https://github.com/ccoreilly/tts

🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production

https://github.com/ccoreilly/tts

Repository

🐸💬 - a deep learning toolkit for Text-to-Speech, battle-tested in research and production

https://github.com/ccoreilly/TTS/blob/main/

# 

TTS is a library for advanced Text-to-Speech generation. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed and quality.
TTS comes with [pretrained models](https://github.com/coqui-ai/TTS/wiki/Released-Models), tools for measuring dataset quality and already used in **20+ languages** for products and research projects.

[![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/main.yml/badge.svg)](https://github.com/coqui-ai/TTS/actions)
[![PyPI version](https://badge.fury.io/py/TTS.svg)](https://badge.fury.io/py/TTS)
[![Covenant](https://camo.githubusercontent.com/7d620efaa3eac1c5b060ece5d6aacfcc8b81a74a04d05cd0398689c01c4463bb/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f436f6e7472696275746f72253230436f76656e616e742d76322e3025323061646f707465642d6666363962342e737667)](https://github.com/coqui-ai/TTS/blob/master/CODE_OF_CONDUCT.md)
[![Downloads](https://pepy.tech/badge/tts)](https://pepy.tech/project/tts)
[![DOI](https://zenodo.org/badge/265612440.svg)](https://zenodo.org/badge/latestdoi/265612440)

[![Docs]()](https://tts.readthedocs.io/en/latest/)
[![Gitter](https://badges.gitter.im/coqui-ai/TTS.svg)](https://gitter.im/coqui-ai/TTS?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
[![License]()](https://opensource.org/licenses/MPL-2.0)

 [**Subscribe to Coqui.ai Newsletter**](https://coqui.ai/?subscription=true)

 [English Voice Samples](https://erogol.github.io/ddc-samples/) and [SoundCloud playlist](https://soundcloud.com/user-565970875/pocket-article-wavernn-and-tacotron2)

 [Text-to-Speech paper collection](https://github.com/erogol/TTS-papers)



##  Where to ask questions
Please use our dedicated channels for questions and discussion. Help is much more valuable if it's shared publicly so that more people can benefit from it.

| Type                            | Platforms                               |
| ------------------------------- | --------------------------------------- |
|  **Bug Reports**              | [GitHub Issue Tracker]                  |
|  **Feature Requests & Ideas** | [GitHub Issue Tracker]                  |
|  **Usage Questions**          | [Github Discussions]                    |
|  **General Discussion**       | [Github Discussions] or [Gitter Room]   |

[github issue tracker]: https://github.com/coqui-ai/tts/issues
[github discussions]: https://github.com/coqui-ai/TTS/discussions
[gitter room]: https://gitter.im/coqui-ai/TTS?utm_source=share-link&utm_medium=link&utm_campaign=share-link
[Tutorials and Examples]: https://github.com/coqui-ai/TTS/wiki/TTS-Notebooks-and-Tutorials


##  Links and Resources
| Type                            | Links                               |
| ------------------------------- | --------------------------------------- |
|  **Documentation**              | [ReadTheDocs](https://tts.readthedocs.io/en/latest/)
|  **Installation**               | [TTS/README.md](https://github.com/coqui-ai/TTS/tree/dev#install-tts)|
|  **Contributing**               | [CONTRIBUTING.md](https://github.com/coqui-ai/TTS/blob/main/CONTRIBUTING.md)|
|  **Road Map**                   | [Main Development Plans](https://github.com/coqui-ai/TTS/issues/378)
|  **Released Models**            | [TTS Releases](https://github.com/coqui-ai/TTS/releases) and [Experimental Models](https://github.com/coqui-ai/TTS/wiki/Experimental-Released-Models)|

##  TTS Performance


Underlined "TTS*" and "Judy*" are TTS models


## Features
- High-performance Deep Learning models for Text2Speech tasks.
    - Text2Spec models (Tacotron, Tacotron2, Glow-TTS, SpeedySpeech).
    - Speaker Encoder to compute speaker embeddings efficiently.
    - Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS, ParallelWaveGAN, WaveGrad, WaveRNN)
- Fast and efficient model training.
- Detailed training logs on the terminal and Tensorboard.
- Support for Multi-speaker TTS.
- Efficient, flexible, lightweight but feature complete `Trainer API`.
- Ability to convert PyTorch models to Tensorflow 2.0 and TFLite for inference.
- Released and read-to-use models.
- Tools to curate Text2Speech datasets under```dataset_analysis```.
- Utilities to use and test your models.
- Modular (but not too much) code base enabling easy implementation of new ideas.

