# 
🐸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, tools for measuring dataset quality and already used in **20+ languages** for products and research projects.
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[](https://badge.fury.io/py/TTS)
[](https://github.com/coqui-ai/TTS/blob/master/CODE_OF_CONDUCT.md)
[](https://pepy.tech/project/tts)
[](https://zenodo.org/badge/latestdoi/265612440)
[![Docs]()](https://tts.readthedocs.io/en/latest/)
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📰 [**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).
## Use TTS
### Single Speaker Models
- List provided models:
```
$ tts --list_models
```
- Run TTS with default models:
```
$ tts --text "Text for TTS"
```
- Run a TTS model with its default vocoder model:
```
$ tts --text "Text for TTS" --model_name "//
```
- Run with specific TTS and vocoder models from the list:
```
$ tts --text "Text for TTS" --model_name "//" --vocoder_name "//" --output_path
```
- Run your own TTS model (Using Griffin-Lim Vocoder):
```
$ tts --text "Text for TTS" --model_path path/to/model.pth.tar --config_path path/to/config.json --out_path output/path/speech.wav
```
- Run your own TTS and Vocoder models:
```
$ tts --text "Text for TTS" --model_path path/to/config.json --config_path path/to/model.pth.tar --out_path output/path/speech.wav
--vocoder_path path/to/vocoder.pth.tar --vocoder_config_path path/to/vocoder_config.json
```
### Multi-speaker Models
- List the available speakers and choose as among them:
```
$ tts --model_name "//" --list_speaker_idxs
```
- Run the multi-speaker TTS model with the target speaker ID:
```
$ tts --text "Text for TTS." --out_path output/path/speech.wav --model_name "//" --speaker_idx
```
- Run your own multi-speaker TTS model:
```
$ tts --text "Text for TTS" --out_path output/path/speech.wav --model_path path/to/config.json --config_path path/to/model.pth.tar --speakers_file_path path/to/speaker.json --speaker_idx
```
## 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)
```