https://github.com/JusperLee/Deep-Clustering-for-Speech-Separation

Pytorch implements Deep Clustering: Discriminative Embeddings For Segmentation And Separation
https://github.com/JusperLee/Deep-Clustering-for-Speech-Separation

deep-clustering pytorch segmentation speech-separation

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Pytorch implements Deep Clustering: Discriminative Embeddings For Segmentation And Separation

• Host: GitHub
• URL: https://github.com/JusperLee/Deep-Clustering-for-Speech-Separation
• Owner: JusperLee
• Created: 2019-12-27T07:54:51.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-07-14T14:16:19.000Z (over 4 years ago)
• Last Synced: 2024-08-02T07:14:10.572Z (6 months ago)
• Topics: deep-clustering, pytorch, segmentation, speech-separation
• Language: Python
• Size: 94.7 KB
• Stars: 120
• Watchers: 4
• Forks: 25
• Open Issues: 12
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-speech-enhancement - [Code

README

        
## Deep Clustering for Speech Separation

Deep clustering in the field of speech separation implemented by pytorch

Demo Pages: [Results of pure speech separation model](https://www.likai.show/Pure-Audio/index.html)

> Hershey J R, Chen Z, Le Roux J, et al. Deep clustering: Discriminative embeddings for segmentation and separation[C]//2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016: 31-35.

## Requirement

- **Pytorch 1.3.0**

- **librosa 0.7.1**

- **PyYAML 5.1.2**

## Code writing log

**2019-12-27 Friday**. It is currently being refined and is not yet complete.

**2020-01-02 Thursday**. The training code is currently complete and the code bug is being tested.

## Training steps

1. First, you can use the create_scp script to generate training and test data scp files.

```shell

python create_scp.py

```

2. Then, in order to reduce the mismatch of training and test environments. Therefore, you need to run the util script to generate a feature normalization file (CMVN).

```shell

python ./utils/util.py

```

3. Finally, use the following command to train the network.

```shell

python train.py -opt ./option/train.yml

```

## Inference steps

1. Use the following command to start testing the model

```shell

python test.py -scp 1.scp -opt ./option/train.yml -save_file ./result

```

2. You can use the [this code](https://github.com/JusperLee/Calculate-SNR-SDR "this code") to calculate the SNR scores.

## Thanks

1. [Pytorch Template](https://github.com/victoresque/pytorch-template "Pytorch Template")