[pull] main from m-bain:main

.github/workflows/python-compatibility.yml

@@ -12,7 +12,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.9", "3.10", "3.11", "3.12"]
+        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
 
     steps:
       - uses: actions/checkout@v4

pyproject.toml

@@ -2,10 +2,10 @@
 urls = { repository = "https://github.com/m-bain/whisperx" }
 authors = [{ name = "Max Bain" }]
 name = "whisperx"
-version = "3.5.0"
+version = "3.7.0"
 description = "Time-Accurate Automatic Speech Recognition using Whisper."
 readme = "README.md"
-requires-python = ">=3.9, <3.13"
+requires-python = ">=3.9, <3.14"
 license = { text = "BSD-2-Clause" }
 
 dependencies = [
@@ -14,7 +14,7 @@ dependencies = [
     "nltk>=3.9.1",
     # Restrict numpy, onnxruntime, pandas, av to be compatible with Python 3.9
     "numpy>=2.0.2,<2.1.0",
-    "onnxruntime>=1.19,<1.20.0",
+    "onnxruntime>=1.19,<1.20.0; python_version <'3.10'",
     "pandas>=2.2.3,<2.3.0",
     "av<16.0.0",
     "pyannote-audio>=3.3.2,<4.0.0",

whisperx/__init__.py

@@ -29,3 +29,29 @@ def load_audio(*args, **kwargs):
 def assign_word_speakers(*args, **kwargs):
     diarize = _lazy_import("diarize")
     return diarize.assign_word_speakers(*args, **kwargs)
+
+
+def setup_logging(*args, **kwargs):
+    """
+    Configure logging for WhisperX.
+
+    Args:
+        level: Logging level (debug, info, warning, error, critical). Default: warning
+        log_file: Optional path to log file. If None, logs only to console.
+    """
+    logging_module = _lazy_import("log_utils")
+    return logging_module.setup_logging(*args, **kwargs)
+
+
+def get_logger(*args, **kwargs):
+    """
+    Get a logger instance for the given module.
+
+    Args:
+        name: Logger name (typically __name__ from calling module)
+
+    Returns:
+        Logger instance configured with WhisperX settings
+    """
+    logging_module = _lazy_import("log_utils")
+    return logging_module.get_logger(*args, **kwargs)

whisperx/__main__.py

@@ -6,6 +6,7 @@
 
 from whisperx.utils import (LANGUAGES, TO_LANGUAGE_CODE, optional_float,
                             optional_int, str2bool)
+from whisperx.log_utils import setup_logging
 
 
 def cli():
@@ -23,6 +24,7 @@ def cli():
     parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs")
     parser.add_argument("--output_format", "-f", type=str, default="all", choices=["all", "srt", "vtt", "txt", "tsv", "json", "aud"], help="format of the output file; if not specified, all available formats will be produced")
     parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages")
+    parser.add_argument("--log-level", type=str, default=None, choices=["debug", "info", "warning", "error", "critical"], help="logging level (overrides --verbose if set)")
 
     parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')")
     parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection")
@@ -80,6 +82,16 @@ def cli():
 
     args = parser.parse_args().__dict__
 
+    log_level = args.get("log_level")
+    verbose = args.get("verbose")
+
+    if log_level is not None:
+        setup_logging(level=log_level)
+    elif verbose:
+        setup_logging(level="info")
+    else:
+        setup_logging(level="warning")
+
     from whisperx.transcribe import transcribe_task
 
     transcribe_task(args, parser)

whisperx/alignment.py

@@ -24,6 +24,9 @@
 )
 import nltk
 from nltk.data import load as nltk_load
+from whisperx.log_utils import get_logger
+
+logger = get_logger(__name__)
 
 LANGUAGES_WITHOUT_SPACES = ["ja", "zh"]
 
