mirror of https://github.com/coqui-ai/TTS.git
43 lines
1.7 KiB
Markdown
43 lines
1.7 KiB
Markdown
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# Tacotron-pytorch
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A pytorch implementation of [Tacotron: A Fully End-to-End Text-To-Speech Synthesis Model](https://arxiv.org/abs/1703.10135).
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<img src="png/model.png">
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## Requirements
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* Install python 3
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* Install pytorch == 0.2.0
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* Install requirements:
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```
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pip install -r requirements.txt
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```
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## Data
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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.
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## File description
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* `hyperparams.py` includes all hyper parameters that are needed.
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* `data.py` loads training data and preprocess text to index and wav files to spectrogram. Preprocessing codes for text is in text/ directory.
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* `module.py` contains all methods, including CBHG, highway, prenet, and so on.
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* `network.py` contains networks including encoder, decoder and post-processing network.
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* `train.py` is for training.
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* `synthesis.py` is for generating TTS sample.
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## Training the network
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* STEP 1. Download and extract LJSpeech data at any directory you want.
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* STEP 2. Adjust hyperparameters in `hyperparams.py`, especially 'data_path' which is a directory that you extract files, and the others if necessary.
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* STEP 3. Run `train.py`.
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## Generate TTS wav file
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* STEP 1. Run `synthesis.py`. Make sure the restore step.
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## Samples
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* You can check the generated samples in 'samples/' directory. Training step was only 60K, so the performance is not good yet.
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## Reference
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* Keith ito: https://github.com/keithito/tacotron
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## Comments
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* Any comments for the codes are always welcome.
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