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Add WavLM (#14354)
* first commit * fix some stuff * fix more readme * Apply suggestions from code review * update * correct * up * attn layer works * push code * make modedls work * Small change * more refactor * finish * up * fix convertsion * fix position bias * Fix style * fix conversion * make fix-copies * add * clean * fix docs * fix * Apply suggestions from code review Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * apply final changes * make fix-copies Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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Patrick von Platen
GitHub
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@ -311,6 +311,7 @@ Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih.
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AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
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1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
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1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
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1. **[WavLM](https://huggingface.co/docs/transformers/master/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
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1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
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1. **[XLM](https://huggingface.co/docs/transformers/model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
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1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlmprophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
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@ -290,6 +290,7 @@ Flax, PyTorch, TensorFlow 설치 페이지에서 이들을 conda로 설치하는
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1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
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1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
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1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
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1. **[WavLM](https://huggingface.co/docs/transformers/master/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
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1. **[XLM](https://huggingface.co/docs/transformers/model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
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1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlmprophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
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1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlmroberta)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
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@ -314,6 +314,7 @@ conda install -c huggingface transformers
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1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (来自 Google AI) 伴随论文 [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) 由 Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby 发布。
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1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (来自 UCLA NLP) 伴随论文 [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) 由 Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang 发布。
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1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (来自 Facebook AI) 伴随论文 [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) 由 Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli 发布。
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1. **[WavLM](https://huggingface.co/docs/transformers/master/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
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1. **[XLM](https://huggingface.co/docs/transformers/model_doc/xlm)** (来自 Facebook) 伴随论文 [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) 由 Guillaume Lample and Alexis Conneau 发布。
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1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlmprophetnet)** (来自 Microsoft Research) 伴随论文 [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) 由 Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou 发布。
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1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlmroberta)** (来自 Facebook AI), 伴随论文 [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) 由 Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov 发布。
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@ -326,6 +326,7 @@ conda install -c huggingface transformers
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1. **[Vision Transformer (ViT)](https://huggingface.co/docs/transformers/model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
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1. **[VisualBERT](https://huggingface.co/docs/transformers/model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
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1. **[Wav2Vec2](https://huggingface.co/docs/transformers/model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
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1. **[WavLM](https://huggingface.co/docs/transformers/master/model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
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1. **[XLM](https://huggingface.co/docs/transformers/model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
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1. **[XLM-ProphetNet](https://huggingface.co/docs/transformers/model_doc/xlmprophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
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1. **[XLM-RoBERTa](https://huggingface.co/docs/transformers/model_doc/xlmroberta)** (from Facebook AI), released together with the paper [Unsupervised Cross-lingual Representation Learning at Scale](https://arxiv.org/abs/1911.02116) by Alexis Conneau*, Kartikay Khandelwal*, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer and Veselin Stoyanov.
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@ -284,6 +284,8 @@
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title: VisualBERT
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- local: model_doc/wav2vec2
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title: Wav2Vec2
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- local: model_doc/wavlm
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title: WavLM
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- local: model_doc/xlm
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title: XLM
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- local: model_doc/xlmprophetnet
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@ -170,6 +170,7 @@ conversion utilities for the following models.
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1. **[UniSpeechSat](model_doc/unispeech_sat)** (from Microsoft Research) released with the paper [UNISPEECH-SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE-TRAINING](https://arxiv.org/abs/2110.05752) by Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu.
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1. **[Vision Transformer (ViT)](model_doc/vit)** (from Google AI) released with the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) by Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, Neil Houlsby.
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1. **[VisualBERT](model_doc/visual_bert)** (from UCLA NLP) released with the paper [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://arxiv.org/pdf/1908.03557) by Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, Kai-Wei Chang.
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1. **[WavLM](model_doc/wavlm)** (from Microsoft Research) released with the paper [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://arxiv.org/abs/2110.13900) by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen, Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu, Michael Zeng, Furu Wei.
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1. **[Wav2Vec2](model_doc/wav2vec2)** (from Facebook AI) released with the paper [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://arxiv.org/abs/2006.11477) by Alexei Baevski, Henry Zhou, Abdelrahman Mohamed, Michael Auli.
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1. **[XLM](model_doc/xlm)** (from Facebook) released together with the paper [Cross-lingual Language Model Pretraining](https://arxiv.org/abs/1901.07291) by Guillaume Lample and Alexis Conneau.
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1. **[XLM-ProphetNet](model_doc/xlmprophetnet)** (from Microsoft Research) released with the paper [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://arxiv.org/abs/2001.04063) by Yu Yan, Weizhen Qi, Yeyun Gong, Dayiheng Liu, Nan Duan, Jiusheng Chen, Ruofei Zhang and Ming Zhou.
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@ -263,6 +264,7 @@ Flax), PyTorch, and/or TensorFlow.
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| VisualBert | ❌ | ❌ | ✅ | ❌ | ❌ |
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| ViT | ❌ | ❌ | ✅ | ✅ | ✅ |
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| Wav2Vec2 | ✅ | ❌ | ✅ | ✅ | ✅ |
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| WavLM | ❌ | ❌ | ✅ | ❌ | ❌ |
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| XLM | ✅ | ❌ | ✅ | ✅ | ❌ |
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| XLM-RoBERTa | ✅ | ✅ | ✅ | ✅ | ❌ |
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| XLMProphetNet | ✅ | ❌ | ✅ | ❌ | ❌ |
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83
docs/source/model_doc/wavlm.rst
Normal file
83
docs/source/model_doc/wavlm.rst
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@ -0,0 +1,83 @@
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..
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Copyright 2021 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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WavLM
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-----------------------------------------------------------------------------------------------------------------------
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Overview
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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The WavLM model was proposed in `WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing
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<https://arxiv.org/abs/2110.13900>`__ by Sanyuan Chen, Chengyi Wang, Zhengyang Chen, Yu Wu, Shujie Liu, Zhuo Chen,
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Jinyu Li, Naoyuki Kanda, Takuya Yoshioka, Xiong Xiao, Jian Wu, Long Zhou, Shuo Ren, Yanmin Qian, Yao Qian, Jian Wu,
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Michael Zeng, Furu Wei.
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The abstract from the paper is the following:
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*Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been
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attempted for other speech processing tasks. As speech signal contains multi-faceted information including speaker
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identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is
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challenging. In this paper, we propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks.
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WavLM is built based on the HuBERT framework, with an emphasis on both spoken content modeling and speaker identity
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preservation. We first equip the Transformer structure with gated relative position bias to improve its capability on
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recognition tasks. For better speaker discrimination, we propose an utterance mixing training strategy, where
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additional overlapped utterances are created unsupervisely and incorporated during model training. Lastly, we scale up
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the training dataset from 60k hours to 94k hours. WavLM Large achieves state-of-the-art performance on the SUPERB
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benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks.*
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Tips:
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- WavLM is a speech model that accepts a float array corresponding to the raw waveform of the speech signal. Please use
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:class:`~transformers.Wav2Vec2Processor` for the feature extraction.
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- WavLM model can be fine-tuned using connectionist temporal classification (CTC) so the model output has to be decoded
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using :class:`~transformers.Wav2Vec2CTCTokenizer`.
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- WavLM performs especially well on speaker verification, speaker identification, and speaker diarization tasks.
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Relevant checkpoints can be found under https://huggingface.co/models?other=wavlm.
