diff --git a/.gitattribute b/.gitattribute new file mode 100644 index 0000000..dfdb8b7 --- /dev/null +++ b/.gitattribute @@ -0,0 +1 @@ +*.sh text eol=lf diff --git a/.gitignore b/.gitignore old mode 100644 new mode 100755 index f9d8610..10cc183 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,49 @@ # Generally ignore data directories data* +docs/build/* +.DS_Store +docs/.DS_Store +ldc_sad_hmm/ +HTK.tar.gz +HTK-3.4.1.tar.gz +MCR_R2017b_glnxa64_installer.zip +Anaconda2-2019.03-Linux-x86_64.sh +opensmile-2-3-0.tar.gz + +# History files +.Rhistory +.Rapp.history + +# Session Data files +.RData + +# Example code in package build process +*-Ex.R + +# Output files from R CMD build +/*.tar.gz + +# Output files from R CMD check +/*.Rcheck/ + +# RStudio files +.Rproj.user/ + +# produced vignettes +vignettes/*.html +vignettes/*.pdf + +# OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3 +.httr-oauth + +# knitr and R markdown default cache directories +/*_cache/ +/cache/ + +# Temporary files created by R markdown +*.utf8.md +*.knit.md + +# To avoid issues with bash scripts when git cloning on windows +*.sh text eol=lf diff --git a/.idea/vcs.xml b/.idea/vcs.xml new file mode 100644 index 0000000..e6e3d41 --- /dev/null +++ b/.idea/vcs.xml @@ -0,0 +1,7 @@ + + + + + + + \ No newline at end of file diff --git a/MED_2s_100ms_htk.conf b/MED_2s_100ms_htk.conf deleted file mode 100644 index edaa202..0000000 --- a/MED_2s_100ms_htk.conf +++ /dev/null @@ -1,318 +0,0 @@ -// OpenSMILE configuration for Large Scale Feature Extraction for Multimedia Event Detection// -// Low Level Descriptors: MFCC, Pitch, Loudness, PLP, LSP ... // - -/////////////////////////////////////////////////////////////////////////////////////// -; -; This section is always required in openSMILE configuration files -; it configures the componentManager and gives a list of all components which are to be loaded -; The order in which the components are listed should match -; the order of the data flow for most efficient processing -; -/////////////////////////////////////////////////////////////////////////////////////// -[componentInstances:cComponentManager] -instance[dataMemory].type=cDataMemory -instance[waveIn].type=cWaveSource -instance[fr1].type=cFramer -instance[pe2].type=cVectorPreemphasis -instance[w1].type=cWindower -instance[fft1].type=cTransformFFT -instance[fftmp1].type=cFFTmagphase -instance[mspec].type=cMelspec -instance[mfcc].type=cMfcc -instance[mzcr].type=cMZcr -instance[acf].type=cAcf -instance[cepstrum].type=cAcf -instance[pitchACF].type=cPitchACF -instance[energy].type=cEnergy -instance[spectral].type=cSpectral -instance[lld].type=cContourSmoother -instance[delta1].type=cDeltaRegression -instance[delta2].type=cDeltaRegression -instance[functL1].type=cFunctionals -instance[htksink].type=cHtkSink -printLevelStats=0 -nThreads=1 - - -///////////////////////////////////////////////////////////////////////////////////////////// -///////////////////////// component configuration //////////////////////////////////////// -///////////////////////////////////////////////////////////////////////////////////////////// -; the following sections configure the components listed above -; a help on configuration parameters can be obtained with -; SMILExtract -H -; or -; SMILExtract -H configTypeName (= componentTypeName) -///////////////////////////////////////////////////////////////////////////////////////////// -[waveIn:cWaveSource] -writer.dmLevel=wave -filename=\cm[inputfile(I){test.wav}:name of input file] -monoMixdown=1 - -[fr1:cFramer] -reader.dmLevel=wave -writer.dmLevel=frames -noPostEOIprocessing = 1 -copyInputName = 1 -frameSize = 0.0250 -frameStep = 0.010 -frameMode = fixed -frameCenterSpecial = right -buffersize = 1000 - -[pe2:cVectorPreemphasis] -reader.dmLevel=frames -writer.dmLevel=framespe -k = 0.97 -de = 0 - -[w1:cWindower] -reader.dmLevel=framespe -writer.dmLevel=winframe -copyInputName = 1 -processArrayFields = 1 -winFunc = ham -gain = 1.0 -offset = 0 - - // ---- LLD ----- - -[fft1:cTransformFFT] -reader.dmLevel=winframe -writer.dmLevel=fftc -copyInputName = 1 -processArrayFields = 1 -inverse = 0 - -[fftmp1:cFFTmagphase] -reader.dmLevel=fftc -writer.dmLevel=fftmag -copyInputName = 1 -processArrayFields = 1 -inverse = 0 -magnitude = 1 -phase = 0 - -[mspec:cMelspec] -nameAppend=melspec -reader.dmLevel=fftmag -writer.dmLevel=mspec1 -copyInputName = 1 -processArrayFields = 1 -htkcompatible = 1 -usePower = 0 -nBands = 26 -lofreq = 0 -hifreq = 8000 -usePower = 0 -inverse = 0 -specScale = mel - -[mfcc:cMfcc] -reader.dmLevel=mspec1 -writer.dmLevel=mfcc1 -copyInputName = 1 -processArrayFields = 1 -firstMfcc = 0 -lastMfcc = 12 -cepLifter = 22.0 -htkcompatible = 1 - - -[acf:cAcf] -reader.dmLevel=fftmag -writer.dmLevel=acf -nameAppend = acf -copyInputName = 1 -processArrayFields = 1 -usePower = 1 -cepstrum = 0 - -[cepstrum:cAcf] -reader.dmLevel=fftmag -writer.dmLevel=cepstrum -nameAppend = acf -copyInputName = 1 -processArrayFields = 1 -usePower = 1 -cepstrum = 1 - -[pitchACF:cPitchACF] - ; the pitchACF component must ALWAYS read from acf AND cepstrum in the given order! -reader.dmLevel=acf;cepstrum -writer.dmLevel=pitch -copyInputName = 1 -processArrayFields = 0 -maxPitch = 500 -voiceProb = 1 -voiceQual = 0 -HNR = 0 -F0 = 1 -F0raw = 0 -F0env = 1 -voicingCutoff = 0.550000 - -[energy:cEnergy] -reader.dmLevel=winframe -writer.dmLevel=energy -nameAppend=energy -copyInputName = 1 -processArrayFields = 0 -htkcompatible = 0 -rms=0 -log=1 - -[mzcr:cMZcr] -reader.dmLevel=frames -writer.dmLevel=mzcr -copyInputName = 1 -processArrayFields = 1 -zcr = 1 -amax = 0 -mcr = 0 -maxmin = 0 -dc = 0 - -[spectral:cSpectral] -reader.dmLevel=fftmag -writer.dmLevel=spectral -copyInputName = 1 -processArrayFields = 1 -squareInput = 1 -bands[0]=0-250 -bands[1]=0-650 -bands[2]=250-650 -bands[3]=1000-4000 -bands[4]=3010-9123 -rollOff[0] = 0.25 -rollOff[1] = 0.50 -rollOff[2] = 0.75 -rollOff[3] = 0.90 -flux = 1 -centroid = 1 -maxPos = 1 -minPos = 1 -entropy = 0 - -[lld:cContourSmoother] -reader.dmLevel=energy;mfcc1;mspec1;mzcr;pitch;spectral -writer.dmLevel=lld -writer.levelconf.nT=10 -;writer.levelconf.noHang=2 -writer.levelconf.isRb=0 -writer.levelconf.growDyn=1 -;processArrayFields=0 -nameAppend = sma -copyInputName = 1 -noPostEOIprocessing = 0 -smaWin = 3 - - // ---- delta regression of LLD ---- - -[delta1:cDeltaRegression] -reader.dmLevel=lld -writer.dmLevel=lld_de -writer.levelconf.isRb=0 -writer.levelconf.growDyn=1 -nameAppend = de -copyInputName = 1 -noPostEOIprocessing = 0 -deltawin=2 -blocksize=1 - -[delta2:cDeltaRegression] -reader.dmLevel=lld_de -writer.dmLevel=lld_dede -writer.levelconf.isRb=0 -writer.levelconf.growDyn=1 -nameAppend = de -copyInputName = 1 -noPostEOIprocessing = 0 -deltawin=2 -blocksize=1 - - -[functL1:cFunctionals] -reader.dmLevel=lld;lld_de;lld_dede -writer.dmLevel=func -copyInputName = 1 -frameMode = fixed - ; frameSize and frameStep = 0 => functionals over complete input - ; (NOTE: buffersize of lld and lld_de levels must be large enough!!) - ; 2 Sec frames with 100 millisec shift -frameSize=2 -frameStep=0.10 -frameCenterSpecial = right -noPostEOIprocessing = 0 -functionalsEnabled=Extremes;Regression;Moments;Percentiles;Crossings;Peaks;Means -Extremes.max = 0 -Extremes.min = 0 -Extremes.range = 1 -Extremes.maxpos = 1 -Extremes.minpos = 1 -Extremes.amean = 0 -Extremes.maxameandist = 1 -Extremes.minameandist = 1 -Extremes.norm = frame -Regression.linregc1 = 1 -Regression.linregc2 = 1 -Regression.linregerrA = 1 -Regression.linregerrQ = 1 -Regression.qregc1 = 1 -Regression.qregc2 = 1 -Regression.qregc3 = 1 -Regression.qregerrA = 1 -Regression.qregerrQ = 1 -Regression.centroid = 1 -Moments.variance = 1 -Moments.stddev = 1 -Moments.skewness = 1 -Moments.kurtosis = 1 -Moments.amean = 0 -Percentiles.quartiles = 1 -Percentiles.quartile1 = 0 -Percentiles.quartile2 = 0 -Percentiles.quartile3 = 0 -Percentiles.iqr = 1 -Percentiles.iqr12 = 0 -Percentiles.iqr23 = 0 -Percentiles.iqr13 = 0 -Percentiles.interp = 1 -Percentiles.percentile[0] = 0.95 -Percentiles.percentile[1] = 0.98 -Crossings.zcr = 1 -Crossings.mcr = 0 -Crossings.amean = 0 -Peaks.numPeaks = 1 -Peaks.meanPeakDist = 1 -Peaks.peakMean = 1 -Peaks.peakMeanMeanDist = 1 -Peaks.overlapFlag = 1 -Means.amean = 1 -Means.absmean = 1 -Means.qmean = 1 -Means.nzamean = 0 -Means.nzabsmean = 1 -Means.nzqmean = 1 -Means.nzgmean = 1 -Means.nnz = 1 - - - - - ////////////////////////////////////////////////////////////////////// - /////////////////// data output configuration ////////////////////// -////////////////////////////////////////////////////////////////////// - -; the HTK sink writes data in HTK parameter format -[htksink:cHtkSink] - ; data from the following dataMemory levels in concattenated - ; to form the output vectors -reader.dmLevel=func - ; this again defines a commandline option for the output file (see waveIn) -filename=\cm[output(O):name of output filename (HTK format)] -append=0 -parmKind=9 - -//////---------------------- END -------------------------/////// - - diff --git a/README.md b/README.md index effa7f2..c2ee12c 100644 --- a/README.md +++ b/README.md @@ -1,421 +1,13 @@ -This repo contains the latest stable version of the ACLEW Diarization Virtual Machine (DiViMe). For the development version, visit https://github.com/srvk/DiViMe/. -# Initial questions +This repo contains the ACLEW Diarization Virtual Machine (DiViMe), as described in: -## What is the ACLEW DiViMe? - -It is a collection of diarization tools, i.e., it allows users to add annotations onto a raw audio recording. At present, we have tools to do the following types of annotation: - -1) Speech activity detection (answers the question: when is someone talking?) - -2) Talker diarization (answers the question: who is talking?) - -We are hoping to add more tools in the future, including register detection, syllable quantification, and vocal maturity estimation. - -## Who is the ACLEW DiViMe for? - -Our target users have "difficult" recordings, e.g. recorded in natural environment, from sensitive populations, etc. Therefore, we are assuming users who are unable to share their audio recordings. Our primary test case involves language acquisition in children 0-3 years of age. - -We are hoping to make the use of these tools as easy as possible, but some command line programming will be unavoidable. If you are worried when reading this, we can recommend the Software Carpentry programming courses for researchers, and particularly their [unix bash](http://swcarpentry.github.io/shell-novice) and [version control](http://swcarpentry.github.io/git-novice/) bootcamps. - -## What exactly is inside the ACLEW DiViMe? - -A virtual machine is actually a mini-computer that gets set up inside your computer. This creates a virtual environment within which we can be sure that our tools run, and run in the same way across all computers (Windows, Mac, Linux). - -Inside this mini-computer, we have put the following tools: - -1) Speech activity detection (answers the question: when is someone talking?) - - * [LDC Speech Activity Detection](https://github.com/aclew/DiViMe#ldc_sad)(coming soon) - * [Speech Activity Detection Using Noisemes](#noisemes_sad) - * [OpenSmile SAD](#opensmile_sad) - * [ToCombo SAD](#tocombo_sad) - - -2) Talker diarization (answers the question: who is talking?) - - * [DiarTK](#diartk) - -3) Evaluation - -If a user has some annotations, they may want to know how good the ACLEW DiViMe parsed their audio recordings. In that case, you can use one tool we soon paln to provide to evaluate: - - * [LDC Diarization Scoring](https://github.com/aclew/DiViMe#ldc-diarization-scoring) - - -# Installation instructions - -Try the following first: - -1. Install [Vagrant](https://www.vagrantup.com/): Click on the download link and follow the prompted instructions - -1. Install [VirtualBox](https://www.virtualbox.org/wiki/Downloads): When we last checked, the links for download for all operating systems were under the header "VirtualBox 5.2.8 platform packages", so look for a title like that one. - -2. Clone this repo: - - - Open terminal - - Navigate to the directory in which you want the VM to be hosted - - type in: - -`$ git clone https://github.com/aclew/DiViMe` - -3. Change into it by - -`$ cd divime` - -2. Install HTK - -HTK is used by some of these tools (until we find and implement an open-source replacement). We are not allowed to distribute HTK, so unfortunately you have to get it yourself. - -- Go to the HTK download page http://htk.eng.cam.ac.uk/download.shtml -- Register by following the instructions on the left (under "Getting HTK": Register) -- Check that you have received your password via email; you will need it for the next step. -- Find the link that reads "HTK source code" under your system (if you have a mac, it will be under "Linux/unix downloads"). Notice that you will need your username and password (from the previous step). The download is probably called HTK-3.4.1.tar.gz, although the numbers may change if they update their code. -- Move the HTK-*.tar.gz file into the root folder of this repository (alongside Vagrantfile), and rename it HTK.tar.gz - -4. Type - -`$ vagrant up` - -The first time you do this, it will take at least 20 minutes to install all the packages that are needed to build the virtual machine. - -The instructions above make the simplest assumptions as to your environment. If you have Amazon Web Services, an ubuntu system, or you do not have admin rights in your computer, you might need to read the [instructions to the eesen-transcriber](https://github.com/srvk/eesen-transcriber/blob/master/INSTALL.md) for fancier options. Or you can just open an issue [here](https://github.com/aclew/DiViMe/issues), describing your situation. - -Advanced topic: [Installing With Docker](https://github.com/srvk/DiViMe/wiki/InstallingWithDocker) - -# Checking your installation - -The very first time you use DiViMe, it is a good idea to run a quickstart test: - -1. Open a terminal -2. Navigate inside the DiViMe folder -3. Do -`$ vagrant up` -4. Do -`$ vagrant ssh -c "tools/test.sh"` - -This should produce the output: - -``` -Testing noisemes... -Noisemes passed the test. -Testing DIARTK... -DiarTK passed the test. -Congratulations, everything is OK! -Connection to 127.0.0.1 closed. -``` - -If something fails, please open an issue [here](https://github.com/aclew/DiViMe/issues). Please paste the complete output there. - - -# Update instructions - -If there is a new version of DiViMe, you'll need to perform the following 3 steps from within the DiViME folder on your terminal: - - -``` -$ vagrant destroy -$ git pull -$ vagrant up -``` - -# Uninstallation instructions - -If you want to get rid of the files completely, you should perform the following 3 steps from within the DiViME folder on your terminal: - -``` -$ vagrant destroy -$ cd .. -$ rm -r -f divime -``` - - - - - -# Use instructions - - -## Short instructions for all tools - -1. Put the files you want to analyze inside the "data" folder inside the DiViMe folder. If your files aren't .wav some of the tools may not work. Please consider converting them into wav with some other program, such as [ffmpeg](https://www.ffmpeg.org/). It is probably safer to make a copy (rather than moving your files into the data folder), in case you later decide to delete the whole folder. - -2. If you have any annotations, put them also in the same "data" folder. Annotations can be in .eaf, .textgrid, or .rttm format, and *they should be named exactly as your wav files*. It is probably safer to make a copy (rather than moving them), in case you later decide to delete the whole vagrant folder. - -[//]: # (Julien, you had a solution for not moving data at all -- can you please describe it in simple terms?) - -3. Launch the virtual machine anytime by navigating to your DiViMe folder on your terminal and performing: - -`$ vagrant up` - -4. For the SAD tools, type a command like the one below, being careful to type the SAD tool name instead of SADTOOLNAME: - -`$ vagrant ssh -c "tools/SADTOOLNAME.sh data/"` - -The SAD options are: -- SADTOOLNAME = ldc_sad (coming soon) -- SADTOOLNAME = noisemes_sad -- SADTOOLNAME = noisemes_full -- SADTOOLNAME = opensmile_sad -- SADTOOLNAME = tocombo_sad - -This will create a set of new rttm files, with the name of the tool added at the beginning. For example, imagine you have a file called participant23.wav, and you decide to run both the LDC_SAD and the Noisemes analyses. You will run the following commands: - - -``` -$ vagrant ssh -c "tools/ldc_sad.sh data/" -$ vagrant ssh -c "tools/noisemes_sad.sh data/" -``` - -And this will result in your having the following three files in your /data/ folder: - -- participant23.wav -- ldc_sad_participant23.rttm -- noisemes_sad_participant23.rttm - -If you look inside one of these .rttm's, say the ldc_sad one, it will look as follows: - -``` -SPEAKER participant23 1 0.00 0.77 speech -SPEAKER participant23 1 0.77 0.61 nonspeech -SPEAKER participant23 1 1.38 2.14 speech -SPEAKER participant23 1 3.52 0.82 nonspeech -``` - -This means that LDC_SAD considered that the first 770 milliseconds of the audio were speech; followed by 610 milliseconds of non-speech, followed by 2.14 seconds of speech; etc. - - -5. For the diarization tools, type a command like the one below, being careful to type the diarization tool name instead of DiarTOOLNAME: - -`$ vagrant ssh -c "tools/DiarTOOLNAME.sh data/ noisemes"` - -The DiarTOOLNAME options are: -- DiarTOOLNAME = diartk -- DiarTOOLNAME = yunitate @riebling please check - -Notice there is one more parameter provided to the system in the call; in the example above "noisemes". This is because the DiarTK tool only does talker diarization (i.e., who speaks) but not speech activity detection (when is someone speaking). Therefore, this system requires some form of SAD. With this last parameter, you are telling the system which annotation to use. At present, you can choose between: - -- ldc_sad: this means you want the system to use the output of the LDC_SAD system. If you have not run LDC_SAD, the system will run it for you. -- noisemes: this means you want the system to use the output of the noisemes system. If you have not run LDC_SAD, the system will run it for you. -- opensmile: this means you want the system to use the output of the opensmile system. If you have not run opensmile, the system will run it for you. -- tocombosad: this means you want the system to use the output of the tocombo_sad system. If you have not ran tocombosad, the system will run it for you. -- textgrid: this means you want the system to use your textgrid annotations. Notice that all tiers count, so if you have some tiers that are non-speech, you should remove them from your textgrids before you start. Please note that the system will convert your textgrids into .rttm in the process. -- eaf: this means you want the system to use your eaf annotations. Notice that all tiers count, so if you have some tiers that are non-speech, you should remove them from your eaf files before you start. Please note that the system will convert your eafs into .rttm in the process. -- rttm: this means you want the system to use your rttm annotations. Notice that all annotations that say "speech" in the eigth column count as such. - - -Finally, if no parameter is provided, the system will default to ldc_sad. - -6. If you have some annotations that you have made, you probably want to know how well our tools did - how close they were to your hard-earned human annotations. To find out, type a command like the one below: - -`$ vagrant ssh -c "tools/eval.sh data/ noisemes"` - -Notice there are 2 parameters provided to the evaluation suite. The first parameter tells the system which folder to analyze (in this case, the whole data/ folder). The second parameter indicates which tool's output to evaluate (in this case, noisemes). The system will use the .rttm annotations if they exist; or the .eaf ones if the former are missing; or the .textgrid of neither .rttm nor .eaf are found. -If you want to evaluate a diarization produced by the diartk tool, you will have to specify a third parameter, to tell the system which SAD was used to compute the diartk outputs you want to evaluate. E.G. : -`$ vagrant ssh -c "tools/eval.sh data/ diartk noisemes_sad` - -7. Last but not least, you should **remember to halt the virtual machine**. If you don't, it will continue running in the background, taking up useful resources! To do so, simply navigate to the DiViMe folder on your terminal and type in: - -`$ vagrant halt` - -## More details for each tool - -### LDC_SAD - -Instructions coming. - -@riebling please add reference (you can find it in the interspeech paper) - -### Noisemes_sad - -#### General intro - -This system will classify slices of the audio recording into one of 17 noiseme classes: - -- background -- speech -- speech non English -- mumble -- singing alone -- music + singing -- music alone -- human sounds -- cheer -- crowd sounds -- animal sounds -- engine -- noise_ongoing -- noise_pulse -- noise_tone -- noise_nature -- white_noise -- radio - -To learn more, read the source file -Wang, Y., Neves, L., & Metze, F. (2016, March). Audio-based multimedia event detection using deep recurrent neural networks. In Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on (pp. 2742-2746). IEEE. [pdf](http://www.cs.cmu.edu/~yunwang/papers/icassp16.pdf) - -#### Instructions for direct use - -You can analyze just one file as follows. Imagine that <$MYFILE> is the name of the file you want to analyze, which you've put inside the `data/` folder in the current working directory. - -``` -$ vagrant ssh -c "OpenSAT/runOpenSAT.sh data/<$MYFILE>" -``` - -You can also analyze a group of files as follows: - -``` -$ vagrant ssh -c "OpenSAT/runDiarNoisemes.sh data/" -``` - -This will analyze all .wav's inside the "data" folder. - -Created annotations will be stored inside the same "data" folder. - -### Some more technical details - -For more fine grained control, you can log into the VM and from a command line, and play around from inside the "Diarization with noisemes" directory, called "OpenSAT": - -``` -$ vagrant ssh -$ cd OpenSAT -``` - -The main script is runOpenSAT.sh and takes one argument: an audio file in .wav format. -Upon successful completion, output will be in the folder (relative to ~/OpenSAT) -`SSSF/data/hyp//confidence.pkl.gz` - -The system will grind first creating features for all the .wav files it found, then will place those features in a subfolder `feature`. Then it will load a model, and process all the features generated, producing output in a subfolder `hyp/` two files per input: `.confidence.mat` and `.confidence.pkl.gz` - a confidence matrix in Matlab v5 mat-file format, and a Python compressed data 'pickle' file. Now, as well, in the `hyp/` folder, `.rttm` with labels found from a config file [noisemeclasses.txt](https://github.com/riebling/OpenSAT/blob/master/noisemeclasses.txt) - --More details on output format- - -The 18 classes are as follows: -``` -0 background -1 speech -2 speech_ne -3 mumble -4 singing -5 music_sing -6 music -7 human -8 cheer -9 crowd -10 animal -11 engine -12 noise_ongoing -13 noise_pulse -14 noise_tone -15 noise_nature -16 white_noise -17 radio -``` -The frame length is 0.1s. The system also uses a 2-second window, so the i-th frame starts at (0.1 * i - 2) seconds and finishes at (0.1 * i) seconds. That's why 60 seconds become 620 frames. 'speech_ne' means non-English speech - --Sample RTTM output snippet- -``` -SPEAKER family 1 4.2 0.4 noise_ongoing 0.37730383873 -SPEAKER family 1 4.6 1.2 background 0.327808111906 -SPEAKER family 1 5.8 1.1 speech 0.430758684874 -SPEAKER family 1 6.9 1.2 background 0.401730179787 -SPEAKER family 1 8.1 0.7 speech 0.407463937998 -SPEAKER family 1 8.8 1.1 background 0.37258502841 -SPEAKER family 1 9.9 1.7 noise_ongoing 0.315185159445 -``` - -The script `runClasses.sh` works like `runDiarNoisemes.sh`, but produces the more detailed results as seen above. - -### OpenSmile_SAD - -F. Eyben, F. Weninger, F. Gross, and B. Schuller, “Recent devel-opments in opensmile, the munich open-source multimedia feature extractor,” in Proceedings of the 21st ACM international conference on Multimedia. ACM, 2013, pp. 835–838. - -### TOCombo_SAD - -A. Ziaei, A. Sangwan, J.H.L. Hansen, "Effective word count estimation for long duration daily naturalistic audio recordings," Speech Communication, vol. 84, pp. 15-23, Nov. 2016. - -### DiarTK - -This tool performs diarization, requiring as input not only .wav audio, but also speech/nonspeech in .rttm format as generated by one of the tools above. A script to run DiarTK (also known as ib_diarization_toolkit) can be found in `tools/diartk.sh`. Here is it's usage: -``` -Usage: diartk.sh -where dirname is the name of the folder -containing the wav files, and transcription -specifies which transcription you want to use. -Choices are: - ldc_sad - noisemes - textgrid - eaf - rttm -``` -To invoke the tool from outside the VM, invoke it with a command like: -``` -vagrant ssh -c 'tools/diartk.sh data noisemes' -``` -where `data/` is in the current working directory, and contains .wav audio as well as speech/nonspeech RTTM files with names based on the tool that generated them, from the set of possible SAD providers `ldc_sad`, `noisemes`, `textgrid`, `eaf`, `rttm` for example `noisemes_sad_myaudio.rttm` or `ldc_sad_myaudio.rttm` - -### LDC Diarization Scoring - -@riebling please add reference (you can find it in the interspeech paper) - - -Instructions coming - -https://github.com/aclew/varia - -# Troubleshooting - -## Installation issues -### Virtual Machine creation -If your computer freezes after `vagrant up`, it may be due to several things. -If your OS is ubuntu 16.04, there's a known incompatibility between VirtualBox and the 4.13 Linux kernel on ubuntu 16.04. What you may do is to install a previous version of the kernel, for example the 4.10, following [these instructions](https://doc.ubuntu-fr.org/kernel#installation_simple), or install the latest version of virtualbox which should fix the problem. -If you are not on ubuntu 16.04, or if the previous fix didn't work, it may also be due to the fact that Vagrant is trying to create a Virtual Machine that asks for too much resources. Please ensure that you have enough space on your computer (you should have at least 15Gb of free space) and check that the memory asked for is okay. If not, you can lower the memory of the VM by changing line 25 of the VagrantFile, -``` -vbox.memory = 3072 -``` -to a lower number, such as -``` -vbox.memory = 2048 -``` -### Resuming the Virtual Machine -If you already used the VM once, shut down your computer, turned it back on and can't seem to be able to do `vagrant up` again, you can simply do -``` -vagrant destroy -``` -and recreate the VM using -``` -vagrant up -``` -If you don't want to destroy it, you can try opening the VirtualBox GUI, go to `File -> Settings or Preferences -> Network `, click on the `Host-only Networks` tab, then click the network card icon with the green plus sign in the right, if there are no networks yet listed. The resulting new default network should appear with the name ‘vboxnet0’. -You can now try again with `vagrant up` -### Tools -If ldc_sad doesn't seem to work after vagrant up, first, please check that you indeed have the htk archive in your folder. If you don't, please put it there and launch: -``` -vagrant up --provision -``` -This step will install HTK inside the VM, which is used by several tools including ldc_sad. -## Problems with some of the Tools -### Noisemes -If you use the noisemes_sad or the noisemes_full tool, one problem you may encounter is that it doesn't treat all of your files and gives you an error that looks like this: -``` -Traceback (most recent call last): - File "SSSF/code/predict/1-confidence-vm5.py", line 59, in - feature = pca(readHtk(os.path.join(INPUT_DIR, filename))).astype('float32') - File "/home/vagrant/G/coconut/fileutils/htk.py", line 16, in readHtk - data = struct.unpack(">%df" % (nSamples * sampSize / 4), f.read(nSamples * sampSize)) -MemoryError -``` -If this happens to you, it's because you are trying to treat more data than the system/your computer can handle. -What you can do is simply put the remaining files that weren't treated in a seperate folder and treat this folder seperately (and do this until all of your files are treated if it happens again on very big datasets). -After that, you can put back all of your data in the same folder. -## Input Format For Transcriptions -If your transcriptions are in TextGrid format but the conversion doesn't seem to work, it's probably because it isn't in the right TextGrid format. -The input TextGrid the system allows is a TextGrid in which all the tiers have speech segments (so remove tiers with no speech segments) and all the annotated segments for each tiers is indeed speech (so remove segments that are noises or other non-speech type). - - -# References - - -WCE: Räsänen, O., Seshadri, S., & Casillas, M. (2018, June). Comparison of Syllabification Algorithms and Training Strategies for Robust Word Count Estimation across Different Languages and Recording Conditions. In Interspeech 2018. +Adrien Le Franc, Eric Riebling, Julien Karadayi, Yun Wang, Camila Scaff, Florian Metze, and Alejandrina Cristia. +[The ACLEW DiViMe: An easy-to-use diarization tool.](http://www.cs.cmu.edu/~fmetze/interACT/Publications_files/publications/aclew-divime-easy.pdf) +In Proc. INTERSPEECH, Hyderabad; India, September 2018. ISCA. +Please see the online documentation at https://divime.readthedocs.io/ for instructions on installation and use. +Quickstart: +- Install vagrant +- vagrant plugin install vagrant-aws vagrant-sshfs vagrant-vbguest +- vagrant up (in the DiViMe folder) diff --git a/Vagrantfile b/Vagrantfile index fdf5f40..5a6791f 100644 --- a/Vagrantfile +++ b/Vagrantfile @@ -1,3 +1,4 @@ +# coding: utf-8 # -*- mode: ruby -*- # vi: set ft=ruby @@ -8,7 +9,7 @@ Vagrant.configure("2") do |config| # . aws.sh; vagrant up --provider aws # Make sure you don't check in aws.sh (maybe make a copy with your "secret" data) # Before that, do - # vagrant plugin install vagrant-aws; vagrant plugin install vagrant-sshfs + # vagrant plugin install vagrant-aws vagrant-sshfs # if you have this plugin (https://github.com/dotless-de/vagrant-vbguest) installed # the following line disables it (for faster startup) @@ -31,7 +32,7 @@ Vagrant.configure("2") do |config| config.vm.network "private_network", ip: "192.168.56.101" vbox.cpus = 2 - vbox.memory = 3072 + vbox.memory = 2048 end config.vm.provider "docker" do |d, override| @@ -39,19 +40,21 @@ Vagrant.configure("2") do |config| d.image = 'tknerr/baseimage-ubuntu:14.04' d.remains_running = true d.has_ssh = true + # This needs to be set on a Mac - not sure if it causes problems on other architectures? + d.force_host_vm = true # (too late?) - override.vm.synced_folder ".", "/vagrant", owner: "vagrant", group: "vagrant", :mount_options => ["dmode=777", "fmode=777"] + override.vm.synced_folder ".", "/vagrant", owner: "vagrant", group: "vagrant" + #, :mount_options => ["dmode=777", "fmode=777"] end config.vm.provider "aws" do |aws, override| - aws.tags["Name"] = "Diarization VM" aws.ami = "ami-663a6e0c" # Ubuntu ("Trusty") Server 14.04 LTS AMI - US-East region aws.instance_type = "m3.xlarge" override.vm.synced_folder ".", "/vagrant", type: "sshfs", ssh_username: ENV['USER'], ssh_port: "22", prompt_for_password: "true" - override.vm.box = "http://speechkitchen.org/dummy.box" + override.vm.box = "http://speech-kitchen.org/dummy.box" # it is assumed these environment variables were set by ". aws.sh" aws.access_key_id = ENV['AWS_KEY'] @@ -95,167 +98,17 @@ Vagrant.configure("2") do |config| #override.vm.box = "https://github.com/azure/vagrant-azure/raw/v2.0/dummy.box" end - config.vm.provision "shell", inline: <<-SHELL - sudo apt-get update -y - sudo apt-get upgrade -y - - if grep --quiet vagrant /etc/passwd - then - user="vagrant" - else - user="ubuntu" - fi - - sudo apt-get install -y git make automake libtool autoconf patch subversion fuse \ - libatlas-base-dev libatlas-dev liblapack-dev sox libav-tools g++ \ - zlib1g-dev libsox-fmt-all sshfs gcc-multilib libncurses5-dev unzip - sudo apt-get install -y openjdk-6-jre || sudo apt-get install -y icedtea-netx-common icedtea-netx -# sudo apt-get install -y libtool-bin apache2 - - # If you wish to train EESEN with a GPU machine, uncomment this section to install CUDA - # also uncomment the line that mentions cudatk-dir in the EESEN install section below - #cd /home/${user} - #wget -nv http://speechkitchen.org/vms/Data/cuda-repo-ubuntu1404-7-5-local_7.5-18_amd64.deb - #dpkg -i cuda-repo-ubuntu1404-7-5-local_7.5-18_amd64.deb - #rm cuda-repo-ubuntu1404-7-5-local_7.5-18_amd64.deb - #apt-get update - #apt-get remove --purge xserver-xorg-video-nouveau - #apt-get install -y cuda - - # Kaldi and others want bash - otherwise the build process fails - [ $(readlink /bin/sh) == "dash" ] && sudo ln -s -f bash /bin/sh - - # Install Anaconda and Theano - echo "Downloading Anaconda-2.3.0..." - cd /home/${user} - wget -q https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda-2.3.0-Linux-x86_64.sh - #bash Anaconda-2.3.0-Linux-x86_64.sh -b # batch install into /home/vagrant/anaconda - echo "Installing Anaconda-2.3.0..." - sudo -S -u vagrant -i /bin/bash -l -c "bash /home/${user}/Anaconda-2.3.0-Linux-x86_64.sh -b" - if ! grep -q -i anaconda .bashrc; then - echo "export PATH=/home/${user}/anaconda/bin:\$PATH" >> /home/${user}/.bashrc - fi - - # install Matlab runtime environment - cd /tmp - wget -q http://ssd.mathworks.com/supportfiles/downloads/R2017b/deployment_files/R2017b/installers/glnxa64/MCR_R2017b_glnxa64_installer.zip - unzip -q MCR_R2017b_glnxa64_installer.zip - ./install -mode silent -agreeToLicense yes - # add Matlab stuff to path - echo 'LD_LIBRARY_PATH="/usr/local/MATLAB/MATLAB_Runtime/v93/runtime/glnxa64:/usr/local/MATLAB/MATLAB_Runtime/v93/bin/glnxa64:/usr/local/MATLAB/MATLAB_Runtime/v93/sys/os/glnxa64:$LD_LIBRARY_PATH"' >> /home/${user}/.bashrc - rm /tmp/MCR_R2017b_glnxa64_installer.zip - - # Install OpenSMILE - echo "Installing OpenSMILE" - cd /home/${user} - wget -q http://audeering.com/download/1131/ -O OpenSMILE-2.1.tar.gz - tar zxvf OpenSMILE-2.1.tar.gz - rm OpenSMILE-2.1.tar.gz - - # optionally Install HTK (without it, some other tools will not work) - # the idea is to make users independently download HTK installer since - # we cannot redistribute - if [ -f /vagrant/HTK.tar.gz ] - then - cd /home/${user} - su ${user} -c "tar zxf /vagrant/HTK.tar.gz" - cd htk - ./configure --without-x --disable-hslab - make all - make install - fi - - # Get OpenSAT and all the tools - # Install DiarTK, LDC SAD, LDC scoring, Rajat's LENA stuff - - cd /home/${user} - git clone http://github.com/srvk/OpenSAT - git clone http://github.com/srvk/ib_diarization_toolkit - #git clone http://github.com/srvk/ldc_sad_hmm - git clone http://github.com/srvk/dscore - git clone https://github.com/rajatkuls/lena-clean - git clone https://github.com/srvk/Yunitator - git clone https://github.com/srvk/To-Combo-SAD - - # Get the Wrapper scripts - - git clone https://github.com/srvk/tools.git - git clone https://github.com/aclew/varia.git - - # Festvox Speech Tools -# wget -nv http://festvox.org/packed/festival/2.4/speech_tools-2.4-release.tar.gz -# tar zxvf speech_tools-2.4-release.tar.gz && rm speech_tools-2.4-release.tar.gz -# cd speech_tools -# ./configure -# make -# cd .. - - # Festvox 2.7.0 -# wget -nv http://festvox.org/festvox-2.7/festvox-2.7.0-release.tar.gz -# tar zxvf festvox-2.7.0-release.tar.gz && rm festvox-2.7.0-release.tar.gz -# cd festvox -# ./configure -# make -j 4 -# cd .. - - # Festival 2.4 -# wget -nv http://festvox.org/packed/festival/2.4/festival-2.4-release.tar.gz -# tar zxvf festival-2.4-release.tar.gz -# wget -nv http://festvox.org/packed/festival/2.4/festlex_CMU.tar.gz -# tar zxvf festlex_CMU.tar.gz -# wget -nv http://festvox.org/packed/festival/2.4/festlex_OALD.tar.gz -# tar zxvf festlex_OALD.tar.gz -# wget -nv http://festvox.org/packed/festival/2.4/festlex_POSLEX.tar.gz -# tar zxvf festlex_POSLEX.tar.gz -# wget -nv http://festvox.org/packed/festival/2.4/voices/festvox_cmu_us_awb_cg.tar.gz -# tar zxvf festvox_cmu_us_awb_cg.tar.gz -# cd festival -# ./configure -# make - - # LIUM Diarization system http://www-lium.univ-fr/diarization -# cd /home/${user} -# mkdir LIUM -# cd LIUM -# wget -nv http://www-lium.univ-lemans.fr/diarization/lib/exe/fetch.php/lium_spkdiarization-8.4.1.jar.gz -# gunzip lium_spkdiarization-8.4.1.jar.gz -# cp /vagrant/diarization.sh . -# ln -s /home/vagrant/tools/eesen-offline-transcriber/models . - - # Get tools: PDNN, coconut, ldc_sad_hmm - cd /home/${user} - mkdir G - cd G - git clone http://github.com/yajiemiao/pdnn - git clone http://github.com/srvk/coconut - - # get theanorc! - cp /vagrant/.theanorc /home/${user}/ - - export PATH=/home/${user}/anaconda/bin:$PATH - - # install theano - su ${user} -c "/home/${user}/anaconda/bin/conda install -y theano=0.8.2" - - # install pympi (for eaf -> rttm conversion) and tgt (for textgrid -> rttm conversion) - # and intervaltree (needed for rttm2scp.py) - su ${user} -c "/home/${user}/anaconda/bin/pip install pympi-ling tgt intervaltree" - - # assume 'conda' is installed now (get path) - su ${user} -c "/home/${user}/anaconda/bin/conda install numpy scipy mkl dill tabulate joblib" - - # now dependencies for Yunitator - su ${user} -c "/home/${user}/anaconda/bin/conda install cudatoolkit" - su ${user} -c "/home/${user}/anaconda/bin/conda install pytorch-cpu -c pytorch" - - # Some cleanup - sudo apt-get autoremove -y - - # Silence error message from missing file - touch /home/${user}/.Xauthority + config.vm.provision "bootstrap-system", type: "shell", run: "once" do |s| + s.path = "conf/bootstrap-system.sh" + end - # Provisioning runs as root; we want files to belong to '${user}' - chown -R ${user}:${user} /home/${user} + config.vm.provision "bootstrap-user", type: "shell", run: "once", privileged: false do |s| + s.path = "conf/bootstrap-user.sh" + end - SHELL + config.vm.provision "update", type: "shell", run: "never" do |s| + # This needs to be completed + s.path = "conf/update.sh" + end + end diff --git a/.theanorc b/conf/.theanorc old mode 100644 new mode 100755 similarity index 100% rename from .theanorc rename to conf/.theanorc diff --git a/conf/Dockerfile b/conf/Dockerfile new file mode 100644 index 0000000..d91f7d3 --- /dev/null +++ b/conf/Dockerfile @@ -0,0 +1,110 @@ +#Download base image ubuntu 16.04 +FROM ubuntu:16.04 + +# Volume configuration: available during `docker build` time, read only +# (use docker build -v ? ) +#VOLUME ["/vagrant"] + +# hassle #1: default user name in Docker is 'root' +# -m forces to make home directory +RUN useradd -m vagrant + +# hassle #2: default Dockerfile shell is sh +SHELL ["/bin/bash", "-c"] + +# Update Software repository # Install packages from ubuntu repository +RUN apt-get update && apt-get install -y git make automake libtool autoconf patch subversion fuse \ + libatlas-base-dev libatlas-dev liblapack-dev sox libav-tools g++ \ + zlib1g-dev libsox-fmt-all apache2 sshfs gcc-multilib libncurses5-dev unzip wget bzip2 libboost-all-dev + +RUN apt-get install -y icedtea-netx-common icedtea-netx +RUN rm -rf /var/lib/apt/lists/* + + # Kaldi and others want bash - otherwise the build process fails +RUN [ $(readlink /bin/sh) == "dash" ] && ln -s -f bash /bin/sh + + # Install Anaconda and Theano +RUN echo "Downloading Anaconda-2.3.0..." +USER vagrant +WORKDIR /home/vagrant +RUN wget -q https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda-2.3.0-Linux-x86_64.sh +RUN bash /home/vagrant/Anaconda-2.3.0-Linux-x86_64.sh -b +RUN rm Anaconda-2.3.0-Linux-x86_64.sh +RUN if ! grep -q -i anaconda .bashrc; then \ + echo "export PATH=/home/vagrant/anaconda/bin:\$PATH" >> /home/vagrant/.bashrc; \ + fi + + # install Matlab runtime environment +USER root +RUN cd /tmp && wget -q http://ssd.mathworks.com/supportfiles/downloads/R2017b/deployment_files/R2017b/installers/glnxa64/MCR_R2017b_glnxa64_installer.zip && unzip -q MCR_R2017b_glnxa64_installer.zip && ./install -mode silent -agreeToLicense yes + # add Matlab stuff to path +RUN echo 'LD_LIBRARY_PATH="/usr/local/MATLAB/MATLAB_Runtime/v93/runtime/glnxa64:/usr/local/MATLAB/MATLAB_Runtime/v93/bin/glnxa64:/usr/local/MATLAB/MATLAB_Runtime/v93/sys/os/glnxa64:$LD_LIBRARY_PATH"' >> /home/vagrant/.bashrc +RUN rm /tmp/MCR_R2017b_glnxa64_installer.zip + + # Install OpenSMILE +RUN echo "Installing OpenSMILE" +USER vagrant +WORKDIR /home/vagrant +RUN wget -q http://audeering.com/download/1131/ -O OpenSMILE-2.1.tar.gz && tar zxvf OpenSMILE-2.1.tar.gz --no-same-owner && rm OpenSMILE-2.1.tar.gz + + # optionally Install HTK (without it, some other tools will not work) + # the idea is to make users independently download HTK installer since + # we cannot redistribute +WORKDIR /home/vagrant +USER root +RUN wget http://speech-kitchen.org/vms/Data/HTK.tar.gz && tar zxf HTK.tar.gz --no-same-owner +WORKDIR /home/vagrant/htk +RUN ./configure --without-x --disable-hslab +# Fix bad Makefile +RUN sed -i "s/ /\t/g" HLMTools/Makefile && make all +RUN make install + + # Get OpenSAT and all the tools + # Install DiarTK, LDC SAD, LDC scoring, Rajat's LENA stuff + + #git clone http://github.com/srvk/ldc_sad_hmm + +USER vagrant +WORKDIR /home/vagrant +RUN git clone http://github.com/srvk/OpenSAT && cp -f OpenSAT/theanorc /home/vagrant/.theanorc && \ + git clone http://github.com/srvk/ib_diarization_toolkit && \ + git clone https://github.com/rajatkuls/lena-clean && \ + git clone https://github.com/srvk/Yunitator && \ + git clone https://github.com/srvk/To-Combo-SAD && \ + git clone https://github.com/srvk/tools.git && \ + git clone https://github.com/aclew/varia.git + + # Get tools: PDNN, coconut, ldc_sad_hmm +USER vagrant +WORKDIR /home/vagrant +RUN mkdir G && \ + cd G && \ + git clone http://github.com/yajiemiao/pdnn && \ + git clone http://github.com/srvk/coconut + + # get theanorc! + # need to figure out how to access this at 'docker build' time. Do it at 'docker run' time instead??? +#RUN cp /vagrant/.theanorc /home/vagrant/ + + # install theano + # install pympi (for eaf -> rttm conversion) and tgt (for textgrid -> rttm conversion) + # and intervaltree (needed for rttm2scp.py) + # assume 'conda' is installed now (get path) + # now dependencies for Yunitator + +RUN export PATH=/home/vagrant/anaconda/bin:$PATH && \ + /home/vagrant/anaconda/bin/conda install -y theano=0.8.2 && \ + /home/vagrant/anaconda/bin/pip install pympi-ling tgt intervaltree && \ + /home/vagrant/anaconda/bin/conda install numpy scipy mkl dill tabulate joblib && \ + /home/vagrant/anaconda/bin/conda install cudatoolkit && \ + /home/vagrant/anaconda/bin/conda install pytorch-cpu -c pytorch + +USER root + # Some cleanup + # If default user is given by USER, passwordless sudo not work??? +RUN touch /home/vagrant/.Xauthority && chown -R vagrant:vagrant /home/vagrant && apt-get autoremove -y + +# Open up HTTP / HTTPS ports +#EXPOSE 80 443 + +CMD ["/bin/bash"] diff --git a/conf/bootstrap-system.sh b/conf/bootstrap-system.sh new file mode 100755 index 0000000..91da650 --- /dev/null +++ b/conf/bootstrap-system.sh @@ -0,0 +1,102 @@ +# Please note that the bootstrap.sh script runs as root and not the vagrant user + +echo "---- Start bootstrapping DiViMe @ `date` ----" + +# Some reporting +echo "-- System information --" +echo "uname -a:" `uname -a` +echo "nproc:" `nproc` +echo "cat /proc/meminfo:" +cat /proc/meminfo +echo "df -h:" +df -h +echo "free:" +free +#dpkg -l + +# These downloads take a long time, start them in the background +#echo "c3100392685b5a62c8509c0588ce9376 */vagrant/Anaconda-2.3.0-Linux-x86_64.sh" | \ +# md5sum -c --quiet || wget -qP /vagrant --no-check-certificate \ +# https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda-2.3.0-Linux-x86_64.sh & +echo "000b186207ad0eec7a667f34d82868f5 */vagrant/opensmile-2-3-0.tar.gz" | \ + md5sum -c --quiet || wget -qO- --no-check-certificate https://www.audeering.com/download/opensmile-2-3-0-tar-gz/?wpdmdl=4782 > /vagrant/opensmile-2-3-0.tar.gz & +echo "dd87c316e211891df8889c52d9167a5d */vagrant/Anaconda2-2019.03-Linux-x86_64.sh" | \ + md5sum -c --quiet || wget -qP /vagrant --no-check-certificate \ + https://repo.anaconda.com/archive/Anaconda2-2019.03-Linux-x86_64.sh & +echo "ef082e99726b14a2b433c59d002ffb3b */vagrant/MCR_R2017b_glnxa64_installer.zip" | \ + md5sum -c --quiet || wget -qP /vagrant --no-check-certificate \ + http://ssd.mathworks.com/supportfiles/downloads/R2017b/deployment_files/R2017b/installers/glnxa64/MCR_R2017b_glnxa64_installer.zip & + +if true; then +echo "-- Updating system --" +apt-get update -y +apt-get upgrade -y + +apt-get install -y git make automake libtool autoconf patch subversion fuse \ + libatlas-base-dev libatlas-dev liblapack-dev sox libav-tools g++ \ + zlib1g-dev libsox-fmt-all sshfs gcc-multilib libncurses5-dev unzip bc \ + openjdk-6-jre icedtea-netx-common icedtea-netx libxt-dev libx11-xcb1 \ + libc6-dev-i386 festival espeak python-setuptools gawk \ + libboost-all-dev + +apt-get autoremove -y && apt-get clean -y && apt-get autoclean -y +fi + +if false; then +if grep --quiet vagrant /etc/passwd +then + user="vagrant" +else + user="ubuntu" +fi +cd /home/${user} +fi + +# Kaldi and others want bash - otherwise the build process fails +[ $(readlink /bin/sh) == "dash" ] && ln -s -f bash /bin/sh + +echo "---- Done system updates @ `date` ----" + + +# Install Anaconda and Theano +#echo "Downloading Anaconda-2.3.0..." +#wget -q --no-check-certificate -P /tmp https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda-2.3.0-Linux-x86_64.sh +echo "Waiting for Anaconda download to finish" +# Anaconda download should be the first background process +#echo "c3100392685b5a62c8509c0588ce9376 */vagrant/Anaconda-2.3.0-Linux-x86_64.sh" | \ +# md5sum -c --quiet || wait `jobs -p|head -n 1` +#echo "Installing Anaconda-2.3.0..." +#sudo -S -u ${user} -i /bin/bash -l -c "bash /vagrant/Anaconda-2.3.0-Linux-x86_64.sh -b" +echo "dd87c316e211891df8889c52d9167a5d */vagrant/Anaconda2-2019.03-Linux-x86_64.sh" | \ + md5sum -c --quiet || wait `jobs -p|head -n 1` + +# install Matlab runtime environment +#echo "Download matlab installer" +#wget -q http://ssd.mathworks.com/supportfiles/downloads/R2017b/deployment_files/R2017b/installers/glnxa64/MCR_R2017b_glnxa64_installer.zip +echo "Waiting for Matlab download to finish" +# This should be our last background process +echo "ef082e99726b14a2b433c59d002ffb3b */vagrant/MCR_R2017b_glnxa64_installer.zip" | \ + md5sum -c --quiet || wait +( + echo "Installing Matlab..." + cd /tmp + unzip -q /vagrant/MCR_R2017b_glnxa64_installer.zip + ./install -mode silent -agreeToLicense yes + +# check if matlab is installed correctly +if [ $? -ne 0 ]; then + echo "*******************************" + echo " matlab installation failed" + echo "*******************************" + exit 1 +fi +) + +echo "---- Done bootstrapping DiViMe @ `date` ----" + + +# To Test: +# - vagrant ssh -c "launcher/test.sh" +# - vagrant ssh -c "yunitate.sh data/" (with a large wav file in data) +# - vagrant ssh -c "utils/high_volubility.py data/7085.wav --diar yunitator_universal --mode CHI --nb_chunks 50" +# ... diff --git a/conf/bootstrap-user.sh b/conf/bootstrap-user.sh new file mode 100755 index 0000000..0781fad --- /dev/null +++ b/conf/bootstrap-user.sh @@ -0,0 +1,175 @@ +#!/bin/bash + +# Please note that the bootstrap.sh script runs as root and not the vagrant user +# bootstrap-user.sh runs as the actual user + +echo "---- Start bootstrapping user (${USER}) part of DiViMe @ `date` ----" + +echo "Installing Anaconda..." +cd ${HOME} +bash /vagrant/Anaconda2-2019.03-Linux-x86_64.sh -b -p ${HOME}/anaconda + +# check if anaconda is installed correctly +if ! [ -x "$(command -v ${HOME}/anaconda/bin/conda)" ]; then + echo "*******************************" + echo " conda installation failed" + echo "*******************************" + exit 1 +fi + +if ! grep -q -i anaconda .bashrc; then + echo -e "\n# For DiViMe and Anaconda:" >> ${HOME}/.bashrc + echo "export PATH=${HOME}/launcher:${HOME}/utils:${HOME}/anaconda/bin:\$PATH" >> ${HOME}/.bashrc +fi + + +# python3 env +echo "Creating python3 env..." +${HOME}/anaconda/bin/conda env create -q -f /vagrant/conf/environment.yml +if [ $? -ne 0 ]; then PYTHON3_INSTALLED=false; fi + +echo "-- Conda report --" +${HOME}/anaconda/bin/conda list + + +# add Matlab stuff to path +echo -e "\n# For Matlab:" >> ${HOME}/.bashrc +echo 'LD_LIBRARY_PATH="/usr/local/MATLAB/MATLAB_Runtime/v93/runtime/glnxa64:/usr/local/MATLAB/MATLAB_Runtime/v93/bin/glnxa64:/usr/local/MATLAB/MATLAB_Runtime/v93/sys/os/glnxa64:$LD_LIBRARY_PATH"' >> ${HOME}/.bashrc + + +# optionally Install HTK (without it, some other tools will not work) +# the idea is to make users independently download HTK installer since +# we cannot redistribute +if [ -f /vagrant/HTK-3.4.1.tar.gz ]; then + echo "Installing HTK..." + cd ${HOME} + if [[ ! -d repos/htk ]]; then + cd ${HOME}/repos/ + tar zxf /vagrant/HTK-3.4.1.tar.gz + cd htk + ./configure --without-x --disable-hslab + sed -i "s/ /\t/g" HLMTools/Makefile # fix bad Makefile + make all + sudo make install + else + echo "Visibly HTK has been already installed..." + fi +else + HTK_INSTALLED=false; + echo "Can't find HTK-3.4.1.tar.gz. Assuming HTK not needed." +fi + + +# POPULATE THE REPOSITORY SECTION +echo "---- Changing into ${HOME}/repos @ `date` ----" +mkdir -p ${HOME}/repos && cd ${HOME}/repos + + +# Install OpenSMILE +( + echo "Installing OpenSMILE" + #wget -qO- --no-check-certificate https://www.audeering.com/download/opensmile-2-3-0-tar-gz/?wpdmdl=4782 | tar -xzf - + tar -xzf /vagrant/opensmile-2-3-0.tar.gz + sudo cp opensmile-2.3.0/bin/linux_x64_standalone_static/SMILExtract /usr/local/bin + sudo chmod +x /usr/local/bin/SMILExtract + +# check if opensmile if installed +if ! [ -x "$(command -v SMILExtract)" ]; then + echo "*******************************" + echo " OPENSMILE installation failed" + echo "*******************************" + OPENSMILE_INSTALLED=false; +fi +) + + +# Get OpenSAT=noisemes and dependencies +git clone -q http://github.com/srvk/OpenSAT --branch yunified # --branch v1.0 # need Dev + +${HOME}/anaconda/bin/pip --disable-pip-version-check install -q ipdb + +cp /vagrant/conf/.theanorc ${HOME}/ +#su ${user} -c "/home/${user}/anaconda/bin/conda install -q -y --no-update-deps theano=0.8.2" +${HOME}/anaconda/bin/conda install -q -y theano cudatoolkit pytorch-cpu -c pytorch + +# Install Yunitator and dependencies +git clone -q https://github.com/srvk/Yunitator +#su ${user} -c "/home/${user}/anaconda/bin/conda install -q -y --no-update-deps cudatoolkit" +#su ${user} -c "/home/${user}/anaconda/bin/conda install -q -y --no-update-deps pytorch-cpu -c pytorch" + +# Install VCM +git clone -q https://github.com/srvk/vcm + +# Install to-combo sad and dependencies (matlab runtime environnement) +git clone -q https://github.com/srvk/To-Combo-SAD +(cd To-Combo-SAD && git checkout 2ce2998) + +# Install DiarTK +git clone -q http://github.com/srvk/ib_diarization_toolkit +(cd ib_diarization_toolkit && git checkout b3e4deb) + +# Install WCE and dependencies +git clone -q https://github.com/aclew/WCE_VM +${HOME}/anaconda/bin/pip --disable-pip-version-check install -q keras tensorflow==1.13.1 +#su ${user} -c "/home/${user}/anaconda/bin/pip --disable-pip-version-check install -q -U tensorflow" + +# Phonemizer installation +git clone -q https://github.com/bootphon/phonemizer +( + cd phonemizer + git checkout 332b8dd + python setup.py build + sudo python setup.py install +) + +# Install pyannote (python 3) +## Need to add anaconda to the PATH to be able to activate divime. +export PATH=${HOME}/anaconda/bin:$PATH +source activate divime +pip --disable-pip-version-check install -q pyannote.metrics pyannote.core +conda deactivate + +#install launcher and utils +# cd /home/${user}/ +# git clone https://github.com/aclew/launcher.git +# chmod +x launcher/* +# git clone https://github.com/aclew/utils.git +# chmod +x utils/* + +# install pympi (for eaf -> rttm conversion) and tgt (for textgrid -> rttm conversion) +# and intervaltree (needed for rttm2scp.py) +# and recommonmark (needed to make html in docs/) +${HOME}/anaconda/bin/pip install --disable-pip-version-check pympi-ling tgt intervaltree recommonmark sphinx-markdown-tables sphinx_rtd_theme + +# Document the version of the tools that we have installed +echo "---- git logs follow ----" +cd ${HOME}/repos/ +for f in /vagrant *; +do + if [[ $f =~ opensmile* ]]; then continue; fi + echo -- git log ${f} -- + git --git-dir=${f}/.git log -1 +done +echo "---- git logs done ----" + + +# Link /vagrant/launcher and /vagrant/utils to home folder where scripts expect them +ln -s /vagrant/launcher /vagrant/utils ${HOME} + +# Silence error message from missing file +touch ${HOME}/.Xauthority + + +# Build the docs +echo "---- Building the docs... ----" +cd /vagrant/docs +make SPHINXBUILD=${HOME}/anaconda/bin/sphinx-build html + +echo "---- Sanity checks in user part of DiViMe @ `date` ----" + +# These files can be 'cached' for faster turn-around +[ -f /vagrant/opensmile-2-3-0.tar.gz ] && echo INFO: You can remove opensmile-2-3-0.tar.gz, if you don\'t plan on re-provisioning DiViMe any time soon. +[ -f /vagrant/Anaconda2-2019.03-Linux-x86_64.sh ] && echo INFO: You can remove Anaconda2-2019.03-Linux-x86_64.sh, if you don\'t plan on re-provisioning DiViMe any time soon. +[ -f /vagrant/MCR_R2017b_glnxa64_installer.zip ] && echo INFO: You can remove MCR_R2017b_glnxa64_installer.zip, if you don\'t plan on re-provisioning DiViMe any time soon. + +echo "---- Done bootstrapping user part of DiViMe @ `date` ----" diff --git a/conf/bootstrap.sh b/conf/bootstrap.sh new file mode 100755 index 0000000..a034679 --- /dev/null +++ b/conf/bootstrap.sh @@ -0,0 +1,292 @@ +# Please note that the bootstrap.sh script runs as root and not the vagrant user + +echo "---- Start bootstrapping DiViMe @ `date` ----" + +# Some reporting +echo "-- System information --" +echo nproc is `nproc` +df -h +cat /proc/meminfo + +# These downloads take a long time, start them in the background +#echo "c3100392685b5a62c8509c0588ce9376 */vagrant/Anaconda-2.3.0-Linux-x86_64.sh" | \ +# md5sum -c --quiet || wget -qP /vagrant --no-check-certificate \ +# https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda-2.3.0-Linux-x86_64.sh & +echo "dd87c316e211891df8889c52d9167a5d */vagrant/Anaconda2-2019.03-Linux-x86_64.sh" | \ + md5sum -c --quiet || wget -qP /vagrant --no-check-certificate \ + https://repo.anaconda.com/archive/Anaconda2-2019.03-Linux-x86_64.sh & +echo "ef082e99726b14a2b433c59d002ffb3b */vagrant/MCR_R2017b_glnxa64_installer.zip" | \ + md5sum -c --quiet || wget -qP /vagrant --no-check-certificate \ + http://ssd.mathworks.com/supportfiles/downloads/R2017b/deployment_files/R2017b/installers/glnxa64/MCR_R2017b_glnxa64_installer.zip & + +echo "-- Updating system --" +apt-get update -y +apt-get upgrade -y + +apt-get install -y git make automake libtool autoconf patch subversion fuse \ + libatlas-base-dev libatlas-dev liblapack-dev sox libav-tools g++ \ + zlib1g-dev libsox-fmt-all sshfs gcc-multilib libncurses5-dev unzip bc \ + openjdk-6-jre icedtea-netx-common icedtea-netx libxt-dev libx11-xcb1 \ + libc6-dev-i386 festival espeak python-setuptools gawk \ + libboost-all-dev + +apt-get autoremove -y && apt-get clean -y && apt-get autoclean -y + +if grep --quiet vagrant /etc/passwd +then + user="vagrant" +else + user="ubuntu" +fi +cd /home/${user} + +# Kaldi and others want bash - otherwise the build process fails +[ $(readlink /bin/sh) == "dash" ] && ln -s -f bash /bin/sh + +echo "-- System statistics report --" +echo $user +uname -a +free +#dpkg -l + +echo "---- Done system updates @ `date` ----" + +# Install OpenSMILE +su ${user} -c "mkdir -p /home/${user}/repos/" +echo "Downloading and installing OpenSMILE" +su ${user} -c "wget -qO- --no-check-certificate https://www.audeering.com/download/opensmile-2-3-0-tar-gz/?wpdmdl=4782 | tar -xzf - -C repos" +cp repos/opensmile-2.3.0/bin/linux_x64_standalone_static/SMILExtract /usr/local/bin && chmod +x /usr/local/bin/SMILExtract + +# check if opensmile if installed +if ! [ -x "$(command -v SMILExtract)" ]; then + echo "*******************************" + echo " OPENSMILE installation failed" + echo "*******************************" + OPENSMILE_INSTALLED=false; +fi + +# Install Anaconda and Theano +#echo "Downloading Anaconda-2.3.0..." +#wget -q --no-check-certificate -P /tmp https://3230d63b5fc54e62148e-c95ac804525aac4b6dba79b00b39d1d3.ssl.cf1.rackcdn.com/Anaconda-2.3.0-Linux-x86_64.sh +echo "Waiting for Anaconda download to finish" +# Anaconda download should be the first background process +#echo "c3100392685b5a62c8509c0588ce9376 */vagrant/Anaconda-2.3.0-Linux-x86_64.sh" | \ +# md5sum -c --quiet || wait `jobs -p|head -n 1` +#echo "Installing Anaconda-2.3.0..." +#sudo -S -u ${user} -i /bin/bash -l -c "bash /vagrant/Anaconda-2.3.0-Linux-x86_64.sh -b" +echo "dd87c316e211891df8889c52d9167a5d */vagrant/Anaconda2-2019.03-Linux-x86_64.sh" | \ + md5sum -c --quiet || wait `jobs -p|head -n 1` +echo "Installing Anaconda..." +sudo -S -u ${user} -i /bin/bash -l -c "bash /vagrant/Anaconda2-2019.03-Linux-x86_64.sh -b -p /home/${user}/anaconda" + +# check if anaconda is installed correctly +if ! [ -x "$(command -v /home/${user}/anaconda/bin/conda)" ]; then + echo "*******************************" + echo " conda installation failed" + echo "*******************************" + exit 1 +fi + +if ! grep -q -i anaconda .bashrc; then + echo -e "\n# For DiViMe and Anaconda:" >> /home/${user}/.bashrc + echo "export PATH=/home/${user}/launcher:/home/${user}/utils:/home/${user}/anaconda/bin:\$PATH" >> /home/${user}/.bashrc +fi + +#su ${user} -c "/home/${user}/anaconda/bin/conda install -q -y numpy scipy mkl dill tabulate joblib sphinx" +# clean up big installer +#rm -f /vagrant/Anaconda-2.3.0-Linux-x86_64.sh + +# To use miniconda (~40MB) instead of anaconda (~350MB), uncomment below block +# echo "Downloading Miniconda-4.5.11..." +# wget -q https://repo.continuum.io/miniconda/Miniconda2-4.5.11-Linux-x86_64.sh +# echo "Install miniconda (as Anaconda)" +# sudo -S -u vagrant -i /bin/bash -l -c "bash /home/${user}/Miniconda2-4.5.11-Linux-x86_64.sh -b -p /home/${user}/anaconda" +# # check if anaconda is installed correctly +# if ! [ -x "$(command -v /home/${user}/anaconda/bin/conda)" ]; then +# echo "*******************************" +# echo " conda installation failed" +# echo "*******************************" +# exit 1 +# fi + +# if ! grep -q -i anaconda .bashrc; then +# echo "export PATH=/home/${user}/launcher:/home/${user}/utils:/home/${user}/anaconda/bin:\$PATH" >> /home/${user}/.bashrc +# fi +# su ${user} -c "/home/${user}/anaconda/bin/conda install numpy scipy mkl dill tabulate joblib cython=0.22.1 sphinx" + +# # clean up big installer in home folder +# rm -f Miniconda2-4.5.11-Linux-x86_64.sh + +# python3 env +echo "Creating python3 env..." +#cp /vagrant/conf/environment.yml /home/${user}/ +su ${user} -c "/home/${user}/anaconda/bin/conda env create -q -f /vagrant/conf/environment.yml" +if [ $? -ne 0 ]; then PYTHON3_INSTALLED=false; fi + +# install Matlab runtime environment +#echo "Download matlab installer" +#wget -q http://ssd.mathworks.com/supportfiles/downloads/R2017b/deployment_files/R2017b/installers/glnxa64/MCR_R2017b_glnxa64_installer.zip +echo "Waiting for Matlab download to finish" +# This should be our last background process +echo "ef082e99726b14a2b433c59d002ffb3b */vagrant/MCR_R2017b_glnxa64_installer.zip" | \ + md5sum -c --quiet || wait +echo "Installing Matlab..." +cd /tmp +unzip -q /vagrant/MCR_R2017b_glnxa64_installer.zip +./install -mode silent -agreeToLicense yes + +# check if matlab is installed correctly +if [ $? -ne 0 ]; then + echo "*******************************" + echo " matlab installation failed" + echo "*******************************" + exit 1 +fi + +# add Matlab stuff to path +echo -e "\n# For Matlab:" >> /home/${user}/.bashrc +echo 'LD_LIBRARY_PATH="/usr/local/MATLAB/MATLAB_Runtime/v93/runtime/glnxa64:/usr/local/MATLAB/MATLAB_Runtime/v93/bin/glnxa64:/usr/local/MATLAB/MATLAB_Runtime/v93/sys/os/glnxa64:$LD_LIBRARY_PATH"' >> /home/${user}/.bashrc +#rm /vagrant/MCR_R2017b_glnxa64_installer.zip + +# optionally Install HTK (without it, some other tools will not work) +# the idea is to make users independently download HTK installer since +# we cannot redistribute +echo "Installing HTK..." +cd /home/${user} +if [ -f /vagrant/HTK-3.4.1.tar.gz ]; then + if [[ ! -d repos/htk ]]; then + cd /home/${user}/repos/ + tar zxf /vagrant/HTK-3.4.1.tar.gz + cd htk + ./configure --without-x --disable-hslab + sed -i "s/ /\t/g" HLMTools/Makefile # fix bad Makefile + make all + make install + else + echo "Visibly HTK has been already installed..." + fi +else + HTK_INSTALLED=false; + echo "Can't find HTK-3.4.1.tar.gz. Assuming HTK not needed." +fi + +echo "-- Conda report --" +su ${user} -c "/home/${user}/anaconda/bin/conda list" + +# POPULATE THE REPOSITORY SECTION +echo "---- Changing into /home/${user}/repos @ `date` ----" +cd /home/${user}/repos/ + +# Get OpenSAT=noisemes and dependencies +su ${user} -c "git clone -q http://github.com/srvk/OpenSAT --branch yunified" # --branch v1.0 # need Dev + +su ${user} -c "/home/${user}/anaconda/bin/pip --disable-pip-version-check install -q ipdb" + +su ${user} -c "cp /vagrant/conf/.theanorc /home/${user}/" +#su ${user} -c "/home/${user}/anaconda/bin/conda install -q -y --no-update-deps theano=0.8.2" +su ${user} -c "/home/${user}/anaconda/bin/conda install -q -y theano cudatoolkit pytorch-cpu -c pytorch" + +# Install Yunitator and dependencies +su ${user} -c "git clone -q https://github.com/srvk/Yunitator" +#su ${user} -c "/home/${user}/anaconda/bin/conda install -q -y --no-update-deps cudatoolkit" +#su ${user} -c "/home/${user}/anaconda/bin/conda install -q -y --no-update-deps pytorch-cpu -c pytorch" + +# Install VCM +su ${user} -c "git clone -q https://github.com/srvk/vcm" + +# Install to-combo sad and dependencies (matlab runtime environnement) +su ${user} -c "git clone -q https://github.com/srvk/To-Combo-SAD" +(cd To-Combo-SAD && su ${user} -c "git checkout 2ce2998") + +# Install DiarTK +su ${user} -c "git clone -q http://github.com/srvk/ib_diarization_toolkit" +(cd ib_diarization_toolkit && su ${user} -c "git checkout b3e4deb") + +# Install WCE and dependencies +git clone -q https://github.com/aclew/WCE_VM +su ${user} -c "/home/${user}/anaconda/bin/pip --disable-pip-version-check install -q keras tensorflow==1.13.1" +#su ${user} -c "/home/${user}/anaconda/bin/pip --disable-pip-version-check install -q -U tensorflow" + +# Phonemizer installation +su ${user} -c "git clone -q https://github.com/bootphon/phonemizer" +( + cd phonemizer + su ${user} -c "git checkout 332b8dd" + python setup.py build + python setup.py install +) + +# Install pyannote (python 3) +## Need to add anaconda to the PATH to be able to activate divime. +export PATH=/home/${user}/anaconda/bin:$PATH +source activate divime +pip --disable-pip-version-check install -q pyannote.metrics pyannote.core +conda deactivate + +#install launcher and utils +# cd /home/${user}/ +# git clone https://github.com/aclew/launcher.git +# chmod +x launcher/* +# git clone https://github.com/aclew/utils.git +# chmod +x utils/* + +# install pympi (for eaf -> rttm conversion) and tgt (for textgrid -> rttm conversion) +# and intervaltree (needed for rttm2scp.py) +# and recommonmark (needed to make html in docs/) +su ${user} -c "/home/${user}/anaconda/bin/pip install --disable-pip-version-check pympi-ling tgt intervaltree recommonmark sphinx-markdown-tables sphinx_rtd_theme" + +# Document the version of the tools that we have installed +echo "---- git logs follow ----" +cd /home/${user}/repos/ +for f in /vagrant *; +do + if [[ $f =~ opensmile* ]]; then continue; fi + echo -- git log ${f} -- + git --git-dir=${f}/.git log -1 +done +echo "---- git logs done ----" + +# Link /vagrant/launcher and /vagrant/utils to home folder where scripts expect them +su ${user} -c "ln -s /vagrant/launcher /home/${user}/" +su ${user} -c "ln -s /vagrant/utils /home/${user}/" + +# Silence error message from missing file +su ${user} -c "touch /home/${user}/.Xauthority" + +# Provisioning runs as root; we want files to belong to '${user}' (may no longer be needed) +chown -R ${user}:${user} /home/${user} + +# Build the docs +echo "---- Building the docs... ----" +cd /vagrant/docs +make SPHINXBUILD=/home/${user}/anaconda/bin/sphinx-build html + +# Installation status +echo "---- Build done ----" +if ! $PYTHON3_INSTALLED; then + echo "*********************************************" + echo "Warning: python3 environment is not installed" + echo "*********************************************" +fi +if ! $OPENSMILE_INSTALLED; then + echo "***********************************" + echo "Warning: OpenSMILE is not installed" + echo "***********************************" +fi +#if ! $HTK_INSTALLED; then +# echo "*****************************" +# echo "Warning: HTK is not installed" +# echo "*****************************" +#fi + +# These files can be 'cached' for faster turn-around +[ -f /vagrant/Anaconda2-2019.03-Linux-x86_64.sh ] && echo INFO: You can remove Anaconda2-2019.03-Linux-x86_64.sh, if you don\'t plan on re-provisioning DiViMe any time soon. +[ -f /vagrant/MCR_R2017b_glnxa64_installer.zip ] && echo INFO: You can remove MCR_R2017b_glnxa64_installer.zip, if you don\'t plan on re-provisioning DiViMe any time soon. + +echo "---- Done bootstrapping DiViMe @ `date` ----" + +# To Test: +# - vagrant ssh -c "launcher/test.sh" +# - vagrant ssh -c "yunitate.sh data/" (with a large wav file in data) +# - vagrant ssh -c "utils/high_volubility.py data/7085.wav --diar yunitator_universal --mode CHI --nb_chunks 50" +# ... diff --git a/conf/environment.yml