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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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- Speech Enhancement |
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- PyTorch |
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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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- eSTOI |
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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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# 1D CNN + Transformer Trained w/ Mimic Loss |
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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 eSTOI | Valid WER | Test WER | |
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|:-----------:|:-----:| :-----:|:----:|:---------:| |
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| 21-03-08 | 2.92 | 85.2 | 3.20 | 2.96 | |
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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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https://drive.google.com/drive/folders/1fcVP52gHgoMX9diNN1JxX_My5KaRNZWs?usp=sharing |
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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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## Referencing SpeechBrain |
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If you find SpeechBrain useful, please cite: |
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|
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``` |
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@misc{SB2021, |
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author = {Ravanelli, Mirco and Parcollet, Titouan and Rouhe, Aku and Plantinga, Peter and Rastorgueva, Elena and Lugosch, Loren and Dawalatabad, Nauman and Ju-Chieh, Chou and Heba, Abdel and Grondin, Francois and Aris, William and Liao, Chien-Feng and Cornell, Samuele and Yeh, Sung-Lin and Na, Hwidong and Gao, Yan and Fu, Szu-Wei and Subakan, Cem and De Mori, Renato and Bengio, Yoshua }, |
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title = {SpeechBrain}, |
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year = {2021}, |
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publisher = {GitHub}, |
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journal = {GitHub repository}, |
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howpublished = {\\url{https://github.com/speechbrain/speechbrain}}, |
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} |
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``` |
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#### About SpeechBrain |
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SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. |
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Website: https://speechbrain.github.io/ |
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GitHub: https://github.com/speechbrain/speechbrain |
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