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This model was contributed by `patrickvonplaten <https://huggingface.co/patrickvonplaten>`__. The Authors' code can be
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found `here <https://github.com/microsoft/unilm/tree/master/wavlm>`__.
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WavLMConfig
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.WavLMConfig
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:members:
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WavLM specific outputs
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.models.wavlm.modeling_wavlm.WavLMBaseModelOutput
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:members:
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WavLMModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.WavLMModel
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:members: forward
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WavLMForCTC
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.WavLMForCTC
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:members: forward
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WavLMForSequenceClassification
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.WavLMForSequenceClassification
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:members: forward
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@ -314,6 +314,10 @@ _import_structure = {
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"Wav2Vec2Tokenizer",
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],
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"models.wav2vec2_with_lm": ["Wav2Vec2ProcessorWithLM"],
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"models.wavlm": [
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"WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP",
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"WavLMConfig",
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],
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"models.xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMTokenizer"],
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"models.xlm_prophetnet": ["XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMProphetNetConfig"],
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"models.xlm_roberta": ["XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMRobertaConfig"],
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@ -1372,6 +1376,15 @@ if is_torch_available():
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"Wav2Vec2PreTrainedModel",
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]
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)
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_import_structure["models.wavlm"].extend(
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[
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"WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
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"WavLMForCTC",
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"WavLMForSequenceClassification",
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"WavLMModel",
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"WavLMPreTrainedModel",
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]
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)
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_import_structure["models.xlm"].extend(
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[
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"XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
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@ -2327,6 +2340,7 @@ if TYPE_CHECKING:
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Wav2Vec2Tokenizer,
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)
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from .models.wav2vec2_with_lm import Wav2Vec2ProcessorWithLM
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from .models.wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig
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from .models.xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMTokenizer
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from .models.xlm_prophetnet import XLM_PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMProphetNetConfig
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from .models.xlm_roberta import XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMRobertaConfig
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@ -3210,6 +3224,13 @@ if TYPE_CHECKING:
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Wav2Vec2Model,
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Wav2Vec2PreTrainedModel,
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)
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from .models.wavlm import (
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WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
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WavLMForCTC,
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WavLMForSequenceClassification,
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WavLMModel,
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WavLMPreTrainedModel,
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)
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from .models.xlm import (
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XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
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XLMForMultipleChoice,
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@ -108,6 +108,7 @@ from . import (
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vit,
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wav2vec2,
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wav2vec2_with_lm,
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wavlm,
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xlm,
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xlm_prophetnet,
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xlm_roberta,
|
||||
|
|
|
|||
|
|
@ -109,6 +109,7 @@ CONFIG_MAPPING_NAMES = OrderedDict(
|
|||
("sew", "SEWConfig"),
|
||||
("unispeech-sat", "UniSpeechSatConfig"),
|
||||
("unispeech", "UniSpeechConfig"),
|
||||
("wavlm", "WavLMConfig"),
|
||||
]
|
||||
)
|
||||
|
||||
|
|
@ -277,6 +278,7 @@ MODEL_NAMES_MAPPING = OrderedDict(
|
|||
("sew", "SEW"),
|
||||
("unispeech-sat", "UniSpeechSat"),
|
||||
("unispeech", "UniSpeech"),
|
||||
("wavlm", "WavLM"),
|
||||
]
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -52,6 +52,7 @@ MODEL_MAPPING_NAMES = OrderedDict(
|
|||
("vit", "ViTModel"),
|
||||
("wav2vec2", "Wav2Vec2Model"),
|
||||
("unispeech-sat", "UniSpeechSatModel"),
|
||||
("wavlm", "WavLMModel"),
|
||||
("unispeech", "UniSpeechModel"),
|
||||
("hubert", "HubertModel"),
|
||||
("m2m_100", "M2M100Model"),
|
||||
|
|
@ -523,6 +524,7 @@ MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
|
|||
("hubert", "HubertForSequenceClassification"),
|
||||
("sew", "SEWForSequenceClassification"),
|
||||
("sew-d", "SEWDForSequenceClassification"),
|
||||
("wavlm", "WavLMForSequenceClassification"),
|
||||
]
|
||||
)
|
||||
|
||||
|
|
@ -535,6 +537,7 @@ MODEL_FOR_CTC_MAPPING_NAMES = OrderedDict(
|
|||
("hubert", "HubertForCTC"),
|
||||
("sew", "SEWForCTC"),
|
||||
("sew-d", "SEWDForCTC"),
|
||||
("wavlm", "WavLMForCTC"),
|
||||
]
|
||||
)
|
||||
|
||||
|
|
|
|||
51
src/transformers/models/wavlm/__init__.py
Normal file
51
src/transformers/models/wavlm/__init__.py
Normal file
|
|
@ -0,0 +1,51 @@
|
|||
# flake8: noqa
|
||||
# There's no way to ignore "F401 '...' imported but unused" warnings in this
|
||||
# module, but to preserve other warnings. So, don't check this module at all.
|
||||
|
||||
# Copyright 2021 The HuggingFace Team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
from ...file_utils import _LazyModule, is_torch_available
|
||||
|
||||
|
||||
_import_structure = {
|
||||
"configuration_wavlm": ["WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "WavLMConfig"],
|
||||
}
|
||||
|
||||
if is_torch_available():
|
||||
_import_structure["modeling_wavlm"] = [
|
||||
"WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST",
|
||||
"WavLMForCTC",
|
||||
"WavLMForSequenceClassification",
|
||||
"WavLMModel",
|
||||
"WavLMPreTrainedModel",
|
||||
]
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig
|
||||
|
||||
if is_torch_available():
|
||||
from .modeling_wavlm import (
|
||||
WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
|
||||
WavLMForCTC,
|
||||
WavLMForSequenceClassification,
|
||||
WavLMModel,
|
||||
WavLMPreTrainedModel,
|
||||
)
|
||||
|
||||
else:
|
||||
import sys
|
||||
|
||||
sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure)
|
||||
304
src/transformers/models/wavlm/configuration_wavlm.py
Normal file
304
src/transformers/models/wavlm/configuration_wavlm.py
Normal file
|
|
@ -0,0 +1,304 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2021 The Fairseq Authors, Microsoft Research, and The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
""" WavLM model configuration """
|
||||
|
||||
from ...configuration_utils import PretrainedConfig
|
||||
from ...utils import logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
|
||||
"facebook/wavlm-base-960h": "https://huggingface.co/facebook/wavlm-base-960h/resolve/main/config.json",
|
||||
# See all WavLM models at https://huggingface.co/models?filter=wavlm
|
||||
}
|
||||
|
||||
|
||||
class WavLMConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a :class:`~transformers.WavLMModel`. It is used to
|
||||
instantiate an WavLM model according to the specified arguments, defining the model architecture. Instantiating a
|
||||
configuration with the defaults will yield a similar configuration to that of the WavLM `facebook/wavlm-base-960h
|
||||
<https://huggingface.co/facebook/wavlm-base-960h>`__ architecture.
|
||||
|
||||
Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
|
||||
outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
|
||||
|
||||
|
||||
Args:
|
||||
vocab_size (:obj:`int`, `optional`, defaults to 32):
|
||||
Vocabulary size of the WavLM model. Defines the number of different tokens that can be represented by the
|
||||
:obj:`inputs_ids` passed when calling :class:`~transformers.WavLMModel`. Vocabulary size of the model.
|
||||
Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of
|
||||
:class:`~transformers.WavLMModel`.