@@ -81,8 +84,9 @@ def load_align_model(language_code: str, device: str, model_name: Optional[str]
         elif language_code in DEFAULT_ALIGN_MODELS_HF:
             model_name = DEFAULT_ALIGN_MODELS_HF[language_code]
         else:
-            print(f"There is no default alignment model set for this language ({language_code}).\
-                Please find a wav2vec2.0 model finetuned on this language in https://huggingface.co/models, then pass the model name in --align_model [MODEL_NAME]")
+            logger.error(f"No default alignment model for language: {language_code}. "
+                        f"Please find a wav2vec2.0 model finetuned on this language at https://huggingface.co/models, "
+                        f"then pass the model name via --align_model [MODEL_NAME]")
             raise ValueError(f"No default align-model for language: {language_code}")
 
     if model_name in torchaudio.pipelines.__all__:
@@ -223,12 +227,12 @@ def align(
 
         # check we can align
         if len(segment_data[sdx]["clean_char"]) == 0:
-            print(f'Failed to align segment ("{segment["text"]}"): no characters in this segment found in model dictionary, resorting to original...')
+            logger.warning(f'Failed to align segment ("{segment["text"]}"): no characters in this segment found in model dictionary, resorting to original')
             aligned_segments.append(aligned_seg)
             continue
 
         if t1 >= MAX_DURATION:
-            print(f'Failed to align segment ("{segment["text"]}"): original start time longer than audio duration, skipping...')
+            logger.warning(f'Failed to align segment ("{segment["text"]}"): original start time longer than audio duration, skipping')
             aligned_segments.append(aligned_seg)
             continue
 
@@ -270,7 +274,7 @@ def align(
         path = backtrack_beam(trellis, emission, tokens, blank_id, beam_width=2)
 
         if path is None:
-            print(f'Failed to align segment ("{segment["text"]}"): backtrack failed, resorting to original...')
+            logger.warning(f'Failed to align segment ("{segment["text"]}"): backtrack failed, resorting to original')
             aligned_segments.append(aligned_seg)
             continue
 

whisperx/asr.py

@@ -14,6 +14,9 @@
 from whisperx.audio import N_SAMPLES, SAMPLE_RATE, load_audio, log_mel_spectrogram
 from whisperx.schema import SingleSegment, TranscriptionResult
 from whisperx.vads import Vad, Silero, Pyannote
+from whisperx.log_utils import get_logger
+
+logger = get_logger(__name__)
 
 
 def find_numeral_symbol_tokens(tokenizer):
@@ -247,7 +250,7 @@ def data(audio, segments):
         if self.suppress_numerals:
             previous_suppress_tokens = self.options.suppress_tokens
             numeral_symbol_tokens = find_numeral_symbol_tokens(self.tokenizer)
-            print(f"Suppressing numeral and symbol tokens")
+            logger.info("Suppressing numeral and symbol tokens")
             new_suppressed_tokens = numeral_symbol_tokens + self.options.suppress_tokens
             new_suppressed_tokens = list(set(new_suppressed_tokens))
             self.options = replace(self.options, suppress_tokens=new_suppressed_tokens)
@@ -285,7 +288,7 @@ def data(audio, segments):
 
     def detect_language(self, audio: np.ndarray) -> str:
         if audio.shape[0] < N_SAMPLES:
-            print("Warning: audio is shorter than 30s, language detection may be inaccurate.")
+            logger.warning("Audio is shorter than 30s, language detection may be inaccurate")
         model_n_mels = self.model.feat_kwargs.get("feature_size")
         segment = log_mel_spectrogram(audio[: N_SAMPLES],
                                       n_mels=model_n_mels if model_n_mels is not None else 80,
@@ -294,7 +297,7 @@ def detect_language(self, audio: np.ndarray) -> str:
         results = self.model.model.detect_language(encoder_output)
         language_token, language_probability = results[0][0]
         language = language_token[2:-2]
-        print(f"Detected language: {language} ({language_probability:.2f}) in first 30s of audio...")
+        logger.info(f"Detected language: {language} ({language_probability:.2f}) in first 30s of audio")
         return language
 