b/conf/environment.yml new file mode 100644 index 0000000..7534d32 --- /dev/null +++ b/conf/environment.yml @@ -0,0 +1,25 @@ +name: divime +channels: + - defaults + - conda-forge + - pytorch +dependencies: + - python=3.6.8 # python2 dependency + - cython + - numpy + - scipy + - mkl + - mkl-service + - dill + - tabulate + - joblib + - cudatoolkit + - pytorch-cpu=1.0.1 + - scikit-learn + - pip: + - theano # install theano + - intervaltree + - pympi-ling # eaf -> rttm conversion + - tgt # textgrid -> rttm conversion + - ipdb # divime specific + diff --git a/conf/reconf-slurm.sh b/conf/reconf-slurm.sh deleted file mode 100644 index 68d9208..0000000 --- a/conf/reconf-slurm.sh +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/bash - -sed -i `lscpu -p|grep -v '#'|wc -l|awk '{printf ("s/Procs=DYN/Procs=%d/",$0)}'` /etc/slurm-llnl/slurm.conf diff --git a/conf/slurm.conf b/conf/slurm.conf deleted file mode 100644 index f1ad0c8..0000000 --- a/conf/slurm.conf +++ /dev/null @@ -1,172 +0,0 @@ -############################################################################### -# Sample configuration file for SLURM 2 -############################################################################### -# -# This file holds the system-wide SLURM configuration. It is read -# by SLURM clients, daemons, and the SLURM API to determine where -# and how to contact the SLURM controller, what other nodes reside -# in the current cluster, and various other configuration information. -# -# SLURM configuration parameters take the form Keyword=Value, where -# at this time, no spacing is allowed to surround the equals (=) sign. -# Many of the config values are not mandatory, and so may be left -# out of the config file. We will attempt to list the default -# values for those parameters in this file. -# -# This simple configuration provides a control machine named "laptop" -# to run the Slurm's central management daemon and a single node -# named "server" which execute jobs. Both machine should have Slurm -# installed and use this configuration file. If you have a similar -# configuration just change the values of ControlMachine, for the -# control machine and PartitionName and NodeName for job execution -# -############################################################################### -# -ControlMachine=localhost -#ControlAddr= -#BackupController= -#BackupAddr= -# -AuthType=auth/munge -#AuthType=auth/none -CacheGroups=0 -#CheckpointType=checkpoint/none -CryptoType=crypto/munge -#CryptoType=none -#DisableRootJobs=NO -#EnforcePartLimits=NO -#Epilog= -#PrologSlurmctld= -#FirstJobId=1 -JobCheckpointDir=/var/lib/slurm-llnl/checkpoint -#JobCredentialPrivateKey= -#JobCredentialPublicCertificate= -#JobFileAppend=0 -#JobRequeue=1 -#KillOnBadExit=0 -#Licenses=foo*4,bar -#MailProg=/usr/bin/mail -#MaxJobCount=5000 -MpiDefault=none -#MpiParams=ports:#-# -#PluginDir= -#PlugStackConfig= -#PrivateData=jobs -ProctrackType=proctrack/pgid -#Prolog= -#PrologSlurmctld= -#PropagatePrioProcess=0 -#PropagateResourceLimits= -#PropagateResourceLimitsExcept= -ReturnToService=1 -#SallocDefaultCommand= -SlurmctldPidFile=/var/run/slurm-llnl/slurmctld.pid -SlurmctldPort=6817 -SlurmdPidFile=/var/run/slurm-llnl/slurmd.pid -SlurmdPort=6818 -SlurmdSpoolDir=/var/lib/slurm-llnl/slurmd -SlurmUser=slurm -#SrunEpilog= -#SrunProlog= -StateSaveLocation=/var/lib/slurm-llnl/slurmctld -SwitchType=switch/none -#TaskEpilog= -TaskPlugin=task/none -#TaskPluginParam= -#TaskProlog= -#TopologyPlugin=topology/tree -#TmpFs=/tmp -#TrackWCKey=no -#TreeWidth= -#UnkillableStepProgram= -#UnkillableStepTimeout= -#UsePAM=0 -# -# -# TIMERS -#BatchStartTimeout=10 -#CompleteWait=0 -#EpilogMsgTime=2000 -#GetEnvTimeout=2 -#HealthCheckInterval=0 -#HealthCheckProgram= -InactiveLimit=0 -KillWait=30 -#MessageTimeout=10 -#ResvOverRun=0 -MinJobAge=300 -#OverTimeLimit=0 -SlurmctldTimeout=300 -SlurmdTimeout=300 -#UnkillableStepProgram= -#UnkillableStepTimeout=60 -Waittime=0 -# -# -# SCHEDULING -#DefMemPerCPU=0 -FastSchedule=0 -#MaxMemPerCPU=0 -#SchedulerRootFilter=1 -#SchedulerTimeSlice=30 -SchedulerType=sched/backfill -SchedulerPort=7321 -SelectType=select/cons_res -SelectTypeParameters=CR_Core_Memory -#SelectType=select/linear -#SelectTypeParameters= -# -# -# JOB PRIORITY -#PriorityType=priority/basic -#PriorityDecayHalfLife= -#PriorityFavorSmall= -#PriorityMaxAge= -#PriorityUsageResetPeriod= -#PriorityWeightAge= -#PriorityWeightFairshare= -#PriorityWeightJobSize= -#PriorityWeightPartition= -#PriorityWeightQOS= -# -# -# LOGGING AND ACCOUNTING -#AccountingStorageEnforce=0 -#AccountingStorageHost= -#AccountingStorageLoc= -#AccountingStoragePass= -#AccountingStoragePort= -AccountingStorageType=accounting_storage/none -#AccountingStorageUser= -ClusterName=cluster -#DebugFlags= -#JobCompHost= -#JobCompLoc= -#JobCompPass= -#JobCompPort= -JobCompType=jobcomp/none -#JobCompUser= -JobAcctGatherFrequency=30 -JobAcctGatherType=jobacct_gather/none -SlurmctldDebug=3 -SlurmctldLogFile=/var/log/slurm-llnl/slurmctld.log -SlurmdDebug=3 -SlurmdLogFile=/var/log/slurm-llnl/slurmd.log -# -# -# POWER SAVE SUPPORT FOR IDLE NODES (optional) -#SuspendProgram= -#ResumeProgram= -#SuspendTimeout= -#ResumeTimeout= -#ResumeRate= -#SuspendExcNodes= -#SuspendExcParts= -#SuspendRate= -#SuspendTime= -# -# -# COMPUTE NODES -NodeName=localhost Procs=1 State=UNKNOWN -PartitionName=standard Nodes=localhost Default=YES MaxTime=INFINITE State=UP -# Shared=Yes diff --git a/conf/slurm.sv.conf b/conf/slurm.sv.conf deleted file mode 100644 index c80d806..0000000 --- a/conf/slurm.sv.conf +++ /dev/null @@ -1,14 +0,0 @@ -[program:munge] -startsecs=0 -command=/usr/sbin/munged -f - -[program:slurmconfig] -startsecs=0 -priority=100 -command=/root/reconf-slurm.sh - -[program:slurmctl] -command=/usr/sbin/slurmctld -D - -[program:slurmd] -command=/usr/sbin/slurmd -D diff --git a/conf/supervisor.conf b/conf/supervisor.conf deleted file mode 100644 index 95242cd..0000000 --- a/conf/supervisor.conf +++ /dev/null @@ -1,7 +0,0 @@ -[supervisord] -nodaemon=true -logfile=/var/log/supervisor/supervisord.log -pidfile=/var/log/supervisor/supervisord.pid - -[include] -files = /etc/supervisor/conf.d/*.conf diff --git a/conf/vad/turnDetector.conf.inc b/conf/turnDetector.conf.inc similarity index 100% rename from conf/vad/turnDetector.conf.inc rename to conf/turnDetector.conf.inc diff --git a/conf/update.sh b/conf/update.sh new file mode 100755 index 0000000..fe1506f --- /dev/null +++ b/conf/update.sh @@ -0,0 +1,26 @@ +if grep --quiet vagrant /etc/passwd +then + user="vagrant" +else + user="ubuntu" +fi + +echo "Check git updates" +############################################# +# Get OpenSAT and all the tools +# change version tag as necessary for each tools +cd /home/${user}/repos +(cd "OpenSAT"; git checkout master; git pull) +# (cd "OpenSAT"; git checkout master; git pull; git checkout v2.0) +(cd "ib_diarization_toolkit"; git checkout master; git pull) +# (cd "ib_diarization_toolkit" ; git checkout master; git pull; git checkout v2.0) +(cd "Yunitator" ;git checkout master; git pull) +# (cd "Yunitator" ; git checkout master; git pull; git checkout v2.0) +(cd "vcm"; git checkout master; git pull) +# (cd "vcm"; git checkout master; git pull; git checkout v2.0) +(cd "To-Combo-SAD" ; git checkout master; git pull) +# (cd "To-Combo-SAD" ; git checkout master; git pull; git checkout v2.0) +(cd "WCE_VM" ; git checkout master; git pull) +# (cd "WCE_VM" ; git checkout master; git pull; git checkout v2.0) + +############################################# diff --git a/conf/vad/README.txt b/conf/vad/README.txt deleted file mode 100644 index 4b1672a..0000000 --- a/conf/vad/README.txt +++ /dev/null @@ -1,55 +0,0 @@ -/////////////////////////////////////////////////////////////////////////////////////// -///////// > openSMILE LSTM-RNN voice activity detector< ////////////////// -///////// ////////////////// -///////// (c) audEERING UG (haftungsbeschränkt), ////////////////// -///////// All rights reserverd. ////////////////// -/////////////////////////////////////////////////////////////////////////////////////// - - -This folder contains configuration files and models for a -prototype LSTM-RNN Voice Activity Detector. -This is meant as an example to show how to implement such -a VAD in openSMILE with the RNN components and to provide -a simple VAD for prototype development. The noise-robustness -of this VAD is *not* state-of-the-art, it has been built on -a small-scale research data-set, mainly for clean speech. - - -This config implements a voice activity detector as described in: - - Eyben, F.; Weninger, F.; Squartini, S.; Schuller, B.: - "Real-life voice activity detection with LSTM Recurrent Neural - Networks and an application to Hollywood movies," - Proc. of ICASSP 2013, pp. 483-487, 26-31 May 2013. - doi: 10.1109/ICASSP.2013.6637694 - -The models are not the same models as used in the paper, but a -preliminary version of the data presented in the paper. - -If you need a more accurate and noise-robust VAD, -contact audEERING at info@audeering.com for a demo of -our latest commercial VAD technology. -We have extremely robust detectors, which are even capable -of reliably detecting sung speech (vocals) in single channel -polyphonic music recordings. - - -There are two main config files to start from here: - vad_opensource.conf : Dumps vad activations per frame to CSV - vad_segmenter.conf : Creates wave files with voice segments - and optionally writes activations to CSV - -Run - SMILExtract -ccmdHelp -C -to see the commandline options supported by these config files. - -If you want to include the VAD as a module in your own -configuration files, you can use: - vad_opensource.conf.inc - turnDetector.conf.inc - -See the comments in these config files for level input/output -naming requirements. See the main config files for examples -of how to use the module includes. - - diff --git a/conf/vad/lstmvad_rplp18d_12.net b/conf/vad/lstmvad_rplp18d_12.net deleted file mode 100644 index be23ad2..0000000 --- a/conf/vad/lstmvad_rplp18d_12.net +++ /dev/null @@ -1,57 +0,0 @@ -autosave true -batchLearn false -bidirectional false -dataFileNum 0 -dataFraction 1.0 -dataset train -display false -displayAll false -errorTest false -gradCheck false -hiddenSize 50 -hiddenType lstm -initWeightRange 0.1 -inputNoiseDev 0.3 -inputSize 36 -labelDelimiter , -learnRate 1e-5 -loadWeights true -maxTestsNoBest 10 -momentum 0.9 -netUseImportanceWeight false -optimiser steepest -randSeed 2 -recurrent true -saveFileBase trained_nets/net4_proto.vad_rplp18d.seed2 -saveFileNoTimestamp true -seqsPerWeightUpdate 1 -sequence 0 -subsampleBias false -subsampleType tanh -targetLabels sil -task regression -testDistortions false -totalEpochs 40 -trainFile /home/iaa1/data/eyb/vad2012/ncout/train_timit_part0.vad_RASTA_PLP_18_new_D_std.nc,/home/iaa1/data/eyb/vad2012/ncout/train_buckeye_part0.vad_RASTA_PLP_18_new_D_std.nc,/home/iaa1/data/eyb/vad2012/ncout/train_timit_part1.vad_RASTA_PLP_18_new_D_std.nc,/home/iaa1/data/eyb/vad2012/ncout/train_buckeye_part1.vad_RASTA_PLP_18_new_D_std.nc,/home/iaa1/data/eyb/vad2012/ncout/train_timit_part2.vad_RASTA_PLP_18_new_D_std.nc,/home/iaa1/data/eyb/vad2012/ncout/train_buckeye_part2.vad_RASTA_PLP_18_new_D_std.nc -valFile /home/iaa1/data/eyb/vad2012/ncout/val_timit_part0.vad_RASTA_PLP_18_new_D_std.nc,/home/iaa1/data/eyb/vad2012/ncout/val_buckeye_part0.vad_RASTA_PLP_18_new_D_std.nc -verbose false -weightDistortion 0 -trainer_epoch 14 -weightContainer_bias_to_hidden_0_0_deltas 200 -1.99516e-05 4.48064e-05 -0.000767233 -9.14626e-05 3.74249e-05 2.83748e-05 0.000239365 5.80469e-05 0.000106838 -7.50933e-05 0.000263419 -0.000377648 -7.56347e-06 2.09836e-05 -0.000531158 1.4857e-05 5.43028e-05 1.542e-05 0.000486663 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-0.417872 0.0559441 0.16125 -0.0321918 0.105246 -0.304421 0.0210794 0.00638279 0.103374 -0.0240736 0.176431 0.00167034 0.00176263 0.019307 0.0437794 -0.0407008 0.15164 0.256638 -0.243051 -0.0234826 0.0427931 0.0314591 0.150012 -0.102223 -0.0941515 -0.0728122 -0.0252282 -0.0547703 0.0272074 -0.0126805 0.00370477 0.0193586 0.0134075 -0.0187448 -0.0449414 diff --git a/conf/vad/output_segment_0001.wav b/conf/vad/output_segment_0001.wav deleted file mode 100644 index c85fd4c..0000000 Binary files a/conf/vad/output_segment_0001.wav and /dev/null differ diff --git a/conf/vad/output_segment_0002.wav b/conf/vad/output_segment_0002.wav deleted file mode 100644 index 3a1d47a..0000000 Binary files a/conf/vad/output_segment_0002.wav and /dev/null differ diff --git a/conf/vad/rplp18d_norm.dat b/conf/vad/rplp18d_norm.dat deleted file mode 100644 index b93271f..0000000 Binary files a/conf/vad/rplp18d_norm.dat and /dev/null differ diff --git a/conf/vad/vad_opensource.conf.inc b/conf/vad/vad_opensource.conf.inc deleted file mode 100644 index bda8551..0000000 --- a/conf/vad/vad_opensource.conf.inc +++ /dev/null @@ -1,189 +0,0 @@ -/////////////////////////////////////////////////////////////////////////////////////// -///////// > openSMILE LSTM-RNN voice activity detector< ////////////////// -///////// ////////////////// -///////// (c) audEERING UG (haftungsbeschränkt), ////////////////// -///////// All rights reserverd. ////////////////// -/////////////////////////////////////////////////////////////////////////////////////// - - -[componentInstances:cComponentManager] -instance[framer].type = cFramer -instance[vectorPreemphasis].type = cVectorPreemphasis -instance[windower].type = cWindower - ; magnitude FFT spectrum -instance[fft].type=cTransformFFT -instance[fftmag].type=cFFTmagphase - ; compute critical-bands from power spectrum -instance[melspec_VAD].type=cMelspec - ; compute PLP 1-12 from critical-band spectrum -instance[plp_VAD].type=cPlp - ; compute log-energy from raw signal frames - ; (not windowed, not pre-emphasised: that's the way HTK does it) -;instance[energy_VAD].type=cEnergy - ; compute delta coefficients from PLP and energy -instance[delta_VAD].type=cDeltaRegression - ; compute acceleration coefficients from delta coefficients of PLP and energy -;instance[accel_VAD].type=cDeltaRegression - ; MVN (2x) -instance[mvn_VAD].type = cVectorMVN -;; experimental MVN adaption, uncomment to enable -;; and also change reader.dmLevel of lstm_vad component -;instance[mn2_VAD].type = cVectorMVN - ; LSTM for VAD -instance[lstm_vad].type=cRnnProcessor -instance[dataSelector].type = cDataSelector - -[framer:cFramer] -reader.dmLevel = wave -writer.dmLevel = frames -// nameAppend = -copyInputName = 1 -frameMode = fixed -// frameListFile = -// frameList = -frameSize = 0.025000 -frameStep = 0.01 -frameCenterSpecial = left -buffersize = 100000 -noPostEOIprocessing = 1 - -[vectorPreemphasis:cVectorPreemphasis] -reader.dmLevel = frames -writer.dmLevel = prframes -// nameAppend = -copyInputName = 1 -processArrayFields = 1 -k = 0.970000 -de = 0 - -[windower:cWindower] -reader.dmLevel = prframes -writer.dmLevel = winframes -// nameAppend = -copyInputName = 1 -processArrayFields = 1 -gain = 1 -offset = 0 -winFunc = ham -;sigma = 0.400000 -;alpha = 0.160000 - -[fft:cTransformFFT] -reader.dmLevel=winframes -writer.dmLevel=fft - -[fftmag:cFFTmagphase] -reader.dmLevel=fft -writer.dmLevel=fftmag - -[melspec_VAD:cMelspec] -reader.dmLevel=fftmag -writer.dmLevel=melspec_power -; no htk compatible sample value scaling -htkcompatible = 0 -nBands = 26 -; use power spectrum instead of magnitude spectrum -usePower = 1 -lofreq = 0 -hifreq = 8000 -specScale = mel - -[plp_VAD:cPlp] -reader.dmLevel=melspec_power -writer.dmLevel=plp_VAD -buffersize=100 -firstCC = 1 -lpOrder = 18 -cepLifter = 22 -compression = 0.33 -htkcompatible = 0 -newRASTA = 1 -RASTA = 0 -rastaUpperCutoff = 29.0 -rastaLowerCutoff = 0.9 -doIDFT = 1 -doLpToCeps = 1 -doLP = 1 -doInvLog = 1 -doAud = 1 -doLog = 1 - -[energy_VAD:cEnergy] -reader.dmLevel=winframes -writer.dmLevel=energy_VAD -htkcompatible=1 -rms = 0 -log = 1 - -[delta_VAD:cDeltaRegression] -reader.dmLevel=plp_VAD -writer.dmLevel=plpde_VAD -deltawin=2 -blocksize=1 - -[accel_VAD:cDeltaRegression] -reader.dmLevel=plpde_VAD -writer.dmLevel=plpdede_VAD -deltawin=2 -blocksize=1 - -[mvn_VAD:cVectorMVN] -reader.dmLevel = plp_VAD;plpde_VAD -writer.dmLevel = plpmvn_VAD -// nameAppend = -copyInputName = 1 -processArrayFields = 0 -mode = transform -initFile = rplp18d_norm.dat -htkcompatible = 0 -meanEnable = 1 -stdEnable = 1 -normEnable = 0 - -; experimental incremental MVN adaptation -; disabled by default -[mn2_VAD:cVectorMVN] -reader.dmLevel = plpmvn_VAD -writer.dmLevel = plpmvn2_VAD -processArrayFields = 0 -mode = incremental -updateMethod = fix -fixedBuffer = 30.0 -meanEnable = 1 -stdEnable = 0 -normEnable = 0 - -[lstm_vad:cRnnProcessor] -reader.dmLevel = plpmvn_VAD -;; enable this, if you enable the -;; experimental incremental adaptaion -; reader.dmLevel = plpmvn2_VAD -writer.dmLevel = vad_VAD -netfile = lstmvad_rplp18d_12.net - -[dataSelector:cDataSelector] -reader.dmLevel = vad_VAD -writer.dmLevel = vad_VAD_voice -nameAppend = vadBin -copyInputName = 1 -selectedRange = 0 -elementMode = 1 - -[turn:cTurnDetector] -reader.dmLevel=vad_VAD_voice -writer.dmLevel=isTurn -readVad=1 -threshold = -0.1 -threshold2 = -0.1 -writer.levelconf.noHang=1 -msgInterval = 0 -messageRecp = waveSinkCut -eventRecp = waveSinkCut -statusRecp = waveSinkCut -debug=\cm[turndebug{4}:set this to 1 to see turn/speaking debug messages] -maxTurnLength=0 -maxTurnLengthGrace=1 -nPre = 8 -nPost = 35 - - diff --git a/conf/vad/vad_segmenter_aclew.conf b/conf/vad_segmenter_aclew.conf similarity index 87% rename from conf/vad/vad_segmenter_aclew.conf rename to conf/vad_segmenter_aclew.conf index 2c81751..2b07ec4 100644 --- a/conf/vad/vad_segmenter_aclew.conf +++ b/conf/vad_segmenter_aclew.conf @@ -24,8 +24,7 @@ buffersize_sec = 10 ; inculdes a VAD module \{vad_opensource.conf.inc} ; and a turn detector module -\{turnDetector.conf.inc} -;\{\cm[turnDetector(T){turnDetector.conf}:filename of turn detector config file]} +\{\cm[turnDetector(T){turnDetector.conf.inc}:filename of turn detector config file]} [componentInstances:cComponentManager] @@ -36,7 +35,7 @@ instance[csvSink].type = cCsvSink printLevelStats = 0 [waveSinkCut:cWaveSinkCut] -reader.dmLevel = frames +reader.dmLevel = framesVAD fileBase = \cm[waveoutput(W){output_segment_}:prefix of WAV output files] fileExtension = .wav fileNameFormatString = %s%04d%s @@ -50,8 +49,7 @@ sampleFormat = 16bit ; round-off errors, so you can force it manually here: ;forceSampleRate = 44100 ;forceSampleRate = 16000 -saveSegmentTimes = \cm[saveSegmentTimes{times.dat}:file to save segment times to] -showSegmentTimes = 1 +saveSegmentTimes = \cm[saveSegmentTimes{?}:file to save segment times to] [csvSink:cCsvSink] reader.dmLevel=vad_VAD_voice diff --git a/data/.gitignore b/data/.gitignore old mode 100644 new mode 100755 diff --git a/docs/Makefile b/docs/Makefile new file mode 100755 index 0000000..bfa2234 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +SPHINXPROJ = DiViMe +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/images/example_visu.png b/docs/images/example_visu.png new file mode 100644 index 0000000..5492cf5 Binary files /dev/null and b/docs/images/example_visu.png differ diff --git a/docs/make.bat b/docs/make.bat new file mode 100755 index 0000000..fba88a1 --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,36 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build +set SPHINXPROJ=DiViMe + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% + +:end +popd diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000..5f0d84a --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1 @@ +sphinx-markdown-tables diff --git a/docs/source/WCEtool.md b/docs/source/WCEtool.md new file mode 100644 index 0000000..767880a --- /dev/null +++ b/docs/source/WCEtool.md @@ -0,0 +1,68 @@ +# Word count estimation (WCE) tool + +This section contains documentation for word count estimation tool. + + +## Basic description + +The purpose of the WCE tool is to provide an estimate of the number of words in an utterance that is provided as an input to the algorithm. It is based on automatic syllabification of speech, followed by mapping of syllable counts + a number of other acoustic features to word counts. The basic functionality is described in Räsänen et al. (submitted). + +There are two basic ways to use the current tool: + +1) An out-of-the-box version that can be used directly on any speech data , and +2) An adapted version, where the WCE system is first adapted to data provided by the user, and then performs more accurately on similar data. NOTE: This version is going to be obsolote, as the DiViMe will move on to using language-independent syllable count estimation instead of word ount estimation. + +In practice, out-of-the-box variant provides only syllable count estimates for the input data, while the adaptation mode can be used +to re-train the system to provide meaningful word count estimates in any language or other data domain. These two operation modes are detailed further below. + +Note that the tool assumes that the incoming signals are already segmented into utterances (e.g., using a SAD and/or a diarization tool), as it does not +have an internal module for separating speech from non-speech contents. + +By default, the WCE tool is using a bi-directional long short-term memory (LSTM) -based syllabifier, trained on four different languages. The package also +contains a syllabifier stage from speaking-rate estimator published by Wang & Narayanan (2007), as implemented for MATLAB in Räsänen et al. (2018), and an +oscillator-based syllabifier described in Räsänen et al. (2018). The default syllabifier can be changed from configuration files (see "Changing configuration"). + +All research or other use utilizing this WCE tool should cite the following paper: + +Räsänen, O., Seshadri, S., Karadayi, J., Riebling, E., Bunce, J., Cristia, A., Metze, F., Casillas, M., Rosemberg, C., Bergelson, E., & Soderstrom, M. (submitted). Automatic word count estimation from daylong child-centered recordings in various language environments using language-independent syllabification of speech. In review. + +## Instructions for direct use (out-of-the-box version) + +To get syllable count estimates on your audio files, run + +vagrant ssh -c "~/launcher/estimateWCE.sh data/my_audiofolder/ data/WCE_output.txt" + +which will run WCE on all .wav files in data/my_audiofolder/ and output results to data/WCE_output.txt. + +## Instructions for running out-of-the-box WCE for SAD-based utterance segments + +If you have .rttm files corresponding to your .wav files that define speech segments in each recording (see SAD tool documentation on DiViMe), you can place your .wav files and corresponding .rttm files into on the VM (e.g., /vagrant/data/mydatafolder/), and then run: + +vagrant ssh -c "~/launcher/WCE_from_SAD_outputs.sh " + +e.g., + +vagrant ssh -c "~/launcher/WCE_from_SAD_outputs.sh /vagrant/data/mydatafolder/ opensmileSad + +Output syllable counts will be wirtten as .rttm files of format WCE__.rttm and located in . + +NOTE: The .rttm files must be named as _.rttm, where is the original .wav file name located in the same . + + +## Instructions for language-adapted use: OBSOLETE. WILL BE UPDATED IN A NEW VERSION OF THE SYSTEM. + + +## Main references for this tool: + +Räsänen, O., Seshadri, S. & Casillas, M. (2018). Comparison of Syllabification Algorithms and Training Strategies for Robust Word Count Estimation across Different Languages and Recording Conditions. Proc. Interspeech-201, Hyderabad, India, pp. 1200–1204. + +Räsänen, O., Seshadri, S., Karadayi, J., Riebling, E., Bunce, J., Cristia, A., Metze, F., Casillas, M., Rosemberg, C., Bergelson, E., & Soderstrom, M. (submitted). Automatic word count estimation from daylong child-centered recordings in various language environments using language-independent syllabification of speech. In review. + +Räsänen, O., Doyle, G., & Frank, M. C. (2018). Pre-linguistic segmentation of speech into syllable-like units. Cognition, 171, 130–150. + +Wang, D., & Narayanan, S. (2007). Robust speech rate estimation for spontaneous speech. IEEE Transactions on Audio, Speech, and Language Processing, 15, 2190–2201. + + +## Questions and bug reports + +Send questions & Bug reports to Okko Räsänen (firstname.surname @ aalto.fi) diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100755 index 0000000..c341069 --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,171 @@ +# -*- coding: utf-8 -*- +# +# DiViMe documentation build configuration file, created by +# sphinx-quickstart on Wed Oct 10 11:23:30 2018. +# +# This file is execfile()d with the current directory set to its +# containing dir. +# +# Note that not all possible configuration values are present in this +# autogenerated file. +# +# All configuration values have a default; values that are commented out +# serve to show the default. + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +# import os +# import sys +# sys.path.insert(0, os.path.abspath('.')) +from recommonmark.parser import CommonMarkParser + +# -- General configuration ------------------------------------------------ + +# If your documentation needs a minimal Sphinx version, state it here. +# +# needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx_markdown_tables', +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +source_suffix = ['.rst', '.md'] +#source_suffix = '.md' +source_parsers = {'.md': 'recommonmark.parser.CommonMarkParser',} + +# The master toctree document. +master_doc = 'index' + +# General information about the project. +project = u'DiViMe' +copyright = u'2018, ACLEW' +author = u'ACLEW' + +# The version info for the project you're documenting, acts as replacement for +# |version| and |release|, also used in various other places throughout the +# built documents. +# +# The short X.Y version. +version = u'1.0' +# The full version, including alpha/beta/rc tags. +release = u'1.0' + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This patterns also effect to html_static_path and html_extra_path +exclude_patterns = [] + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = 'sphinx' + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = False + + +# -- Options for HTML output ---------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'sphinx_rtd_theme' + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +# +# html_theme_options = {} + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + +# Custom sidebar templates, must be a dictionary that maps document names +# to template names. +# +# This is required for the alabaster theme +# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars +html_sidebars = { + '**': [ + 'relations.html', # needs 'show_related': True theme option to display + 'searchbox.html', + ] +} + + +# -- Options for HTMLHelp output ------------------------------------------ + +# Output file base name for HTML help builder. +htmlhelp_basename = 'DiViMedoc' + + +# -- Options for LaTeX output --------------------------------------------- + +latex_elements = { + # The paper size ('letterpaper' or 'a4paper'). + # + # 'papersize': 'letterpaper', + + # The font size ('10pt', '11pt' or '12pt'). + # + # 'pointsize': '10pt', + + # Additional stuff for the LaTeX preamble. + # + # 'preamble': '', + + # Latex figure (float) alignment + # + # 'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, 'DiViMe.tex', u'DiViMe Documentation', + u'ACLEW', 'manual'), +] + + +# -- Options for manual page output --------------------------------------- + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + (master_doc, 'divime', u'DiViMe Documentation', + [author], 1) +] + + +# -- Options for Texinfo output ------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'DiViMe', u'DiViMe Documentation', + author, 'DiViMe', 'One line description of project.', + 'Miscellaneous'), +] + + + diff --git a/docs/source/extra-tools.md b/docs/source/extra-tools.md new file mode 100755 index 0000000..538301e --- /dev/null +++ b/docs/source/extra-tools.md @@ -0,0 +1,74 @@ +# Extra tools + +## Getting sample data + +### ACLEW Starter Dataset + +The ACLEW Starter dataset is freely available, and can be downloaded in order to test the tools. +To download it, using your terminal, as explained before, go in the DiViMe folder and do: +```$ vagrant ssh -c "launcher/get_aclewStarter.sh data/aclewStarter/"``` + +This will create a folder called aclewStarter inside data, in which you will find the audio files from the public dataset and their corresponding .rttm annotations. At the present time, there are only two matched annotation-wav files, totalling 10 minutes of running recordings + +You can then use the tools mentioned before, by replacing the "data/" folder in the command given in the previous paragraph by "aclewStarter/", E.G for noisemes: + +```$ vagrant ssh -c "launcher/noisemesSad.sh /"``` + +#### Reference for the ACLEW Starter dataset: + +Bergelson, E., Warlaumont, A., Cristia, A., Casillas, M., Rosemberg, C., Soderstrom, M., Rowland, C., Durrant, S. & Bunce, J. (2017). Starter-ACLEW. Databrary. Retrieved August 15, 2018 from http://doi.org/10.17910/B7.390. + + +## Using scripts in the Utilities + +### elan2rttm.py + +Convert annotations made using the ELAN tool, in .eaf format, into rttm transcriptions. Please note that, using this script, some information is lost, notably +the vocal maturity annotation (coded in tier vcm@CHI), does not appear in the RTTM format. These information could be retrieved and put in the rttm. If you need this information in the RTTM, please raise an issue on github. + +### textgrid2rttm.py + +Convert annotations made using Praat, in .TextGrid format, into rttm transcriptions. Requires: + +* [pympi](https://github.com/dopefishh/pympi) +* [tgt](https://github.com/hbuschme/TextGridTools/) + +We provide code to translate annotations from other formats into RTTM: + +**ELAN .eaf format** + +WARNING: the current version does not handle subtypes when parsing annotations e.g. TIER\_ID 'CHI' would be written in the RTTM output file but 'vmc@CHI' would not. This is due to the time references being based on other TIER\_ID's annotations for subtypes. + +From within the machine, you would run the script as follows: + +``` +python utils/elan2rttm.py -i my_file.eaf -o my_output_folder +``` + +**Praat TextGrid format** + +From within the machine, you would run the script as follows: + +``` +python utils/textgrid2rttm.py my_input_folder +``` + + +### adjust_timestamps.py + +This script is specific to the data in ACLEW, with the ACLEW annotations conventions. It takes as input a daylong recording in wav format (available on databrary), and a transcription in .eaf format that contains annotated segment coded in an "on_off" tier (or "code" tier for some corpora that were annotated before the new convention). +It then takes each annotated segment of 2 minutes, extract it from the daylong recording to output a small wav file of 2 minutes, with the name: +corpus_id_onset_offset.wav +where corpus is the name of the original corpus, id is the name of the daylong recording (which is itself the id given to the recorded child), onset is where the segment starts in the daylong recording (in seconds, with 6 digits padded with 0's if necessary), offset is where the segment ends in the daylong recording (with the same convention). +For each of these segments extracted, it also writes the annotations in rttm format, with the timestamps adapted to correspond to the small wav, and with the same name as the small wav previously written. + +### remove_overlap_rttm.py + +Take a transcription in RTTM format, and convert it to a SAD annotation in RTTM format. The SAD annotation contains less information, as it only indicated "speech" segment (i.e. the talker is written as "speech", no matter who the original talker is), and there are no overlap between speech segments. + +### make_big_corpus.sh + +This script is called to treat all the daylong recording with their annotations, using the previous adjust_timestamps.py script. It also creates gold SAD rttm using the remove_overlap_rttm.py script previously described. + + + diff --git a/docs/source/formats.md b/docs/source/formats.md new file mode 100644 index 0000000..ac98215 --- /dev/null +++ b/docs/source/formats.md @@ -0,0 +1,115 @@ +# Formats + +This section explains the input and output formats. + +## Overview + +The basic file format within DiViMe is a modified form of [rttm](https://catalog.ldc.upenn.edu/docs/LDC2004T12/RTTM-format-v13.pdf), which is standard in key diarization tasks, and which allows us to evaluate most tools using a standardized evaluation routine. The different ways of using this same general format are explained below. + +Many users, however, will be interested in knowing how to convert into and out of this format into something that is more commonly used for annotation. DiViMe includes routines to convert from any .TextGrid file (produced by the program [Praat](praat.org)); from any .cha file (produced by the program [CLAN](http://dali.talkbank.org/clan/)); and from .eaf files (produced by the program [ELAN](https://tla.mpi.nl/tools/tla-tools/elan/)) that have been generated using the [ACLEW Annotation Scheme](https://osf.io/b2jep/wiki/home/) template. Users who rely on a different program (or for .eaf, on a different template) are advised to use the tools in their program or similar others to convert into one of these, or directly into the [rttm](https://catalog.ldc.upenn.edu/docs/LDC2004T12/RTTM-format-v13.pdf) format explained further below. We also provide code to convert the .its files outputted by the [LENA(R)](www.lena.org) software, so you can evaluate them against your own coding and/or DiViMe's output. + +The following subsections explain how to convert from each of these input formats into a basic reference [rttm](https://catalog.ldc.upenn.edu/docs/LDC2004T12/RTTM-format-v13.pdf). + +### Input: TextGrid + +Our process assumes that you will have the speech of each talker diarized in a different tier, using filled intervals when the person is talking, and empty intervals when they are not talking. + +Notice that all tiers count, so if you have some tiers that are non-speech, you should remove them from your textgrids before you start. For example, if you have three tiers associated with a talker, coding e.g., what they say, how they say it, and to whom, you should remove 2 of these three tiers, because otherwise each of these tiers will be treated as a different talker. + +Furthermore, any interval that is empty will be seen as *not* containing vocalizations from that speaker. Thus, if your coding is sparse (i.e., if you have a day-long recording, but have only coded some clips here and there), then you should extract the audio clips and annotations for the sections that have been coded, and not process the whole day-long recording. (If you do, then your evaluation will be off, because all the speech found in sections you have not coded count towards false positives, as if the system had found speech when none was there.) + +Additionally, the name of the tier is what will be taken to be the speaker's name. Therefore, if you have tiers that code speech by a speaker but are named differently, change the tier's name before starting. In fact, some of the tools assume a specific set of names, and thus the tool's output can only be properly evaluated if you use those names. In particular, the child wearing the recording device should be called "CHI". Other children should be called "XC0", where the X is the child's sex (F for Female, M for Male, U for uncertain/undecided/unknown) and 0 is a number 0-9 to identify a unique child. Similarly, adults should be called "XA0" where the X is the child's sex (F for Female, M for Male, U for uncertain/undecided/unknown) and 0 is a number 0-9 to identify a unique adult. Further, you can also define "XU0", a person of unknown age; and "EE0," a voice from a non-human source, such as a toy, radio, or TV. + +Once you have removed all tiers that do not pertain to speakers, made sure that all the empty intervals really are non-vocalizations, renamed the tiers with the speaker's name, and ideally used this set of names, you are ready to convert your .TextGrid files into [rttm](https://catalog.ldc.upenn.edu/docs/LDC2004T12/RTTM-format-v13.pdf). After you have put all the files you want to convert inside the folder data/mydata/, you would next run: + +``` +vagrant ssh -c "textgrid2rttm_folder.sh $j" +``` + +### Input: cha + +Our process extracts time stamps from bullet points, assuming that all vocalizations are coded. Therefore, if some of your entries do not have bullet points (e.g., "*CHI: 0 [=! crying]."), they will be treated as if there was no speech/vocalizations at that point. Furthermore, additional pre-processing steps are necessary if your coding is sparse (i.e., if you have a day-long recording, but have only coded some clips here and there) since our automatic extraction method has no way of knowing that you have skipped sections. If this is the case, then you should extract the audio clips and annotations for the sections that have been coded, and not process the whole day-long recording. (If you do, then your evaluation will be off, because all the speech found in sections you have not coded count towards false positives, as if the system had found speech when none was there.) + +Additionally, some of the tools assume a specific set of names, and thus the tool's output can only be properly evaluated if you use those names. In particular, the child wearing the recording device should be called "CHI". Other children should be called "XC0", where the X is the child's sex (F for Female, M for Male, U for uncertain/undecided/unknown) and 0 is a number 0-9 to identify a unique child. Similarly, adults should be called "XA0" where the X is the child's sex (F for Female, M for Male, U for uncertain/undecided/unknown) and 0 is a number 0-9 to identify a unique adult. Further, you can also define "XU0", a person of unknown age; and "EE0," a voice from a non-human source, such as a toy, radio, or TV. + +Once you have made sure that all lines of interest has bullet points, that there are no regions of the recording that have been skipped, and (if you want to use all tools) that your speakers follow this naming convention, you are ready to convert your .cha files into [rttm](https://catalog.ldc.upenn.edu/docs/LDC2004T12/RTTM-format-v13.pdf). After you have put all the files you want to convert inside the folder data/mydata/, you would next run: + +``` +vagrant ssh -c "chat2rttm_folder.sh data/mydata/" +``` +### Input: Eaf + +Since .eaf files can vary a lot in structure, we only provide tools to properly process .eaf files that follow the [ACLEW Annotation Scheme](https://osf.io/b2jep/wiki/home/) template. One of the perks of using this format is that you can make full use of all tools in DiViMe, including a phonologization of your orthographic transcriptions into phonemic transcriptions, which will allow you to evaluate WCE in your data. For the phonologization stage, you need to provide the language, which can be: spanish, english, tzeltal. + +The fourth column indicates the onset of a speech region; the forth column the duration of that speech region. All other columns may be ignored. Regions of non-speech are all the others (e.g., in this example, between .77 and 1.38). + +## Diarization style (diarization or role assignment) output + +RTTM is an annotation format for audio files well designed for diarization. Explanations about how to write and read .rttm files can be found [here](https://catalog.ldc.upenn.edu/docs/LDC2004T12/RTTM-format-v13.pdf) +This format is used by the [DiViMe](https://github.com/srvk/DiViMe). + + +Diarization-type tools return one rttm per audio file, named toolnameDiar_filename.rttm, which looks like this: + +``` +SPEAKER file17 1 4.2 0.4 talker0 +SPEAKER file17 1 4.6 1.2 talker0 +SPEAKER file17 1 5.8 1.1 talker1 +SPEAKER file17 1 6.9 1.2 talker0 +SPEAKER file17 1 8.1 0.7 talker1 +``` + + +The fourth column indicates the onset of a region, the identity being indicated in ; the forth column the duration of that speech region. All other columns may be ignored. Regions of non-speech are all the others (e.g., in this example, between .77 and 1.38). + + + +Tools that are of the speech/voice activity detection type return one [rttm](https://catalog.ldc.upenn.edu/docs/LDC2004T12/RTTM-format-v13.pdf) per audio file, named toolnameSad_filename.rttm, which looks like this: + +``` +SPEAKER file17 1 0.00 0.77 speech +SPEAKER file17 1 1.38 2.14 speech + +``` + +The third column indicates the onset of a region; the forth column the duration of that region. All other columns may be ignored. Regions of non-speech are all the others (e.g., in this example, between .77 and 1.38). + +## Output: rttm's from diarization tools + +Diarization-type tools return one [rttm](https://catalog.ldc.upenn.edu/docs/LDC2004T12/RTTM-format-v13.pdf) per audio file, named toolnameDiar_filename.rttm, which looks like this: + +``` +SPEAKER file17 1 4.2 0.4 talker0 +SPEAKER file17 1 5.8 1.1 talker1 +SPEAKER file17 1 6.9 1.2 talker2 +SPEAKER file17 1 8.1 0.7 talker1 +``` + +The third column indicates the onset of a region, the forth column the duration of that region; and the identity of the speaker being indicated in the eighth column. All other columns may be ignored. Regions of non-speech are all the others (e.g., in this example, between 4.6 and 5.8). + + +## Output: rttm's from talker type tools + +Talker type tools return one [rttm](https://catalog.ldc.upenn.edu/docs/LDC2004T12/RTTM-format-v13.pdf) per audio file, named toolname_filename.rttm, which looks like this: + +``` +SPEAKER file17 1 4.2 0.4 CHI +SPEAKER file17 1 5.8 1.1 FA +SPEAKER file17 1 6.9 1.2 MA +SPEAKER file17 1 8.1 0.7 FA +``` + +The third column indicates the onset of a region, the forth column the duration of that region; and the speaker type being indicated in the eighth column: CHI is the key child, C is a child (target or other), FA is female adult, MA is male adult. All other columns may be ignored. Regions of non-speech are all the others (e.g., in this example, between 4.6 and 5.8). + +## Output: rttm's from vocal maturity tools + +Vocal maturity tools return one [rttm](https://catalog.ldc.upenn.edu/docs/LDC2004T12/RTTM-format-v13.pdf) per audio file, named toolname_filename.rttm, which looks like this: + + +``` +SPEAKER FILENAME 1 31.4 1.6 CNS 0.71 +SPEAKER FILENAME 1 34.6 1.1 NCS 0.81 +SPEAKER FILENAME 1 39.0 0.8 CRY 0.80 +SPEAKER FILENAME 1 47.9 0.5 NCS 0.62 +``` +The third column indicates the onset of a region, the forth column the duration of that region; and the vocalization type being indicated in the eighth column: canonical syllable (CNS), non-cannoical syllable (NCS), crying (CRY), and others (OTH, normally refer to laughing); followed by the likelihood of that class (higher means the system was more "certain"). All other columns may be ignored. Regions of non-vocalization as well as regions where people other than the child vocalize are not marked (e.g., in this example, between 33 and 34.6). diff --git a/docs/source/further-info.md b/docs/source/further-info.md new file mode 100755 index 0000000..a509f7a --- /dev/null +++ b/docs/source/further-info.md @@ -0,0 +1,40 @@ +# More information about DiViMe + +DiViMe is a virtual machine, whose characteristics and contents are almost completely determined by provisioning scripts found in the DiViMe folder created when you did `git clone https://github.com/srvk/DiViMe/.` This section provides information to help you understand the DiViMe structure in conceptual terms (the "pipeline"). We also explain the process of "bootstrapping" or creation of this virtual machine. Finally, we describe the folder structure. If you just want to use the tools in DiViMe, you can probably skip this whole section, or read just the `Pipeline structure` section. + +## Pipeline Structure + +DiViMe is a platform for tools to analyze naturalistic, unannotated audiorecordings. We consider this process to involve three kinds of processes: + +- speech activity detection and voice activity detection = “detecting vocalizations”, +- diarization = “deciding to whom the vocalizations belong”, and +- “additional annotations” + + +Some tools actually combine two of these stages (e.g. a tool may do both speech activity detection and role attribution in one fell swoop). + +## Building a virtual machine + +The structures and contents of the VM are actually built from reproducible directives which are executed when you do `vagrant up`. In technical terms, this is called VM is _provisioning_. In a nutshell, a file called "Vagrantfile" builds the machine (e.g., creates a virtual machine, allocates memory, installs an operating systems). Another file (conf/bootstrap.sh) installs tools within this virtual machine. For detailed documentation, see https://www.vagrantup.com/docs/vagrantfile/. + + + +## Folder Structure + +By virtue of how DiViMe is provisioned, there is some information that is accessible both from the host machine (i.e., your computer) and the virtual machine (i.e., the minicomputer built inside your computer). These are called `shared folders`. + +*The following folders are shared between the host and the virtual machine, and thus accessible from both:* + +- *utils/* contains ancillary files, typically used across more than one tool +- *launcher/* contains files that target users will use to launch different tools +- *conf/* is for configuration files for two potential reasons: they are shared across more than one tool, or to make them more easily editable from outside the VM +- *data/* folder where users put the data to be analyzed, and recover the automatically annotated files + +From within the virtual machine, these are inside /vagrant: /vagrant/utils, /vagrant/launcher, etc. Some of these are also accessible from the home of the default user, through links, ie, ~/utils and ~/launcher. (the default user is vagrant for virtualbox) + +*The following folders can only be found within the virtual machine*: + +- *repos/* contains any repository that is cloned into the machine, typically tools for the user, but also tools used by other tools to do e.g. feature extraction. + +From within the virtual machine, ~/repos is inside the vagrant user’s home folder, so you can access it as ~/repos/ or /home/vagrant/repos. + diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100755 index 0000000..7856954 --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,31 @@ +.. DiViMe documentation master file, created by + sphinx-quickstart on Wed Oct 10 11:23:30 2018. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to DiViMe's documentation! +================================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + + initial_questions + install + usage + further-info + formats + tool_doc + WCEtool + extra-tools + troubleshoot + instructions_for_contributors + references + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/source/initial_questions.md b/docs/source/initial_questions.md new file mode 100755 index 0000000..f3e98fd --- /dev/null +++ b/docs/source/initial_questions.md @@ -0,0 +1,40 @@ +# Before starting + +## What is the ACLEW DiViMe? + +It is a collection of speech processing tools allowing users to automatically add annotations onto a raw audio recording. At present, we have tools to do the following types of annotation: + +1) Speech activity detection (_when is someone talking?_) + +2) Talker diarization (_who is talking?_) + +3) Role diarization (_what kind of person is talking?_) + +4) Vocal type classification (_what kind of vocalization is this one?_) + +We are hoping to add more tools in the future, including register detection, and syllable quantification. + +## Who is the ACLEW DiViMe for? + +ACLEW DiViMe is for researchers dealing with speech recorded in naturalistic environments (speech in the wild), typically in daylong recording settings. The population recorded may be sensitive, therefore researchers may not be able to share their audio recordings. Our primary test case involves language acquisition in children 0-3 years of age. + +We are trying to make the use of these tools as easy as possible, but some command line programming/scripting is unavoidable. If you are worried when reading this, we can recommend the Software Carpentry programming courses for researchers, and particularly their [unix bash](http://swcarpentry.github.io/shell-novice) and [version control](http://swcarpentry.github.io/git-novice/) bootcamps. + +## What exactly is inside the ACLEW DiViMe? + +A virtual machine (VM) is actually a mini-computer that gets set up inside your computer. This creates a virtual environment within which we can be sure that our tools run, and run in the same way across all computers (Windows, Mac, Linux). + +Inside this mini-computer, we have tried to put several tools for each one of our three questions. Please note that some of the tools are developed by fellow researchers and programmers, and since we do not control them, we cannot be absolutely certain they will work. Therefore, we provide a general introduction to the contents in the usage section, and a specific list of tools in dedicated Detailed instructions sections. + +## How should I cite ACLEW DiViMe? + +The main citation is this paper, which explains the structure and idea, and provides some evaluation: + +Adrien Le Franc, Eric Riebling, Julien Karadayi, Yun Wang, Camila Scaff, Florian Metze, and Alejandrina Cristia. +[The ACLEW DiViMe: An easy-to-use diarization tool](https://www.isca-speech.org/archive/Interspeech_2018/pdfs/2324.pdf). In Proc. INTERSPEECH, Hyderabad; India, September 2018. + +The idea of using virtual machines to package speech tools comes from this work: + +Florian Metze, Eric Fosler-Lussier, and Rebecca Bates. The speech recognition virtual kitchen. In Proc. INTERSPEECH, Lyon; France, August 2013. [https://github.org/srvk](https://github.org/srvk). + +Depending on the particular tool that you are using, you should potentially cite additional papers that describe the underlying software or methods - please check. diff --git a/docs/source/install.md b/docs/source/install.md new file mode 100755 index 0000000..1668d24 --- /dev/null +++ b/docs/source/install.md @@ -0,0 +1,132 @@ +# Installing DiViMe + +## Requirements + +DiViMe can be installed in any operating system and computer with at least 2 CPUs, 8GB of RAM, and 25GB of available disc space. You may need more of everything to actually use the tools, specifically when running on large files. *Before following the instructions under "First Installation"*, you must follow the instructions in the relevant subsection of the Troubleshooting section, at the end of this page, in the following cases: + +- your computer has only one core (or you don't know) +- your computer has 25 GB or less of disc space +- your computer has 6 GB or less of RAM +- your computer is running Ubuntu (e.g., 16.04) + + +## First Installation + +1. Install [Vagrant](https://www.vagrantup.com/): Click on the download link for your operating system and follow the prompted instructions + +2. Install [VirtualBox](https://www.virtualbox.org/wiki/Downloads): When we last checked, the links for download for all operating systems were under the header "VirtualBox 5.2.18 platform packages", so look for a title like that one (picking the latest version, most likely). + +3. Clone the present repository: To do this, you must use a terminal. If you don't know what this means, we recommend that you first follow the [Software Carpentry Shell Tutorial](https://swcarpentry.github.io/shell-novice/) (up to 00:45, namely "Introducing the shell", and "Navigating files and directories"). Next, navigate to the directory in which you want the VM to be hosted and type in: `$ git clone https://github.com/srvk/DiViMe` - or use another way or tool to "clone" a [Git](https://git-scm.com) repository + +4. Change into this folder: `$ cd DiViMe` + +5. Install several Vagrant plugins: `$ vagrant plugin install vagrant-aws vagrant-sshfs vagrant-vbguest` + +Depending on how you want to use DiViMe, and which provider you will be using, you may or may not need to install the above plugins (or you may need to install additional plugins), but this quickly turns into an advanced topic, because not all plugins will work equally well on all host platforms ... + +6. Type `$ vagrant up` + +The first time you do this, it will take at least 15 minutes to install all the packages that are needed to build the virtual machine. You are done when you see something like this: + +``` +FloriansMBP2019:DiViMe metze$ tail -n 8 example-logs/vagrant-up.log + default: build succeeded, 4 warnings. + default: The HTML pages are in build/html. + default: INFO: You can remove Anaconda2-2019.03-Linux-x86_64.sh, if you don't plan on re-provisioning DiViMe any time soon. + default: INFO: You can remove MCR_R2017b_glnxa64_installer.zip, if you don't plan on re-provisioning DiViMe any time soon. + default: ---- Done bootstrapping DiViMe @ Wed Jul 3 03:30:01 UTC 2019 ---- + default: root@vagrant-ubuntu-trusty-64:/home/vagrant# + default: exit +``` + +The instructions above make the simplest assumptions as to your environment. If you have Amazon Web Services, an Ubuntu system, or you do not have admin rights in your computer, you might need to read the [instructions to the eesen-transcriber](https://github.com/srvk/eesen-transcriber/blob/master/INSTALL.md) for fancier options. Or you can just open an issue [here](https://github.com/srvk/DiViMe/issues), describing your situation. + +We are working on [Installing With Docker](https://github.com/srvk/DiViMe/wiki/InstallingWithDocker), but this option is not yet fully functional. + +Please note that there is a large amount of documentation on Vagrant and Virtualbox online, explaining on how to fix assorted errors. It is often a good idea to simply "google" errors that these tools throw; you may find a solution to your specific problem quickly, because the problem may not lie with DiViMe, but the VM, before DiViMe gets involved. + +## Checking your installation + +The very first time you use DiViMe, it is a good idea to run a quick start test, which will be performed using data from the [VanDam Public Daylong](https://homebank.talkbank.org/access/Public/VanDam-Daylong.html) [HomeBank](homebank.talkbank.org) corpus (VanDam et al., 2015): + +1. Open a terminal +2. Navigate inside the DiViMe folder +3. Do `$ vagrant up` (if you haven't done it already) +4. Do `$ vagrant ssh -c "launcher/test.sh"` + +This should produce the following output: + +``` +FloriansMBP2019:DiViMe metze$ vagrant ssh -c "launcher/test.sh" +Starting tests +Checking for HTK... + HTK missing. You can probably ignore this warning, HTK is no longer needed. +Testing noisemes... +Noisemes passed the test. +Testing OpenSmile SAD... +OpenSmile SAD passed the test. +[...] +Congratulations, everything is OK! +[...] +``` + +## Using it + +You can now run commands and experiment on multiple data files, using as many `$ vagrant ssh -c "[...]"` calls as you like. It *should* also be safe to have multiple ssh connections to the VM and run many experiments in parallel, if the tool that you are using supports it. There *may* be some that are not "thread-safe". + +## When you are done with DiViMe, Teardown + +After working with DiViMe, you can shut down the virtual machine, which will free up CPU and RAM resources on your computer (but not disc space). To do this, type `$ vagrant halt` or `$ vagrant suspend`. To continue working with the VM at a later point, simply issue another `$ vagrant up` command. + +When you do `$ vagrant up` again (without previously shutting down the VM), its output will probably end in: + +``` + default: /vagrant => /Users/metze/Work/Kitchen/DiViMe +==> default: Machine already provisioned. Run `vagrant provision` or use the `--provision` +==> default: flag to force provisioning. Provisioners marked to run always will still run. +``` + +This is safe to ignore, and you can continue with `$vagrant ssh -c "[...]"