|
||||
hidden_size (:obj:`int`, `optional`, defaults to 768):
|
||||
Dimensionality of the encoder layers and the pooler layer.
|
||||
num_hidden_layers (:obj:`int`, `optional`, defaults to 12):
|
||||
Number of hidden layers in the Transformer encoder.
|
||||
num_attention_heads (:obj:`int`, `optional`, defaults to 12):
|
||||
Number of attention heads for each attention layer in the Transformer encoder.
|
||||
intermediate_size (:obj:`int`, `optional`, defaults to 3072):
|
||||
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
|
||||
hidden_act (:obj:`str` or :obj:`function`, `optional`, defaults to :obj:`"gelu"`):
|
||||
The non-linear activation function (function or string) in the encoder and pooler. If string,
|
||||
:obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported.
|
||||
hidden_dropout (:obj:`float`, `optional`, defaults to 0.1):
|
||||
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
|
||||
attention_dropout (:obj:`float`, `optional`, defaults to 0.1):
|
||||
The dropout ratio for the attention probabilities.
|
||||
final_dropout (:obj:`float`, `optional`, defaults to 0.1):
|
||||
The dropout probability for the final projection layer of :class:`WavLMForCTC`.
|
||||
initializer_range (:obj:`float`, `optional`, defaults to 0.02):
|
||||
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||
layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12):
|
||||
The epsilon used by the layer normalization layers.
|
||||
feat_extract_norm (:obj:`str`, `optional`, defaults to :obj:`"group"`):
|
||||
The norm to be applied to 1D convolutional layers in feature extractor. One of :obj:`"group"` for group
|
||||
normalization of only the first 1D convolutional layer or :obj:`"layer"` for layer normalization of all 1D
|
||||
convolutional layers.
|
||||
feat_proj_dropout (:obj:`float`, `optional`, defaults to 0.0):
|
||||
The dropout probability for output of the feature extractor.
|
||||
feat_extract_activation (:obj:`str, `optional`, defaults to :obj:`"gelu"`):
|
||||
The non-linear activation function (function or string) in the 1D convolutional layers of the feature
|
||||
extractor. If string, :obj:`"gelu"`, :obj:`"relu"`, :obj:`"selu"` and :obj:`"gelu_new"` are supported.
|
||||
feat_quantizer_dropout (obj:`float`, `optional`, defaults to 0.0):
|
||||
The dropout probabilitiy for quantized feature extractor states.
|
||||
conv_dim (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(512, 512, 512, 512, 512, 512, 512)`):
|
||||
A tuple of integers defining the number of input and output channels of each 1D convolutional layer in the
|
||||
feature extractor. The length of `conv_dim` defines the number of 1D convolutional layers.
|
||||
conv_stride (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(5, 2, 2, 2, 2, 2, 2)`):
|
||||
A tuple of integers defining the stride of each 1D convolutional layer in the feature extractor. The length
|
||||
of `conv_stride` defines the number of convolutional layers and has to match the the length of `conv_dim`.
|
||||
conv_kernel (:obj:`Tuple[int]`, `optional`, defaults to :obj:`(10, 3, 3, 3, 3, 3, 3)`):
|
||||
A tuple of integers defining the kernel size of each 1D convolutional layer in the feature extractor. The
|
||||
length of `conv_kernel` defines the number of convolutional layers and has to match the the length of
|
||||
`conv_dim`.
|
||||
conv_bias (:obj:`bool`, `optional`, defaults to :obj:`False`):
|
||||
Whether the 1D convolutional layers have a bias.
|
||||
num_conv_pos_embeddings (:obj:`int`, `optional`, defaults to 128):
|
||||
Number of convolutional positional embeddings. Defines the kernel size of 1D convolutional positional
|
||||
embeddings layer.
|
||||
num_conv_pos_embedding_groups (:obj:`int`, `optional`, defaults to 16):
|
||||
Number of groups of 1D convolutional positional embeddings layer.
|
||||
do_stable_layer_norm (:obj:`bool`, `optional`, defaults to :obj:`False`):
|
||||
Whether to apply `stable` layer norm architecture of the Transformer encoder. ``do_stable_layer_norm is
|
||||
True`` corresponds to applying layer norm before the attention layer, whereas ``do_stable_layer_norm is
|
||||
False`` corresponds to applying layer norm after the attention layer.
|
||||
apply_spec_augment (:obj:`bool`, `optional`, defaults to :obj:`True`):
|
||||
Whether to apply *SpecAugment* data augmentation to the outputs of the feature extractor. For reference see
|
||||
`SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
|
||||
<https://arxiv.org/abs/1904.08779>`__.
|
||||
mask_time_prob (:obj:`float`, `optional`, defaults to 0.05):
|
||||
Propability of each feature vector along the time axis to be chosen as the start of the vector span to be
|
||||
masked. Approximately ``mask_time_prob * sequence_length // mask_time_length`` feature vectors will be
|
||||
masked along the time axis. This is only relevant if ``apply_spec_augment is True``.
|
||||
mask_time_length (:obj:`int`, `optional`, defaults to 10):
|
||||
Length of vector span along the time axis.
|
||||
mask_time_min_masks (:obj:`int`, `optional`, defaults to 2),:
|
||||
The minimum number of masks of length ``mask_feature_length`` generated along the time axis, each time
|
||||
step, irrespectively of ``mask_feature_prob``. Only relevant if
|
||||
''mask_time_prob*len(time_axis)/mask_time_length < mask_time_min_masks''
|
||||
mask_feature_prob (:obj:`float`, `optional`, defaults to 0.0):
|
||||
Propability of each feature vector along the feature axis to be chosen as the start of the vector span to
|
||||
be masked. Approximately ``mask_time_prob * hidden_size // mask_time_length`` feature vectors will be
|
||||
masked along the time axis. This is only relevant if ``apply_spec_augment is True``.
|
||||
mask_feature_length (:obj:`int`, `optional`, defaults to 10):
|
||||
Length of vector span along the feature axis.
|
||||
num_codevectors_per_group (:obj:`int`, `optional`, defaults to 320):
|
||||
Number of entries in each quantization codebook (group).
|
||||
num_codevector_groups (:obj:`int`, `optional`, defaults to 2):
|
||||
Number of codevector groups for product codevector quantization.
|
||||
contrastive_logits_temperature (:obj:`float`, `optional`, defaults to 0.1):
|
||||
The temperature `kappa` in the contrastive loss.
|
||||
feat_quantizer_dropout (:obj:`float`, `optional`, defaults to 0.0):
|
||||
The dropout probabilitiy for the output of the feature extractor that's used by the quantizer.
|
||||
num_negatives (:obj:`int`, `optional`, defaults to 100):
|
||||
Number of negative samples for the contrastive loss.
|
||||
codevector_dim (:obj:`int`, `optional`, defaults to 256):
|
||||
Dimensionality of the quantized feature vectors.
|
||||
proj_codevector_dim (:obj:`int`, `optional`, defaults to 256):
|
||||
Dimensionality of the final projection of both the quantized and the transformer features.
|
||||
diversity_loss_weight (:obj:`int`, `optional`, defaults to 0.1):
|
||||
The weight of the codebook diversity loss component.
|
||||
ctc_loss_reduction (:obj:`str`, `optional`, defaults to :obj:`"mean"`):
|
||||
Specifies the reduction to apply to the output of ``torch.nn.CTCLoss``. Only relevant when training an
|
||||
instance of :class:`~transformers.WavLMForCTC`.
