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 given 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.

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