` as needed. Note that the VM is not set up to actually support a proper separate provisioning step, so `$ vagrant provision` probably will not work reliably. + +## Updating DiViMe + +If you want to install a new release of DiViMe, you will need to perform the following 3 steps from within the DiViME folder on your terminal: + +``` +$ vagrant destroy +$ git pull +$ vagrant up +``` + +## Uninstallation + +If you want to get rid of the files completely, you should perform the following 3 steps from within the DiViME folder on your terminal (assuming you are on Unix): + +``` +$ vagrant destroy +$ cd .. +$ rm -rf DiViMe +``` + +## Troubleshooting + +DiViMe is very powerful (and complex), and despite our best efforts, there are many ways to fail. The first order of business is therefore to pin-point the problem, which may or may not be related to DiViMe specifically. First, look at DiViMe's issue tracking system [here](https://github.com/srvk/DiViMe/issues) - maybe you can find the solution already. If not, read on below. + +In many cases, the problem will be generic, and related to Vagrant, Virtualbox, or some other underlying tool. In this case, there are often online resources that can often be found with a bit of googleing - and you should add the solution to the DiViMe knowledge base by opening an issue, and posting the solution. + +If you cannot find a solution after reading https://divime.readthedocs.io/en/latest/troubleshoot.html, open an issue and supply as much information as needed for someone else to be able to (ideally) reproduce your error. Typically, this means + +- Your operating system and computer specs (RAM, HDD, CPUs) +- Your version of Vagrant +- Your version of Virtualbox (or any other provider that you run) +- Your version of DiViMe +- The command(s) that you type and the output that you get, ideally a log file (complete - not just the last line) +- You can find some example log files in the logs/ folder, so you can compare your output against the output we see +- Anything else that may be relevant + +We are monitoring the issues and will try to get back yo you. + + +## References + +VanDam, M., De Palma, P., Strong, W. E. (2015, May). Fundamental frequency of speech directed to children who have hearing loss. Poster presented at the 169th Meeting of the Acoustical Society of America, Pittsburgh, PA. diff --git a/docs/source/instructions_for_contributors.md b/docs/source/instructions_for_contributors.md new file mode 100755 index 0000000..930a5d0 --- /dev/null +++ b/docs/source/instructions_for_contributors.md @@ -0,0 +1,197 @@ +# Instructions For Contributors + +Temporary instructions: +We have reorganized DiViMe to try to facilitate tool incorporation and VM use, but these changes have not yet made it to the main branch. Therefore, all of the following instructions are NOT in the master branch, but in the major_reorganization branch. This should make no difference to you, except that you need to check out the right branch to view the VM fitting with these instructions. We provide the code for doing this below. +## Overview +This detailed guide provides you with step-by-step, specific instructions for adapting a tool into the DiViMe environment. The following is a Summary of the steps: +1) Install the VM for yourself +2) Adapt & test your tools in the VM environment, and build the necessary links to other modules. +3) Put your tools and all your custom scripts somewhere where they can be downloaded (e.g., GitHub repo(s)) +4) Create a bash script to download and install your tools on the VM (the same steps will be added to a Vagrantfile that controls the entire VM installation) +5) ... +## Before You Start + +Before you start, you'll need to get the VM up and running to establish the environment in which your tool will run. + +1. Install DiViMe as per the [installation instructions](https://divime.readthedocs.io/en/latest/install.html), including the `vagrant up` that wakes the machine up. + +Temporary instructions: +IMPORTANT: After you’ve cloned DiViMe, you should check out the major_reorganization branch, as follows: +``` +$ git checkout remotes/origin/major_reorganization +``` +If ever you want to go back to the master branch, you’d do: +``` +$ git checkout master +``` +If you’ve already built one version of the VM, you’ll need to do: +``` +vagrant destroy +vagrant up +``` + + +2. Run the test.sh routine routine. It downloads audio recordings and annotations that you will need to make sure your tool is compatible with the rest of the workflow. Additionally, it will help you spot any issues there may be in your local environment. + +## Understanding the general structure of DiViMe + +DiViMe is a virtual machine, whose characteristics and contents are almost completely determined by provisioning scripts found at the top level when you clone DiViMe. That means that if you want to contribute to DiViMe, you need to understand its structure, and bootstrapping of the virtual machine. + +In the next section, we will walk you through some stages of modifying the contents of the VM, which you will do initially “by hand”. However, remember the contents of the VM are actually built from reproducible directives ran when you do `vagrant up`. (In technical terms, when the VM is _provisioned_.) Therefore, any changes you make by hand when you are logged into the VM will be lost when you run `vagrant destroy`. Please bear that in mind when trying out the steps in the next section. + +Additionally, by the end of these instructions you will be ready to propose a revised version of the VM - i.e., a new recipe to _provision_ the VM. Therefore, any changes made by hand that you wish to make permanent in the VM should eventually be added to a file called `util/bootstraph.sh`. So you may want to have a copy of bootstraph.sh open to make notes in there of the steps you take, which should be reproduced when provisioning the VM. + +## Adapting Your Tool to the VM Environment + +1. For this section, you'll be working almost entirely within the virtual machine. So start by doing `vagrant ssh` to log in. This logs you in as the `vagrant` user, which also has sudo privileges to do things like install software, change permissions, etc. in the virtual machine. + +2. Decide where your tool is made available so that it can be copied into the VM. Ideally, it will be somewhere public, such as GitHub, so that anyone rebuilding the machine will get access to it. Alternatively, your tool might be stored on a server in a location you control, and then pulled into the virtual machine. Please note that the latter solution only preserves the anonymity of your code temporarily; if you get to the end of this document, when you are proposing a new version of the VM including your tool, then the URL needs to be known to the world, and accessible to everyone. Please note that neither alternative forces you to make your code open source. You can also share a binary executable (ask us for instructions if you need help). + +3. Import your tool into the VM. Once you have decided where to put your tool, install your code by hand in the location where it will be within the machine: `/home/vagrant/repos`. For example if your code is in a public GitHub repository `https://github.com/srvk/OpenSAT`, you would type into the terminal: +``` +cd /home/vagrant/repos +git clone http://github.com/srvk/OpenSAT +``` + +If your tool is accessible via URL `https://toolnameurl.com/tool.zip`, you would type into the terminal: + +``` +cd /home/vagrant/repos +wget -q -N https://toolnameurl.com/toolname.zip +unzip -q -o toolname.zip +``` +In bootstrap.sh, add the same code to make this step permanently reproducible. + + + +4. If your code requires certain linux packages that are not installed, first install them ‘by hand’ in the VM with a command like `sudo apt-get install `. Any packages installed this way should be added similarly to one of the `apt-get` lines in bootstraph.sh like: +``` +sudo apt-get install -y libxt-dev libx11-xcb1 +``` + +5. The next step is to install additional dependencies, if any are needed. The VM already includes code to install OpenSmile 2.1, matlab v93, python 3, and anaconda 2.3.0. As in the two previous steps, you can do this by hand within the terminal, but you need to add the step to bootstraph.sh to make it permanent. For instance, if you need the python package `pympi-ling`, you would type `pip install pympi-ling` by hand into the terminal. Additionally, to make this change permanent (and have the dependencies installed when you reprovision the VM or when someone else rebuilds the VM), you need to add it to the Vagrantfile section where python packages are installed, with code like this: + +``` +su ${user} -c "/home/${user}/anaconda/bin/pip install pympi-ling” +``` + +## Write a Wrapper + +In this section, we provide detailed instructions for writing a wrapper that allows users to run your tool in a standardized way. The wrapper should be written in bash. Please look at the other launcher wrappers to have an idea of how to structure it, and read on for important considerations. + +### Naming Conventions + +- You choose your own tool's name. Use anything you want except the other names already in use. We refer to your tool name later as ‘TOOLNAME’ +- It may be useful to you to decide at what “stage” of diarization your tool operates. A few things will be clearer if you have a good notion of when this is, such as the number of arguments and whether there is already an evaluation/scoring tool that can be re-used with your tool. We explain these things in more detail below. +- To facilitate end users’ understanding of what tool they are using, we have systematically added the stage name to the wrapper’s name, but you don’t have to follow this procedure if you think it will not be useful. +- We have identified three stages with a fixed name: Sad (for both speech activity detection and voice activity detection), Diar (for speaker diarization and role assignment), and Add (for adding annotation dependent on role assignment). +- Other stages or stage combinations do not have fixed names. But please feel free to use these stage names. For instance, if your tool only requires audio files as input, then you can use Sad; if it operates on both audio and speech activity detection, then use Diar; and if it is specific to one talker role as input, use Add. + +### Tool Autonomy + +Tools should be self-aware of where they have been installed, and they should use this awareness to find their dependencies. Said differently, a tool should run "in place" independent of the absolute path it's installed in. Tool dependency paths should be relative to the tool home folder, which should serve as the working directory. Again, please look at the other launcher wrappers to reuse the code present at the top of the wrappers, which correctly reconstructs the absolute path of this folder. + + +### Input, Output, and Parameters + + Your wrapper should take at least one argument, namely the name of a folder containing data. This folder is appended to “/vagrant” so from your script and the VM’s perspective, data appears in /vagrant/data/. This is actually a shared folder, coming from the host computer working directory. Everything in data/ on the host will in /vagrant/data in the VM, and vice-versa. The default wrapper argument, then, is typically "data/", but users could also supply “data/mystudy/” or “data/mystudy/baby1/” as the data folder argument. This supports the notion of having multiple datasets in different folders on the host. You can see how other wrappers use this, typically setting the variable `$audio_dir` to /vagrant/data. For the rest of this explanation, we’ll be referring to this folder as DATAFOLDER. +- The wrapper should process all .wav files inside DATAFOLDER and, optionally, associated annotation files, which are in rttm format. (For more information on the rttm output, read [NIST's 2009 eval plan](https://web.archive.org/web/20170119114252/http://www.itl.nist.gov/iad/mig/tests/rt/2009/docs/rt09-meeting-eval-plan-v2.pdf)) +- Your wrapper should support processing long sound files. If you have no better way of achieving this, look to utils/chunk.sh as an example; it breaks up long files into smaller 5 minute chunks, then iteratively calls a tool (in this case, yours), and concatenates the results. +- Your tool must process many sound files. This may require some optimization, for example loading very large model files into memory for each sound file is less optimal than loading once, then iterating over many files. +- The wrapper should write its *main output* into DATAFOLDER. Typically, this will be one annotation file for each .wav file. If your tool does VAD, SAD, or diarization, this annotation file should respect the rttm format. Additionally, the output file it should be named according to the pattern: toolname_file_original_name.rttm. If your tool does something other than VAD, SAD, or diarization, use a format that seems reasonable to you. If your tool returns results on individual files (and not e.g., summary statistics over multiple files), we still ask you to name the file toolnameStage_file_original_name.ext -- where “ext” is any extension that is reasonable for the annotation format you have chosen. +- You probably also generate two types of ancillary files: Intermediary representation files, such as features extracted from the wav files; and log files, with detailed information of what was done and how. Both should be initially stored in the DATAFOLDER/temp/TOOLNAME folder. +- Intermediary representation files should be deleted -- with one exception, introduced below. +- It is a good idea to print out a short log accompanying each batch of files analyzed, stating the key parameters, version number, and preferred citation. You may want to write out a lot more information. However, our target user may not be technically advanced, and thus including long technical logs may do more to confuse than to help. Log files should also be deleted if they are large (>5MB) -- with one exception, introduced next. +- Your wrapper should expect an optional argument “--keep-temp”, which is optionally passed in final position. If the last argument to the wrapper is not “--keep-temp”, then ancillary files should be deleted. Code snippet example: + +``` +KEEPTEMP=false +if [ $BASH_ARGV == "--keep-temp" ]; then + KEEPTEMP=true +fi +… +if ! $KEEPTEMP; then + rm -rf $MYTEMP +fi +``` + See any script in `launcher/` for examples. + +## Document Your Tool +Add a documentation file to the `DiViMe/docs/` folder on your host, in markdown format, containing at least the following three pieces of information: +A one-paragraph explanation of what your tool does +A reference or citation that people using your tool must employ +A short section explaining how to use it. This will typically include a description of input & output formats, which can be replaced with references to the Format section of the docs. You should also include an example command line of how to run the tool. Often, this will be `vagrant ssh ‘toolname.sh data/`. +Please include any other information that would be useful to users, such as what parameters are available, how to access them, a tiny example input and output, further technical information on how the tool was built, additional references for work using this tool or on which the tool was built, etc. + +For an example, see the tocomboSad section in the [tools documentation](https://divime.readthedocs.io/en/latest/tool_doc.html). + +## Create a Reproducible Test for Your Tool +DiViMe comes equipped with a test script that downloads a publicly available daylong audio file and transcript, which all tools within DiViMe can process (and many can be evaluated). In this section, we provide instructions for you to add your tool to the test.sh routine. + +By default, all launchers are read-only to avoid accidental editing by newbie users. For the next step, you need to change file permissions so as to be able to edit test.sh. From the host machine, type into the terminal: + +``` +chmod +rw test.sh +``` + +Open the file test.sh, which is inside the launcher folder, and add a section testing your tool, modeling it on the other tests present there. Typically, you will need these lines: + +Under “# Paths to Tools” add a line with the path to your tool, eg: +TOOLNAMEDIR=$REPOS/TOOLNAME + +b) Before “# test finished”, add a section like the following: + +Example for a Sad type tool: +``` +echo "Testing TOOLNAME..." +cd $TOOLNAMEDIR +TESTDIR=$WORKDIR/TOOLNAME-test +rm -rf $TESTDIR; mkdir -p $TESTDIR +ln -fs $TEST_WAV $TESTDIR +$LAUNCHERS/toolnameStage.sh $DATADIR/TOOLNAME-test >$TESTDIR/TOOLNAME-test.log || { echo " TOOLNAME failed - dependencies"; FAILURES=true;} + +if [ -s $TESTDIR/toolnameStage_$BASETEST.rttm ]; then + echo "TOOLNAME passed the test." +else + FAILURES=true + echo " TOOLNAME failed - no RTTM output" +fi +``` +In the above example: +`DATADIR` is predefined as the test 5 minute data folder `data/VanDam-Daylong/BN32` +`TOOLNAME` is whatever human readable name you have given your tool +`toolnameStage` is the pattern for the system/launcher name for your tool, for example `opensmileSad` +`BASETEST` is the basename of a test input file e.g. `BN32_010007_test` for the 5 minute input file `BN32_010007_test.wav` + + +Example for a Diar type tool: +** todo: complete** + +Example for an Add type tool: +** todo: complete** + + + Run test.sh. Only proceed to the next phase if your tool passes the test. + +## Check reproducibility of your version of the VM by reprovisioning + +Throughout the steps above, you have modified `Vagrantfile/bootstrap.sh` to automatically install your code, any required packages, and any required dependencies. If you have been keeping your Vagrantfile/bootstrap.sh in a good state, you should be able to rebuild your version of the virtual machine from scratch. + +If necessary, log out from the virtual machine with control+D. Then, from the host machine, run the following code to destroy, re-build, and re-run the test: +``` +vagrant destroy +vagrant up +vagrant ssh -c “test.sh” +``` + +*WARNING:* any changes you made by hand when you were logged into the VM will be lost when you run `vagrant destroy`: to make sure they show up automatically with `vagrant up`, all such dependencies need to be automated (added to Vagrantfile/bootstrap.sh). +If your tool passes the test in this condition, you are ready to integrate your tool to DiViMe for real. + +## Integrate Your Tool Into the Public Version of DiViMe + +1. Fork the DiViMe repo +2. Feplace Vagrantfile/bootstrap.sh with your version of Vagrantfile/bootstrap.sh +3. Add in the docs/ your tool’s doc +4. Add your wrapper to launcher/ +5. Replace test.sh with your version containing an additional test case specific to your tool +6. Create a pull request to DiViMe requesting your additions be incoporated diff --git a/docs/source/references.md b/docs/source/references.md new file mode 100755 index 0000000..f441f5a --- /dev/null +++ b/docs/source/references.md @@ -0,0 +1,17 @@ + + +# References + +Our work builds directly on that of others. The main references for tools currently included and/or data currently used to perform tests are: + +- Bergelson, E., Warlaumont, A., Cristia, A., Casillas, M., Rosemberg, C., Soderstrom, M., Rowland, C., Durrant, S. & Bunce, J. (2017). Starter-ACLEW. Databrary. Retrieved October 1, 2018 from http://doi.org/10.17910/B7.390. +- Eyben, F. Weninger, F., Gross, F. & B. Schuller. (2013). Recent developments in opensmile, the munich open-source multimedia feature extractor. Proceedings of the 21st ACM international conference on Multimedia, 835–838. +- Eyben, F., Weninger, F., Squartini, S., & Schuller, B. (2013, May). Real-life voice activity detection with lstm recurrent neural networks and an application to hollywood movies. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on (pp. 483-487). IEEE. +- Räsänen, O., Seshadri, S., & Casillas, M. (2018, June). Comparison of Syllabification Algorithms and Training Strategies for Robust Word Count Estimation across Different Languages and Recording Conditions. In Interspeech 2018. +- Sadjadi, S.O. & Hansen, J.H.L. (2013). Unsupervised Speech Activity Detection using Voicing Measures and Perceptual Spectral Flux. IEEE Signal Processing Letters, 20(3), 197-200. +- VanDam, M., & Tully, T. (2016, May). Quantity of mothers’ and fathers’ speech to sons and daughters. Talk presented at the 171st Meeting of the Acoustical Society of America, Salt Lake City, UT. +- Vijayasenan, D. & Valente, F. (2012) Diartk: An open source toolkit for research in multistream speaker diarization and its application to meetings recordings. Thirteenth Annual Conference of the International Speech Communication Association, 2012. +- Wang, Y., Neves, L., & Metze, F. (2016, March). Audio-based multimedia event detection using deep recurrent neural networks. In Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on (pp. 2742-2746). IEEE. [pdf](http://www.cs.cmu.edu/~yunwang/papers/icassp16.pdf) +- Young, S., Evermann, G., Gales, M., Hain, T. , Kershaw, D., Liu, X., Moore, G., Odell, J., Ollason, D., Povey,D. et al. (2002) The HTK book. Cambridge University Engineering Department. +- Ziaei, A. Sangwan, A., & Hansen, J.H.L. (2016). Effective word count estimation for long duration daily naturalistic audio recordings. Speech Communication, 84, 15-23. +- Bredin, H. (2017). A toolkit for reproducible evaluation, diagnostic, and error analysis of speaker diarization systems, https://github.com/pyannote/pyannote-metrics diff --git a/docs/source/tool_doc.md b/docs/source/tool_doc.md new file mode 100755 index 0000000..e91a733 --- /dev/null +++ b/docs/source/tool_doc.md @@ -0,0 +1,299 @@ +# Speech or Voice activity detection tools + +This section contains documentation from the different Speech or Voice activity detection tools. + + +## NoisemesSad + +### General intro + +Noiseme SAD was actually not specifically built as a SAD but rather as a broader “noiseme classifier”. It is a neural network that can predict frame-level probabilities of 17 types of sound events (called “noisemes”), including speech, singing, engine noise, etc. The network consists of one single bidirectional LSTM layer with 400 hidden units in each direction. It was trained on 10h of basically web videos data (Strassel et al., 2012), with the Theano toolkit. The OpenSMILE toolkit (Eyben et al., 2013) is used to extract 6,669 low-level acoustic features, which are reduced to 50 dimensions with PCA. For our purposes, we summed the probabilities of the classes “speech” and “speech non-english” and labeled a region as speech if this probability was higher than all others. The original method in Wang et al. (2016) used 983 features selected using information gain criterion, but we used an updated version from authors Y. Wang and F. Metze in this paper. + + + +### Instructions for direct use (ATTENTION, MAY BE OUTDATED) + +You can analyze just one file as follows. Imagine that <$MYFILE> is the name of the file you want to analyze, which you've put inside the `data/` folder in the current working directory. + +``` +$ vagrant ssh -c "OpenSAT/runOpenSAT.sh data/<$MYFILE>" +``` + +You can also analyze a group of files as follows: + +``` +$ vagrant ssh -c "OpenSAT/runDiarNoisemes.sh data/" +``` + +This will analyze all .wav's inside the "data" folder. + +Created annotations will be stored inside the same "data" folder. + +This system will classify slices of the audio recording into one of 17 noiseme classes: + +- background +- speech +- speech non English +- mumble +- singing alone +- music + singing +- music alone +- human sounds +- cheer +- crowd sounds +- animal sounds +- engine +- noise_ongoing +- noise_pulse +- noise_tone +- noise_nature +- white_noise +- radio + +##### Some more technical details + +For more fine grained control, you can log into the VM and from a command line, and play around from inside the "Diarization with noisemes" directory, called "OpenSAT": + +``` +$ vagrant ssh +$ cd OpenSAT +``` + +The main script is runOpenSAT.sh and takes one argument: an audio file in .wav format. +Upon successful completion, output will be in the folder (relative to ~/OpenSAT) +`SSSF/data/hyp//confidence.pkl.gz` + +The system will grind first creating features for all the .wav files it found, then will place those features in a subfolder `feature`. Then it will load a model, and process all the features generated, producing output in a subfolder `hyp/` two files per input: `.confidence.mat` and `.confidence.pkl.gz` - a confidence matrix in Matlab v5 mat-file format, and a Python compressed data 'pickle' file. Now, as well, in the `hyp/` folder, `.rttm` with labels found from a config file [noisemeclasses.txt](https://github.com/riebling/OpenSAT/blob/master/noisemeclasses.txt) + +-More details on output format- + +The 18 classes are as follows: +``` +0 background +1 speech +2 speech_ne +3 mumble +4 singing +5 musicSing +6 music +7 human +8 cheer +9 crowd +10 animal +11 engine +12 noise_ongoing +13 noise_pulse +14 noise_tone +15 noise_nature +16 white_noise +17 radio +``` +The frame length is 0.1s. The system also uses a 2-second window, so the i-th frame starts at (0.1 * i - 2) seconds and finishes at (0.1 * i) seconds. That's why 60 seconds become 620 frames. 'speech_ne' means non-English speech + +-Sample RTTM output snippet- +``` +SPEAKER family 1 4.2 0.4 noise_ongoing 0.37730383873 +SPEAKER family 1 4.6 1.2 background 0.327808111906 +SPEAKER family 1 5.8 1.1 speech 0.430758684874 +SPEAKER family 1 6.9 1.2 background 0.401730179787 +SPEAKER family 1 8.1 0.7 speech 0.407463937998 +SPEAKER family 1 8.8 1.1 background 0.37258502841 +SPEAKER family 1 9.9 1.7 noise_ongoing 0.315185159445 +``` + +The script `runClasses.sh` works like `runDiarNoisemes.sh`, but produces the more detailed results as seen above. + + + +### Main references: + +Wang, Y., Neves, L., & Metze, F. (2016, March). Audio-based multimedia event detection using deep recurrent neural networks. In Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on (pp. 2742-2746). IEEE. [pdf](http://www.cs.cmu.edu/~yunwang/papers/icassp16.pdf) + + +### Associated references: + +S.Burger,Q.Jin,P.F.Schulam,andF.Metze,“Noisemes:Man- ual annotation of environmental noise in audio streams,” Carnegie Mellon University, Pittsburgh, PA; U.S.A., Tech. Rep. CMU-LTI- 12-07, 2012. +S.Strassel,A.Morris,J.G.Fiscus,C.Caruso,H.Lee,P.D.Over, J. Fiumara, B. L. Shaw, B. Antonishek, and M. Michel, “Creating havic: Heterogeneous audio visual internet collection,” in Proc. +LREC. Istanbul, Turkey: ELRA, May 2012. +F. Eyben, F. Weninger, F. Gross, and B. Schuller, “Recent developments in opensmile, the munich open-source multimedia fea- ture extractor,” in Proceedings of the 21st ACM international con- ference on Multimedia. ACM, 2013, pp. 835–838. + +### Questions and bug reports + +http://github.com/srvk/OpenSAT/issues + +## OpenSmile SAD + + +### General intro + +openSMILE SAD relies on openSMILE (Eyben et al., 2013a) to generate an 18-coefficient RASTA-PLP plus first order delta features. It then uses a long short-term memory recurrent neural network (see details in Eyben et al., 2013b) that has been pre-trained on two corpora of read and spontaneous speech by adults recorded in laboratory conditions, augmented with various noise types. + +### Some more technical details + +These are the parameters that are being used with values that depart from the openSMILE default settings (quoted material comes from either of the openSMILE manuals): + +monoMixdown = 1, means "mix down all recorded channels to 1 mono channel" +noHeader = 0, means read the RIFF header (don't need to specify the parameters ‘sampleRate’, ‘channels’, and possibly ‘sampleSize’) +preSil = 0.1 "Specifies the amount of silence at the turn beginning in seconds, i.e. the lag of the turn +detector. This is the length of the data that will be added to the current segment prior to +the turn start time received in the message from the turn detector component"; we use a tighter criterion than the default (.2) +postSil = 0.1 "Specifies the amount of silence at the turn end in seconds. This is the length of the data +that will be added to the current segment after to the turn end time received in the message +from the turn detector component."; we use a tighter criterion than the default (.3) + +You can change these parameters locally by doing: +``` +$ vagrant ssh +$ nano /vagrant/conf/vad/vadSegmenter_aclew.conf +``` + +openSMILE manuals consulted: + +- Eyben, F., Woellmer, M., & Schuller, B. (2013). openSMILE: The Munich open Speech and Music Interpretation by Large space Extraction toolkit. Institute for Human-Machine Communication, version 2.0. http://download2.nust.na/pub4/sourceforge/o/project/op/opensmile/openSMILE_book_2.0-rc1.pdf +- Eyben, F., Woellmer, M., & Schuller, B. (2016). openSMILE: open Source Media Interpretation by Large feture-space Extraction toolkit. Institute for Human-Machine Communication, version 2.3. https://www.audeering.com/research-and-open-source/files/openSMILE-book-latest.pdf + + +### Main references for this tool: + +Eyben, F. Weninger, F. Gross, F., &1 Schuller, B. (2013a). Recent developments in OpenSmile, the Munich open-source multimedia feature extractor. Proceedings of the 21st ACM international conference on Multimedia, 835–838. + +Eyben, F., Weninger, F., Squartini, S., & Schuller, B. (2013b). Real-life voice activity detection with lstm recurrent neural networks and an application to hollywood movies. In Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on (pp. 483-487). IEEE. + +### Questions and bug reports + +https://www.audeering.com/technology/opensmile/#support + +## TOCombo SAD + +### General intro + +This tool's name stands for "Threshold-Optimized" "combo" SAD; we explain each part in turn. It is a SAD because the goal is to extract speech activity. It is called "combo" because it combines linearly 4 different aspects of voicing (harmonicity , clarity, prediction gain, periodicity) in addition to one perceptual spectral flux feature (see details in Sadjadi & Hansen, 2013). These are extracted in 32-ms frames (with a 10 ms stride). The specific version included here corresponds to the Combo SAD introduced in Ziaei et al. (2014) and used further in Ziaei et al (2016). In this work, a threshold was optimized for daylong recordings, which typically have long silent periods, in order to avoid the usual overly large false alarm rates found in typical SAD systems provided with these data. + +### Main references: + +Ziaei, A., Sangwan, A., & Hansen, J. H. (2014). A speech system for estimating daily word counts. In Fifteenth Annual Conference of the International Speech Communication Association. http://193.6.4.39/~czap/letoltes/IS14/IS2014/PDF/AUTHOR/IS141028.PDF +A. Ziaei, A. Sangwan, J.H.L. Hansen, "Effective word count estimation for long duration daily naturalistic audio recordings," Speech Communication, vol. 84, pp. 15-23, Nov. 2016. +S.O. Sadjadi, J.H.L. Hansen, "Unsupervised Speech Activity Detection using Voicing Measures and Perceptual Spectral Flux," IEEE Signal Processing Letters, vol. 20, no. 3, pp. 197-200, March 2013. + +### Questions and bug reports + +Not available + +# Talker diarization tools + +This section contains documentation from the different Talker diarization tools (i.e., given a speech segment, decide who speaks). + + +## DiarTK + +### General intro + +This tool performs diarization, requiring as input not only .wav audio, but also speech/nonspeech in .rttm format, from human annotation, or potentially from one of the SAD or VAD tools included in this VM. + +The DiarTK model imported in the VM is a C++ open source toolkit by Vijayasenan & Valente (2012). The algorithm first extracts MFCC features, then performs non-parametric clustering of the frames using agglomerative information bottleneck clustering. At the end of the process, the resulting clusters correspond to a set of speakers. The most likely Diarization sequence between those speakers is computed by Viterbi realignement. + +We use this tool with the following parameter values: + +- weight MFCC = 1 (default) +- Maximum Segment Duration 250 (default) +- Maximum number of clusters possible: 10 (default) +- Normalized Mutual Information threshold: 0.5 (default) +- Beta value: 10 (passed as parameter) +- Number of threads: 3 (passed as parameter) + + +### Main references: + +D. Vijayasenan and F. Valente, “Diartk: An open source toolkit for research in multistream speaker diarization and its application to meetings recordings,” in Thirteenth Annual Conference of the International Speech Communication Association, 2012. https://pdfs.semanticscholar.org/71e3/9d42aadd9ec44a42aa5cd21202fedb5eaec5.pdf + +### Questions and bug reports + +http://htk.eng.cam.ac.uk/bugs/buglist.shtml + + +# Other tools + +This section contains documentation from other tools. + + + +## Yunitate + +### General intro + + +There is no reference for this tool, we provide a more extensive introduction based on a presentation Florian Metze gave on 2018-08-13 in an ACLEW Meeting. + +The data used for training were: + +- ACLEW Starter dataset +- VanDam public 5-min dataset (about 13h; https://homebank.talkbank.org/access/Public/VanDam-5minute.html); noiseme-sad used to detect and remove intraturn silences + +Talker identity annotations collapsed into the following 4 types: + +- children (including both the child wearing the device and other children; class prior: .13) +- female adults (class prior .09) +- male adults (class prior .03) +- non-vocalizations (class prior .75) + +The features were MED (multimedia event detection) feature, extracted with OpenSMILE. They were extracted in 2s windows moving 100ms each step. There were 6,669 dims at first, PCA-ed down to 50 dims + +The model was a RNN, with 1 bidirectional GRU layer and 200 units in each direction. There was a softmax output layer, which therefore doesn't predict overlaps.. + +The training regime used 5-fold cross-validation, with 5 models trained on 4/5 of the data and tested on the remainder. The outputs are poooled together to measure performance. The final model was trained on all the data. + +The loss function was cross entropy with classes weighted by 1/prior. The batch size was 5 sequences of 500 frames. The optimizer was SGD with Nesterov momentum=.9, the inital LR was .01 and the LR schedule was *=0.8 if frame accuracy doesn’t reach new best in 4 epochs + +The resulting F1 for the key classes were: + +- Child .55 (Precision .55, recall .55) +- Male adult .43 (P .31, R .61) +- Female adult .55 (P .5, R .62) + + +### Main references: + +There is no official reference for this tool. + +### Questions and bug reports + +Not available + +## VCM + +### General intro +Two independent models: one (modelLing) to predicts linguistic vs. non-linguistic infant vocalisations; the other one (modelSyll) predicts canonical vs. non-canonical syllables if given a linguistic infant vocalization. + +Specifically, the modelLing was trained on an infant linguistic dataset (refer to this paper: https://static1.squarespace.com/static/591f756559cc68d09fc2e308/t/5b3a94cb758d4645603085db/1530565836736/ZhangEtAl_2018.pdf), and modelSyll was trained on another infant syllable vocalisation dataset (refer to this paper: https://pdfs.semanticscholar.org/2b25/bc84d2c4668e6d17f4f9343106f726198cd0.pdf). + +Feature set: 88 eGeMAPS extracted by openSMILE-2.3.0 on the segment level. + +Model: two hidden layers feed-forward neural networks with 1024 hidden nodes per each hidden layer. A log_softmax layer is stacked as an output layer. The optimiser was set to SGD with a learning rate 0.01, and the batch size is 64. + +Setups: Both the infant linguistic and syllable vocalisation datasets were split into train, development, and test partitions following a speaker independent strategy. + +Results: The results are 67.5% UAR and 76.6% WAR on the test set for the lingustic voc classification; and are 70.4% UAR and 69.2% WAR for the syllable voc classification. + + +### Main references: + +There is no official reference for this tool. + +### Questions and bug reports + +https://github.com/srvk/vcm/issues/ + + +# Evaluation + +## Speech/voice activity detection and diarization evaluation + +For SAD, we employ an evaluation, which returns the false alarm (FA) rate (proportion of frames labeled as speech that were non-speech in the gold annotation) and missed speech rate (proportion of frames labeled as non-speech that were speech in the gold annotation). For TD, we employ the evaluation developed for the DiHARD Challenge, which returns a Diarization error rate (DER), which sums percentage of speaker error (mismatch in speaker IDs), false alarm speech (non-speech segments assigned to a speaker) and missed speech (unassigned speech). + +One important consideration is in order: What to do with files that have no speech to begin with, or where the system does not return any speech at the SAD stage or any labels at the TD stage. This is not a case that is often discussed in the literature because recordings are typically targeted at moments where there is speech. However, in naturalistic recordings, some extracts may not contain any speech activity, and thus one must adopt a coherent framework for the evaluation of such instances. We opted for the following decisions. + +If the gold annotation was empty, and the SAD system returned no speech labels, then the FA = 0 and M = 0; but if the SAD system returned some speech labels, then FA = 100 and M = 0. Also, if the gold annotation was not empty and the system did not find any speech, then this was treated as FA = 0 and M=100. + +As for the TD evaluation, the same decisions were used above for FA and M, and the following decisions were made for mismatch. If the gold annotation was empty, regardless of what the system returned, the mismatch rate was treated as 0. If the gold annotation was empty but a pipeline returned no TD labels (either because the SAD in that system did not detect any speech, or because the diarization failed), then this was penalized via a miss of 100 (as above), but not further penalized in terms of talker mismatch, which was set at 0. + + diff --git a/docs/source/troubleshoot.md b/docs/source/troubleshoot.md new file mode 100755 index 0000000..dff5cd0 --- /dev/null +++ b/docs/source/troubleshoot.md @@ -0,0 +1,91 @@ +# Troubleshooting + +## Installation issues + +### If your computer only has one core + +Before doing `vagrant up`, open the file called `DiViMe/Vagrantfile` in a text editor. Change the following line: +``` +> vbox.cpus = 2 +``` +into: +``` +> vbox.cpus = 1 +``` +Then proceed with the installation. Also, if you have more than one CPU, and you do not want DiViMe to take over your entire computer, you can set it to any value >= 2, and you should be fine. DiViMe uses multiple processors, but we have not yet fully optimized for many cores. + +### If your computer has 25 GB or less of storage space + +If your computer has less than 25 GB of storage space, then *you cannot build a fully working DiViMe*. In this case, clean up your files to free up space. + +### If your computer has 6 GB or less of RAM + +If your computer has less than about 8 GB of RAM, then you may or may not be able to build and use DiViMe. You probably need to change the space allocated to the virtual machine. Before doing `vagrant up`, open the file called `DiViMe/Vagrantfile` in a text editor. Change the following line: +``` +> vbox.memory = 4096 +``` +into: +``` +> vbox.memory = 2048 +``` +Then proceed with the Installation. Also, if you have more RAM, and you experience issues during installation (or use), you may benefit from increasing this value, which should normally not exceed half of your total installed RAM (as a rule of thumb). + +### If your computer is running Ubuntu (16.04) + +There is a known incompatibility between VirtualBox and the 4.13 Linux kernel on Ubuntu 16.04. What you may do is to install a previous version of the kernel, for example the 4.10, following [these instructions](https://doc.ubuntu-fr.org/kernel#installationSimple), or install the latest version of VirtualBox, which should fix the problem. + +Again, there is often a lot of information available online, because Vagrant and VirtualBox are widely used tools. + +### If something else fails + +Please open an issue [here](https://github.com/srvk/DiViMe/issues). Please paste the complete output of the failing run there, so we can better provide you with a solution. Also, please provide detailed information on your host system (which OS, RAM, CPU, HDD), which changes you made to the Vagrantfile, and also provide access to the data the system chokes on (if any). + +### Virtual Machine creation + +If your computer freezes after `vagrant up`, it may be due to several things. +If your OS is ubuntu 16.04, there's a known incompatibility between VirtualBox and the 4.13 Linux kernel on ubuntu 16.04. What you may do is to install a previous version of the kernel, for example the 4.10, following [these instructions](https://doc.ubuntu-fr.org/kernel#installationSimple), or install the latest version of virtualbox which should fix the problem. +If you are not on ubuntu 16.04, or if the previous fix didn't work, it may also be due to the fact that Vagrant is trying to create a Virtual Machine that asks for too much resources. Please ensure that you have enough space on your computer (you should have at least 15Gb of free space) and check that the memory asked for is okay. If not, you can lower the memory of the VM by changing line 25 of the VagrantFile, +``` +vbox.memory = 3072 +``` +to a lower number, such as +``` +vbox.memory = 2048 +``` + +## Resuming the Virtual Machine + +If you already used the VM once, shut down your computer, turned it back on and can't seem to be able to do `vagrant up` again, you can simply do +``` +vagrant destroy +``` +and recreate the VM using +``` +vagrant up +``` +If you don't want to destroy it, you can try opening the VirtualBox GUI, go to `File -> Settings or Preferences -> Network `, click on the `Host-only Networks` tab, then click the network card icon with the green plus sign in the right, if there are no networks yet listed. The resulting new default network should appear with the name ‘vboxnet0’. +You can now try again with `vagrant up` + + +## Problems with some of the Tools + +### OpenSmile, DiarTK + +If OpenSmile, DiarTK don't seem to work after `vagrant up`, first, please check that you indeed have the htk archive in your folder. If you don't, please put it there and launch: +``` +vagrant up --provision +``` +This step will install HTK inside the VM, which is used by several tools. + +If you use the noisemesSad or the noisemes_full tool, one problem you may encounter is that it doesn't treat all of your files and gives you an error that looks like this: +``` +Traceback (most recent call last): + File "SSSF/code/predict/1-confidence-vm5.py", line 59, in + feature = pca(readHtk(os.path.join(INPUT_DIR, filename))).astype('float32') + File "/home/vagrant/G/coconut/fileutils/htk.py", line 16, in readHtk + data = struct.unpack(">%df" % (nSamples * sampSize / 4), f.read(nSamples * sampSize)) +MemoryError +``` +If this happens to you, it's because you are trying to treat more data than the system/your computer can handle. +What you can do is simply put the remaining files that weren't treated in a separate folder and treat this folder separately (and do this until all of your files are treated if it happens again on very big datasets). +After that, you can put back all of your data in the same folder. diff --git a/docs/source/usage.md b/docs/source/usage.md new file mode 100755 index 0000000..2074e10 --- /dev/null +++ b/docs/source/usage.md @@ -0,0 +1,315 @@ +# Using DiViMe + +## Overview + +This is an overview of the full tool presentation found in the next sextion, recapping the main steps: + +1. Put your data in the ```data``` shared directory. +2. Do `$ vagrant up` to "wake the machine up" + +Next we provide instructions for all tools. More detailed information about each tool can be found in separate ReadMe files. + +Assuming the installation of the virtual machine is complete and some of the tests have passed, you can now use at least some of the tools in the virtual machine. We explain more about each step below, but in a nutshell, the steps to use DiViMe are always the same: + +1. Put the data you want to process in the ```data/``` directory (or any subdirectory within ```data/```) +2. Go to the DiViMe folder +3. Do `$ vagrant up` to "wake the machine up" +4. Use tools on data, typically by doing `vagrant ssh -c "script.sh [arguments]"`. You can also run a recipe. +5. Finally, remember to put DiViMe back to sleep with `$ vagrant halt` + +## An example use case: analyzing .wav files using the full pipeline of current tools + +1. Put the data you want to process in ```data/mydata/``` under the DiViMe main directory. Create a folder if it does not exist. +2. Make sure DiViMe is running (`vagrant up`) +3. Run `vagrant ssh -c "analyze.sh data/mydata/"` +4. Check ```data/mydata/``` for ELAN-readable .rttm files that contain full tool outputs (currently: speech detection & role diarization, vocal maturity classification, and syllable count estimation"). + + +## Further information on Step 1, putting your data into the ```data/``` directory + +Put the sound files that you want analyzed (and annotations, if you have any) inside the shared ```data``` folder. It is probably safer to make a copy of your files (rather than moving them), in case you later decide to delete the whole folder. Also, for greater security, DiViMe (as a VM) can only see data within the ```DiViMe``` folder, so soft links to files outside of that fodler will not work. + +You can drop a whole folder into ```data```. You will provide the path to the specific folder to be analyzed when running the tools (as per instructions below). All `.wav` files in that folder will be analyzed. + +If your files aren't .wav some of the tools may not work. Please consider converting them into wav with some other program, such as [ffmpeg](https://www.ffmpeg.org/). It is probably safer to make a copy (rather than moving your files into the data folder), in case you later decide to delete the whole folder. + +If you have any annotations, put them also in the same ```data``` folder. Annotations must be in .rttm format, and *they should be named exactly as your wav files*. If you have annotations in .cha, .eaf, .textgrid, or .its, see the Format section for instructions on converting them into .rttm. + +IMPORTANT: If you already analyzed a file with a given tool, re-running the tool will result in the previous analysis being overwritten. + +## Further information on Step 2, going to the DiViMe folder + +To interact with the virtual machine, you must use a terminal. If you don't know what this means, we recommend that you first follow the [Software Carpentry Shell Tutorial](https://swcarpentry.github.io/shell-novice/) (up to 00:45, namely "Introducing the shell", and "Navigating files and directories"). + +Next, navigate in the terminal window to the DiViMe directory that was created when you did `git clone https://github.com/srvk/DiViMe` when installing DiViMe. + +## Further information on Step 3, Waking the machine up + +Remember that you will be using a mini-computer within your computer. Typically, the machine will be down - i.e., it will not be running. This is good, because when it is running, it will use memory and other resources from your computer (which we call "the host", because it is hosting the other computer). With this step, you launch the virtual machine: + +`$ vagrant up` + +## Further information on Step 4, Using tools on data + +### Overview of tools + +If all tools passed the test, then you'll be able to automatically add the following types of annotation to your audio files: + +1) Speech activity detection (_when is someone talking?_): The tools available for this task are the following: noisemesSad, tocomboSad, opensmileSad + +2) Talker diarization (_who is talking?_) The tools available for this task are the following: diartk + +3) Role diarization (_what kind of person is talking?_) The tools available for this task are the following: yunitate + +4) Vocal type classification (_what kind of vocalization is this one?_) The tools available for this task are the following: vcm + +5) Evaluation (_how good is the automatic annotation?_) There is an evaluation available for the following tools: noisemesSad, tocomboSad, opensmileSad, diartk, yunitate + +### The concept of "pipelines" + +DiViMe is a platform for tools to analyze naturalistic, unannotated audio recordings. We consider this process to involve three kinds of processes: + +- speech activity detection and voice activity detection = “detecting vocalizations”, +- diarization = “deciding to whom the vocalizations belong”, and +- “additional annotations” + +Some tools actually combine two of these stages (e.g. a tool may do both speech activity detection and role attribution in one fell swoop). This [flowchart](https://docs.google.com/presentation/d/1vh2rTFdVZDZKh4WQ-UEzzPvHpr4-k-Q6Lf-5fvotRXw/edit#slide=id.g44f4e7b6a3_0_9) may help. + +We call a *pipeline* a sequence of those processes; i.e., it involves using one tool after another. For example, you may do *speech activity detection* + *talker diarization* + *vocal type classification* + +Starting from an audio file with no annotation, typically, you may want to run a *speech activity detection* tool followed by a *talker diarization* tool; then you will end up with an annotation showing who spoke when. However, you may not know who "talker0" and "talker1" are. (You could decide this by listening to some samples of each, and mapping them to different roles. However, we do not provide tools to do this.) + +Alternatively, we provide a *role diarization* tool that directly segments recordings into 3 main roles, namely child, male adult, female adult; and these separated from silence. + +In both cases, you may want to classify each vocalizations into different types with the *vocal type classification* tool. + +### How to run a Speech or Voice activity detection tool + +For these tools, type a command like this one: + +`$ vagrant ssh -c "noisemesSad.sh data/mydata/"` + +You can read that command as follows: + +*vagrant ssh -c*: This tells DiViMe that it needs to run a tool. + +*noisemesSad.sh*: This first argument tells DiViMe which tool to run. The options are: noisemesSad.sh, tocomboSad.sh, opensmileSad.sh + +*data/mydata/*: This second argument tells DiViMe where are the sound files to analyze. Note that the directory containing the input files should be located in the ```data/``` directory (or it can be ```data/``` itself). The directory does not need to be called `mydata` - you can choose any name. + +For each input wav file, there will be one rttm file created in the same directory, with the name of the tool added at the beginning. For example, imagine you have put a single file called participant23.wav into ```data/```, and you decided to run two SADs: + +``` +$ vagrant ssh -c "opensmileSad.sh data/" +$ vagrant ssh -c "noisemesSad.sh data/" +``` + +This will result in your having the following three files in your ```data/``` folder: + +- participant23.wav +- opensmileSad_participant23.rttm +- noisemesSad_participant23.rttm + +If you look inside one of these .rttm's, say the opensmileSad one, it will look as follows: + +``` +SPEAKER participant23 1 0.00 0.77 speech +SPEAKER participant23 1 1.38 2.14 speech +``` + +This means that opensmileSad considered that the first 770 milliseconds of the audio were speech; followed by 610 milliseconds of non-speech, followed by 2.14 seconds of speech; etc. + +### How to run a Talker diarization tool + +For these tools, type a command like this one: + +`$ vagrant ssh -c "diartk.sh data/mydata/ noisemesSad"` + +You can read that command as follows: + +*vagrant ssh -c*: This tells DiViMe that it needs to run a tool. + +*diartk.sh*: This first argument tells DiViMe which tool to run. The options are: diartk.sh. + +*data/mydata/*: This second argument tells DiViMe where are the sound files to analyze. Note that the directory containing the input files should be located in the ```data/``` directory (or it can be ```data/``` itself). The directory does not need to be called `mydata` - you can choose any name. + +*noisemesSad*: Remember that this tool does "talker diarization": Given some speech, attribute it to a speaker. Therefore, this type of tool necessitates speech/voice activity detection. This third argument tells DiViMe what file contains information about which sections of the sound file contain speech. + +You can only use one of the following options: rttm, opensmileSad, tocomboSad, noisemesSad. We explain each of these options next. + +You can provide annotations done by a human or in some other way, and encoded as rttms. If you have a different format, see the Format section. *What is crucial for this procedure to work is that your rttm's reflection your human-annotation are called exactly like your sound files.* Notice that all annotations that say "speech" in the eighth column count as such. + +Alternatively, you can use automatic annotations generated by DiViMe's speech/voice activity detection systems, encoded in rttm files. In this case, you would pass one of the following options: + +- noisemesSad: this means you want the system to use the output of the noisemesSad system. If you have not run noisemesSad, the system will fail. +- opensmileSad: this means you want the system to use the output of the opensmile system. If you have not run this system before, the system will fail. +- tocomboSad: this means you want the system to use the output of the tocomboSad system. If you have not ran this system before, the system will fail. + +If the third parameter is not provided, the system will give an error. + +If all three parameters are provided, then the system will first find all the annotation files matching the third parameter (e.g., all the human-annotated files *.rttm; or all the tocomboSad_*.rttm files), and then find the corresponding sound files. For example, imagine you have put into your ```data/mydata/``` folder the following files: + +- participant23.wav +- opensmileSad_participant23.rttm +- participant24.wav +- participant24.rttm + +If you run: + +`$ vagrant ssh -c "diartk.sh data/mydata/ opensmileSad"` + +then only participant23.wav will be analyzed. + + +If you run: + +`$ vagrant ssh -c "diartk.sh data/mydata/ rttm"` + +then only participant24.wav will be analyzed. + +At the end of the process, there will be an added rttm file for each analyzed file. For instance, if you have just one sound file (participant23.wav) at the beginning and you run opensmileSad followed by diartk, then you will end up with the following three files: + +- `participant23.wav`: your original sound file +- `opensmileSad_participant23.rttm`: the output of opensmileSad, which states where there is speech +- `diartk_opensmileSad_participant23.rttm`: the output of opensmileSad followed by diartk, which states which speech sections belong to which speakers. + +See Format section for explanation on how to read the resulting rttm. + +### How to run a talker type tool + +For these tools, type a command like this one: + +`$ vagrant ssh -c "yunitate.sh data/mydata/"` + +You can read that command as follows: + +*vagrant ssh -c*: This tells DiViMe that it needs to run a tool. + +*yunitate.sh*: This first argument tells DiViMe which tool to run. The options are: yunitate. + +*data/mydata/*: This second argument tells DiViMe where are the sound files to analyze. Note that the directory containing the input files should be located in the ```data/``` directory (or it can be ```data/``` itself). The directory does not need to be called `mydata` - you can choose any name. + +It returns one rttm per sound file, with an estimation of where there are vocalizations by children, female adults, and male adults. See Format section for explanation on how to read the resulting rttm. + + +### How to run a Vocalization classification tool + +For these tools, type a command like this one: + +`$ vagrant ssh -c "vcm.sh data/mydata/"` + +You can read that command as follows: + +*vagrant ssh -c*: This tells DiViMe that it needs to run a tool. + +*vcm.sh*: This first argument tells DiViMe which tool to run. The options are: vcm. + +*data/mydata/*: This second argument tells DiViMe where are the sound files to analyze. Note that the directory containing the input files should be located in the ```data/``` directory (or it can be ```data/``` itself). The directory does not need to be called `mydata` - you can choose any name. + +**The vocalization classification tool depends on the output of the talker type tool yunitate. Therefore, the directory where you put your clips to analyze must contain files called yunitate_*.rttm (e.g., yunitate_participant23.wav).** + +The vocalization classification tool returns one rttm per sound file, with an estimation for each CHI vocalzsation to be a canonical syllable (CNS), non-canonical syllable (NCS), crying (CRY), and others (OTH, normally refer to laughing). + +See Format section for explanation on how to read the resulting rttm. + + +### How to run an Evaluation + +If you have some annotations that you have made, you probably want to know how well our tools did - how close they were to your hard-earned human annotations. + +You may first need to generate .rttm files for the evaluation. We provide some undocumented tools for this in `utils`, which you can use at your own risk. The only case that is currently supported and documented here is when you have an ELAN file following the ACLEW annotation scheme exhaustively (i.e., the whole file has been annotated by humans) at least in terms of how participants are called. To generate rttms for evaluation, + +`vagrant ssh -c "eafAAS2rttm_folder.sh data/"` + +You should see appear files that are called exactly like your .eaf's but that have an rttm format. See Format section for explanation on how to read the resulting rttm. + +**IMPORTANT** The rttm's containing this human annotation should be named exactly the same as your sound files!!! + + +Type a command like the one below: + +`vagrant ssh -c "eval.sh data/ tocomboSad accuracy"`` + +You can read that command as follows: + +*vagrant ssh -c*: This tells DiViMe that it needs to run a tool. + +*eval.sh*: This first argument tells DiViMe that we want to perform an evaluation. + +*data/*: This second argument tells DiViMe where are the sound files that need to be evaluated. This directory must contain both the annotations generated by the model and the human-made ones. + +*noisemesSad*: The third argument indicates which tool's output to evaluate. + +*accuracy*: The fourth argument tells DiViMe which metric need to be used to assess the model's performances. Here, we want to use the well-known accuracy measure. + +The output should look like this : + +``` +accuracy report + detection accuracy true positive true negative false positive false negative + % +item +my_file1.rttm 48.73 30.49 27.16 44.47 16.18 +my_file2.rttm 57.00 12.32 55.13 40.36 10.53 +TOTAL 52.86 42.81 82.29 84.83 26.71 +``` + +It generates a table showing the scores obtained for each file. +Since it is usually not enough to look at the final metric (the detection accuracy here), the table also shows intermediate metrics, therefore allowing the user to have a better insight of model's performances. +Note that this table will be saved in the .csv format in the *data/* folder. + +We use the program pyannote-metrics to compute these metrics. Therefore, please see the [pyannote-metrics docs](http://pyannote.github.io/pyannote-metrics/reference.html) for explanations. + +%
+% +%| Speech Activity Detection | Diarization | Identification | +%|---|---|---| +%| Accuracy | Completeness | Identification Error Rate | +%| Detection Error Rate | Coverage | Precision | +%| Precision | Diarization Error Rate | Recall | +%| Recall | Homogeneity | | +%| | Purity | | +% +% +% +%Note that the identification task is the same as the diarization task when the one-to-one mapping between hypothesis classes and reference classes share the same labels. To assess a role diarization model in identification mode, you need to type the following command : +% +%`vagrant ssh -c "eval.sh data/ diartk_tocomboSad completeness --identification"` +% +%If the flag *-\-identification* is not passed, the script will run in the diarization mode. +% +%Note that you can ask to compute several metrics at once by typing : +% +%`vagrant ssh -c "eval.sh data/ tocomboSad accuracy precision recall"` +% +%It will generated a report for each metric. + +If you're not a math person, you can add the -\-visualization flag by typing : + +`vagrant ssh -c "eval.sh data/ tocomboSad accuracy precision recall --visualization"` + +It will extract the minute that contains the most speech for each file and align the reference and the hypothesis segments : + +![
One minute alignement that has been obtained by adding the flag --visualization
](../images/example_visu.png) + +Note that the process of calculating the minute that contains the most speech can be time-consuming. + +## An alternative for Step 4: using recipes + +It is possible to combine multiple steps into one program, which can then execute an entire complex analysis all by itself. Feel free to experiment. If you have good examples, **feel free to share**. + +## Further information on Step 5, putting DiViMe back to sleep + +Last but not least, you should **remember to halt the virtual machine**. If you don't, it will continue running in the background, taking up useful resources! To do so, simply navigate to the DiViMe folder on your terminal and type in: + +`$ vagrant halt` + +## Updating DiViMe + +1. Go to DiViMe main directory, and run +2. `vagrant halt` +3. `vagrant destroy` +4. `git pull` +5. `vagrant up` + +Note that this procedure will install newest version of DiViMe from scracth, and may take up to several hours depending on your network connection and computer. Note that vagrant destroy will *not* destroy your own files under ```data/```. diff --git a/launcher/README.md b/launcher/README.md new file mode 100755 index 0000000..86d93f7 --- /dev/null +++ b/launcher/README.md @@ -0,0 +1,30 @@ +# Launcher +This repository contains scripts that launch tools in the ACLEW Diarization VM. The scripts in this folder are called when you "[use a tool on data](https://divime.readthedocs.io/en/latest/usage.html#further-information-on-step-4-using-tools-on-data)". + +### SAD tools +``` +noisemesSad.sh +opensmileSad.sh +tocomboSad.sh +``` + +### Talker Diarization tools +``` +diartk.sh +``` + +### Role assignment tools +``` +yunitate.sh +``` + +### Scoring tools +``` +evalDiar.sh +evalSAD.sh +``` + +### VM Self-test +``` +test.sh +``` diff --git a/launcher/WCE_from_SAD_outputs.sh b/launcher/WCE_from_SAD_outputs.sh new file mode 100644 index 0000000..b5c75ce --- /dev/null +++ b/launcher/WCE_from_SAD_outputs.sh @@ -0,0 +1,33 @@ +# Runs WCE for .wav files using the corresponding speech activity detection (SAD) +# segments defined in .rttm files named as _.rttm , all located +# in the same directory . + +if [ $# -lt 1 ]; then + echo "Usage: sh WCE_from_SAD_outputs.sh " + echo "where dirname is a folder containing audio .wav files and the corresponding .rttm files" + echo "created using ." + echo "Output .rttm files with WCE counts will be produced to " + exit 1 +fi + + +SCRIPT_DIR=$(dirname "$0") +DATA_FOLDER=$1 + +if [ "$#" -ne 2 ]; then +SADNAME="tocomboSad" +else +SADNAME=$2 +fi + +if [ -d "${DATA_FOLDER}/wav_tmp/" ]; then +rm ${DATA_FOLDER}/wav_tmp/*.wav +fi + +python /home/vagrant/repos/WCE_VM/aux_VM/rttm_to_wavs.py ${DATA_FOLDER} ${SADNAME} + +echo "Running WCE..." +sh /home/vagrant/launcher/estimateWCE.sh ${DATA_FOLDER}/wav_tmp/ ${DATA_FOLDER}/wav_tmp/WCE_output.txt > /dev/null 2>&1 + +python /home/vagrant/repos/WCE_VM/aux_VM/WCE_to_rttm.py ${DATA_FOLDER}/wav_tmp/WCE_output.txt ${DATA_FOLDER} +echo "WCE complete." diff --git a/launcher/diartk.sh b/launcher/diartk.sh new file mode 100755 index 0000000..555d13d --- /dev/null +++ b/launcher/diartk.sh @@ -0,0 +1,131 @@ +#!/bin/bash +# Launcher onset routine +SCRIPT=$(readlink -f $0) +BASEDIR=/home/vagrant +REPOS=$BASEDIR/repos +UTILS=$BASEDIR/utils +# end of launcher onset routine + +if [ $# -lt 1 ] || [ $# -gt 3 ]; then + echo "Usage: $0 " + echo "where audio-dir is the name of the folder" + echo "containing the wav files" + echo "and is one of:" + echo " noisemesSad" + echo " tocomboSad" + echo " opensmileSad" + echo " textgrid" + echo " eaf" + echo " rttm" + + exit 1 +fi + +KEEPTEMP=false +if [ $BASH_ARGV == "--keep-temp" ]; then + KEEPTEMP=true +fi + +### Read in variables from user +audio_dir=/vagrant/$1 +trs_format=$2 + +### Other variables specific to this script +# create temp dir +workdir=`mktemp -d --tmpdir=${audio_dir}` + +### SCRIPT STARTS +cd $BASEDIR/repos/ib_diarization_toolkit + + +# Check audio_dir to see if empty or if contains empty wav +bash $UTILS/check_folder.sh ${audio_dir} + + +for fin in `ls ${audio_dir}/*.wav`; do + filename=$(basename "$fin") + basename="${filename%.*}" + echo "treating $basename" + + featfile=$workdir/$basename.fea + scpfile=$workdir/$basename.scp + + # first-first convert RTTM to DiarTK's version of a .scp file + # SCP format: + # __=[start,end] + # RTTM format: + # Type file chan tbeg tdur ortho stype name conf Slat + # math: convert RTTM seconds to HTK (10ms default) frames = multiply by 100 + case $trs_format in + "noisemesSad") + sys="noisemesSad" + python $UTILS/rttm2scp.py ${audio_dir}/noisemesSad_${basename}.rttm $scpfile + ;; + "tocomboSad") + sys="tocomboSad" + python $UTILS/rttm2scp.py ${audio_dir}/tocomboSad_${basename}.rttm $scpfile + ;; + "opensmileSad") + sys="opensmileSad" + python $UTILS/rttm2scp.py ${audio_dir}/opensmileSad_${basename}.rttm $scpfile + ;; + "textgrid") + sys="goldSad" + python /home$UTILS/textgrid2rttm.py ${audio_dir}/${basename}.TextGrid $workdir/${basename}.rttm + python $UTILS/rttm2scp.py $workdir/${basename}.rttm $scpfile + rm $workdir/$basename.rttm + ;; + "eaf") + sys="goldSad" + python /home$UTILS/elan2rttm.py ${audio_dir}/${basename}.eaf $workdir/${basename}.rttm + python $UTILS/rttm2scp.py $workdir/${basename}.rttm $scpfile + rm $workdir/$basename.rttm + ;; + "rttm") + sys="goldSad" + # Since some reference rttm files are spaced rather than tabbed, we need to + # tab them before using them. + cp ${audio_dir}/${basename}.rttm $workdir/${basename}.rttm + sed -i 's/ \+/\t/g' $workdir//${basename}.rttm + python $UTILS/rttm2scp.py $workdir/${basename}.rttm $scpfile + ;; + *) + echo "ERROR: please choose SAD system between:" + echo " noisemesSad" + echo " tocomboSad" + echo " opensmileSad" + echo " textgrid" + echo " eaf" + echo " rttm" + echo "Now exiting..." + exit 1 + ;; + esac + + # don't process files with empty transcription + if [ -s $scpfile ]; then + # first generate HTK features + #HCopy -T 2 -C htkconfig $fin $featfile + >&2 echo WARNING for $featfile: replacing HCopy htconfig with SMILExtract MFCC12_E_D_A is untested + LD_LIBRARY_PATH=/usr/local/lib \ + SMILExtract \ + -C ~/repos/opensmile-2.3.0/config/MFCC12_E_D_A.conf \ + -I $fin -O $featfile \ + -logfile $workdir/opensmile-diartk.log + + # next run DiarTK + scripts/run.diarizeme.sh $featfile $scpfile $workdir $basename + + # print results + #cat $workdir/$basename.out + cp $workdir/$basename.rttm ${audio_dir}/diartk_${sys}_${basename}.rttm + else + # Create empty output file + touch ${audio_dir}/diartk_${sys}_${basename}.rttm + fi +done + +# Delete temporary folder +if ! $KEEPTEMP; then + rm -rf $workdir +fi diff --git a/launcher/estimateWCE.sh b/launcher/estimateWCE.sh new file mode 100755 index 0000000..90ae424 --- /dev/null +++ b/launcher/estimateWCE.sh @@ -0,0 +1,61 @@ +#!/usr/bin/env bash + +SCRIPT_DIR=$(dirname "$0") + +FILES_TEST=$1 + +if [ "$#" -ne 1 ] && [ "$#" -ne 2 ] && [ "$#" -ne 3 ] ; then +echo "Usage: $0 (optional) (optional)" +echo "Example:" +echo "$0 /vagrant/data/my_audiodata/ ~/my_WCE_output.txt" +echo "or" +echo "$0 /vagrant/data/list_of_audiofiles.txt ~/my_WCE_output.txt" +exit 1 +fi + + +# If input is a directory, find all .wavs in it and write to a file to be used as input +# to the WCE tool +if [[ -d $1 ]]; then + find $1*.wav > ~/repos/WCE_VM/file_list.txt + FILES_TEST=~/repos/WCE_VM/file_list.txt +elif [ ! -d "$1" ] && [ ! -f $1 ]; then + if [[ -d /vagrant/$1 ]]; then + find /vagrant/$1*.wav > ~/repos/WCE_VM/file_list.txt + FILES_TEST=~/repos/WCE_VM/file_list.txt + fi +fi + + +# Replace DiViMe way of addressing data-folder with an absolute path +if [ -n "$2" ]; then + OUTPUTFILE=$2 + OUTPUTFILE=${OUTPUTFILE/"data/"/"/vagrant/data/"} + OUTPUTFILE=${OUTPUTFILE/"/vagrant//vagrant/"/"/vagrant/"} +else + OUTPUTFILE='~/repos/WCE_VM/outputs/default_output.csv' +fi + +if [ -n "$3" ]; then + MODEL=$3 +else + MODEL="~/repos/WCE_VM/models/model_default.mat" +fi + +if [ ! -d "/vagrant/data/WCE_VM_TEMP/" ]; then + mkdir -p /vagrant/data/WCE_VM_TEMP/ +fi + +MATPATH="/usr/local/MATLAB/MATLAB_Runtime/v93/" + +echo "Running WCE module (this might take a while...)" +# The 'DISPLAY= ' part prevents an X-Server from popping up on some machines +DISPLAY= ~/repos/WCE_VM/run_WCEestimate.sh ${MATPATH} ${FILES_TEST} ${MODEL} ${OUTPUTFILE} > /vagrant/data/WCE_VM_TEMP/WCE_process_log.log + +# Combine filenames and output counts into one file +paste -d ', ' $FILES_TEST $OUTPUTFILE > /vagrant/data/WCE_VM_TEMP/tempout.txt +cp /vagrant/data/WCE_VM_TEMP/tempout.txt $OUTPUTFILE +sed -i 's+/vagrant/data/+data/+g' $OUTPUTFILE + + +echo "WCE processing complete. Wrote output to $OUTPUTFILE" diff --git a/launcher/eval.sh b/launcher/eval.sh new file mode 100755 index 0000000..351aa19 --- /dev/null +++ b/launcher/eval.sh @@ -0,0 +1,94 @@ +#!/usr/bin/env bash + +display_usage() { + echo "Usage: eval.sh " + echo "where :" + echo -e "\tdata is the folder containing the data" + echo -e "\tmodel is the model that needs to be evaluated" + echo -e "\tmetrics is a list of metrics that will assess the performances" + echo -e "\toptional flags, are the flags that change the behaviour of this script :" + echo -e "\t\t --identification to assess the model as an identification task (when the labels are same in the reference and the hypothesis)" + echo -e "\t\t --visualization to generate .png files showing the alignement between the reference and the hypothesis.\n\n" + echo "Choices for the model are:" + echo -e "\t - noisemesSad" + echo -e "\t - tocomboSad" + echo -e "\t - opensmileSad" + echo -e "\t - diartk" + echo -e "\t - yunitator" + echo "If evaluating diartk or yunitator, please give which flavour" + echo "of SAD you used to produce the transcription" + echo "you want to evaluate" + echo -e "\nChoices for the metrics are:" + echo -e "\t- for the speech activity detection task :" + echo -e "\t\t accuracy, deter, precision, recall" + echo -e "\t- for the diarization task :" + echo -e "\t\t completeness, coverage, diaer, homogeneity, purity" + echo -e "\t- for the identification task :" + echo -e "\t\t ider, precision, recall" + exit 1 +} + +### Read in variables from user +DATA=$1 +MODEL=$2 +shift; shift; +if [[ "$MODEL" == "diartk" ]] || [[ "$MODEL" == "yunitator" ]]; then + if [[ "$1" == "rttm" ]]; then + MODEL=${MODEL}_goldSad + else + MODEL=${MODEL}_$1 + fi + shift; +fi; + +# Read metrics +METRICS=() +while [ ! -z $1 ] && [ ! ${1:0:2} == "--" ]; do + METRICS+=("$1") + shift ; +done + +# Read optional flags that are used to run +# the script in a "non-classical" way. +FLAGS=() +while true ; do + case "$1" in + --identification) + FLAGS+=("--identification") + shift ;; + --visualization) + FLAGS+=("--visualization") + shift ;; + *) + if [[ ! $1 == "" ]]; then + echo "Flag $1 not recognized." + exit 1 + fi + break;; + esac +done + +echo -e "\nEvaluating ${MODEL} on ${DATA} with respect to : ${METRICS[*]}" +echo -e "${FLAGS[*]}" +### Launcher onset routine +SCRIPT=$(readlink -f $0) +BASEDIR=/home/vagrant +REPOS=$BASEDIR/repos +UTILS=$BASEDIR/utils +LAUNCHER=$BASEDIR/launcher +############################### + +#### SCRIPT STARTS +source activate divime +case $MODEL in +"tocomboSad"|"opensmileSad"|"noisemesSad") + python $UTILS/compute_metrics.py --reference $DATA/.. --hypothesis $DATA --prefix $MODEL --task detection --metrics ${METRICS[*]} ${FLAGS[*]} + ;; +"yunitator_old"|"yunitator_english"|"yunitator_universal"|"lena"|"diartk_noisemesSad"|"diartk_tocomboSad"|"diartk_opensmileSad"|"diartk_goldSad") + python $UTILS/compute_metrics.py --reference $DATA/.. --hypothesis $DATA --prefix $MODEL --task diarization --metrics ${METRICS[*]} ${FLAGS[*]} + ;; +*) + display_usage + ;; +esac +conda deactivate diff --git a/launcher/evalWCE_LOSO.sh b/launcher/evalWCE_LOSO.sh new file mode 100755 index 0000000..cfb8274 --- /dev/null +++ b/launcher/evalWCE_LOSO.sh @@ -0,0 +1,52 @@ +#!/usr/bin/env bash + +SCRIPT_DIR=$(dirname "$0") +DIROP_FOLDER="/vagrant/data/WCE_VM_TEMP/" + +MATPATH="/usr/local/MATLAB/MATLAB_Runtime/v93/" +FILES_TRAIN="${DIROP_FOLDER}/WAVFILES_TRAIN.txt" +TRAIN_COUNTS="${DIROP_FOLDER}/WORDCOUNTS_TRAIN.txt" +MODEL="~/repos/WCE_VM/models/default_model.mat" +ALPHA="${DIROP_FOLDER}/ALPHA.txt" +CONFIG="~/repos/WCE_VM/configs/config_default.txt" + +FILES_TEST="${DIROP_FOLDER}/WAVFILES_TEST.txt" +TEST_COUNTS="${DIROP_FOLDER}/WORDCOUNTS_TEST.txt" +OUTPUTS_COUNTS="${DIROP_FOLDER}/OUTPUTS_COUNTS.csv" +OUTPUTS_EVAL="${DIROP_FOLDER}/OUTPUTS_EVAL.txt" + + +[ -e "${DIROP_FOLDER}/all_estimates.csv" ] && rm "${DIROP_FOLDER}/all_estimates.csv" +[ -e "${DIROP_FOLDER}/all_references.csv" ] && rm "${DIROP_FOLDER}/all_references.csv" + +c=0 +for en_path in ${DIROP_FOLDER}*_totWords.txt; do + ((c++)) + ids[c]=${en_path##*/} + ids[c]="${ids[c]%_totWords.txt}" # this neglects corpus name, only 4 digits + + line=$(head -n 1 $en_path) + echo $line >> "${DIROP_FOLDER}/all_references.csv" +done + +c=0 +for en_path in ${DIROP_FOLDER}*_totWords.txt; do + #python $SCRIPT_DIR/get_train_LOO_inputs.py ${c} ${DIROP_FOLDER} ${ids[@]} + python ~/repos/WCE_VM/aux_VM/get_train_LOO_inputs.py ${c} ${DIROP_FOLDER} ${ids[@]} + ~/repos/WCE_VM/run_WCEtrain.sh ${MATPATH} ${FILES_TRAIN} ${TRAIN_COUNTS} ${MODEL} ${CONFIG} ${ALPHA} + ~/repos/WCE_VM/run_WCEestimate.sh ${MATPATH} ${FILES_TEST} ${MODEL} "${DIROP_FOLDER}/OUTPUTS_COUNTS_${c}_ESTIMATE.csv" + python ~/repos/WCE_VM/evaluate_WCE.py "${DIROP_FOLDER}/OUTPUTS_COUNTS_${c}_ESTIMATE.csv" ${TEST_COUNTS} ${DIROP_FOLDER}"/OUTPUTS_EVAL_"${c}".txt" # code to run eval must bre filled + cp ${TEST_COUNTS} "${DIROP_FOLDER}/OUTPUTS_COUNTS_${c}_REFERENCE.csv" + + ((c++)) +done + +c=0 +for en_path in ${DIROP_FOLDER}*_ESTIMATE.csv; do + awk '{ sum += $1 } END { print sum }' "${DIROP_FOLDER}/OUTPUTS_COUNTS_${c}_ESTIMATE.csv" >> "${DIROP_FOLDER}/all_estimates.csv" + ((c++)) +done + +python ~/repos/WCE_VM/evaluate_WCE.py "${DIROP_FOLDER}/all_estimates.csv" "${DIROP_FOLDER}/all_references.csv" ${DIROP_FOLDER}"/OUTPUTS_LOO.txt" # code to run eval must bre filled + +more ${DIROP_FOLDER}"/OUTPUTS_LOO.txt" \ No newline at end of file diff --git a/launcher/fulltrainWCE.sh b/launcher/fulltrainWCE.sh new file mode 100755 index 0000000..bec111d --- /dev/null +++ b/launcher/fulltrainWCE.sh @@ -0,0 +1,30 @@ +#!