|
||||
ctc_zero_infinity (:obj:`bool`, `optional`, defaults to :obj:`False`):
|
||||
Whether to zero infinite losses and the associated gradients of ``torch.nn.CTCLoss``. Infinite losses
|
||||
mainly occur when the inputs are too short to be aligned to the targets. Only relevant when training an
|
||||
instance of :class:`~transformers.WavLMForCTC`.
|
||||
use_weighted_layer_sum (:obj:`bool`, `optional`, defaults to :obj:`False`):
|
||||
Whether to use a weighted average of layer outputs with learned weights. Only relevant when using an
|
||||
instance of :class:`~transformers.WavLMForSequenceClassification`.
|
||||
classifier_proj_size (:obj:`int`, `optional`, defaults to 256):
|
||||
Dimensionality of the projection before token mean-pooling for classification.
|
||||
add_adapter (:obj:`bool`, `optional`, defaults to :obj:`False`):
|
||||
Whether a convolutional network should be stacked on top of the Wav2Vec2 Encoder. Can be very useful for
|
||||
warm-starting Wav2Vec2 for SpeechEncoderDecoder models.
|
||||
adapter_kernel_size (:obj:`int`, `optional`, defaults to 3):
|
||||
Kernel size of the convolutional layers in the adapter network. Only relevant if ``add_adapter is True``.
|
||||
adapter_stride (:obj:`int`, `optional`, defaults to 2):
|
||||
Stride of the convolutional layers in the adapter network. Only relevant if ``add_adapter is True``.
|
||||
num_adapter_layers (:obj:`int`, `optional`, defaults to 3):
|
||||
Number of convolutional layers that should be used in the adapter network. Only relevant if ``add_adapter
|
||||
is True``.
|
||||
output_hidden_size (:obj:`int`, `optional`):
|
||||
Dimensionality of the encoder output layer. If not defined, this defaults to `hidden-size`. Only relevant
|
||||
if ``add_adapter is True``.
|
||||
|
||||
Example::
|
||||
|
||||
Example::
|
||||
|
||||
>>> from transformers import WavLMModel, WavLMConfig
|
||||
|
||||
>>> # Initializing a WavLM facebook/wavlm-base-960h style configuration
|
||||
>>> configuration = WavLMConfig()
|
||||
|
||||
>>> # Initializing a model from the facebook/wavlm-base-960h style configuration
|
||||
>>> model = WavLMModel(configuration)
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
"""
|
||||
model_type = "wavlm"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=32,
|
||||
hidden_size=768,
|
||||
num_hidden_layers=12,
|
||||
num_attention_heads=12,
|
||||
intermediate_size=3072,
|
||||
hidden_act="gelu",
|
||||
hidden_dropout=0.1,
|
||||
activation_dropout=0.1,
|
||||
attention_dropout=0.1,
|
||||
feat_proj_dropout=0.0,
|
||||
feat_quantizer_dropout=0.0,
|
||||
final_dropout=0.1,
|
||||
layerdrop=0.1,
|
||||
initializer_range=0.02,
|
||||
layer_norm_eps=1e-5,
|
||||
feat_extract_norm="group",
|
||||
feat_extract_activation="gelu",
|
||||
conv_dim=(512, 512, 512, 512, 512, 512, 512),
|
||||
conv_stride=(5, 2, 2, 2, 2, 2, 2),
|
||||
conv_kernel=(10, 3, 3, 3, 3, 2, 2),
|
||||
conv_bias=False,
|
||||
num_conv_pos_embeddings=128,
|
||||
num_conv_pos_embedding_groups=16,
|
||||
num_buckets=320,
|
||||
max_bucket_distance=800,
|
||||
do_stable_layer_norm=False,
|
||||
apply_spec_augment=True,
|
||||
mask_time_prob=0.05,
|
||||
mask_time_length=10,
|
||||
mask_time_min_masks=2,
|
||||
mask_feature_prob=0.0,
|
||||
mask_feature_length=10,
|
||||
num_codevectors_per_group=320,
|
||||
num_codevector_groups=2,
|
||||
contrastive_logits_temperature=0.1,
|
||||
num_negatives=100,
|
||||
codevector_dim=256,
|
||||
proj_codevector_dim=256,
|
||||
diversity_loss_weight=0.1,
|
||||
ctc_loss_reduction="mean",
|
||||
ctc_zero_infinity=False,
|
||||
use_weighted_layer_sum=False,
|
||||
classifier_proj_size=256,
|
||||
num_ctc_classes=80,
|
||||
pad_token_id=0,
|
||||
bos_token_id=1,
|
||||
eos_token_id=2,
|
||||
add_adapter=False,
|
||||
adapter_kernel_size=3,
|
||||
adapter_stride=2,
|
||||
num_adapter_layers=3,
|
||||
output_hidden_size=None,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__(**kwargs, pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id)
|
||||
self.hidden_size = hidden_size
|
||||
self.feat_extract_norm = feat_extract_norm
|
||||
self.feat_extract_activation = feat_extract_activation
|
||||
self.conv_dim = list(conv_dim)
|
||||
self.conv_stride = list(conv_stride)
|
||||
self.conv_kernel = list(conv_kernel)
|
||||
self.conv_bias = conv_bias
|
||||
self.num_buckets = num_buckets
|
||||
self.max_bucket_distance = max_bucket_distance
|
||||
self.num_conv_pos_embeddings = num_conv_pos_embeddings
|
||||
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
|
||||
self.num_feat_extract_layers = len(self.conv_dim)
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.intermediate_size = intermediate_size
|
||||
self.hidden_act = hidden_act
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.hidden_dropout = hidden_dropout
|
||||
self.attention_dropout = attention_dropout
|
||||
self.activation_dropout = activation_dropout
|
||||
self.feat_proj_dropout = feat_proj_dropout
|
||||
self.final_dropout = final_dropout
|
||||
self.layerdrop = layerdrop
|
||||
self.layer_norm_eps = layer_norm_eps
|
||||
self.initializer_range = initializer_range
|
||||
self.num_ctc_classes = num_ctc_classes
|
||||
self.vocab_size = vocab_size
|
||||
self.do_stable_layer_norm = do_stable_layer_norm
|
||||
self.use_weighted_layer_sum = use_weighted_layer_sum
|
||||
self.classifier_proj_size = classifier_proj_size
|
||||
|
||||
if (
|
||||
(len(self.conv_stride) != self.num_feat_extract_layers)
|
||||
or (len(self.conv_kernel) != self.num_feat_extract_layers)
|
||||
or (len(self.conv_dim) != self.num_feat_extract_layers)
|
||||
):
|
||||
raise ValueError(
|
||||
"Configuration for convolutional layers is incorrect. "
|
||||
"It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`, "
|
||||
f"but is `len(config.conv_dim) = {len(self.conv_dim)}`, `len(config.conv_stride) "
|
||||
f"= {len(self.conv_stride)}`, `len(config.conv_kernel) = {len(self.conv_kernel)}`."
