Fast-GeCo

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Source code and demo for INTERSPEECH 2024 paper: Noise-robust Speech Separation with Fast Generative Correction

Fast-GeCo 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Fast-GeCo: Noise-robust Speech Separation with Fast Generative Correction
[![Paper](https://img.shields.io/badge/arXiv-2406.07461-brightgreen.svg?style=flat-square)](https://arxiv.org/pdf/2406.07461.pdf)  [![HuggingFace](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/OpenSound/Fast-GeCo) [![Demo page](https://img.shields.io/badge/Audio_Samples-blue?logo=Github&style=flat-square)](https://fastgeco.github.io/Fast-GeCo/)

In this paper, we propose a generative correction method to enhance the output of a discriminative separator. By leveraging a generative corrector based on a diffusion model, we refine the separation process for single-channel mixture speech by removing noises and perceptually unnatural distortions. Furthermore, we optimize the generative model using a predictive loss to streamline the diffusion model’s reverse process into a single step and rectify any associated errors by the reverse process. Our method achieves state-of-the-art performance on the in-domain Libri2Mix noisy dataset, and out-of-domain WSJ with a variety of noises, improving SI-SNR by 22-35% relative to SepFormer, demonstrating robustness and strong generalization capabilities.

Try our Huggingface space!!!

## NEWS & TODO
- [x] Release model weights and inference code
- [x] HuggingFace Spaces demo
- [ ] Scale up training

## Environment setup

```
conda create -n geco python=3.8.19
conda activate geco
pip install -r requirements.txt
```

## Inference examples

To inference with SepFormer and Fast-GeCo, please install `speechbrain`:

```bash
pip install speechbrain==1.0.0
```

Then download our pretrained models on [Huggingface](https://huggingface.co/westbrook/Fast-GeCo/) and run:

```bash
python inference.py  \
    --test_file 'demo/item0_mix.wav' \
    --save_name 'item0' \
    --save_folder 'output_demo' \
    --ckpt 'YOUR_MODEL_PATH'
```

## Data Preparation

To train GeCo or Fast-GeCo, you should prepare a data folder in the following way:

```
libri2mix-train100/
    -1_mix.wav
    -1_source1.wav
    -1_source1hatP.wav
    -2_mix.wav
    -2_source1.wav
    -2_source1hatP.wav
    ....
```

Here,  `*_mix.wav` is the mixture audio, `*_source1.wav` is the grouth truth audio, and `*_source1hatP.wav` is the estimated audio by a speech separation model like SepFormer.

## Train GeCo
with 1 GPU, run:

```bash
CUDA_VISIBLE_DEVICES=0 python train-geco.py --gpus 1 --batch_size 16
```

## Train Fast-GeCo
with 1 GPU, run:

```bash
CUDA_VISIBLE_DEVICES=0 python train-fastgeco.py --gpus 1 --batch_size 32
```

## Evaluate GeCo

```bash
CUDA_VISIBLE_DEVICES=0 python eval-geco.py
```

## Evaluate Fast-GeCo

```bash
CUDA_VISIBLE_DEVICES=0 python eval-fastgeco.py
```

## Run baseline SepFormer

We also provide codes to train and evaluate the SepFormer model, the same as in our paper.

See [speechbrain](https://github.com/speechbrain) for more details of training and test.

## License
The codebase is under [MIT LICENSE](./LICENSE).

## Citations & References
We kindly ask you to cite our paper in your publication when using any of our research or code:

```
@inproceedings{wang24i_interspeech,
  title     = {Noise-robust Speech Separation with Fast Generative Correction},
  author    = {Helin Wang and Jesús Villalba and Laureano Moro-Velazquez and Jiarui Hai and Thomas Thebaud and Najim Dehak},
  year      = {2024},
  booktitle = {Interspeech 2024},
  pages     = {2165--2169},
  doi       = {10.21437/Interspeech.2024-327},
  issn      = {2958-1796},
}
```

## Acknowledgement

[1] [speechbrain](https://github.com/speechbrain)

[2] [Conv-TasNet](https://github.com/JusperLee/Conv-TasNet)

[3] [sgmse-bbed](https://github.com/sp-uhh/sgmse-bbed)

[4] [sgmse-crp](https://github.com/sp-uhh/sgmse_crp)

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