---
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](https://speechbrain.github.io). 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:

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 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