Medix-AI/coqui-tts
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coqui-tts
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# <img src="images/coqui-log-green-TTS.png" height="56"/> # <img src="images/coqui-log-green-TTS.png" height="56"/>
:frog: 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 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.
:frog: 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. 🐸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.
<!-- [![CircleCI](TODO)]() --> <!-- [![CircleCI](TODO)]() -->
[![License](<https://img.shields.io/badge/License-MPL%202.0-brightgreen.svg>)](https://opensource.org/licenses/MPL-2.0) [![License](<https://img.shields.io/badge/License-MPL%202.0-brightgreen.svg>)](https://opensource.org/licenses/MPL-2.0)
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## 🥇 TTS Performance ## 🥇 TTS Performance
<p align="center"><img src="images/TTS-performance.png" width="800" /></p> <p align="center"><img src="images/TTS-performance.png" width="800" /></p>
Underlined "TTS*" and "Judy*" are :frog:TTS models Underlined "TTS*" and "Judy*" are 🐸TTS models
<!-- [Details...](https://github.com/coqui-ai/TTS/wiki/Mean-Opinion-Score-Results) --> <!-- [Details...](https://github.com/coqui-ai/TTS/wiki/Mean-Opinion-Score-Results) -->
## Features ## Features
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- WaveRNN: [origin](https://github.com/fatchord/WaveRNN/) - WaveRNN: [origin](https://github.com/fatchord/WaveRNN/)
- WaveGrad: [paper](https://arxiv.org/abs/2009.00713) - WaveGrad: [paper](https://arxiv.org/abs/2009.00713)
You can also help us implement more models. Some :frog: TTS related work can be found [here](https://github.com/erogol/TTS-papers). You can also help us implement more models. Some 🐸TTS related work can be found [here](https://github.com/erogol/TTS-papers).
## Install TTS ## Install TTS
:frog: TTS is tested on Ubuntu 18.04 with **python >= 3.6, < 3.9**. 🐸TTS is tested on Ubuntu 18.04 with **python >= 3.6, < 3.9**.
If you are only interested in [synthesizing speech](https://github.com/coqui-ai/TTS/tree/dev#example-synthesizing-speech-on-terminal-using-the-released-models) with the released :frog: TTS models, installing from PyPI is the easiest option. If you are only interested in [synthesizing speech](https://github.com/coqui-ai/TTS/tree/dev#example-synthesizing-speech-on-terminal-using-the-released-models) with the released 🐸TTS models, installing from PyPI is the easiest option.
```bash ```bash
pip install TTS pip install TTS
``` ```
If you plan to code or train models, clone :frog: TTS and install it locally. If you plan to code or train models, clone 🐸TTS and install it locally.
```bash ```bash
git clone https://github.com/coqui-ai/TTS git clone https://github.com/coqui-ai/TTS
@ -142,11 +142,11 @@ Audio examples: [soundcloud](https://soundcloud.com/user-565970875/pocket-articl
<img src="images/example_model_output.png?raw=true" alt="example_output" width="400"/> <img src="images/example_model_output.png?raw=true" alt="example_output" width="400"/>
## Datasets and Data-Loading ## Datasets and Data-Loading
:frog: TTS provides a generic dataloader easy to use for your custom dataset. 🐸TTS provides a generic dataloader easy to use for your custom dataset.
You just need to write a simple function to format the dataset. Check ```datasets/preprocess.py``` to see some examples. You just need to write a simple function to format the dataset. Check ```datasets/preprocess.py``` to see some examples.
After that, you need to set ```dataset``` fields in ```config.json```. After that, you need to set ```dataset``` fields in ```config.json```.
Some of the public datasets that we successfully applied :frog: TTS: Some of the public datasets that we successfully applied 🐸TTS:
- [LJ Speech](https://keithito.com/LJ-Speech-Dataset/) - [LJ Speech](https://keithito.com/LJ-Speech-Dataset/)
- [Nancy](http://www.cstr.ed.ac.uk/projects/blizzard/2011/lessac_blizzard2011/) - [Nancy](http://www.cstr.ed.ac.uk/projects/blizzard/2011/lessac_blizzard2011/)
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## Example: Synthesizing Speech on Terminal Using the Released Models. ## Example: Synthesizing Speech on Terminal Using the Released Models.
After the installation, :frog: TTS provides a CLI interface for synthesizing speech using pre-trained models. You can either use your own model or the release models under :frog: TTS. After the installation, 🐸TTS provides a CLI interface for synthesizing speech using pre-trained models. You can either use your own model or the release models under 🐸TTS.
Listing released :frog: TTS models. Listing released 🐸TTS models.
```bash ```bash
tts --list_models tts --list_models
``` ```
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You can also enjoy Tensorboard, if you point Tensorboard argument```--logdir``` to the experiment folder. You can also enjoy Tensorboard, if you point Tensorboard argument```--logdir``` to the experiment folder.
## Contribution guidelines ## Contribution guidelines
Please follow the steps below as you send a PR for :frog:. It helps us to keep things organized. Please follow the steps below as you send a PR to 🐸. It helps us to keep things organized.
1. Create a new branch. 1. Create a new branch.
2. Implement your changes. 2. Implement your changes.