/usr/bin/env bash + +# Perform full training of WCE module using data prepared by WCE_preprocess.sh + +SCRIPT_DIR=$(dirname "$0") +DIROP_FOLDER="/vagrant/data/WCE_VM_TEMP/" + +MATPATH="/usr/local/MATLAB/MATLAB_Runtime/v93/" +FILES_TRAIN="${DIROP_FOLDER}/WAVFILES_TRAIN.txt" +TRAIN_COUNTS="${DIROP_FOLDER}/WORDCOUNTS_TRAIN.txt" +MODEL_FINAL="~/repos/WCE_VM/models/model_final.mat" +ALPHA="${DIROP_FOLDER}/ALPHA.txt" +CONFIG="~/repos/WCE_VM/configs/config_default.txt" + +FILES_TEST="${DIROP_FOLDER}/WAVFILES_TEST.txt" +TEST_COUNTS="${DIROP_FOLDER}/WORDCOUNTS_TEST.txt" +OUTPUTS_COUNTS="${DIROP_FOLDER}/OUTPUTS_COUNTS.csv" +OUTPUTS_EVAL="${DIROP_FOLDER}/OUTPUTS_EVAL.txt" + +c=0 +for en_path in ${DIROP_FOLDER}*_totWords.txt; do + ((c++)) + ids[c]=${en_path##*/} + ids[c]="${ids[c]%_totWords.txt}" +done + +#python ... write python code that averages all the results of the speakers +c=-1 +python $SCRIPT_DIR/get_train_LOO_inputs.py ${c} ${DIROP_FOLDER} ${ids[@]} +~/repos/WCE_VM/run_WCEtrain.sh ${MATPATH} ${FILES_TRAIN} ${TRAIN_COUNTS} ${MODEL_FINAL} ${CONFIG} ${ALPHA} diff --git a/launcher/noisemesFull.sh b/launcher/noisemesFull.sh new file mode 100644 index 0000000..70bafd6 --- /dev/null +++ b/launcher/noisemesFull.sh @@ -0,0 +1,3 @@ +#!/bin/bash + +./launcher/noisemesSad.sh $@ --full-classes \ No newline at end of file diff --git a/launcher/noisemesSad.sh b/launcher/noisemesSad.sh new file mode 100755 index 0000000..ff8e008 --- /dev/null +++ b/launcher/noisemesSad.sh @@ -0,0 +1,98 @@ +#!/bin/bash +# noisemes_sad.sh +# Since the script is built to be launched outside of the vm, source +# the .bashrc which is not necessarily sourced! +source ~/.bashrc + +source activate divime + +# Absolute path to this script. /home/vagrant/launcher/noisemesSad.sh +SCRIPT=$(readlink -f $0) +# home folder +BASEDIR=/home/vagrant +YUNITATORDIR=/home/vagrant/repos/Yunitator + +if [ $# -lt 1 ]; then + echo "Usage: noisemesSad.sh [--keep-temp] [--full-classes]" + echo "where dirname is a folder on the host" + echo "containing the wav files (/vagrant/dirname/ in the VM)" + exit 1 +fi + +KEEPTEMP=false +FULLCLASSES=false +for a in ${BASH_ARGV[*]} ; do + if [ $a == "--keep-temp" ]; then + KEEPTEMP=true + fi + if [ $a == "--full-classes" ]; then + FULLCLASSES=true + fi +done + +audio_dir=/vagrant/$1 +TEMPNAME=feature +filename=$(basename "${audio_dir}") +dirname=$(dirname "${audio_dir}") +extension="${filename##*.}" +basename="${filename%.*}" + +# Check audio_dir to see if empty or if contains empty wav +bash $BASEDIR/utils/check_folder.sh ${audio_dir} + +# let's get our bearings: set CWD to path of Yunitator +cd $YUNITATORDIR + +# make output folder for features, below input folder +mkdir -p ${audio_dir}/$TEMPNAME + +# first features +echo "extracting features for speech activity detection" + +for file in `ls ${audio_dir}/*.wav`; do + ./extract-htk-vm2.sh $file $TEMPNAME +done + +# Choose chunksize based off memory. Currently this is equivalent to 200 +# frames per 100MB of memory. +# Ex: 3GB -> 6000 frames +# Ex: 2048MB -> 4000 frames +# This setting was chosen arbitrarily and was successful for tests at 2GB-4GB. +chunksize=$(free | awk '/^Mem:/{print $2}') +let chunksize=$chunksize/100000*200 + +# then confidences +#python SSSF/code/predict/1-confidence-vm3.py $1 +echo "detecting speech and non speech segments" + +python yunified.py noisemes ${audio_dir} $chunksize + +echo "finished detecting speech and non speech segments" + +# take all the .rttm in /vagrant/data/hyp and move them to /vagrant/data - move features and hyp to another folder also. +for sad in `ls ${audio_dir}/hyp_sum/*.lab`; do + base=$(basename $sad .lab) + + if $FULLCLASSES; then + rttm_out=noisemesFull_${base}.rttm + else + rttm_out=noisemesSad_${base}.rttm + fi + + if [ -s $sad ]; then + if $FULLCLASSES; then + grep '' $sad | awk -v fname=$base '{print $4 " " fname " " 1 " " $1 " " $2-$1 " " "" " " "" " " $3 " " ""}' > ${audio_dir}/$rttm_out + else + grep ' speech' $sad | awk -v fname=$base '{print $4 " " fname " " 1 " " $1 " " $2-$1 " " "" " " "" " " $3 " " ""}' > ${audio_dir}/$rttm_out + fi + else + touch ${audio_dir}/$rttm_out + fi +done + +# simple remove hyp and feature +if ! $KEEPTEMP; then + rm -rf ${audio_dir}/hyp_sum ${audio_dir}/$TEMPNAME +fi + +conda deactivate diff --git a/launcher/opensmileSad.sh b/launcher/opensmileSad.sh new file mode 100755 index 0000000..85739fe --- /dev/null +++ b/launcher/opensmileSad.sh @@ -0,0 +1,76 @@ +#!/bin/bash +# Launcher onset routine +SCRIPT=$(readlink -f $0) +BASEDIR=`dirname $SCRIPT` # this is the home folder of this script + # not the home folder of the 'vagrant' user in the VM +REPOS=/home/vagrant/repos +UTILS=/home/vagrant/utils +CONF=/vagrant/conf +# end of launcher onset routine + +### Read in variables from user +audio_dir=/vagrant/$1 + +### Other variables specific to this script +OSHOME=$REPOS/opensmile-2.3.0 +CONFIG_FILE=$CONF/vad_segmenter_aclew.conf +OPENSMILE=SMILExtract +workdir=${audio_dir}/temp/opensmileSad +mkdir -p $workdir + +### SCRIPT STARTS + +if [ $# -lt 1 ]; then + echo "USAGE: $0 " + exit 1 +fi + +KEEPTEMP=false +if [ $BASH_ARGV == "--keep-temp" ]; then + KEEPTEMP=true +fi + +filename=$(basename "$1") +dirname=$(dirname "$1") +extension="${filename##*.}" +basename="${filename%.*}" + + +cd $OSHOME/scripts/vad + +# Use OpenSMILE 2.3.0 +for sad in `ls ${audio_dir}/*.wav`; do + file=$sad + id=`basename $file` + id=${id%.wav} + > ${audio_dir}/${id}.txt #Make it empty if already present + echo "Processing $id ..." + echo $workdir/${id}.txt + LD_LIBRARY_PATH=/usr/local/lib \ + $OPENSMILE \ + -C $CONFIG_FILE \ + -I $file \ + -turndebug 1 \ + -noconsoleoutput 1 \ + -saveSegmentTimes $workdir/${id}.txt \ + -logfile $workdir/opensmile-vad.log + + if [[ ! -f $workdir/${id}.txt ]]; then + echo "No output produced by opensmileSad." + echo "Generating empty rttm... (check the log file)" + touch $workdir/${id}.txt + fi +done + +# clean up generated WAV files in working directory (tool_home/scripts/vad) +rm -f output_segment_*.wav + +for output in $(ls $workdir/*.txt); do + id=$(basename $output .txt) + awk -F ';|,' -v FN=$id '{ start_on = $2; start_off = $3 ; print "SPEAKER "FN" 1 "start_on" "(start_off-start_on)" speech " }' $output > ${audio_dir}/opensmileSad_$id.rttm +done + +# Delete temporary folder +if ! $KEEPTEMP; then + rm -rf $workdir +fi diff --git a/launcher/python3/README.md b/launcher/python3/README.md new file mode 100644 index 0000000..413b7c7 --- /dev/null +++ b/launcher/python3/README.md @@ -0,0 +1,18 @@ +### python3 +``` +# activate divime environment to use python 3.6.5 +# put below in run script +source activate divime + +# list of libararies installed can be found with +# conda list +# to install new packages, edit conf/environment.yml file + +# to switch back to python 2, run +conda deactivate + +# And to use python3, make sure you use correct syntax +# in python files and/or checkout python3 branch from +# each tool +# see python3/ for examples +``` \ No newline at end of file diff --git a/launcher/python3/noisemesSad.sh b/launcher/python3/noisemesSad.sh new file mode 100755 index 0000000..5ddac5a --- /dev/null +++ b/launcher/python3/noisemesSad.sh @@ -0,0 +1,70 @@ +#!/bin/bash +source activate divime + +# run OpenSAT with hard coded models & configs found here and in /vagrant + + +# Absolute path to this script. /home/user/bin/foo.sh +SCRIPT=$(readlink -f $0) +# Absolute path this script is in. /home/user/bin +BASEDIR=`dirname $SCRIPT` +# Path to OpenSAT (go on folder up and to opensat) + + + +if [ $# -lt 1 ]; then + echo "Usage: noisemesSad.sh " + echo "where dirname is a folder on the host" + echo "containing the wav files (/vagrant/dirname/ in the VM)" + exit 1 +fi + +KEEPTEMP=false +if [ $BASH_ARGV == "--keep-temp" ]; then + KEEPTEMP=true +fi + +audio_dir=/vagrant/$1 +filename=$(basename "$audio_dir") +dirname=$(dirname "$audio_dir") +extension="${filename##*.}" +basename="${filename%.*}" + +# Check audio_dir to see if empty or if contains empty wav +bash $UTILS/check_folder.sh $audio_dir + +# let's get our bearings: set CWD to path of OpenSAT +cd $OPENSATDIR +git checkout develop/python3 + +# make output folder for features, below input folder +mkdir -p $audio_dir/feature + +# first features +echo "extracting features for speech activity detection" +for file in `ls $audio_dir/*.wav`; do + SSSF/code/feature/extract-htk-vm2.sh $file +done + +# then confidences +#python SSSF/code/predict/1-confidence-vm3.py $1 +echo "detecting speech and non speech segments" +python SSSF/code/predict/1-confidence-vm5.py $audio_dir +echo "finished detecting speech and non speech segments" + +# take all the .rttm in /vagrant/data/hyp and move them to /vagrant/data - move features and hyp to another folder also. +for sad in `ls $audio_dir/hyp_sum/*.lab`; do + base=$(basename $sad .lab) + rttm_out=noisemesSad_${base}.rttm + if [ -s $sad ]; then + grep ' speech' $sad | awk -v fname=$base '{print "SPEAKER" " " fname " " 1 " " $1 " " $2-$1 " " "" " " "" " " $3 " " ""}' > $audio_dir/$rttm_out + else + touch $audio_dir/$rttm_out + fi +done + +# simple remove hyp and feature +if ! $KEEPTEMP; then + rm -rf $audio_dir/hyp_sum $audio_dir/feature +fi +git checkout master \ No newline at end of file diff --git a/launcher/python3/opensmileSad.sh b/launcher/python3/opensmileSad.sh new file mode 100755 index 0000000..4547a41 --- /dev/null +++ b/launcher/python3/opensmileSad.sh @@ -0,0 +1,66 @@ +#!/bin/bash +# Launcher onset routine +source activate divime + +SCRIPT=$(readlink -f $0) +BASEDIR=`dirname $SCRIPT` # this is the home folder of this script + # not the home folder of the 'vagrant' user in the VM +# end of launcher onset routine + +### Read in variables from user +audio_dir=/vagrant/$1 + +### Other variables specific to this script +OSHOME=$REPOS/openSMILE-2.1.0/ +CONFIG_FILE=$UTILS/vad_segmenter_aclew.conf.txt +OPENSMILE=$OSHOME/bin/linux_x64_standalone_static/SMILExtract +workdir=$audio_dir/temp/opensmileSad +mkdir -p $workdir + +### SCRIPT STARTS + +if [ $# -lt 1 ]; then + echo "USAGE: $0 " + exit 1 +fi + +KEEPTEMP=false +if [ $BASH_ARGV == "--keep-temp" ]; then + KEEPTEMP=true +fi + +filename=$(basename "$1") +dirname=$(dirname "$1") +extension="${filename##*.}" +basename="${filename%.*}" + + +cd $OSHOME/scripts/vad + +# Use OpenSMILE 2.1.0 +for sad in `ls $audio_dir/*.wav`; do + + file=$sad + id=`basename $file` + id=${id%.wav} +# > $audio_dir/${id}.txt #Make it empty if already present + echo "Processing $id ..." + LD_LIBRARY_PATH=/usr/local/lib \ + $OPENSMILE \ + -C $CONFIG_FILE \ + -I $file \ + -turndebug 1 \ + -noconsoleoutput 1 \ + -saveSegmentTimes $workdir/${id}.txt \ + -logfile $workdir/opensmile-vad.log > /dev/null +done + +for output in $(ls $workdir/*.txt); do + id=$(basename $output .txt) + awk -F ';|,' -v FN=$id '{ start_on = $2; start_off = $3 ; print "SPEAKER "FN" 1 "start_on" "(start_off-start_on)" speech " }' $output > $audio_dir/opensmileSad_$id.rttm +done + +# Delete temporary folder +if ! $KEEPTEMP; then + rm -rf $workdir +fi diff --git a/launcher/python3/test.sh b/launcher/python3/test.sh new file mode 100644 index 0000000..f149624 --- /dev/null +++ b/launcher/python3/test.sh @@ -0,0 +1,186 @@ +#!/bin/bash +# +# This script tests numerous tools +# from a downloaded 5 minute section of the HomeBank VanDam daylong audio sample +# ("ACLEW Starter" data) + +KEEPTEMP="" +if [ $# -eq 1 ]; then + if [ $BASH_ARGV == "--keep-temp" ]; then + KEEPTEMP="--keep-temp" + fi +fi + + +# Absolute path to this script. /home/vagrant/launcher +SCRIPT=$(readlink -f $0) +# Absolute path this script is in. /home/vagrant/ - works out to being user home folder +BASEDIR=`dirname $SCRIPT` +export LAUNCHERS=/home/vagrant/launcher/python3 +export REPOS=/home/vagrant/repos +export UTILS=/home/vagrant/utils + +# Paths to Tools +export OPENSATDIR=$REPOS/OpenSAT # noisemes +OPENSMILEDIR=$REPOS/openSMILE-2.1.0/ +TOCOMBOSAD=$REPOS/To-Combo-SAD +DIARTKDIR=$REPOS/ib_diarization_toolkit +#TALNETDIR=$REPOS/TALNet +YUNITATORDIR=$REPOS/Yunitator + +FAILURES=false + +echo "Starting tests" +echo "Downloading test audio..." + +cd /vagrant/data +# get transcript +wget -q -N https://homebank.talkbank.org/data/Public/VanDam-Daylong.zip +# cp /vagrant/VanDam-Daylong.zip . +unzip -q -o VanDam-Daylong.zip + +# This is the working directory for the tests; right beside the input +cd VanDam-Daylong/BN32/ +WORKDIR=`pwd` + +# Get daylong recording from the web +wget -q -N https://media.talkbank.org/homebank/Public/VanDam-Daylong/BN32/BN32_010007.mp3 +# cp /vagrant/BN32_010007.mp3 . + +DATADIR=data/VanDam-Daylong/BN32 # relative to /vagrant, used by launcher scripts +BASE=BN32_010007 # base filename for test input file, minus .wav or .rttm suffix +BASETEST=${BASE}_test +START=2513 # 41:53 in seconds +STOP=2813 # 46:53 in seconds + +# get 5 minute subset of audio +sox $BASE.mp3 $BASETEST.wav trim $START 5:00 >& /dev/null 2>&1 # silence output + +# convert CHA to reliable STM +$UTILS/chat2stm.sh $BASE.cha > $BASE.stm 2>/dev/null +# convert STM to RTTM as e.g. BN32_010007.rttm +# shift audio offsets to be 0-relative +cat $BASE.stm | awk -v start=$START -v stop=$STOP -v file=$BASE -e '{if (($4 > start) && ($4 < stop)) print "SPEAKER",file,"1",($4 - start),($5 - $4),"","","","","" }' > $BASETEST.rttm +TEST_RTTM=$WORKDIR/$BASETEST.rttm +TEST_WAV=$WORKDIR/$BASETEST.wav + + +# Check for HTK +echo "Checking for HTK..." +if [ -s /usr/local/bin/HCopy ]; then + echo "HTK is installed." +else + echo " HTK missing; did you first download HTK-3.4.1 from http://htk.eng.cam.ac.uk/download.shtml" + echo " and rename it to HTK.tar.gz ?" +fi + + + +# now test Noisemes +echo "Testing noisemes..." +cd $OPENSATDIR +TESTDIR=$WORKDIR/noisemes-test +rm -rf $TESTDIR; mkdir -p $TESTDIR +ln -fs $TEST_WAV $TESTDIR +#./runDiarNoisemes.sh $TESTDIR > $TESTDIR/noisemes-test.log 2>&1 +$LAUNCHERS/noisemesSad.sh $DATADIR/noisemes-test $KEEPTEMP > $TESTDIR/noisemes-test.log 2>&1 || { echo " Noisemes failed - dependencies"; FAILURES=true;} + +if [ -s $TESTDIR/noisemesSad_$BASETEST.rttm ]; then + echo "Noisemes passed the test." +else + FAILURES=true + echo " Noisemes failed - no RTTM output" +fi +# clean up +#rm -rf $OPENSATDIR/SSSF/data/feature $OPENSATDIR/SSSF/data/hyp + + +# now test OPENSMILEDIR +echo "Testing OpenSmile SAD..." +cd $OPENSMILEDIR +TESTDIR=$WORKDIR/opensmile-test +rm -rf $TESTDIR; mkdir -p $TESTDIR +ln -fs $TEST_WAV $TESTDIR +$LAUNCHERS/opensmileSad.sh $DATADIR/opensmile-test $KEEPTEMP >$TESTDIR/opensmile-test.log || { echo " OpenSmile SAD failed - dependencies"; FAILURES=true;} + +if [ -s $TESTDIR/opensmileSad_$BASETEST.rttm ]; then + echo "OpenSmile SAD passed the test." +else + FAILURES=true + echo " OpenSmile SAD failed - no RTTM output" +fi + +# now test TOCOMBOSAD +echo "Testing ToCombo SAD..." +cd $TOCOMBOSAD +TESTDIR=$WORKDIR/tocombo_sad-test +rm -rf $TESTDIR; mkdir -p $TESTDIR +ln -fs $TEST_WAV $TESTDIR +$LAUNCHERS/tocomboSad.sh $DATADIR/tocombo_sad-test $KEEPTEMP > $TESTDIR/tocombo_sad_test.log 2>&1 || { echo " TOCOMBO SAD failed - dependencies"; FAILURES=true;} + +if [ -s $TESTDIR/tocomboSad_$BASETEST.rttm ]; then + echo "TOCOMBO SAD passed the test." +else + FAILURES=true + echo " TOCOMBO SAD failed - no output RTTM" +fi +exit 0; + + +# # finally test DIARTK +echo "Testing DIARTK..." +cd $DIARTKDIR +TESTDIR=$WORKDIR/diartk-test +rm -rf $TESTDIR; mkdir -p $TESTDIR +ln -fs $TEST_WAV $TESTDIR +cp $TEST_RTTM $TESTDIR +# run like the wind +$LAUNCHERS/diartk.sh $DATADIR/diartk-test rttm $KEEPTEMP > $TESTDIR/diartk-test.log 2>&1 +if grep -q "command not found" $TESTDIR/diartk-test.log; then + echo " Diartk failed - dependencies (probably HTK)" + FAILURES=true +else + if [ -s $TESTDIR/diartk_goldSad_$BASETEST.rttm ]; then + echo "DiarTK passed the test." + else + FAILURES=true + echo " Diartk failed - no output RTTM" + fi +fi +rm $TESTDIR/$BASETEST.rttm + +# finally test Yunitator +echo "Testing Yunitator..." +cd $YUNITATORDIR +TESTDIR=$WORKDIR/yunitator-test +rm -rf $TESTDIR; mkdir -p $TESTDIR +ln -fs $TEST_WAV $TESTDIR +# let 'er rip +#./runYunitator.sh $TESTDIR/$BASETEST.wav > $TESTDIR/yunitator-test.log 2>&1 || { echo " Yunitator failed - dependencies"; FAILURES=true;} +$LAUNCHERS/yunitate.sh $DATADIR/yunitator-test $KEEPTEMP > $TESTDIR/yunitator-test.log 2>&1 || { echo " Yunitator failed - dependencies"; FAILURES=true;} +if [ -s $TESTDIR/yunitator_$BASETEST.rttm ]; then + echo "Yunitator passed the test." +else + FAILURES=true + echo " Yunitator failed - no output RTTM" +fi + + + +git checkout master + + + + +# test finished +if $FAILURES; then + echo "Some tools did not pass the test, but you can still use others" +else + echo "Congratulations, everything is OK!" +fi + +# results +echo "RESULTS:" +for f in /vagrant/$DATADIR/*-test/*.rttm; do $UTILS/sum-rttm.sh $f; done +echo "EVAL_SAD:" +cat $TESTDIR/opensmileSad_eval.df diff --git a/launcher/python3/tocomboSad.sh b/launcher/python3/tocomboSad.sh new file mode 100755 index 0000000..c896644 --- /dev/null +++ b/launcher/python3/tocomboSad.sh @@ -0,0 +1,83 @@ +#!/bin/bash +# Launcher onset routine +SCRIPT=$(readlink -f $0) +BASEDIR=`dirname $SCRIPT` +# end of launcher onset routine + + +### Read in variables from user +audio_dir=/vagrant/$1 +trs_format=$2 + + +### Other variables specific to this script +# create temp dir +workdir=$audio_dir/temp/tocomboSad +mkdir -p $workdir +TOCOMBOSADDIR=$REPOS/To-Combo-SAD +MCR=/usr/local/MATLAB/MATLAB_Runtime/v93 + +### SCRIPT STARTS + +if [ $# -lt 1 ]; then + echo "Usage: tocombo_sad.sh " + echo "where dirname is a folder on the host" + echo "containing the wav files (/vagrant/dirname/ in the VM)" + exit 1 +fi + +KEEPTEMP=false +if [ $BASH_ARGV == "--keep-temp" ]; then + KEEPTEMP=true +fi + +filename=$(basename "$audio_dir") +dirname=$(dirname "$audio_dir") +extension="${filename##*.}" +basename="${filename%.*}" + +# Check audio_dir to see if empty or if contains empty wav +bash /home/vagrant/utils/check_folder.sh $audio_dir + +# let's get our bearings: set CWD to path of ToComboSAD +cd $TOCOMBOSADDIR +git checkout develop/python3 + +mkdir -p $workdir/feat +rm -f $workdir/filelist.txt +touch $workdir/filelist.txt + +# create temp dir to store audio files with 1 channels, if needed (i.e. if audio to treat has 2 or more channels.) +# Indeed, To Combo Sad Fails when there are more than 1 channels. +for f in $audio_dir/*.wav; do + # Check if audio has 1 channel or more. If it has more, use sox to create a temp audio file w/ 1 channel. + n_chan=$(soxi $f | grep Channels | cut -d ':' -f 2) + if [[ $n_chan -gt 1 ]]; then + base=$(basename $f) + sox -c $n_chan $f -c 1 $workdir/$base + f=$workdir/$base + fi + + echo $f >> $workdir/filelist.txt + +done +echo "finished" + +export LD_LIBRARY_PATH=$MCR/runtime/glnxa64:$MCR/bin/glnxa64:$MCR/sys/os/glnxa64: + +./run_get_TOcomboSAD_output_v3.sh $MCR $workdir/filelist.txt 0 0.5 $TOCOMBOSADDIR/UBMnodct256Hub5.txt + +# Retrieve the outputs from the temp folder +mv $workdir/*ToCombo.txt $audio_dir + +#convert to rttms +for f in $audio_dir/*.ToCombo.txt; do + bn=`basename $f .wav.ToCombo.txt` + python $TOCOMBOSADDIR/tocombo2rttm.py $f $bn > $audio_dir/tocomboSad_$bn.rttm +done + +# Delete temporary folder +if ! $KEEPTEMP; then + rm -rf $workdir +fi +git checkout master diff --git a/launcher/python3/yunitate.sh b/launcher/python3/yunitate.sh new file mode 100755 index 0000000..32ea71e --- /dev/null +++ b/launcher/python3/yunitate.sh @@ -0,0 +1,73 @@ +#!/bin/bash +# Since the script is built to be launched outside of the vm, source +# the .bashrc which is not necessarily sourced! +source activate divime + +# run Yunitator with hard coded models & configs + +# Absolute path to this script. /home/vagrant/launcher/yunitate.sh +SCRIPT=$(readlink -f $0) +# Absolute path this script is in. /home/vagrant/launcher +BASEDIR=`dirname $SCRIPT` +# Path to Yunitator (go one folder up and to Yunitator) +YUNITATDIR=/home/vagrant/repos/Yunitator + +if [ $# -lt 1 ] || [ $# -gt 2 ]; then + echo "Usage: $0 " + echo "where dirname is the name of the folder" + echo "containing the wav files" + exit 1 +fi + +KEEPTEMP=false +if [ $BASH_ARGV == "--keep-temp" ]; then + KEEPTEMP=true +fi + +audio_dir=/vagrant/$1 +YUNITEMP=$audio_dir/Yunitemp +filename=$(basename "$audio_dir") +dirname=$(dirname "$audio_dir") +extension="${filename##*.}" +basename="${filename%.*}" +# Check audio_dir to see if empty or if contains empty wav +bash $UTILS/check_folder.sh $audio_dir + + +# let's get our bearings: set CWD to the path of Yunitator +cd $YUNITATDIR +git checkout develop/python3 + + +# make output folder for features, below input folder +mkdir -p $YUNITEMP + +# Iterate over files +echo "Starting $0" +for f in `ls $audio_dir/*.wav`; do + + basename=`basename $f .wav` + # first features + ./extract-htk-vm2.sh $f + + # then confidences + python diarize.py $YUNITEMP/$basename.htk $YUNITEMP/$basename.rttm.sorted + sort -V -k3 $YUNITEMP/$basename.rttm.sorted > $YUNITEMP/$basename.rttm +done + +echo "$0 finished running" + +# take all the .rttm in $audio_dir/Yunitemp/ and move them to /vagrant/data +for sad in `ls $YUNITEMP/*.rttm`; do + _rttm=$(basename $sad) + rttm=$audio_dir/yunitator_${_rttm} + # Remove not needed SIL lines + # sed -i '/ SIL /d' $sad + mv $sad $rttm +done + +# simply remove hyp and feature +if ! $KEEPTEMP; then + rm -rf $YUNITEMP +fi +git checkout master \ No newline at end of file diff --git a/launcher/talnet.sh b/launcher/talnet.sh new file mode 100755 index 0000000..ba98015 --- /dev/null +++ b/launcher/talnet.sh @@ -0,0 +1,40 @@ +#!/bin/bash + +# run 537 class classifier with hard coded models & configs found here + +# Absolute path to this script. /home/user/bin/foo.sh +SCRIPT=$(readlink -f $0) +# Absolute path this script is in. /home/user/bin +BASEDIR=`dirname $SCRIPT` +# Path to classify tool (go one folder up and to 537cls) +CLASSIFY=/home/vagrant/repos/TALNet + +if [ $# -ne 1 ]; then + echo "Usage: $0 " + echo "where dirname is the name of the folder" + echo "in /vagrant containing wav files" + exit 1 +fi + +audio_dir=/vagrant/$1 +filename=$(basename "${audio_dir}") +dirname=$(dirname "${audio_dir}") +extension="${filename##*.}" +basename="${filename%.*}" +# Check audio_dir to see if empty or if contains empty wav +bash $BASEDIR/check_folder.sh ${audio_dir} + + + +# let's get our bearings: set CWD to the path of TALNet +cd $CLASSIFY + +# Iterate over files +echo "Starting" +for f in `ls ${audio_dir}/*.wav`; do + ./runTALNet.sh $f + base=$(basename $f .wav) + mv ${audio_dir}/${base}.frame_prob.mat ${audio_dir}/${base}_talnet.frame_prob.mat +done + +echo "$0 finished running" diff --git a/launcher/test.sh b/launcher/test.sh new file mode 100755 index 0000000..991d40a --- /dev/null +++ b/launcher/test.sh @@ -0,0 +1,246 @@ +#!/bin/bash +# +# This script tests numerous tools +# from a downloaded 5 minute section of the HomeBank VanDam daylong audio sample +# ("ACLEW Starter" data) + +KEEPTEMP="" +if [ $# -eq 1 ]; then + if [ $BASH_ARGV == "--keep-temp" ]; then + KEEPTEMP="--keep-temp" + fi +fi + + +# Absolute path to this script. /home/vagrant/launcher +SCRIPT=$(readlink -f $0) +# Absolute path this script is in. /home/vagrant/ - works out to being user home folder +BASEDIR=`dirname $SCRIPT` +LAUNCHERS=/home/vagrant/launcher +REPOS=/home/vagrant/repos +UTILS=/home/vagrant/utils + +# Paths to Tools +OPENSATDIR=$REPOS/Yunitator # same code for noisemes and Yunitator +OPENSMILEDIR=$REPOS/opensmile-2.3.0/ +TOCOMBOSAD=$REPOS/To-Combo-SAD +DIARTKDIR=$REPOS/ib_diarization_toolkit +#TALNETDIR=$REPOS/TALNet +DSCOREDIR=$REPOS/dscore +YUNITATORDIR=$REPOS/Yunitator +VCMDIR=$REPOS/vcm + +FAILURES=false + +( + cd /vagrant + echo -e "Welcome to DiViMe's test.sh\n\$ git show-branch" + git show-branch + echo "$ git log -1" + git log -1 + echo "$ git diff --name-status" + git diff --name-status + echo "################################################################################" + echo "Starting tests" +) + +cd /vagrant/data + +if [ ! -s VanDam-Daylong.zip ]; then + echo "Downloading test transcript..." + wget -q -N https://homebank.talkbank.org/data/Public/VanDam-Daylong.zip + unzip -q -o VanDam-Daylong.zip +fi + +# This is the working directory for the tests; right beside the input +cd VanDam-Daylong/BN32/ +WORKDIR=`pwd` + +# Get daylong recording from the web +if [ ! -s BN32_010007.mp3 ]; then + echo "Downloading test audio..." + wget -q -N https://media.talkbank.org/homebank/Public/VanDam-Daylong/BN32/BN32_010007.mp3 +fi + +DATADIR=data/VanDam-Daylong/BN32 # relative to /vagrant, used by launcher scripts +BASE=BN32_010007 # base filename for test input file, minus .wav or .rttm suffix +BASETEST=${BASE}_test +START=2513 # 41:53 in seconds +STOP=2813 # 46:53 in seconds + +# get 5 minute subset of audio +sox $BASE.mp3 $BASETEST.wav trim $START 5:00 >& /dev/null 2>&1 # silence output + +# convert CHA to reliable STM +$UTILS/chat2stm.sh $BASE.cha > $BASE.stm 2>/dev/null +# convert STM to RTTM as e.g. BN32_010007.rttm +# shift audio offsets to be 0-relative +cat $BASE.stm | awk -v start=$START -v stop=$STOP -v file=$BASE -e '{if (($4 > start) && ($4 < stop)) print "SPEAKER",file"_test","1",($4 - start),($5 - $4),"","","speech","","" }' > $BASETEST.rttm +TEST_RTTM=$WORKDIR/$BASETEST.rttm +TEST_WAV=$WORKDIR/$BASETEST.wav +TESTDIR=$WORKDIR/test + + +# Check for HTK +if false; then +echo "Checking for HTK..." +if [ -s /usr/local/bin/HCopy ]; then + echo "HTK is installed." +else + #echo " HTK missing; did you first download HTK-3.4.1 from http://htk.eng.cam.ac.uk/download.shtml ?" + #echo " If so, then you may need to re-install it. Run: vagrant ssh -c \"utils/install_htk.sh\" " + echo " HTK missing. You can probably ignore this warning, HTK is no longer needed." +fi +fi + +rm -rf $TESTDIR; mkdir -p $TESTDIR +ln -fs $TEST_WAV $TESTDIR +cp $WORKDIR/$BASETEST.rttm $TESTDIR + + +# now test Noisemes +echo "Testing noisemes..." + +$LAUNCHERS/noisemesSad.sh $DATADIR/test $KEEPTEMP > $TESTDIR/noisemes-test.log 2>&1 || { echo " Noisemes failed - dependencies"; FAILURES=true;} + +if [ -s $TESTDIR/noisemesSad_$BASETEST.rttm ]; then + echo "Noisemes passed the test." +else + FAILURES=true + echo " Noisemes failed - no RTTM output" +fi + + +# now test OPENSMILEDIR +echo "Testing OpenSmile SAD..." + +$LAUNCHERS/opensmileSad.sh $DATADIR/test $KEEPTEMP >$TESTDIR/opensmile-test.log || { echo " OpenSmile SAD failed - dependencies"; FAILURES=true;} + +if [ -s $TESTDIR/opensmileSad_$BASETEST.rttm ]; then + echo "OpenSmile SAD passed the test." +else + FAILURES=true + echo " OpenSmile SAD failed - no RTTM output" +fi + + +# now test TOCOMBOSAD +echo "Testing ToCombo SAD..." + +$LAUNCHERS/tocomboSad.sh $DATADIR/test $KEEPTEMP > $TESTDIR/tocombo_sad_test.log 2>&1 || { echo " ToCombo SAD failed - dependencies"; FAILURES=true;} + +if [ -s $TESTDIR/tocomboSad_$BASETEST.rttm ]; then + echo "ToCombo SAD passed the test." +else + FAILURES=true + echo " ToCombo SAD failed - no output RTTM" +fi + + +# test DIARTK +echo "Testing DiarTk..." + +cp $TEST_RTTM $TESTDIR + +# run like the wind +$LAUNCHERS/diartk.sh $DATADIR/test rttm $KEEPTEMP > $TESTDIR/diartk-test.log 2>&1 +if grep -q "command not found" $TESTDIR/diartk-test.log; then + echo " DiarTk failed - dependencies (probably HTK)" + FAILURES=true +else + if [ -s $TESTDIR/diartk_goldSad_$BASETEST.rttm ]; then + echo "DiarTk passed the test." + else + FAILURES=true + echo " DiarTk failed - no output RTTM" + fi +fi +#rm $TESTDIR/$BASETEST.rttm + + +# test Yunitator +echo "Testing Yunitator..." + +# let 'er rip +yun="old" +$LAUNCHERS/yunitate.sh $DATADIR/test $yun $KEEPTEMP > $TESTDIR/yunitator-test.log 2>&1 || { echo " Yunitator failed - dependencies"; FAILURES=true;} +if [ -s $TESTDIR/yunitator_${yun}_$BASETEST.rttm ]; then + echo "Yunitator passed the test." +else + FAILURES=true + echo " Yunitator failed - no output RTTM" +fi + + +# Test the evaluation +echo "Testing the evaluation pipeline..." +source activate divime +rm -f $TESTDIR/accuracy_noisemesSad_report.csv +$LAUNCHERS/eval.sh $TESTDIR noisemesSad accuracy > $TESTDIR/eval-test.log || { echo " The evaluation pipeline failed - dependencies"; FAILURES=true;} +# $TESTDIR/.. assumes that eval.sh puts the reference one directory up from the hypotheses +if [ -s $TESTDIR/../accuracy_noisemesSad_report.csv ]; then + echo "The evaluation pipeline passed the test." +else + echo " The evaluation pipeline failed the test - output does not match expected" + FAILURES=true +fi +conda deactivate + + +# Testing VCM +echo "Testing VCM..." + +$LAUNCHERS/vcm.sh $DATADIR/test $yun $KEEPTEMP > $TESTDIR/vcm-test.log 2>&1 || { echo " VCM failed - dependencies"; FAILURES=true;} +if [ -s $TESTDIR/vcm_$BASETEST.rttm ]; then + echo "VCM passed the test." +else + FAILURES=true + echo " VCM failed - no output RTTM" +fi + + +# Testing WCE +echo "Testing WCE..." + +sh estimateWCE.sh $DATADIR/test/ $TESTDIR/WCE_$BASETEST.csv +if [ -s $TESTDIR/WCE_$BASETEST.csv ]; then + echo "WCE passed the test" +else + echo "WCE tests failed" + FAILURES=true +fi + + +# test finished +if $FAILURES; then + echo "Some tools did not pass the test, but you can still use others" +else + echo "Congratulations, everything is OK!" +fi + +# results +echo "################################################################################" +echo "To wrap up, we will print out the results of the analyses that we ran during the test." +echo "Compare the following results against the reference results printed out below." +echo "If the numbers are similar, then your system is working ok." +echo "If you see bigger changes, then please paste this output onto an issue on https://github.com/srvk/DiViMe/issues/." +echo "****** YOUR RESULTS BEGIN ******." +for f in /vagrant/$DATADIR/test/*.rttm; do $UTILS/sum-rttm.sh $f; done +echo "****** REFERENCE RESULTS BEGIN ******." +echo "LINES: 101 DURATION SUM: 298.637 FILE: /vagrant/data/VanDam-Daylong/BN32/test/BN32_010007_test.rttm" +echo "LINES: 10 DURATION SUM: 296.23 FILE: /vagrant/data/VanDam-Daylong/BN32/test/diartk_goldSad_BN32_010007_test.rttm" +echo "LINES: 37 DURATION SUM: 31.9 FILE: /vagrant/data/VanDam-Daylong/BN32/test/noisemesSad_BN32_010007_test.rttm" +echo "LINES: 88 DURATION SUM: 212.22 FILE: /vagrant/data/VanDam-Daylong/BN32/test/opensmileSad_BN32_010007_test.rttm" +echo "LINES: 56 DURATION SUM: 63.66 FILE: /vagrant/data/VanDam-Daylong/BN32/test/tocomboSad_BN32_010007_test.rttm" +echo "LINES: 31 DURATION SUM: 24.7 FILE: /vagrant/data/VanDam-Daylong/BN32/test/vcm_BN32_010007_test.rttm" +echo "LINES: 60 DURATION SUM: 42.3 FILE: /vagrant/data/VanDam-Daylong/BN32/test/yunitator_BN32_010007_test.rttm" +echo "****** REFERENCE RESULTS END ******." + +echo "Evaluation pipeline YOURS:" +cat /vagrant/data/VanDam-Daylong/BN32/test/eval-test.log +echo "Evaluation pipeline REFERENCE:" +echo " detection accuracy true positive true negative false positive false " +echo " % " +echo "item " +echo "BN32_010007_test.rttm 11.24 30.80 3.11 0.20 267.57" +echo "TOTAL 11.24 30.80 3.11 0.20 267.57" diff --git a/launcher/tocomboSad.sh b/launcher/tocomboSad.sh new file mode 100755 index 0000000..ee57a9b --- /dev/null +++ b/launcher/tocomboSad.sh @@ -0,0 +1,93 @@ +#!/bin/bash +# Launcher onset routine +SCRIPT=$(readlink -f $0) +BASEDIR=`dirname $SCRIPT` +REPOS=/home/vagrant/repos +UTILS=/home/vagrant/utils +# end of launcher onset routine + + +### Read in variables from user +audio_dir=/vagrant/$1 +trs_format=$2 + + +### Other variables specific to this script +# create temp dir +#workdir=${audio_dir}/temp +#mkdir -p $workdir +workdir=`mktemp -d --tmpdir=${audio_dir}` +TOCOMBOSADDIR=$REPOS/To-Combo-SAD +MCR=/usr/local/MATLAB/MATLAB_Runtime/v93 + +### SCRIPT STARTS + +if [ $# -lt 1 ]; then + echo "Usage: tocombo_sad.sh " + echo "where dirname is a folder on the host" + echo "containing the wav files (/vagrant/dirname/ in the VM)" + exit 1 +fi + +KEEPTEMP=false +if [ $BASH_ARGV == "--keep-temp" ]; then + KEEPTEMP=true +fi + +filename=$(basename "${audio_dir}") +dirname=$(dirname "${audio_dir}") +extension="${filename##*.}" +basename="${filename%.*}" + +# Check audio_dir to see if empty or if contains empty wav +bash /home/vagrant/utils/check_folder.sh ${audio_dir} + +# let's get our bearings: set CWD to path of ToComboSAD +cd $TOCOMBOSADDIR + +mkdir -p $workdir/feat +rm -f $workdir/filelist.txt +touch $workdir/filelist.txt + +# create temp dir to store audio files with 1 channels, if needed (i.e. if audio to treat has 2 or more channels.) +# Indeed, To Combo Sad Fails when there are more than 1 channels. +for f in ${audio_dir}/*.wav; do + # Check if audio has 1 channel or more. If it has more, use sox to create a temp audio file w/ 1 channel. + n_chan=$(soxi $f | grep Channels | cut -d ':' -f 2) + if [[ $n_chan -gt 1 ]]; then + base=$(basename $f) + sox -c $n_chan $f -c 1 $workdir/$base + f=$workdir/$base + fi + + echo $f >> $workdir/filelist.txt + +done +echo "finished" + +export LD_LIBRARY_PATH=$MCR/runtime/glnxa64:$MCR/bin/glnxa64:$MCR/sys/os/glnxa64: + +# The 'DISPLAY= ' part prevents an X-Server from popping up +DISPLAY= ./run_get_TOcomboSAD_output_v3.sh $MCR $workdir/filelist.txt 0 0.5 $TOCOMBOSADDIR/UBMnodct256Hub5.txt + +#convert to rttms +for f in `ls ${audio_dir}/*.ToCombo.txt ${workdir}/*.ToCombo.txt`; do + echo converting to rttm $f + bn=`basename $f .wav.ToCombo.txt` + python $TOCOMBOSADDIR/tocombo2rttm.py $f $bn > ${workdir}/tocomboSad_$bn.rttm +done + +# same in the temp folder which has the .wav that were not monochannel +#for f in ${workdir}/*.ToCombo.txt; do +# bn=`basename $f .wav.ToCombo.txt` +# python $TOCOMBOSADDIR/tocombo2rttm.py $f $bn > ${workdir}/tocomboSad_$bn.rttm +#done + +# get the rttm +mv ${workdir}/*.rttm ${audio_dir} + +# move the txt files and delete temporary folder +mv ${audio_dir}/*ToCombo.txt $workdir +if ! $KEEPTEMP; then + rm -rf $workdir +fi diff --git a/launcher/vcm.sh b/launcher/vcm.sh new file mode 100755 index 0000000..ede5435 --- /dev/null +++ b/launcher/vcm.sh @@ -0,0 +1,57 @@ +#!/bin/bash + +# run OpenSAT with hard coded models & configs found here and in /vagrant +# assumes Python environment in /home/${user}/ + +# Absolute path to this script. /home/user/bin/foo.sh +SCRIPT=$(readlink -f $0) +# Absolute path this script is in. /home/user/bin +BASEDIR=`dirname $SCRIPT` +#| Path to VCM (go one folder up and to VCM) +VCMDIR=/home/vagrant/repos/vcm +UTIL=/home/vagrant/utils + +if [ $# -gt 2 ]; then + echo "Usage: $0 [yun_mode]" + echo "where dirname is the name of the folder" + echo "containing the wav files" + exit 1 +fi + +audio_dir=/vagrant/$1 +if [ $# -gt 1 ]; then + yun=$2 +else + yun="universal" +fi +filename=$(basename "${audio_dir}") +dirname=$(dirname "${audio_dir}") +extension="${filename##*.}" +basename="${filename%.*}" +# Check audio_dir to see if empty or if contains empty wav +bash $UTIL/check_folder.sh ${audio_dir} + +KEEPTEMP=false +if [ $BASH_ARGV == "--keep-temp" ]; then + KEEPTEMP=true +fi +mkdir -p ${audio_dir}/VCMtemp +echo ${audio_dir}/VCMtemp + +# let's get our bearings: set CWD to the path of VCM +cd $VCMDIR + +# Iterate over files +echo "Starting" +for f in `ls ${audio_dir}/*.wav`; do + echo $f + ./runVCM.sh $f $yun +done + +echo "$0 finished running" + + +# simply remove hyp and feature +if ! $KEEPTEMP; then + rm -rf ${audio_dir}/VCMtemp +fi diff --git a/launcher/yunitate.sh b/launcher/yunitate.sh new file mode 100755 index 0000000..c221f63 --- /dev/null +++ b/launcher/yunitate.sh @@ -0,0 +1,114 @@ +#!/bin/bash +# Since the script is built to be launched outside of the vm, source +# the .bashrc which is not necessarily sourced! +source ~/.bashrc + +source activate divime + +# run Yunitator with hard coded models & configs + +# Absolute path to this script. /home/vagrant/launcher/yunitate.sh +SCRIPT=$(readlink -f $0) +# Absolute path this script is in. /home/vagrant/launcher +BASEDIR=`dirname $SCRIPT` +# Path to Yunitator (go one folder up and to Yunitator) +YUNITATDIR=/home/vagrant/repos/Yunitator + +if [ $# -lt 1 ] || [ $# -gt 3 ]; then + echo "Usage: $0 " + echo "where dirname is the name of the folder" + echo "containing the wav files, and the second parameter (optional)" + echo "whether to use the old model, the english model, or the universal one." + exit 1 +fi + +DELSIL=true +KEEPTEMP=false +if [ $BASH_ARGV == "--keep-temp" ]; then + KEEPTEMP=true +fi + +MODE=$2 # old, english or universal +if [[ $MODE == "" ]]; then + MODE="old" +fi + +case $MODE in + old|english|universal) + ;; + *) + echo "MODE = $MODE but needs to be amongst {old, english, universal}"; + exit 1;; +esac + +audio_dir=/vagrant/$1 +TEMPNAME=Yunitemp +YUNITEMP=${audio_dir}/$TEMPNAME +filename=$(basename "${audio_dir}") +dirname=$(dirname "${audio_dir}") +extension="${filename##*.}" +basename="${filename%.*}" + +# Check audio_dir to see if empty or if contains empty wav +bash /home/vagrant/utils/check_folder.sh ${audio_dir} + +# let's get our bearings: set CWD to the path of Yunitator +cd $YUNITATDIR + +# make output folder for features, below input folder +mkdir -p $YUNITEMP + +# Iterate over files +echo "Starting $0" +if true; then + # I think it is safe to do the feature extraction in parallel by default + ls ${audio_dir}/*.wav | xargs -P `nproc` -i ./extract-htk-vm2.sh '{}' $TEMPNAME +else +for f in `ls ${audio_dir}/*.wav`; do + basename=`basename $f .wav` + # first features + ./extract-htk-vm2.sh $f $TEMPNAME +done +fi + + +# Choose chunksize based off memory. Currently this is equivalent to 200 +# frames per 100MB of memory. +# Ex: 3GB -> 6000 frames +# Ex: 2048MB -> 4000 frames +# This setting was chosen arbitrarily and was successful for tests at 2GB-4GB. +chunksize=$(free | awk '/^Mem:/{print $2}') +let chunksize=$chunksize/100000*200 +[ $chunksize -eq 0 ] && echo You seem to have very little free memory. Things may be slow: +[ $chunksize -eq 0 ] && free +[ $chunksize -eq 0 ] && let chunksize=1000 + +echo python yunified.py yunitator $audio_dir $chunksize $MODE +python yunified.py yunitator $audio_dir $chunksize $MODE # MODE equal to old, english or universal + +for f in `ls $YUNITEMP/*.rttm.sorted`; do + filename=$(basename "$f") + basename="${filename%.*}" + + sort -V -k3 $f > $YUNITEMP/$basename +done + +echo "$0 finished running" + +# take all the .rttm in ${audio_dir}/Yunitemp/ and move them to /vagrant/data +for sad in `ls $YUNITEMP/*.rttm`; do + _rttm=$(basename $sad) + rttm=${audio_dir}/yunitator_${MODE}_${_rttm} + if $DELSIL; then + # Remove not needed SIL lines + sed -i '/ SIL /d' $sad + fi + mv $sad $rttm +done + +# simply remove hyp and feature +if ! $KEEPTEMP; then + rm -rf $YUNITEMP +fi + +conda deactivate diff --git a/logs/launcher-test.log b/logs/launcher-test.log new file mode 100644 index 0000000..b7d1fc9 --- /dev/null +++ b/logs/launcher-test.log @@ -0,0 +1,76 @@ +Florians-MacBook-Pro-2019:DiViMe-2 metze$ vagrant ssh -c "launcher/test.sh" +Welcome to DiViMe's test.sh +$ git show-branch +[master] Update install.md +$ git log -1 +commit 2af79066d1a17c1349c6071538d5e78ab33e14ae +Author: Florian Metze +Date: Sun Jul 28 00:26:24 2019 +0200 + + Update install.md +$ git diff --name-status +M Vagrantfile +###################################################################################### +Starting tests +Testing noisemes... +Noisemes passed the test. +Testing OpenSmile SAD... +OpenSmile SAD passed the test. +Testing ToCombo SAD... +ToCombo SAD passed the test. +Testing DiarTk... +DiarTk passed the test. +Testing Yunitator... +Yunitator passed the test. +Testing the evaluation pipeline... +The evaluation pipeline passed the test. +Testing VCM... +VCM passed the test. +Testing WCE... +Running WCE module (this might take a while...) +WCE processing complete. Wrote output to /vagrant/data/VanDam-Daylong/BN32/test/WCE_BN32_010007_test.csv +WCE passed the test +Congratulations, everything is OK! +###################################################################################### +To wrap up, we will print out the results of the analyses that we ran during the test. +Compare the following results against the reference results printed out below. +If the numbers are similar, then your system is working ok. +If you see bigger changes, then please paste this output onto an issue on https://github.com/srvk/DiViMe/issues/. +****** YOUR RESULTS BEGIN ******. +LINES: 101 DURATION SUM: 298.637 FILE: /vagrant/data/VanDam-Daylong/BN32/test/BN32_010007_test.rttm +LINES: 10 DURATION SUM: 296.23 FILE: /vagrant/data/VanDam-Daylong/BN32/test/diartk_goldSad_BN32_010007_test.rttm +LINES: 36 DURATION SUM: 31 FILE: /vagrant/data/VanDam-Daylong/BN32/test/noisemesSad_BN32_010007_test.rttm +LINES: 88 DURATION SUM: 212.22 FILE: /vagrant/data/VanDam-Daylong/BN32/test/opensmileSad_BN32_010007_test.rttm +LINES: 56 DURATION SUM: 63.66 FILE: /vagrant/data/VanDam-Daylong/BN32/test/tocomboSad_BN32_010007_test.rttm +LINES: 31 DURATION SUM: 24.7 FILE: /vagrant/data/VanDam-Daylong/BN32/test/vcm_BN32_010007_test.rttm +LINES: 60 DURATION SUM: 42.3 FILE: /vagrant/data/VanDam-Daylong/BN32/test/yunitator_old_BN32_010007_test.rttm +****** REFERENCE RESULTS BEGIN ******. +LINES: 101 DURATION SUM: 298.637 FILE: /vagrant/data/VanDam-Daylong/BN32/test/BN32_010007_test.rttm +LINES: 10 DURATION SUM: 296.23 FILE: /vagrant/data/VanDam-Daylong/BN32/test/diartk_goldSad_BN32_010007_test.rttm +LINES: 37 DURATION SUM: 31.9 FILE: /vagrant/data/VanDam-Daylong/BN32/test/noisemesSad_BN32_010007_test.rttm +LINES: 88 DURATION SUM: 212.22 FILE: /vagrant/data/VanDam-Daylong/BN32/test/opensmileSad_BN32_010007_test.rttm +LINES: 56 DURATION SUM: 63.66 FILE: /vagrant/data/VanDam-Daylong/BN32/test/tocomboSad_BN32_010007_test.rttm +LINES: 31 DURATION SUM: 24.7 FILE: /vagrant/data/VanDam-Daylong/BN32/test/vcm_BN32_010007_test.rttm +LINES: 60 DURATION SUM: 42.3 FILE: /vagrant/data/VanDam-Daylong/BN32/test/yunitator_BN32_010007_test.rttm +****** REFERENCE RESULTS END ******. +Evaluation pipeline YOURS: + +Evaluating noisemesSad on /vagrant/data/VanDam-Daylong/BN32/test with respect to : accuracy + +Pairs that have been found : +BN32_010007_test.rttm / noisemesSad_BN32_010007_test.rttm + +accuracy report + detection accuracy true positive true negative false positive false negative + % +item +BN32_010007_test.rttm 11.24 30.80 3.11 0.20 267.57 +TOTAL 11.24 30.80 3.11 0.20 267.57 +Evaluation pipeline REFERENCE: + detection accuracy true positive true negative false positive false + % +item +BN32_010007_test.rttm 11.24 30.80 3.11 0.20 267.57 +TOTAL 11.24 30.80 3.11 0.20 267.57 +Connection to 127.0.0.1 closed. +Florians-MacBook-Pro-2019:DiViMe-2 metze$ diff --git a/logs/vagrant-up-again.log b/logs/vagrant-up-again.log new file mode 100644 index 0000000..55d4003 --- /dev/null +++ b/logs/vagrant-up-again.log @@ -0,0 +1,33 @@ +Florians-MacBook-Pro-2019:DiViMe metze$ vagrant up +==> vagrant: A new version of Vagrant is available: 2.2.5 (installed version: 2.2.4)! +==> vagrant: To upgrade visit: https://www.vagrantup.com/downloads.html + +Bringing machine 'default' up with 'virtualbox' provider... +==> default: Checking if box 'ubuntu/trusty64' version '20190429.0.1' is up to date... +==> default: Clearing any previously set forwarded ports... +==> default: Clearing any previously set network interfaces... +==> default: Preparing network interfaces based on configuration... + default: Adapter 1: nat + default: Adapter 2: hostonly + default: Adapter 3: hostonly + default: Adapter 4: hostonly + default: Adapter 5: hostonly + default: Adapter 6: hostonly + default: Adapter 7: hostonly +==> default: Forwarding ports... + default: 22 (guest) => 2222 (host) (adapter 1) +==> default: Running 'pre-boot' VM customizations... +==> default: Booting VM... +==> default: Waiting for machine to boot. This may take a few minutes... + default: SSH address: 127.0.0.1:2222 + default: SSH username: vagrant + default: SSH auth method: private key +==> default: Machine booted and ready! +[default] GuestAdditions 6.0.8 running --- OK. +==> default: Checking for guest additions in VM... +==> default: Configuring and enabling network interfaces... +==> default: Mounting shared folders... + default: /vagrant => /Users/metze/Work/Kitchen/DiViMe +==> default: Machine already provisioned. Run `vagrant provision` or use the `--provision` +==> default: flag to force provisioning. Provisioners marked to run always will still run. +Florians-MacBook-Pro-2019:DiViMe metze$ diff --git a/logs/vagrant-up.log b/logs/vagrant-up.log new file mode 100644 index 0000000..dfe27c2 --- /dev/null +++ b/logs/vagrant-up.log @@ -0,0 +1,3150 @@ +Florians-MacBook-Pro-2019:DiViMe-2 metze$ vagrant up +Bringing machine 'default' up with 'virtualbox' provider... +==> default: Importing base box 'ubuntu/trusty64'... +==> default: Matching MAC address for NAT networking... +==> default: Checking if box 'ubuntu/trusty64' version '20190429.0.1' is up to date... +==> default: Setting the name of the VM: DiViMe-2_default_1564386654149_44009 +==> default: Clearing any previously set forwarded ports... +==> default: Clearing any previously set network interfaces... +==> default: Preparing network interfaces based on configuration... + default: Adapter 1: nat + default: Adapter 2: hostonly + default: Adapter 3: hostonly + default: Adapter 4: hostonly + default: Adapter 5: hostonly + default: Adapter 6: hostonly + default: Adapter 7: hostonly +==> default: Forwarding ports... + default: 22 (guest) => 2222 (host) (adapter 1) +==> default: Running 'pre-boot' VM customizations... +==> default: Booting VM... +==> default: Waiting for machine to boot. This may take a few minutes... + default: SSH address: 127.0.0.1:2222 + default: SSH username: vagrant + default: SSH auth method: private key + default: + default: Vagrant insecure key detected. Vagrant will automatically replace + default: this with a newly generated keypair for better security. + default: + default: Inserting generated public key within guest... + default: Removing insecure key from the guest if it's present... + default: Key inserted! Disconnecting and reconnecting using new SSH key... +==> default: Machine booted and ready! +[default] GuestAdditions versions on your host (6.0.8) and guest (4.3.40) do not match. + vboxguest); sleep 1; umount -a -t vboxsf; sleep 1; rmmod vboxsf; sleep 1; rmmod + * Stopping VirtualBox Additions + ...done. +root@vagrant-ubuntu-trusty-64:/home/vagrant# exit +exit +t-dkms virtualbox-guest-utils virtualbox-guest-x11et -y -q purge virtualbox-gues +Reading package lists... +Building dependency tree... +Reading state information... +The following packages were automatically installed and are no longer required: + acl at-spi2-core colord dconf-gsettings-backend dconf-service dkms fakeroot + fontconfig fontconfig-config fonts-dejavu-core gcc gcc-4.8 + hicolor-icon-theme libasan0 libasound2 libasound2-data libatk-bridge2.0-0 + libatk1.0-0 libatk1.0-data libatomic1 libatspi2.0-0 libavahi-client3 + libavahi-common-data libavahi-common3 libc-dev-bin libc6-dev + libcairo-gobject2 libcairo2 libcanberra-gtk3-0 libcanberra-gtk3-module + libcanberra0 libcolord1 libcolorhug1 libcups2 libdatrie1 libdconf1 + libdrm-intel1 libdrm-nouveau2 libdrm-radeon1 libexif12 libfakeroot + libfontconfig1 libfontenc1 libgcc-4.8-dev libgd3 libgdk-pixbuf2.0-0 + libgdk-pixbuf2.0-common libgl1-mesa-dri libgl1-mesa-glx libglapi-mesa + libgomp1 libgphoto2-6 libgphoto2-l10n libgphoto2-port10 libgraphite2-3 + libgtk-3-0 libgtk-3-bin libgtk-3-common libgudev-1.0-0 libgusb2 + libharfbuzz0b libice6 libieee1284-3 libitm1 libjasper1 libjbig0 + libjpeg-turbo8 libjpeg8 liblcms2-2 libllvm3.4 libltdl7 libnotify-bin + libnotify4 libogg0 libpango-1.0-0 libpangocairo-1.0-0 libpangoft2-1.0-0 + libpciaccess0 libpixman-1-0 libquadmath0 libsane libsane-common libsm6 + libtdb1 libthai-data libthai0 libtiff5 libtsan0 libtxc-dxtn-s2tc0 libv4l-0 + libv4lconvert0 libvorbis0a libvorbisfile3 libvpx1 libwayland-client0 + libwayland-cursor0 libx11-xcb1 libxaw7 libxcb-dri2-0 libxcb-dri3-0 + libxcb-glx0 libxcb-present0 libxcb-render0 libxcb-shm0 libxcb-sync1 + libxcomposite1 libxcursor1 libxdamage1 libxfixes3 libxfont1 libxi6 + libxinerama1 libxkbcommon0 libxkbfile1 libxmu6 libxpm4 libxrandr2 + libxrender1 libxshmfence1 libxt6 libxtst6 libxxf86vm1 linux-libc-dev + manpages-dev notification-daemon sound-theme-freedesktop x11-common + x11-xkb-utils xfonts-base xfonts-encodings xfonts-utils xserver-common + xserver-xorg-core +Use 'apt-get autoremove' to remove them. +The following packages will be REMOVED: + virtualbox-guest-dkms* virtualbox-guest-utils* virtualbox-guest-x11* +0 upgraded, 0 newly installed, 3 to remove and 0 not upgraded. +After this operation, 12.2 MB disk space will be freed. +(Reading database ... 63174 files and directories currently installed.) +Removing virtualbox-guest-dkms (4.3.40-dfsg-0ubuntu14.04.1) ... + +-------- Uninstall Beginning -------- +Module: virtualbox-guest +Version: 4.3.40 +Kernel: 3.13.0-170-generic (x86_64) +------------------------------------- + +Status: Before uninstall, this module version was ACTIVE on this kernel. + +vboxguest.ko: + - Uninstallation + - Deleting from: /lib/modules/3.13.0-170-generic/updates/dkms/ + - Original module + - No original module was found for this module on this kernel. + - Use the dkms install command to reinstall any previous module version. + + +vboxsf.ko: + - Uninstallation + - Deleting from: /lib/modules/3.13.0-170-generic/updates/dkms/ + - Original module + - No original module was found for this module on this kernel. + - Use the dkms install command to reinstall any previous module version. + + +vboxvideo.ko: + - Uninstallation + - Deleting from: /lib/modules/3.13.0-170-generic/updates/dkms/ + - Original module + - No original module was found for this module on this kernel. + - Use the dkms install command to reinstall any previous module version. + +depmod.... + +DKMS: uninstall completed. + +------------------------------ +Deleting module version: 4.3.40 +completely from the DKMS tree. +------------------------------ +Done. +Removing virtualbox-guest-x11 (4.3.40-dfsg-0ubuntu14.04.1) ... +Purging configuration files for virtualbox-guest-x11 (4.3.40-dfsg-0ubuntu14.04.1) ... +Removing virtualbox-guest-utils (4.3.40-dfsg-0ubuntu14.04.1) ... +Purging configuration files for virtualbox-guest-utils (4.3.40-dfsg-0ubuntu14.04.1) ... +Processing triggers for man-db (2.6.7.1-1ubuntu1) ... +Processing triggers for libc-bin (2.19-0ubuntu6.15) ... +root@vagrant-ubuntu-trusty-64:/home/vagrant# exit +exit +name -r` dkmsubuntu-trusty-64:/home/vagrant# apt-get install -y linux-headers-`u +Reading package lists... +Building dependency tree... +Reading state information... +dkms is already the newest version. +dkms set to manually installed. +linux-headers-3.13.0-170-generic is already the newest version. +linux-headers-3.13.0-170-generic set to manually installed. +The following packages were automatically installed and are no longer required: + acl at-spi2-core colord dconf-gsettings-backend dconf-service fontconfig + fontconfig-config fonts-dejavu-core hicolor-icon-theme libasound2 + libasound2-data libatk-bridge2.0-0 libatk1.0-0 libatk1.0-data libatspi2.0-0 + libavahi-client3 libavahi-common-data libavahi-common3 libcairo-gobject2 + libcairo2 libcanberra-gtk3-0 libcanberra-gtk3-module libcanberra0 libcolord1 + libcolorhug1 libcups2 libdatrie1 libdconf1 libdrm-intel1 libdrm-nouveau2 + libdrm-radeon1 libexif12 libfontconfig1 libfontenc1 libgd3 + libgdk-pixbuf2.0-0 libgdk-pixbuf2.0-common libgl1-mesa-dri libgl1-mesa-glx + libglapi-mesa libgphoto2-6 libgphoto2-l10n libgphoto2-port10 libgraphite2-3 + libgtk-3-0 libgtk-3-bin libgtk-3-common libgudev-1.0-0 libgusb2 + libharfbuzz0b libice6 libieee1284-3 libjasper1 libjbig0 libjpeg-turbo8 + libjpeg8 liblcms2-2 libllvm3.4 libltdl7 libnotify-bin libnotify4 libogg0 + libpango-1.0-0 libpangocairo-1.0-0 libpangoft2-1.0-0 libpciaccess0 + libpixman-1-0 libsane libsane-common libsm6 libtdb1 libthai-data libthai0 + libtiff5 libtxc-dxtn-s2tc0 libv4l-0 libv4lconvert0 libvorbis0a + libvorbisfile3 libvpx1 libwayland-client0 libwayland-cursor0 libx11-xcb1 + libxaw7 libxcb-dri2-0 libxcb-dri3-0 libxcb-glx0 libxcb-present0 + libxcb-render0 libxcb-shm0 libxcb-sync1 libxcomposite1 libxcursor1 + libxdamage1 libxfixes3 libxfont1 libxi6 libxinerama1 libxkbcommon0 + libxkbfile1 libxmu6 libxpm4 libxrandr2 libxrender1 libxshmfence1 libxt6 + libxtst6 libxxf86vm1 notification-daemon sound-theme-freedesktop x11-common + x11-xkb-utils xfonts-base xfonts-encodings xfonts-utils xserver-common + xserver-xorg-core +Use 'apt-get autoremove' to remove them. +0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. +root@vagrant-ubuntu-trusty-64:/home/vagrant# exit +exit +Copy iso file /Applications/VirtualBox.app/Contents/MacOS/VBoxGuestAdditions.iso into the box /tmp/VBoxGuestAdditions.iso +Mounting Virtualbox Guest Additions ISO to: /mnt +o loop /mntt-ubuntu-trusty-64:/home/vagrant# mount /tmp/VBoxGuestAdditions.iso - +mount: block device /tmp/VBoxGuestAdditions.iso is write-protected, mounting read-only +root@vagrant-ubuntu-trusty-64:/home/vagrant# exit +exit +Installing Virtualbox Guest Additions 6.0.8 - guest version is 4.3.40 +-nox11agrant-ubuntu-trusty-64:/home/vagrant# yes | /mnt/VBoxLinuxAdditions.run - +Verifying archive integrity... All good. +Uncompressing VirtualBox 6.0.8 Guest Additions for Linux........ +VirtualBox Guest Additions installer +Copying additional installer modules ... +Installing additional modules ... +VirtualBox Guest Additions: Starting. +VirtualBox Guest Additions: Building the VirtualBox Guest Additions kernel +modules. This may take a while. +VirtualBox Guest Additions: To build modules for other installed kernels, run +VirtualBox Guest Additions: /sbin/rcvboxadd quicksetup +VirtualBox Guest Additions: or +VirtualBox Guest Additions: /sbin/rcvboxadd quicksetup all +VirtualBox Guest Additions: Building the modules for kernel 3.13.0-170-generic. + Adding system startup for /etc/init.d/vboxadd ... + /etc/rc0.d/K90vboxadd -> ../init.d/vboxadd + /etc/rc1.d/K90vboxadd -> ../init.d/vboxadd + /etc/rc6.d/K90vboxadd -> ../init.d/vboxadd + /etc/rc2.d/S10vboxadd -> ../init.d/vboxadd + /etc/rc3.d/S10vboxadd -> ../init.d/vboxadd + /etc/rc4.d/S10vboxadd -> ../init.d/vboxadd + /etc/rc5.d/S10vboxadd -> ../init.d/vboxadd + Adding system startup for /etc/init.d/vboxadd-service ... + /etc/rc0.d/K65vboxadd-service -> ../init.d/vboxadd-service + /etc/rc1.d/K65vboxadd-service -> ../init.d/vboxadd-service + /etc/rc6.d/K65vboxadd-service -> ../init.d/vboxadd-service + /etc/rc2.d/S35vboxadd-service -> ../init.d/vboxadd-service + /etc/rc3.d/S35vboxadd-service -> ../init.d/vboxadd-service + /etc/rc4.d/S35vboxadd-service -> ../init.d/vboxadd-service + /etc/rc5.d/S35vboxadd-service -> ../init.d/vboxadd-service +Building the VirtualBox Guest Additions kernel modules. This may take a while. +To build modules for other installed kernels, run + /sbin/rcvboxadd quicksetup +or + /sbin/rcvboxadd quicksetup all +Running kernel modules will not be replaced until the system is restarted +vboxadd-service.sh: Starting VirtualBox Guest Addition service. +bash: [3160: 2 (255)] tcsetattr: Inappropriate ioctl for device +root@vagrant-ubuntu-trusty-64:/home/vagrant# exit +exit +root@vagrant-ubuntu-trusty-64:/home/vagrant# /usr/sbin/service vboxadd start +VirtualBox Guest Additions: Starting. +VirtualBox Guest Additions: Building the VirtualBox Guest Additions kernel +modules. This may take a while. +VirtualBox Guest Additions: To build modules for other installed kernels, run +VirtualBox Guest Additions: /sbin/rcvboxadd quicksetup +VirtualBox Guest Additions: or +VirtualBox Guest Additions: /sbin/rcvboxadd quicksetup all +VirtualBox Guest Additions: Running kernel modules will not be replaced until +the system is restarted +root@vagrant-ubuntu-trusty-64:/home/vagrant# exit +exit +Unmounting Virtualbox Guest Additions ISO from: /mnt +root@vagrant-ubuntu-trusty-64:/home/vagrant# umount /mnt +root@vagrant-ubuntu-trusty-64:/home/vagrant# exit +exit +vagrant@vagrant-ubuntu-trusty-64:~$ +t +e +s +t + +- +mp/VBoxGuestAdditions.iso.iso && rm /t +vagrant@vagrant-ubuntu-trusty-64:~$ +e +x +i +t + +exit +==> default: Checking for guest additions in VM... +==> default: Configuring and enabling network interfaces... +==> default: Mounting shared folders... + default: /vagrant => /Users/metze/Work/Kitchen/DiViMe-2 +==> default: Running provisioner: bootstrap-system (shell)... + default: Running: /var/folders/0p/f4ssc1410_sc6_9wkzz2d9100000gq/T/vagrant-shell20190729-49213-15owjqt.sh + default: root@vagrant-ubuntu-trusty-64:/home/vagrant# chmod +x '/tmp + default: / + default: vagrant-s +mp/vagrant-shelll' && /t + default: ---- Start bootstrapping DiViMe @ Mon Jul 29 07:51:53 UTC 2019 ---- + default: -- System information -- + default: uname -a: Linux vagrant-ubuntu-trusty-64 3.13.0-170-generic #220-Ubuntu SMP Thu May 9 12:40:49 UTC 2019 x86_64 x86_64 x86_64 GNU/Linux + default: nproc: 2 + default: cat /proc/meminfo: + default: MemTotal: 2049736 kB + default: MemFree: 1523944 kB + default: Buffers: 18880 kB + default: Cached: 358112 kB + default: SwapCached: 0 kB + default: Active: 243108 kB + default: Inactive: 215260 kB + default: Active(anon): 81508 kB + default: Inactive(anon): 324 kB + default: Active(file): 161600 kB + default: Inactive(file): 214936 kB + default: Unevictable: 0 kB + default: Mlocked: 0 kB + default: SwapTotal: 0 kB + default: SwapFree: 0 kB + default: Dirty: 25272 kB + default: Writeback: 0 kB + default: AnonPages: 81336 kB + default: Mapped: 9324 kB + default: Shmem: 460 kB + default: Slab: 37236 kB + default: SReclaimable: 26924 kB + default: SUnreclaim: 10312 kB + default: KernelStack: 936 kB + default: PageTables: 3352 kB + default: NFS_Unstable: 0 kB + default: Bounce: 0 kB + default: WritebackTmp: 0 kB + default: CommitLimit: 1024868 kB + default: Committed_AS: 144456 kB + default: VmallocTotal: 34359738367 kB + default: VmallocUsed: 11116 kB + default: VmallocChunk: 34359723004 kB + default: HardwareCorrupted: 0 kB + default: AnonHugePages: 0 kB + default: HugePages_Total: 0 + default: HugePages_Free: 0 + default: HugePages_Rsvd: 0 + default: HugePages_Surp: 0 + default: Hugepagesize: 2048 kB + default: DirectMap4k: 38848 kB + default: DirectMap2M: 2058240 kB + default: df -h: + default: Filesystem Size Used Avail Use% Mounted on + default: udev 996M 12K 996M 1% /dev + default: tmpfs 201M 424K 200M 1% /run + default: /dev/sda1 40G 1.5G 37G 4% / + default: none 4.0K 0 4.0K 0% /sys/fs/cgroup + default: none 5.0M 24K 5.0M 1% /run/lock + default: none 1001M 0 1001M 0% /run/shm + default: none 100M 0 100M 0% /run/user + default: vagrant 1.9T 1.5T 368G 81% /vagrant + default: free: + default: total used free shared buffers cached + default: Mem: 2049736 525708 1524028 460 18880 358112 + default: -/+ buffers/cache: 148716 1901020 + default: Swap: 0 0 0 + default: -- Updating system -- + default: Ign http://archive.ubuntu.com trusty InRelease + default: Get:1 http://archive.ubuntu.com trusty-updates InRelease [65.9 kB] + default: Get:2 http://security.ubuntu.com trusty-security InRelease [65.9 kB] + default: Hit http://archive.ubuntu.com trusty-backports InRelease + default: Hit http://archive.ubuntu.com trusty Release.gpg + default: Get:3 http://archive.ubuntu.com trusty-updates/main Sources [431 kB] + default: Get:4 http://archive.ubuntu.com trusty-updates/restricted Sources [6,313 B] + default: Get:5 http://security.ubuntu.com trusty-security/main Sources [172 kB] + default: Get:6 http://archive.ubuntu.com trusty-updates/universe Sources [231 kB] + default: Get:7 http://archive.ubuntu.com trusty-updates/multiverse Sources [7,535 B] + default: Get:8 http://archive.ubuntu.com trusty-updates/main amd64 Packages [1,177 kB] + default: Get:9 http://archive.ubuntu.com trusty-updates/restricted amd64 Packages [17.2 kB] + default: Get:10 http://archive.ubuntu.com trusty-updates/universe amd64 Packages [526 kB] + default: Get:11 http://security.ubuntu.com trusty-security/universe Sources [102 kB] + default: Get:12 http://archive.ubuntu.com trusty-updates/multiverse amd64 Packages [14.6 kB] + default: Get:13 http://archive.ubuntu.com trusty-updates/main Translation-en [582 kB] + default: Get:14 http://archive.ubuntu.com trusty-updates/multiverse Translation-en [7,616 B] + default: Get:15 http://security.ubuntu.com trusty-security/main amd64 Packages [835 kB] + default: Get:16 http://archive.ubuntu.com trusty-updates/restricted Translation-en [4,028 B] + default: Get:17 http://archive.ubuntu.com trusty-updates/universe Translation-en [281 kB] + default: Get:18 http://archive.ubuntu.com trusty-backports/main Sources [9,709 B] + default: Get:19 http://archive.ubuntu.com trusty-backports/restricted Sources [28 B] + default: Get:20 http://archive.ubuntu.com trusty-backports/universe Sources [35.4 kB] + default: Get:21 http://archive.ubuntu.com trusty-backports/multiverse Sources [1,896 B] + default: Hit http://archive.ubuntu.com trusty-backports/main amd64 Packages + default: Hit http://archive.ubuntu.com trusty-backports/restricted amd64 Packages + default: Hit http://archive.ubuntu.com trusty-backports/universe amd64 Packages + default: Hit http://archive.ubuntu.com trusty-backports/multiverse amd64 Packages + default: Hit http://archive.ubuntu.com trusty-backports/main Translation-en + default: Hit http://archive.ubuntu.com trusty-backports/multiverse Translation-en + default: Hit http://archive.ubuntu.com trusty-backports/restricted Translation-en + default: Hit http://archive.ubuntu.com trusty-backports/universe Translation-en + default: Hit http://archive.ubuntu.com trusty Release + default: Get:22 http://security.ubuntu.com trusty-security/universe amd64 Packages [294 kB] + default: Get:23 http://archive.ubuntu.com trusty/main Sources [1,064 kB] + default: Get:24 http://security.ubuntu.com trusty-security/main Translation-en [448 kB] + default: Get:25 http://archive.ubuntu.com trusty/restricted Sources [5,433 B] + default: Get:26 http://archive.ubuntu.com trusty/universe Sources [6,399 kB] + default: Get:27 http://security.ubuntu.com trusty-security/universe Translation-en [162 kB] + default: Get:28 http://archive.ubuntu.com trusty/multiverse Sources [174 kB] + default: Hit http://archive.ubuntu.com trusty/main amd64 Packages + default: Hit http://archive.ubuntu.com trusty/restricted amd64 Packages + default: Hit http://archive.ubuntu.com trusty/universe amd64 Packages + default: Hit http://archive.ubuntu.com trusty/multiverse amd64 Packages + default: Hit http://archive.ubuntu.com trusty/main Translation-en + default: Hit http://archive.ubuntu.com trusty/multiverse Translation-en + default: Hit http://archive.ubuntu.com trusty/restricted Translation-en + default: Hit http://archive.ubuntu.com trusty/universe Translation-en + default: Ign http://archive.ubuntu.com trusty/main Translation-en_US + default: Ign http://archive.ubuntu.com trusty/multiverse Translation-en_US + default: Ign http://archive.ubuntu.com trusty/restricted Translation-en_US + default: Ign http://archive.ubuntu.com trusty/universe Translation-en_US + default: Fetched 13.1 MB in 5s (2,589 kB/s) + default: Reading package lists... + default: Reading package lists... + default: Building dependency tree... + default: Reading state information... + default: The following packages were automatically installed and are no longer required: + default: acl at-spi2-core colord dconf-gsettings-backend dconf-service fontconfig + default: fontconfig-config fonts-dejavu-core hicolor-icon-theme libasound2 + default: libasound2-data libatk-bridge2.0-0 libatk1.0-0 libatk1.0-data libatspi2.0-0 + default: libavahi-client3 libavahi-common-data libavahi-common3 libcairo-gobject2 + default: libcairo2 libcanberra-gtk3-0 libcanberra-gtk3-module libcanberra0 libcolord1 + default: libcolorhug1 libcups2 libdatrie1 libdconf1 libdrm-intel1 libdrm-nouveau2 + default: libdrm-radeon1 libexif12 libfontconfig1 libfontenc1 libgd3 + default: libgdk-pixbuf2.0-0 libgdk-pixbuf2.0-common libgl1-mesa-dri libgl1-mesa-glx + default: libglapi-mesa libgphoto2-6 libgphoto2-l10n libgphoto2-port10 libgraphite2-3 + default: libgtk-3-0 libgtk-3-bin libgtk-3-common libgudev-1.0-0 libgusb2 + default: libharfbuzz0b libice6 libieee1284-3 libjasper1 libjbig0 libjpeg-turbo8 + default: libjpeg8 liblcms2-2 libllvm3.4 libltdl7 libnotify-bin libnotify4 libogg0 + default: libpango-1.0-0 libpangocairo-1.0-0 libpangoft2-1.0-0 libpciaccess0 + default: libpixman-1-0 libsane libsane-common libsm6 libtdb1 libthai-data libthai0 + default: libtiff5 libtxc-dxtn-s2tc0 libv4l-0 libv4lconvert0 libvorbis0a + default: libvorbisfile3 libvpx1 libwayland-client0 libwayland-cursor0 libx11-xcb1 + default: libxaw7 libxcb-dri2-0 libxcb-dri3-0 libxcb-glx0 libxcb-present0 + default: libxcb-render0 libxcb-shm0 libxcb-sync1 libxcomposite1 libxcursor1 + default: libxdamage1 libxfixes3 libxfont1 libxi6 libxinerama1 libxkbcommon0 + default: libxkbfile1 libxmu6 libxpm4 libxrandr2 libxrender1 libxshmfence1 libxt6 + default: libxtst6 libxxf86vm1 notification-daemon sound-theme-freedesktop x11-common + default: x11-xkb-utils xfonts-base xfonts-encodings xfonts-utils xserver-common + default: xserver-xorg-core + default: Use 'apt-get autoremove' to remove them. + default: 0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded. + default: Reading package lists... + default: Building dependency tree... + default: Reading state information... + default: bc is already the newest version. + default: gawk is already the newest version. + default: make is already the newest version. + default: make set to manually installed. + default: fuse is already the newest version. + default: libx11-xcb1 is already the newest version. + default: libx11-xcb1 set to manually installed. + default: patch is already the newest version. + default: The following packages were automatically installed and are no longer required: + default: acl at-spi2-core colord dconf-gsettings-backend dconf-service + default: libatk-bridge2.0-0 libatspi2.0-0 libcairo-gobject2 libcanberra-gtk3-0 + default: libcanberra-gtk3-module libcanberra0 libcolord1 libcolorhug1 libdconf1 + default: libdrm-intel1 libdrm-nouveau2 libdrm-radeon1 libexif12 libfontenc1 libgd3 + default: libgl1-mesa-dri libgl1-mesa-glx