readme: ---
  language: "en"
  tags:
  - Robust ASR
  - audio-to-audio 
  - speech-enhancement
  - PyTorch
  - speechbrain
  license: "apache-2.0"
  datasets:
  - Voicebank
  - DEMAND
  metrics:
  - WER
  - PESQ
  - COVL
  ---
  
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  <br/><br/>
  
  # ResNet-like model
  
  This repository provides all the necessary tools to perform enhancement and
  robust ASR training (EN) within
  SpeechBrain. For a better experience we encourage you to learn more about
  [SpeechBrain](https://speechbrain.github.io). The model performance is:
  
  | Release | Test PESQ | Test COVL | Valid WER | Test WER |
  |:--------:|:----:|:----:|:----:|:----:|
  | 22-06-21 | 3.05 | 3.74 | 2.89 | 2.80 |
  
  Works with SpeechBrain v0.5.12
  
  ## Pipeline description
  
  The mimic loss training system consists of three steps:
  
  1. A perceptual model is pre-trained on clean speech features, the
  same type used for the enhancement masking system.
  2. An enhancement model is trained with mimic loss, using the
  pre-trained perceptual model.
  3. A large ASR model pre-trained on LibriSpeech is fine-tuned
  using the enhancement front-end.
  
  The enhancement and ASR models can be used together or
  independently.
  
  ## Install SpeechBrain
  
  First of all, please install SpeechBrain with the following command:
  
  ```
  pip install speechbrain
  ```
  
  Please notice that we encourage you to read our tutorials and learn more about
  [SpeechBrain](https://speechbrain.github.io).
  
  ## Pretrained Usage
  
  To use the mimic-loss-trained model for enhancement, use the following simple code:
  
  ```python
  import torchaudio
  from speechbrain.pretrained import WaveformEnhancement
  
  enhance_model = WaveformEnhancement.from_hparams(
      source="speechbrain/mtl-mimic-voicebank",
      savedir="pretrained_models/mtl-mimic-voicebank",
  )
  enhanced = enhance_model.enhance_file("speechbrain/mtl-mimic-voicebank/example.wav")
  
  # Saving enhanced signal on disk
  torchaudio.save('enhanced.wav', enhanced.unsqueeze(0).cpu(), 16000)
  ```
  
  The system is trained with recordings sampled at 16kHz (single channel).
  The code will automatically normalize your audio (i.e., resampling + mono channel selection) when calling *enhance_file* if needed. Make sure your input tensor is compliant with the expected sampling rate if you use *enhance_batch* as in the example.
  
  
  ### Inference on GPU
  To perform inference on the GPU, add  `run_opts={"device":"cuda"}`  when calling the `from_hparams` method.
  
  ### Training
  The model was trained with SpeechBrain (150e1890).
  To train it from scratch follows these steps:
  1. Clone SpeechBrain:
  ```bash
  git clone https://github.com/speechbrain/speechbrain/
  ```
  2. Install it:
  ```
  cd speechbrain
  pip install -r requirements.txt
  pip install -e .
  ```
  
  3. Run Training:
  ```
  cd  recipes/Voicebank/MTL/ASR_enhance
  python train.py hparams/enhance_mimic.yaml --data_folder=your_data_folder
  ```
  
  You can find our training results (models, logs, etc) [here](https://drive.google.com/drive/folders/1HaR0Bq679pgd1_4jD74_wDRUq-c3Wl4L?usp=sharing).
  
  ### Limitations
  The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.
  
  ## Referencing Mimic Loss
  
  If you find mimic loss useful, please cite:
  
  ```
  @inproceedings{bagchi2018spectral,
  title={Spectral Feature Mapping with Mimic Loss for Robust Speech Recognition},
  author={Bagchi, Deblin and Plantinga, Peter and Stiff, Adam and Fosler-Lussier, Eric},
  booktitle={IEEE Conference on Audio, Speech, and Signal Processing (ICASSP)},
  year={2018}
  }
  ```
  
  # **About SpeechBrain**
  - Website: https://speechbrain.github.io/
  - Code: https://github.com/speechbrain/speechbrain/
  - HuggingFace: https://huggingface.co/speechbrain/
  
  
  # **Citing SpeechBrain**
  Please, cite SpeechBrain if you use it for your research or business.
  
  ```bibtex
  @misc{speechbrain,
    title={{SpeechBrain}: A General-Purpose Speech Toolkit},
    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},
    year={2021},
    eprint={2106.04624},
    archivePrefix={arXiv},
    primaryClass={eess.AS},
    note={arXiv:2106.04624}
  }
  ```
  