Skip to content

Commit

Permalink
Initial code release
Browse files Browse the repository at this point in the history
  • Loading branch information
hubertsiuzdak committed Jun 11, 2023
1 parent 9875037 commit 416f24d
Show file tree
Hide file tree
Showing 26 changed files with 2,766 additions and 1 deletion.
26 changes: 26 additions & 0 deletions .github/workflows/pypi-release.yml
Original file line number Diff line number Diff line change
@@ -0,0 +1,26 @@
name: Publish Python package

on:
release:
types: [published]

jobs:
publish:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: "3.x"
- name: Install pypa/setuptools
run: >-
python -m
pip install wheel
- name: Build a binary wheel
run: >-
python setup.py sdist bdist_wheel
- name: Publish to PyPI
uses: pypa/gh-action-pypi-publish@release/v1
with:
password: ${{ secrets.PYPI_API_TOKEN }}
164 changes: 164 additions & 0 deletions .gitignore
Original file line number Diff line number Diff line change
@@ -0,0 +1,164 @@
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
.pybuilder/
target/

# Jupyter Notebook
.ipynb_checkpoints

# IPython
profile_default/
ipython_config.py

# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version

# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock

# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock

# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml

# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/

# Celery stuff
celerybeat-schedule
celerybeat.pid

# SageMath parsed files
*.sage.py

# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/

# Spyder project settings
.spyderproject
.spyproject

# Rope project settings
.ropeproject

# mkdocs documentation
/site

# mypy
.mypy_cache/
.dmypy.json
dmypy.json

# Pyre type checker
.pyre/

# pytype static type analyzer
.pytype/

# Cython debug symbols
cython_debug/

# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
.idea/

logs/
*.pt
*.ckpt
21 changes: 21 additions & 0 deletions LICENSE
Original file line number Diff line number Diff line change
@@ -0,0 +1,21 @@
MIT License

Copyright (c) 2023 Charactr Inc.

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
123 changes: 122 additions & 1 deletion README.md
Original file line number Diff line number Diff line change
@@ -1,3 +1,124 @@
# Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis

[Audio samples](https://charactr-platform.github.io/vocos/) | Paper [[abs]](https://arxiv.org/abs/2306.00814) [[pdf]](https://arxiv.org/pdf/2306.00814.pdf)
[Audio samples](https://charactr-platform.github.io/vocos/) |
Paper [[abs]](https://arxiv.org/abs/2306.00814) [[pdf]](https://arxiv.org/pdf/2306.00814.pdf)

## Installation

To use Vocos only in inference mode, install it using:

```bash
pip install vocos
```

If you wish to train the model, install it with additional dependencies:

```bash
pip install vocos[train]
```

## Usage

### Reconstruct audio from mel-spectrogram

```python
import torch

from vocos import Vocos

vocos = Vocos.from_pretrained("charactr/vocos-mel-24khz")

mel = torch.randn(1, 100, 256) # B, C, T

with torch.no_grad():
audio = vocos.decode(mel)
```

Copy-synthesis from a file:

```python
import torchaudio

y, sr = torchaudio.load(YOUR_AUDIO_FILE)
if y.size(0) > 1: # mix to mono
y = y.mean(dim=0, keepdim=True)
y = torchaudio.functional.resample(y, orig_freq=sr, new_freq=24000)

with torch.no_grad():
y_hat = vocos(y)
```

### Reconstruct audio from EnCodec

Additionally, you need to provide a `bandwidth_id` which corresponds to the lookup embedding for bandwidth from the
list: `[1.5, 3.0, 6.0, 12.0]`.

```python
vocos = Vocos.from_pretrained("charactr/vocos-encodec-24khz")

quantized_features = torch.randn(1, 128, 256)
bandwidth_id = torch.tensor([3]) # 12 kbps

with torch.no_grad():
audio = vocos.decode(quantized_features, bandwidth_id=bandwidth_id)
```

Copy-synthesis from a file: It extracts and quantizes features with EnCodec, then reconstructs them with Vocos in a
single forward pass.

```python
y, sr = torchaudio.load(YOUR_AUDIO_FILE)
if y.size(0) > 1: # mix to mono
y = y.mean(dim=0, keepdim=True)
y = torchaudio.functional.resample(y, orig_freq=sr, new_freq=24000)

with torch.no_grad():
y_hat = vocos(y, bandwidth_id=bandwidth_id)
```

## Pre-trained models

The provided models were trained up to 2.5 million generator iterations, which resulted in slightly better objective
scores
compared to those reported in the paper.

| Model Name | Dataset | Training Iterations | Parameters
|-------------------------------------------------------------------------------------|---------------|---------------------|------------|
| [charactr/vocos-mel-24khz](https://huggingface.co/charactr/vocos-mel-24khz) | LibriTTS | 2.5 M | 13.5 M
| [charactr/vocos-encodec-24khz](https://huggingface.co/charactr/vocos-encodec-24khz) | DNS Challenge | 2.5 M | 7.9 M

## Training

Prepare a filelist of audio files for the training and validation set:

```bash
find $TRAIN_DATASET_DIR -name *.wav > filelist.train
find $VAL_DATASET_DIR -name *.wav > filelist.val
```

Fill a config file, e.g. [vocos.yaml](configs%2Fvocos.yaml), with your filelist paths and start training with:

```bash
python train.py -c configs/vocos.yaml
```

Refer to [Pytorch Lightning documentation](https://lightning.ai/docs/pytorch/stable/) for details about customizing the
training pipeline.

## Citation

If this code contributes to your research, please cite our work:

```
@article{siuzdak2023vocos,
title={Vocos: Closing the gap between time-domain and Fourier-based neural vocoders for high-quality audio synthesis},
author={Siuzdak, Hubert},
journal={arXiv preprint arXiv:2306.00814},
year={2023}
}
```

## License

The code in this repository is released under the MIT license as found in the
[LICENSE](LICENSE) file.
Loading

0 comments on commit 416f24d

Please sign in to comment.