https://github.com/yoyololicon/wavenet-like-vocoder

Basic wavenet and fftnet vocoder model.
https://github.com/yoyololicon/wavenet-like-vocoder

fftnet mel-spectrogram pytorch vocoder wavenet

Last synced: 21 days ago
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Basic wavenet and fftnet vocoder model.

• Host: GitHub
• URL: https://github.com/yoyololicon/wavenet-like-vocoder
• Owner: yoyololicon
• Created: 2018-12-10T07:28:54.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2022-02-07T13:16:50.000Z (almost 3 years ago)
• Last Synced: 2024-10-04T13:31:23.537Z (about 1 month ago)
• Topics: fftnet, mel-spectrogram, pytorch, vocoder, wavenet
• Language: Python
• Size: 46.9 KB
• Stars: 19
• Watchers: 3
• Forks: 2
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

        
# WaveNet-like vocoder models

Basic implementations of WaveNet and modified FFTNet in PyTorch. The project structre is brought from [pytorch-template].

## Requirements

* NumPy

* SciPy

* PyTorch >= 0.4.1

* tqdm

* librosa

## Quick Start

The code in this repo by default will train a WaveNet (or FFTNet) using 80-dimension mel-spectrogram with linear interpolation.

### Preprocess

Use `preprocess.py` to convert your wave files into mel-spectrograms.

```

python preprocess.py wave/files/folder -c config.json --out data

```

The preprocessed data will be stored in `./data`.

You can change the configurations of "feature" in the `.json` file.

### Train

```

python train.py -c config.json

```

### Test

Use `preprocess.py` to convert a single wave file into mel-spectrogram feature.

```

python preprocess.py example.wav -c config.json --out test

```

The result is stored in `test.npz`.

Then use the latest checkpoint file in the `./saved` folder to decoded `test.npz` back to waveform.

The generating process will run on gpu if you add `--cuda`.

```

python test.py test.npz outfile.wav -r saved/your-model-name/XXXX_XXXXXX/checkpoint-stepXXXXX.pth --cuda

```

That's it. Other instructions and advanced usage can be found in [pytorch-template], I didn't change too much of the whole structure. 

## Customization

I add a new folder `feature` which is different from [pytorch-template].

To use other feature like mfcc instead of mel-spectrogram, you can add your own function in `./feature/features.py` with similar arguments style of `get_logmel()`.

Other customization method can be found in [pytorch-template].

[pytorch-template]: https://github.com/victoresque/pytorch-template

## Fast inference

In `test.py` I implement fast-wavenet generation process in a very naive way. Use `fast_inference.py` you can get a huge speed up (CPU only).

The speed is around 1500 samples/s on FFTNet and 300 samples/s on WaveNet.