|
||||
)
|
||||
|
||||
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
|
||||
self.apply_spec_augment = apply_spec_augment
|
||||
self.mask_time_prob = mask_time_prob
|
||||
self.mask_time_length = mask_time_length
|
||||
self.mask_time_min_masks = mask_time_min_masks
|
||||
self.mask_feature_prob = mask_feature_prob
|
||||
self.mask_feature_length = mask_feature_length
|
||||
|
||||
# parameters for pretraining with codevector quantized representations
|
||||
self.num_codevectors_per_group = num_codevectors_per_group
|
||||
self.num_codevector_groups = num_codevector_groups
|
||||
self.contrastive_logits_temperature = contrastive_logits_temperature
|
||||
self.feat_quantizer_dropout = feat_quantizer_dropout
|
||||
self.num_negatives = num_negatives
|
||||
self.codevector_dim = codevector_dim
|
||||
self.proj_codevector_dim = proj_codevector_dim
|
||||
self.diversity_loss_weight = diversity_loss_weight
|
||||
|
||||
# ctc loss
|
||||
self.ctc_loss_reduction = ctc_loss_reduction
|
||||
self.ctc_zero_infinity = ctc_zero_infinity
|
||||
|
||||
# adapter
|
||||
self.add_adapter = add_adapter
|
||||
self.adapter_kernel_size = adapter_kernel_size
|
||||
self.adapter_stride = adapter_stride
|
||||
self.num_adapter_layers = num_adapter_layers
|
||||
self.output_hidden_size = output_hidden_size or hidden_size
|
||||
|
|
@ -0,0 +1,203 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2021 The HuggingFace Inc. team.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Convert WavLM checkpoint."""
|
||||
|
||||
|
||||
import argparse
|
||||
|
||||
import torch
|
||||
|
||||
from transformers import WavLMConfig, WavLMModel, logging
|
||||
|
||||
# Step 1. clone https://github.com/microsoft/unilm
|
||||
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
|
||||
# Step 3. cd unilm
|
||||
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
|
||||
# import classes
|
||||
from unilm.wavlm.WavLM import WavLM as WavLMOrig
|
||||
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig
|
||||
|
||||
|
||||
logging.set_verbosity_info()
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
MAPPING = {
|
||||
"post_extract_proj": "feature_projection.projection",
|
||||
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
|
||||
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
|
||||
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
|
||||
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
|
||||
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
|
||||
"self_attn.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
|
||||
"self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
|
||||
"self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
|
||||
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
|
||||
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
|
||||
"fc2": "encoder.layers.*.feed_forward.output_dense",
|
||||
"final_layer_norm": "encoder.layers.*.final_layer_norm",
|
||||
"encoder.layer_norm": "encoder.layer_norm",
|
||||
"w2v_model.layer_norm": "feature_projection.layer_norm",
|
||||
"quantizer.weight_proj": "quantizer.weight_proj",
|
||||
"quantizer.vars": "quantizer.codevectors",
|
||||
"project_q": "project_q",
|
||||
"final_proj": "project_hid",
|
||||
"w2v_encoder.proj": "ctc_proj",
|
||||
"mask_emb": "masked_spec_embed",
|
||||
}
|
||||
TOP_LEVEL_KEYS = [
|
||||
"ctc_proj",
|
||||
"quantizer.weight_proj",
|
||||
"quantizer.codevectors",
|
||||
"project_q",
|
||||
"project_hid",
|
||||
]
|
||||
|
||||
|
||||
def set_recursively(hf_pointer, key, value, full_name, weight_type):
|
||||
for attribute in key.split("."):
|
||||
hf_pointer = getattr(hf_pointer, attribute)
|
||||
|
||||
if weight_type is not None:
|
||||
hf_shape = getattr(hf_pointer, weight_type).shape
|
||||
else:
|
||||
hf_shape = hf_pointer.shape
|
||||
|
||||
assert (
|
||||
hf_shape == value.shape
|
||||
), f"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be {value.shape} for {full_name}"
|
||||
|
||||
if weight_type == "weight":
|
||||
hf_pointer.weight.data = value
|
||||
elif weight_type == "weight_g":
|
||||
hf_pointer.weight_g.data = value
|
||||
elif weight_type == "weight_v":
|
||||
hf_pointer.weight_v.data = value
|
||||
elif weight_type == "bias":
|
||||
hf_pointer.bias.data = value
|
||||
else:
|
||||
hf_pointer.data = value
|
||||
|
||||
logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.")
|
||||
|
||||
|
||||
def recursively_load_weights(fairseq_model, hf_model):
|
||||
unused_weights = []
|
||||
fairseq_dict = fairseq_model.state_dict()
|
||||
|
||||
feature_extractor = hf_model.feature_extractor
|
||||
|
||||
for name, value in fairseq_dict.items():
|
||||
is_used = False
|
||||
if "conv_layers" in name:
|
||||
load_conv_layer(
|
||||
name,
|
||||
value,
|
||||
feature_extractor,
|
||||
unused_weights,
|
||||
hf_model.config.feat_extract_norm == "group",
|
||||
)
|
||||
is_used = True
|
||||
else:
|
||||
for key, mapped_key in MAPPING.items():
|
||||
if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
|
||||
is_used = True
|
||||
if "*" in mapped_key:
|
||||
layer_index = name.split(key)[0].split(".")[-2]
|
||||
mapped_key = mapped_key.replace("*", layer_index)
|
||||
if "weight_g" in name:
|
||||
weight_type = "weight_g"
|
||||
elif "weight_v" in name:
|
||||
weight_type = "weight_v"
|
||||
elif "bias" in name and "relative_attention_bias" not in name:
|
||||
weight_type = "bias"
|
||||
elif "weight" in name:
|
||||
# TODO: don't match quantizer.weight_proj
|
||||
weight_type = "weight"
|
||||
else:
|
||||
weight_type = None
|
||||
|
||||
set_recursively(hf_model, mapped_key, value, name, weight_type)
|
||||
continue
|
||||
if not is_used:
|
||||
unused_weights.append(name)
|
||||
|
||||
logger.warning(f"Unused weights: {unused_weights}")
|
||||
|
||||
|
||||
def load_conv_layer(full_name, value, feature_extractor, unused_weights, use_group_norm):
|
||||
name = full_name.split("conv_layers.")[-1]
|
||||
items = name.split(".")
|
||||
layer_id = int(items[0])
|
||||
type_id = int(items[1])
|
||||
|
||||
if type_id == 0:
|
||||
if "bias" in name:
|
||||
assert (
|
||||
value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape
|
||||
), f"{full_name} has size {value.shape}, but {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
|
||||
feature_extractor.conv_layers[layer_id].conv.bias.data = value
|
||||
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
|
||||
elif "weight" in name:
|
||||
assert (
|
||||
value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape
|
||||
), f"{full_name} has size {value.shape}, but {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
|
||||
feature_extractor.conv_layers[layer_id].conv.weight.data = value
|
||||
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}.")
|
||||
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
|
||||
if "bias" in name:
|
||||
assert (
|
||||
value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape
|
||||
), f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."
|
||||
feature_extractor.conv_layers[layer_id].layer_norm.bias.data = value
|
||||
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
|
||||
elif "weight" in name:
|
||||
assert (
|
||||
value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape
|
||||
), f"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
|
||||
feature_extractor.conv_layers[layer_id].layer_norm.weight.data = value
|
||||
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.")
