https://github.com/r9y9/tacotron_pytorch

PyTorch implementation of Tacotron speech synthesis model.
https://github.com/r9y9/tacotron_pytorch

python pytorch speech speech-synthesis tacotron

Last synced: 2 days ago
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PyTorch implementation of Tacotron speech synthesis model.

• Host: GitHub
• URL: https://github.com/r9y9/tacotron_pytorch
• Owner: r9y9
• License: other
• Created: 2017-09-15T11:42:38.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2019-07-12T04:37:27.000Z (over 5 years ago)
• Last Synced: 2025-01-08T11:08:04.074Z (9 days ago)
• Topics: python, pytorch, speech, speech-synthesis, tacotron
• Language: Jupyter Notebook
• Homepage: http://nbviewer.jupyter.org/github/r9y9/tacotron_pytorch/blob/master/notebooks/Test%20Tacotron.ipynb
• Size: 20.7 MB
• Stars: 309
• Watchers: 16
• Forks: 79
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# tacotron_pytorch

[![Build Status](https://travis-ci.org/r9y9/tacotron_pytorch.svg?branch=master)](https://travis-ci.org/r9y9/tacotron_pytorch)

PyTorch implementation of [Tacotron](https://arxiv.org/abs/1703.10135) speech synthesis model.

Inspired from [keithito/tacotron](https://github.com/keithito/tacotron). Currently not as much good speech quality as [keithito/tacotron](https://github.com/keithito/tacotron) can generate, but it seems to be basically working. You can find some generated speech examples trained on [LJ Speech Dataset](https://keithito.com/LJ-Speech-Dataset/) at [here](http://nbviewer.jupyter.org/github/r9y9/tacotron_pytorch/blob/master/notebooks/Test%20Tacotron.ipynb).

If you are comfortable working with TensorFlow, I'd recommend you to try

https://github.com/keithito/tacotron instead. The reason to rewrite it in PyTorch is that it's easier to debug and extend (multi-speaker architecture, etc) at least to me.

## Requirements

- PyTorch

- TensorFlow (if you want to run the training script. This definitely can be optional, but for now required.)

## Installation

```

git clone --recursive https://github.com/r9y9/tacotron_pytorch

pip install -e . # or python setup.py develop

```

If you want to run the training script, then you need to install additional dependencies.

```

pip install -e ".[train]"

```

## Training

The package relis on [keithito/tacotron](https://github.com/keithito/tacotron) for text processing, audio preprocessing and audio reconstruction (added as a submodule). Please follows the quick start section at https://github.com/keithito/tacotron and prepare your dataset accordingly.

If you have your data prepared, assuming your data is in `"~/tacotron/training"` (which is the default), then you can train your model by:

```

python train.py

```

Alignment, predicted spectrogram, target spectrogram, predicted waveform and checkpoint (model and optimizer states) are saved per 1000 global step in `checkpoints` directory. Training progress can be monitored by:

```

tensorboard --logdir=log

```

## Testing model

Open the notebook in `notebooks` directory and change `checkpoint_path` to your model.