 
@@ -344,7 +347,7 @@ def load_model(
     if language is not None:
         tokenizer = Tokenizer(model.hf_tokenizer, model.model.is_multilingual, task=task, language=language)
     else:
-        print("No language specified, language will be first be detected for each audio file (increases inference time).")
+        logger.info("No language specified, language will be detected for each audio file (increases inference time)")
         tokenizer = None
 
     default_asr_options =  {

whisperx/diarize.py

@@ -6,6 +6,9 @@
 
 from whisperx.audio import load_audio, SAMPLE_RATE
 from whisperx.schema import TranscriptionResult, AlignedTranscriptionResult
+from whisperx.log_utils import get_logger
+
+logger = get_logger(__name__)
 
 
 class DiarizationPipeline:
@@ -18,6 +21,7 @@ def __init__(
         if isinstance(device, str):
             device = torch.device(device)
         model_config = model_name or "pyannote/speaker-diarization-3.1"
+        logger.info(f"Loading diarization model: {model_config}")
         self.model = Pipeline.from_pretrained(model_config, use_auth_token=use_auth_token).to(device)
 
     def __call__(

whisperx/log_utils.py

@@ -0,0 +1,67 @@
+import logging
+import sys
+from typing import Optional
+
+_LOG_FORMAT = "%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+_DATE_FORMAT = "%Y-%m-%d %H:%M:%S"
+
+
+def setup_logging(
+    level: str = "info",
+    log_file: Optional[str] = None,
+) -> None:
+    """
+    Configure logging for WhisperX.
+
+    Args:
+        level: Logging level (debug, info, warning, error, critical). Default: info
+        log_file: Optional path to log file. If None, logs only to console.
+    """
+    logger = logging.getLogger("whisperx")
+
+    logger.handlers.clear()
+
+    try:
+        log_level = getattr(logging, level.upper())
+    except AttributeError:
+        log_level = logging.WARNING
+    logger.setLevel(log_level)
+
+    formatter = logging.Formatter(_LOG_FORMAT, datefmt=_DATE_FORMAT)
+
+    console_handler = logging.StreamHandler(sys.stdout)
+    console_handler.setLevel(log_level)
+    console_handler.setFormatter(formatter)
+
+    logger.addHandler(console_handler)
+
+    if log_file:
+        try:
+            file_handler = logging.FileHandler(log_file)
+            file_handler.setLevel(log_level)
+            file_handler.setFormatter(formatter)
+            logger.addHandler(file_handler)
+        except (OSError) as e:
+            logger.warning(f"Failed to create log file '{log_file}': {e}")
+            logger.warning("Continuing with console logging only")
+
+    # Don't propagate to root logger to avoid duplicate messages
+    logger.propagate = False
+
+
+def get_logger(name: str) -> logging.Logger:
+    """
+    Get a logger instance for the given module.
+
+    Args:
+        name: Logger name (typically __name__ from calling module)
+
+    Returns:
+        Logger instance configured with WhisperX settings
+    """
+    whisperx_logger = logging.getLogger("whisperx")
+    if not whisperx_logger.handlers:
+        setup_logging()
+
+    logger_name = "whisperx" if name == "__main__" else name
+    return logging.getLogger(logger_name)

whisperx/transcribe.py

@@ -12,6 +12,9 @@
 from whisperx.diarize import DiarizationPipeline, assign_word_speakers
 from whisperx.schema import AlignedTranscriptionResult, TranscriptionResult
 from whisperx.utils import LANGUAGES, TO_LANGUAGE_CODE, get_writer
+from whisperx.log_utils import get_logger
+
+logger = get_logger(__name__)
 
