metadata

language: en
tags:
  - Robust ASR
  - Speech Enhancement
  - PyTorch
license: apache-2.0
datasets:
  - Voicebank
  - DEMAND
metrics:
  - WER
  - PESQ
  - eSTOI

1D CNN + Transformer Trained w/ Mimic Loss

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. The model performance is:

Release	Test PESQ	Test eSTOI	Valid WER	Test WER
21-03-08	2.92	85.2	3.20	2.96

Pipeline description

The mimic loss training system consists of three steps:

A perceptual model is pre-trained on clean speech features, the same type used for the enhancement masking system.
An enhancement model is trained with mimic loss, using the pre-trained perceptual model.
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.

Pretrained Usage

To use the mimic-loss-trained model for enhancement, use the following simple code:

import torchaudio
from speechbrain.pretrained import SpectralMaskEnhancement

enhance_model = SpectralMaskEnhancement.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)

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:

Clone SpeechBrain:

git clone https://github.com/speechbrain/speechbrain/

Install it:

cd speechbrain
pip install -r requirements.txt
pip install -e .

Run Training:

cd  recipes/Voicebank/MTL/ASR_enhance
python train.py hparams/enhance_mimic.yaml --data_folder=your_data_folder

https://drive.google.com/drive/folders/1fcVP52gHgoMX9diNN1JxX_My5KaRNZWs?usp=sharing

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} }


## Referencing SpeechBrain

If you find SpeechBrain useful, please cite:

@misc{SB2021, 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 }, title = {SpeechBrain}, year = {2021}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/speechbrain/speechbrain}}, }


#### About SpeechBrain
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.
Website: https://speechbrain.github.io/
GitHub: https://github.com/speechbrain/speechbrain