https://github.com/xcmyz/fastspeech

The Implementation of FastSpeech based on pytorch.
https://github.com/xcmyz/fastspeech

deep-learning pytorch speech-synthesis

Last synced: 7 days ago
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The Implementation of FastSpeech based on pytorch.

• Host: GitHub
• URL: https://github.com/xcmyz/fastspeech
• Owner: xcmyz
• License: mit
• Created: 2019-05-31T12:27:47.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2023-07-06T22:00:13.000Z (over 1 year ago)
• Last Synced: 2024-12-01T09:40:04.937Z (about 1 month ago)
• Topics: deep-learning, pytorch, speech-synthesis
• Language: Python
• Homepage:
• Size: 17.3 MB
• Stars: 858
• Watchers: 34
• Forks: 213
• Open Issues: 14
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# FastSpeech-Pytorch

The Implementation of FastSpeech Based on Pytorch.

## Update (2020/07/20)

1. Optimize the training process.

2. Optimize the implementation of length regulator.

3. Use the same hyper parameter as FastSpeech2.

4. **The measures of the 1, 2 and 3 make the training process 3 times faster than before.**

5. **Better speech quality.**

## Model



    



## My Blog

- [FastSpeech Reading Notes](https://zhuanlan.zhihu.com/p/67325775)

- [Details and Rethinking of this Implementation](https://zhuanlan.zhihu.com/p/67939482)

## Prepare Dataset

1. Download and extract [LJSpeech dataset](https://keithito.com/LJ-Speech-Dataset/).

2. Put LJSpeech dataset in `data`.

3. Unzip `alignments.zip`.

4. Put [Nvidia pretrained waveglow model](https://drive.google.com/file/d/1WsibBTsuRg_SF2Z6L6NFRTT-NjEy1oTx/view?usp=sharing) in the `waveglow/pretrained_model` and rename as `waveglow_256channels.pt`;

5. Run `python3 preprocess.py`.

## Training

Run `python3 train.py`.

## Evaluation

Run `python3 eval.py`.

## Notes

- In the paper of FastSpeech, authors use pre-trained Transformer-TTS model to provide the target of alignment. I didn't have a well-trained Transformer-TTS model so I use Tacotron2 instead.

- I use the same hyper-parameter as [FastSpeech2](https://arxiv.org/abs/2006.04558).

- The examples of audio are in `sample`.

- [pretrained model](https://drive.google.com/file/d/1vMrKtbjPj9u_o3Y-8prE6hHCc6Yj4Nqk/view?usp=sharing).

## Reference

### Repository

- [The Implementation of Tacotron Based on Tensorflow](https://github.com/keithito/tacotron)

- [The Implementation of Transformer Based on Pytorch](https://github.com/jadore801120/attention-is-all-you-need-pytorch)

- [The Implementation of Transformer-TTS Based on Pytorch](https://github.com/xcmyz/Transformer-TTS)

- [The Implementation of Tacotron2 Based on Pytorch](https://github.com/NVIDIA/tacotron2)

- [The Implementation of FastSpeech2 Based on Pytorch](https://github.com/ming024/FastSpeech2)

### Paper

- [Tacotron2](https://arxiv.org/abs/1712.05884)

- [Transformer](https://arxiv.org/abs/1706.03762)

- [FastSpeech](https://arxiv.org/abs/1905.09263)

- [FastSpeech2](https://arxiv.org/abs/2006.04558)