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--- |
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language: "en" |
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tags: |
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- Robust ASR |
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- audio-to-audio |
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- speech-enhancement |
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- PyTorch |
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- speechbrain |
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license: "apache-2.0" |
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datasets: |
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- Voicebank |
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- DEMAND |
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metrics: |
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- WER |
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- PESQ |
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- COVL |
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--- |
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<iframe src="https://ghbtns.com/github-btn.html?user=speechbrain&repo=speechbrain&type=star&count=true&size=large&v=2" frameborder="0" scrolling="0" width="170" height="30" title="GitHub"></iframe> |
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<br/><br/> |
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# ResNet-like model |
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This repository provides all the necessary tools to perform enhancement and |
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robust ASR training (EN) within |
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SpeechBrain. For a better experience we encourage you to learn more about |
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[SpeechBrain](https://speechbrain.github.io). The model performance is: |
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| Release | Test PESQ | Test COVL | Valid WER | Test WER | |
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|:--------:|:----:|:----:|:----:|:----:| |
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| 21-07-25 | 3.05 | 3.74 | 2.89 | 2.80 | |
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## Pipeline description |
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The mimic loss training system consists of three steps: |
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1. A perceptual model is pre-trained on clean speech features, the |
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same type used for the enhancement masking system. |
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2. An enhancement model is trained with mimic loss, using the |
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pre-trained perceptual model. |
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3. A large ASR model pre-trained on LibriSpeech is fine-tuned |
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using the enhancement front-end. |
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The enhancement and ASR models can be used together or |
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independently. |
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## Install SpeechBrain |
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First of all, please install SpeechBrain with the following command: |
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``` |
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pip install speechbrain |
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``` |
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Please notice that we encourage you to read our tutorials and learn more about |
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[SpeechBrain](https://speechbrain.github.io). |
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## Pretrained Usage |
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To use the mimic-loss-trained model for enhancement, use the following simple code: |
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```python |
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import torchaudio |
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from speechbrain.pretrained import SpectralMaskEnhancement |
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enhance_model = SpectralMaskEnhancement.from_hparams( |
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source="speechbrain/mtl-mimic-voicebank", |
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savedir="pretrained_models/mtl-mimic-voicebank", |
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) |
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enhanced = enhance_model.enhance_file("speechbrain/mtl-mimic-voicebank/example.wav") |
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# Saving enhanced signal on disk |
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torchaudio.save('enhanced.wav', enhanced.unsqueeze(0).cpu(), 16000) |
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``` |
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### Inference on GPU |
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To perform inference on the GPU, add `run_opts={"device":"cuda"}` when calling the `from_hparams` method. |
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### Training |
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The model was trained with SpeechBrain (150e1890). |
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To train it from scratch follows these steps: |
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1. Clone SpeechBrain: |
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```bash |
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git clone https://github.com/speechbrain/speechbrain/ |
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``` |
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2. Install it: |
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``` |
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cd speechbrain |
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pip install -r requirements.txt |
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pip install -e . |
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``` |
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3. Run Training: |
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``` |
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cd recipes/Voicebank/MTL/ASR_enhance |
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python train.py hparams/enhance_mimic.yaml --data_folder=your_data_folder |
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``` |
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You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1HaR0Bq679pgd1_4jD74_wDRUq-c3Wl4L?usp=sharing). |
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### Limitations |
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The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets. |
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## Referencing Mimic Loss |
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If you find mimic loss useful, please cite: |
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``` |
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@inproceedings{bagchi2018spectral, |
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title={Spectral Feature Mapping with Mimic Loss for Robust Speech Recognition}, |
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author={Bagchi, Deblin and Plantinga, Peter and Stiff, Adam and Fosler-Lussier, Eric}, |
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booktitle={IEEE Conference on Audio, Speech, and Signal Processing (ICASSP)}, |
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year={2018} |
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} |
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``` |
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# **About SpeechBrain** |
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- Website: https://speechbrain.github.io/ |
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- Code: https://github.com/speechbrain/speechbrain/ |
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- HuggingFace: https://huggingface.co/speechbrain/ |
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# **Citing SpeechBrain** |
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Please, cite SpeechBrain if you use it for your research or business. |
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```bibtex |
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@misc{speechbrain, |
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title={{SpeechBrain}: A General-Purpose Speech Toolkit}, |
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author={Mirco Ravanelli and Titouan Parcollet and Peter Plantinga and Aku Rouhe and Samuele Cornell and Loren Lugosch and Cem Subakan and Nauman Dawalatabad and Abdelwahab Heba and Jianyuan Zhong and Ju-Chieh Chou and Sung-Lin Yeh and Szu-Wei Fu and Chien-Feng Liao and Elena Rastorgueva and François Grondin and William Aris and Hwidong Na and Yan Gao and Renato De Mori and Yoshua Bengio}, |
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year={2021}, |
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eprint={2106.04624}, |
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archivePrefix={arXiv}, |
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primaryClass={eess.AS}, |
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note={arXiv:2106.04624} |
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} |
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``` |
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