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mtl-mimic-voicebank

Audio-to-Audio
speechbrain
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Robust ASR
speech-enhancement
PyTorch
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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:

  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.

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:

  1. Clone SpeechBrain:
git clone https://github.com/speechbrain/speechbrain/
  1. Install it:
cd speechbrain
pip install -r requirements.txt
pip install -e .
  1. 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

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