## Implemented Models
### Text-to-Spectrogram
- Tacotron: [paper](https://arxiv.org/abs/1703.10135)
- Tacotron2: [paper](https://arxiv.org/abs/1712.05884)
- Glow-TTS: [paper](https://arxiv.org/abs/2005.11129)
- Speedy-Speech: [paper](https://arxiv.org/abs/2008.03802)
- Align-TTS: [paper](https://arxiv.org/abs/2003.01950)
- FastPitch: [paper](https://arxiv.org/pdf/2006.06873.pdf)
- FastSpeech: [paper](https://arxiv.org/abs/1905.09263)

### End-to-End Models
- VITS: [paper](https://arxiv.org/pdf/2106.06103)

### Attention Methods
- Guided Attention: [paper](https://arxiv.org/abs/1710.08969)
- Forward Backward Decoding: [paper](https://arxiv.org/abs/1907.09006)
- Graves Attention: [paper](https://arxiv.org/abs/1910.10288)
- Double Decoder Consistency: [blog](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/)
- Dynamic Convolutional Attention: [paper](https://arxiv.org/pdf/1910.10288.pdf)
- Alignment Network: [paper](https://arxiv.org/abs/2108.10447)

### Speaker Encoder
- GE2E: [paper](https://arxiv.org/abs/1710.10467)
- Angular Loss: [paper](https://arxiv.org/pdf/2003.11982.pdf)

### Vocoders
- MelGAN: [paper](https://arxiv.org/abs/1910.06711)
- MultiBandMelGAN: [paper](https://arxiv.org/abs/2005.05106)
- ParallelWaveGAN: [paper](https://arxiv.org/abs/1910.11480)
- GAN-TTS discriminators: [paper](https://arxiv.org/abs/1909.11646)
- WaveRNN: [origin](https://github.com/fatchord/WaveRNN/)
- WaveGrad: [paper](https://arxiv.org/abs/2009.00713)
- HiFiGAN: [paper](https://arxiv.org/abs/2010.05646)
- UnivNet: [paper](https://arxiv.org/abs/2106.07889)

You can also help us implement more models.

## Install TTS
TTS is tested on Ubuntu 18.04 with **python >= 3.6, < 3.9**.

If you are only interested in [synthesizing speech](https://tts.readthedocs.io/en/latest/inference.html) with the released TTS models, installing from PyPI is the easiest option.

```bash
pip install TTS
```

By default, this only installs the requirements for PyTorch. To install the tensorflow dependencies as well, use the `tf` extra.

```bash
pip install TTS[tf]
```

If you plan to code or train models, clone TTS and install it locally.

```bash
git clone https://github.com/coqui-ai/TTS
pip install -e .[all,dev,notebooks,tf]  # Select the relevant extras
```

If you are on Ubuntu (Debian), you can also run following commands for installation.

```bash
$ make system-deps  # intended to be used on Ubuntu (Debian). Let us know if you have a diffent OS.
$ make install
```

If you are on Windows, @GuyPaddock wrote installation instructions [here](https://stackoverflow.com/questions/66726331/how-can-i-run-mozilla-tts-coqui-tts-training-with-cuda-on-a-windows-system).

## Directory Structure
```
|- notebooks/       (Jupyter Notebooks for model evaluation, parameter selection and data analysis.)
|- utils/           (common utilities.)
|- TTS
    |- bin/             (folder for all the executables.)
      |- train*.py                  (train your target model.)
      |- distribute.py              (train your TTS model using Multiple GPUs.)
      |- compute_statistics.py      (compute dataset statistics for normalization.)
      |- convert*.py                (convert target torch model to TF.)
      |- ...
    |- tts/             (text to speech models)
        |- layers/          (model layer definitions)
        |- models/          (model definitions)
        |- tf/              (Tensorflow 2 utilities and model implementations)
        |- utils/           (model specific utilities.)
    |- speaker_encoder/ (Speaker Encoder models.)
        |- (same)
    |- vocoder/         (Vocoder models.)
        |- (same)
```

Owner

GitHub Events