libglapi-mesa libgphoto2-6 libgphoto2-l10n + default: libgphoto2-port10 libgtk-3-0 libgtk-3-bin libgtk-3-common libgudev-1.0-0 + default: libgusb2 libieee1284-3 libllvm3.4 libnotify-bin libnotify4 libsane + default: libsane-common libtdb1 libtxc-dxtn-s2tc0 libv4l-0 libv4lconvert0 + default: libwayland-client0 libwayland-cursor0 libxaw7 libxcb-dri2-0 libxcb-dri3-0 + default: libxcb-glx0 libxcb-present0 libxcb-sync1 libxfont1 libxkbcommon0 libxkbfile1 + default: libxmu6 libxpm4 libxshmfence1 libxxf86vm1 notification-daemon + default: sound-theme-freedesktop x11-xkb-utils xfonts-base xfonts-encodings + default: xfonts-utils xserver-common xserver-xorg-core + default: Use 'apt-get autoremove' to remove them. + default: The following extra packages will be installed: + default: alsa-base alsa-utils autotools-dev ca-certificates-java espeak-data + default: festlex-cmu festlex-poslex festvox-kallpc16k fonts-dejavu-extra g++-4.8 + default: gcc-4.8-multilib git-man icedtea-6-jre-cacao icedtea-6-jre-jamvm + default: icu-devtools java-common lib32asan0 lib32atomic1 lib32gcc-4.8-dev lib32gcc1 + default: lib32gomp1 lib32itm1 lib32quadmath0 libao-common libao4 libapr1 libaprutil1 + default: libasyncns0 libatk-wrapper-java libatk-wrapper-java-jni libatlas3-base + default: libavcodec54 libavdevice53 libavfilter3 libavformat54 libavresample1 + default: libavutil52 libblas-dev libblas3 libboost-atomic-dev libboost-atomic1.54-dev + default: libboost-atomic1.54.0 libboost-chrono-dev libboost-chrono1.54-dev + default: libboost-chrono1.54.0 libboost-context-dev libboost-context1.54-dev + default: libboost-context1.54.0 libboost-coroutine-dev libboost-coroutine1.54-dev + default: libboost-date-time-dev libboost-date-time1.54-dev libboost-date-time1.54.0 + default: libboost-dev libboost-exception-dev libboost-exception1.54-dev + default: libboost-filesystem-dev libboost-filesystem1.54-dev + default: libboost-filesystem1.54.0 libboost-graph-dev libboost-graph-parallel-dev + default: libboost-graph-parallel1.54-dev libboost-graph-parallel1.54.0 + default: libboost-graph1.54-dev libboost-graph1.54.0 libboost-iostreams-dev + default: libboost-iostreams1.54-dev libboost-locale-dev libboost-locale1.54-dev + default: libboost-locale1.54.0 libboost-log-dev libboost-log1.54-dev + default: libboost-log1.54.0 libboost-math-dev libboost-math1.54-dev + default: libboost-math1.54.0 libboost-mpi-dev libboost-mpi-python-dev + default: libboost-mpi-python1.54-dev libboost-mpi-python1.54.0 libboost-mpi1.54-dev + default: libboost-mpi1.54.0 libboost-program-options-dev + default: libboost-program-options1.54-dev libboost-program-options1.54.0 + default: libboost-python-dev libboost-python1.54-dev libboost-python1.54.0 + default: libboost-random-dev libboost-random1.54-dev libboost-random1.54.0 + default: libboost-regex-dev libboost-regex1.54-dev libboost-regex1.54.0 + default: libboost-serialization-dev libboost-serialization1.54-dev + default: libboost-serialization1.54.0 libboost-signals-dev libboost-signals1.54-dev + default: libboost-signals1.54.0 libboost-system-dev libboost-system1.54-dev + default: libboost-system1.54.0 libboost-test-dev libboost-test1.54-dev + default: libboost-test1.54.0 libboost-thread-dev libboost-thread1.54-dev + default: libboost-thread1.54.0 libboost-timer-dev libboost-timer1.54-dev + default: libboost-timer1.54.0 libboost-tools-dev libboost-wave-dev + default: libboost-wave1.54-dev libboost-wave1.54.0 libboost1.54-dev + default: libboost1.54-tools-dev libc6-dev-x32 libc6-i386 libc6-x32 libcaca0 + default: libcdio-cdda1 libcdio-paranoia1 libcdio13 libcr0 libdc1394-22 liberror-perl + default: libespeak1 libestools2.1 libexpat1-dev libflac8 libgfortran3 libgif4 libgsm1 + default: libgtk2.0-0 libgtk2.0-bin libgtk2.0-common libhwloc-dev libhwloc-plugins + default: libhwloc5 libibverbs-dev libibverbs1 libice-dev libicu-dev libid3tag0 + default: libjack-jackd2-0 liblapack3 libltdl-dev libmad0 libmp3lame0 libnspr4 libnss3 + default: libnss3-nssdb libopencore-amrnb0 libopencore-amrwb0 libopenjpeg2 + default: libopenmpi-dev libopenmpi1.6 libopus0 liborc-0.4-0 libpci-dev libpcsclite1 + default: libportaudio2 libpthread-stubs0-dev libpulse0 libpython-dev libpython2.7-dev + default: libraw1394-11 libsamplerate0 libschroedinger-1.0-0 libsdl1.2debian + default: libserf-1-1 libsm-dev libsndfile1 libsonic0 libsox-fmt-alsa libsox-fmt-ao + default: libsox-fmt-base libsox-fmt-mp3 libsox-fmt-oss libsox-fmt-pulse libsox2 + default: libspeex1 libstdc++-4.8-dev libsvn1 libswscale2 libtheora0 libtinfo-dev + default: libtorque2 libtwolame0 libva1 libvorbisenc2 libwavpack1 libx11-dev + default: libx11-doc libx264-142 libx32asan0 libx32atomic1 libx32gcc-4.8-dev + default: libx32gcc1 libx32gomp1 libx32itm1 libx32quadmath0 libxau-dev libxcb1-dev + default: libxdmcp-dev libxml2-dev libxvidcore4 linux-sound-base m4 mpi-default-bin + default: mpi-default-dev ocl-icd-libopencl1 openjdk-6-jre-headless openjdk-6-jre-lib + default: openmpi-bin openmpi-common ttf-dejavu-extra tzdata-java x11proto-core-dev + default: x11proto-input-dev x11proto-kb-dev xorg-sgml-doctools xtrans-dev + default: Suggested packages: + default: apmd alsa-oss oss-compat autoconf2.13 autoconf-archive gnu-standards + default: autoconf-doc gettext pidgin-festival festival-freebsoft-utils g++-multilib + default: g++-4.8-multilib gcc-4.8-doc libstdc++6-4.8-dbg git-daemon-run + default: git-daemon-sysvinit git-doc git-el git-email git-gui gitk gitweb git-arch + default: git-bzr git-cvs git-mediawiki git-svn default-jre equivs libaudio2 libesd0 + default: libesd-alsa0 libblas-doc liblapack-doc frei0r-plugins libboost-doc graphviz + default: libboost1.54-doc python-pyste libboost-coroutine.54-dev libboost-log.54-dev + default: libmpfrc++-dev libntl-dev xsltproc doxygen docbook-xml docbook-xsl + default: default-jdk fop blcr-dkms librsvg2-common gvfs libhwloc-contrib-plugins + default: libice-doc icu-doc jackd2 libtool-doc ncurses-doc opus-tools pcscd + default: pulseaudio libraw1394-doc libsm-doc speex libstdc++-4.8-doc automaken + default: gfortran fortran95-compiler gcj-jdk libxcb-doc pkg-config libxt-doc + default: opencl-icd icedtea-plugin libnss-mdns sun-java6-fonts fonts-ipafont-gothic + default: fonts-ipafont-mincho ttf-wqy-microhei ttf-wqy-zenhei ttf-indic-fonts-core + default: ttf-telugu-fonts ttf-oriya-fonts ttf-kannada-fonts ttf-bengali-fonts + default: openmpi-checkpoint subversion-tools db5.3-util zip + default: The following NEW packages will be installed: + default: alsa-base alsa-utils autoconf automake autotools-dev ca-certificates-java + default: espeak espeak-data festival festlex-cmu festlex-poslex festvox-kallpc16k + default: fonts-dejavu-extra g++ g++-4.8 gcc-4.8-multilib gcc-multilib git git-man + default: icedtea-6-jre-cacao icedtea-6-jre-jamvm icedtea-netx icedtea-netx-common + default: icu-devtools java-common lib32asan0 lib32atomic1 lib32gcc-4.8-dev lib32gcc1 + default: lib32gomp1 lib32itm1 lib32quadmath0 libao-common libao4 libapr1 libaprutil1 + default: libasyncns0 libatk-wrapper-java libatk-wrapper-java-jni libatlas-base-dev + default: libatlas-dev libatlas3-base libav-tools libavcodec54 libavdevice53 + default: libavfilter3 libavformat54 libavresample1 libavutil52 libblas-dev libblas3 + default: libboost-all-dev libboost-atomic-dev libboost-atomic1.54-dev + default: libboost-atomic1.54.0 libboost-chrono-dev libboost-chrono1.54-dev + default: libboost-chrono1.54.0 libboost-context-dev libboost-context1.54-dev + default: libboost-context1.54.0 libboost-coroutine-dev libboost-coroutine1.54-dev + default: libboost-date-time-dev libboost-date-time1.54-dev libboost-date-time1.54.0 + default: libboost-dev libboost-exception-dev libboost-exception1.54-dev + default: libboost-filesystem-dev libboost-filesystem1.54-dev + default: libboost-filesystem1.54.0 libboost-graph-dev libboost-graph-parallel-dev + default: libboost-graph-parallel1.54-dev libboost-graph-parallel1.54.0 + default: libboost-graph1.54-dev libboost-graph1.54.0 libboost-iostreams-dev + default: libboost-iostreams1.54-dev libboost-locale-dev libboost-locale1.54-dev + default: libboost-locale1.54.0 libboost-log-dev libboost-log1.54-dev + default: libboost-log1.54.0 libboost-math-dev libboost-math1.54-dev + default: libboost-math1.54.0 libboost-mpi-dev libboost-mpi-python-dev + default: libboost-mpi-python1.54-dev libboost-mpi-python1.54.0 libboost-mpi1.54-dev + default: libboost-mpi1.54.0 libboost-program-options-dev + default: libboost-program-options1.54-dev libboost-program-options1.54.0 + default: libboost-python-dev libboost-python1.54-dev libboost-python1.54.0 + default: libboost-random-dev libboost-random1.54-dev libboost-random1.54.0 + default: libboost-regex-dev libboost-regex1.54-dev libboost-regex1.54.0 + default: libboost-serialization-dev libboost-serialization1.54-dev + default: libboost-serialization1.54.0 libboost-signals-dev libboost-signals1.54-dev + default: libboost-signals1.54.0 libboost-system-dev libboost-system1.54-dev + default: libboost-system1.54.0 libboost-test-dev libboost-test1.54-dev + default: libboost-test1.54.0 libboost-thread-dev libboost-thread1.54-dev + default: libboost-thread1.54.0 libboost-timer-dev libboost-timer1.54-dev + default: libboost-timer1.54.0 libboost-tools-dev libboost-wave-dev + default: libboost-wave1.54-dev libboost-wave1.54.0 libboost1.54-dev + default: libboost1.54-tools-dev libc6-dev-i386 libc6-dev-x32 libc6-i386 libc6-x32 + default: libcaca0 libcdio-cdda1 libcdio-paranoia1 libcdio13 libcr0 libdc1394-22 + default: liberror-perl libespeak1 libestools2.1 libexpat1-dev libflac8 libgfortran3 + default: libgif4 libgsm1 libgtk2.0-0 libgtk2.0-bin libgtk2.0-common libhwloc-dev + default: libhwloc-plugins libhwloc5 libibverbs-dev libibverbs1 libice-dev libicu-dev + default: libid3tag0 libjack-jackd2-0 liblapack-dev liblapack3 libltdl-dev libmad0 + default: libmp3lame0 libncurses5-dev libnspr4 libnss3 libnss3-nssdb + default: libopencore-amrnb0 libopencore-amrwb0 libopenjpeg2 libopenmpi-dev + default: libopenmpi1.6 libopus0 liborc-0.4-0 libpci-dev libpcsclite1 libportaudio2 + default: libpthread-stubs0-dev libpulse0 libpython-dev libpython2.7-dev libraw1394-11 + default: libsamplerate0 libschroedinger-1.0-0 libsdl1.2debian libserf-1-1 libsm-dev + default: libsndfile1 libsonic0 libsox-fmt-all libsox-fmt-alsa libsox-fmt-ao + default: libsox-fmt-base libsox-fmt-mp3 libsox-fmt-oss libsox-fmt-pulse libsox2 + default: libspeex1 libstdc++-4.8-dev libsvn1 libswscale2 libtheora0 libtinfo-dev + default: libtool libtorque2 libtwolame0 libva1 libvorbisenc2 libwavpack1 libx11-dev + default: libx11-doc libx264-142 libx32asan0 libx32atomic1 libx32gcc-4.8-dev + default: libx32gcc1 libx32gomp1 libx32itm1 libx32quadmath0 libxau-dev libxcb1-dev + default: libxdmcp-dev libxml2-dev libxt-dev libxvidcore4 linux-sound-base m4 + default: mpi-default-bin mpi-default-dev ocl-icd-libopencl1 openjdk-6-jre + default: openjdk-6-jre-headless openjdk-6-jre-lib openmpi-bin openmpi-common + default: python-setuptools sox sshfs subversion ttf-dejavu-extra tzdata-java unzip + default: x11proto-core-dev x11proto-input-dev x11proto-kb-dev xorg-sgml-doctools + default: xtrans-dev zlib1g-dev + default: 0 upgraded, 251 newly installed, 0 to remove and 0 not upgraded. + default: Need to get 162 MB of archives. + default: After this operation, 600 MB of additional disk space will be used. + default: Get:1 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libc6-i386 amd64 2.19-0ubuntu6.15 [2,208 kB] + default: Get:2 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libc6-dev-i386 amd64 2.19-0ubuntu6.15 [1,153 kB] + default: Get:3 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libc6-x32 amd64 2.19-0ubuntu6.15 [2,431 kB] + default: Get:4 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libc6-dev-x32 amd64 2.19-0ubuntu6.15 [1,414 kB] + default: Get:5 http://archive.ubuntu.com/ubuntu/ trusty-updates/main lib32gcc1 amd64 1:4.9.3-0ubuntu4 [47.8 kB] + default: Get:6 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libx32gcc1 amd64 1:4.9.3-0ubuntu4 [38.6 kB] + default: Get:7 http://archive.ubuntu.com/ubuntu/ trusty-updates/main lib32gomp1 amd64 4.8.4-2ubuntu1~14.04.4 [25.0 kB] + default: Get:8 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libx32gomp1 amd64 4.8.4-2ubuntu1~14.04.4 [22.5 kB] + default: Get:9 http://archive.ubuntu.com/ubuntu/ trusty-updates/main lib32itm1 amd64 4.8.4-2ubuntu1~14.04.4 [28.5 kB] + default: Get:10 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libx32itm1 amd64 4.8.4-2ubuntu1~14.04.4 [28.4 kB] + default: Get:11 http://archive.ubuntu.com/ubuntu/ trusty-updates/main lib32atomic1 amd64 4.8.4-2ubuntu1~14.04.4 [8,250 B] + default: Get:12 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libx32atomic1 amd64 4.8.4-2ubuntu1~14.04.4 [8,604 B] + default: Get:13 http://archive.ubuntu.com/ubuntu/ trusty-updates/main lib32asan0 amd64 4.8.4-2ubuntu1~14.04.4 [64.2 kB] + default: Get:14 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libx32asan0 amd64 4.8.4-2ubuntu1~14.04.4 [63.1 kB] + default: Get:15 http://archive.ubuntu.com/ubuntu/ trusty-updates/main lib32quadmath0 amd64 4.8.4-2ubuntu1~14.04.4 [187 kB] + default: Get:16 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libx32quadmath0 amd64 4.8.4-2ubuntu1~14.04.4 [128 kB] + default: Get:17 http://archive.ubuntu.com/ubuntu/ trusty-updates/main lib32gcc-4.8-dev amd64 4.8.4-2ubuntu1~14.04.4 [1,685 kB] + default: Get:18 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libx32gcc-4.8-dev amd64 4.8.4-2ubuntu1~14.04.4 [1,558 kB] + default: Get:19 http://archive.ubuntu.com/ubuntu/ trusty-updates/main gcc-4.8-multilib amd64 4.8.4-2ubuntu1~14.04.4 [982 B] + default: Get:20 http://archive.ubuntu.com/ubuntu/ trusty/main gcc-multilib amd64 4:4.8.2-1ubuntu6 [1,024 B] + default: Get:21 http://archive.ubuntu.com/ubuntu/ trusty/main libao-common all 1.1.0-2ubuntu2 [6,278 B] + default: Get:22 http://archive.ubuntu.com/ubuntu/ trusty/main libao4 amd64 1.1.0-2ubuntu2 [31.5 kB] + default: Get:23 http://archive.ubuntu.com/ubuntu/ trusty/main libapr1 amd64 1.5.0-1 [85.1 kB] + default: Get:24 http://archive.ubuntu.com/ubuntu/ trusty/main libaprutil1 amd64 1.5.3-1 [76.4 kB] + default: Get:25 http://archive.ubuntu.com/ubuntu/ trusty/main libasyncns0 amd64 0.8-4ubuntu2 [11.9 kB] + default: Get:26 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libgtk2.0-common all 2.24.23-0ubuntu1.4 [121 kB] + default: Get:27 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libgtk2.0-0 amd64 2.24.23-0ubuntu1.4 [1,739 kB] + default: Get:28 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe openjdk-6-jre-lib all 6b41-1.13.13-0ubuntu0.14.04.1 [6,007 kB] + default: Get:29 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libnspr4 amd64 2:4.13.1-0ubuntu0.14.04.1 [110 kB] + default: Get:30 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libnss3-nssdb all 2:3.28.4-0ubuntu0.14.04.5 [10.6 kB] + default: Get:31 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libnss3 amd64 2:3.28.4-0ubuntu0.14.04.5 [1,124 kB] + default: Get:32 http://archive.ubuntu.com/ubuntu/ trusty/main ca-certificates-java all 20130815ubuntu1 [13.4 kB] + default: Get:33 http://archive.ubuntu.com/ubuntu/ trusty-updates/main tzdata-java all 2019a-0ubuntu0.14.04 [70.0 kB] + default: Get:34 http://archive.ubuntu.com/ubuntu/ trusty/main java-common all 0.51 [130 kB] + default: Get:35 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libpcsclite1 amd64 1.8.10-1ubuntu1.1 [21.1 kB] + default: Get:36 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe openjdk-6-jre-headless amd64 6b41-1.13.13-0ubuntu0.14.04.1 [31.2 MB] + default: Get:37 http://archive.ubuntu.com/ubuntu/ trusty/main libgif4 amd64 4.1.6-11 [28.6 kB] + default: Get:38 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libflac8 amd64 1.3.0-2ubuntu0.14.04.1 [80.2 kB] + default: Get:39 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libvorbisenc2 amd64 1.3.2-1.3ubuntu1.2 [84.6 kB] + default: Get:40 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libsndfile1 amd64 1.0.25-7ubuntu2.2 [136 kB] + default: Get:41 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libpulse0 amd64 1:4.0-0ubuntu11.1 [225 kB] + default: Get:42 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe openjdk-6-jre amd64 6b41-1.13.13-0ubuntu0.14.04.1 [192 kB] + default: Get:43 http://archive.ubuntu.com/ubuntu/ trusty/main libatk-wrapper-java all 0.30.4-4 [30.2 kB] + default: Get:44 http://archive.ubuntu.com/ubuntu/ trusty/main libatk-wrapper-java-jni amd64 0.30.4-4 [25.2 kB] + default: Get:45 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libavutil52 amd64 6:9.20-0ubuntu0.14.04.1 [66.9 kB] + default: Get:46 http://archive.ubuntu.com/ubuntu/ trusty/universe libgsm1 amd64 1.0.13-4 [27.1 kB] + default: Get:47 http://archive.ubuntu.com/ubuntu/ trusty/universe libmp3lame0 amd64 3.99.5+repack1-3ubuntu1 [158 kB] + default: Get:48 http://archive.ubuntu.com/ubuntu/ trusty/universe libopenjpeg2 amd64 1.3+dfsg-4.7ubuntu1 [64.3 kB] + default: Get:49 http://archive.ubuntu.com/ubuntu/ trusty/main libopus0 amd64 1.1-0ubuntu1 [153 kB] + default: Get:50 http://archive.ubuntu.com/ubuntu/ trusty/main liborc-0.4-0 amd64 1:0.4.18-1ubuntu1 [136 kB] + default: Get:51 http://archive.ubuntu.com/ubuntu/ trusty/universe libschroedinger-1.0-0 amd64 1.0.11-2ubuntu1 [268 kB] + default: Get:52 http://archive.ubuntu.com/ubuntu/ trusty/main libspeex1 amd64 1.2~rc1.1-1ubuntu1 [50.7 kB] + default: Get:53 http://archive.ubuntu.com/ubuntu/ trusty/main libtheora0 amd64 1.1.1+dfsg.1-3.2 [163 kB] + default: Get:54 http://archive.ubuntu.com/ubuntu/ trusty/universe libva1 amd64 1.3.0-2 [38.6 kB] + default: Get:55 http://archive.ubuntu.com/ubuntu/ trusty/universe libx264-142 amd64 2:0.142.2389+git956c8d8-2 [575 kB] + default: Get:56 http://archive.ubuntu.com/ubuntu/ trusty/universe libxvidcore4 amd64 2:1.3.2-9ubuntu1 [298 kB] + default: Get:57 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libavcodec54 amd64 6:9.20-0ubuntu0.14.04.1 [2,350 kB] + default: Get:58 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libavformat54 amd64 6:9.20-0ubuntu0.14.04.1 [481 kB] + default: Get:59 http://archive.ubuntu.com/ubuntu/ trusty/main libcdio13 amd64 0.83-4.1ubuntu1 [53.6 kB] + default: Get:60 http://archive.ubuntu.com/ubuntu/ trusty/main libcdio-cdda1 amd64 0.83-4.1ubuntu1 [15.5 kB] + default: Get:61 http://archive.ubuntu.com/ubuntu/ trusty/main libcdio-paranoia1 amd64 0.83-4.1ubuntu1 [15.2 kB] + default: Get:62 http://archive.ubuntu.com/ubuntu/ trusty/main libraw1394-11 amd64 2.1.0-1ubuntu1 [29.9 kB] + default: Get:63 http://archive.ubuntu.com/ubuntu/ trusty/universe libdc1394-22 amd64 2.2.1-2ubuntu2 [75.0 kB] + default: Get:64 http://archive.ubuntu.com/ubuntu/ trusty/main libsamplerate0 amd64 0.1.8-7 [938 kB] + default: Get:65 http://archive.ubuntu.com/ubuntu/ trusty/main libjack-jackd2-0 amd64 1.9.9.5+20130622git7de15e7a-1ubuntu1 [182 kB] + default: Get:66 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libavdevice53 amd64 6:9.20-0ubuntu0.14.04.1 [32.3 kB] + default: Get:67 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libavresample1 amd64 6:9.20-0ubuntu0.14.04.1 [39.2 kB] + default: Get:68 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libswscale2 amd64 6:9.20-0ubuntu0.14.04.1 [81.7 kB] + default: Get:69 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libavfilter3 amd64 6:9.20-0ubuntu0.14.04.1 [93.6 kB] + default: Get:70 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libcaca0 amd64 0.99.beta18-1ubuntu5.1 [202 kB] + default: Get:71 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libsdl1.2debian amd64 1.2.15-8ubuntu1.1 [162 kB] + default: Get:72 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libav-tools amd64 6:9.20-0ubuntu0.14.04.1 [3,310 kB] + default: Get:73 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-atomic1.54.0 amd64 1.54.0-4ubuntu3.1 [8,076 B] + default: Get:74 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-system1.54.0 amd64 1.54.0-4ubuntu3.1 [10.1 kB] + default: Get:75 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-chrono1.54.0 amd64 1.54.0-4ubuntu3.1 [12.5 kB] + default: Get:76 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-context1.54.0 amd64 1.54.0-4ubuntu3.1 [8,028 B] + default: Get:77 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-date-time1.54.0 amd64 1.54.0-4ubuntu3.1 [20.8 kB] + default: Get:78 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-filesystem1.54.0 amd64 1.54.0-4ubuntu3.1 [34.2 kB] + default: Get:79 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-serialization1.54.0 amd64 1.54.0-4ubuntu3.1 [126 kB] + default: Get:80 http://archive.ubuntu.com/ubuntu/ trusty/universe libcr0 amd64 0.8.5-2.1 [18.6 kB] + default: Get:81 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libhwloc5 amd64 1.8-1ubuntu1.14.04.1 [78.0 kB] + default: Get:82 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libibverbs1 amd64 1.1.7-1ubuntu1.1 [23.6 kB] + default: Get:83 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libtorque2 amd64 2.4.16+dfsg-1.3ubuntu1.1 [76.6 kB] + default: Get:84 http://archive.ubuntu.com/ubuntu/ trusty/universe libopenmpi1.6 amd64 1.6.5-8 [1,396 kB] + default: Get:85 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-mpi1.54.0 amd64 1.54.0-4ubuntu3.1 [45.6 kB] + default: Get:86 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-graph-parallel1.54.0 amd64 1.54.0-4ubuntu3.1 [71.3 kB] + default: Get:87 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-regex1.54.0 amd64 1.54.0-4ubuntu3.1 [261 kB] + default: Get:88 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-graph1.54.0 amd64 1.54.0-4ubuntu3.1 [94.7 kB] + default: Get:89 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-thread1.54.0 amd64 1.54.0-4ubuntu3.1 [26.5 kB] + default: Get:90 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-locale1.54.0 amd64 1.54.0-4ubuntu3.1 [212 kB] + default: Get:91 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-math1.54.0 amd64 1.54.0-4ubuntu3.1 [252 kB] + default: Get:92 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-python1.54.0 amd64 1.54.0-4ubuntu3.1 [122 kB] + default: Get:93 http://archive.ubuntu.com/ubuntu/ trusty/universe openmpi-common all 1.6.5-8 [101 kB] + default: Get:94 http://archive.ubuntu.com/ubuntu/ trusty/universe openmpi-bin amd64 1.6.5-8 [84.5 kB] + default: Get:95 http://archive.ubuntu.com/ubuntu/ trusty/universe mpi-default-bin amd64 1.0.2ubuntu1 [3,252 B] + default: Get:96 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-mpi-python1.54.0 amd64 1.54.0-4ubuntu3.1 [215 kB] + default: Get:97 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-program-options1.54.0 amd64 1.54.0-4ubuntu3.1 [115 kB] + default: Get:98 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-random1.54.0 amd64 1.54.0-4ubuntu3.1 [9,834 B] + default: Get:99 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-signals1.54.0 amd64 1.54.0-4ubuntu3.1 [29.8 kB] + default: Get:100 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-test1.54.0 amd64 1.54.0-4ubuntu3.1 [178 kB] + default: Get:101 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-timer1.54.0 amd64 1.54.0-4ubuntu3.1 [9,926 B] + default: Get:102 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-wave1.54.0 amd64 1.54.0-4ubuntu3.1 [161 kB] + default: Get:103 http://archive.ubuntu.com/ubuntu/ trusty/main libportaudio2 amd64 19+svn20140130-1 [62.9 kB] + default: Get:104 http://archive.ubuntu.com/ubuntu/ trusty/main libsonic0 amd64 0.1.18-0ubuntu1 [11.5 kB] + default: Get:105 http://archive.ubuntu.com/ubuntu/ trusty/main espeak-data amd64 1.47.11-1ubuntu1 [853 kB] + default: Get:106 http://archive.ubuntu.com/ubuntu/ trusty/main libespeak1 amd64 1.47.11-1ubuntu1 [138 kB] + default: Get:107 http://archive.ubuntu.com/ubuntu/ trusty/universe libestools2.1 amd64 1:2.1~release-6 [1,292 kB] + default: Get:108 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libgfortran3 amd64 4.8.4-2ubuntu1~14.04.4 [248 kB] + default: Get:109 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libmad0 amd64 0.15.1b-9ubuntu14.04.1 [62.0 kB] + default: Get:110 http://archive.ubuntu.com/ubuntu/ trusty/universe libopencore-amrnb0 amd64 0.1.3-2ubuntu1 [90.4 kB] + default: Get:111 http://archive.ubuntu.com/ubuntu/ trusty/universe libopencore-amrwb0 amd64 0.1.3-2ubuntu1 [45.3 kB] + default: Get:112 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libexpat1-dev amd64 2.1.0-4ubuntu1.4 [115 kB] + default: Get:113 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libpython2.7-dev amd64 2.7.6-8ubuntu0.5 [22.0 MB] + default: Get:114 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libserf-1-1 amd64 1.3.3-1ubuntu0.1 [42.2 kB] + default: Get:115 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libsox2 amd64 14.4.1-3ubuntu1.1 [221 kB] + default: Get:116 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libsvn1 amd64 1.8.8-1ubuntu3.3 [918 kB] + default: Get:117 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libwavpack1 amd64 4.70.0-1ubuntu0.2 [74.7 kB] + default: Get:118 http://archive.ubuntu.com/ubuntu/ trusty/universe ocl-icd-libopencl1 amd64 2.1.3-4 [28.1 kB] + default: Get:119 http://archive.ubuntu.com/ubuntu/ trusty/main linux-sound-base all 1.0.25+dfsg-0ubuntu4 [14.5 kB] + default: Get:120 http://archive.ubuntu.com/ubuntu/ trusty/main alsa-base all 1.0.25+dfsg-0ubuntu4 [151 kB] + default: Get:121 http://archive.ubuntu.com/ubuntu/ trusty/main alsa-utils amd64 1.0.27.2-1ubuntu2 [933 kB] + default: Get:122 http://archive.ubuntu.com/ubuntu/ trusty/main m4 amd64 1.4.17-2ubuntu1 [195 kB] + default: Get:123 http://archive.ubuntu.com/ubuntu/ trusty/main autoconf all 2.69-6 [322 kB] + default: Get:124 http://archive.ubuntu.com/ubuntu/ trusty/main autotools-dev all 20130810.1 [44.3 kB] + default: Get:125 http://archive.ubuntu.com/ubuntu/ trusty/main automake all 1:1.14.1-2ubuntu1 [510 kB] + default: Get:126 http://archive.ubuntu.com/ubuntu/ trusty/main espeak amd64 1.47.11-1ubuntu1 [60.0 kB] + default: Get:127 http://archive.ubuntu.com/ubuntu/ trusty/universe festival amd64 1:2.1~release-6ubuntu1 [1,016 kB] + default: Get:128 http://archive.ubuntu.com/ubuntu/ trusty/universe festlex-cmu all 1.4.0-6 [881 kB] + default: Get:129 http://archive.ubuntu.com/ubuntu/ trusty/universe festlex-poslex all 1.4.0-5 [235 kB] + default: Get:130 http://archive.ubuntu.com/ubuntu/ trusty/main fonts-dejavu-extra all 2.34-1ubuntu1 [1,736 kB] + default: Get:131 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libstdc++-4.8-dev amd64 4.8.4-2ubuntu1~14.04.4 [1,051 kB] + default: Get:132 http://archive.ubuntu.com/ubuntu/ trusty-updates/main g++-4.8 amd64 4.8.4-2ubuntu1~14.04.4 [18.0 MB] + default: Get:133 http://archive.ubuntu.com/ubuntu/ trusty/main g++ amd64 4:4.8.2-1ubuntu6 [1,490 B] + default: Get:134 http://archive.ubuntu.com/ubuntu/ trusty/main liberror-perl all 0.17-1.1 [21.1 kB] + default: Get:135 http://archive.ubuntu.com/ubuntu/ trusty-updates/main git-man all 1:1.9.1-1ubuntu0.10 [700 kB] + default: Get:136 http://archive.ubuntu.com/ubuntu/ trusty-updates/main git amd64 1:1.9.1-1ubuntu0.10 [2,737 kB] + default: Get:137 http://archive.ubuntu.com/ubuntu/ trusty-updates/main icu-devtools amd64 52.1-3ubuntu0.8 [163 kB] + default: Get:138 http://archive.ubuntu.com/ubuntu/ trusty/universe libatlas3-base amd64 3.10.1-4 [2,677 kB] + default: Get:139 http://archive.ubuntu.com/ubuntu/ trusty/main libblas3 amd64 1.2.20110419-7 [215 kB] + default: Get:140 http://archive.ubuntu.com/ubuntu/ trusty/main libblas-dev amd64 1.2.20110419-7 [240 kB] + default: Get:141 http://archive.ubuntu.com/ubuntu/ trusty/universe libatlas-dev all 3.10.1-4 [22.7 kB] + default: Get:142 http://archive.ubuntu.com/ubuntu/ trusty/universe libatlas-base-dev amd64 3.10.1-4 [3,348 kB] + default: Get:143 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost1.54-dev amd64 1.54.0-4ubuntu3.1 [5,682 kB] + default: Get:144 http://archive.ubuntu.com/ubuntu/ trusty/main libboost-dev amd64 1.54.0.1ubuntu1 [3,076 B] + default: Get:145 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost1.54-tools-dev amd64 1.54.0-4ubuntu3.1 [1,048 kB] + default: Get:146 http://archive.ubuntu.com/ubuntu/ trusty/universe libboost-tools-dev amd64 1.54.0.1ubuntu1 [2,992 B] + default: Get:147 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-atomic1.54-dev amd64 1.54.0-4ubuntu3.1 [4,914 B] + default: Get:148 http://archive.ubuntu.com/ubuntu/ trusty/universe libboost-atomic-dev amd64 1.54.0.1ubuntu1 [3,128 B] + default: Get:149 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-chrono1.54-dev amd64 1.54.0-4ubuntu3.1 [14.2 kB] + default: Get:150 http://archive.ubuntu.com/ubuntu/ trusty/main libboost-chrono-dev amd64 1.54.0.1ubuntu1 [3,450 B] + default: Get:151 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-context1.54-dev amd64 1.54.0-4ubuntu3.1 [4,760 B] + default: Get:152 http://archive.ubuntu.com/ubuntu/ trusty/universe libboost-context-dev amd64 1.54.0.1ubuntu1 [3,010 B] + default: Get:153 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-coroutine1.54-dev amd64 1.54.0-4ubuntu3.1 [6,860 B] + default: Get:154 http://archive.ubuntu.com/ubuntu/ trusty/universe libboost-coroutine-dev amd64 1.54.0.1ubuntu1 [3,084 B] + default: Get:155 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-serialization1.54-dev amd64 1.54.0-4ubuntu3.1 [151 kB] + default: Get:156 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-date-time1.54-dev amd64 1.54.0-4ubuntu3.1 [25.2 kB] + default: Get:157 http://archive.ubuntu.com/ubuntu/ trusty/main libboost-date-time-dev amd64 1.54.0.1ubuntu1 [2,826 B] + default: Get:158 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-exception1.54-dev amd64 1.54.0-4ubuntu3.1 [4,392 B] + default: Get:159 http://archive.ubuntu.com/ubuntu/ trusty/universe libboost-exception-dev amd64 1.54.0.1ubuntu1 [2,812 B] + default: Get:160 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-system1.54-dev amd64 1.54.0-4ubuntu3.1 [8,428 B] + default: Get:161 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-filesystem1.54-dev amd64 1.54.0-4ubuntu3.1 [41.7 kB] + default: Get:162 http://archive.ubuntu.com/ubuntu/ trusty/main libboost-filesystem-dev amd64 1.54.0.1ubuntu1 [2,854 B] + default: Get:163 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-test1.54-dev amd64 1.54.0-4ubuntu3.1 [213 kB] + default: Get:164 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-graph1.54-dev amd64 1.54.0-4ubuntu3.1 [98.3 kB] + default: Get:165 http://archive.ubuntu.com/ubuntu/ trusty/universe libboost-graph-dev amd64 1.54.0.1ubuntu1 [2,912 B] + default: Get:166 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-graph-parallel1.54-dev amd64 1.54.0-4ubuntu3.1 [72.5 kB] + default: Get:167 http://archive.ubuntu.com/ubuntu/ trusty/universe libboost-graph-parallel-dev amd64 1.54.0.1ubuntu1 [2,948 B] + default: Get:168 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libicu-dev amd64 52.1-3ubuntu0.8 [7,615 kB] + default: Get:169 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-regex1.54-dev amd64 1.54.0-4ubuntu3.1 [302 kB] + default: Get:170 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-iostreams1.54-dev amd64 1.54.0-4ubuntu3.1 [32.7 kB] + default: Get:171 http://archive.ubuntu.com/ubuntu/ trusty/main libboost-iostreams-dev amd64 1.54.0.1ubuntu1 [2,816 B] + default: Get:172 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-locale1.54-dev amd64 1.54.0-4ubuntu3.1 [277 kB] + default: Get:173 http://archive.ubuntu.com/ubuntu/ trusty/universe libboost-locale-dev amd64 1.54.0.1ubuntu1 [3,150 B] + default: Get:174 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-log1.54.0 amd64 1.54.0-4ubuntu3.1 [529 kB] + default: Get:175 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-thread1.54-dev amd64 1.54.0-4ubuntu3.1 [26.0 kB] + default: Get:176 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-log1.54-dev amd64 1.54.0-4ubuntu3.1 [621 kB] + default: Get:177 http://archive.ubuntu.com/ubuntu/ trusty/universe libboost-log-dev amd64 1.54.0.1ubuntu1 [3,012 B] + default: Get:178 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-math1.54-dev amd64 1.54.0-4ubuntu3.1 [621 kB] + default: Get:179 http://archive.ubuntu.com/ubuntu/ trusty/universe libboost-math-dev amd64 1.54.0.1ubuntu1 [3,018 B] + default: Get:180 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libibverbs-dev amd64 1.1.7-1ubuntu1.1 [70.0 kB] + default: Get:181 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libxml2-dev amd64 2.9.1+dfsg1-3ubuntu4.13 [630 kB] + default: Get:182 http://archive.ubuntu.com/ubuntu/ trusty-updates/main zlib1g-dev amd64 1:1.2.8.dfsg-1ubuntu1.1 [166 kB] + default: Get:183 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libpci-dev amd64 1:3.2.1-1ubuntu5.1 [43.9 kB] + default: Get:184 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libhwloc-dev amd64 1.8-1ubuntu1.14.04.1 [129 kB] + default: Get:185 http://archive.ubuntu.com/ubuntu/ trusty/universe libopenmpi-dev amd64 1.6.5-8 [379 kB] + default: Get:186 http://archive.ubuntu.com/ubuntu/ trusty/universe mpi-default-dev amd64 1.0.2ubuntu1 [3,822 B] + default: Get:187 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-mpi1.54-dev amd64 1.54.0-4ubuntu3.1 [118 kB] + default: Get:188 http://archive.ubuntu.com/ubuntu/ trusty/universe libboost-mpi-dev amd64 1.54.0.1ubuntu1 [2,904 B] + default: Get:189 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-mpi-python1.54-dev amd64 1.54.0-4ubuntu3.1 [23.5 kB] + default: Get:190 http://archive.ubuntu.com/ubuntu/ trusty/universe libboost-mpi-python-dev amd64 1.54.0.1ubuntu1 [2,940 B] + default: Get:191 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-program-options1.54-dev amd64 1.54.0-4ubuntu3.1 [133 kB] + default: Get:192 http://archive.ubuntu.com/ubuntu/ trusty/main libboost-program-options-dev amd64 1.54.0.1ubuntu1 [2,840 B] + default: Get:193 http://archive.ubuntu.com/ubuntu/ trusty/main libpython-dev amd64 2.7.5-5ubuntu3 [7,078 B] + default: Get:194 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libboost-python1.54-dev amd64 1.54.0-4ubuntu3.1 [119 kB] + default: Get:195 http://archive.ubuntu.com/ubuntu/ trusty/main libboost-python-dev amd64 1.54.0.1ubuntu1 [3,204 B] + default: Get:196 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-random1.54-dev amd64 1.54.0-4ubuntu3.1 [6,366 B] + default: Get:197 http://archive.ubuntu.com/ubuntu/ trusty/universe libboost-random-dev amd64 1.54.0.1ubuntu1 [2,812 B] + default: Get:198 http://archive.ubuntu.com/ubuntu/ trusty/main libboost-regex-dev amd64 1.54.0.1ubuntu1 [3,084 B] + default: Get:199 http://archive.ubuntu.com/ubuntu/ trusty/main libboost-serialization-dev amd64 1.54.0.1ubuntu1 [3,050 B] + default: Get:200 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-signals1.54-dev amd64 1.54.0-4ubuntu3.1 [31.0 kB] + default: Get:201 http://archive.ubuntu.com/ubuntu/ trusty/universe libboost-signals-dev amd64 1.54.0.1ubuntu1 [2,884 B] + default: Get:202 http://archive.ubuntu.com/ubuntu/ trusty/main libboost-system-dev amd64 1.54.0.1ubuntu1 [2,956 B] + default: Get:203 http://archive.ubuntu.com/ubuntu/ trusty/main libboost-test-dev amd64 1.54.0.1ubuntu1 [2,858 B] + default: Get:204 http://archive.ubuntu.com/ubuntu/ trusty/main libboost-thread-dev amd64 1.54.0.1ubuntu1 [2,848 B] + default: Get:205 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-timer1.54-dev amd64 1.54.0-4ubuntu3.1 [9,554 B] + default: Get:206 http://archive.ubuntu.com/ubuntu/ trusty/universe libboost-timer-dev amd64 1.54.0.1ubuntu1 [2,940 B] + default: Get:207 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libboost-wave1.54-dev amd64 1.54.0-4ubuntu3.1 [210 kB] + default: Get:208 http://archive.ubuntu.com/ubuntu/ trusty/universe libboost-wave-dev amd64 1.54.0.1ubuntu1 [2,852 B] + default: Get:209 http://archive.ubuntu.com/ubuntu/ trusty/universe libboost-all-dev amd64 1.54.0.1ubuntu1 [2,096 B] + default: Get:210 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libgtk2.0-bin amd64 2.24.23-0ubuntu1.4 [9,774 B] + default: Get:211 http://archive.ubuntu.com/ubuntu/ trusty/main xorg-sgml-doctools all 1:1.11-1 [12.9 kB] + default: Get:212 http://archive.ubuntu.com/ubuntu/ trusty-updates/main x11proto-core-dev all 7.0.26-1~ubuntu2 [700 kB] + default: Get:213 http://archive.ubuntu.com/ubuntu/ trusty/main libice-dev amd64 2:1.0.8-2 [57.6 kB] + default: Get:214 http://archive.ubuntu.com/ubuntu/ trusty/main libid3tag0 amd64 0.15.1b-10ubuntu1 [28.9 kB] + default: Get:215 http://archive.ubuntu.com/ubuntu/ trusty/main liblapack3 amd64 3.5.0-2ubuntu1 [1,730 kB] + default: Get:216 http://archive.ubuntu.com/ubuntu/ trusty/main liblapack-dev amd64 3.5.0-2ubuntu1 [1,740 kB] + default: Get:217 http://archive.ubuntu.com/ubuntu/ trusty/main libltdl-dev amd64 2.4.2-1.7ubuntu1 [157 kB] + default: Get:218 http://archive.ubuntu.com/ubuntu/ trusty/main libtinfo-dev amd64 5.9+20140118-1ubuntu1 [76.3 kB] + default: Get:219 http://archive.ubuntu.com/ubuntu/ trusty/main libncurses5-dev amd64 5.9+20140118-1ubuntu1 [170 kB] + default: Get:220 http://archive.ubuntu.com/ubuntu/ trusty/main libpthread-stubs0-dev amd64 0.3-4 [4,068 B] + default: Get:221 http://archive.ubuntu.com/ubuntu/ trusty/main libsm-dev amd64 2:1.2.1-2 [19.9 kB] + default: Get:222 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libsox-fmt-alsa amd64 14.4.1-3ubuntu1.1 [7,116 B] + default: Get:223 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libsox-fmt-ao amd64 14.4.1-3ubuntu1.1 [4,108 B] + default: Get:224 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libsox-fmt-base amd64 14.4.1-3ubuntu1.1 [28.5 kB] + default: Get:225 http://archive.ubuntu.com/ubuntu/ trusty/universe libtwolame0 amd64 0.3.13-1ubuntu1 [48.8 kB] + default: Get:226 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libsox-fmt-mp3 amd64 14.4.1-3ubuntu1.1 [12.2 kB] + default: Get:227 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libsox-fmt-oss amd64 14.4.1-3ubuntu1.1 [4,572 B] + default: Get:228 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libsox-fmt-pulse amd64 14.4.1-3ubuntu1.1 [4,040 B] + default: Get:229 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libsox-fmt-all amd64 14.4.1-3ubuntu1.1 [1,712 B] + default: Get:230 http://archive.ubuntu.com/ubuntu/ trusty/main libtool amd64 2.4.2-1.7ubuntu1 [188 kB] + default: Get:231 http://archive.ubuntu.com/ubuntu/ trusty/main libxau-dev amd64 1:1.0.8-1 [11.1 kB] + default: Get:232 http://archive.ubuntu.com/ubuntu/ trusty/main libxdmcp-dev amd64 1:1.1.1-1 [26.9 kB] + default: Get:233 http://archive.ubuntu.com/ubuntu/ trusty/main x11proto-input-dev all 2.3-1 [139 kB] + default: Get:234 http://archive.ubuntu.com/ubuntu/ trusty/main x11proto-kb-dev all 1.0.6-2 [269 kB] + default: Get:235 http://archive.ubuntu.com/ubuntu/ trusty-updates/main xtrans-dev all 1.3.5-1~ubuntu14.04.2 [70.7 kB] + default: Get:236 http://archive.ubuntu.com/ubuntu/ trusty/main libxcb1-dev amd64 1.10-2ubuntu1 [76.6 kB] + default: Get:237 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libx11-dev amd64 2:1.6.2-1ubuntu2.1 [632 kB] + default: Get:238 http://archive.ubuntu.com/ubuntu/ trusty-updates/main libx11-doc all 2:1.6.2-1ubuntu2.1 [1,447 kB] + default: Get:239 http://archive.ubuntu.com/ubuntu/ trusty/main libxt-dev amd64 1:1.1.4-1 [455 kB] + default: Get:240 http://archive.ubuntu.com/ubuntu/ trusty-updates/main python-setuptools all 3.3-1ubuntu2 [230 kB] + default: Get:241 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe sox amd64 14.4.1-3ubuntu1.1 [97.1 kB] + default: Get:242 http://archive.ubuntu.com/ubuntu/ trusty/main sshfs amd64 2.5-1ubuntu1 [41.7 kB] + default: Get:243 http://archive.ubuntu.com/ubuntu/ trusty-updates/main subversion amd64 1.8.8-1ubuntu3.3 [281 kB] + default: Get:244 http://archive.ubuntu.com/ubuntu/ trusty/main ttf-dejavu-extra all 2.34-1ubuntu1 [3,104 B] + default: Get:245 http://archive.ubuntu.com/ubuntu/ trusty-updates/main unzip amd64 6.0-9ubuntu1.5 [157 kB] + default: Get:246 http://archive.ubuntu.com/ubuntu/ trusty/universe festvox-kallpc16k all 1.4.0-5 [4,094 kB] + default: Get:247 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe icedtea-6-jre-cacao amd64 6b41-1.13.13-0ubuntu0.14.04.1 [334 kB] + default: Get:248 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe icedtea-6-jre-jamvm amd64 6b41-1.13.13-0ubuntu0.14.04.1 [400 kB] + default: Get:249 http://archive.ubuntu.com/ubuntu/ trusty-updates/main icedtea-netx-common all 1.5.3-0ubuntu0.14.04.1 [1,121 kB] + default: Get:250 http://archive.ubuntu.com/ubuntu/ trusty-updates/main icedtea-netx amd64 1.5.3-0ubuntu0.14.04.1 [20.4 kB] + default: Get:251 http://archive.ubuntu.com/ubuntu/ trusty-updates/universe libhwloc-plugins amd64 1.8-1ubuntu1.14.04.1 [12.0 kB] + default: dpkg-preconfigure: unable to re-open stdin: No such file or directory + default: Fetched 162 MB in 1min 6s (2,446 kB/s) + default: Selecting previously unselected package libc6-i386. + default: (Reading database ... 62899 files and directories currently installed.) + default: Preparing to unpack .../libc6-i386_2.19-0ubuntu6.15_amd64.deb ... + default: Unpacking libc6-i386 (2.19-0ubuntu6.15) ... + default: Selecting previously unselected package libc6-dev-i386. + default: Preparing to unpack .../libc6-dev-i386_2.19-0ubuntu6.15_amd64.deb ... + default: Unpacking libc6-dev-i386 (2.19-0ubuntu6.15) ... + default: Selecting previously unselected package libc6-x32. + default: Preparing to unpack .../libc6-x32_2.19-0ubuntu6.15_amd64.deb ... + default: Unpacking libc6-x32 (2.19-0ubuntu6.15) ... + default: Selecting previously unselected package libc6-dev-x32. + default: Preparing to unpack .../libc6-dev-x32_2.19-0ubuntu6.15_amd64.deb ... + default: Unpacking libc6-dev-x32 (2.19-0ubuntu6.15) ... + default: Selecting previously unselected package lib32gcc1. + default: Preparing to unpack .../lib32gcc1_1%3a4.9.3-0ubuntu4_amd64.deb ... + default: Unpacking lib32gcc1 (1:4.9.3-0ubuntu4) ... + default: Selecting previously unselected package libx32gcc1. + default: Preparing to unpack .../libx32gcc1_1%3a4.9.3-0ubuntu4_amd64.deb ... + default: Unpacking libx32gcc1 (1:4.9.3-0ubuntu4) ... + default: Selecting previously unselected package lib32gomp1. + default: Preparing to unpack .../lib32gomp1_4.8.4-2ubuntu1~14.04.4_amd64.deb ... + default: Unpacking lib32gomp1 (4.8.4-2ubuntu1~14.04.4) ... + default: Selecting previously unselected package libx32gomp1. + default: Preparing to unpack .../libx32gomp1_4.8.4-2ubuntu1~14.04.4_amd64.deb ... + default: Unpacking libx32gomp1 (4.8.4-2ubuntu1~14.04.4) ... + default: Selecting previously unselected package lib32itm1. + default: Preparing to unpack .../lib32itm1_4.8.4-2ubuntu1~14.04.4_amd64.deb ... + default: Unpacking lib32itm1 (4.8.4-2ubuntu1~14.04.4) ... + default: Selecting previously unselected package libx32itm1. + default: Preparing to unpack .../libx32itm1_4.8.4-2ubuntu1~14.04.4_amd64.deb ... + default: Unpacking libx32itm1 (4.8.4-2ubuntu1~14.04.4) ... + default: Selecting previously unselected package lib32atomic1. + default: Preparing to unpack .../lib32atomic1_4.8.4-2ubuntu1~14.04.4_amd64.deb ... + default: Unpacking lib32atomic1 (4.8.4-2ubuntu1~14.04.4) ... + default: Selecting previously unselected package libx32atomic1. + default: Preparing to unpack .../libx32atomic1_4.8.4-2ubuntu1~14.04.4_amd64.deb ... + default: Unpacking libx32atomic1 (4.8.4-2ubuntu1~14.04.4) ... + default: Selecting previously unselected package lib32asan0. + default: Preparing to unpack .../lib32asan0_4.8.4-2ubuntu1~14.04.4_amd64.deb ... + default: Unpacking lib32asan0 (4.8.4-2ubuntu1~14.04.4) ... + default: Selecting previously unselected package libx32asan0. + default: Preparing to unpack .../libx32asan0_4.8.4-2ubuntu1~14.04.4_amd64.deb ... + default: Unpacking libx32asan0 (4.8.4-2ubuntu1~14.04.4) ... + default: Selecting previously unselected package lib32quadmath0. + default: Preparing to unpack .../lib32quadmath0_4.8.4-2ubuntu1~14.04.4_amd64.deb ... + default: Unpacking lib32quadmath0 (4.8.4-2ubuntu1~14.04.4) ... + default: Selecting previously unselected package libx32quadmath0. + default: Preparing to unpack .../libx32quadmath0_4.8.4-2ubuntu1~14.04.4_amd64.deb ... + default: Unpacking libx32quadmath0 (4.8.4-2ubuntu1~14.04.4) ... + default: Selecting previously unselected package lib32gcc-4.8-dev. + default: Preparing to unpack .../lib32gcc-4.8-dev_4.8.4-2ubuntu1~14.04.4_amd64.deb ... + default: Unpacking lib32gcc-4.8-dev (4.8.4-2ubuntu1~14.04.4) ... + default: Selecting previously unselected package libx32gcc-4.8-dev. + default: Preparing to unpack .../libx32gcc-4.8-dev_4.8.4-2ubuntu1~14.04.4_amd64.deb ... + default: Unpacking libx32gcc-4.8-dev (4.8.4-2ubuntu1~14.04.4) ... + default: Selecting previously unselected package gcc-4.8-multilib. + default: Preparing to unpack .../gcc-4.8-multilib_4.8.4-2ubuntu1~14.04.4_amd64.deb ... + default: Unpacking gcc-4.8-multilib (4.8.4-2ubuntu1~14.04.4) ... + default: Selecting previously unselected package gcc-multilib. + default: Preparing to unpack .../gcc-multilib_4%3a4.8.2-1ubuntu6_amd64.deb ... + default: Unpacking gcc-multilib (4:4.8.2-1ubuntu6) ... + default: Selecting previously unselected package libao-common. + default: Preparing to unpack .../libao-common_1.1.0-2ubuntu2_all.deb ... + default: Unpacking libao-common (1.1.0-2ubuntu2) ... + default: Selecting previously unselected package libao4:amd64. + default: Preparing to unpack .../libao4_1.1.0-2ubuntu2_amd64.deb ... + default: Unpacking libao4:amd64 (1.1.0-2ubuntu2) ... + default: Selecting previously unselected package libapr1:amd64. + default: Preparing to unpack .../libapr1_1.5.0-1_amd64.deb ... + default: Unpacking libapr1:amd64 (1.5.0-1) ... + default: Selecting previously unselected package libaprutil1:amd64. + default: Preparing to unpack .../libaprutil1_1.5.3-1_amd64.deb ... + default: Unpacking libaprutil1:amd64 (1.5.3-1) ... + default: Selecting previously unselected package libasyncns0:amd64. + default: Preparing to unpack .../libasyncns0_0.8-4ubuntu2_amd64.deb ... + default: Unpacking libasyncns0:amd64 (0.8-4ubuntu2) ... + default: Selecting previously unselected package libgtk2.0-common. + default: Preparing to unpack .../libgtk2.0-common_2.24.23-0ubuntu1.4_all.deb ... + default: Unpacking libgtk2.0-common (2.24.23-0ubuntu1.4) ... + default: Selecting previously unselected package libgtk2.0-0:amd64. + default: Preparing to unpack .../libgtk2.0-0_2.24.23-0ubuntu1.4_amd64.deb ... + default: Unpacking libgtk2.0-0:amd64 (2.24.23-0ubuntu1.4) ... + default: Selecting previously unselected package openjdk-6-jre-lib. + default: Preparing to unpack .../openjdk-6-jre-lib_6b41-1.13.13-0ubuntu0.14.04.1_all.deb ... + default: Unpacking openjdk-6-jre-lib (6b41-1.13.13-0ubuntu0.14.04.1) ... + default: Selecting previously unselected package libnspr4:amd64. + default: Preparing to unpack .../libnspr4_2%3a4.13.1-0ubuntu0.14.04.1_amd64.deb ... + default: Unpacking libnspr4:amd64 (2:4.13.1-0ubuntu0.14.04.1) ... + default: Selecting previously unselected package libnss3-nssdb. + default: Preparing to unpack .../libnss3-nssdb_2%3a3.28.4-0ubuntu0.14.04.5_all.deb ... + default: Unpacking libnss3-nssdb (2:3.28.4-0ubuntu0.14.04.5) ... + default: Selecting previously unselected package libnss3:amd64. + default: Preparing to unpack .../libnss3_2%3a3.28.4-0ubuntu0.14.04.5_amd64.deb ... + default: Unpacking libnss3:amd64 (2:3.28.4-0ubuntu0.14.04.5) ... + default: Selecting previously unselected package ca-certificates-java. + default: Preparing to unpack .../ca-certificates-java_20130815ubuntu1_all.deb ... + default: Unpacking ca-certificates-java (20130815ubuntu1) ... + default: Selecting previously unselected package tzdata-java. + default: Preparing to unpack .../tzdata-java_2019a-0ubuntu0.14.04_all.deb ... + default: Unpacking tzdata-java (2019a-0ubuntu0.14.04) ... + default: Selecting previously unselected package java-common. + default: Preparing to unpack .../java-common_0.51_all.deb ... + default: Unpacking java-common (0.51) ... + default: Selecting previously unselected package libpcsclite1:amd64. + default: Preparing to unpack .../libpcsclite1_1.8.10-1ubuntu1.1_amd64.deb ... + default: Unpacking libpcsclite1:amd64 (1.8.10-1ubuntu1.1) ... + default: Selecting previously unselected package openjdk-6-jre-headless:amd64. + default: Preparing to unpack .../openjdk-6-jre-headless_6b41-1.13.13-0ubuntu0.14.04.1_amd64.deb ... + default: Unpacking openjdk-6-jre-headless:amd64 (6b41-1.13.13-0ubuntu0.14.04.1) ... + default: Selecting previously unselected package libgif4:amd64. + default: Preparing to unpack .../libgif4_4.1.6-11_amd64.deb ... + default: Unpacking libgif4:amd64 (4.1.6-11) ... + default: Selecting previously unselected package libflac8:amd64. + default: Preparing to unpack .../libflac8_1.3.0-2ubuntu0.14.04.1_amd64.deb ... + default: Unpacking libflac8:amd64 (1.3.0-2ubuntu0.14.04.1) ... + default: Selecting previously unselected package libvorbisenc2:amd64. + default: Preparing to unpack .../libvorbisenc2_1.3.2-1.3ubuntu1.2_amd64.deb ... + default: Unpacking libvorbisenc2:amd64 (1.3.2-1.3ubuntu1.2) ... + default: Selecting previously unselected package libsndfile1:amd64. + default: Preparing to unpack .../libsndfile1_1.0.25-7ubuntu2.2_amd64.deb ... + default: Unpacking libsndfile1:amd64 (1.0.25-7ubuntu2.2) ... + default: Selecting previously unselected package libpulse0:amd64. + default: Preparing to unpack .../libpulse0_1%3a4.0-0ubuntu11.1_amd64.deb ... + default: Unpacking libpulse0:amd64 (1:4.0-0ubuntu11.1) ... + default: Selecting previously unselected package openjdk-6-jre:amd64. + default: Preparing to unpack .../openjdk-6-jre_6b41-1.13.13-0ubuntu0.14.04.1_amd64.deb ... + default: Unpacking openjdk-6-jre:amd64 (6b41-1.13.13-0ubuntu0.14.04.1) ... + default: Selecting previously unselected package libatk-wrapper-java. + default: Preparing to unpack .../libatk-wrapper-java_0.30.4-4_all.deb ... + default: 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default: Setting up libboost-serialization1.54-dev:amd64 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-date-time1.54-dev:amd64 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-date-time-dev:amd64 (1.54.0.1ubuntu1) ... + default: Setting up libboost-exception1.54-dev:amd64 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-exception-dev:amd64 (1.54.0.1ubuntu1) ... + default: Setting up libboost-system1.54-dev:amd64 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-filesystem1.54-dev:amd64 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-filesystem-dev:amd64 (1.54.0.1ubuntu1) ... + default: Setting up libboost-test1.54-dev:amd64 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-graph1.54-dev:amd64 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-graph-dev:amd64 (1.54.0.1ubuntu1) ... + default: Setting up libboost-graph-parallel1.54-dev (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-graph-parallel-dev (1.54.0.1ubuntu1) ... + default: Setting up libicu-dev:amd64 (52.1-3ubuntu0.8) ... + default: Setting up libboost-regex1.54-dev:amd64 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-iostreams1.54-dev:amd64 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-iostreams-dev:amd64 (1.54.0.1ubuntu1) ... + default: Setting up libboost-locale1.54-dev:amd64 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-locale-dev:amd64 (1.54.0.1ubuntu1) ... + default: Setting up libboost-log1.54.0 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-thread1.54-dev:amd64 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-log1.54-dev (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-log-dev (1.54.0.1ubuntu1) ... + default: Setting up libboost-math1.54-dev:amd64 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-math-dev:amd64 (1.54.0.1ubuntu1) ... + default: Setting up libibverbs-dev (1.1.7-1ubuntu1.1) ... + default: Setting up libxml2-dev:amd64 (2.9.1+dfsg1-3ubuntu4.13) ... + default: Setting up zlib1g-dev:amd64 (1:1.2.8.dfsg-1ubuntu1.1) ... + default: Setting up libpci-dev (1:3.2.1-1ubuntu5.1) ... + default: Setting up libhwloc-dev:amd64 (1.8-1ubuntu1.14.04.1) ... + default: Setting up libopenmpi-dev (1.6.5-8) ... + default: update-alternatives: using /usr/lib/openmpi/include to provide /usr/include/mpi (mpi) in auto mode + default: Setting up mpi-default-dev (1.0.2ubuntu1) ... + default: Setting up libboost-mpi1.54-dev (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-mpi-dev (1.54.0.1ubuntu1) ... + default: Setting up libboost-mpi-python1.54-dev (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-mpi-python-dev (1.54.0.1ubuntu1) ... + default: Setting up libboost-program-options1.54-dev:amd64 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-program-options-dev:amd64 (1.54.0.1ubuntu1) ... + default: Setting up libpython-dev:amd64 (2.7.5-5ubuntu3) ... + default: Setting up libboost-python1.54-dev:amd64 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-python-dev (1.54.0.1ubuntu1) ... + default: Setting up libboost-random1.54-dev:amd64 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-random-dev:amd64 (1.54.0.1ubuntu1) ... + default: Setting up libboost-regex-dev:amd64 (1.54.0.1ubuntu1) ... + default: Setting up libboost-serialization-dev:amd64 (1.54.0.1ubuntu1) ... + default: Setting up libboost-signals1.54-dev:amd64 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-signals-dev:amd64 (1.54.0.1ubuntu1) ... + default: Setting up libboost-system-dev:amd64 (1.54.0.1ubuntu1) ... + default: Setting up libboost-test-dev:amd64 (1.54.0.1ubuntu1) ... + default: Setting up libboost-thread-dev:amd64 (1.54.0.1ubuntu1) ... + default: Setting up libboost-timer1.54-dev:amd64 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-timer-dev:amd64 (1.54.0.1ubuntu1) ... + default: Setting up libboost-wave1.54-dev:amd64 (1.54.0-4ubuntu3.1) ... + default: Setting up libboost-wave-dev:amd64 (1.54.0.1ubuntu1) ... + default: Setting up libboost-all-dev (1.54.0.1ubuntu1) ... + default: Setting up libgtk2.0-bin (2.24.23-0ubuntu1.4) ... + default: Setting up xorg-sgml-doctools (1:1.11-1) ... + default: Setting up x11proto-core-dev (7.0.26-1~ubuntu2) ... + default: Setting up libice-dev:amd64 (2:1.0.8-2) ... + default: Setting up libid3tag0 (0.15.1b-10ubuntu1) ... + default: Setting up liblapack3 (3.5.0-2ubuntu1) ... + default: update-alternatives: using /usr/lib/lapack/liblapack.so.3 to provide /usr/lib/liblapack.so.3 (liblapack.so.3) in auto mode + default: Setting up liblapack-dev (3.5.0-2ubuntu1) ... + default: update-alternatives: using /usr/lib/lapack/liblapack.so to provide /usr/lib/liblapack.so (liblapack.so) in auto mode + default: Setting up libltdl-dev:amd64 (2.4.2-1.7ubuntu1) ... + default: Setting up libtinfo-dev:amd64 (5.9+20140118-1ubuntu1) ... + default: Setting up libncurses5-dev:amd64 (5.9+20140118-1ubuntu1) ... + default: Setting up libpthread-stubs0-dev:amd64 (0.3-4) ... + default: Setting up libsm-dev:amd64 (2:1.2.1-2) ... + default: Setting up libsox-fmt-alsa:amd64 (14.4.1-3ubuntu1.1) ... + default: Setting up libsox-fmt-ao:amd64 (14.4.1-3ubuntu1.1) ... + default: Setting up libsox-fmt-base:amd64 (14.4.1-3ubuntu1.1) ... + default: Setting up libtwolame0 (0.3.13-1ubuntu1) ... + default: Setting up libsox-fmt-mp3:amd64 (14.4.1-3ubuntu1.1) ... + default: Setting up libsox-fmt-oss:amd64 (14.4.1-3ubuntu1.1) ... + default: Setting up libsox-fmt-pulse:amd64 (14.4.1-3ubuntu1.1) ... + default: Setting up libsox-fmt-all:amd64 (14.4.1-3ubuntu1.1) ... + default: Setting up libtool (2.4.2-1.7ubuntu1) ... + default: Setting up libxau-dev:amd64 (1:1.0.8-1) ... + default: Setting up libxdmcp-dev:amd64 (1:1.1.1-1) ... + default: Setting up x11proto-input-dev (2.3-1) ... + default: Setting up x11proto-kb-dev (1.0.6-2) ... + default: Setting up xtrans-dev (1.3.5-1~ubuntu14.04.2) ... + default: Setting up libxcb1-dev:amd64 (1.10-2ubuntu1) ... + default: Setting up libx11-dev:amd64 (2:1.6.2-1ubuntu2.1) ... + default: Setting up libx11-doc (2:1.6.2-1ubuntu2.1) ... + default: Setting up libxt-dev:amd64 (1:1.1.4-1) ... + default: Setting up python-setuptools (3.3-1ubuntu2) ... + default: Setting up sox (14.4.1-3ubuntu1.1) ... + default: Setting up sshfs (2.5-1ubuntu1) ... + default: Setting up subversion (1.8.8-1ubuntu3.3) ... + default: Setting up ttf-dejavu-extra (2.34-1ubuntu1) ... + default: Setting up unzip (6.0-9ubuntu1.5) ... + default: Setting up icedtea-netx-common (1.5.3-0ubuntu0.14.04.1) ... + default: Setting up libhwloc-plugins (1.8-1ubuntu1.14.04.1) ... + default: Processing triggers for ureadahead (0.100.0-16) ... + default: Setting up festival (1:2.1~release-6ubuntu1) ... + default: Processing triggers for sgml-base (1.26+nmu4ubuntu1) ... + default: Setting up festlex-cmu (1.4.0-6) ... + default: Setting up festlex-poslex (1.4.0-5) ... + default: Setting up festvox-kallpc16k (1.4.0-5) ... + default: Setting up libnss3-nssdb (2:3.28.4-0ubuntu0.14.04.5) ... + default: Setting up openjdk-6-jre-lib (6b41-1.13.13-0ubuntu0.14.04.1) ... + default: Setting up libatk-wrapper-java (0.30.4-4) ... + default: Setting up libnss3:amd64 (2:3.28.4-0ubuntu0.14.04.5) ... + default: Setting up openjdk-6-jre-headless:amd64 (6b41-1.13.13-0ubuntu0.14.04.1) ... + default: update-alternatives: + default: using /usr/lib/jvm/java-6-openjdk-amd64/jre/bin/java to provide /usr/bin/java (java) in auto mode + default: update-alternatives: + default: using /usr/lib/jvm/java-6-openjdk-amd64/jre/bin/keytool to provide /usr/bin/keytool (keytool) in auto mode + default: update-alternatives: + default: using /usr/lib/jvm/java-6-openjdk-amd64/jre/bin/pack200 to provide /usr/bin/pack200 (pack200) in auto mode + default: update-alternatives: + default: using /usr/lib/jvm/java-6-openjdk-amd64/jre/bin/rmid to provide /usr/bin/rmid (rmid) in auto mode + default: update-alternatives: + default: using /usr/lib/jvm/java-6-openjdk-amd64/jre/bin/rmiregistry to provide /usr/bin/rmiregistry (rmiregistry) in auto mode + default: update-alternatives: + default: using /usr/lib/jvm/java-6-openjdk-amd64/jre/bin/unpack200 to provide /usr/bin/unpack200 (unpack200) in auto mode + default: update-alternatives: + default: using /usr/lib/jvm/java-6-openjdk-amd64/jre/bin/orbd to provide /usr/bin/orbd (orbd) in auto mode + default: update-alternatives: + default: using /usr/lib/jvm/java-6-openjdk-amd64/jre/bin/servertool to provide /usr/bin/servertool (servertool) in auto mode + default: update-alternatives: + default: using /usr/lib/jvm/java-6-openjdk-amd64/jre/bin/tnameserv to provide /usr/bin/tnameserv (tnameserv) in auto mode + default: update-alternatives: + default: using /usr/lib/jvm/java-6-openjdk-amd64/jre/lib/jexec to provide /usr/bin/jexec (jexec) in auto mode + default: Setting up ca-certificates-java (20130815ubuntu1) ... + default: Adding debian:Hellenic_Academic_and_Research_Institutions_RootCA_2011.pem + default: Adding debian:GeoTrust_Primary_Certification_Authority_-_G2.pem + default: Adding debian:Certum_Root_CA.pem + default: Adding debian:NetLock_Arany_=Class_Gold=_Főtanúsítvány.pem + default: Adding debian:DST_ACES_CA_X6.pem + default: Adding debian:CNNIC_ROOT.pem + default: Adding debian:GlobalSign_Root_CA_-_R2.pem + default: Adding debian:PSCProcert.pem + default: Adding debian:QuoVadis_Root_CA_2_G3.pem + default: Adding debian:VeriSign_Class_3_Public_Primary_Certification_Authority_-_G4.pem + default: Adding debian:Buypass_Class_2_Root_CA.pem + default: Adding debian:Amazon_Root_CA_2.pem + default: Adding debian:T-TeleSec_GlobalRoot_Class_3.pem + default: Adding debian:Cybertrust_Global_Root.pem + default: Adding debian:XRamp_Global_CA_Root.pem + default: Adding debian:Certinomis_-_Autorité_Racine.pem + default: Adding debian:OISTE_WISeKey_Global_Root_GA_CA.pem + default: Adding debian:AffirmTrust_Commercial.pem + default: Adding debian:DigiCert_Assured_ID_Root_G2.pem + default: Adding debian:Izenpe.com.pem + default: Adding debian:Starfield_Root_Certificate_Authority_-_G2.pem + default: Adding debian:T-TeleSec_GlobalRoot_Class_2.pem + default: Adding debian:Global_Chambersign_Root_-_2008.pem + default: Adding debian:GeoTrust_Global_CA_2.pem + default: Adding debian:Comodo_Secure_Services_root.pem + default: Adding debian:Certum_Trusted_Network_CA.pem + default: Adding debian:DST_Root_CA_X3.pem + default: Adding debian:TWCA_Root_Certification_Authority.pem + default: Adding debian:IdenTrust_Public_Sector_Root_CA_1.pem + default: Adding debian:Network_Solutions_Certificate_Authority.pem + default: Adding debian:SecureSign_RootCA11.pem + default: Adding debian:OpenTrust_Root_CA_G3.pem + default: Adding debian:GeoTrust_Universal_CA_2.pem + default: Adding debian:D-TRUST_Root_Class_3_CA_2_2009.pem + default: Adding debian:Visa_eCommerce_Root.pem + default: Adding debian:Security_Communication_Root_CA.pem + default: Adding debian:Entrust.net_Premium_2048_Secure_Server_CA.pem + default: Adding debian:GeoTrust_Global_CA.pem + default: Adding debian:Starfield_Class_2_CA.pem + default: Adding debian:TÜRKTRUST_Elektronik_Sertifika_Hizmet_Sağlayıcısı_H5.pem + default: Adding debian:Hellenic_Academic_and_Research_Institutions_RootCA_2015.pem + default: Adding debian:Microsec_e-Szigno_Root_CA_2009.pem + default: Adding debian:TeliaSonera_Root_CA_v1.pem + default: Adding debian:D-TRUST_Root_Class_3_CA_2_EV_2009.pem + default: Adding debian:QuoVadis_Root_CA_3.pem + default: Adding debian:Atos_TrustedRoot_2011.pem + default: Adding debian:Staat_der_Nederlanden_Root_CA_-_G3.pem + default: Adding debian:VeriSign_Class_3_Public_Primary_Certification_Authority_-_G5.pem + default: Adding debian:Amazon_Root_CA_1.pem + default: Adding debian:TUBITAK_Kamu_SM_SSL_Kok_Sertifikasi_-_Surum_1.pem + default: Adding debian:Certum_Trusted_Network_CA_2.pem + default: Adding debian:GeoTrust_Primary_Certification_Authority.pem + default: Adding debian:VeriSign_Universal_Root_Certification_Authority.pem + default: Adding debian:Certplus_Root_CA_G1.pem + default: Adding debian:Camerfirma_Chambers_of_Commerce_Root.pem + default: Adding debian:Entrust_Root_Certification_Authority_-_EC1.pem + default: Adding debian:ePKI_Root_Certification_Authority.pem + default: Adding debian:COMODO_Certification_Authority.pem + default: Adding debian:DigiCert_Trusted_Root_G4.pem + default: Adding debian:CA_Disig_Root_R2.pem + default: Adding debian:TWCA_Global_Root_CA.pem + default: Adding debian:E-Tugra_Certification_Authority.pem + default: Adding debian:IdenTrust_Commercial_Root_CA_1.pem + default: Adding debian:Go_Daddy_Class_2_CA.pem + default: Adding debian:EE_Certification_Centre_Root_CA.pem + default: Adding debian:SwissSign_Silver_CA_-_G2.pem + default: Adding debian:GeoTrust_Universal_CA.pem + default: Adding debian:UTN_USERFirst_Hardware_Root_CA.pem + default: Adding debian:GlobalSign_Root_CA_-_R3.pem + default: Adding debian:AffirmTrust_Premium_ECC.pem + default: Adding debian:DigiCert_Global_Root_G3.pem + default: Adding debian:SwissSign_Gold_CA_-_G2.pem + default: Adding debian:Certigna.pem + default: Adding debian:Certinomis_-_Root_CA.pem + default: Adding debian:DigiCert_High_Assurance_EV_Root_CA.pem + default: Adding debian:DigiCert_Assured_ID_Root_G3.pem + default: Adding debian:Comodo_AAA_Services_root.pem + default: Adding debian:Certplus_Root_CA_G2.pem + default: Adding debian:TÜBİTAK_UEKAE_Kök_Sertifika_Hizmet_Sağlayıcısı_-_Sürüm_3.pem + default: Adding debian:Secure_Global_CA.pem + default: Adding debian:Actalis_Authentication_Root_CA.pem + default: Adding debian:AddTrust_External_Root.pem + default: Adding debian:QuoVadis_Root_CA_1_G3.pem + default: Adding debian:DigiCert_Assured_ID_Root_CA.pem + default: Adding debian:Camerfirma_Global_Chambersign_Root.pem + default: Adding debian:Security_Communication_EV_RootCA1.pem + default: Adding debian:Entrust_Root_Certification_Authority.pem + default: Adding debian:DigiCert_Global_Root_G2.pem + default: Adding debian:Staat_der_Nederlanden_Root_CA_-_G2.pem + default: Adding debian:ACEDICOM_Root.pem + default: Adding debian:Go_Daddy_Root_Certificate_Authority_-_G2.pem + default: Adding debian:QuoVadis_Root_CA_3_G3.pem + default: Adding debian:CA_Disig_Root_R1.pem + default: Adding debian:Certplus_Class_2_Primary_CA.pem + default: Adding debian:GlobalSign_ECC_Root_CA_-_R4.pem + default: Adding debian:EC-ACC.pem + default: Adding debian:LuxTrust_Global_Root_2.pem + default: Adding debian:SZAFIR_ROOT_CA2.pem + default: Adding debian:Starfield_Services_Root_Certificate_Authority_-_G2.pem + default: Adding debian:CFCA_EV_ROOT.pem + default: Adding debian:certSIGN_ROOT_CA.pem + default: Adding debian:Staat_der_Nederlanden_EV_Root_CA.pem + default: Adding debian:Sonera_Class_2_Root_CA.pem + default: Adding debian:Verisign_Class_3_Public_Primary_Certification_Authority_-_G3.pem + default: Adding debian:Buypass_Class_3_Root_CA.pem + default: Adding debian:Baltimore_CyberTrust_Root.pem + default: Adding debian:Amazon_Root_CA_4.pem + default: Adding debian:SecureTrust_CA.pem + default: Adding debian:OISTE_WISeKey_Global_Root_GB_CA.pem + default: Adding debian:USERTrust_ECC_Certification_Authority.pem + default: Adding debian:Swisscom_Root_CA_1.pem + default: Adding debian:QuoVadis_Root_CA.pem + default: Adding debian:thawte_Primary_Root_CA_-_G2.pem + default: Adding debian:Swisscom_Root_CA_2.pem + default: Adding debian:Comodo_Trusted_Services_root.pem + default: Adding debian:AddTrust_Qualified_Certificates_Root.pem + default: Adding debian:COMODO_ECC_Certification_Authority.pem + default: Adding debian:Taiwan_GRCA.pem + default: Adding debian:ISRG_Root_X1.pem + default: Adding debian:OpenTrust_Root_CA_G2.pem + default: Adding debian:GlobalSign_ECC_Root_CA_-_R5.pem + default: Adding debian:Hongkong_Post_Root_CA_1.pem + default: Adding debian:Autoridad_de_Certificacion_Firmaprofesional_CIF_A62634068.pem + default: Adding debian:AddTrust_Low-Value_Services_Root.pem + default: Adding debian:DigiCert_Global_Root_CA.pem + default: Adding debian:GeoTrust_Primary_Certification_Authority_-_G3.pem + default: Adding debian:Deutsche_Telekom_Root_CA_2.pem + default: Adding debian:GlobalSign_Root_CA.pem + default: Adding debian:QuoVadis_Root_CA_2.pem + default: Adding debian:China_Internet_Network_Information_Center_EV_Certificates_Root.pem + default: Adding debian:ACCVRAIZ1.pem + default: Adding debian:AffirmTrust_Premium.pem + default: Adding debian:Trustis_FPS_Root_CA.pem + default: Adding debian:Chambers_of_Commerce_Root_-_2008.pem + default: Adding debian:OpenTrust_Root_CA_G1.pem + default: Adding debian:Swisscom_Root_EV_CA_2.pem + default: Adding debian:Entrust_Root_Certification_Authority_-_G2.pem + default: Adding debian:USERTrust_RSA_Certification_Authority.pem + default: Adding debian:AffirmTrust_Networking.pem + default: Adding debian:thawte_Primary_Root_CA.pem + default: Adding debian:COMODO_RSA_Certification_Authority.pem + default: Adding debian:AddTrust_Public_Services_Root.pem + default: Adding debian:Security_Communication_RootCA2.pem + default: Adding debian:thawte_Primary_Root_CA_-_G3.pem + default: Adding debian:Amazon_Root_CA_3.pem + default: Adding debian:AC_RAIZ_FNMT-RCM.pem + default: Adding debian:TURKTRUST_Certificate_Services_Provider_Root_2007.pem + default: Adding debian:Hellenic_Academic_and_Research_Institutions_ECC_RootCA_2015.pem + default: done. + default: Setting up libatk-wrapper-java-jni:amd64 (0.30.4-4) ... + default: Setting up openjdk-6-jre:amd64 (6b41-1.13.13-0ubuntu0.14.04.1) ... + default: update-alternatives: + default: using /usr/lib/jvm/java-6-openjdk-amd64/jre/bin/policytool to provide /usr/bin/policytool (policytool) in auto mode + default: Setting up icedtea-6-jre-cacao:amd64 (6b41-1.13.13-0ubuntu0.14.04.1) ... + default: Setting up icedtea-6-jre-jamvm:amd64 (6b41-1.13.13-0ubuntu0.14.04.1) ... + default: Setting up icedtea-netx:amd64 (1.5.3-0ubuntu0.14.04.1) ... + default: update-alternatives: using /usr/lib/jvm/java-6-openjdk-amd64/jre/bin/javaws to provide /usr/bin/javaws (javaws) in auto mode + default: update-alternatives: using /usr/lib/jvm/java-6-openjdk-amd64/jre/bin/itweb-settings to provide /usr/bin/itweb-settings (itweb-settings) in auto mode + default: update-alternatives: warning: not replacing /usr/share/man/man1/itweb-settings.1.gz with a link + default: update-alternatives: using /usr/lib/jvm/java-7-openjdk-amd64/jre/bin/javaws to provide /usr/bin/javaws (javaws) in auto mode + default: update-alternatives: using /usr/lib/jvm/java-7-openjdk-amd64/jre/bin/itweb-settings to provide /usr/bin/itweb-settings (itweb-settings) in auto mode + default: update-alternatives: warning: not replacing /usr/share/man/man1/itweb-settings.1.gz with a link + default: Processing triggers for libc-bin (2.19-0ubuntu6.15) ... + default: Processing triggers for ca-certificates (20170717~14.04.2) ... + default: Updating certificates in /etc/ssl/certs... + default: 0 added, 0 removed; done. + default: Running hooks in /etc/ca-certificates/update.d.... + default: done. + default: done. + default: Reading package lists... + default: Building dependency tree... + default: Reading state information... + default: The following packages will be REMOVED: + default: acl at-spi2-core colord dconf-gsettings-backend dconf-service + default: libatk-bridge2.0-0 libatspi2.0-0 libcairo-gobject2 libcanberra-gtk3-0 + default: libcanberra-gtk3-module libcanberra0 libcolord1 libcolorhug1 libdconf1 + default: libdrm-intel1 libdrm-nouveau2 libdrm-radeon1 libexif12 libfontenc1 libgd3 + default: libgl1-mesa-dri libgl1-mesa-glx libglapi-mesa libgphoto2-6 libgphoto2-l10n + default: libgphoto2-port10 libgtk-3-0 libgtk-3-bin libgtk-3-common libgudev-1.0-0 + default: libgusb2 libieee1284-3 libllvm3.4 libnotify-bin libnotify4 libsane + default: libsane-common libtdb1 libtxc-dxtn-s2tc0 libv4l-0 libv4lconvert0 + default: libwayland-client0 libwayland-cursor0 libxaw7 libxcb-dri2-0 libxcb-dri3-0 + default: libxcb-glx0 libxcb-present0 libxcb-sync1 libxfont1 libxkbcommon0 libxkbfile1 + default: libxmu6 libxpm4 libxshmfence1 libxxf86vm1 notification-daemon + default: sound-theme-freedesktop x11-xkb-utils xfonts-base xfonts-encodings + default: xfonts-utils xserver-common xserver-xorg-core + default: 0 upgraded, 0 newly installed, 64 to remove and 0 not upgraded. + default: After this operation, 109 MB disk space will be freed. + default: (Reading database ... 