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https://github.com/facebookresearch/vocoder-benchmark
A repository for benchmarking neural vocoders by their quality and speed.
https://github.com/facebookresearch/vocoder-benchmark
Last synced: about 22 hours ago
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A repository for benchmarking neural vocoders by their quality and speed.
- Host: GitHub
- URL: https://github.com/facebookresearch/vocoder-benchmark
- Owner: facebookresearch
- License: other
- Created: 2021-05-07T19:22:16.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2024-10-11T21:39:28.000Z (about 1 month ago)
- Last Synced: 2024-11-06T12:12:17.699Z (8 days ago)
- Language: Python
- Size: 365 KB
- Stars: 203
- Watchers: 18
- Forks: 27
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# VocBench: A Neural Vocoder Benchmark for Speech Synthesis
PyTorch implementation for VocBench framework.
[[arXiv](https://arxiv.org/abs/2112.03099)]
## Installation
1. **Python** >= 3.6
2. Get VocBench code
```sh
$ git clone https://github.com/facebookresearch/vocoder-benchmark.git
$ cd vocoder-benchmark
```
3. Install dependencies
```sh
$ python3 -m venv vocbench
# activate the virtualenv
$ source vocbench/bin/activate
# Upgrade pip
$ python -m pip install --upgrade pip
# Install dependences
$ pip install -e .
```
4. To use VocBench cli, make sure to set paths in your `.bashrc` or `.bash_profile`
appropriately.
```sh
VOCODER_BENCHMARK=/path/to/vocoder-benchmark
export PATH=$VOCODER_BENCHMARK/bin:$PATH
```
5. Change the binary file permission and test your installation
```sh
$ chomd +x $VOCODER_BENCHMARK/bin/vocoder
$ vocoder --help
Usage: cli.py [OPTIONS] COMMAND [ARGS]...
Vocoder benchmarking CLI.
Options:
--help Show this message and exit.
Commands:
dataset Dataset processing.
diffwave Create, train, or use diffwave models.
parallel_wavegan Create, train, or use parallel_wavegan models.
wavegrad Create, train, or use wavegrad models.
wavenet Create, train, or use wavenet models.
wavernn Create, train, or use wavernn models.
```
## Usage
### Download dataset
```sh
$ vocoder dataset --help # For more information on how to download/split dataset
# e.g. download and split LJ Speech
$ vocoder dataset download --dataset ljspeech --path ~/local/datasets/lj # Download and unzip dataset files
$ vocoder dataset split --dataset ljspeech --path ~/local/datasets/lj # Create train / validation / test splits
```
### Training
```sh
$ vocoder [model-cmd] train --help
# e.g. train wavenet on LJ Speech dataset
$ vocoder wavenet train --path ~/local/models/wavenet --dataset ~/local/datasets/lj --config $VOCODER_BENCHMARK/config/wavenet_mulaw_normal.yaml
```
*For MelGAN and Parallel WaveGAN, they both use the same model cmd. You will need to choose the right configuration for each of them
```sh
# MelGAN
$ vocoder parallel_wavegan train --path ~/local/models/melgan --dataset ~/local/datasets/lj --config $VOCODER_BENCHMARK/config/melgan.v1.yaml
# Parallel WaveGAN
$ vocoder parallel_wavegan train --path ~/local/models/parallel_wavegan --dataset ~/local/datasets/lj --config $VOCODER_BENCHMARK/config/parallel_wavegan.yaml
```
Example of configuration files for each model is provided under `config` directory.
### Synthesize
```sh
$ vocoder [model-cmd] synthesize --help
Usage: cli.py [model-cmd] synthesize [OPTIONS] INPUT_FILE OUTPUT_FILE
Synthesize with the model.
Options:
--path TEXT Directory for the model [required]
--length TEXT The length of the output sample in seconds
--offset FLOAT Offset in seconds of the sample
--help Show this message and exit.
```
### Evaluate
```sh
$ vocoder [model-cmd] evaluate --help
Usage: cli.py [model-cmd] evaluate [OPTIONS]
Evaluate a given vocoder.
Options:
--path TEXT Directory for the model [required]
--dataset TEXT Name of the dataset to use [required]
--checkpoint TEXT Checkpoint path (default: load latest checkpoint)
--help Show this message and exit.
```
*Frechet Audio Distance is currently not implemented. We use Google Research opensource [repository](https://github.com/google-research/google-research/tree/master/frechet_audio_distance) to get FAD results.
## Reference Repositories
* [Pytorch](https://github.com/pytorch/pytorch), Pytorch.
* [Audio](https://github.com/pytorch/audio), Pytorch.
* [FAD](https://github.com/google-research/google-research/tree/master/frechet_audio_distance), Google Research.
* [WaveNet](https://github.com/r9y9/wavenet_vocoder), Ryuichi Yamamoto.
* [Parallel WaveGAN](https://github.com/kan-bayashi/ParallelWaveGAN), Tomoki Hayashi.
* [WaveGrad](https://github.com/ivanvovk/WaveGrad), Ivan Vovk.
* [DiffWave](https://github.com/lmnt-com/diffwave), LMNT.
* [Flops counter](https://github.com/sovrasov/flops-counter.pytorch), Vladislav Sovrasov.
## License
The majority of VocBench is licensed under CC-BY-NC, however portions of the project are available under separate license terms: Wavenet, ParallelWaveGAN, and flops counter are licensed under the MIT license; diffwave is licensed under the Apache 2.0 license; WaveGrad is licensed under the BSD-3 license.
## Used by
List of papers that used our work (Feel free to add your own paper by making a pull request)