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https://github.com/funcwj/deep-clustering
deep clustering method for single-channel speech separation
https://github.com/funcwj/deep-clustering
pytorch speech-separation
Last synced: about 2 months ago
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deep clustering method for single-channel speech separation
- Host: GitHub
- URL: https://github.com/funcwj/deep-clustering
- Owner: funcwj
- Created: 2018-06-14T15:50:16.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-06-21T21:19:45.000Z (over 2 years ago)
- Last Synced: 2024-06-06T14:35:11.833Z (4 months ago)
- Topics: pytorch, speech-separation
- Language: Python
- Size: 23.4 KB
- Stars: 108
- Watchers: 6
- Forks: 35
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-speech-enhancement - [Code
README
## Deep clustering for single-channel speech separation
Implement of "Deep Clustering Discriminative Embeddings for Segmentation and Separation"
### Requirements
see [requirements.txt](requirements.txt)
### Usage
1. Configure experiments in .yaml files, for example: `train.yaml`
2. Training:
```shell
python ./train_dcnet.py --config conf/train.yaml --num-epoches 20 > train.log 2>&1 &
```
3. Inference:
```
python ./separate.py --num-spks 2 $mdl_dir/train.yaml $mdl_dir/final.pkl egs.scp
```
### Experiments
| Configure | Epoch | FM | FF | MM | FF/MM | AVG |
| :-------: | :---: | :---: | :--: | :--: | :---: | :--: |
| [config-1](conf/1.config.yaml) | 25 | 11.42 | 6.85 | 7.88 | 7.36 | 9.54 |
### Q & A
1. The format of the `.scp` file?
The format of the `wav.scp` file follows the definition in kaldi toolkit. Each line contains a `key value` pair, where key is a unique string to index audio file and the value is the path of the file. For example
```
mix-utt-00001 /home/data/train/mix-utt-00001.wav
...
mix-utt-XXXXX /home/data/train/mix-utt-XXXXX.wav
```
2. How to prepare training dataset?
Original paper use MATLAB scripts from [create-speaker-mixtures.zip](http://www.merl.com/demos/deep-clustering/create-speaker-mixtures.zip) to simulate two- and three-speaker dataset. You can use you own data source (egs: Librispeech, TIMIT) and create mixtures, keeping clean sources at meanwhile.
### Reference
1. Hershey J R, Chen Z, Le Roux J, et al. Deep clustering: Discriminative embeddings for segmentation and separation[C]//Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. IEEE, 2016: 31-35.
2. Isik Y, Roux J L, Chen Z, et al. Single-channel multi-speaker separation using deep clustering[J]. arXiv preprint arXiv:1607.02173, 2016.