 
 def transcribe_task(args: dict, parser: argparse.ArgumentParser):
@@ -142,7 +145,7 @@ def transcribe_task(args: dict, parser: argparse.ArgumentParser):
     for audio_path in args.pop("audio"):
         audio = load_audio(audio_path)
         # >> VAD & ASR
-        print(">>Performing transcription...")
+        logger.info("Performing transcription...")
         result: TranscriptionResult = model.transcribe(
             audio,
             batch_size=batch_size,
@@ -175,13 +178,13 @@ def transcribe_task(args: dict, parser: argparse.ArgumentParser):
             if align_model is not None and len(result["segments"]) > 0:
                 if result.get("language", "en") != align_metadata["language"]:
                     # load new language
-                    print(
+                    logger.info(
                         f"New language found ({result['language']})! Previous was ({align_metadata['language']}), loading new alignment model for new language..."
                     )
                     align_model, align_metadata = load_align_model(
                         result["language"], device
                     )
-                print(">>Performing alignment...")
+                logger.info("Performing alignment...")
                 result: AlignedTranscriptionResult = align(
                     result["segments"],
                     align_model,
@@ -203,12 +206,12 @@ def transcribe_task(args: dict, parser: argparse.ArgumentParser):
     # >> Diarize
     if diarize:
         if hf_token is None:
-            print(
-                "Warning, no --hf_token used, needs to be saved in environment variable, otherwise will throw error loading diarization model..."
+            logger.warning(
+                "No --hf_token provided, needs to be saved in environment variable, otherwise will throw error loading diarization model"
             )
         tmp_results = results
-        print(">>Performing diarization...")
-        print(">>Using model:", diarize_model_name)
+        logger.info("Performing diarization...")
+        logger.info(f"Using model: {diarize_model_name}")
         results = []
         diarize_model = DiarizationPipeline(model_name=diarize_model_name, use_auth_token=hf_token, device=device)
         for result, input_audio_path in tmp_results:

whisperx/vads/pyannote.py

@@ -13,6 +13,9 @@
 
 from whisperx.diarize import Segment as SegmentX
 from whisperx.vads.vad import Vad
+from whisperx.log_utils import get_logger
+
+logger = get_logger(__name__)
 
 
 def load_vad_model(device, vad_onset=0.500, vad_offset=0.363, use_auth_token=None, model_fp=None):
@@ -232,7 +235,7 @@ def apply(self, file: AudioFile, hook: Optional[Callable] = None) -> Annotation:
 class Pyannote(Vad):
 
     def __init__(self, device, use_auth_token=None, model_fp=None, **kwargs):
-        print(">>Performing voice activity detection using Pyannote...")
+        logger.info("Performing voice activity detection using Pyannote...")
         super().__init__(kwargs['vad_onset'])
         self.vad_pipeline = load_vad_model(device, use_auth_token=use_auth_token, model_fp=model_fp)
 
@@ -257,7 +260,7 @@ def merge_chunks(segments,
             segments_list.append(SegmentX(speech_turn.start, speech_turn.end, "UNKNOWN"))
 
         if len(segments_list) == 0:
-            print("No active speech found in audio")
+            logger.warning("No active speech found in audio")
             return []
         assert segments_list, "segments_list is empty."
         return Vad.merge_chunks(segments_list, chunk_size, onset, offset)

whisperx/vads/silero.py

@@ -8,14 +8,17 @@
 
 from whisperx.diarize import Segment as SegmentX
 from whisperx.vads.vad import Vad
+from whisperx.log_utils import get_logger
+
+logger = get_logger(__name__)
 
 AudioFile = Union[Text, Path, IOBase, Mapping]
 
 
 class Silero(Vad):
     # check again default values
     def __init__(self, **kwargs):
-        print(">>Performing voice activity detection using Silero...")
+        logger.info("Performing voice activity detection using Silero...")
         super().__init__(kwargs['vad_onset'])
 
         self.vad_onset = kwargs['vad_onset']
@@ -60,7 +63,7 @@ def merge_chunks(segments_list,
                      ):
         assert chunk_size > 0
         if len(segments_list) == 0:
-            print("No active speech found in audio")
+            logger.warning("No active speech found in audio")
             return []
         assert segments_list, "segments_list is empty."
         return Vad.merge_chunks(segments_list, chunk_size, onset, offset)