|
||||
else:
|
||||
unused_weights.append(full_name)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def convert_wavlm_checkpoint(checkpoint_path, pytorch_dump_folder_path, config_path=None):
|
||||
|
||||
# load the pre-trained checkpoints
|
||||
checkpoint = torch.load(checkpoint_path)
|
||||
cfg = WavLMConfigOrig(checkpoint["cfg"])
|
||||
model = WavLMOrig(cfg)
|
||||
model.load_state_dict(checkpoint["model"])
|
||||
model.eval()
|
||||
|
||||
if config_path is not None:
|
||||
config = WavLMConfig.from_pretrained(config_path)
|
||||
else:
|
||||
config = WavLMConfig()
|
||||
|
||||
hf_wavlm = WavLMModel(config)
|
||||
|
||||
recursively_load_weights(model, hf_wavlm)
|
||||
|
||||
hf_wavlm.save_pretrained(pytorch_dump_folder_path)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
|
||||
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
|
||||
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
|
||||
args = parser.parse_args()
|
||||
convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
|
||||
1441
src/transformers/models/wavlm/modeling_wavlm.py
Executable file
1441
src/transformers/models/wavlm/modeling_wavlm.py
Executable file
File diff suppressed because it is too large
Load diff
|
|
@ -5174,6 +5174,50 @@ class Wav2Vec2PreTrainedModel:
|
|||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
||||
|
||||
|
||||
class WavLMForCTC:
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class WavLMForSequenceClassification:
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, *args, **kwargs):
|
||||
requires_backends(cls, ["torch"])
|
||||
|
||||
def forward(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class WavLMModel:
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, *args, **kwargs):
|
||||
requires_backends(cls, ["torch"])
|
||||
|
||||
def forward(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
class WavLMPreTrainedModel:
|
||||
def __init__(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, *args, **kwargs):
|
||||
requires_backends(cls, ["torch"])
|
||||
|
||||
def forward(self, *args, **kwargs):
|
||||
requires_backends(self, ["torch"])
|
||||
|
||||
|
||||
XLM_PRETRAINED_MODEL_ARCHIVE_LIST = None
|
||||
|
||||
|
||||
|
|
|
|||
493
tests/test_modeling_wavlm.py
Normal file
493
tests/test_modeling_wavlm.py
Normal file
|
|
@ -0,0 +1,493 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
""" Testing suite for the PyTorch WavLM model. """
|
||||
|
||||
import math
|
||||
import unittest
|
||||
|
||||
import pytest
|
||||
from datasets import load_dataset
|
||||
|
||||
from tests.test_modeling_common import floats_tensor, ids_tensor, random_attention_mask
|
||||
from transformers import WavLMConfig, is_torch_available
|
||||
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
|
||||
|
||||
from .test_configuration_common import ConfigTester
|
||||
from .test_modeling_common import ModelTesterMixin, _config_zero_init
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
import torch
|
||||
|
||||
from transformers import Wav2Vec2FeatureExtractor, WavLMForCTC, WavLMForSequenceClassification, WavLMModel
|
||||
|
||||
|
||||
class WavLMModelTester:
|
||||
def __init__(
|
||||
self,
|
||||
parent,
|
||||
batch_size=13,
|
||||
seq_length=1024, # speech is longer
|
||||
is_training=False,
|
||||
hidden_size=16,
|
||||
feat_extract_norm="group",
|
||||
feat_extract_dropout=0.0,
|
||||
feat_extract_activation="gelu",
|
||||
conv_dim=(32, 32, 32),
|
||||
conv_stride=(4, 4, 4),
|
||||
conv_kernel=(8, 8, 8),
|
||||
conv_bias=False,
|
||||
num_conv_pos_embeddings=16,
|
||||
num_conv_pos_embedding_groups=2,
|
||||
num_hidden_layers=4,
|
||||
num_attention_heads=2,
|
||||
hidden_dropout_prob=0.1, # this is most likely not correctly set yet
|
||||
intermediate_size=20,
|
||||
layer_norm_eps=1e-5,
|
||||
hidden_act="gelu",
|
||||
initializer_range=0.02,
|
||||
vocab_size=32,
|
||||
do_stable_layer_norm=False,
|
||||
scope=None,
|
||||
):
|
||||
self.parent = parent
|
||||
self.batch_size = batch_size
|
||||
self.seq_length = seq_length
|
||||
self.is_training = is_training
|
||||
self.hidden_size = hidden_size
|
||||
self.feat_extract_norm = feat_extract_norm
|
||||
self.feat_extract_dropout = feat_extract_dropout
|
||||
self.feat_extract_activation = feat_extract_activation
|
||||
self.conv_dim = conv_dim
|
||||
self.conv_stride = conv_stride
|
||||
self.conv_kernel = conv_kernel
|
||||
self.conv_bias = conv_bias
|
||||
self.num_conv_pos_embeddings = num_conv_pos_embeddings
|
||||
self.num_conv_pos_embedding_groups = num_conv_pos_embedding_groups
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.hidden_dropout_prob = hidden_dropout_prob
|
||||
self.intermediate_size = intermediate_size
|
||||
self.layer_norm_eps = layer_norm_eps
|
||||
self.hidden_act = hidden_act
|
||||
self.initializer_range = initializer_range
|
||||
self.vocab_size = vocab_size
|
||||
self.do_stable_layer_norm = do_stable_layer_norm
|
||||
self.scope = scope
|
||||
|
||||
output_seq_length = self.seq_length
|
||||
for kernel, stride in zip(self.conv_kernel, self.conv_stride):
|
||||
output_seq_length = (output_seq_length - (kernel - 1)) / stride
|
||||
self.output_seq_length = int(math.ceil(output_seq_length))
|
||||
self.encoder_seq_length = self.output_seq_length
|
||||
|
||||
def prepare_config_and_inputs(self):
|
||||
input_values = floats_tensor([self.batch_size, self.seq_length], self.vocab_size)
|
||||
attention_mask = random_attention_mask([self.batch_size, self.seq_length])
|
||||
|
||||
config = self.get_config()
|
||||
|
||||
return config, input_values, attention_mask
|
||||
|
||||
def get_config(self):
|
||||
return WavLMConfig(
|
||||
hidden_size=self.hidden_size,
|
||||
feat_extract_norm=self.feat_extract_norm,
|
||||
feat_extract_dropout=self.feat_extract_dropout,
|
||||
feat_extract_activation=self.feat_extract_activation,
|
||||
conv_dim=self.conv_dim,
|
||||
conv_stride=self.conv_stride,
|
||||
conv_kernel=self.conv_kernel,
|
||||
conv_bias=self.conv_bias,
|
||||
num_conv_pos_embeddings=self.num_conv_pos_embeddings,
|
||||
num_conv_pos_embedding_groups=self.num_conv_pos_embedding_groups,
|
||||
num_hidden_layers=self.num_hidden_layers,
|
||||
num_attention_heads=self.num_attention_heads,
|
||||
hidden_dropout_prob=self.hidden_dropout_prob,
|
||||
intermediate_size=self.intermediate_size,
|
||||
layer_norm_eps=self.layer_norm_eps,
|
||||
hidden_act=self.hidden_act,
|
||||
initializer_range=self.initializer_range,
|
||||
vocab_size=self.vocab_size,
|
||||
)
|
||||
|
||||
def create_and_check_model(self, config, input_values, attention_mask):
|
||||
model = WavLMModel(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
result = model(input_values, attention_mask=attention_mask)
|
||||
self.parent.assertEqual(
|
||||
result.last_hidden_state.shape, (self.batch_size, self.output_seq_length, self.hidden_size)
|
||||
)
|
||||
|
