https://github.com/npuichigo/waveglow

A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis
https://github.com/npuichigo/waveglow

neural-vocoder text-to-speech waveglow

Last synced: about 2 months ago
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A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis

• Host: GitHub
• URL: https://github.com/npuichigo/waveglow
• Owner: npuichigo
• License: apache-2.0
• Created: 2018-11-04T16:30:44.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2018-11-06T14:42:38.000Z (over 5 years ago)
• Last Synced: 2024-01-26T20:35:24.603Z (4 months ago)
• Topics: neural-vocoder, text-to-speech, waveglow
• Language: Python
• Size: 25.4 KB
• Stars: 206
• Watchers: 17
• Forks: 35
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• Awesome-pytorch-list - torch_waveglow - based Generative Network for Speech Synthesis. (Paper implementations / Other libraries:)
• my-awesome-stars - npuichigo/waveglow - A PyTorch implementation of the WaveGlow: A Flow-based Generative Network for Speech Synthesis (Python)
• Awesome-pytorch-list-CNVersion - torch_waveglow

README

        
# WaveGlow 

A PyTorch implementation of the [WaveGlow: A Flow-based Generative Network for Speech Synthesis](https://arxiv.org/abs/1811.00002)

## Quick Start:

1. Install requirements:

```

pip install -r requirements.txt

```

2. Download dataset:

```

wget http://festvox.org/cmu_arctic/cmu_arctic/packed/cmu_us_slt_arctic-0.95-release.tar.bz2

tar xf cmu_us_slt_arctic-0.95-release.tar.bz2

```

3. Extract features: 

feature extracting pipeline is the same as [tacotron](https://github.com/keithito/tacotron)

4. Training with default hyperparams:

```

python train.py

```

5. Synthesize from model:

```

python generate.py --checkpoint=/path/to/model --local_condition_file=/path/to/local_conditon

```

## Notes:

  * This is not official implementation, some details are not necessarily correct.

  * Work in progress.