82676 files and directories currently installed.) + default: Removing colord (1.0.6-1) ... + default: Removing libsane:amd64 (1.0.23-3ubuntu3.1) ... + default: Removing acl (2.2.52-1) ... + default: Removing at-spi2-core (2.10.2.is.2.10.1-0ubuntu1) ... + default: Removing libgtk-3-bin (3.10.8-0ubuntu1.6) ... + default: Removing 'diversion of /usr/sbin/update-icon-caches to /usr/sbin/update-icon-caches.gtk2 by libgtk-3-bin' + default: Removing 'diversion of /usr/share/man/man8/update-icon-caches.8.gz to /usr/share/man/man8/update-icon-caches.gtk2.8.gz by libgtk-3-bin' + default: Removing notification-daemon (0.7.6-1) ... + default: Removing libcanberra-gtk3-module:amd64 (0.30-0ubuntu3) ... + default: Removing libcanberra-gtk3-0:amd64 (0.30-0ubuntu3) ... + default: Removing libcanberra0:amd64 (0.30-0ubuntu3) ... + default: Removing libcolorhug1:amd64 (1.0.6-1) ... + default: Removing libgl1-mesa-dri:amd64 (10.1.3-0ubuntu0.6) ... + default: Removing libdrm-intel1:amd64 (2.4.67-1ubuntu0.14.04.2) ... + default: Removing libdrm-nouveau2:amd64 (2.4.67-1ubuntu0.14.04.2) ... + default: Removing libdrm-radeon1:amd64 (2.4.67-1ubuntu0.14.04.2) ... + default: Removing libgphoto2-6:amd64 (2.5.3.1-1ubuntu2.2) ... + default: Removing libexif12:amd64 (0.6.21-1ubuntu1) ... + default: 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default: Removing xfonts-encodings (1:1.0.4-1ubuntu1) ... + default: Removing libgtk-3-0:amd64 (3.10.8-0ubuntu1.6) ... + default: Removing libgtk-3-common (3.10.8-0ubuntu1.6) ... + default: Removing dconf-gsettings-backend:amd64 (0.20.0-1) ... + default: Removing dconf-service (0.20.0-1) ... + default: Removing libatk-bridge2.0-0:amd64 (2.10.2-2ubuntu1) ... + default: Removing libatspi2.0-0:amd64 (2.10.2.is.2.10.1-0ubuntu1) ... + default: Removing libcairo-gobject2:amd64 (1.13.0~20140204-0ubuntu1.1) ... + default: Removing libcolord1:amd64 (1.0.6-1) ... + default: Removing libdconf1:amd64 (0.20.0-1) ... + default: Removing libwayland-cursor0:amd64 (1.4.0-1ubuntu1.1) ... + default: Removing libwayland-client0:amd64 (1.4.0-1ubuntu1.1) ... + default: Removing libxkbcommon0:amd64 (0.4.1-0ubuntu1.1) ... + default: Processing triggers for libglib2.0-0:amd64 (2.40.2-0ubuntu1.1) ... + default: No schema files found: removed existing output file. + default: Processing triggers for man-db (2.6.7.1-1ubuntu1) ... + default: Processing triggers for libc-bin (2.19-0ubuntu6.15) ... + default: Processing triggers for mime-support (3.54ubuntu1.1) ... + default: Processing triggers for udev (204-5ubuntu20.31) ... + default: Processing triggers for fontconfig (2.11.0-0ubuntu4.2) ... + default: Reading package lists... + default: Building dependency tree... + default: Reading state information... + default: ---- Done system updates @ Mon Jul 29 07:53:42 UTC 2019 ---- + default: Waiting for Anaconda download to finish + default: Waiting for Matlab download to finish + default: Installing Matlab... + default: Preparing installation files ... + default: Installing ... + default: (Jul 29, 2019 07:54:04) ################################################################## + default: (Jul 29, 2019 07:54:04) # + default: (Jul 29, 2019 07:54:04) # Today's Date: + default: (Jul 29, 2019 07:54:04) Mon Jul 29 07:54:04 UTC 2019 + default: (Jul 29, 2019 07:54:04) + default: (Jul 29, 2019 07:54:04) System Info + default: (Jul 29, 2019 07:54:04) OS: Linux 3.13.0-170-generic + default: (Jul 29, 2019 07:54:04) Arch: amd64 + default: (Jul 29, 2019 07:54:04) Data Model: 64 + default: (Jul 29, 2019 07:54:04) Language: en + default: (Jul 29, 2019 07:54:04) Java Vendor: Oracle Corporation + default: (Jul 29, 2019 07:54:04) Java Home: /tmp/mathworks_20942/sys/java/jre/glnxa64/jre + default: (Jul 29, 2019 07:54:04) Java Version: 1.8.0_121 + default: (Jul 29, 2019 07:54:04) Java VM Name: Java HotSpot(TM) 64-Bit Server VM + default: (Jul 29, 2019 07:54:04) Java Class Path: /tmp/mathworks_20942/java/config/installagent/pathlist.jar + default: (Jul 29, 2019 07:54:04) User Name: root + default: (Jul 29, 2019 07:54:04) Current Directory: /tmp/mathworks_20942 + default: (Jul 29, 2019 07:54:04) Input arguments: + default: (Jul 29, 2019 07:54:04) root /tmp + default: (Jul 29, 2019 07:54:04) libdir /tmp/mathworks_20942 + default: (Jul 29, 2019 07:54:04) mode silent + default: (Jul 29, 2019 07:54:04) agreeToLicense yes + default: (Jul 29, 2019 07:54:04) standalone true + default: (Jul 29, 2019 07:54:04) connectionMode OFFLINE_ONLY + default: (Jul 29, 2019 07:54:05) Starting local product/component search in download directory + default: (Jul 29, 2019 07:54:05) Searching for archives... + default: (Jul 29, 2019 07:54:05) Reading /tmp/archives + default: (Jul 29, 2019 07:54:05) 1569 files found in /tmp/archives + default: (Jul 29, 2019 07:54:05) Reading /tmp + default: (Jul 29, 2019 07:54:05) 16 files found in /tmp + default: (Jul 29, 2019 07:54:05) Archive search complete. 1585 total files found. + default: (Jul 29, 2019 07:54:05) Assembling product list... + default: (Jul 29, 2019 07:54:06) Completed local product/component search + default: (Jul 29, 2019 07:54:06) Starting local product/component search in download directory + default: (Jul 29, 2019 07:54:06) Searching for archives... + default: (Jul 29, 2019 07:54:06) /usr/local/MATLAB/MATLAB_Runtime/v93/archives doesn't exist ... skipping. + default: (Jul 29, 2019 07:54:06) Archive search complete. 0 total files found. + default: (Jul 29, 2019 07:54:06) Completed local product/component search + default: (Jul 29, 2019 07:54:06) Installing Product: MATLAB Runtime - Builder JA 9.3 + default: (Jul 29, 2019 07:54:07) Installing Product: MATLAB Runtime - Core 9.3 + default: (Jul 29, 2019 07:54:32) Installing Product: MATLAB Runtime - GPU 9.3 + default: (Jul 29, 2019 07:54:54) Installing Product: MATLAB Runtime - Hadoop And Spark 9.3 + default: (Jul 29, 2019 07:54:54) Installing Product: MATLAB Runtime - NET And XL 9.3 + default: (Jul 29, 2019 07:54:54) Installing Product: MATLAB Runtime - Numerics 9.3 + default: (Jul 29, 2019 07:55:01) Notes: + default: On the target computer, append the following to your LD_LIBRARY_PATH environment variable: + default: + default: /usr/local/MATLAB/MATLAB_Runtime/v93/runtime/glnxa64:/usr/local/MATLAB/MATLAB_Runtime/v93/bin/glnxa64:/usr/local/MATLAB/MATLAB_Runtime/v93/sys/os/glnxa64: + default: + default: If MATLAB Runtime is to be used with MATLAB Production Server, you do not need to modify the above environment variable. + default: (Jul 29, 2019 07:55:01) Exiting with status 0 + default: (Jul 29, 2019 07:55:01) End - Successful. + default: Finished + default: ---- Done bootstrapping DiViMe @ Mon Jul 29 07:55:01 UTC 2019 ---- + default: root@vagrant-ubuntu-trusty-64:/home/vagrant# + default: e + default: x + default: i + default: t + default: exit +==> default: Running provisioner: bootstrap-user (shell)... + default: Running: /var/folders/0p/f4ssc1410_sc6_9wkzz2d9100000gq/T/vagrant-shell20190729-49213-6vqrk0.sh + default: vagrant@vagrant-ubuntu-trusty-64:~$ chmod +x + default: + default: '/tmp/vagrant +t-shellault: -shell' && /tmp/vagran + default: ---- Start bootstrapping user (vagrant) part of DiViMe @ Mon Jul 29 07:55:01 UTC 2019 ---- + default: Installing Anaconda... + default: PREFIX=/home/vagrant/anaconda + default: installing: python-2.7.16-h9bab390_0 ... + default: Python 2.7.16 :: Anaconda, Inc. + default: installing: conda-env-2.6.0-1 ... + default: installing: blas-1.0-mkl ... + default: installing: ca-certificates-2019.1.23-0 ... + default: installing: intel-openmp-2019.3-199 ... + default: installing: libgcc-ng-8.2.0-hdf63c60_1 ... + default: installing: libgfortran-ng-7.3.0-hdf63c60_0 ... + default: installing: libstdcxx-ng-8.2.0-hdf63c60_1 ... + default: installing: bzip2-1.0.6-h14c3975_5 ... + default: installing: expat-2.2.6-he6710b0_0 ... + default: installing: fribidi-1.0.5-h7b6447c_0 ... + default: installing: gmp-6.1.2-h6c8ec71_1 ... + default: installing: graphite2-1.3.13-h23475e2_0 ... + default: installing: icu-58.2-h9c2bf20_1 ... + default: installing: jbig-2.1-hdba287a_0 ... + default: installing: jpeg-9b-h024ee3a_2 ... + default: installing: 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backports.shutil_get_terminal_size-1.0.0-py27_2 ... + default: installing: cffi-1.12.2-py27h2e261b9_1 ... + default: installing: configparser-3.7.3-py27_1 ... + default: installing: cycler-0.10.0-py27hc7354d3_0 ... + default: installing: cytoolz-0.9.0.1-py27h14c3975_1 ... + default: installing: harfbuzz-1.8.8-hffaf4a1_0 ... + default: installing: html5lib-1.0.1-py27_0 ... + default: installing: jedi-0.13.3-py27_0 ... + default: installing: llvmlite-0.28.0-py27hd408876_0 ... + default: installing: mkl_random-1.0.2-py27hd81dba3_0 ... + default: installing: more-itertools-5.0.0-py27_0 ... + default: installing: multipledispatch-0.6.0-py27_0 ... + default: installing: nltk-3.4-py27_1 ... + default: installing: openpyxl-2.6.1-py27_1 ... + default: installing: packaging-19.0-py27_0 ... + default: installing: partd-0.3.10-py27_1 ... + default: installing: pathlib2-2.3.3-py27_0 ... + default: installing: pexpect-4.6.0-py27_0 ... + default: installing: pillow-5.4.1-py27h34e0f95_0 ... + default: 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installing: cython-0.29.6-py27he6710b0_0 ... + default: installing: entrypoints-0.3-py27_0 ... + default: installing: get_terminal_size-1.0.0-haa9412d_0 ... + default: installing: gevent-1.4.0-py27h7b6447c_0 ... + default: installing: importlib_metadata-0.8-py27_0 ... + default: installing: jinja2-2.10-py27_0 ... + default: installing: jsonschema-3.0.1-py27_0 ... + default: installing: jupyter_core-4.4.0-py27_0 ... + default: installing: navigator-updater-0.2.1-py27_0 ... + default: installing: networkx-2.2-py27_1 ... + default: installing: nose-1.3.7-py27_2 ... + default: installing: pango-1.42.4-h049681c_0 ... + default: installing: pickleshare-0.7.5-py27_0 ... + default: installing: pygments-2.3.1-py27_0 ... + default: installing: pytest-4.3.1-py27_0 ... + default: installing: tornado-5.1.1-py27h7b6447c_0 ... + default: installing: unittest2-1.1.0-py27_0 ... + default: installing: wheel-0.33.1-py27_0 ... + default: installing: astroid-1.6.5-py27_0 ... + default: installing: conda-verify-3.1.1-py27_0 ... + default: installing: distributed-1.26.0-py27_1 ... + default: installing: flask-1.0.2-py27_1 ... + default: installing: isort-4.3.16-py27_0 ... + default: installing: jupyter_client-5.2.4-py27_0 ... + default: installing: nbformat-4.4.0-py27hed7f2b2_0 ... + default: installing: path.py-11.5.0-py27_0 ... + default: installing: pip-19.0.3-py27_0 ... + default: installing: prompt_toolkit-1.0.15-py27h1b593e1_0 ... + default: installing: pyopenssl-19.0.0-py27_0 ... + default: installing: soupsieve-1.8-py27_0 ... + default: installing: terminado-0.8.1-py27_1 ... + default: installing: beautifulsoup4-4.7.1-py27_1 ... + default: installing: ipython-5.8.0-py27_0 ... + default: installing: nbconvert-5.4.1-py27_3 ... + default: installing: pylint-1.9.2-py27_0 ... + default: installing: urllib3-1.24.1-py27_0 ... + default: installing: ipykernel-4.10.0-py27_0 ... + default: installing: requests-2.21.0-py27_0 ... + default: installing: anaconda-client-1.7.2-py27_0 ... + default: installing: conda-4.6.11-py27_0 ... + default: installing: jupyter_console-5.2.0-py27_1 ... + default: installing: notebook-5.7.8-py27_0 ... + default: installing: qtconsole-4.4.3-py27_0 ... + default: installing: sphinx-1.8.5-py27_0 ... + default: installing: spyder-kernels-0.4.2-py27_0 ... + default: installing: anaconda-navigator-1.9.7-py27_0 ... + default: installing: anaconda-project-0.8.2-py27_0 ... + default: installing: conda-build-3.17.8-py27_0 ... + default: installing: jupyterlab_launcher-0.11.2-py27h28b3542_0 ... + default: installing: numpydoc-0.8.0-py27_0 ... + default: installing: widgetsnbextension-3.4.2-py27_0 ... + default: installing: ipywidgets-7.4.2-py27_0 ... + default: installing: jupyterlab-0.33.11-py27_0 ... + default: installing: spyder-3.3.3-py27_0 ... + default: installing: _ipyw_jlab_nb_ext_conf-0.1.0-py27_0 ... + default: installing: jupyter-1.0.0-py27_7 ... + default: installing: astropy-2.0.9-py27hdd07704_0 ... + default: installing: bokeh-1.0.4-py27_0 ... + default: installing: bottleneck-1.2.1-py27h035aef0_1 ... + default: installing: h5py-2.9.0-py27h7918eee_0 ... + default: installing: imageio-2.5.0-py27_0 ... + default: installing: matplotlib-2.2.3-py27hb69df0a_0 ... + default: installing: mkl_fft-1.0.10-py27ha843d7b_0 ... + default: installing: numpy-1.16.2-py27h7e9f1db_0 ... + default: installing: numba-0.43.1-py27h962f231_0 ... + default: installing: numexpr-2.6.9-py27h9e4a6bb_0 ... + default: installing: pandas-0.24.2-py27he6710b0_0 ... + default: installing: pywavelets-1.0.2-py27hdd07704_0 ... + default: installing: scipy-1.2.1-py27h7c811a0_0 ... + default: installing: bkcharts-0.2-py27h241ae91_0 ... + default: installing: dask-1.1.4-py27_1 ... + default: installing: patsy-0.5.1-py27_0 ... + default: installing: pytables-3.5.1-py27h71ec239_0 ... + default: installing: scikit-image-0.14.2-py27he6710b0_0 ... + default: installing: scikit-learn-0.20.3-py27hd81dba3_0 ... + default: installing: statsmodels-0.9.0-py27h035aef0_0 ... + default: installing: seaborn-0.9.0-py27_0 ... + default: installing: anaconda-2019.03-py27_0 ... + default: installation finished. + default: Creating python3 env... + default: WARNING: The conda.compat module is deprecated and will be removed in a future release. + default: WARNING: The conda.compat module is deprecated and will be removed in a future release. + default: Warning: you have pip-installed dependencies in your environment file, but you do not list pip itself as one of your conda dependencies. Conda may not use the correct pip to install your packages, and they may end up in the wrong place. Please add an explicit pip dependency. I'm adding one for you, but still nagging you. + default: Collecting package metadata: + default: ...working... + default: done + default: Solving environment: + default: ...working... + default: done + default: + default: CondaMultiError: ('Connection broken: error("(104, \'ECONNRESET\')",)', error("(104, 'ECONNRESET')",)) + default: -- Conda report -- + default: WARNING: The conda.compat module is deprecated and will be removed in a future release. + default: # packages in environment at /home/vagrant/anaconda: + default: # + default: # Name Version Build Channel + default: _ipyw_jlab_nb_ext_conf 0.1.0 py27_0 + default: alabaster 0.7.12 py27_0 + default: anaconda 2019.03 py27_0 + default: anaconda-client 1.7.2 py27_0 + default: anaconda-navigator 1.9.7 py27_0 + default: anaconda-project 0.8.2 py27_0 + default: asn1crypto 0.24.0 py27_0 + default: astroid 1.6.5 py27_0 + default: astropy 2.0.9 py27hdd07704_0 + default: atomicwrites 1.3.0 py27_1 + default: attrs 19.1.0 py27_1 + default: 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40.8.0 py27_0 + default: simplegeneric 0.8.1 py27_2 + default: singledispatch 3.4.0.3 py27h9bcb476_0 + default: sip 4.19.8 py27hf484d3e_0 + default: six 1.12.0 py27_0 + default: snappy 1.1.7 hbae5bb6_3 + default: snowballstemmer 1.2.1 py27h44e2768_0 + default: sortedcollections 1.1.2 py27_0 + default: sortedcontainers 2.1.0 py27_0 + default: soupsieve 1.8 py27_0 + default: sphinx 1.8.5 py27_0 + default: sphinxcontrib 1.0 py27_1 + default: sphinxcontrib-websupport 1.1.0 py27_1 + default: spyder 3.3.3 py27_0 + default: spyder-kernels 0.4.2 py27_0 + default: sqlalchemy 1.3.1 py27h7b6447c_0 + default: sqlite 3.27.2 h7b6447c_0 + default: ssl_match_hostname 3.7.0.1 py27_0 + default: statsmodels 0.9.0 py27h035aef0_0 + default: subprocess32 3.5.3 py27h7b6447c_0 + default: sympy 1.3 py27_0 + default: tblib 1.3.2 py27h51fe5ba_0 + default: terminado 0.8.1 py27_1 + default: testpath 0.4.2 py27_0 + default: tk 8.6.8 hbc83047_0 + default: toolz 0.9.0 py27_0 + default: tornado 5.1.1 py27h7b6447c_0 + default: tqdm 4.31.1 py27_1 + default: traceback2 1.4.0 py27_0 + default: traitlets 4.3.2 py27hd6ce930_0 + default: typing 3.6.6 py27_0 + default: unicodecsv 0.14.1 py27h5062da9_0 + default: unittest2 1.1.0 py27_0 + default: unixodbc 2.3.7 h14c3975_0 + default: urllib3 1.24.1 py27_0 + default: wcwidth 0.1.7 py27h9e3e1ab_0 + default: webencodings 0.5.1 py27_1 + default: werkzeug 0.14.1 py27_0 + default: wheel 0.33.1 py27_0 + default: widgetsnbextension 3.4.2 + default: py27_0 + default: wrapt 1.11.1 py27h7b6447c_0 + default: wurlitzer 1.0.2 py27_0 + default: xlrd 1.2.0 py27_0 + default: xlsxwriter 1.1.5 py27_0 + default: xlwt 1.3.0 py27h3d85d97_0 + default: xz 5.2.4 h14c3975_4 + default: yaml 0.1.7 had09818_2 + default: zeromq 4.3.1 he6710b0_3 + default: zict 0.1.4 py27_0 + default: zipp 0.3.3 py27_1 + default: zlib 1.2.11 h7b6447c_3 + default: zstd 1.3.7 h0b5b093_0 + default: Can't find HTK-3.4.1.tar.gz. Assuming HTK not needed. + default: ---- Changing into /home/vagrant/repos @ Mon Jul 29 09:26:03 UTC 2019 ---- + default: Installing OpenSMILE + default: DEPRECATION: Python 2.7 will reach the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 won't be maintained after that date. A future version of pip will drop support for Python 2.7. + default: WARNING: The conda.compat module is deprecated and will be removed in a future release. + default: Collecting package metadata: + default: ...working... + default: done + default: Solving environment: ...working... + default: done + default: + default: ## Package Plan ## + default: + default: environment location: /home/vagrant/anaconda + default: + default: added / updated specs: + default: - cudatoolkit + default: - pytorch-cpu + default: - theano + default: + default: + default: The following packages will be downloaded: + default: + default: package | build + default: ---------------------------|----------------- + default: binutils_impl_linux-64-2.31.1| h6176602_1 16.5 MB + default: binutils_linux-64-2.31.1 | h6176602_7 9 KB + default: conda-4.7.10 | py27_0 3.0 MB + default: conda-package-handling-1.3.11| py27_0 260 KB + default: cudatoolkit-10.1.168 | 0 516.0 MB + default: gcc_impl_linux-64-7.3.0 | habb00fd_1 73.2 MB + default: gcc_linux-64-7.3.0 | h553295d_7 10 KB + default: gxx_impl_linux-64-7.3.0 | hdf63c60_1 18.7 MB + default: gxx_linux-64-7.3.0 | h553295d_7 10 KB + default: libgpuarray-0.7.6 | h14c3975_0 264 KB + default: mako-1.0.10 | py_0 61 KB + default: ninja-1.9.0 | py27hfd86e86_0 1.6 MB + default: pygpu-0.7.6 | py27h035aef0_0 685 KB + default: pytorch-cpu-1.1.0 | py2.7_cpu_0 53.5 MB pytorch + default: theano-1.0.3 | py27hfd86e86_0 3.6 MB + default: ------------------------------------------------------------ + default: Total: 687.4 MB + default: + default: The following NEW packages will be INSTALLED: + default: + default: _libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main + default: binutils_impl_lin~ pkgs/main/linux-64::binutils_impl_linux-64-2.31.1-h6176602_1 + default: binutils_linux-64 pkgs/main/linux-64::binutils_linux-64-2.31.1-h6176602_7 + default: conda-package-han~ pkgs/main/linux-64::conda-package-handling-1.3.11-py27_0 + default: cudatoolkit pkgs/main/linux-64::cudatoolkit-10.1.168-0 + default: gcc_impl_linux-64 pkgs/main/linux-64::gcc_impl_linux-64-7.3.0-habb00fd_1 + default: gcc_linux-64 pkgs/main/linux-64::gcc_linux-64-7.3.0-h553295d_7 + default: gxx_impl_linux-64 pkgs/main/linux-64::gxx_impl_linux-64-7.3.0-hdf63c60_1 + default: gxx_linux-64 pkgs/main/linux-64::gxx_linux-64-7.3.0-h553295d_7 + default: libgpuarray pkgs/main/linux-64::libgpuarray-0.7.6-h14c3975_0 + default: mako pkgs/main/noarch::mako-1.0.10-py_0 + default: ninja pkgs/main/linux-64::ninja-1.9.0-py27hfd86e86_0 + default: pygpu pkgs/main/linux-64::pygpu-0.7.6-py27h035aef0_0 + default: pytorch-cpu pytorch/linux-64::pytorch-cpu-1.1.0-py2.7_cpu_0 + default: theano pkgs/main/linux-64::theano-1.0.3-py27hfd86e86_0 + default: + default: The following packages will be UPDATED: + default: + default: conda 4.6.11-py27_0 --> 4.7.10-py27_0 + default: + default: + default: Preparing transaction: ...working... + default: done + default: Verifying transaction: + default: ...working... + default: done + default: Executing transaction: + default: ...working... + default: done + default: Note: checking out '2ce2998'. + default: + default: You are in 'detached HEAD' state. You can look around, make experimental + default: changes and commit them, and you can discard any commits you make in this + default: state without impacting any branches by performing another checkout. + default: + default: If you want to create a new branch to retain commits you create, you may + default: do so (now or later) by using -b with the checkout command again. Example: + default: + default: git checkout -b new_branch_name + default: HEAD is now at 2ce2998... Change tabulation to space rttm + default: Note: checking out 'b3e4deb'. + default: + default: You are in 'detached HEAD' state. You can look around, make experimental + default: changes and commit them, and you can discard any commits you make in this + default: state without impacting any branches by performing another checkout. + default: + default: If you want to create a new branch to retain commits you create, you may + default: do so (now or later) by using -b with the checkout command again. Example: + default: + default: git checkout -b new_branch_name + default: HEAD is now at b3e4deb... don't exit early; process every RTTM line + default: DEPRECATION: Python 2.7 will reach the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 won't be maintained after that date. A future version of pip will drop support for Python 2.7. + default: Note: checking out '332b8dd'. + default: + default: You are in 'detached HEAD' state. You can look around, make experimental + default: changes and commit them, and you can discard any commits you make in this + default: state without impacting any branches by performing another checkout. + default: + default: If you want to create a new branch to retain commits you create, you may + default: do so (now or later) by using -b with the checkout command again. Example: + default: + default: git checkout -b new_branch_name + default: HEAD is now at 332b8dd... fixed #14 + default: running build + default: running build_py + default: creating build + default: creating build/lib.linux-x86_64-2.7 + default: creating build/lib.linux-x86_64-2.7/phonemizer + default: copying phonemizer/main.py -> build/lib.linux-x86_64-2.7/phonemizer + default: copying phonemizer/espeak.py -> build/lib.linux-x86_64-2.7/phonemizer + default: copying phonemizer/festival.py -> build/lib.linux-x86_64-2.7/phonemizer + default: copying phonemizer/separator.py -> build/lib.linux-x86_64-2.7/phonemizer + default: copying phonemizer/lispy.py -> build/lib.linux-x86_64-2.7/phonemizer + default: copying phonemizer/__init__.py -> build/lib.linux-x86_64-2.7/phonemizer + default: copying phonemizer/phonemize.py -> build/lib.linux-x86_64-2.7/phonemizer + default: creating build/lib.linux-x86_64-2.7/phonemizer/share + default: copying phonemizer/share/phonemize.scm -> build/lib.linux-x86_64-2.7/phonemizer/share + default: running install + default: running bdist_egg + default: running egg_info + default: creating phonemizer.egg-info + default: writing requirements to phonemizer.egg-info/requires.txt + default: writing phonemizer.egg-info/PKG-INFO + default: writing top-level names to phonemizer.egg-info/top_level.txt + default: writing dependency_links to phonemizer.egg-info/dependency_links.txt + default: writing entry points to phonemizer.egg-info/entry_points.txt + default: writing manifest file 'phonemizer.egg-info/SOURCES.txt' + default: reading manifest file 'phonemizer.egg-info/SOURCES.txt' + default: writing manifest file 'phonemizer.egg-info/SOURCES.txt' + default: installing library code to build/bdist.linux-x86_64/egg + default: running install_lib + default: running build_py + default: creating build/bdist.linux-x86_64 + default: creating build/bdist.linux-x86_64/egg + default: creating build/bdist.linux-x86_64/egg/phonemizer + default: copying build/lib.linux-x86_64-2.7/phonemizer/main.py -> build/bdist.linux-x86_64/egg/phonemizer + default: copying build/lib.linux-x86_64-2.7/phonemizer/espeak.py -> build/bdist.linux-x86_64/egg/phonemizer + default: copying build/lib.linux-x86_64-2.7/phonemizer/festival.py -> build/bdist.linux-x86_64/egg/phonemizer + default: copying build/lib.linux-x86_64-2.7/phonemizer/separator.py -> build/bdist.linux-x86_64/egg/phonemizer + default: creating build/bdist.linux-x86_64/egg/phonemizer/share + default: copying build/lib.linux-x86_64-2.7/phonemizer/share/phonemize.scm -> build/bdist.linux-x86_64/egg/phonemizer/share + default: copying build/lib.linux-x86_64-2.7/phonemizer/lispy.py -> build/bdist.linux-x86_64/egg/phonemizer + default: copying build/lib.linux-x86_64-2.7/phonemizer/__init__.py -> build/bdist.linux-x86_64/egg/phonemizer + default: copying build/lib.linux-x86_64-2.7/phonemizer/phonemize.py -> build/bdist.linux-x86_64/egg/phonemizer + default: byte-compiling build/bdist.linux-x86_64/egg/phonemizer/main.py to main.pyc + default: byte-compiling build/bdist.linux-x86_64/egg/phonemizer/espeak.py to espeak.pyc + default: byte-compiling build/bdist.linux-x86_64/egg/phonemizer/festival.py to festival.pyc + default: byte-compiling build/bdist.linux-x86_64/egg/phonemizer/separator.py to separator.pyc + default: byte-compiling build/bdist.linux-x86_64/egg/phonemizer/lispy.py to lispy.pyc + default: byte-compiling build/bdist.linux-x86_64/egg/phonemizer/__init__.py to __init__.pyc + default: byte-compiling build/bdist.linux-x86_64/egg/phonemizer/phonemize.py to phonemize.pyc + default: creating build/bdist.linux-x86_64/egg/EGG-INFO + default: copying phonemizer.egg-info/PKG-INFO -> build/bdist.linux-x86_64/egg/EGG-INFO + default: copying phonemizer.egg-info/SOURCES.txt -> build/bdist.linux-x86_64/egg/EGG-INFO + default: copying phonemizer.egg-info/dependency_links.txt -> build/bdist.linux-x86_64/egg/EGG-INFO + default: copying phonemizer.egg-info/entry_points.txt -> build/bdist.linux-x86_64/egg/EGG-INFO + default: copying phonemizer.egg-info/requires.txt -> build/bdist.linux-x86_64/egg/EGG-INFO + default: copying phonemizer.egg-info/top_level.txt -> build/bdist.linux-x86_64/egg/EGG-INFO + default: copying phonemizer.egg-info/zip-safe -> build/bdist.linux-x86_64/egg/EGG-INFO + default: creating dist + default: creating 'dist/phonemizer-0.3.2-py2.7.egg' and adding 'build/bdist.linux-x86_64/egg' to it + default: removing 'build/bdist.linux-x86_64/egg' (and everything under it) + default: Processing phonemizer-0.3.2-py2.7.egg + default: Copying phonemizer-0.3.2-py2.7.egg to /usr/local/lib/python2.7/dist-packages + default: Adding phonemizer 0.3.2 to easy-install.pth file + default: Installing phonemize script to /usr/local/bin + default: + default: Installed /usr/local/lib/python2.7/dist-packages/phonemizer-0.3.2-py2.7.egg + default: Processing dependencies for phonemizer==0.3.2 + default: Searching for joblib + default: Reading https://pypi.python.org/simple/joblib/ + default: Best match: joblib 0.13.2 + default: Downloading https://files.pythonhosted.org/packages/2c/7f/c1da4e341d81840c2fa46d583cbc92bb02e5274fe601e9cbc72bcb78b2d7/joblib-0.13.2.tar.gz#sha256=315d6b19643ec4afd4c41c671f9f2d65ea9d787da093487a81ead7b0bac94524 + default: Processing joblib-0.13.2.tar.gz + default: Writing /tmp/easy_install-WZQDCy/joblib-0.13.2/setup.cfg + default: Running joblib-0.13.2/setup.py -q bdist_egg --dist-dir /tmp/easy_install-WZQDCy/joblib-0.13.2/egg-dist-tmp-WhDswX + default: warning: no files found matching '*.rst' under directory 'joblib' + default: warning: no previously-included files matching '*~' found anywhere in distribution + default: warning: no previously-included files matching '*.swp' found anywhere in distribution + default: zip_safe flag not set; analyzing archive contents... + default: joblib.format_stack: module references __file__ + default: joblib.format_stack: module MAY be using inspect.getinnerframes + default: joblib.format_stack: module MAY be using inspect.getouterframes + default: joblib.func_inspect: module MAY be using inspect.getsourcefile + default: joblib.test.test_func_inspect: module references __file__ + default: joblib.test.test_numpy_pickle: module references __file__ + default: joblib.test.test_parallel: module references __file__ + default: joblib.externals.cloudpickle.cloudpickle: module references __file__ + default: joblib.externals.loky.backend.spawn: module references __file__ + default: Adding joblib 0.13.2 to easy-install.pth file + default: + default: Installed /usr/local/lib/python2.7/dist-packages/joblib-0.13.2-py2.7.egg + default: Finished processing dependencies for phonemizer==0.3.2 + default: Could not find conda environment: divime + default: You can list all discoverable environments with `conda info --envs`. + default: DEPRECATION: Python 2.7 will reach the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 won't be maintained after that date. A future version of pip will drop support for Python 2.7. + default: DEPRECATION: Python 2.7 will reach the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 won't be maintained after that date. A future version of pip will drop support for Python 2.7. + default: Collecting pympi-ling + default: Downloading https://files.pythonhosted.org/packages/92/c7/90b4821910509e9c888d93386e6fe00efaf7dd5ae37fc607391d6838df40/pympi-ling-1.69.tar.gz + default: Collecting tgt + default: Downloading https://files.pythonhosted.org/packages/51/9a/25d82ed43c926e0234c8705e374323d7f9bfe0b311019fe3b1e2e445fbb6/tgt-1.4.4.tar.gz + default: Collecting intervaltree + default: Downloading https://files.pythonhosted.org/packages/e8/f9/76237755b2020cd74549e98667210b2dd54d3fb17c6f4a62631e61d31225/intervaltree-3.0.2.tar.gz + default: Collecting recommonmark + default: Downloading https://files.pythonhosted.org/packages/9b/3d/92ea48401622510e57b4bdaa74dc9db2fb9e9e892324b48f9c02d716a93a/recommonmark-0.5.0-py2.py3-none-any.whl + default: Collecting sphinx-markdown-tables + default: Downloading https://files.pythonhosted.org/packages/3d/d0/4f6e12f06f87bcae7a97997fc50862222ee78f859edece0da0ad75d8ada4/sphinx-markdown-tables-0.0.9.tar.gz + default: Collecting sphinx_rtd_theme + default: Downloading https://files.pythonhosted.org/packages/60/b4/4df37087a1d36755e3a3bfd2a30263f358d2dea21938240fa02313d45f51/sphinx_rtd_theme-0.4.3-py2.py3-none-any.whl (6.4MB) + default: Requirement already satisfied: sortedcontainers<3.0,>=2.0 in /home/vagrant/anaconda/lib/python2.7/site-packages (from intervaltree) (2.1.0) + default: Requirement already satisfied: docutils>=0.11 in /home/vagrant/anaconda/lib/python2.7/site-packages (from recommonmark) (0.14) + default: Requirement already satisfied: sphinx>=1.3.1 in /home/vagrant/anaconda/lib/python2.7/site-packages (from recommonmark) (1.8.5) + default: Collecting commonmark>=0.7.3 (from recommonmark) + default: Downloading https://files.pythonhosted.org/packages/a7/65/2ea45a38e8c6a0a13453c5cadcc9b725049425c8628dbe7da87b30944573/commonmark-0.9.0-py2.py3-none-any.whl (51kB) + default: Collecting markdown==2.6.11 (from sphinx-markdown-tables) + default: Downloading https://files.pythonhosted.org/packages/6d/7d/488b90f470b96531a3f5788cf12a93332f543dbab13c423a5e7ce96a0493/Markdown-2.6.11-py2.py3-none-any.whl (78kB) + default: Requirement already satisfied: six>=1.5 in /home/vagrant/anaconda/lib/python2.7/site-packages (from sphinx>=1.3.1->recommonmark) (1.12.0) + default: Requirement already satisfied: Jinja2>=2.3 in /home/vagrant/anaconda/lib/python2.7/site-packages (from sphinx>=1.3.1->recommonmark) (2.10) + default: Requirement already satisfied: Pygments>=2.0 in /home/vagrant/anaconda/lib/python2.7/site-packages (from sphinx>=1.3.1->recommonmark) (2.3.1) + default: Requirement already satisfied: snowballstemmer>=1.1 in /home/vagrant/anaconda/lib/python2.7/site-packages (from sphinx>=1.3.1->recommonmark) (1.2.1) + default: Requirement already satisfied: babel!=2.0,>=1.3 in /home/vagrant/anaconda/lib/python2.7/site-packages (from sphinx>=1.3.1->recommonmark) (2.6.0) + default: Requirement already satisfied: alabaster<0.8,>=0.7 in /home/vagrant/anaconda/lib/python2.7/site-packages (from sphinx>=1.3.1->recommonmark) (0.7.12) + default: Requirement already satisfied: imagesize in /home/vagrant/anaconda/lib/python2.7/site-packages (from sphinx>=1.3.1->recommonmark) (1.1.0) + default: Requirement already satisfied: requests>=2.0.0 in /home/vagrant/anaconda/lib/python2.7/site-packages (from sphinx>=1.3.1->recommonmark) (2.21.0) + default: Requirement already satisfied: setuptools in /home/vagrant/anaconda/lib/python2.7/site-packages (from sphinx>=1.3.1->recommonmark) (40.8.0) + default: Requirement already satisfied: packaging in /home/vagrant/anaconda/lib/python2.7/site-packages (from sphinx>=1.3.1->recommonmark) (19.0) + default: Requirement already satisfied: sphinxcontrib-websupport in /home/vagrant/anaconda/lib/python2.7/site-packages (from sphinx>=1.3.1->recommonmark) (1.1.0) + default: Requirement already satisfied: typing in /home/vagrant/anaconda/lib/python2.7/site-packages (from sphinx>=1.3.1->recommonmark) (3.6.6) + default: Requirement already satisfied: future in /home/vagrant/anaconda/lib/python2.7/site-packages (from commonmark>=0.7.3->recommonmark) (0.17.1) + default: Requirement already satisfied: MarkupSafe>=0.23 in /home/vagrant/anaconda/lib/python2.7/site-packages (from Jinja2>=2.3->sphinx>=1.3.1->recommonmark) (1.1.1) + default: Requirement already satisfied: pytz>=0a in /home/vagrant/anaconda/lib/python2.7/site-packages (from babel!=2.0,>=1.3->sphinx>=1.3.1->recommonmark) (2018.9) + default: Requirement already satisfied: urllib3<1.25,>=1.21.1 in /home/vagrant/anaconda/lib/python2.7/site-packages (from requests>=2.0.0->sphinx>=1.3.1->recommonmark) (1.24.1) + default: Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /home/vagrant/anaconda/lib/python2.7/site-packages (from requests>=2.0.0->sphinx>=1.3.1->recommonmark) (3.0.4) + default: Requirement already satisfied: idna<2.9,>=2.5 in /home/vagrant/anaconda/lib/python2.7/site-packages (from requests>=2.0.0->sphinx>=1.3.1->recommonmark) (2.8) + default: Requirement already satisfied: certifi>=2017.4.17 in /home/vagrant/anaconda/lib/python2.7/site-packages (from requests>=2.0.0->sphinx>=1.3.1->recommonmark) (2019.3.9) + default: Requirement already satisfied: pyparsing>=2.0.2 in /home/vagrant/anaconda/lib/python2.7/site-packages (from packaging->sphinx>=1.3.1->recommonmark) (2.3.1) + default: Building wheels for collected packages: pympi-ling, tgt, intervaltree, sphinx-markdown-tables + default: Building wheel for pympi-ling (setup.py): started + default: Building wheel for pympi-ling (setup.py): finished with status 'done' + default: Stored in directory: /home/vagrant/.cache/pip/wheels/34/c2/ab/1107ef0b833e770faf53b41179884b4bfa6f5bd641dc74e8f4 + default: Building wheel for tgt (setup.py): started + default: Building wheel for tgt (setup.py): finished with status 'done' + default: Stored in directory: /home/vagrant/.cache/pip/wheels/d0/01/c2/7b18446d6ce6b1ced14c50044844180f89a51b960d0799802d + default: Building wheel for intervaltree (setup.py): started + default: Building wheel for intervaltree (setup.py): finished with status 'done' + default: Stored in directory: /home/vagrant/.cache/pip/wheels/08/99/c0/5a5942f5b9567c59c14aac76f95a70bf11dccc71240b91ebf5 + default: Building wheel for sphinx-markdown-tables (setup.py): started + default: Building wheel for sphinx-markdown-tables (setup.py): finished with status 'done' + default: Stored in directory: /home/vagrant/.cache/pip/wheels/df/ae/13/2e436636f69f99f623599b532c2c11bdd4331c5030e246700f + default: Successfully built pympi-ling tgt intervaltree sphinx-markdown-tables + default: Installing collected packages: pympi-ling, tgt, intervaltree, commonmark, recommonmark, markdown, sphinx-markdown-tables, sphinx-rtd-theme + default: Found existing installation: Markdown 3.1.1 + default: Uninstalling Markdown-3.1.1: + default: Successfully uninstalled Markdown-3.1.1 + default: Successfully installed commonmark-0.9.0 intervaltree-3.0.2 markdown-2.6.11 pympi-ling-1.69 recommonmark-0.5.0 sphinx-markdown-tables-0.0.9 sphinx-rtd-theme-0.4.3 tgt-1.4.4 + default: ---- git logs follow ---- + default: -- git log /vagrant -- + default: commit 2af79066d1a17c1349c6071538d5e78ab33e14ae + default: Author: Florian Metze + default: Date: Sun Jul 28 00:26:24 2019 +0200 + default: + default: Update install.md + default: -- git log ib_diarization_toolkit -- + default: commit b3e4deb2465b36270cec0c934bd3f7be2f88e3fa + default: Author: riebling + default: Date: Thu Aug 16 11:28:15 2018 -0400 + default: + default: don't exit early; process every RTTM line + default: + default: assume we process every line in RTTM, don't look for standard symbols in certain columns + default: -- git log OpenSAT -- + default: commit e47575acecdde5651e585b167722c3b98f510d27 + default: Author: jaden-w + default: Date: Tue Nov 27 12:45:05 2018 -0500 + default: + default: Modify RNN.py for python3 (and encoding issues) + default: -- git log phonemizer -- + default: commit 332b8dd8bb399e16fce025c4eaa95d9e21da45bf + default: Author: Mathieu Bernard + default: Date: Mon Oct 22 13:08:12 2018 +0200 + default: + default: fixed #14 + default: -- git log To-Combo-SAD -- + default: commit 2ce29984ef621439fd7b58a337e59df88139307c + default: Author: Marvin Lavechin + default: Date: Tue Oct 23 14:21:37 2018 +0200 + default: + default: Change tabulation to space rttm + default: -- git log vcm -- + default: commit 8d00df0db4014c3c275a504bd85d376fe7185fab + default: Author: Florian Metze + default: Date: Wed Jul 3 03:15:38 2019 +0000 + default: + default: added yunitator mode + default: -- git log WCE_VM -- + default: commit 0f78f59b9be6e9ed56367c3c81feb707638b5f2d + default: Author: orasanen + default: Date: Wed Jul 24 13:10:16 2019 -0400 + default: + default: fixed rttm_to_wavs underscoer bug + default: -- git log Yunitator -- + default: commit d4b8b36b90347b7740a7d6835ffe119665e45049 + default: Author: Florian Metze + default: Date: Wed Jul 3 01:05:08 2019 -0400 + default: + default: Delete 1-confidence-vm.py~ + default: ---- git logs done ---- + default: ---- Building the docs... ---- + default: Running Sphinx v1.8.5 + default: loading pickled environment... + default: done + default: building [mo]: targets for 0 po files that are out of date + default: building [html]: targets for 12 source files that are out of date + default: updating environment: + default: 0 added, 0 changed, 0 removed + default: looking for now-outdated files... + default: none found + default: preparing documents... + default: done + default: writing output... [ 8%] WCEtool + default: writing output... [ 16%] extra-tools + default: writing output... [ 25%] formats + default: writing output... [ 33%] further-info + default: writing output... [ 41%] index + default: writing output... [ 50%] initial_questions + default: writing output... [ 58%] install + default: writing output... [ 66%] instructions_for_contributors + default: writing output... [ 75%] references + default: writing output... [ 83%] tool_doc + default: writing output... [ 91%] troubleshoot + default: writing output... [100%] usage + default: /vagrant/docs/source/formats.md:9: WARNING: None:any reference target not found: praat.org + default: /vagrant/docs/source/formats.md:9: WARNING: None:any reference target not found: www.lena.org + default: /vagrant/docs/source/install.md:49: WARNING: None:any reference target not found: homebank.talkbank.org + default: generating indices... + default: genindex + default: writing additional pages... + default: search + default: copying images... [100%] ../images/example_visu.png + default: copying static files... + default: WARNING: html_static_path entry u'/vagrant/docs/source/_static' does not exist + default: done + default: copying extra files... + default: done + default: dumping search index in English (code: en) ... + default: done + default: dumping object inventory... + default: done + default: build succeeded, 4 warnings. + default: The HTML pages are in build/html. + default: ---- Sanity checks in user part of DiViMe @ Mon Jul 29 09:33:25 UTC 2019 ---- + default: INFO: You can remove opensmile-2-3-0.tar.gz, if you don't plan on re-provisioning DiViMe any time soon. + default: INFO: You can remove Anaconda2-2019.03-Linux-x86_64.sh, if you don't plan on re-provisioning DiViMe any time soon. + default: INFO: You can remove MCR_R2017b_glnxa64_installer.zip, if you don't plan on re-provisioning DiViMe any time soon. + default: ---- Done bootstrapping user part of DiViMe @ Mon Jul 29 09:33:25 UTC 2019 ---- + default: vagrant@vagrant-ubuntu-trusty-64:~$ + default: e + default: x + default: i + default: t + default: exit +Florians-MacBook-Pro-2019:DiViMe-2 metze$ diff --git a/test.wav b/test.wav deleted file mode 100644 index 3b424b7..0000000 Binary files a/test.wav and /dev/null differ diff --git a/test2.mp3 b/test2.mp3 deleted file mode 100644 index 1918b17..0000000 Binary files a/test2.mp3 and /dev/null differ diff --git a/test2.rttm b/test2.rttm deleted file mode 100644 index b8d7d53..0000000 --- a/test2.rttm +++ /dev/null @@ -1,4 +0,0 @@ -SPEAKER test2 1 0.00 2.03 speech -SPEAKER test2 1 2.49 3.29 speech -SPEAKER test2 1 6.45 3.5 speech -SPEAKER test2 1 10.71 2.92 speech diff --git a/toolbox/rttm2scp.py b/toolbox/rttm2scp.py deleted file mode 100755 index e27e35d..0000000 --- a/toolbox/rttm2scp.py +++ /dev/null @@ -1,141 +0,0 @@ -#!/usr/bin/env python -# -# author: The ACLEW Team -# -# This script takes as input gold rttm file -# that transcribes a diarization, and converts it -# to a gold scp file indicating Speech Activity. -# It is necessary, to avoid overlaps between speech intervals -# and to sort the intervals by onsets. - -import os -import sys -import argparse -from intervaltree import IntervalTree, Interval -from operator import itemgetter - -def read_rttm(input_path): - """Read a RTTM file indicating gold diarization""" - with open(input_path, 'r') as fin: - # RTTM format is - # SPEAKER fname 1 onset duration spkr - rttm = fin.readlines() - sad = IntervalTree() - fname = "" - for line in rttm: - _, fname, _, onset, dur, _, _, _, _ = line.strip('\n').split() - if float(dur) == 0: - # Remove empty intervals - continue - elif float(dur) < 0: - print("{} shows an interval with negative duration." - " Please inspect file, this shouldn't happen".format( - line)) - continue - - interval = Interval(float(onset), float(onset) + float(dur)) - - # Search for intervals already added that overlap with current - # interval. If we find some, then we truncate the current - # interval to remove all overalps - ov = sad.search(interval) - interval, other_intervals = remove_overlap(ov, interval) - if interval[0] == interval[1]: - # continue if interval was removed - continue - - sad.add(interval) - - # if other_intervals is not empty, add these intervals to tree - for new_interv in other_intervals: - if new_interv[0] == new_interv[1]: - # continue if interval was removed - continue - - sad.add(new_interv) - - return sad, fname - -def remove_overlap(ov, interval): - """Take as input an interval, and the set of the intervals it overlaps """ - """with, and output the interval trimmed (possibly to 0) so that there """ - """are no overlaps in the output""" - onset, offset = interval[0], interval[1] - other_intervals = [] - - for covered_int in ov: - if (onset >= covered_int[0]) and (offset <= covered_int[1]): - # if interval is already covered, return empty interval - onset = 0 - offset = 0 - elif (onset < covered_int[0]) and (offset >= covered_int[0])\ - and (offset <= covered_int[1]): - # change onset/offset to avoid overlap - onset = onset - offset = covered_int[0] - elif (onset <= covered_int[1]) and (offset > covered_int[1])\ - and (onset >= covered_int[0]): - onset = covered_int[1] - offset = offset - elif(onset <= covered_int[0]) and (offset >= covered_int[1]): - # case where the new interval contains a previously add one - # in this case return second interval - new_onset = covered_int[1] - new_offset = offset - onset = onset - offset = covered_int[0] - - temp_interval = Interval(new_onset, new_offset) - # call remove_overlap again with the newly created interval - # in case it also has overlap in ov - new_interval, _other_intervals = remove_overlap(ov, - temp_interval) - - other_intervals.append(new_interval) - other_intervals += _other_intervals - - - #else: - # # Do nothing, all problems are probably allready resolved ! - # # print('{} {} {}'.format(interval, ov, other_intervals)) - - return Interval(onset, offset), other_intervals - -def write_scp(tree, fname, output): - """Write output in SCP format""" - # First order the intervals - out_intervals = [] - for interval in tree: - out_intervals.append((interval[0], interval[1])) - out_intervals = sorted(out_intervals, key=itemgetter(0)) - - with open(output, 'w') as fout: - fout.write(u'') # write empty string in case out_intervals is empty - for onset, offset in out_intervals: - fout.write(u'{fname}_{on}_{off}={fname}.fea[{on},{off}]\n'.format( - fname=fname, - on=int(onset)*100, - off=int(offset)*100)) - -def main(): - """Take diarization in RTTM format as input, and write """ - """SAD in scp format, with ordered intervals, as output.""" - parser = argparse.ArgumentParser( - description='Take diarization in RTTM format as input, and write ' - 'SAD in scp format, with ordered intervals, as output.', - add_help=True, - usage='%(prog)s [RTTM] [SCP]') - parser.add_argument( - 'rttm', type=str, metavar='PATH', - help='Path to the RTTM input') - parser.add_argument( - 'scp', type=str, metavar='PATH', - help='Path to the SCP output') - - args = parser.parse_args() - - sad_tree, fname = read_rttm(args.rttm) - write_scp(sad_tree, fname, args.scp) - -if __name__ == "__main__": - main() diff --git a/utils/.gitignore b/utils/.gitignore new file mode 100755 index 0000000..fcff087 --- /dev/null +++ b/utils/.gitignore @@ -0,0 +1,33 @@ +# History files +.Rhistory +.Rapp.history + +# Session Data files +.RData + +# Example code in package build process +*-Ex.R + +# Output files from R CMD build +/*.tar.gz + +# Output files from R CMD check +/*.Rcheck/ + +# RStudio files +.Rproj.user/ + +# produced vignettes +vignettes/*.html +vignettes/*.pdf + +# OAuth2 token, see https://github.com/hadley/httr/releases/tag/v0.3 +.httr-oauth + +# knitr and R markdown default cache directories +/*_cache/ +/cache/ + +# Temporary files created by R markdown +*.utf8.md +*.knit.md diff --git a/utils/README.md b/utils/README.md new file mode 100755 index 0000000..1735de1 --- /dev/null +++ b/utils/README.md @@ -0,0 +1,7 @@ +# Utils + +This folder contains utilities that are potentially useful to more than one tool in the ACLEW Diarization VM. It also contains some launchers that are in quarantine at present: +- noisemes_full.sh +- yuniSeg.sh + +The end user should not need to deal with files in this folder, they are meant to be called by the "tools" that reside in the [DiViMe/launcher](https://github.com/srvk/DiViMe/tree/master/launcher) folder. diff --git a/utils/WCE_preprocess.sh b/utils/WCE_preprocess.sh new file mode 100755 index 0000000..6fc7a3e --- /dev/null +++ b/utils/WCE_preprocess.sh @@ -0,0 +1,83 @@ +# Perform pre-porcessing of speech data for WCE module training and/or cross-validation. Requires +# wav-files following ACLEW file naming convention, and .eafs containing daylong annotations. + +SCRIPT_DIR=$(dirname "$0") + +WAV_FOLDER=$1 +EAF_FOLDER=$2 +LANGUAGE=$3 + +display_usage() { + echo "usage: $0 [wav_folder] [eaf_folder] [language] [SAD]" + echo " wav_folder The folder where to find the wav files (REQUIRED)." + echo " eaf_folder The folder where to find the eaf files (REQUIRED)." + echo " language The language of the transcription : english, spanish or tzeltal (REQUIRED)." + echo " SAD The SAD used to detect speech: opensmileSad (DEFAULT), tocomboSad" + echo "" + echo "Wav files have to follow ACLEW file naming conventions:" + echo "COR_baby_yyyyyy_zzzzzz.wav where COR is the three-character ID of the corpus, baby is four-digit" + echo "identifier of the baby, and yyyyyy and zzzzzz are 2/5min segment onsets and offsets in seconds" + echo "measured from the beginning of the daylong file, e.g., BER_0396_005220_005340.wav" + echo "" + echo "Eaf-files must be of form xxxx.eaf, where xxxx is the babyID corerresponding to the .wav files." + exit 1 + } + +if [ -z "$1" ] || [ -z "$2" ] || [ -z "$3" ] || ! [[ $LANGUAGE =~ ^(english|spanish|tzeltal)$ ]]; then + display_usage +fi + +if [ -z "$4" ]; then + SAD="opensmileSad" +else + SAD=$4 +fi; + + + +# Check that WCE_VM temp dir exists and create if needed +if [ ! -d "/vagrant/data/WCE_VM_TEMP/" ]; then + mkdir /vagrant/data/WCE_VM_TEMP/ + mkdir /vagrant/data/WCE_VM_TEMP/RTTM/ + mkdir /vagrant/data/WCE_VM_TEMP/ENRICH/ +fi; + + +# 1) Put .wav files to {wav_folder} under {data} and .eafs to another {eaf_folder}. + +# 2) Run: SAD on the data + +echo "Running SAD on the .wav files" + +~/launcher/${SAD}.sh $WAV_FOLDER + +#for file in "/vagrant/${WAV_FOLDER}/*.rttm"; do cp "$file" /vagrant/data/WCE_VM_TEMP/RTTM/;done +#cp "/vagrant/${WAV_FOLDER}*.rttm" /vagrant/data/WCE_VM_TEMP/RTTM/ + +curdir=$(pwd);cd /vagrant/${WAV_FOLDER};cp *.rttm /vagrant/data/WCE_VM_TEMP/RTTM/;cd $curdir + +# 3) Call eaf2enriched.sh to create .rttm files from .eaf files + +echo "Converting .eafs to .rttms..." + +~/utils/eaf2enriched_txt.sh ${EAF_FOLDER} english + +curdir=$(pwd);cd /vagrant/${EAF_FOLDER};cp *enriched.txt /vagrant/data/WCE_VM_TEMP/ENRICH/;cd $curdir + +# 4) Call SADsplit to create annotation file for each SAD output segment + +echo "Creating .wav files and annotation files for each SAD segment..." + +~/repos/WCE_VM/aux_VM/SADsplit.sh /vagrant/data/WCE_VM_TEMP/ENRICH/ /vagrant/data/WCE_VM_TEMP/RTTM/ $WAV_FOLDER + +echo " " +echo "WCE data preprocessing complete. Use WCE_LOSO_eval.sh to run leave-one-subject-out validation +of the system, or WCE_fulltrain.sh to adapt the WCE for all the data." + +# 5) Do leave-one-subject-out validation of the WCE + +#~/launcher/WCE_LOSO_eval.sh + +# 6) Train with all data + +#~/launcher/WCE_fulltrain.sh diff --git a/utils/adjust_timestamps.py b/utils/adjust_timestamps.py new file mode 100755 index 0000000..c598301 --- /dev/null +++ b/utils/adjust_timestamps.py @@ -0,0 +1,308 @@ +#!/usr/bin/env python +# +# +import pympi as pmp +import shutil +import os +import argparse +import subprocess +from collections import defaultdict +from operator import itemgetter + + +def eaf2rttm(path_to_eaf): + """ + function to write a new .rttm file which is a transcription of the .eaf + given as input + + """ + + # in EAF, timestamps are in milliseconds, convert them to seconds + # TODO read scale from header of EAF + sampling_freq = 1000.0 + + print('\n') + # read eaf file + EAF = pmp.Elan.Eaf(path_to_eaf) + + participants = [] + + # gather all the talker's names + for k in EAF.tiers.keys(): + + if 'PARTICIPANT' in EAF.tiers[k][2].keys(): + + if EAF.tiers[k][2]['PARTICIPANT'] not in participants: + + participants.append(EAF.tiers[k][2]['PARTICIPANT']) + + print('participants: {}'.format(participants)) + + base = os.path.basename(path_to_eaf) + name = os.path.splitext(base)[0] + + print('parsing file: {}'.format(name)) + + # get the begining, ending and transcription for each annotation of + # each tier + rttm = [] + for participant in participants: + if participant not in EAF.tiers: + continue + for _, val in EAF.tiers[participant][0].items(): + # Get timestamps + start = val[0] + end = val[1] + + t0 = EAF.timeslots[start] / sampling_freq + length = EAF.timeslots[end] / sampling_freq - t0 + + # get transcription + transcript = val[2] + + rttm.append((name, t0, length, transcript, participant)) + + return rttm + +def eaf2rttm_CAS(path_to_eaf): + """ + function to write a new .rttm file which is a transcription of the .eaf + given as input + + """ + + sampling_freq = 1000.0 + + print('\n') + EAF = pmp.Elan.Eaf(path_to_eaf) + + participants = [] + + for k in EAF.tiers.keys(): + + if 'PARTICIPANT' in EAF.tiers[k][2].keys(): + + if EAF.tiers[k][2]['PARTICIPANT'] not in participants: + + participants.append(EAF.tiers[k][2]['PARTICIPANT']) + + print('participants: {}'.format(participants)) + + base = os.path.basename(path_to_eaf) + name = os.path.splitext(base)[0] + + print('parsing file: {}'.format(name)) + + # get the begining, ending and transcription for each annotation of + # each tier + rttm = [] + for participant in participants: + + for _, val in EAF.tiers[participant][0].items(): + # Get timestamps + start = val[0] + end = val[1] + + t0 = EAF.timeslots[start] / sampling_freq + length = EAF.timeslots[end] / sampling_freq - t0 + + # get transcription + transcript = val[2] + + rttm.append((name, t0, length, transcript, participant)) + return rttm + +def write_rttm(output, rttm_path, annotations): + """ write annotations to rttm_path""" + + with open(os.path.join(output, rttm_path), 'w') as fout: + rttm_name = rttm_path.split('.')[0] + print len(annotations) + for name, t0, length, transcript, participant in annotations: + fout.write(u"SPEAKER\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n".format + (rttm_name, 1, "%.3f" %t0, "%.3f" %length, transcript, + "", participant, 1 )) + +def get_all_on_offs(eaf): + """ + Return all the annotated intervals from the current file + """ + EAF = pmp.Elan.Eaf(eaf) + + all_intervals = EAF.tiers['on_off'][0] + + # get the segments delimited for "on_off" tier, + # as those give the timestamps between which are the annotations + on_offs = [] + for key in all_intervals: + interv = all_intervals[key] + beg_end = interv[2] + beg, end = [float(time) for time in beg_end.split('_')] + # store in seconds, not milliseconds + on_offs.append((beg/1000.0, end/1000.0)) + + return on_offs + +def get_all_on_offs_CAS(eaf): + """ + Return all the annotated intervals from the current file + """ + EAF = pmp.Elan.Eaf(eaf) + + all_intervals = EAF.tiers['code'][0] + + on_offs = [] + for key in all_intervals: + interv = all_intervals[key] + beg_end = interv[2] + _beg = interv[0] + _end = interv[1] + beg = EAF.timeslots[_beg] + end = EAF.timeslots[_end] + + # store in seconds, not milliseconds + on_offs.append((beg/1000.0, end/1000.0)) + + return on_offs + +def cut_audio(on_offs, input_audio, dest): + """ + Extract from the daylong recordings the small parts that have + been annotated + """ + + # for each annotated segment, call sox to extract the part from the + # wav file + # Also, write each onset/offset with 6 digits + for on, off in on_offs: + audio_base = os.path.splitext(input_audio)[0] + wav_name = os.path.basename(audio_base) + dir_name = os.path.split(os.path.dirname(audio_base))[-1] + + # add the necessary number of 0's to the onsets/offsets + # to have 6 digits + str_on = str(int(on)) + str_off = str(int(off)) + + str_on = (6 - len(str_on)) * '0' + str_on + str_off = (6 - len(str_off)) * '0' + str_off + output_audio = '_'.join([dir_name, wav_name, + str_on, str_off]) + '.wav' + cmd = ['sox', input_audio, os.path.join(dest, output_audio), + 'trim', str(on), str(off - on)] + print " ".join(cmd) + subprocess.call(cmd) + +def extract_from_rttm(on_offs, rttm): + """ + For each minute of annotation, extract the annotation of that minute + from the transcription and write a distinct .rttm file with all the + timestamps with reference to the begining of that segment. + """ + sorted_rttm = sorted(rttm, key=itemgetter(1)) + + # create dict { (annotated segments) -> [annotation] } + extract_rttm = defaultdict(list) + for on, off in on_offs: + for name, t0, length, transcript, participant in sorted_rttm: + end = t0 + length + if (on <= t0 < off) or (on <= end < off): + # if the current annotation is (at least partially) + # contained in the current segment, append it. + # Adjust the segment to strictly fit in on-off + t0 = max(t0, on) + end = min(end, off) + length = end - t0 + extract_rttm[(on, off)].append((name, t0 - on, + length, + transcript, participant)) + elif (on > t0) and (end >= off): + # if the current annotation completely contains the annotated + # segment, add it also. This shouldn't happen, so print a + # warning also. + print('Warning: speaker speaks longer than annotated segment.\n' + 'Please check annotation from speaker {},' + 'between {} {}, segment {} {}.\n'.format(name, t0, + end, on, off)) + extract_rttm[(on, off)].append((name, 0, off - on, + transcript, participant)) + elif (end < on): + # wait until reach segment + continue + elif (t0 >= off): + # no point in continuing further since the rttm is sorted. + break + + return extract_rttm + +def main(): + """ + Take as input one eaf and wav file, and extract the segments from the + wav that have been annotated. + """ + parser = argparse.ArgumentParser(description="extract annotated segments") + parser.add_argument('eaf', type=str, + help='''Path to the transcription of the wave file, ''' + ''' in eaf format.''') + parser.add_argument('wav', type=str, + help='''Path to the wave file to treat''') + parser.add_argument('output', type=str) + parser.add_argument('-c', '--CAS', action='store_true', + help='''By default the script detects the segments''' + ''' using the "on_off" tier. For the CAS corpus,''' + ''' we should use the "code" tier.\n''' + ''' Enable this option when treating the CAS corpus''') + args = parser.parse_args() + + output = args.output + print output + #if not os.path.isdir( os.path.join(output, 'treated')): + # os.makedirs(os.path.join(output, 'treated')) + #if not os.path.isdir( os.path.join(output, 'treated', 'talker_role')): + # os.makedirs(os.path.join(output, 'treated', 'talker_role')) + + if args.CAS: + # read transcriptions + complete_rttm = eaf2rttm_CAS(args.eaf) + + # extract annotated segments + on_offs = get_all_on_offs_CAS(args.eaf) + else: + # read transcriptions + complete_rttm = eaf2rttm(args.eaf) + + # extract annotated segments + on_offs = get_all_on_offs(args.eaf) + + # cut audio files according to on_off/code tier in eaf annotations + cut_audio(on_offs, args.wav, output) + + # store in dict the annotations to write in rttm format + extract_rttm = extract_from_rttm(on_offs, complete_rttm) + + # write one rttm file per on_off/tier segment + for key in extract_rttm: + base = os.path.basename(args.eaf) + + # get the name of the corpus by taking the name of the folder and removing "raw" + dir_name = os.path.split( os.path.dirname(args.eaf) )[-1].split('_')[-1] + + name = os.path.splitext(base)[0] + # check is initials of annotator are in eaf name + if '-' in name: + name = name.split('-')[0] + + # add 0's to have exactly 6 digits (i.e. 1 second is 000001 s) + str_on = str(int(key[0])) + str_off = str(int(key[1])) + + str_on = (6 - len(str_on)) * '0' + str_on + str_off = (6 - len(str_off)) * '0' + str_off + + rttm_path = '_'.join([dir_name, name, + str_on, str_off]) + '.rttm' + write_rttm(output, rttm_path, extract_rttm[key]) + + +if __name__ == '__main__': + main() diff --git a/utils/analyze.sh b/utils/analyze.sh new file mode 100755 index 0000000..560f288 --- /dev/null +++ b/utils/analyze.sh @@ -0,0 +1,65 @@ +#!/bin/bash +# pipeline producing a 3-class diarization (all children, female adult, male adult), a word count estimator for all speaker types, and a vocal class for children speakers +# Okko Räsänen & A. Cristia + +SCRIPT_DIR=$(dirname "$0") + +if [ "$#" -ne 1 ]; then +DATA_FOLDER="data/to_analysis/" +else +DATA_FOLDER=$1 +fi + +if [ ! -d "/vagrant/${DATA_FOLDER}/logs/" ]; then + mkdir /vagrant/${DATA_FOLDER}/logs/ +fi + +sh ~/launcher/yunitate.sh ${DATA_FOLDER} english > /vagrant/${DATA_FOLDER}/logs/yunitate.log +sh ~/launcher/vcm.sh ${DATA_FOLDER} english > /vagrant/${DATA_FOLDER}/logs/vcm.log +sh WCE_from_SAD_outputs.sh /vagrant/${DATA_FOLDER} yunitator_english > /vagrant/${DATA_FOLDER}/logs/WCE.log + +if [ -d "/vagrant/${DATA_FOLDER}/detailed_outputs/" ]; then +rm /vagrant/${DATA_FOLDER}/detailed_outputs/*.rttm +else +mkdir /vagrant/${DATA_FOLDER}/detailed_outputs/ +fi + +mv /vagrant/${DATA_FOLDER}/*.rttm /vagrant/${DATA_FOLDER}/detailed_outputs/ + +if [ -d "/vagrant/${DATA_FOLDER}/wav_tmp/" ]; then +rm -rf /vagrant/${DATA_FOLDER}/wav_tmp/ +fi + +ls /vagrant/${DATA_FOLDER}/detailed_outputs/yunitator*.rttm | sed "s/.*english_//g" > /vagrant/${DATA_FOLDER}/files.txt + +cat /vagrant/${DATA_FOLDER}/files.txt | while read -r line ; do + #get first five cols & speaker col + awk '{print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $8}' < /vagrant/${DATA_FOLDER}/detailed_outputs/yunitator_english_$line > /vagrant/${DATA_FOLDER}/base.temp + + #add WCE info + awk '{print $8}' < /vagrant/${DATA_FOLDER}/detailed_outputs/WCE_yunitator_english_$line > /vagrant/${DATA_FOLDER}/wce.temp + paste /vagrant/${DATA_FOLDER}/base.temp /vagrant/${DATA_FOLDER}/wce.temp > /vagrant/${DATA_FOLDER}/base2.temp + + #sort by speaker column to add the + sort -k6 /vagrant/${DATA_FOLDER}/base2.temp > /vagrant/${DATA_FOLDER}/base3.temp + + #pull out chi info & complete with empties + awk '{print "{vcm@ " $8 "}"}' < /vagrant/${DATA_FOLDER}/detailed_outputs/vcm_$line > /vagrant/${DATA_FOLDER}/vcm.temp + + #todo implement adding of ADS for adult lines + nchi=`wc -l /vagrant/${DATA_FOLDER}/vcm.temp | awk '{print $1}'` + nall=`wc -l /vagrant/${DATA_FOLDER}/base3.temp | awk '{print $1}'` + if [ "$nall" -gt "$nchi" ] ; then + i=$nchi + while [ "$i" -lt "$nall" ] ; do + echo "{xds@ 0}" >> /vagrant/${DATA_FOLDER}/vcm.temp + i=$((i + 1)) + done + fi + paste /vagrant/${DATA_FOLDER}/base3.temp /vagrant/${DATA_FOLDER}/vcm.temp > /vagrant/${DATA_FOLDER}/base4.temp + sort -k4 -n /vagrant/${DATA_FOLDER}/base4.temp | sed "s/.rttm//" | sed "s/NCS/N/" | sed "s/CNS/C/" | awk -F"\t" '{print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $7 "\t" $8 "\t" "" "\t" $6}' > /vagrant/${DATA_FOLDER}/${line}.txt + awk -F"\t" '{print $9 "-wce," $9 "," $4 "," $5 "," $6}' < /vagrant/${DATA_FOLDER}/${line}.txt > /vagrant/${DATA_FOLDER}/${line}_forELAN.txt + grep "vcm" /vagrant/${DATA_FOLDER}/${line}.txt | awk -F"\t" '{print $9 "-vcm," $9 "," $4 "," $5 "," $7}'| sed "s/xds@//" | tr -d "{" | tr -d "}" >> /vagrant/${DATA_FOLDER}/${line}_forELAN.txt + + rm /vagrant/${DATA_FOLDER}/*.temp +done diff --git a/utils/catspa-syllabify-corpus.pl b/utils/catspa-syllabify-corpus.pl new file mode 100755 index 0000000..36a8c4a --- /dev/null +++ b/utils/catspa-syllabify-corpus.pl @@ -0,0 +1,93 @@ +#!