Mtl Mimic Voicebank

🚀 ResNet-like model

This repository offers all the essential tools for performing enhancement and robust ASR training (EN) within SpeechBrain. To have a better experience, we recommend you to learn more about SpeechBrain. The model's performance metrics are as follows:

It is compatible with SpeechBrain v0.5.12.

🚀 Quick Start

This repository provides all the necessary tools to perform enhancement and robust ASR training (EN) within SpeechBrain. For a better experience, it's recommended to learn more about SpeechBrain.

✨ Features

• Robust ASR: Capable of performing robust automatic speech recognition.
• Audio-to-audio: Supports audio-to-audio processing for speech enhancement.
• Speech Enhancement: Can enhance the quality of speech signals.

📦 Installation

First, install SpeechBrain using the following command:

pip install speechbrain

Note that we encourage you to read our tutorials and learn more about SpeechBrain.

💻 Usage Examples

Basic Usage

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

import torchaudio
from speechbrain.inference.enhancement 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.

Advanced Usage

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, follow these steps:

1. Clone SpeechBrain:

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.

📚 Documentation

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.

Limitations

The SpeechBrain team does not provide any warranty on the performance achieved by this model when used on other datasets.

📄 License

This project is licensed under the "apache-2.0" license.

📚 Referencing

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}
}

Citing SpeechBrain

Please cite SpeechBrain if you use it for your research or business.

@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}
}

📋 Information Table

🔗 About SpeechBrain

• Website: https://speechbrain.github.io/
• Code: https://github.com/speechbrain/speechbrain/
• HuggingFace: https://huggingface.co/speechbrain/