||||
def create_and_check_batch_inference(self, config, input_values, *args):
|
||||
# test does not pass for models making use of `group_norm`
|
||||
# check: https://github.com/pytorch/fairseq/issues/3227
|
||||
model = WavLMModel(config=config)
|
||||
model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
input_values = input_values[:3]
|
||||
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.bool)
|
||||
|
||||
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
|
||||
|
||||
# pad input
|
||||
for i in range(len(input_lengths)):
|
||||
input_values[i, input_lengths[i] :] = 0.0
|
||||
attention_mask[i, input_lengths[i] :] = 0.0
|
||||
|
||||
batch_outputs = model(input_values, attention_mask=attention_mask).last_hidden_state
|
||||
|
||||
for i in range(input_values.shape[0]):
|
||||
input_slice = input_values[i : i + 1, : input_lengths[i]]
|
||||
output = model(input_slice).last_hidden_state
|
||||
|
||||
batch_output = batch_outputs[i : i + 1, : output.shape[1]]
|
||||
self.parent.assertTrue(torch.allclose(output, batch_output, atol=1e-3))
|
||||
|
||||
def check_ctc_loss(self, config, input_values, *args):
|
||||
model = WavLMForCTC(config=config)
|
||||
model.to(torch_device)
|
||||
|
||||
# make sure that dropout is disabled
|
||||
model.eval()
|
||||
|
||||
input_values = input_values[:3]
|
||||
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)
|
||||
|
||||
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
|
||||
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
|
||||
labels = ids_tensor((input_values.shape[0], min(max_length_labels) - 1), model.config.vocab_size)
|
||||
|
||||
# pad input
|
||||
for i in range(len(input_lengths)):
|
||||
input_values[i, input_lengths[i] :] = 0.0
|
||||
attention_mask[i, input_lengths[i] :] = 0
|
||||
|
||||
model.config.ctc_loss_reduction = "sum"
|
||||
sum_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
|
||||
|
||||
model.config.ctc_loss_reduction = "mean"
|
||||
mean_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
|
||||
|
||||
self.parent.assertTrue(isinstance(sum_loss, float))
|
||||
self.parent.assertTrue(isinstance(mean_loss, float))
|
||||
|
||||
def check_seq_classifier_loss(self, config, input_values, *args):
|
||||
model = WavLMForSequenceClassification(config=config)
|
||||
model.to(torch_device)
|
||||
|
||||
# make sure that dropout is disabled
|
||||
model.eval()
|
||||
|
||||
input_values = input_values[:3]
|
||||
attention_mask = torch.ones(input_values.shape, device=torch_device, dtype=torch.long)
|
||||
|
||||
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
|
||||
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
|
||||
|
||||
# pad input
|
||||
for i in range(len(input_lengths)):
|
||||
input_values[i, input_lengths[i] :] = 0.0
|
||||
attention_mask[i, input_lengths[i] :] = 0
|
||||
|
||||
masked_loss = model(input_values, attention_mask=attention_mask, labels=labels).loss.item()
|
||||
unmasked_loss = model(input_values, labels=labels).loss.item()
|
||||
|
||||
self.parent.assertTrue(isinstance(masked_loss, float))
|
||||
self.parent.assertTrue(isinstance(unmasked_loss, float))
|
||||
self.parent.assertTrue(masked_loss != unmasked_loss)
|
||||
|
||||
def check_ctc_training(self, config, input_values, *args):
|
||||
config.ctc_zero_infinity = True
|
||||
model = WavLMForCTC(config=config)
|
||||
model.to(torch_device)
|
||||
model.train()
|
||||
|
||||
# freeze feature encoder
|
||||
model.freeze_feature_extractor()
|
||||
|
||||
input_values = input_values[:3]
|
||||
|
||||
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
|
||||
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
|
||||
labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size)
|
||||
|
||||
# pad input
|
||||
for i in range(len(input_lengths)):
|
||||
input_values[i, input_lengths[i] :] = 0.0
|
||||
|
||||
if max_length_labels[i] < labels.shape[-1]:
|
||||
# it's important that we make sure that target lenghts are at least
|
||||
# one shorter than logit lenghts to prevent -inf
|
||||
labels[i, max_length_labels[i] - 1 :] = -100
|
||||
|
||||
loss = model(input_values, labels=labels).loss
|
||||
self.parent.assertFalse(torch.isinf(loss).item())
|
||||
|
||||
loss.backward()
|
||||
|
||||
def check_seq_classifier_training(self, config, input_values, *args):
|
||||
config.ctc_zero_infinity = True
|
||||
model = WavLMForSequenceClassification(config=config)
|
||||
model.to(torch_device)
|
||||
model.train()
|
||||
|
||||
# freeze everything but the classification head
|
||||
model.freeze_base_model()
|
||||
|
||||
input_values = input_values[:3]
|
||||
|
||||
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
|
||||
labels = ids_tensor((input_values.shape[0], 1), len(model.config.id2label))
|
||||
|
||||
# pad input
|
||||
for i in range(len(input_lengths)):
|
||||
input_values[i, input_lengths[i] :] = 0.0
|
||||
|
||||
loss = model(input_values, labels=labels).loss
|
||||
self.parent.assertFalse(torch.isinf(loss).item())
|
||||
|
||||
loss.backward()
|
||||
|
||||
def check_labels_out_of_vocab(self, config, input_values, *args):
|
||||
model = WavLMForCTC(config)
|
||||
model.to(torch_device)
|
||||
model.train()
|
||||
|
||||
input_values = input_values[:3]
|
||||
|
||||
input_lengths = [input_values.shape[-1] // i for i in [4, 2, 1]]
|
||||
max_length_labels = model._get_feat_extract_output_lengths(torch.tensor(input_lengths))
|
||||
labels = ids_tensor((input_values.shape[0], max(max_length_labels) - 2), model.config.vocab_size + 100)
|
||||
|
||||
with pytest.raises(ValueError):
|
||||
model(input_values, labels=labels)
|
||||
|
||||
def prepare_config_and_inputs_for_common(self):
|
||||
config, input_values, attention_mask = self.prepare_config_and_inputs()
|
||||
inputs_dict = {"input_values": input_values, "attention_mask": attention_mask}
|
||||
return config, inputs_dict
|
||||
|
||||
|
||||
@require_torch
|
||||
class WavLMModelTest(ModelTesterMixin, unittest.TestCase):
|
||||
all_model_classes = (WavLMForCTC, WavLMModel, WavLMForSequenceClassification) if is_torch_available() else ()
|
||||
test_pruning = False
|
||||
test_headmasking = False
|
||||
test_torchscript = False
|
||||
|
||||
def setUp(self):
|
||||
self.model_tester = WavLMModelTester(self)
|
||||
self.config_tester = ConfigTester(self, config_class=WavLMConfig, hidden_size=37)
|
||||
|
||||
def test_config(self):
|
||||
self.config_tester.run_common_tests()
|
||||
|
||||
def test_model(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.create_and_check_model(*config_and_inputs)
|
||||
|
||||
def test_ctc_loss_inference(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_ctc_loss(*config_and_inputs)
|
||||
|
||||
def test_seq_classifier_loss_inference(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_seq_classifier_loss(*config_and_inputs)
|
||||
|
||||