/usr/bin/perl + +# Alex Cristia alecristia@gmail.com 2015-07-13 adapted VERY MINIMALLY this code from scripts +# distributed by Lawrence Phillips & Lisa Pearl, 12/23/13 (UCI-Brent-Syllabic Corpus) - credit is owed mainly to those authors + +# Uses the maximum onset principle to fully syllabify a corpus +use File::Basename; + +$language=$ARGV[0]; +$filecorpus=$ARGV[1]; +$output=$ARGV[2]; +$dirname=dirname($filecorpus); + +#print "\n the language is $language\n"; +# /input/ OR /scripts/ ? +# Save valid onsets from ValidOnsets.txt +%onsets = {}; +open(ONSETS, "<$dirname/$language-ValidOnsets.txt") or die("Couldn't open $dirname/$language-ValidOnsets.txt\n"); +while(defined($fileline = )){ + chomp($fileline); + #print "$fileline\n"; + $onsets{$fileline} = 1; #This is an odd way of stating things + #print "added"; +} +#print "out of the while"; +close(ONSETS); + +# Save valid vowels from Vowels.txt +%vowels = {}; +open(VOWELS, "<$dirname/$language-Vowels.txt") or die("Couldn't open $dirname/$language-Vowels.txt\n"); +my $vowels = ; +close(VOWELS); +#print "$vowels\n"; + +# Go through CORPUS.txt, +# for nonsyllabified words: for each syllable, find its vowel, and its maximum onset, given acceptable onsets and beginning of word. +# print syllabified version to syllabified-CORPUS.txt. +open(SYLLABIFIED, ">$output") or die("Couldn't open $output for writing\n"); +open(CORPUS, "<$filecorpus") or die("Couldn't open $filecorpus for reading"); + + + +while(defined($fileline = )){ + #print "entered first while\n"; + chomp($fileline); + $currline = $fileline; + @wordarray = split(" ", $currline); # divide the line into a set of words + $syllline="";#we start with a clean slate for each line + while(@wordarray > 0){ + $currword = pop(@wordarray); # cut out the last word in the word array & put it in currword + #print "now looking at $currword\n"; + @chararray = split(//, $currword); + + $syllword="";#we start with a clean slate for each word + $currsyllable=""; #and for the syllable + + while(@chararray > 0){ + $currchar = pop(@chararray); # cut out the last char in the char array for this word & put it in currchar + $currsyllable = $currchar.$currsyllable; # append currchar to current syllable - that will be necessary regardless of whether it's a vowel or a coda + # if hit a vowel.. + #if($currchar =~ /[ae3EiOo0u]/){ + if($currchar =~ /[$vowels]/){ + # print "$currchar\n"; + #if(@chararray[@chararray-1] !=~ /[ae3EiOo0u]/){ + if(@chararray[@chararray-1] !=~ /[$vowels]/){ + # print "@chararray[@chararray-1]\n"; + #if this char is a vowel and the previous one is not, then we need to make the onset + $onset = ""; #we start with nothing as the onset + #then we want to take one letter at a time and check whether their concatenation makes a good onset + while(@chararray > 0 && exists($onsets{@chararray[@chararray-1] . $onset})){ + #print "$onsets{@chararray[@chararray-1] . $onset}\n"; + $currchar = pop(@chararray); + $onset = $currchar . $onset; + } + #we get here either because we've concatenated the onset+rest or because there was no onset and the preceding element is a vowel, so this is the end of the syllable + $currsyllable = $onset . $currsyllable; + # add syllable to word entry + } + $syllword = "\/" . $currsyllable . $syllword; + $currsyllable = ""; + }#we end the if we are looking at a vowel + }#when we end this while there are no more characters in this word, so we can add it + $syllline=$syllword. " " . $syllline; + #print "$syllword\n"; + + } #while we work our way through the words in the line -- so when we exit this, we are ready to print out a syllabified line + if($syllline){ + print SYLLABIFIED "$syllline\n"; + } +}#while there are lines in this file + +close(SYLLABIFIED); +close(CORPUS); \ No newline at end of file diff --git a/utils/change_tsi_lena_names.py b/utils/change_tsi_lena_names.py new file mode 100755 index 0000000..88134ce --- /dev/null +++ b/utils/change_tsi_lena_names.py @@ -0,0 +1,43 @@ +import glob +import os +import re +import argparse + +parser = argparse.ArgumentParser(description="Convert the old naming convention of tsi/lena files into the new one.") +parser.add_argument('-f', '--folder', type=str, required=True, + help="path to the input folder tsi/lena (containing C25_NA_M14_20170719_56040.{wav|rttm} files).") +args = parser.parse_args() + +path_tsi_lena=os.path.join("/vagrant", args.folder) +rttm_files = glob.iglob(os.path.join(path_tsi_lena, '*.rttm')) + +for rttm in rttm_files: + dirname = os.path.dirname(rttm) + basename = os.path.splitext(os.path.basename(rttm))[0] + basename_splitted = basename.split('_') + + old_name = os.path.splitext(basename)[0] + if basename[0] == 'C' and len(basename_splitted) == 5: + new_name = '_'.join([basename_splitted[0], + basename_splitted[3], + basename_splitted[4]]) + + # Modify rttm name + new_path_rttm = os.path.join(dirname, new_name+'.rttm') + os.rename(rttm ,new_path_rttm) + + # Modify wav name + old_path_wav = os.path.join(dirname, old_name+'.wav') + new_path_wav = os.path.join(dirname, new_name+'.wav') + os.rename(old_path_wav, new_path_wav) + + # Do the change within the rttm (column 2 containing the filename) + new_name = os.path.splitext(new_name)[0] + f = open(new_path_rttm, 'r+b') + f_content = f.read() + f_content = re.sub(old_name, new_name, f_content) + f.seek(0) + f.truncate() + f.write(f_content) + f.close() + diff --git a/utils/chat2rttm_folder.sh b/utils/chat2rttm_folder.sh new file mode 100755 index 0000000..762e474 --- /dev/null +++ b/utils/chat2rttm_folder.sh @@ -0,0 +1,11 @@ +#!/bin/bash +# +# Shell script to convert all .CHA files inside a folder to .rttm format +# +for j in ${1}/*.cha; do + stm=${j/.cha/.stm} + rttm=${j/.cha/.rttm} + chat2stm.sh $j >> $stm + cat $stm | awk -v file="$(basename ${rttm/.rttm/})" '{print "SPEAKER",file,"1",$4,($5 - $4),"","",$2,"","" }' > $rttm +# rm ${j/.cha/.stm} +done \ No newline at end of file diff --git a/utils/chat2stm.sh b/utils/chat2stm.sh new file mode 100755 index 0000000..d1420dd --- /dev/null +++ b/utils/chat2stm.sh @@ -0,0 +1,53 @@ +#!/bin/bash +# +# Shell script to convert .CHA format to .STM format +# +# ./cha2stm.sh .cha produces .stm +# +# To have this use replacement words instead of marking OOV words as "" +# swap (uncomment) the last line to change the arguments to parse_cha_xml.sh + +BASEDIR=$(dirname $0) + +if [ $# -ne 1 ]; then + echo "Usage: cha2stm.sh .cha" + echo + exit 1; +fi + +filename=$(basename "$1") +dirname=$(dirname "$1") +extension="${filename##*.}" +basename="${filename%.*}" + +# Install Java 8 and CHATTER +# First get chatter +mkdir -p ~/bin/lib +( +cd ~/bin/lib +if [ ! -f chatter.jar ]; then + wget http://talkbank.org/software/chatter.jar || exit 1 +fi + +# now get java 8 +if [ -d zulu8.17.0.3-jdk8.0.102-linux_x64 ]; then + : + #echo "Not installing Java 8 since it is already there." 1>&2 +else + echo "Downloading and installing Java 8" 1>&2 + wget http://cdn.azul.com/zulu/bin/zulu8.17.0.3-jdk8.0.102-linux_x64.tar.gz 1>&2 || exit 1 + tar -zxvf zulu8.17.0.3-jdk8.0.102-linux_x64.tar.gz 1>&2 + rm zulu8.17.0.3-jdk8.0.102-linux_x64.tar.gz 1>&2 + echo "Done installing Java 8" 1>&2 +fi +) + +if [ -f $dirname/$basename.cha ]; then + # + # First convert CHA to CHATTER xml + ~/bin/lib/zulu8.17.0.3-jdk8.0.102-linux_x64/bin/java -cp ~/bin/lib/chatter.jar org.talkbank.chatter.App -inputFormat cha -outputFormat xml -output $dirname/$basename.xml $dirname/$basename.cha + + # Convert CHATTER xml to STM + #python scripts/parse_cha_xml.py $dirname/$basename.xml --stm --replacement + python $BASEDIR/parse_cha_xml.py $dirname/$basename.xml --stm --oov +fi diff --git a/utils/check_folder.sh b/utils/check_folder.sh new file mode 100755 index 0000000..d7be677 --- /dev/null +++ b/utils/check_folder.sh @@ -0,0 +1,35 @@ +#!/bin/bash + +check_folder=$(readlink -f $1) + +# First check that the folder is not empty +if [[ -z "$(ls -A $check_folder)" ]]; then + echo "data folder is empty!" + exit +else + echo "wavs and transcriptions found !" +fi + +# Then, check the consistency of all the files -> check if +# the wav have no amplitude +# (credit to http://decided.ly/2013/02/06/find-silent-audio-files/) +## A quick hack like script to list all the files that have +## a low amplitude. + +Max=0.0 # Any amplitude greater than this will NOT be listed +#OutList=~/output.list # The name of the file that contains a +# list of file names only of all the +# low-amplitude files. + +# rm $OutList +for each in `ls $check_folder/*.wav` +do + amplitude=$(sox "$each" -n stat 2>&1 | grep "Maximum amplitude" | cut -d ":" -f 2 | sed 's/ //g') + if [[ $(echo "if (${amplitude} > ${Max}) 1 else 0" | bc) -eq 0 ]] + then + echo "$each --> $amplitude" >&2 + echo "$each seems empty !" + fi +done + +echo "Tests finished" diff --git a/utils/chunk.sh b/utils/chunk.sh new file mode 100755 index 0000000..5219a97 --- /dev/null +++ b/utils/chunk.sh @@ -0,0 +1,61 @@ +#!/bin/bash + +# chunk.sh - break files into 5 minute segments in a special folder +# then run a processing script on the folder of chunks +# then merge the resulting RTTMs back together into the +# folder where the input file resides +# +# assumes $1 is full path to a large WAV file +# assumes $2 is path to script to run (such as are found in utils/ +# that takes a folder name in /vagrant, and processes all WAVs in it) +# +# produces a single .rttm in the same folder $1 was found in +# +# uses /vagrant/chunk/ for temporary results + +if [[ $# < 2 ]]; then + echo "Usage: " $0 " " + echo " e.g. utils/chunk.sh /vagrant/ami.wav launcher/yunitate.sh" + exit +fi + +WORKFOLDER="/vagrant/data/temp/chunk" + +# remember basename for later ;) +filename=$(basename "$1") +basename="${filename%.*}" # name without extension +dirname=`dirname $1` # input folder + +if [[ -f $WORKFOLDER ]]; then + # clean out the folder first + rm -rf $WORKFOLDER/* +else + # create the folder + mkdir -p $WORKFOLDER +fi + +# Create 5 minute (except for last piece) named chunk-001.wav chunk-002.wav etc. +sox $1 $WORKFOLDER/chunk-.wav trim 0 300 : newfile : restart + +# run whichever tool you were going to run over the $WORKFOLDER folder +# assume programs are like in ~/launcher and assume path is /vagrant/ +# but are only given +$2 `basename $WORKFOLDER` + +# assume output is in $WORKFOLDER/chunk-00x.rttm - rename/ rejoin + +OUTFILE=$dirname/$basename.rttm +rm -f $OUTFILE # don't want to append to existing one! +touch $OUTFILE + +COUNT=0 +for f in `ls $WORKFOLDER/*chunk-*.rttm`; do + + # add $COUNT seconds to start time (column 4) of RTTM, and concatenate to $OUTFILE + cat $f | awk -v ADDME=$COUNT '{print $1,$2,$3,($4+ADDME),$5,$6,$7,$8,$9}' >> $OUTFILE + + # increment COUNT in 5 minutes' worth of seconds (300) + COUNT=$(($COUNT + 300)) + +done + diff --git a/utils/compute_metrics.py b/utils/compute_metrics.py new file mode 100644 index 0000000..05a8076 --- /dev/null +++ b/utils/compute_metrics.py @@ -0,0 +1,232 @@ +from pyannote.core import Segment, Timeline, Annotation +from pyannote.metrics.errors.identification import IdentificationErrorAnalysis +import pyannote.metrics.diarization as diarization +import pyannote.metrics.identification as identification +import pyannote.metrics.detection as detection +import pyannote.core.notebook as notebook +import matplotlib.pyplot as plt +import os, glob, errno +import argparse +import numpy as np +import warnings +import re +warnings.filterwarnings("ignore", category=UserWarning, module='pyannote') +# Here, we want to filter the following warning : +# UserWarning: 'uem' was approximated by the union of 'reference' and 'hypothesis' extents. +# "'uem' was approximated by the union of 'reference'" + +def get_best_start(beginnings, durations, tot_duration): + STEP_SIZE = 10 + beginnings = np.array(beginnings) + lst = list(range(0, int(tot_duration), STEP_SIZE)) + best_sum_speech = 0.0 + best_start = 0 + for i in range(0, len(lst)): + if i+7 > len(lst): + break + beg, end = lst[i], lst[i+6] + condition = (beginnings > beg) & (beginnings < end) + sum_speech = np.sum(np.extract(condition, durations)) + if sum_speech > best_sum_speech: + best_start = beg + best_sum_speech = sum_speech + return best_start + +def find_1mn_highest_volubility(annotation): + support = list(annotation.get_timeline().support().__iter__()) + if len(support) > 0: + tot_duration = support[-1].end + if tot_duration <= 60.0: + return 0.0, 60.0 + else: + durations = [seg.duration for seg in support] + beginnings = [seg.start for seg in support] + best_start = get_best_start(beginnings, durations, tot_duration) + return best_start, best_start + 60 + +def rttm_to_annotation(input_rttm, collapse_to_speech=False): + """ + Given a path to a rttm file, create the corresponding Annotation objects + containing the triplets (t_beg, t_end, activity) + + Parameters + ---------- + input_rttm + A path to a rttm file that must exist. + + Returns + ------- + An Annotation object. + """ + anno = Annotation(uri=input_rttm) + if os.path.isfile(input_rttm): + with open(input_rttm) as fn: + for line in fn: + row = re.sub(' +', ' ', line).replace('\t', ' ').replace(' ', ' ').split(' ') + t_beg, t_dur, spkr = float(row[3]), float(row[4]), row[7] + if row[7] == "": + raise ValueError("Speaker role is empty in %s" % os.path.basename(input_rttm)) + anno[Segment(t_beg, t_beg + t_dur)] = spkr + return anno + else: + raise ValueError("%s not found. Please create it (even though it's empty) or remove the wav from the folder you want to evaluate." % os.path.basename(input_rttm)) + + +def run_metrics(references_f, hypothesis_f, metrics, visualization=False): + if len(references_f) != len(hypothesis_f): + raise ValueError("The number of reference files and hypothesis files must match ! (%d != %d)" + % (len(references_f), len(hypothesis_f))) + if visualization: + visualization_dir = os.path.join(os.path.dirname(hypothesis_f[0]), "visualization") + if not os.path.exists(visualization_dir): + os.makedirs(visualization_dir) + for ref_f, hyp_f in zip(references_f, hypothesis_f): + ref, hyp = rttm_to_annotation(ref_f), rttm_to_annotation(hyp_f) + basename = os.path.basename(ref_f) + # Set the uri as the basename for both reference and hypothesis + ref.uri, hyp.uri = basename, basename + # Let's accumulate the score for each metrics + for m in metrics.values(): + res = m(ref, hyp) + + # Let's generate a visualization of the results + if visualization: + moment = find_1mn_highest_volubility(ref) + if moment is not None: + # Set figure size, and crop the annotation + # for the highest volubile moment + start, end = moment[0], moment[1] + notebook.width = end / 4 + plt.rcParams['figure.figsize'] = (notebook.width, 10) + notebook.crop = Segment(start, end) + + # Plot reference + plt.subplot(211) + notebook.plot_annotation(ref, legend=True, time=False) + plt.gca().set_title('reference '+ os.path.basename(ref_f).replace('.rttm', ''), fontdict={'fontsize':18}) + + # Plot hypothesis + plt.subplot(212) + notebook.plot_annotation(hyp, legend=True, time=True) + plt.gca().set_title('hypothesis '+os.path.basename(hyp_f).replace('.rttm', ''), fontdict={'fontsize':18}) + + plt.savefig(os.path.join(visualization_dir, os.path.basename(hyp_f).replace('.rttm', '.png'))) + plt.close() + return metrics + + +def get_couple_files(ref_path, hyp_path=None, prefix=None): + # If hyp_path is None, then the hyp files are stored where the ref files are + if prefix is not None: + prefix = prefix + "_" + + ref_path = os.path.join("/vagrant", ref_path) + + if hyp_path is None: + hyp_files = list(glob.iglob(os.path.join(ref_path, prefix+'*.rttm'))) + ref_files = [os.path.join(os.path.dirname(f), os.path.basename(f).replace(prefix, '')) for f in hyp_files] + else: + hyp_path = os.path.join("/vagrant", hyp_path) + # Hyp files are stored in a different place, we check if a folder has been provided or if it just a single file + if os.path.isdir(hyp_path): + hyp_files = list(glob.iglob(os.path.join(hyp_path, prefix+'*.rttm'))) + ref_files = [os.path.join(ref_path, os.path.basename(f).replace(prefix, '')) for f in hyp_files] + elif os.path.isfile(hyp_path): + hyp_files = [hyp_path] + else: + raise ValueError("%s doesn't exist" % hyp_path) + + if len(ref_files) == 0 or len(hyp_files) == 0: + raise FileNotFoundError("No reference, or no hypothesis found were found.") + return sorted(ref_files), sorted(hyp_files) + + +def main(): + parser = argparse.ArgumentParser(description="Scripts that computes metrics between reference and hypothesis files." + "Inputs can be both path to folders or single file.") + parser.add_argument('-ref', '--reference', type=str, required=True, + help="Path of the reference.") + parser.add_argument('-hyp', '--hypothesis', type=str, required=False, default=None, + help="Path of the hypothesis" + "If None, consider that the hypothesis is stored where the reference is.") + parser.add_argument('-p', '--prefix', required=True, choices=["lena", "noisemesSad", "opensmileSad", + "tocomboSad", "yunitator_old", "yunitator_english", + "yunitator_universal", "diartk_noisemesSad", + "diartk_tocomboSad","diartk_opensmileSad", + "diartk_goldSad", "yuniseg_noisemesSad", + "yuniseg_opensmileSad", "yuniseg_tocomboSad", + "yuniseg_goldSad"], + help="Prefix that filenames of the hypothesis must match.") + parser.add_argument('-t', '--task', type=str, required=True, choices=["detection", "diarization", "identification"]) + parser.add_argument('-m', '--metrics', required=True, nargs='+', type=str, choices=["diaer", "coverage", + "completeness", "homogeneity", + "purity", "accuracy", + "precision", "recall", "deter", + "ider","idea"], + help="Metrics that need to be run.") + parser.add_argument('--visualization', action='store_true') + parser.add_argument('--identification', action='store_true') + args = parser.parse_args() + + if args.identification: + args.task = "identification" + + # Let's create the metrics + metrics = {} + for m in args.metrics: + if m == "accuracy": # All the 3 tasks can be evaluated as a detection task + metrics[m] = detection.DetectionAccuracy(parallel=True) + elif m == "precision": + metrics[m] = detection.DetectionPrecision(parallel=True) + elif m == "recall": + metrics[m] = detection.DetectionRecall(parallel=True) + elif m == "deter": + metrics[m] = detection.DetectionErrorRate(parallel=True) + elif args.task == "diarization" or args.task == "identification": # The diarization and the identification task can be both evaluated as a diarization task + if m == "diaer": + metrics[m] = diarization.DiarizationErrorRate(parallel=True) + elif m == "coverage": + metrics[m] = diarization.DiarizationCoverage(parallel=True) + elif m == "completeness": + metrics[m] = diarization.DiarizationCompleteness(parallel=True) + elif m == "homogeneity": + metrics[m] = diarization.DiarizationHomogeneity(parallel=True) + elif m == "purity": + metrics[m] = diarization.DiarizationPurity(parallel=True) + elif args.task == "identification": # Only the identification task can be evaluated as an identification task + if m == "ider": + metrics[m] = identification.IdentificationErrorRate(parallel=True) + elif m == "precision": + metrics[m] = identification.IdentificationPrecision(parallel=True) + elif m == "recall": + metrics[m] = identification.IdentificationRecall(parallel=True) + else: + print("Filtering out %s, which is not available for the %s task." % (m, args.task)) + else: + print("Filtering out %s, which is not available for the %s task." % (m, args.task)) + + # Get files and run the metrics + references_f, hypothesis_f = get_couple_files(args.reference, args.hypothesis, args.prefix) + + print("Pairs that have been found : ") + for ref, hyp in zip(references_f, hypothesis_f): + print("%s / %s "% (os.path.basename(ref), os.path.basename(hyp))) + + metrics = run_metrics(references_f, hypothesis_f, metrics, args.visualization) + + # Display a report for each metrics + for name, m in metrics.items(): + print("\n%s report" % name) + rep = m.report(display=True) + colnames = list(rep.columns.get_level_values(0)) + percent_or_count = rep.columns.get_level_values(1) + for i in range(0,len(percent_or_count)): + if percent_or_count[i] == '%': + colnames[i] = colnames[i] +' %' + rep.columns = colnames + rep.to_csv(os.path.join("/vagrant", args.reference, name+'_'+args.prefix+"_report.csv")) + + +if __name__ == '__main__': + main() + diff --git a/diarization.sh b/utils/diarization.sh similarity index 100% rename from diarization.sh rename to utils/diarization.sh diff --git a/utils/eaf2enriched_txt.sh b/utils/eaf2enriched_txt.sh new file mode 100755 index 0000000..30b09c9 --- /dev/null +++ b/utils/eaf2enriched_txt.sh @@ -0,0 +1,55 @@ +#!/bin/bash +INPUT=$1 +LANG=$2 +SCRIPT_DIR=$(dirname "$0") + +display_usage() { + echo "Given a folder containing eaf file, creates an enriched txt version of them." + echo "For each files, this script produces a txt version containing :" + echo -e "\t onset" + echo -e "\t off" + echo -e "\t speaker" + echo -e "\t cleaned up transcription" + echo -e "\t number of words" + echo -e "\t phonemized (or syllabified) version of the transcription" + echo -e "\t number of syllables" + + echo "usage: $0 [input] [language]" + echo " input The folder where to find the eaf files, or the single eaf file (REQUIRED)." + echo " output The language of the transcription : english, spanish or tzeltal (REQUIRED)." + exit 1 + } + +if [ -z "$1" ] || [ -z "$2" ] || ! [[ $LANG =~ ^(english|spanish|tzeltal)$ ]]; then + display_usage +fi + +if [ -f "/vagrant/"$1 ]; then + echo "File found." + eaf_path=${1#/vagrant/} + without_extension="${eaf_path%.*}" + echo "Converting $eaf_path files to ${without_extension}.txt ..." + python $SCRIPT_DIR/eaf2txt.py -i $eaf_path + + echo "Enriching ${without_extension}.txt" + $SCRIPT_DIR/selcha2clean.sh ${without_extension}.txt ${without_extension}_enriched.txt $LANG + + rm /vagrant/${without_extension}.txt +elif [ -d "/vagrant/"$1 ]; then + echo "Directory found." + for eaf_path in /vagrant/$1/*.eaf; do + eaf_path=${eaf_path#/vagrant/} + without_extension="${eaf_path%.*}" + echo "Converting $eaf_path files to ${without_extension}.txt ..." + python $SCRIPT_DIR/eaf2txt.py -i $eaf_path + + echo "Enriching ${without_extension}.txt" + $SCRIPT_DIR/selcha2clean.sh ${without_extension}.txt ${without_extension}_enriched.txt $LANG + + rm /vagrant/${without_extension}.txt + done +else + echo "File or directory not found." + display_usage +fi; + diff --git a/utils/eaf2txt.py b/utils/eaf2txt.py new file mode 100755 index 0000000..4878b61 --- /dev/null +++ b/utils/eaf2txt.py @@ -0,0 +1,115 @@ +#!/usr/bin/env python + +""" +This script converts an eaf file into a txt file +containing the following information : + + onset offset transcription receiver speaker_tier + +It can be run either on a single eaf file, +or on a whole folder containing eaf files. + +Example of use : + python tools/eaf2txt.py -i data/0396.eaf # One one file + python tools/eaf2txt.py -i data/ # On a whole folder + +About the naming convention of the output : + For each file called input_file.eaf, + the result will be stored in input_file.txt +""" + +import pympi as pmp +import argparse +import os +import glob +import sys +reload(sys) +sys.setdefaultencoding('utf8') + +def eaf2txt(path_to_eaf, output_folder, cleanup=False, format='okko'): + """ + Convert an eaf file to the txt format by extracting the onset, offset, ortho, + and the speaker tier. Note that the ortho field has been made by a human and needs + to be cleaned up. + + Parameters + ---------- + path_to_eaf : path to the eaf file. + + Write a txt whose name is the same than the eaf's one in output_folder + """ + basename = os.path.splitext(os.path.basename(path_to_eaf))[0] + output_path = os.path.join(output_folder, basename + '.txt') + output_file = open(output_path, 'w') + EAF = pmp.Elan.Eaf(path_to_eaf) + tiers = EAF.tiers + tiers = {key: value for key, value in tiers.items() if key.startswith(('xds@', 'vcm@'))} + for tier in tiers: + try: + annotations = EAF.get_annotation_data_for_tier(tier) + except KeyError: + print("Tier %s ignored..." %tier) + for annotation in annotations: + parameters = EAF.get_parameters_for_tier(tier) + if 'PARTICIPANT' in parameters: + if len(annotation) == 4: + onset, offset, receiver, transcript = annotation[0], annotation[1], annotation[2], annotation[3] + elif len(annotation) == 3: + onset, offset, receiver, transcript = annotation[0], annotation[1], '', annotation[2] + else: + raise ValueError("Format unknown : %s\n" % annotation) + speaker = parameters['PARTICIPANT'] + + if cleanup: + transcript = clean_up_annotation(transcript) + if format == 'okko': + output_file.write("%d\t%d\t%s\t%s\t%s\n" % (onset, offset, str(receiver), str(transcript), str(speaker))) + elif 'LINGUISTIC_TYPE_REF' in parameters and parameters['LINGUISTIC_TYPE_REF'] == 'XDS': + output_file.write("%s\t%d\t%d\t%s\t%s\t%s\t%s\t%s\n" % (str(speaker), onset, offset, str(receiver), 'NA', 'NA', 'NA', str(transcript))) + elif 'LINGUISTIC_TYPE_REF' in parameters and parameters['LINGUISTIC_TYPE_REF'] == 'VCM': + l=len(str.split(str(transcript))) + lex='0' if l == 0 else 'W' + mwu='M' if l > 1 else '1' + output_file.write("%s\t%d\t%d\t%s\t%s\t%s\t%s\t%s\n" % (str(speaker), onset, offset, 'NA', str(receiver), lex, mwu, str(transcript))) + else: + pass + + output_file.close() + +def main(): + parser = argparse.ArgumentParser(description="convert .eaf into .rttm") + parser.add_argument('-i', '--input', type=str, required=True, + help="path to the input .eaf file or the folder containing eaf files.") + parser.add_argument('-f', '--format', type=str, required=False, default='okko', + help="format flag, 'okko' or 'marisa'.") + args = parser.parse_args() + + # Removing extra beginning / that might break the code + if args.input[0] == '/': + args.input = args.input[1:] + + # Initialize the output folder as the same folder than the input + # if not provided by the user. + if args.input[-4:] == '.eaf': + output = os.path.dirname(args.input) + else: + output = args.input + + data_dir = '/vagrant' + + args.input = os.path.join(data_dir, args.input) + + output = os.path.join(data_dir, output) + if not os.path.isdir(output): + os.mkdir(output) + + if args.input[-4:] == '.eaf': # A single file has been provided by the user + eaf2txt(args.input, output, format=args.format) + else: # A whole folder has been provided + eaf_files = glob.iglob(os.path.join(args.input, '*.eaf')) + for eaf_path in eaf_files: + print("Processing %s" % eaf_path) + eaf2txt(eaf_path, output, format=args.format) + +if __name__ == '__main__': + main() diff --git a/utils/eafAAS2enriched_folder.sh b/utils/eafAAS2enriched_folder.sh new file mode 100755 index 0000000..d6e0b94 --- /dev/null +++ b/utils/eafAAS2enriched_folder.sh @@ -0,0 +1,10 @@ +#!/bin/bash +# +# Shell script to convert all .eaf files following the ACLEW Annotation Scheme inside a folder to .rttm format +# +for j in ${1}/*.eaf + do elan2rttm.py $j #generate the basic rttms +done + +#extra processing for WCE +eaf2enriched_txt.sh ${1}/ $2 diff --git a/utils/eafAAS2rttm_folder.sh b/utils/eafAAS2rttm_folder.sh new file mode 100755 index 0000000..3ad879f --- /dev/null +++ b/utils/eafAAS2rttm_folder.sh @@ -0,0 +1,9 @@ +#!/bin/bash +# +# Shell script to convert all .eaf files following the ACLEW Annotation Scheme inside a folder to .rttm format +# +for j in ${1}/*.eaf; do + r=`echo $j | sed "s/.eaf/rttm/"` + elan2rttm.py -i $j -o $r +#generate the basic rttms +done \ No newline at end of file diff --git a/utils/easy_chat2rttm.sh b/utils/easy_chat2rttm.sh new file mode 100755 index 0000000..cf1c485 --- /dev/null +++ b/utils/easy_chat2rttm.sh @@ -0,0 +1,79 @@ +#!/bin/bash +# Done in bash thinking that it would be faster. +# Maybe need to redo it in Python. + +INPUT=$1 + +display_usage() { + echo "Given a folder containing .cha files, convert them into .rttm files." + echo "usage: $0 [input] [optional flag]" + echo " input The folder where to find the .cha files, or the single .cha file (REQUIRED)." + exit 1 +} + +cha_2_rttm() { + if [ "$#" -eq 0 ]; then + echo "Illegal number of parameters" + exit 1 + else + filename=$1 + fi + + if [ ! -f $filename ]; then + echo "File not found" + exit 1 + fi +# # Read header +# header="$(cat $filename | grep "^@")" +# NB_PARTICIPANTS=$(echo "${header,,}" | grep "^@participants" | awk -F"," '{print NF}') +# +# PARTICIPANTS=$(echo "${header,,}" | grep "^@id") +# PARTICIPANTS=$(echo "$PARTICIPANTS" | awk -F"|" '{print "p_"$3 " a_"$4}') +# >&2 echo "Number of participants : $NB_PARTICIPANTS" +# >&2 echo "Participants & their age: ${PARTICIPANTS//$'\n'/, }" + + new_file=$(basename $filename) + new_file=${new_file%.*}.rttm + + # Read body + body="$(cat $filename | sed ':a;N;s/\n\t/ok/;ta;P;D' | grep "^*")" # Ensuring that one transcript = one and only one line + while IFS= read -r line; do + # Get class + SPKR=$(grep -oP '(?<=\*).*?(?=:)' <<< "$line") + + # Get onset / offset + ONOFF=$(echo $line | grep -oP '(?<=\025)\d+_\d+(?=\025)') + ONSET=${ONOFF%_*} + OFFSET=${ONOFF#*_} + + if [ "$ONSET" == "" ] || [ "$OFFSET" == "" ]; then + echo -e "SPEAKER\t${new_file%.*}\t1\t-1.0\t-1.0\t\t\t$SPKR\t\t" + else + # Get seconds instead of ms + ONSET=$(echo "scale=4; $ONSET/1000.0" | bc -l) + OFFSET=$(echo "scale=4; $OFFSET/1000.0" | bc -l) + DURATION=$(echo "scale=4; $OFFSET-$ONSET" | bc -l) + echo -e "SPEAKER\t${new_file%.*}\t1\t$ONSET\t$DURATION\t\t\t$SPKR\t\t" + fi + done < <(printf '%s\n' "$body") > $(dirname $filename)/$new_file + >&2 echo "Done ${new_file%.*}" +} + +if [ -z "$1" ]; then + display_usage +fi + + +if [ -f "/vagrant/"$1 ]; then + echo "File found." + cha_2_rttm $cha_path +elif [ -d "/vagrant/"$1 ]; then + echo "Directory found." + for cha_path in /vagrant/$1/*.cha; do + cha_2_rttm $cha_path + done +else + echo "File or directory not found." + display_usage +fi; + diff --git a/utils/elan2rttm.py b/utils/elan2rttm.py new file mode 100755 index 0000000..a4d355d --- /dev/null +++ b/utils/elan2rttm.py @@ -0,0 +1,77 @@ +#!/usr/bin/env python +""" +written in python3.5 + +script for translating .eaf annotation files into .rttm format + +WARNING: this version results in a loss of information since .rttm +only keeps speaker ID regardless of the nature of the speech (whereas +.eaf contains additional information such as speech nature e.g. MWU, VCM ...) + +This information might be recovered in an advanced version of this script + +""" + + +import pympi as pmp +import argparse +import os + + +def eaf2rttm(path_to_eaf, path_to_write_rttm): + """ + function to write a new .rttm file which is a transcription of the .eaf + given as input + + """ + + sampling_freq = 1000 + + print('\n') + EAF = pmp.Elan.Eaf(path_to_eaf) + + participants = [] + + for k in EAF.tiers.keys(): + + if 'PARTICIPANT' in EAF.tiers[k][2].keys(): + + if EAF.tiers[k][2]['PARTICIPANT'] not in participants: + + participants.append(EAF.tiers[k][2]['PARTICIPANT']) + + print('participants: {}'.format(participants)) + base = os.path.basename(path_to_eaf) + name = os.path.splitext(base)[0] + + print('parsing file: {}'.format(name)) + + with open(os.path.join(path_to_write_rttm, name + ".rttm"), "w") as rttm: + + for participant in participants: + if participant not in EAF.tiers.keys(): + continue + + for _, val in EAF.tiers[participant][0].items(): + + start = val[0] + end = val[1] + + t0 = float(EAF.timeslots[start]) / sampling_freq + length = float(EAF.timeslots[end]) / sampling_freq - t0 + + rttm.write(u"SPEAKER {} {} {} {} {} {} {} {}\n".format + (name, 1, "%.2f" % t0, "%.2f" % length, "", "", participant, 1 )) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description="convert .eaf into .rttm") + parser.add_argument('-i', '--input', type=str, required=True, + help="path to the input .eaf file.") + parser.add_argument('-o', '--output', type=str, required=True, + help="path to the folder in which to write the output." + "The output .rttm file will have the same name as the " + "input file - except for the extension") + args = parser.parse_args() + +eaf2rttm(args.input, args.output) diff --git a/utils/empty_transcription.sh b/utils/empty_transcription.sh new file mode 100755 index 0000000..a5084ae --- /dev/null +++ b/utils/empty_transcription.sh @@ -0,0 +1,15 @@ +#!/bin/bash +# +# author: the ACLEW team +# +# after all the transcriptions are extracted, +# look for wav file for which no transcription +# exist, and create empty file + +for wav in `ls $1/*.wav`; do + id=$(basename $wav .wav) + eaf=$1/$id.rttm + if [ ! -f $eaf ]; then + touch $eaf + fi +done diff --git a/utils/extract_stats_chi.py b/utils/extract_stats_chi.py new file mode 100755 index 0000000..44bfdd7 --- /dev/null +++ b/utils/extract_stats_chi.py @@ -0,0 +1,127 @@ +import sys +import argparse +import glob +import os +import re +from collections import OrderedDict + + +def detect_ad_chi_tt(previous_activity, curr_activity, last_silence_dur, chi, mal, fem): + if last_silence_dur <= 2.0: + if (previous_activity in mal and curr_activity in chi) or \ + (previous_activity in fem and curr_activity in chi) or \ + (previous_activity in chi and curr_activity in mal) or \ + (previous_activity in chi and curr_activity in fem): + return 1 + return 0 + + +def compute_statistics(rttm_path, chi, mal, fem, trash): + chi_dur=0.0 + chi_utt=0 + ad_dur=0.0 + ad_utt=0 + ad_chi_tt=0 + + silence_dur = 0 + prev_activity = None + onset_prev = 0 + dur_prev = 0 + + with open(rttm_path,'r') as rttm: + for line in rttm: + line = line.replace('\t', ' ') + line = re.sub('\s+', ' ', line).strip() + anno_fields = line.split(' ') + curr_activity = anno_fields[7] + if curr_activity != 'SIL' and curr_activity != 'S': # We're managing things as if 'SIL' lines weren't exist + onset = float(anno_fields[3]) + dur = float(anno_fields[4]) + + if curr_activity in chi: + chi_dur += dur + chi_utt += 1 + elif curr_activity in mal or curr_activity in fem: + ad_dur += dur + ad_utt +=1 + elif curr_activity not in trash: + print("Activity %s not recognized" % (curr_activity)) + print("In file %s" % (os.path.basename(rttm_path))) + sys.exit(1) + + if onset_prev + dur_prev == onset: + silence_dur = 0.0 + else: + silence_dur = onset - onset_prev - dur_prev + + ad_chi_tt += detect_ad_chi_tt(prev_activity, curr_activity, silence_dur, chi, mal, fem) + + # We're managing things as if SIL lines weren't exist + + prev_activity=curr_activity + onset_prev=onset + dur_prev=dur + + filename = os.path.basename(rttm_path).split('.')[0] + res = [filename, chi_dur, chi_utt, ad_dur, ad_utt, ad_chi_tt] + return res + +def write_stats(list_stats, folder): + filename=os.path.join(folder,"stats.txt") + + with open(filename,'w') as fn: + fn.write("filename\tchi_dur\tchi_utt\tad_dur\tad_utt\tad_chi_tt\n") + for stats in list_stats: + fn.write('\t'.join(map(str,stats))+'\n') + +def main(): + parser = argparse.ArgumentParser(description="convert .txt into .rttm") + parser.add_argument('-f', '--folder', type=str, required=True, + help="path to the folder where to find the rttm to analyze." + "Note that all of the rttm are scanned.") + parser.add_argument('--chi', nargs='+', type=str, required=True, + help="labels that need to be considered as being child vocalization.") + parser.add_argument('--mal', nargs='+', type=str, required=True, + help="labels that need to be considered as being male adult speech.") + parser.add_argument('--fem', nargs='+', type=str, required=True, + help="labels that need to be considered as being female adult speech.") + parser.add_argument('--trash', nargs='+', type=str, required=False, default=None, + help="labels that need to not be considered.") + args = parser.parse_args() + + # Below the values that need to be consider when evaluating tsi/lena folder + # prob C22_20170717_5640 + ## Gold files + # chi=['CHI*','C1', 'C2'] + # mal=['MA1','MA2'] + # fem=['MOT*','FA1','FA2'] + + ## Yunitator + # chi = ['CHI'] + # mal = ['MAL'] + # fem = ['FEM'] + + ## Lena N tag + # chi = ['CXN', 'CHN'] + # mal = ['MAN'] + # fem = ['FAN'] + + ## Lena MFC + # chi = ['C'] + # mal = ['M'] + # fem = ['F'] + + ## Lena N and F separated (should be the equivalent of MFC + # chi = ['CHN', 'CXN', 'CHF', 'CXF'] + # mal = ['MAN', 'MAF'] + # fem = ['FAN', 'FAF'] + args.folder=os.path.join('/vagrant', args.folder) + rttm_files = [fn for fn in glob.iglob(os.path.join(args.folder, '*.rttm')) + if 'cutted' not in fn] + list_stats=[] + for rttm_path in rttm_files: + list_stats.append(compute_statistics(rttm_path, args.chi, args.mal, args.fem, args.trash)) + + write_stats(list_stats, args.folder) +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/utils/frame_cutter.py b/utils/frame_cutter.py new file mode 100755 index 0000000..1f8eb97 --- /dev/null +++ b/utils/frame_cutter.py @@ -0,0 +1,221 @@ +import sys +import os +import argparse +import re +import glob +import numpy as np +from scipy.io import wavfile +import math + +def cutter(path_to_rttm, frame_length, output_file, prefix): + """ + Given a rttm file, create a new file whose represents the same rttm cutted in frames. + If one frame has been classified as several classes in the original rttm, it concatenates classes. + Note that all of the frames that have not been classified in the original rttm are considered as being SIL. + Note that it will read the associated wav file to get the total duration of the recording. + + Parameters + ---------- + path_to_rttm : path to the rttm file. + frame_length : frame length. + output_file : path to the output file. + prefix : the prefix that needs to be remove (from rttm) to map the rttm to the wav file. + + Write a rttm whose name is the same than the rttm's one suffixed by _cutted.rttm + """ + basename = os.path.splitext(os.path.basename(path_to_rttm))[0] + if prefix != "" and prefix in basename: + basename = basename.split(prefix)[1] + dirname = os.path.dirname(path_to_rttm) + wav_path = os.path.join(dirname,basename+'.wav') + if os.path.isfile(wav_path): + fs, data = wavfile.read(wav_path) + tot_dur_s = len(data)*1.0/fs + else: + print("Something went wrong while reading the wav file %s. Can not get total duration..." % wav_path) + sys.exit(1) + + frame_length_s = float(frame_length)/1000.0 + + with open(path_to_rttm, 'r') as rttm: + with open(output_file, 'w') as output: + onset_prev_s = 0.0 + dur_prev_s = 0.0 + onset_s = None + + for line in rttm: + line = line.replace('\t', ' ') + line = re.sub('\s+', ' ', line).strip() + anno_fields = line.split(' ') + onset_s = float(anno_fields[3]) + dur_s = float(anno_fields[4]) + curr_activity = anno_fields[7] + + if onset_s > onset_prev_s + dur_prev_s : #There's been silence just before ! + sil_dur_s = onset_s - onset_prev_s - dur_prev_s + onset_sil_s = onset_prev_s + dur_prev_s + single_activity_cutter(basename, output, frame_length_s, + sil_dur_s, onset_sil_s, 'SIL', tot_dur_s) + + single_activity_cutter(basename, output, frame_length_s, + dur_s, onset_s, curr_activity, tot_dur_s) + + # Update previous fields + dur_prev_s = dur_s + onset_prev_s = onset_s + + # Fill the last one by SILENCE + ## Handle empty rttm + if onset_s is None: + onset_s = 0.0 + dur_s = 0.0 + + if onset_s + dur_s < tot_dur_s: + + sil_dur_s = tot_dur_s - onset_s-dur_s + onset_sil_s = onset_s + dur_s + single_activity_cutter(basename, output, frame_length_s, + sil_dur_s, onset_sil_s, 'SIL', tot_dur_s) + + +def single_activity_cutter(basename, output, frame_length_s, dur_s, onset_s, curr_activity, tot_dur_s): + """ + Given an activity, its onset and its duration, cut it into frames of length frame_length_s. + + Parameters + ---------- + basename The basename of the input rttm. + output The path of the output file. + frame_length_s The frame length (in s). + dur_s The duration of the current activity (in s). + onset_s The onset of the current activity (in s). + curr_activity The current activity. + + """ + + # We don't want to consider any fake labels generated after the duration of the wav file + if onset_s + dur_s > tot_dur_s: + dur_s = max(tot_dur_s - onset_s, 0) + if onset_s > tot_dur_s: + onset_s = 0.0 + + diff_s = onset_s - int(round(onset_s / frame_length_s)) * frame_length_s + + onset_s = int(round(onset_s / frame_length_s)) * frame_length_s + n_frames = int((dur_s+diff_s) / frame_length_s) + # Get the output label (we want a full match or nothing) + + for i in range(0, n_frames): + output.write("SPEAKER %s 1 %s %s %s \n" % \ + (basename, onset_s + frame_length_s * i, + str(frame_length_s), curr_activity)) + + if (not np.isclose(onset_s + frame_length_s * n_frames, onset_s+dur_s+diff_s, rtol=1e-05, atol=1e-08, equal_nan=False)) and (onset_s + frame_length_s * n_frames < tot_dur_s): + output.write("SPEAKER %s 1 %s %s %s \n" % \ + (basename, onset_s + frame_length_s * n_frames, + str(frame_length_s), curr_activity)) + + +def aggregate_overlap(path_to_rttm, output_file): + """ + Given a cutted rttm file, aggregate the activities that happen in the same time. + The class of the generated frame will take the form of spkr1/spkr2 ... + + Parameters + ---------- + path_to_rttm Path to the input rttm file that have been previously cutted. + output_file Path to the output file. + """ + basename = os.path.splitext(os.path.basename(path_to_rttm))[0] + with open(path_to_rttm, 'r') as rttm: + with open(output_file, 'w') as output: + lines = rttm.readlines() + lines = sorted(lines, key=lambda line: float(line.split()[3])) + k = 0 + while k < len(lines): # Loop through the whole file + line_k = lines[k].split() + onset_k, dur_k, act_k = line_k[3], line_k[4], line_k[7] + frame_activity = [act_k] + j = k + 1 + while j < len(lines): # Loop through the activities that have the same onset + line_j = lines[j].split() + onset_j, dur_j, act_j = line_j[3], line_j[4], line_j[7] + if onset_k != onset_j: + if len(frame_activity) >= 2 and 'SIL' in frame_activity: + frame_activity.remove("SIL") + output.write("SPEAKER %s 1 %s %s %s \n" % \ + (basename, onset_k, dur_k, '/'.join(frame_activity))) + break + else: # onset_k == onset_j: + if act_j not in frame_activity: + frame_activity.append(act_j) + frame_activity.sort() # Consider alphabetical order + k += 1 + j += 1 + k += 1 + + if len(frame_activity) >= 2 and 'SIL' in frame_activity: + frame_activity.remove("SIL") + output.write("SPEAKER %s 1 %s %s %s \n" % \ + (basename, onset_k, dur_k, '/'.join(frame_activity))) + + + + +def main(): + parser = argparse.ArgumentParser(description="convert a rttm file into another rttm cutted in frames.") + parser.add_argument('-i', '--input', type=str, required=True, + help="path to the input .rttm file or the folder containing rttm and wav files.") + parser.add_argument('-l', '--length', type=float, required=False, default=10.0, + help="the frame length in ms (Default to 10 ms).") + parser.add_argument('-p', '--prefix', type=str, default="", + help="the prefix that needs to be removed to map the rttm to the wav.") + args = parser.parse_args() + + # labels_map = {"CHI": "CHI.?|CXN|CHN|C.?", + # "FEM": "FAN|FAF|FEM|F|MOT.?|FA.?", + # "MAL": "MAL|M|MAN|MA.?", + # "SIL": "SIL|S"} + # labels_map = {"CHF":"CHF", + # "CHI":"CHI", + # "OCHI": "OCHI", + # "CHN":"CHN", + # "FAF":"FAF", + # "FAN":"FAN", + # "FEM":"FEM", + # "MAF":"MAF", + # "MAL":"MAL", + # "MAN":"MAN", + # "OLF":"OLF", + # "OLN":"OLN", + # "SIL":"SIL"} + # Initialize the output folder as the same folder than the input + # if not provided by the user. + if args.input[-5:] == '.rttm': + output = os.path.dirname(args.input) + else: + output = args.input + + data_dir = '/vagrant' + args.input = os.path.join(data_dir, args.input) + output = os.path.join(data_dir, output) + + if not os.path.isdir(output): + os.mkdir(output) + + if args.input[-5:] == '.rttm': # A single file has been provided by the user + output = os.path.splitext(args.input)[0]+'_cutted.rttm' + cutter(args.input, args.length, output+'.tmp', args.prefix) + aggregate_overlap(output+'.tmp', output) + os.remove(output+'.tmp') + else: # A whole folder has been provided + rttm_files = glob.iglob(os.path.join(args.input, '*.rttm')) + for rttm_path in rttm_files: + print("Processing %s" % rttm_path) + output = os.path.splitext(rttm_path)[0] + '_cutted.rttm' + cutter(rttm_path, args.length, output + '.tmp', args.prefix) + aggregate_overlap(output + '.tmp', output) + os.remove(output + '.tmp') + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/utils/generate_html.py b/utils/generate_html.py new file mode 100755 index 0000000..a5ab577 --- /dev/null +++ b/utils/generate_html.py @@ -0,0 +1,44 @@ +# generate_html.py +""" +Given a list of folders that have been fed to the different models, +generate a html file presenting the following results : + +wip + +Use case : + python tools/generate_html.py -f data/ -o data/html +It will scan folder data/ and generates the html interface at data/html. +""" + +import argparse +import os +import glob +from html_python.files_page import FilesPage +from html_python.models_page import ModelsPage +from html_python.style_css import StyleCSS +from html_python.page import Page + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('-f', '--folder', type=str, + help=' Folder that needs to be analyzed', required=True) + parser.add_argument('-o', '--output', type = str, + help=' The folder where the html files will be generated.', required=True) + + args = parser.parse_args() + + # define data dir + data_dir = "/vagrant" + args.folder = os.path.join(data_dir, args.folder) + args.output = os.path.join(data_dir, args.output) + + style_css = StyleCSS(args.output) + files_html = FilesPage(args.output, args.folder) + files_html.write_statistics() + + models_html = ModelsPage(args.output, args.folder) + models_html.write_statistics() + models_html.close() + +if __name__ == '__main__': + main() diff --git a/utils/get_aclewStarter.sh b/utils/get_aclewStarter.sh new file mode 100755 index 0000000..a1909bf --- /dev/null +++ b/utils/get_aclewStarter.sh @@ -0,0 +1,58 @@ +#!/bin/bash +# +# author = the ACLEW Team +# +# Use this script to download the aclew Starter from databrary and start using +# it with the DiViMe. +# This script should be launched from the DiViMe folder, which also contains +# the VagrantFile. + +# Absolute path to this script. /home/user/bin/foo.sh +SCRIPT=$(readlink -f $0) +# Absolute path this script is in. /home/user/bin +BASEDIR=`dirname $SCRIPT` + +# url to dataset in zip format +aclewStarter_url=https://nyu.databrary.org/volume/390/zip/true +aclewStarter_folder=databrary390-Bergelson-Warlaumont-Cristia-Casillas-Rosemberg-Soderstrom-Rowland-Durrant-Bunce-Starter-ACLEW + +# create data directory to store dataset and download zip file +if [[ ! -d "aclewStarter" ]]; then + mkdir $BASEDIR/aclewStarter +fi + +cd $BASEDIR/aclewStarter + +echo "downloading the ACLEW Starter Dataset..." +wget $aclewStarter_url + +# unzip archive and keep data when there is both the audio file and +# the transcription. Delete the archive file +unzip true +rm true + +# regroup wavs and transcriptions and remove those that don't have a match +cd $aclewStarter_folder +mkdir transcript +mkdir wav + +mv sessions/*/*.wav wav/ +mv materials*/*.eaf transcript/ + +echo "processing all the ACLEW Starter files..." +for eaf in $(ls transcript/); do + base=$(echo $eaf | cut -d '-' -f 1) + if [[ -f wav/${base}.wav ]]; then + mv transcript/$eaf transcript/${base}.eaf + vagrant ssh -c "python varia/elan2rttm.py -i /vagrant/aclewStarter/$aclewStarter_folder/transcript/${base}.eaf -o /vagrant/aclewStarter/" + mv wav/${base}.wav ../ + else + rm transcript/$eaf + fi +done + +# When all the files are seen, delete the extracted raw folder +cd ../ +rm -rf $aclewStarter_folder + +echo "You can now use the ACLEW Starter dataset!" diff --git a/utils/high_volubility.py b/utils/high_volubility.py new file mode 100755 index 0000000..945e8de --- /dev/null +++ b/utils/high_volubility.py @@ -0,0 +1,615 @@ +#!/usr/bin/env python +# +# author = The ACLEW Team +# +""" +This script extract short snippets of sound (approx. 10s long), +and runs them through a SAD or diarization tool to detect chunks of audio with : + +1) a lot of speech. +--> python utils/high_volubility.py file_path.wav --sad noisemesSad + +2) a lot of child speech +--> python utils/high_volubility.py file_path.wav --diar yunitator_old --mode CHI --nb_chunks 3 + +3) a lot of parent-child conversations +--> python utils/high_volubility.py file_path.wav --diar yunitator_english --mode PCCONV + +4) a lot of adults conversations with a child minimally aware +--> python utils/high_volubility.py file_path.wav --diar yunitator_universal --mode ACCA +""" + +import os +import sys +import re +import wave +import math +import numpy +import shutil +import argparse +import subprocess +import collections +import tempfile +import shutil +from operator import itemgetter + +LAUNCHER_FOLDER = "/home/vagrant/launcher" + +def get_audio_length(wav): + """ Return duration of Wave file. + + Input: + wav: path to the wav file. + + Output: + dur: float, duration, in seconds, of wav file. + """ + + audio = wave.open(wav, 'r') + frames = audio.getnframes() + rate = audio.getframerate() + duration = frames / float(rate) + audio.close() + + return duration + +def select_onsets(duration, step, min_chunk_size=-1.0, max_chunk_size=-1.0): + """ Return list of onsets on which this script will extract the chunks of + 10s + + Input: + duration: float, duration of the daylong recording + + Output: + onsets: list[floats], list the onsets on which this script will extract + the chunks of 10s to be run through the SAD tools + """ + return list(numpy.arange((max_chunk_size-min_chunk_size)/2, duration, step)) + + +def extract_chunks(wav, onset_list, chunk_size, temp, duration=-1.0): + """ Given a list of onset and a length in seconds, extract a snippet of + audio at each onset of this length. The extraction will be done using + SoX, called by subprocess. + + Input: + wav: path to the wav file. + onset_list: list[float], list of the onsets in the 'wav' file at which + we'll extract the segments + chunk_size: float, length in seconds of the chunks of audio this script + will extract. + + Output: + 'temp': the output of this function is the set of small wav file of + 'chunk_size' seconds in the temp folder. + The name of the wav files will be: + id_onset_length.wav + where id is the name of the original wav file, onset is the + onset at which the chunk occurs in the original wav file, and + length is the length of the chunk. + """ + + print("extract_chunks("+wav+", "+str(onset_list)+", "+str(chunk_size)+", "+str(temp)+", "+str(duration)+")") + + # get "id" basename of wav file + basename = os.path.splitext(os.path.basename(wav))[0] + + # for each onset, call SoX using subprocess to extract a chunk. + for on in onset_list: + # create name of output + off = on + chunk_size + if on < 0.0: + on = 0.0 + if duration > 0 and off > duration: + off = duration + str_on = '{:.1f}'.format(on) + #(6 - len(str(on))) * '0' + str(on) + str_off = '{:.1f}'.format(off) + #(6 - len(str(off))) * '0' + str(off) + # {:08.1f} + + #chunk_duration = float(str_off) - float(str_on) + #if chunk_duration != chunk_size: + # missed_duration = chunk_size - chunk_duration + # if float(str_on) == 0.0: + # str_off = str(float(str_off)+missed_duration) + # else: + # str_on = str(float(str_on)-missed_duration) + chunk_name = '_'.join([basename, str_on, str_off]) + + # call subprocess + cmd = ['sox', wav, + os.path.join(temp, '{}.wav'.format(chunk_name)), + 'trim', '{:f}'.format(on), '{:f}'.format(off-on)] + try: + cpi = subprocess.check_output(cmd, stderr=subprocess.STDOUT) + if len(cpi): + print(str(cmd), "->", str(cpi)) + except subprocess.CalledProcessError: + print("Error caused by: "+str(cmd)) + sys.exit(str(cpi)) + + +def run_Model(temp_rel, temp_abs, sad, diar=None, output_dir=None, del_temp=True): + """ When all the snippets of sound are extracted, and stored in the temp + file, run them through a sad or diar tool, and keep + the 10% that have the most speech. + By default, the function is on the SAD mode. That means that it will + run a SAD model (by default noiseme). However, if the diar parameter is + provided, this function will be on the diarization mode. + + Input: + temp_rel: relative path to the temp folder in which the snippets of + sounds are stored. Here we need the relative path, not the + abs path, since the SAD tool will add the "/vagrant/" part of + the path. + temp_abs: absolute path to the temp folder in which the snippets of + sounds are stored. + sad: name of the sad tool to be used to analyze the snippets of + sound + diar: name of the diar tool to be used to analyze the snippets of + sound. Note that it will use that sad parameter as a choice + of SAD provider. + + Output: + _: In temp, the SAD analyses will be written in RTTM format. + """ + + print("run_Model("+temp_rel+", "+temp_abs+", "+sad+", diar="+str(diar)+", output_dir="+str(output_dir)+", del_temp="+str(del_temp)+")") + + if diar is not None: # Diarization mode + if diar.startswith('yunitator'): + mode = diar.split("_")[1] + available_flavors = ["old", "english", "universal"] + if mode not in available_flavors: + sys.exit("Yunitator's flavor not recognized. Should be in %s" % available_flavors) + cmd = [os.path.join(LAUNCHER_FOLDER, 'yunitate.sh'), '{}'.format(temp_rel), '{}'.format(mode)] + if not del_temp: + cmd.append('--keep-temp') + elif diar == 'diartk': + cmd = [os.path.join(LAUNCHER_FOLDER, '{}.sh').format(diar), '{}'.format(temp_rel), '{}'.format(sad)] + else: + cmd = ['exit 1'] + else: # SAD mode + cmd = [os.path.join(LAUNCHER_FOLDER, '{}.sh').format(sad), '{}'.format(temp_rel)] + + try: + cpi = subprocess.check_output(cmd, stderr=subprocess.STDOUT) + except subprocess.CalledProcessError: + print("Error caused by: "+str(cmd)) + sys.exit(str(cpi)) + + # after the model has finished running, remove the wav files + if output_dir is None: + for fin in os.listdir(temp_abs): + if fin.endswith('.wav'): + os.remove(os.path.join(temp_abs, fin)) + else: + output_files = [] + for fin in os.listdir(temp_abs): + if not os.path.isfile(os.path.join(output_dir, fin)): + shutil.move(os.path.join(temp_abs, fin), output_dir) + if fin[-5:] == ".rttm": + output_files.append(os.path.join(output_dir, fin)) + return output_files + + + +def extract_base_name(filename, diar): + """ + Giving a filename generated by a model and composed of : + model_name + wav_filename + tbeg + tdur + Extracts the base name composed of the three last elements. + + Input: + filename: a filename generated by a model + diar: a diarization model (if provided) + + Output: + base : wavfilename_tbeg_tdur + """ + if diar in ["noisemesSad", "tocomboSad", "opensmileSad"]: + base = filename.split('_')[1:] + else: # diartk and yunitator + base = filename.split('_')[2:] + + base = os.path.splitext('_'.join(base))[0] + return base + + +def detect_parent_child_conv(previous_activity, curr_activity, last_silence_dur): + """ + Attempts to try if a turn-taking happened between a child and his/her parents + + Input: + previous_activity: the last activity amongst ['CHI', 'MAL', 'FEM'] + curr_activity: the current activity amongst ['CHI', 'MAL', 'FEM'] + last_silence_dur: the duration of the last silence + + Output: + A boolean indicating if a turn-taking happened between a child and his/her parents + True : a turn-taking happened + False : nothing happened + """ + if last_silence_dur <= 2.0: + if (previous_activity == 'MAL' and curr_activity == 'CHI') or \ + (previous_activity == 'FEM' and curr_activity == 'CHI') or \ + (previous_activity == 'CHI' and curr_activity == 'MAL') or \ + (previous_activity == 'CHI' and curr_activity == 'FEM'): + + return True + return False + +def detect_adults_conv(previous_activity, curr_activity, last_silence_dur): + """ + Attempts to try if a turn-taking happened between two adults + + Input: + previous_activity: the last activity amongst ['CHI', 'MAL', 'FEM'] + curr_activity: the current activity amongst ['CHI', 'MAL', 'FEM'] + last_silence_dur: the duration of the last silence + + Output: + A boolean indicating if a turn-taking happened between a child and his/her parents + True : a turn-taking happened + False : nothing happened + """ + if last_silence_dur <= 2.0: + if (previous_activity == 'MAL' and curr_activity == 'FEM') or \ + (previous_activity == 'FEM' and curr_activity == 'MAL') or \ + (previous_activity == 'FEM' and curr_activity == 'FEM') or \ + (previous_activity == 'MAL' and curr_activity == 'MAL'): + + return True + return False + +def read_analyses(temp_abs, sad, nb_chunks, diar=None, mode='CHI', child_aware=False, keep_rttm=False): + """ When the model has finished producing its outputs, read all the + transcriptions and sort the files by the quantity of their speech + content or the quantity of their speech belonging to the noiseme_class. + + Input: + temp: path to the temp folder in which the snippets of sound are + stored. Here we need the relative path, not the absolute + path, since the SAD tool will add the "/vagrant/" part of + the path. + sad: name of the sad tool to be used to analyze the snippets of + sound. + nb_chunks: the number of chunks that need to be kept (the ones containing the most speech). + diar: the diarization model (if provided). + mode: the type of speech that needs to be kept (has to be amongst ['CHI']). + child_aware : (only used if mode == ACCA) Indicates, if we should filter the snippets + that don't present any child activity. + + Output: + sorted_files: list(str), list of the files, sorted by the quantity + of the speech content (as returned by the SAD tool) + or the quantity of the speech content classified as + belonging to the noiseme class (as returned by the + diarization tool). + only 10 %% of all files, that contain the most speech + """ + + # get list of model outputs + all_files = os.listdir(temp_abs) + diar_mode = diar is not None + if diar_mode: + model = diar + else: + model = sad + + # we consider only the annotations that have been generated by the model + annotations = [file for file in all_files if file.startswith(model[:-1])] + + # read all annotations and store duple in list (filename, speech_dur) + files_n_dur = [] + for file in annotations: + + # we extract the base name composed of wav_file_name + t_deg + t_dur + base = extract_base_name(file, diar) + with open(os.path.join(temp_abs, file), 'r') as fin: + speech_activity = fin.readlines() + + # total duration of the speech of interest + tot_dur = 0.0 + # type of the last activity + previous_activity = None + onset_prev = 0.0 + dur_prev = 0.0 + # variable to detect if the child is aware in ACCA mode. + chi_points = 0.0 + + for line in speech_activity: + anno_fields = line.split(' ') + dur = float(anno_fields[4]) + onset = float(anno_fields[3]) + curr_activity = anno_fields[7] + + if onset_prev+dur_prev == onset: + silence_dur = 0.0 + else: + silence_dur = onset-onset_prev-dur_prev + + if not diar_mode: + # SAD mode + tot_dur += dur + elif diar_mode and mode == 'CHI' and curr_activity == 'CHI': + # Child detection mode + tot_dur += 1 + elif diar_mode and mode == 'PCCONV': + # Parent-child detection mode + if detect_parent_child_conv(previous_activity, curr_activity, silence_dur): + # Here we consider more an objective function (number of turn-taking) that + # we want to maximize. That comes from the fact that adults speak during a + # longer time while children speak only for few seconds. And we don't want + # to put too much weight on the adults speech. + tot_dur += 1 + elif diar_mode and mode == 'ACCA': + # Adults conversation child aware detection + if detect_adults_conv(previous_activity, curr_activity, silence_dur): + tot_dur += 1 + elif curr_activity == 'CHI': + chi_points += 1 + + previous_activity = curr_activity + onset_prev = onset + dur_prev = dur + + files_n_dur.append((base, tot_dur, chi_points)) + + # remove annotation when finished reading + if keep_rttm: + os.move(os.path.join(temp_abs, file), ) + else: + os.remove(os.path.join(temp_abs, file)) + + if child_aware and diar_mode == 'ACCA': + files_n_dur = [file for file in files_n_dur if file[2] > 3] + + if len(files_n_dur) == 0: + sys.exit("No "+mode+" speech found, try to decrease the step parameter or to increase the size of the chunks.") + + files_n_dur = sorted(files_n_dur, key=itemgetter(1), reverse=True) + + # Extract the nb_chunks snippets that contain the have the highest score + nb_chunks = min(len(files_n_dur), nb_chunks) + + sorted_files = files_n_dur[:nb_chunks] + + return sorted_files + +def new_onsets(sorted_files, duration=0.0, chunk_size=120.0): + """ + Given a selection of file with lots of speech, + extract new 2minutes long chunks of audio in the original wav, + centered around the short 10s snippets that were analysed. + + Input: + sorted_files: list of the snippets that were selected + because they had lot of speech + temp_abs: absolute path to the temp folder that contains the + snippets + wav: path to the daylong recording + Ouput: + _: in the temp folder, new two minutes long chunks of + audio will be stored. + """ + + # loop over selected files and retrieve their onsets from their name + new_onset_list = [] + for snippet, speech_dur, chi_points in sorted_files: + onset = float(os.path.splitext(snippet)[0].split('_')[-2]) + offset = float(os.path.splitext(snippet)[0].split('_')[-1]) + length = offset - onset + + # new segment is centered around snippet, so get middle of snippet + new_onset = onset + length/2 - chunk_size/2 + if new_onset + chunk_size > 0 and new_onset < duration: + new_onset_list.append(new_onset) + + return new_onset_list + +def write_final_stats(output_rttm): + stats = collections.OrderedDict([("filename", None), + ("CHI_duration", 0.0), + ("MAL_duration", 0.0), + ("FEM_duration", 0.0), + ("ADU_ADU_turntaking", 0), + ("ADU_CHI_turntaking", 0)]) + original_name = '_'.join(os.path.basename(output_rttm[0]).split('_')[:-2]) + output_stats = os.path.join(os.path.dirname(output_rttm[0]), "high_volubility_stats_" + original_name + ".txt") + with open(output_stats, "w") as fout: + fout.write(",".join(stats.keys())) + fout.write("\n") + + for file in output_rttm: + stats["filename"] = os.path.basename(file).replace(".rttm", "") + with open(file, "r") as rttm: + # type of the last activity + previous_activity = None + onset_prev = 0.0 + dur_prev = 0.0 + for line in rttm: + anno_fields = line.split(' ') + dur = float(anno_fields[4]) + onset = float(anno_fields[3]) + curr_activity = anno_fields[7] + + if onset_prev + dur_prev == onset: + silence_dur = 0.0 + else: + silence_dur = onset - onset_prev - dur_prev + + stats[curr_activity+"_duration"] += dur + + if detect_adults_conv(previous_activity, curr_activity, silence_dur): + stats["ADU_ADU_turntaking"] += 1 + if detect_parent_child_conv(previous_activity, curr_activity, silence_dur): + stats["ADU_CHI_turntaking"] += 1 + + previous_activity = curr_activity + onset_prev = onset + dur_prev = dur + fout.write(",".join(str(x) for x in stats.values())) + fout.write("\n") + + # Go back to 0 + stats = collections.OrderedDict([("filename", None), + ("CHI_duration", 0.0), + ("MAL_duration", 0.0), + ("FEM_duration", 0.0), + ("ADU_ADU_turntaking", 0), + ("ADU_CHI_turntaking", 0)]) +def main(): + """ + Get duration of wav file + Given duration of wav file, extract list of onsets + Given list of onsets in wav file, extract chunks of wav + + Input: + daylong: path to the daylong recording + --step: (optional) step in seconds between each chunk. + By default 600 seconds. + --chunk_size: (optional) size of the chunks to extract. + --temp: (optional) path to a temporary folder to store the + extracted chunks. + --sad: (optional) name of the SAD tool to call to analyse + the chunks. By default noiseme + --diar: (optional) name of the diarization tool to call to analyse + the chunks. No default option + + """ + parser = argparse.ArgumentParser() + + parser.add_argument('daylong', metavar='AUDIO_FILE', + help='''Give RELATIVE path to the daylong recording''' + '''in wav format.''') + parser.add_argument('--step', default=600.0, type=float, + help='''(Optional) Step, in seconds, between each chunk of ''' + '''audio that will be extracted to be analysed by the SAD ''' + '''tool. By default, step=600 seconds (10 minutes)''') + parser.add_argument('--chunk_sizes', nargs=3, + default=[10.0, 120.0, 300.0], type=float, + help='''(Optional) Size of the chunks to extract and analyze. ''' + '''By default it's 10.0 120.0 300.0: \n''' + '''10s chunks are extracted, analyzed by the ''' + '''SAD tool, the 10%% chunks that contain the most speech ''' + '''are kept, than 120s chunks centered on the 10s chunks, ''' + '''these are again analysed by the SAD tool, the 10%% that ''' + ''' contain the most speech are kept, and 300.0s chunks ''' + ''' are finally extracted around these kept chunks.''') + parser.add_argument('--nb_chunks', default=5, type=int, + help='''(Optional) Number of snippets to keep at the last stage. ''' + '''By default, we keep the top 5 snippets that have the most speech content.\n''') + parser.add_argument('--temp', default='(auto)', + help='''(Optional) Path to a temp folder in which the small wav ''' + '''segments will be stored. If it doesn't exist, it will be ''' + '''created.''') + parser.add_argument('--output', default=None, + help='''(Optional) Path to the output folder in which the results ''' + '''will be stored. If it doesn't exist, it will be ''' + '''created.''') + parser.add_argument('--sad', default='noisemes_sad', + help='''(Optional) name of the sad tool that will be used to ''' + '''analyze the snippets of sound''') + parser.add_argument('--diar', + help='''(Optional) name of the diar tool that will be used to ''' + '''analyze the snippets of sound. If this argument is provided by + the user, this script will be on the diarization mode. Otherwise, it will + be on the SAD mode.''') + parser.add_argument('--mode', default='CHI', choices=['CHI', 'PCCONV', 'ACCA'], + help='''(Optional) the noiseme class(es) of interest. ''' + '''Used only when the diar argument is provided.''') + + args = parser.parse_args() + + if args.diar == 'yunitate' and (args.mode == 'PCCONV' or args.mode == 'ACCA' or args.mode == 'CHI'): + if args.chunk_sizes[0] == 10.0: + print("Resetting chunk size at 20.0 seconds (more suitable for CHI/PCCONV/ACCA mode).") + args.chunk_sizes[0] = 20.0 + if args.step == 600.0: + print("Resetting step at 300.0 seconds (more suitable for CHI/PCCONV/ACCA mode).") + args.step = 300.0 + + # Sanity check and auto-generation of temp dir + if os.path.isabs(args.temp): + sys.exit("Temp path must be relative, not "+args.temp) + if os.path.isabs(args.daylong): + sys.exit("Daylong path must be relative, not "+args.daylong) + + # Define data dir + data_dir = "/vagrant" + + # to launch SAD tool we need the relative path to temp + if args.temp == '(auto)': + temp_abs = tempfile.mkdtemp(dir=data_dir) + temp_rel = os.path.basename(temp_abs) + del_temp = True + else: + temp_abs = os.path.join(data_dir, args.temp) + temp_rel = args.temp + del_temp = False + os.makedirs(temp_abs) + + # Define absolute path to wav file + wav_abs = os.path.join(data_dir, args.daylong) + + if args.output is None: + output_dir = os.path.dirname(wav_abs) + elif os.path.isabs(args.output): + sys.exit("Output path must be relative, not "+args.output) + else: + output_dir = os.path.join(data_dir, args.output) + + # get number of chunks that need to be kept + nb_chunks = int(args.nb_chunks) + + # get duration + duration = get_audio_length(wav_abs) + + # get list of onsets + onset_list = select_onsets(duration, args.step, + min_chunk_size=args.chunk_sizes[0], max_chunk_size=args.chunk_sizes[2]) + + # call subprocess to extract the chunks + extract_chunks(wav_abs, onset_list, args.chunk_sizes[0], temp_abs, duration=duration) + + # analyze using SAD tool + run_Model(temp_rel, temp_abs, args.sad, diar=args.diar, del_temp=del_temp) + + # sort by speech duration + sorted_files = read_analyses(temp_abs, args.sad, nb_chunks*3, args.diar, args.mode) + # get new onsets for two minutes chunks + new_onset_list = new_onsets(sorted_files, duration=duration, chunk_size=args.chunk_sizes[1]) + # extract two minutes chunks + extract_chunks(wav_abs, new_onset_list, args.chunk_sizes[1], temp_abs, duration=duration) + + # analyze using SAD tool + run_Model(temp_rel, temp_abs, args.sad, diar=args.diar, del_temp=del_temp) + + # sort by speech duration again + child_aware = False + if args.mode == 'ACCA': + child_aware = True + + sorted_files = read_analyses(temp_abs, args.sad, nb_chunks, args.diar, args.mode, child_aware) + # get new onsets for five minutes chunks + new_onset_list = new_onsets(sorted_files, duration=duration, chunk_size=args.chunk_sizes[2]) + # extract final five minutes long chunks + extract_chunks(wav_abs, new_onset_list, args.chunk_sizes[2], temp_abs, duration=duration) + + ## Run one last time the model to get the transcription + # analyze using SAD tool + if not os.path.exists(output_dir): + os.makedirs(output_dir) + output_rttm = run_Model(temp_rel, temp_abs, args.sad, diar=args.diar, output_dir=output_dir, del_temp=del_temp) + + #output_rttm = ["/vagrant/data/daylong/yunitator_english_0396_sub_1085.0_1385.0.rttm"] + write_final_stats(output_rttm) + + if del_temp: + shutil.rmtree(temp_abs) + + +if __name__ == '__main__': + main() diff --git a/utils/html_python/__init__.py b/utils/html_python/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/utils/html_python/files_page.py b/utils/html_python/files_page.py new file mode 100755 index 0000000..bc56fdd --- /dev/null +++ b/utils/html_python/files_page.py @@ -0,0 +1,179 @@ +import html_python.page as page +import html_python.utils as utils +import html_python.style_css as style_css +import numpy as np +import os +import glob +import random +import numbers +from collections import OrderedDict + + +class FilesPage(page.Page): + + def __init__(self, output_folder, results_folder): + # Initialize self.output_folder & self.results_folder + page.Page.__init__(self, output_folder, results_folder) + + self.N_files = 20 # Fix the number of files to print in the table + + # List of gold files + self.gold_files = [fn for fn in glob.iglob(os.path.join(self.results_folder, '*.rttm')) + if not os.path.basename(fn).startswith(('ldc_sad', 'diartk', 'noisemes_sad', + 'opensmile', 'tocombo_sad', 'lena', 'yunitator'))] + # Name of the html that will be generated + self.name = 'files.html' + + # File descriptor + self.html = open(os.path.join(self.output_folder, self.name), 'w') + + tabs = '\n' + \ + '' + self.html.write(tabs)#write tabs + + # Compute statistics + self._compute_statistics() + + if len(self.audio_stats) > self.N_files: + self.random_indices = np.sort(random.sample(range(0, len(self.audio_stats)), self.N_files)) + else: + self.random_indices = range(0, len(self.audio_stats)) + + def _compute_statistics(self): + + self.audio_stats = [] + self.gold_stats = [] + + # Empty OrderedDict that will contains the unique keys of the gold file + # We use only the keys, it's to fake an OrderedSet that doesn't exist natively in python. + # The difficulty here is that gold2.rttm could have some participants that gold1.rttm doesn't have. + # We still want to consider them in all of the rttm to present the results in a table. + self.gold_keys = OrderedDict({}) + for gold in self.gold_files: #for each reference rttm file + + wav_path = os.path.splitext(gold)[0]+'.wav' + + # Get .wav information + audio_stats_file = utils.analyze_wav(wav_path) + self.audio_stats.append(audio_stats_file) + + # Analyze gold + dur_wav = audio_stats_file['duration (s)'] + gold_stats_file = utils.analyze_rttm(gold, dur_wav) + self.gold_keys = OrderedDict.fromkeys(self.gold_keys.keys() + gold_stats_file.keys()) + self.gold_stats.append(gold_stats_file) + + self._compute_averages() + self.audio_stats = np.array(self.audio_stats) + self.gold_stats = np.array(self.gold_stats) + + def _compute_averages(self): + + # Get audio stats averages + self.averages_audio = OrderedDict(zip(self.audio_stats[0].keys()[2:], [0.0] * (len(self.audio_stats[0]) - 2))) + for line in self.audio_stats: + for key in line.keys(): + if isinstance(line[key], numbers.Number) and line[key] is not None: # None and non numbers are considered as being 0.0 + self.averages_audio[key] += line[key] + + for k, v in self.averages_audio.items(): + self.averages_audio[k] = v/len(self.audio_stats) + + # Get gold stats averages + self.averages_gold = OrderedDict(zip(self.gold_keys.keys()[1:], [0.0] * (len(self.gold_keys) - 1))) + for line in self.gold_stats: + for key in line.keys(): + if isinstance(line[key], numbers.Number) and line[key] is not None: # None and non numbers are considered as being 0.0 + self.averages_gold[key] += line[key] + + for k, v in self.averages_gold.items(): + self.averages_gold[k] = v/len(self.gold_stats) + + + def _write_value(self, value, number_of_digits = 4): + if isinstance(value, str): + self.html.write(' %s \n' % value) + elif isinstance(value, float) or isinstance(value, np.float64) or isinstance(value, np.float32): + self.html.write(' %.*f\n' % (number_of_digits,value)) + elif isinstance(value, int): + self.html.write(' %d \n' % value) + + def write_audio_stats(self): + keys = self.audio_stats[0].keys() + + # Open table + self.html.write('

