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https://github.com/NVIDIA/waveglow
A Flow-based Generative Network for Speech Synthesis
https://github.com/NVIDIA/waveglow
Last synced: about 1 month ago
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A Flow-based Generative Network for Speech Synthesis
- Host: GitHub
- URL: https://github.com/NVIDIA/waveglow
- Owner: NVIDIA
- License: bsd-3-clause
- Created: 2018-11-08T00:41:44.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2023-10-19T15:19:59.000Z (about 1 year ago)
- Last Synced: 2024-10-29T18:10:27.227Z (about 1 month ago)
- Language: Python
- Size: 427 KB
- Stars: 2,279
- Watchers: 77
- Forks: 529
- Open Issues: 79
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-colab-project - WaveGlow
- awesome-robotic-tooling - waveglow - A Flow-based Generative Network for Speech Synthesis. (User Interaction / Acoustic User Interface)
README
## WaveGlow: a Flow-based Generative Network for Speech Synthesis
### Ryan Prenger, Rafael Valle, and Bryan Catanzaro
In our recent [paper], we propose WaveGlow: a flow-based network capable of
generating high quality speech from mel-spectrograms. WaveGlow combines insights
from [Glow] and [WaveNet] in order to provide fast, efficient and high-quality
audio synthesis, without the need for auto-regression. WaveGlow is implemented
using only a single network, trained using only a single cost function:
maximizing the likelihood of the training data, which makes the training
procedure simple and stable.
Our [PyTorch] implementation produces audio samples at a rate of 1200
kHz on an NVIDIA V100 GPU. Mean Opinion Scores show that it delivers audio
quality as good as the best publicly available WaveNet implementation.
Visit our [website] for audio samples.
## Setup
1. Clone our repo and initialize submodule
```command
git clone https://github.com/NVIDIA/waveglow.git
cd waveglow
git submodule init
git submodule update
```
2. Install requirements `pip3 install -r requirements.txt`
3. Install [Apex]
## Generate audio with our pre-existing model
1. Download our [published model]
2. Download [mel-spectrograms]
3. Generate audio `python3 inference.py -f <(ls mel_spectrograms/*.pt) -w waveglow_256channels.pt -o . --is_fp16 -s 0.6`
N.b. use `convert_model.py` to convert your older models to the current model
with fused residual and skip connections.
## Train your own model
1. Download [LJ Speech Data]. In this example it's in `data/`
2. Make a list of the file names to use for training/testing
```command
ls data/*.wav | tail -n+10 > train_files.txt
ls data/*.wav | head -n10 > test_files.txt
```
3. Train your WaveGlow networks
```command
mkdir checkpoints
python train.py -c config.json
```
For multi-GPU training replace `train.py` with `distributed.py`. Only tested with single node and NCCL.
For mixed precision training set `"fp16_run": true` on `config.json`.
4. Make test set mel-spectrograms
`python mel2samp.py -f test_files.txt -o . -c config.json`
5. Do inference with your network
```command
ls *.pt > mel_files.txt
python3 inference.py -f mel_files.txt -w checkpoints/waveglow_10000 -o . --is_fp16 -s 0.6
```
[//]: # (TODO)
[//]: # (PROVIDE INSTRUCTIONS FOR DOWNLOADING LJS)
[pytorch 1.0]: https://github.com/pytorch/pytorch#installation
[website]: https://nv-adlr.github.io/WaveGlow
[paper]: https://arxiv.org/abs/1811.00002
[WaveNet implementation]: https://github.com/r9y9/wavenet_vocoder
[Glow]: https://blog.openai.com/glow/
[WaveNet]: https://deepmind.com/blog/wavenet-generative-model-raw-audio/
[PyTorch]: http://pytorch.org
[published model]: https://drive.google.com/open?id=1rpK8CzAAirq9sWZhe9nlfvxMF1dRgFbF
[mel-spectrograms]: https://drive.google.com/file/d/1g_VXK2lpP9J25dQFhQwx7doWl_p20fXA/view?usp=sharing
[LJ Speech Data]: https://keithito.com/LJ-Speech-Dataset
[Apex]: https://github.com/nvidia/apex