def test_ctc_train(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_ctc_training(*config_and_inputs)
|
||||
|
||||
def test_seq_classifier_train(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_seq_classifier_training(*config_and_inputs)
|
||||
|
||||
def test_labels_out_of_vocab(self):
|
||||
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
||||
self.model_tester.check_labels_out_of_vocab(*config_and_inputs)
|
||||
|
||||
# WavLM has no inputs_embeds
|
||||
def test_inputs_embeds(self):
|
||||
pass
|
||||
|
||||
# `input_ids` is renamed to `input_values`
|
||||
def test_forward_signature(self):
|
||||
pass
|
||||
|
||||
# WavLM cannot resize token embeddings
|
||||
# since it has no tokens embeddings
|
||||
def test_resize_tokens_embeddings(self):
|
||||
pass
|
||||
|
||||
# WavLM has no inputs_embeds
|
||||
# and thus the `get_input_embeddings` fn
|
||||
# is not implemented
|
||||
def test_model_common_attributes(self):
|
||||
pass
|
||||
|
||||
# WavLM uses PyTorch's multi-head-attention class
|
||||
# and thus can't retain gradients on attentions
|
||||
def test_retain_grad_hidden_states_attentions(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
config.output_hidden_states = True
|
||||
config.output_attentions = True
|
||||
|
||||
# no need to test all models as different heads yield the same functionality
|
||||
model_class = self.all_model_classes[0]
|
||||
model = model_class(config)
|
||||
model.to(torch_device)
|
||||
|
||||
# set layer drop to 0
|
||||
model.config.layerdrop = 0.0
|
||||
|
||||
input_values = inputs_dict["input_values"]
|
||||
|
||||
input_lengths = torch.tensor(
|
||||
[input_values.shape[1] for _ in range(input_values.shape[0])], dtype=torch.long, device=torch_device
|
||||
)
|
||||
output_lengths = model._get_feat_extract_output_lengths(input_lengths)
|
||||
|
||||
labels = ids_tensor((input_values.shape[0], output_lengths[0] - 2), self.model_tester.vocab_size)
|
||||
inputs_dict["attention_mask"] = torch.ones_like(inputs_dict["attention_mask"])
|
||||
inputs_dict["labels"] = labels
|
||||
|
||||
outputs = model(**inputs_dict)
|
||||
|
||||
output = outputs[0]
|
||||
|
||||
# Encoder-/Decoder-only models
|
||||
hidden_states = outputs.hidden_states[0]
|
||||
hidden_states.retain_grad()
|
||||
|
||||
output.flatten()[0].backward(retain_graph=True)
|
||||
|
||||
self.assertIsNotNone(hidden_states.grad)
|
||||
|
||||
def test_initialization(self):
|
||||
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
||||
|
||||
configs_no_init = _config_zero_init(config)
|
||||
for model_class in self.all_model_classes:
|
||||
model = model_class(config=configs_no_init)
|
||||
for name, param in model.named_parameters():
|
||||
uniform_init_parms = [
|
||||
"conv.weight",
|
||||
"masked_spec_embed",
|
||||
"codevectors",
|
||||
"quantizer.weight_proj.weight",
|
||||
"project_hid.weight",
|
||||
"project_hid.bias",
|
||||
"project_q.weight",
|
||||
"project_q.bias",
|
||||
"feature_projection.projection.weight",
|
||||
"feature_projection.projection.bias",
|
||||
"label_embeddings_concat",
|
||||
"rel_attn_embed",
|
||||
]
|
||||
if param.requires_grad:
|
||||
if any([x in name for x in uniform_init_parms]):
|
||||
self.assertTrue(
|
||||
-1.0 <= ((param.data.mean() * 1e9).round() / 1e9).item() <= 1.0,
|
||||
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
||||
)
|
||||
else:
|
||||
self.assertIn(
|
||||
((param.data.mean() * 1e9).round() / 1e9).item(),
|
||||
[0.0, 1.0],
|
||||
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
||||
)
|
||||
|
||||
# overwrite from test_modeling_common
|
||||
def _mock_init_weights(self, module):
|
||||
if hasattr(module, "weight") and module.weight is not None:
|
||||
module.weight.data.fill_(3)
|
||||
if hasattr(module, "weight_g") and module.weight_g is not None:
|
||||
module.weight_g.data.fill_(3)
|
||||
if hasattr(module, "weight_v") and module.weight_v is not None:
|
||||
module.weight_v.data.fill_(3)
|
||||
if hasattr(module, "bias") and module.bias is not None:
|
||||
module.bias.data.fill_(3)
|
||||
if hasattr(module, "codevectors") and module.codevectors is not None:
|
||||
module.codevectors.data.fill_(3)
|
||||
if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None:
|
||||
module.masked_spec_embed.data.fill_(3)
|
||||
|
||||
@slow
|
||||
def test_model_from_pretrained(self):
|
||||
model = WavLMModel.from_pretrained("microsoft/wavlm-base-plus")
|
||||
self.assertIsNotNone(model)
|
||||
|
||||
|
||||
@require_torch
|
||||
@require_torchaudio
|
||||
@slow
|
||||
class WavLMModelIntegrationTest(unittest.TestCase):
|
||||
def _load_datasamples(self, num_samples):
|
||||
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
||||
# automatic decoding with librispeech
|
||||
speech_samples = ds.sort("id").filter(
|
||||
lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
|
||||
)[:num_samples]["audio"]
|
||||
|
||||
return [x["array"] for x in speech_samples]
|
||||
|
||||
def test_inference_base(self):
|
||||
model = WavLMModel.from_pretrained("microsoft/wavlm-base-plus").to(torch_device)
|
||||
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
|
||||
"microsoft/wavlm-base-plus", return_attention_mask=True
|
||||
)
|
||||
|
||||
input_speech = self._load_datasamples(2)
|
||||
|
||||
inputs = feature_extractor(input_speech, return_tensors="pt", padding=True)
|
||||
|
||||
input_values = inputs.input_values.to(torch_device)
|
||||
attention_mask = inputs.attention_mask.to(torch_device)
|
||||
|
||||
with torch.no_grad():
|
||||
hidden_states_slice = (
|
||||
model(input_values, attention_mask=attention_mask).last_hidden_state[:, -2:, -2:].cpu()
|
||||
)
|
||||
|
||||
EXPECTED_HIDDEN_STATES_SLICE = torch.tensor(
|
||||
[[[0.0554, 0.1138], [0.0555, 0.1144]], [[0.0200, 0.1240], [0.0059, 0.0607]]]
|
||||
)
|
||||
self.assertTrue(torch.allclose(hidden_states_slice, EXPECTED_HIDDEN_STATES_SLICE, rtol=1e-2))
|
||||
|
||||
def test_inference_large(self):
|
||||
model = WavLMModel.from_pretrained("microsoft/wavlm-large").to(torch_device)
|
||||
feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(
|
||||
"microsoft/wavlm-base-plus", return_attention_mask=True
|
||||
)
|
||||
|
||||
input_speech = self._load_datasamples(2)
|
||||
|
||||
inputs = feature_extractor(input_speech, return_tensors="pt", padding=True)
|
||||
|
||||
input_values = inputs.input_values.to(torch_device)
|
||||
attention_mask = inputs.attention_mask.to(torch_device)
|
||||
|
||||
with torch.no_grad():
|
||||
hidden_states_slice = (
|
||||
model(input_values, attention_mask=attention_mask).last_hidden_state[:, -2:, -2:].cpu()
|
||||
)
|
||||
|
||||
EXPECTED_HIDDEN_STATES_SLICE = torch.tensor(
|
||||
[[[0.2132, 0.0486], [0.2119, 0.0571]], [[0.1386, 0.1837], [0.2455, 0.0614]]]
|
||||
)
|
||||
self.assertTrue(torch.allclose(hidden_states_slice, EXPECTED_HIDDEN_STATES_SLICE, rtol=1e-2))
|
||||
Loading…
Reference in a new issue