Audio level statistics

\n\n') + + # Write first line of the table + self.html.write('\n') + for k in keys: + self.html.write('\n' % k) + self.html.write('\n') + + # Write audio statistics + for d in self.audio_stats[self.random_indices]: + self.html.write('\n') + for key, value in d.items(): + self._write_value(value, 4) + + self.html.write('\n') + + # Write averages + self.averages_audio["filename"] = "averages" + for k in keys: + if k in self.averages_audio: + self._write_value(self.averages_audio[k], 4) + else: + self._write_value("") + + # Close table + self.html.write('
%s
') + + + def write_gold_stats(self): + + keys = self.gold_stats[0].keys() + + # Open table + self.html.write('

Annotation level statistics

\n\n') + + # Write first line of the table + self.html.write('\n') + for k in self.gold_keys: + self.html.write('\n' % k) + self.html.write('\n') + + # Write gold statistics + for d in self.gold_stats[self.random_indices]: + self.html.write('\n') + for key in self.gold_keys: + if key in d: + self._write_value(d[key], 2) + else: + self._write_value("") + self.html.write('\n') + + # Write averages + self.averages_gold["filename"] = "averages" + for k in self.gold_keys: + if k in self.averages_gold: + self._write_value(self.averages_gold[k], 2) + else: + self._write_value("") + + # Close table + self.html.write('
%s
') + + def write_statistics(self): + self.write_audio_stats() + self.write_gold_stats() + if len(self.audio_stats) > self.N_files: + self.html.write("

Note : a random sample of %d files has been made to lighten the presentation." % self.N_files) + + def close(self): + self.html.close() \ No newline at end of file diff --git a/utils/html_python/models_page.py b/utils/html_python/models_page.py new file mode 100755 index 0000000..249872c --- /dev/null +++ b/utils/html_python/models_page.py @@ -0,0 +1,103 @@ +import html_python.page as page +import html_python.utils as utils +import numpy as np +import os +import glob +import html_python.style_css as style_css +from collections import OrderedDict + + +class ModelsPage(page.Page): + + def __init__(self, output_folder, results_folder): + # Initialize self.output_folder & self.results_folder + page.Page.__init__(self, output_folder, results_folder) + + # Name of the html that will be generated + self.name = 'models.html' + + # File descriptor + self.html = open(os.path.join(self.output_folder, self.name), 'w') + + tabs = '\n' + \ + '' + self.html.write(tabs) #write tabs + + # List of df files present in the results_folder + self.df_files = [fn for fn in glob.iglob(os.path.join(self.results_folder, '*.df'))] + + def _get_statistics(self): + self.sad_stats = [] + self.diar_stats = [] + for df_path in self.df_files: + basename = os.path.splitext(os.path.basename(df_path))[0] + if basename.startswith(('diartk','lena','yunitator')): + self.diar_stats.append(utils.get_averages(df_path)) + else: + self.sad_stats.append(utils.get_averages(df_path)) + + def _write_SAD_statistics(self): + # Write SAD table + if len(self.sad_stats) == 0: + self.html.write("No SAD model evaluations have been found.") + else: + # Open table + self.html.write('

SAD statistics

\n\n') + # Write first line of the table + self.html.write('\n') + keys = self.sad_stats[0].keys() + for k in keys: + self.html.write('\n' % k) + self.html.write('\n') + + # Write statistics + for d in self.sad_stats: + self.html.write('\n') + for key, value in d.items(): + utils.write_value(self.html, value, 4) + self.html.write('\n') + # Close table + self.html.write('
%s
') + + def _write_diar_statistics(self): + # Write SAD table + if len(self.diar_stats) == 0: + self.html.write("No diarization model evaluations have been found.") + else: + # Open table + self.html.write('

Diarization statistics

\n\n') + # Write first line of the table + self.html.write('\n') + keys = self.diar_stats[0].keys() + for k in keys: + self.html.write('\n' % k) + self.html.write('\n') + + # Write statistics + for d in self.diar_stats: + self.html.write('\n') + for key, value in d.items(): + utils.write_value(self.html, value, 2) + self.html.write('\n') + + # Close table + self.html.write('
%s
') + + def write_statistics(self): + if len(self.df_files) == 0: + self.html.write("No df files have been found... Please run the evaluation of choose another folder.") + else: + self._get_statistics() + self._write_SAD_statistics() + self._write_diar_statistics() + + + + + def close(self): + self.html.close() \ No newline at end of file diff --git a/utils/html_python/page.py b/utils/html_python/page.py new file mode 100755 index 0000000..0c59ba0 --- /dev/null +++ b/utils/html_python/page.py @@ -0,0 +1,30 @@ +from abc import ABCMeta, abstractmethod +import os + +# Used to read the rttm generated by all the different models. +# The first element '' is to handle gold rttm +sad_prefixes = ['ldc_sad_', 'noisemes_sad_', 'tocombo_sad_', 'opensmile_sad_'] +diar_prefixes = ['diartk_goldSad_', 'diartk_ldcSad_', 'diartk_tocomboSad_', + 'diartk_noisemesSad_', 'diartk_opensmileSad_'] + +class Page: + __metaclass__ = ABCMeta + + @abstractmethod + def __init__(self, output_folder, results_folder): + # The output folder where to store the html/css files. + self.output_folder = output_folder + + # The folders that we need to analyze/aggregate + self.results_folder = results_folder + + if not os.path.exists(self.output_folder): + os.makedirs(self.output_folder) + + @abstractmethod + def write_statistics(self): + raise NotImplementedError('subclasses must override write_statistics()!') + + @abstractmethod + def close(self): + raise NotImplementedError('subclasses must override close()!') diff --git a/utils/html_python/style_css.py b/utils/html_python/style_css.py new file mode 100755 index 0000000..db1bc75 --- /dev/null +++ b/utils/html_python/style_css.py @@ -0,0 +1,33 @@ +import os + +css_name = "style.css" # global variable + + +class StyleCSS: + + def __init__(self, output_folder): + # The output folder where to store the html/css files. + self.output_folder = output_folder + + if not os.path.exists(self.output_folder): + os.makedirs(self.output_folder) + + self.css = open(os.path.join(self.output_folder, 'style.css'), 'w') + + # tabs style + tabs_style = "ul {background: #333;list-style: none;overflow: hidden;text-align: center;}\n" + \ + "ul li {display: inline-block;}\n" + \ + "ul li a {padding: 5px 10px;background: #333;color: #fff;line-height: 3em;display: block; }\n" + \ + "ul li a:hover {background: #e4e4e4;color: #333;}\n" + \ + "li.active>a {background: #e4e4e4;color: #333;}\n" + \ + "a {display : block;color : #666;text-decoration : none;padding : 4px;}\n" + + # table style + table_style = "table {margin: 0 auto;}\n" +\ + "td, th {border: 1px solid #ddd;padding: 8px;text-align: center;}\n" +\ + "tr:nth-child(even){background-color: #f2f2f2;}\n" +\ + "tr:hover {background-color: #ddd;}\n" +\ + "th {padding-top: 12px;padding-bottom: 12px;text-align: center;background-color: #333;color: white;}\n" + self.css.write(tabs_style) + self.css.write(table_style) + self.css.close() \ No newline at end of file diff --git a/utils/html_python/utils.py b/utils/html_python/utils.py new file mode 100755 index 0000000..82a1678 --- /dev/null +++ b/utils/html_python/utils.py @@ -0,0 +1,164 @@ +from scipy.io import wavfile +import collections +import numpy as np +import os + + +# Utility functions +def write_value(fn, value, number_of_digits=4): + if isinstance(value, str): + fn.write(' %s \n' % value) + elif isinstance(value, float) or isinstance(value, np.float64) or isinstance(value, np.float32): + fn.write(' %.*f\n' % (number_of_digits, value)) + elif isinstance(value, int): + fn.write(' %d \n' % value) + + +def signaltonoise(a, axis=0, ddof=0): + """ + The signal-to-noise ratio of the input data. + Returns the signal-to-noise ratio of `a`, here defined as the mean + divided by the standard deviation. + Parameters + ---------- + a : array_like + An array_like object containing the sample data. + axis : int or None, optional + If axis is equal to None, the array is first ravel'd. If axis is an + integer, this is the axis over which to operate. Default is 0. + ddof : int, optional + Degrees of freedom correction for standard deviation. Default is 0. + Returns + ------- + s2n : ndarray + The mean to standard deviation ratio(s) along `axis`, or 0 where the + standard deviation is 0. + """ + a = np.asanyarray(a) + m = a.mean(axis) + sd = a.std(axis=axis, ddof=ddof) + return np.where(sd == 0, 0, m / sd) + +def analyze_wav(filepath): + """ + Analyzes a wav file and returns some audio statistics. + Parameters + ---------- + filepath + + Returns + ------- + statistics : + """ + basename = os.path.splitext(os.path.basename(filepath))[0] + + if os.path.splitext(filepath)[1] == '.wav' and os.path.isfile(filepath): + fs, data = wavfile.read(filepath) + data_type = type(data[0]) + if data_type == np.int16: + wav_format = '16-bit PCM' + MAX_WAV_AMP = 32767.0 + elif data_type == np.int32: + wav_format = '32-bit PCM' + MAX_WAV_AMP = 2147483647.0 + elif data_type == np.uint8: + wav_format = '8-bit PCM' + MAX_WAV_AMP = 255.0 + elif data_type == np.float32: + wav_format = '32-bit floating-point' + MAX_WAV_AMP = 1.0 + + # Normalization between -1 and 1 + data = data/MAX_WAV_AMP + + # Compute metrics + duration = len(data)*1.0/fs + mean_amplitude = np.mean(data) + max_amplitude = np.max(data) + snr = float(signaltonoise(data)) + statistics = collections.OrderedDict([("filename", basename), + ("wav format", wav_format), + ("duration (s)", duration), + ("mean amplitude", mean_amplitude), + ("max amplitude", max_amplitude), + ("signal to noise ratio", snr)]) + return statistics + + +def _detect_overlap(t_beg, dur, t_beg_prev, dur_prev): + + if t_beg_prev + dur_prev > t_beg: + if t_beg_prev+dur_prev > t_beg+dur: + return dur + else: + return t_beg_prev + dur_prev - t_beg + return 0.0 + + +def analyze_rttm(filepath, dur_wav): + basename = os.path.splitext(os.path.basename(filepath))[0] + dict = collections.OrderedDict([("filename", basename), + ("% total speech", 0.0), + ("% overlap", 0.0), + ("number of participants", 0)]) + + if os.path.splitext(filepath)[1] == '.rttm' and os.path.isfile(filepath): + fn = open(filepath) + t_beg_prev, dur_prev, participant_prev = 0.0, 0.0, None + for line in fn: + fields = line.split() + type, t_beg, dur, participant = fields[0], float(fields[3]), float(fields[4]), fields[7] + + # Add information about the participant + prop_key = "% of "+participant + if prop_key in dict: + dict[prop_key] += dur + else: + dict[prop_key] = dur + + # Add information about the overall speech + if type == 'SPEAKER': + dict["% total speech"] += dur + + # Compute overlap + dict["% overlap"] += _detect_overlap(t_beg, dur, t_beg_prev,dur_prev) + + # Update previous + t_beg_prev, dur_prev, participant_prev = t_beg, dur, participant + + # Normalization step + # Normalize participants speech duration by total speech + if dict["% total speech"]: + for k, v in dict.items(): + if k.startswith("% of "): + dict["number of participants"] += 1 + dict[k] /= dict["% total speech"] + # Normalize % overlap by total speech + dict["% overlap"] /= dict["% total speech"] + + # Normalize total speech by wav duration + dict["% total speech"] /= dur_wav + + fn.close() + return dict + +def get_averages(df_path): + column_sums = None + basename = os.path.splitext(os.path.basename(df_path))[0] + with open(df_path) as file: + header=file.readline() + header=header.replace('\n','').split('\t') + if header[0] == 'filename': + header=header[1:] + + lines = file.readlines() + rows_of_numbers = [map(float, line.replace('%','').replace('\n','').replace('NA','0.0').split('\t')[1:]) for line in lines] + sums = map(sum, zip(*rows_of_numbers)) + averages = [sum_item / len(lines) for sum_item in sums] + + # Add model name + header.insert(0, 'filename') + averages.insert(0, basename) + averages = collections.OrderedDict(zip(header, averages)) + return averages + diff --git a/utils/install_htk.sh b/utils/install_htk.sh new file mode 100755 index 0000000..1094b67 --- /dev/null +++ b/utils/install_htk.sh @@ -0,0 +1,31 @@ +#!/bin/bash +# install HTK after the fact + +if grep --quiet vagrant /etc/passwd +then + user="vagrant" +else + user="ubuntu" +fi + +# optionally Install HTK (without it, some other tools will not work) +# the idea is to make users independently download HTK installer since +# we cannot redistribute +cd /home/${user} +if [ -f /vagrant/HTK-3.4.1.tar.gz ]; then + if [[ ! -d repos/htk ]]; then + cd /home/${user}/repos/ + sudo tar zxf /vagrant/HTK-3.4.1.tar.gz + cd htk + sudo ./configure --without-x --disable-hslab + sudo sed -i "s/ /\t/g" HLMTools/Makefile # fix bad Makefile + sudo make all + sudo make install + else + echo "Visibly htk has been already installed..." + fi +else + echo "Can't find HTK-3.4.1.tar.gz. Check that you installed the right version." +fi + + diff --git a/utils/its2rttm.py b/utils/its2rttm.py new file mode 100755 index 0000000..f3e190a --- /dev/null +++ b/utils/its2rttm.py @@ -0,0 +1,80 @@ +#!/usr/bin/env python +# +# author: The ACLEW Team +# +# This script takes as input its file +# and convert it to rttm file. + +import os +import re +import argparse + + +def its_line_2_rttm_line(its_line, rttm_name): + """ + < Segment spkr = "TVF" average_dB = "-49.61" peak_dB = "-38.96" startTime = "PT31819.24S" endTime = "PT31820.61S" / > + to + SPEAKER rttm_name 1 t_beg dur class + """ + its_line = its_line[its_line.find("<"):] + + rttm_line = "" + if its_line[:8] == " " +\ + " " +\ + spkr + " " +\ + " " +\ + "\n" + + return rttm_line + + +def its_2_rttm(input_path, output_path): + """Read a RTTM file indicating gold diarization""" + rttm_name = os.path.splitext(os.path.basename(output_path))[0] + + with open(input_path, 'r') as its: + with open(output_path, 'w') as rttm: + for its_line in its: + rttm_line = its_line_2_rttm_line(its_line, rttm_name) + rttm.write(rttm_line) + +def main(): + """Take transcription file in its format as input, and write """ + """it into rttm format.""" + parser = argparse.ArgumentParser( + description='Take transcription file in its format as input, and write ' + 'it into rttm format.', + add_help=True, + usage='%(prog)s [its] [rttm]') + parser.add_argument( + 'its', type=str, metavar='PATH', + help='Path to the its input') + parser.add_argument( + 'rttm', type=str, metavar='PATH', + help='Path to the rttm output') + + args = parser.parse_args() + + its_2_rttm(args.its, args.rttm) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/utils/its2rttm.sh b/utils/its2rttm.sh new file mode 100755 index 0000000..7f54580 --- /dev/null +++ b/utils/its2rttm.sh @@ -0,0 +1,23 @@ +#!/usr/bin/env bash +# Given a folder, convert all of the its files to rttm + +SCRIPT_PATH=$(dirname "$(readlink -f "$0")") + +folder=$1 + +if [ -z "$folder" ]; then + echo "You must specify a folder containing its files." + exit +fi + +if [ ! -n "$(ls -A $folder/*.its 2>/dev/null)" ] +then + echo "The folder you specified does not contain any its files." + +fi + +for its_file in $folder/*.its +do + rttm_name=${its_file%.its}.rttm + python $SCRIPT_PATH/its2rttm.py $its_file $rttm_name +done diff --git a/toolbox/lab2rttm.sh b/utils/lab2rttm.sh similarity index 79% rename from toolbox/lab2rttm.sh rename to utils/lab2rttm.sh index f2143eb..57f8103 100755 --- a/toolbox/lab2rttm.sh +++ b/utils/lab2rttm.sh @@ -26,5 +26,5 @@ do type="" fi subf=`echo $subf | sed "s/_lena//"` - grep ' speech' $j | awk '{print "SPEAKER" "\t" "'$subf'" "\t" 1 "\t" $1 "\t" $2-$1 "\t" "" "\t" "" "\t" $3 "\t" ""}' > $folder/${subf}${type}.rttm + grep ' speech' $j | awk '{print "SPEAKER" " " "'$subf'" " " 1 " " $1 " " $2-$1 " " "" " " "" " " $3 " " ""}' > $folder/${subf}${type}.rttm done diff --git a/utils/make_big_corpus.sh b/utils/make_big_corpus.sh new file mode 100755 index 0000000..12d08a2 --- /dev/null +++ b/utils/make_big_corpus.sh @@ -0,0 +1,101 @@ +#!/bin/bash +# this script takes as input, for $corpus, the folder containing the daylong recordings +# and the transcriptions in eaf format in a folder raw_$corpus. It reads the transcriptions +# in the eaf files and cuts the wav to only keep the transcribed part, and convert the transcribed +# part into rttm with the same names as the cut wavs. + +# take the name of the corpus as input and define the hard coded paths - they shall not change! +corpus=$1 # should be chosen between BER, WAR, SOD and CAS +working_dir=/DATA2T/aclew_data_update +daylong_dir=/DATA2T/aclew_daylong_data +temp_dir=$working_dir/temp # create temp dir to store wav. will be removed at end of script + +# Define function to check if all small wavs have an rttm. If not, create an empty one. +create_empty_rttm() { + # check the wav files and the talker_roles to see if the rttm exists + # if it doesn't, create an empty rttm file. + wav=$1 + rttms=$2 + for fin in `ls $wav/*.wav`; do + basename=$(basename $fin .wav) + if [[ ! -f $rttms/${basename}.rttm ]]; then + touch $rttms/${basename}.rttm + fi + done +} + +## Treat BER WAR the "simple way", add parameters to take into account the format of CAS and SOD/SOD2 +#if [[ $corpus == 'BER' ]] || [[ $corpus == 'WAR' ]] || [[ $corpus == 'CAS' ]]; then +if [[ $corpus != 'SOD2' ]] ; then + mkdir -p $temp_dir/$corpus/SAD + for wav in `ls $daylong_dir/$corpus/*.wav`; do + base=$(basename $wav .wav) + eaf=raw_$corpus/${base}.eaf + if [[ $corpus == 'CAS' ]]; then + python $working_dir/adjust_timestamps.py $working_dir/github_data/$eaf $wav $temp_dir --CAS + else + python $working_dir/adjust_timestamps.py $working_dir/github_data/$eaf $wav $temp_dir 2>&1 + fi + # now remove old wav and transcriptions: + + done + rm $working_dir/ACLEW_data/databrary_ACLEW/wavs/${corpus}_*wav $working_dir/ACLEW_data/databrary_ACLEW/talker_role/${corpus}_*rttm + mv $temp_dir/*.rttm $working_dir/ACLEW_data/databrary_ACLEW/talker_role/ + mv $temp_dir/*.wav $working_dir/ACLEW_data/databrary_ACLEW/wavs/ + + # create empty rttms when the wav has no transcription (nobody's talking)' + #create_empty_rttm $corpus/treated $corpus/treated/talker_role + + # Now create the SAD + rm $working_dir/ACLEW_data/databrary_ACLEW/SAD/${corpus}_*rttm + python remove_overlap_rttm.py $working_dir/ACLEW_data/databrary_ACLEW/talker_role/ $working_dir/ACLEW_data/databrary_ACLEW/SAD +fi + +# SOD : the eaf have the same name as the wav, but they added '-$name' where $name is the initials of the annotator +if [[ $corpus == 'SOD2' ]]; then + echo "treating SOD" 2>&1 + corpus=SOD + mkdir -p $temp_dir/$corpus/SAD + for wav in `ls $daylong_dir/$corpus/*.wav`; do + base=$(basename $wav .wav) + eaf=raw_$corpus/${base}-TS.eaf + if [ ! -f $working_dir/github_data/$eaf ]; then + eaf=raw_$corpus/${base}-JK.eaf + fi + python $working_dir/adjust_timestamps.py $working_dir/github_data/$eaf $wav $temp_dir 2>&1 + + done + mv $temp_dir/*.rttm $working_dir/ACLEW_data/databrary_ACLEW/talker_role/ + mv $temp_dir/*.wav $working_dir/ACLEW_data/databrary_ACLEW/wavs/ + + # Now create the SAD + python remove_overlap_rttm.py $working_dir/ACLEW_data/databrary_ACLEW/talker_role/ $working_dir/ACLEW_data/databrary_ACLEW/SAD + +fi +# create empty rttms when the wav has no transcription (nobody's talking)' + +#create_empty_rttm $corpus/treated $corpus/treated/talker_role + +# Now create the SAD +#python remove_overlap_rttm.py $corpus/treated/talker_role $corpus/treated/SAD + + +## CAS : the eaf don't have the 'on_off' tier to encode the segments, instead it's called "code" - this is taken +## into account in adjust_timestamps.py with the --CAS parameter +#if [[ $corpus == 'CAS' ]]; then +# #convert2wav $corpus +# mkdir -p $corpus/treated/SAD +# for wav in `ls $corpus/*.wav`; do +# base=$(basename $wav .wav) +# eaf=$corpus/raw_$corpus/${base}.eaf +# python $working_dir/adjust_timestamps.py $eaf $wav --CAS +# done +#fi +## create empty rttms when the wav has no transcription (nobody's talking)' +# +#create_empty_rttm $corpus/treated $corpus/treated/talker_role +# +## Now create the SAD +#python remove_overlap_rttm.py $corpus/treated/talker_role $corpus/treated/SAD +##done +# diff --git a/utils/noisemes_full.sh b/utils/noisemes_full.sh new file mode 100755 index 0000000..9d6a2cf --- /dev/null +++ b/utils/noisemes_full.sh @@ -0,0 +1,72 @@ +#!/bin/bash + +# run OpenSAT with hard coded models & configs found here and in /vagrant +# assumes Python environment in /home/${user}/ + +# Absolute path to this script. /home/user/bin/foo.sh +SCRIPT=$(readlink -f $0) +# Absolute path this script is in. /home/user/bin +BASEDIR=`dirname $SCRIPT` +# Path to OpenSAT (go on folder up and to opensat) +OPENSATDIR=$(dirname $BASEDIR)/OpenSAT + +if [ $# -ne 1 ]; then + echo "Usage: noisemes_full.sh " + echo "where dirname is the name of the folder" + echo "containing the wav files" + exit 1 +fi + +audio_dir=/vagrant/$1 +filename=$(basename "${audio_dir}") +dirname=$(dirname "${audio_dir}") +extension="${filename##*.}" +basename="${filename%.*}" +# Check audio_dir to see if empty or if contains empty wav +bash $BASEDIR/check_folder.sh ${audio_dir} + +# let's get our bearings: set CWD to the path of OpenSAT +cd $OPENSATDIR + +# first features +echo "extracting features for noisemes_full" +for file in `ls ${audio_dir}/*.wav`; do + SSSF/code/feature/extract-htk-vm2.sh $file +done + + + + +# then confidences +# python SSSF/code/predict/1-confidence-vm.py $BASEDIR/SSSF/data/feature/evl.med.htk/$basename.htk $basename +echo "predicting classes" +# python SSSF/code/predict/1-confidence-vm.py $BASEDIR/SSSF/data/feature/evl.med.htk/$basename.htk $basename +python SSSF/code/predict/1-confidence-vm4.py ${audio_dir} +echo "noisemes_full finished running" + +# take all the .rttm in /vagrant/data/hyp_sum and move them to /vagrant/data - move features and hyp_sum to another folder also. +for sad in `ls ${audio_dir}/hyp_sum/*.rttm`; do + _rttm=$(basename $sad) + rttm=${audio_dir}/noiseme_full_${_rttm} + mv $sad $rttm +done + +# simply remove hyp and feature +rm -rf ${audio_dir}/feature ${audio_dir}/hyp_sum + +#if [ ! -d "${audio_dir}/noiseme_full_temp" ]; then +# mkdir -p ${audio_dir}/noiseme_full_temp +#fi +# +#if [! -d "${audio_dir}/noiseme_full_temp" ]; then +# mv ${audio_dir}/hyp_sum ${audio_dir}/noiseme_full_temp +#else +# echo "can't move hyp_sum/ folder to noiseme_full_temp/ because temp is already full" +#fi +# +#if [! -d "${audio_dir}/noiseme_full_temp" ]; then +# mv ${audio_dir}/feature ${audio_dir}/noiseme_full_temp +#else +# echo "can't move features/ folder to noiseme_full_temp/ because temp is already full" +#fi +# diff --git a/utils/parse_cha_xml.py b/utils/parse_cha_xml.py new file mode 100755 index 0000000..f9a561f --- /dev/null +++ b/utils/parse_cha_xml.py @@ -0,0 +1,191 @@ +# parse_cha_xml.py +# +# Given CHATTER format xml (http://talkbank.org/software/chatter.html) +# supplied as an argument, or via a pipe, print out STM format text +# +# To toggle whether UNIBET words are printed out, or instead appear as "" +# set the switch --oov. To instead print their replacements, set the switch --replacment +# +# usage ./parse_cha_xml.py P1_6W_SE_C6.xml +# +# Change Log: +# 17 Jan 2018 - print plaintext utterances even if no timecode present +# - added handling of "happening" and formType elements +# to support things like "singing", yell, shout etc. +# - added metadata for speaker e.g. as +# found in 'participant' tags + +from xml.dom.minidom import parse +import sys,argparse,os + +reload(sys) +sys.setdefaultencoding("utf-8") + +parser=argparse.ArgumentParser(description="""Description""") +parser.add_argument('--oov', action='store_true', help='print symbols for nonwords') +parser.add_argument('--replacement', action='store_true', help='print replacement words') +parser.add_argument('--stm', action='store_true', help='produce STM format') +parser.add_argument('infile', nargs='?', type=argparse.FileType('r'), help='CHATTER (xml) format input file', + default=sys.stdin) +args=parser.parse_args() + +# a table of speaker IDs and related metadata + +infile = args.infile.name +if infile == '': + dom = parse(sys.stdin) +else: + dom = parse(infile) +utts = dom.getElementsByTagName('u') # utterances +participants = dom.getElementsByTagName('participant') + +oov = args.oov +stm = args.stm +replace = args.replacement +#recording = infile[infile.rfind("/")+1:] +# get only base name (chop off .xml extension) +recording = os.path.splitext(os.path.basename(infile))[0] + +#utterance = "" + +parts = {} +for part in participants: + label = "<"; ident = "" + name = ""; role = ""; sex = "u" + for key in part.attributes.keys(): + if key == "id": + ident = part.attributes[key].nodeValue.encode('utf8') + if key == "name": + name = part.attributes[key].nodeValue.encode('utf8') + name = name.replace(" ","") + if key == "role": + role = part.attributes[key].nodeValue.encode('utf8') + role = role.replace(" ","") + if key == "sex": + sex = part.attributes[key].nodeValue.encode('utf8') + if sex == "male": sex = "m" + if sex == "female": sex = "f" + parts[ident]=("<"+name+","+sex+","+role+">").lower() +# print parts[ident] + +# add the word found inside node passed in as argument +def addWord( node ): + global utterance + for wordlet in node.childNodes: + if wordlet.nodeType == wordlet.TEXT_NODE: + unibet = False + s = wordlet.nodeValue + try: s.decode('ascii') + except UnicodeDecodeError: unibet = True + utterance += " " + if oov and unibet: + utterance += "" + else: + utterance += wordlet.nodeValue.encode('utf8') + + +def addReplacement ( group ): + added = False + for subword in group.childNodes: + if subword.nodeType == subword.ELEMENT_NODE and subword.tagName == 'replacement': + for replacement in subword.childNodes: + if replacement.nodeType == replacement.ELEMENT_NODE and replacement.tagName == 'w': + addWord( replacement ) + added = True + # didn't find a - just output the word + if not added: addWord( group ) + +def addUnibetOrReplacement( node ): + global utterance + for key in node.attributes.keys(): + if key == "untranscribed": + if oov: + utterance += " " + "" + else: + addWord( node ) + if key == "type" and (node.attributes[key].nodeValue == "fragment"): + utterance += " " + node.firstChild.nodeValue + if key == "formType": + if node.attributes[key].nodeValue == "UNIBET": + if replace: + addReplacement( node ) + else: + addWord( node ) + else: # for all other form types + # e.g. singing, babbling, kana, onomotapoeia, family-specific, letter, child-invented + # output word + addWord( node ) + +for utt in utts: + utterance = "" + start = 0 + has_timecode = False + # speaker + for key in utt.attributes.keys(): + if key == "who": + speaker=utt.attributes[key].nodeValue + spk_reco_clause = recording+" "+speaker+" "+recording+"_"+speaker + + for word in utt.childNodes: + # time code + if word.nodeType == word.ELEMENT_NODE and word.tagName == 'media': + has_timecode = True + for key in word.attributes.keys(): + if key == "start": + start = word.attributes[key].nodeValue + if key == "end": + end = word.attributes[key].nodeValue + if stm: + # Don't output if start == end; confuses downstream systems + if start != end: + print spk_reco_clause+" "+start+" "+end+" "+parts[speaker]+\ + utterance.lower().replace("_"," ").replace("-","") + sys.stdout.flush() + else: + print utterance.lower().replace("_"," ").replace("-","") + sys.stdout.flush() + utterance = "" + + # tb:wordType ("" tag) + if word.nodeType == word.ELEMENT_NODE and word.tagName == 'w': + if len(word.attributes.keys())==0: + if word.childNodes.length == 1: + addWord( word ) + else: + if replace: + addReplacement( word ) + else: + addWord( word ) + else: + addUnibetOrReplacement( word ) + # tb:element type ("" tag): + if word.nodeType == word.ELEMENT_NODE and word.tagName == 'e': + for wordlet in word.childNodes: + if wordlet.nodeType == wordlet.ELEMENT_NODE and wordlet.tagName == 'happening': + for txt in wordlet.childNodes: + thetxt = txt.nodeValue + utterance += " <" + thetxt.encode('utf8') + ">" + # tb:groupType or phoneticGroupType ("" or "" tag): + if word.nodeType == word.ELEMENT_NODE and (word.tagName == 'g' or word.tagName == 'pg'): + for group in word.childNodes: + if group.nodeType == group.ELEMENT_NODE and group.tagName == 'w': + if len(group.attributes.keys())==0: + if group.childNodes.length == 1: + addWord( group ) + else: + if replace: + addReplacement( group ) + else: + addWord( group ) + else: + # print oov (or unibet encoded word) + if oov: + utterance += " " + "" + else: + addUnibetOrReplacement( group ) + # done with utterance. if it had no timecode, was not printed(!) so + # print now + if not has_timecode: + print utterance.lower().replace("_"," ").replace("-","") + sys.stdout.flush() + utterance = "" diff --git a/utils/rttm2labels.py b/utils/rttm2labels.py new file mode 100755 index 0000000..3f593dd --- /dev/null +++ b/utils/rttm2labels.py @@ -0,0 +1,40 @@ +#! /usr/bin/python +''' +Created on Oct 4, 2014 +@author: fmetze,er1k +convert RTTM (segmented) to Audacity labels format +''' + +import sys +import datetime +import re + +def printbuf (begin, end, text): + datetime1 = datetime.datetime.utcfromtimestamp(begin) + datetime2 = datetime.datetime.utcfromtimestamp(end) + allseconds1 = 60 * datetime1.minute + 3600 * datetime1.hour + datetime1.second + allseconds2 = 60 * datetime2.minute + 3600 * datetime2.hour + datetime2.second + print "%s.%s\t%s.%s\t%s" % (allseconds1, datetime1.strftime('%f'), allseconds2, datetime2.strftime('%f'), text) + + +for l in sys.stdin: + m = l.split(' ') + if m: + type, file = m[0], m[1] + channel = int(m[2]) + starttime = float(m[3]) + duration = float(m[4]) + ortho = m[5] + stype = m[6] + name = m[7] + conf = m[8] + + printbuf (starttime, starttime+duration, name) + begin = starttime + + elif re.match(";.*", l) or re.match("#.*", l): + pass + + else: + raise Exception("cannot process line: " + l) + diff --git a/utils/rttm2scp.py b/utils/rttm2scp.py new file mode 100755 index 0000000..cf8fba8 --- /dev/null +++ b/utils/rttm2scp.py @@ -0,0 +1,103 @@ +#!/usr/bin/env python +# +# author: The ACLEW Team +# +# This script takes as input gold rttm file +# that transcribes a diarization, and converts it +# to a gold scp file indicating Speech Activity. +# It is necessary, to avoid overlaps between speech intervals +# and to sort the intervals by onsets. + +import os +import argparse +import errno + +def read_rttm(input_path): + """Read a RTTM file indicating gold diarization""" + if os.path.isfile(input_path): + with open(input_path, 'r') as fin: + # RTTM format is + # SPEAKER fname 1 onset duration spkr + rttm = fin.readlines() + #sad = IntervalTree() + all_intervals = [] + fname = "" + for line in rttm: + row = line.strip('\n').split() + fname, onset, dur = row[1], row[3], row[4] + if float(dur) == 0: + # Remove empty intervals + continue + elif float(dur) < 0: + print("{} shows an interval with negative duration." + " Please inspect file, this shouldn't happen".format( + line)) + continue + + # add interval to list + all_intervals.append((float(onset), float(onset) + float(dur))) + + # sort intervals by their onset + all_intervals.sort() + + # look at each interval, add them in growing order of onset, + # trim the beginning if it overlaps with previous interval, + # and completely delete if it is contained by the previous interval. + sad = [] + prev_on = 0 + prev_off = 0 + for onset, offset in all_intervals: + if len(sad) == 0: + # don't check anything for first interval + sad.append((onset, offset)) + prev_on = onset + prev_off = offset + continue + + if onset < prev_off: + if offset <= prev_off: + # interval is completely contained in the previous one + continue + onset = prev_off + + sad.append((onset, offset)) + prev_on = onset + prev_off = offset + + return sad, fname + else: + raise IOError(errno.ENOENT, os.strerror(errno.ENOENT), input_path) + +def write_scp(out_intervals, fname, output): + """Write output in SCP format""" + if len(out_intervals) != 0: + with open(output, 'w') as fout: + fout.write(u'') # write empty string in case out_intervals is empty + for onset, offset in out_intervals: + fout.write(u'{fname}_{on}_{off}={fname}.fea[{on},{off}]\n'.format( + fname=fname, + on=int(onset*100), + off=int(offset*100))) + +def main(): + """Take diarization in RTTM format as input, and write """ + """SAD in scp format, with ordered intervals, as output.""" + parser = argparse.ArgumentParser( + description='Take diarization in RTTM format as input, and write ' + 'SAD in scp format, with ordered intervals, as output.', + add_help=True, + usage='%(prog)s [RTTM] [SCP]') + parser.add_argument( + 'rttm', type=str, metavar='PATH', + help='Path to the RTTM input') + parser.add_argument( + 'scp', type=str, metavar='PATH', + help='Path to the SCP output') + + args = parser.parse_args() + + sad_tree, fname = read_rttm(args.rttm) + write_scp(sad_tree, fname, args.scp) + +if __name__ == "__main__": + main() diff --git a/toolbox/rttm2scp.sh b/utils/rttm2scp.sh similarity index 100% rename from toolbox/rttm2scp.sh rename to utils/rttm2scp.sh diff --git a/utils/runTALNet.sh b/utils/runTALNet.sh new file mode 100755 index 0000000..629bb38 --- /dev/null +++ b/utils/runTALNet.sh @@ -0,0 +1,38 @@ +#!/bin/bash + +# run 537 class classifier with hard coded models & configs found here + +# Absolute path to this script. /home/user/bin/foo.sh +SCRIPT=$(readlink -f $0) +# Absolute path this script is in. /home/user/bin +BASEDIR=`dirname $SCRIPT` +# Path to classify tool (go one folder up and to 537cls) +CLASSIFY=$(dirname $BASEDIR)/TALNet + +if [ $# -ne 1 ]; then + echo "Usage: $0 " + echo "where dirname is the name of the folder" + echo "in /vagrant containing wav files" + exit 1 +fi + +audio_dir=/vagrant/$1 +filename=$(basename "${audio_dir}") +dirname=$(dirname "${audio_dir}") +extension="${filename##*.}" +basename="${filename%.*}" +# Check audio_dir to see if empty or if contains empty wav +bash $BASEDIR/check_folder.sh ${audio_dir} + +# let's get our bearings: set CWD to the path of TALNet +cd $CLASSIFY + +# Iterate over files +echo "Starting" +for f in `ls ${audio_dir}/*.wav`; do + ./runTALNet.sh $f + base=$(basename $f .wav) + mv ${audio_dir}/${base}.frame_prob.mat ${audio_dir}/${base}_talnet.frame_prob.mat +done + +echo "$0 finished running" diff --git a/utils/selcha2clean.sh b/utils/selcha2clean.sh new file mode 100755 index 0000000..379a017 --- /dev/null +++ b/utils/selcha2clean.sh @@ -0,0 +1,169 @@ +#!/usr/bin/env bash +# Given a txt file containing the following fields : onset offset transcription receiver speaker_tier (Note that eaf2txt.py generates such kind of files) +# Returns an enriched version of it by cleaning the transcription field, syllabifying (or phonemizing it), counting the number of words, and the number of syllables + +LC_CTYPE=C +#########VARIABLES +#Variables that have been passed by the user +SELFILE=$1 +ORTHO=$2 +LANG=$3 +######### + + +display_usage() { + echo "Given a txt files containing the following fields : onset offset transcription receiver speaker_tier" + echo "and a language, returns an enriched version of this file by :" + echo -e "\t 1) Cleaning the transcription field (removing human-made errors)" + echo -e "\t 2) Counting the number of words" + echo -e "\t 3) Phonemizing (if english)/Syllabifying (if spanish or tzeltal) the transcription" + echo -e "\t 4) Counting the number of syllables" + echo "usage: $0 [input] [output] [language]" + echo " input The file path where to find the input txt file (REQUIRED)." + echo " output The output path (REQUIRED)." + echo " language The language of the transcription : english, spanish or tzeltal (REQUIRED)." + exit 1 + } + +if [ -z "$1" ] || [ -z "$2" ] || [ -z "$3" ] || ! [[ $LANG =~ ^(english|spanish|tzeltal)$ ]]; then + display_usage +fi + +# Get relative and absolute paths that we need. +DATA_DIR=/vagrant +SELFILE_ABS="$DATA_DIR/$SELFILE" +ORTHO_ABS="$DATA_DIR/$ORTHO" +DIRNAME_ABS=$(dirname "${ORTHO_ABS}") +CLEAN_TRANSCRIPT_ABS=$DIRNAME_ABS"/clean_transcript.txt" +PHONEMIZED_ABS=$DIRNAME_ABS"/phonemized.txt" +DIRNAME_REL=$(dirname "${ORTHO}") +CLEAN_TRANSCRIPT_REL=$DIRNAME_REL"/clean_transcript.txt" +PHONEMIZED_REL=$DIRNAME_REL"/phonemized.txt" + +echo "Cleaning $SELFILE" + + +### HANDLING REPETITIONS ### +###  word [x2] ### +### or [x2]   ### +cut -f 4 -d$'\t' $SELFILE_ABS | +sed "s/\[ \+/\[/g" | +sed "s/ \+\[/\[/g" | +sed -r "s/\[X([0-9]+)/\[x\1/g" | +sed "s/> \+/>/g" > ${CLEAN_TRANSCRIPT_ABS}.tmp + +python -c " +import re +for line in open(\"${CLEAN_TRANSCRIPT_ABS}.tmp\"): + + if '<' in line and '>' in line: + newline = re.sub('<(.*)>\[x([0-9]+)\]', r'\1 \2', line) + if newline != line: + n = int(newline[-2:-1]) + newline = newline[:-2]*n + else: + newline = line + else: + reg = re.sub('(.*)\[x([0-9]+)\]', r'\1\2', line) + newline=[] + for word in reg.split(): + if word[-1].isdigit(): + newline += [word[:-1]]*int(word[-1]) + else: + newline += [word] + newline = ' '.join(newline) + newline = newline.rstrip() # Remove all \newline and let the print function puts only one of them + print(newline) +" > ${CLEAN_TRANSCRIPT_ABS}2.tmp + + +### CLEAN human-made inconsistencies + +cat ${CLEAN_TRANSCRIPT_ABS}2.tmp | +sed "s/\_/ /g" | +sed '/^0(.*) .$/d' | +sed 's/\..*$//g' | #this code deletes bulletpoints (Û+numbers +sed 's/\?.*$//g' | +sed 's/\!.*$//g' | +tr -d '\"' | +tr -d '\^' | #used to be identical to previous line +tr -d '\/' | +sed 's/\+/ /g' | +tr -d '\.' | +tr -d '\?' | +tr -d '!' | +tr -d ';' | +tr -d '\<' | +tr -d '\>' | +tr -d ',' | +tr -d ':' | +tr -d '~' | +sed 's/&=[^ ]*//g' | +sed 's/&[^ ]*//g' | #delete words beginning with & ##IMPORTANT CHOICE COULD HAVE CHOSEN TO NOT DELETE SUCH NEOLOGISMS/NONWORDS +sed 's/\[[^[]*\]//g' | #delete comments +#sed 's/([^(]*)//g' | #IMPORTANT CHOICE -- UNCOMMENT THIS LINE AND COMMENT OUT THE NEXT TO DELETE MATERIAL NOTED AS NOT PRONOUNCED +sed 's/(//g' | sed 's/)//g' | #IMPORTANT CHOICE -- UNCOMMENT THIS LINE AND COMMENT OUT THE PRECEDING TO REMOVE PARENTHESES TAGGING UNPRONOUNCED MATERIAL +sed 's/xxx//g' | +sed 's/www//g' | +sed 's/XXX//g' | +sed 's/yyy//g' | +sed 's/0*//g' | +sed 's/[^ ]*@s:[^ ]*//g' | #delete words tagged as being a switch into another language +#sed 's/[^ ]*@o//g' | #delete words tagged as onomatopeic +sed 's/@[^ ]*//g' | #delete tags beginning with @ IMPORTANT CHOICE, COULD HAVE CHOSEN TO DELETE FAMILIAR/ONOMAT WORDS +sed "s/\'/ /g" | +tr -s ' ' | +sed 's/ $//g' | +sed 's/^ //g' | +sed 's/^[ ]*//g' | +sed 's/ $//g' | +#sed '/^$/d' | # We don't want to remove end lines here +sed '/^ $/d' | +sed 's/\^//g' | +sed 's/\-//g' | +sed 's/\[\=//g' | # We observed [= occurrences that we're not interested in. Has to be careful about that one +sed 's/[0-9]//g' | # We remove all of the remaining numbers +#tr -d '\t' | +awk '{gsub("\"",""); print}' > ${CLEAN_TRANSCRIPT_ABS}3.tmp + +SCRIPT_DIR=$(dirname "$0") +$SCRIPT_DIR/syllabify.sh ${CLEAN_TRANSCRIPT_REL}3.tmp ${PHONEMIZED_REL} $LANG + +## Append number of words to the clean transcription +cat ${CLEAN_TRANSCRIPT_ABS}3.tmp | awk -F'[ ]' '{print $0"\t"NF}' > ${CLEAN_TRANSCRIPT_ABS} + +## Concatenate those 2 files +python -c " +import re +transcript_f = open(\"${CLEAN_TRANSCRIPT_ABS}\") +phonemized_f = open(\"${PHONEMIZED_ABS}\") + +for transcript_l in transcript_f.readlines(): + nb_words = transcript_l.split('\t')[1] + if int(nb_words) == 0: + x=2 + print(\"\t0\t\t0\") + else: + phoneme_l = phonemized_f.readline() + transcript_l = transcript_l.rstrip() + phoneme_l = phoneme_l.rstrip() + print(transcript_l+'\t'+phoneme_l) +" > $ORTHO_ABS.tmp + +## Now we concatenate the original csv files and the clean ortho (by column) +### Extract everything except transcript column from the original file +cut -f1,2,3,5 -d$'\t' $SELFILE_ABS > ${ORTHO_ABS}2.tmp + +### Concatenate the latter columns to the clean one contained in _tmp3.txt +paste -d$'\t' ${ORTHO_ABS}2.tmp ${ORTHO_ABS}.tmp > $ORTHO_ABS + +##This is to process all the "junk" that were generated when making the +##changes from included to ortho. For e.g., the cleaning process +##generated double spaces between 2 words (while not present in +##included) +sed -i -e 's/ $//g' $ORTHO_ABS + +# Remove temporary files +rm ${DIRNAME_ABS}/*.tmp +rm ${CLEAN_TRANSCRIPT_ABS} +rm ${PHONEMIZED_ABS} \ No newline at end of file diff --git a/utils/speech_extractor.py b/utils/speech_extractor.py new file mode 100755 index 0000000..5e76b80 --- /dev/null +++ b/utils/speech_extractor.py @@ -0,0 +1,78 @@ +import argparse +import os +import sys +import subprocess + + +def extracts_speech(wav, sad, out): + """ + Read a .rttm file and extracts segments of the wav files that + have been annotated as being speech. + + :param wav: Path to the audio file (.wav). + :param sad: Path to the rttm file (.rttm). + :param out: Folder where the segments need to be stored. + """ + if not os.path.isfile(wav): + print("The audio file %s has not been found." % wav) + sys.exit(1) + + if not os.path.isfile(sad): + print("The SAD file %s has not been found." % sad) + sys.exit(1) + + with open(sad, 'r') as rttm: + for line in rttm: + # Replace tabulations by spaces + fields = line.replace('\t', ' ') + # Remove several successive spaces + fields = ' '.join(fields.split()) + fields = fields.split(' ') + onset, duration, activity = float(fields[3]), float(fields[4]), fields[7] + offset = onset+duration + if activity == 'speech': + basename = os.path.basename(wav).split('.wav')[0] + output = os.path.join(out, '_'.join([basename, str(onset), str(offset)])+'.wav') + cmd = ['sox', wav, output, + 'trim', str(onset), str(duration)] + print("Cutting %s from %s to %s " % (os.path.basename(wav), str(onset), str(offset))) + subprocess.call(cmd) + +def main(): + parser = argparse.ArgumentParser(description="Extracts segments that have" + "been annotated as speech by" + "a SAD tool.") + parser.add_argument('-i', '--input', type=str, required=True, + help="Path to the .wav files (or to the folder containing wav files).") + parser.add_argument('-s', '--sad', type=str, required=False, default="gold", + choices=["ldc_sad", "noisemes", "opensmile", "tocombosad", "gold"], + help="Indicates which SAD needs to be used to extract the audio file.") + parser.add_argument('-o', '--output', type=str, required=False, + help="Path to the folder where the extracted segments need to be stored. " + "(Relative to the input folder)") + args = parser.parse_args() + + wav = os.path.join("/vagrant", args.input) + if args.output is not None: + out = os.path.join(os.path.dirname(wav), args.output) + else: + out = os.path.dirname(wav) + + if not os.path.exists(out): + os.makedirs(out) + + name_map = { + 'ldc_sad': 'ldcSad_', + 'noisemes': 'noisemes_sad_', + 'opensmile': 'opensmileSad_', + 'tocombosad': 'tocomboSad_', + 'gold': '', + } + + sad = os.path.join(os.path.dirname(wav), name_map[args.sad] + os.path.basename(wav).replace(".wav", ".rttm")) + print(sad) + extracts_speech(wav, sad, out) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/utils/sum-rttm.sh b/utils/sum-rttm.sh new file mode 100755 index 0000000..056d831 --- /dev/null +++ b/utils/sum-rttm.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +# sum-rttm.sh +# +# Compute and print the number of lines in an RTTM, and the sum of durations +# for use in comparing system outputs +# +# Takes 1 argument: path to an RTTM file + +if [ $# -ne 1 ]; then + echo "Usage: $0 " + echo "where RTTMfile is an RTTM filename" + exit 1 +fi + +LINES=`cat $1 | wc -l` +SUM=`awk '{SUM+=$5} END {print SUM}' $1` + +echo -e 'LINES: '$LINES'\tDURATION SUM: '$SUM'\tFILE: '$1 diff --git a/utils/syllabify.sh b/utils/syllabify.sh new file mode 100755 index 0000000..0c3444f --- /dev/null +++ b/utils/syllabify.sh @@ -0,0 +1,77 @@ +#!/usr/bin/env bash +# Given an input file (containing raw text), an output one, and a language (english, spanish or tzeltal) +# Write in the output file the phonemization (if english) or the syllabification (if spanish or tzeltal) of it, +# and count the number of syllables +### Script parameters +INPUT=$1 +OUTPUT=$2 +LANG=$3 +VOWELS=$4 +### +DATA_DIR="/vagrant" +INPUT=$DATA_DIR"/"$INPUT +OUTPUT=$DATA_DIR"/"$OUTPUT +DIRNAME=${INPUT%/*} +EXTENSION=${INPUT##*.} + +display_usage() { + echo "Given the path to a file (.tmp or .txt) containing sentences, a language (english, spanish or tzeltal)," + echo "and a list of vowels, create a .syll file containing the original sentence, the phonemized/syllabified" + echo "version of it, and the number of syllables present in the original sentence." + echo "usage: $0 [input] [output] [language] [vowels]" + echo " input The file path where to find the transcription. Has to be txt or tmp extension. (REQUIRED)" + echo " output The output path. (REQUIRED)" + echo " language The language of the transcription (OPTIONAL, default = english)" + echo " vowels The list of vowels of the language if language set on spanish or tzeltal (OPTIONAL, default = aeiou)" + exit 1 + } + +if [ -z "$1" ] || [ -z "$2" ] || ! [[ $EXTENSION =~ ^(txt|tmp)$ ]]; then + display_usage +fi + +if [ -z "$3" ]; then + echo "No languages has been provided. Setting this parameter to english." + LANG="english" +fi + +if [ "$3" == "spanish" ] || [ "$3" == "tzeltal" ]; then + if [ -z "$4" ]; then + VOWELS="aeiouáéíóúü" + echo "Language set on spanish or tzeltal. But no vowels have been provided." + echo "Setting this parameter to $VOWELS" + echo $VOWELS > $DIRNAME"/"$LANG"-Vowels.txt" + fi +fi + +if [ "$3" == "english" ]; then + echo "Pĥonemizing $INPUT ..." + # Phonemize the clean version + phonemize ${INPUT} -o ${OUTPUT}.tmp -s - + + ## Append number of syllables to the phonemized transcription + cat ${OUTPUT}.tmp | awk -F- '{print $0"\t"NF-1}' > ${OUTPUT} +elif [ "$3" == "spanish" ] || [ "$3" == "tzeltal" ]; then + # Changing upper case to lower case + cat $INPUT | tr '[:upper:]' '[:lower:]' | tr 'A-ZÂÁÀÄÊÉÈËÏÍÎÖÓÔÖÚÙÛÑÇ' 'a-zâáàäêéèëïíîöóôöúùûñç' > $INPUT.tmp + # Get all the different words in the corpus + # and get the different onsets by removing what follows the first vowel + cat $INPUT.tmp | tr ' ' '\n' | sort | uniq | + sed 's/[aeiou].*//g' | grep .| uniq > $DIRNAME"/"$LANG"-ValidOnsets.txt" + SCRIPT_DIR=$(dirname "$0") + perl $SCRIPT_DIR/catspa-syllabify-corpus.pl $LANG $INPUT.tmp $OUTPUT.tmp + + ## Append number of syllables + cat $OUTPUT.tmp | awk -F'/' '{print $0"\t"NF-1}' > $OUTPUT + + rm $INPUT.tmp $DIRNAME"/"$LANG"-ValidOnsets.txt" $DIRNAME"/"$LANG"-Vowels.txt" +else + echo "Language unknown." + exit 1 +fi + +echo "Done." + +rm $OUTPUT.tmp + + diff --git a/utils/textgrid2rttm.py b/utils/textgrid2rttm.py new file mode 100755 index 0000000..7c2634e --- /dev/null +++ b/utils/textgrid2rttm.py @@ -0,0 +1,81 @@ +#!/usr/bin/env python +# +# author = julien karadayi +# +# This script converts transcription in Text Grid / Praat format +# to RTTM format. This is useful for evaluating performances of +# Speech detection algorithms with the *dscore* package, +# in the DiarizationVM virtual machine. +# All non-empty labels are written in the output; empty labels +# are not written in output (which means it is described as "non speech") + +import os +import argparse +import tgt # tgt is better than praatio for our application + # because it allows to manipulate the timestamps, + # which is something we cannot do with praatio. +import glob +import sys + +def textgrid2rttm(textgrid): + ''' + Take in input the path to a text grid, + and output a dictionary of lists *{spkr: [ (onset, duration) ]}* + that can easily be written in rttm format. + ''' + # init output + rttm_out = dict() + + tg = tgt.io.read_textgrid(textgrid) + # loop over all speakers in this text grid + for spkr in tg.get_tier_names(): + spkr_timestamps = [] + # loop over all annotations for this speaker + for _interval in tg.get_tiers_by_name(spkr): + for interval in _interval: + bg, ed, label = interval.start_time,\ + interval.end_time,\ + interval.text + spkr_timestamps.append((bg, ed-bg)) + + # add list of onsets, durations for each speakers + rttm_out[spkr] = spkr_timestamps + return rttm_out + + +def write_rttm(rttm_out, basename_whole): + ''' + take a dictionary {spkr:[ (onset, duration) ]} as input + and write on rttm output by speaker + ''' + # write one rttm file for the whole wav, indicating + # only regions of speech, and not the speaker + with open(basename_whole, 'w') as fout: + for spkr in rttm_out: + for bg, dur in rttm_out[spkr]: + fout.write(u'SPEAKER {} 1 {} {} ' + ' {} \n'.format( + basename_whole.split('/')[-1].replace('.rttm',''), bg, dur, spkr)) + + +if __name__ == '__main__': + command_example = "python textgrid2rttm.py /folder/" + parser = argparse.ArgumentParser(epilog=command_example) + parser.add_argument('input', help=''' Input file, or folder containing .TextGrid files''') + + args = parser.parse_args() + + if os.path.isfile(args.input): + print("File found. Converting it.") + rttm = textgrid2rttm(args.input) + write_rttm(rttm, args.input.replace(".TextGrid", ".rttm")) + elif os.path.isdir(args.input): + print("Folder found. Scanning .TextGrid files.") + for txtgr in glob.glob(os.path.join(args.input, "*.TextGrid")): + print("Converting %s" % os.path.basename(txtgr)) + rttm = textgrid2rttm(txtgr) + write_rttm(rttm, txtgr.replace(".TextGrid", ".rttm")) + else: + print("Nothing found.") + sys.exit(1) + print("Done.") \ No newline at end of file diff --git a/toolbox/tocombo2rttm.py b/utils/tocombo2rttm.py old mode 100644 new mode 100755 similarity index 95% rename from toolbox/tocombo2rttm.py rename to utils/tocombo2rttm.py index c1e363a..1479bdc --- a/toolbox/tocombo2rttm.py +++ b/utils/tocombo2rttm.py @@ -48,5 +48,5 @@ vad = "speech" else: continue - fout.write(u"SPEAKER\t{}\t{}\t{}\t{}\t{}\t{}\t{}\t{}\n".format + fout.write(u"SPEAKER {} {} {} {} {} {} {} {}\n".format (fname, 1, on, off - on, "", "", vad, 1 )) diff --git a/utils/txt2rttm.py b/utils/txt2rttm.py new file mode 100755 index 0000000..262bb0c --- /dev/null +++ b/utils/txt2rttm.py @@ -0,0 +1,134 @@ +""" +This script converts an txt file into a rttm one. +The txt file must contain : class onset offset. +It can be run either on a single txt file, or on a whole folder containing txt files. + +Example of use : + python tools/txt2rttm.py -i data/my_file.txt # One one file + python tools/txt2rttm.py -i data/ # On a whole folder + python tools/txt2rttm.py -i data/ -l True # On a whole folder + lena mode activated + +If the lena mode is activated, it will look for a file called tsi_key_info.xlsx in the input folder +that contains a column file_lena (storing lena names, ex : e20170722_093015_009456_2040_2100_lena) +and a column key (containg the associated ACLEW name) +About the naming convention of the output : + For each file called input_file.txt, the result will be stored in input_file.rttm +""" + +import pympi as pmp +import argparse +import os +import sys +import glob +from openpyxl import load_workbook + +def lena_to_aclew_name(tsi_key_info, basename): + """ + Convert lena names to ACLEW names. + + Parameters + ---------- + tsi_key_info : the path to the tsi_key_infos.xlsx file + basename : the basename of the txt file that needs to be converted. This name follows the LENA naming convention. + + Returns + ------- + The name respecting the ACLEW naming convention + """ + is_BER_ROW_SOD_WAR = any(substring in basename for substring in ['BER','ROW','SOD','WAR']) + if not is_BER_ROW_SOD_WAR: + # Should come from TSI recordings + onset = basename.split('_')[3] + basename_beg = '_'.join(basename.split('_')[0:3]) # Get the first 3 elements + wb = load_workbook(tsi_key_info,data_only=True) + wb = wb.worksheets[0] + first_row = wb.rows[0] + file_lena_num_col = None + key_num_col = None + + # Get num of the column + for idx in range(0, len(first_row)): + cell = first_row[idx] + if cell.value == "file_lena": + file_lena_num_col = idx + if cell.value == "key": + key_num_col = idx + + # Loop through the cells to look for the basename + if file_lena_num_col is not None and key_num_col is not None: + for row in wb.rows[1:]: + file_lena = row[file_lena_num_col].value + key_num = row[key_num_col].value + if file_lena == basename_beg: + child, good_date = key_num.split('_') + return '_'.join(['lena',child,good_date,onset]) + else: + return '_'.join(['lena',basename.replace('_lena','')]) + + +def txt2rttm(path_to_txt, output_folder, lena_mode=False): + """ + Convert an txt file to the rttm format by extracting the + Parameters + ---------- + path_to_txt : path to the txt file. + output_folder : where to store the output files + + Write a rttm whose name is the same than the txt's one in output_folder + """ + basename = os.path.splitext(os.path.basename(path_to_txt))[0] + if lena_mode: + dirname = os.path.dirname(path_to_txt) + output_basename = lena_to_aclew_name(os.path.join(dirname, 'tsi_key_info.xlsx'), basename) + output_path = os.path.join(dirname, output_basename + '.rttm') + # Change the output_basename because we don't want to write the model prefix lena_ + # in the rttm fil + output_basename = '_'.join(output_basename.split('_')[1:]) + else: + output_path = os.path.join(output_folder, basename + '.rttm') + output_basename = os.path.splitext(os.path.basename(output_path))[0] + + with open(path_to_txt, 'r') as txt: + with open(output_path, 'w') as rttm: + for line in txt: + activity, onset, offset = line.rstrip().split('\t') + dur = float(offset)-float(onset) + + rttm.write("SPEAKER\t%s\t1\t%s\t%s\t\t\t%s\t\n" % (output_basename, onset, str(dur), activity)) + + +def main(): + parser = argparse.ArgumentParser(description="convert .txt into .rttm") + parser.add_argument('-i', '--input', type=str, required=True, + help="path to the input .txt file or the folder containing txt files.") + parser.add_argument('-l', '--lena_mode', type=bool, required=False, default=False, + help="indicates whether to use this script in the lena mode or not. If the lena mode" + "is activated, it will read the table tsi_key_info.xlsx in the input folder and" + "will change the naming convention of the output in consequences") + args = parser.parse_args() + + + # Initialize the output folder as the same folder than the input + # if not provided by the user. + if args.input[-4:] == '.txt': + output = os.path.dirname(args.input) + else: + output = args.input + + data_dir = '/vagrant' + args.input = os.path.join(data_dir, args.input) + output = os.path.join(data_dir, output) + + if not os.path.isdir(output): + os.mkdir(output) + if args.input[-4:] == '.txt': # A single file has been provided by the user + txt2rttm(args.input, output, args.to_keep, args.lena_mode) + else: # A whole folder has been provided + txt_files = glob.iglob(os.path.join(args.input, '*.txt')) + for txt_path in txt_files: + print("Processing %s" % txt_path) + txt2rttm(txt_path, output, args.lena_mode) + + +if __name__ == '__main__': + main() \ No newline at end of file diff --git a/conf/vad/vad_segmenter.conf b/utils/vad_segmenter_aclew.conf.txt old mode 100644 new mode 100755 similarity index 96% rename from conf/vad/vad_segmenter.conf rename to utils/vad_segmenter_aclew.conf.txt index 2c81751..35f1636 --- a/conf/vad/vad_segmenter.conf +++ b/utils/vad_segmenter_aclew.conf.txt @@ -37,7 +37,7 @@ printLevelStats = 0 [waveSinkCut:cWaveSinkCut] reader.dmLevel = frames -fileBase = \cm[waveoutput(W){output_segment_}:prefix of WAV output files] +fileBase = \cm[waveoutput(W){?}:prefix of WAV output files] fileExtension = .wav fileNameFormatString = %s%04d%s startIndex = 1 diff --git a/utils/yuniSeg.sh b/utils/yuniSeg.sh new file mode 100755 index 0000000..3ba088a --- /dev/null +++ b/utils/yuniSeg.sh @@ -0,0 +1,112 @@ +#!/bin/bash + +# run YuniSeg with hard coded models & configs found here and in /vagrant +# assumes Python environment in /home/${user}/ + +# Absolute path to this script. /home/user/bin/foo.sh +SCRIPT=$(readlink -f $0) +# Absolute path this script is in. /home/user/bin +BASEDIR=`dirname $SCRIPT` +# Path to YuniSeg (go one folder up and to Yunitator) +YUNITATDIR=$(dirname $BASEDIR)/Yunitator +# let's get our bearings: set CWD to the path of Yunitator +cd $YUNITATDIR + +if [ $# -ne 2 ]; then + echo "Usage: $0 " + echo "where dirname is the name of the folder" + echo "containing the wav and rttm files" + echo "and SADtoolname is the SAD to use." + echo "Choices are:" + echo " ldc_sad" + echo " noisemes" + echo " tocombosad" + echo " opensmile" + echo " textgrid" + echo " eaf" + echo " rttm" + exit 1 +fi + +audio_dir=/vagrant/$1 +trs_format=$2 + +# Check audio_dir to see if empty or if contains empty wav +bash $BASEDIR/check_folder.sh ${audio_dir} + +# Iterate over files +echo "Starting" +for f in `ls ${audio_dir}/*.wav`; do + filename=$(basename "$f") + basename="${filename%.*}" + echo "treating $basename" + + # output filename produced by runYuniSegs + outfile=${audio_dir}/$basename.yuniSeg.rttm + + case $trs_format in + "ldc_sad") + sys="ldcSad" + model_prefix="ldc_sad_" + ;; + "") + # add default case + echo "Warning: no SAD source specified, using Noisemes by default, at your own risk." + echo "Next time, please specify SAD." + sys="noisemesSad" + model_prefix="noisemes_sad_" + ;; + "noisemes") + sys="noisemesSad" + model_prefix="noisemes_sad_" + ;; + "tocombosad") + sys="tocomboSad" + model_prefix="tocombo_sad_" + ;; + "opensmile") + sys="opensmileSad" + model_prefix="opensmile_sad_" + ;; + "textgrid") + sys="goldSad" + model_prefix=${trs_format}_ + python /home/vagrant/utils/textgrid2rttm.py ${audio_dir}/${basename}.TextGrid ${trs_format}_${basename}.rttm + ;; + "eaf") + sys="goldSad" + model_prefix=${trs_format}_ + python /home/vagrant/utils/elan2rttm.py ${audio_dir}/${basename}.eaf ${trs_format}_${basename}.rttm + ;; + "rttm") + sys="goldSad" + model_prefix="" + ;; + *) + echo "ERROR: please choose SAD system between:" + echo " ldc_sad" + echo " noisemes" + echo " tocombosad" + echo " opensmile" + echo " textgrid" + echo " eaf" + echo " rttm" + echo "Now exiting..." + exit 1 + ;; + esac + + ./runYuniSegs.sh $f ${audio_dir}/${model_prefix}${basename}.rttm + cp $outfile ${audio_dir}/yuniseg_${sys}_${basename}.rttm + + if [ ! -s ${audio_dir}/yuniseg_${sys}_${basename}.rttm ]; then + # if diarization failed, still write an empty file... + touch ${audio_dir}/yuniseg_${sys}_${basename}.rttm + fi +done + +echo "$0 finished running" + +# simply remove hyp and feature +rm $outfile +rm -rf ${audio_dir}/Yunitemp