https://github.com/tugstugi/pytorch-dc-tts

Text to Speech with PyTorch (English and Mongolian)
https://github.com/tugstugi/pytorch-dc-tts

convolutional-neural-networks deep-learning mongolian python pytorch speech-synthesis text-to-speech tts

Last synced: about 13 hours ago
JSON representation

Text to Speech with PyTorch (English and Mongolian)

• Host: GitHub
• URL: https://github.com/tugstugi/pytorch-dc-tts
• Owner: tugstugi
• License: mit
• Created: 2018-08-10T17:05:24.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2021-09-12T17:25:08.000Z (about 3 years ago)
• Last Synced: 2023-11-07T19:20:03.361Z (about 1 year ago)
• Topics: convolutional-neural-networks, deep-learning, mongolian, python, pytorch, speech-synthesis, text-to-speech, tts
• Language: Jupyter Notebook
• Homepage:
• Size: 11.3 MB
• Stars: 174
• Watchers: 13
• Forks: 71
• Open Issues: 5
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
PyTorch implementation of

[Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention](https://arxiv.org/abs/1710.08969)

based partially on the following projects:

* https://github.com/Kyubyong/dc_tts (audio pre processing)

* https://github.com/r9y9/deepvoice3_pytorch (data loader sampler)

## Online Text-To-Speech Demo

The following notebooks are executable on [https://colab.research.google.com ](https://colab.research.google.com):

* [Mongolian Male Voice TTS Demo](https://colab.research.google.com/github/tugstugi/pytorch-dc-tts/blob/master/notebooks/MongolianTTS.ipynb)

* [English Female Voice TTS Demo (LJ-Speech)](https://colab.research.google.com/github/tugstugi/pytorch-dc-tts/blob/master/notebooks/EnglishTTS.ipynb)

For audio samples and pretrained models, visit the above notebook links.

## Training/Synthesizing English Text-To-Speech

The English TTS uses the [LJ-Speech](https://keithito.com/LJ-Speech-Dataset/) dataset.

1. Download the dataset: `python dl_and_preprop_dataset.py --dataset=ljspeech`

2. Train the Text2Mel model: `python train-text2mel.py --dataset=ljspeech`

3. Train the SSRN model: `python train-ssrn.py --dataset=ljspeech`

4. Synthesize sentences: `python synthesize.py --dataset=ljspeech`

   * The WAV files are saved in the `samples` folder.

## Training/Synthesizing Mongolian Text-To-Speech

The Mongolian text-to-speech uses 5 hours audio from the [Mongolian Bible](https://www.bible.com/mn/versions/1590-2013-ariun-bibli-2013).

1. Download the dataset: `python dl_and_preprop_dataset.py --dataset=mbspeech`

2. Train the Text2Mel model: `python train-text2mel.py --dataset=mbspeech`

3. Train the SSRN model: `python train-ssrn.py --dataset=mbspeech`

4. Synthesize sentences: `python synthesize.py --dataset=mbspeech`

   * The WAV files are saved in the `samples` folder.