https://github.com/soobinseo/Tacotron-pytorch
Pytorch implementation of Tacotron
https://github.com/soobinseo/Tacotron-pytorch
pytorch tacotron text-to-speech tts
Last synced: 5 months ago
JSON representation
Pytorch implementation of Tacotron
- Host: GitHub
- URL: https://github.com/soobinseo/Tacotron-pytorch
- Owner: soobinseo
- License: apache-2.0
- Created: 2017-11-23T07:03:44.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-11-01T14:58:32.000Z (over 6 years ago)
- Last Synced: 2024-08-04T00:11:32.600Z (8 months ago)
- Topics: pytorch, tacotron, text-to-speech, tts
- Language: Python
- Size: 1.02 MB
- Stars: 205
- Watchers: 9
- Forks: 42
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-pytorch-list-CNVersion - Tacotron-pytorch
- Awesome-pytorch-list - Tacotron-pytorch - to-End Speech Synthesis. (Pytorch & related libraries / NLP & Speech Processing:)
README
# Tacotron-pytorch
A pytorch implementation of [Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model](https://arxiv.org/abs/1703.10135).
## Requirements
* Install python 3
* Install pytorch == 0.2.0
* Install requirements:
```
pip install -r requirements.txt
```
## Data
I used LJSpeech dataset which consists of pairs of text script and wav files. The complete dataset (13,100 pairs) can be downloaded [here](https://keithito.com/LJ-Speech-Dataset/). I referred https://github.com/keithito/tacotron for the preprocessing code.
## File description
* `hyperparams.py` includes all hyper parameters that are needed.
* `data.py` loads training data and preprocess text to index and wav files to spectrogram. Preprocessing codes for text is in text/ directory.
* `module.py` contains all methods, including CBHG, highway, prenet, and so on.
* `network.py` contains networks including encoder, decoder and post-processing network.
* `train.py` is for training.
* `synthesis.py` is for generating TTS sample.
## Training the network
* STEP 1. Download and extract LJSpeech data at any directory you want.
* STEP 2. Adjust hyperparameters in `hyperparams.py`, especially 'data_path' which is a directory that you extract files, and the others if necessary.
* STEP 3. Run `train.py`.
## Generate TTS wav file
* STEP 1. Run `synthesis.py`. Make sure the restore step.
## Samples
* You can check the generated samples in 'samples/' directory. Training step was only 60K, so the performance is not good yet.
## Reference
* Keith ito: https://github.com/keithito/tacotron
## Comments
* Any comments for the codes are always welcome.