[tune](deps): Bump transformers from 4.9.1 to 4.12.3 in /python/requirements/tune

[dependabot]

Bumps transformers from 4.9.1 to 4.12.3.

Release notes

Sourced from transformers's releases.

v4.12.3: Patch release

• Add PushToHubCallback in main init (#14246)
• Supports huggingface_hub >= 0.1.0

v4.12.2: Patch release

Fixes an issue with the image segmentation pipeline and PyTorch's inference mode.

v4.12.1: Patch release

Enables torch 1.10.0

v4.12.0: TrOCR, SEW & SEW-D, Unispeech & Unispeech-SAT, BARTPho

TrOCR and VisionEncoderDecoderModel

One new model is released as part of the TrOCR implementation: TrOCRForCausalLM, in PyTorch. It comes along a new VisionEncoderDecoderModel class, which allows to mix-and-match any vision Transformer encoder with any text Transformer as decoder, similar to the existing SpeechEncoderDecoderModel class.

The TrOCR model was proposed in TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models, by Minghao Li, Tengchao Lv, Lei Cui, Yijuan Lu, Dinei Florencio, Cha Zhang, Zhoujun Li, Furu Wei.

The TrOCR model consists of an image transformer encoder and an autoregressive text transformer to perform optical character recognition in an end-to-end manner.

• Add TrOCR + VisionEncoderDecoderModel by @​NielsRogge in huggingface/transformers#13874

Compatible checkpoints can be found on the Hub: https://huggingface.co/models?other=trocr

SEW & SEW-D

SEW and SEW-D (Squeezed and Efficient Wav2Vec) were proposed in Performance-Efficiency Trade-offs in Unsupervised Pre-training for Speech Recognition by Felix Wu, Kwangyoun Kim, Jing Pan, Kyu Han, Kilian Q. Weinberger, Yoav Artzi.

SEW and SEW-D models use a Wav2Vec-style feature encoder and introduce temporal downsampling to reduce the length of the transformer encoder. SEW-D additionally replaces the transformer encoder with a DeBERTa one. Both models achieve significant inference speedups without sacrificing the speech recognition quality.

• Add the SEW and SEW-D speech models by @​anton-l in huggingface/transformers#13962
• Add SEW CTC models by @​anton-l in huggingface/transformers#14158

Compatible checkpoints are available on the Hub: https://huggingface.co/models?other=sew and https://huggingface.co/models?other=sew-d

DistilHuBERT

DistilHuBERT was proposed in DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT, by Heng-Jui Chang, Shu-wen Yang, Hung-yi Lee.

DistilHuBERT is a distilled version of the HuBERT model. Using only two transformer layers, the model scores competitively on the SUPERB benchmark tasks.

Compatible checkpoint is available on the Hub: https://huggingface.co/ntu-spml/distilhubert

TensorFlow improvements

Several bug fixes and UX improvements for TensorFlow

Keras callback

Introduction of a Keras callback to push to the hub each epoch, or after a given number of steps:

... (truncated)

Commits

• 3ea15d2 Style
• 294a920 Add maximum check for hf hub
• 9ab10fc Release v4.12.3
• 872c4f3 Bump huggingface_hub
• ac77639 Add PushToHubCallback in main init (#14246)
• 2191373 Release: v4.12.2
• cde7d78 Fixing image segmentation with inference mode. (#14204)
• e0a5154 Release v4.12.1
• 9f3f335 Torch 1.10 (#14169)
• 62bf536 Release v4.12.0
• Additional commits viewable in compare view

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[dependabot]

Superseded by #78.