<img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/coqui-log-green-TTS.png" height="56"/>

----

### 📣 Clone your voice with a single click on [🐸Coqui.ai](https://app.coqui.ai/auth/signin)

----

🐸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.

[![Dicord](https://img.shields.io/discord/1037326658807533628?color=%239B59B6&label=chat%20on%20discord)](https://discord.gg/5eXr5seRrv)
[![License](<https://img.shields.io/badge/License-MPL%202.0-brightgreen.svg>)](https://opensource.org/licenses/MPL-2.0)
[![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)

![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/aux_tests.yml/badge.svg)
![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/data_tests.yml/badge.svg)
![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/docker.yaml/badge.svg)
![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/inference_tests.yml/badge.svg)
![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/style_check.yml/badge.svg)
![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/text_tests.yml/badge.svg)
![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/tts_tests.yml/badge.svg)
![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/vocoder_tests.yml/badge.svg)
![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/zoo_tests0.yml/badge.svg)
![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/zoo_tests1.yml/badge.svg)
![GithubActions](https://github.com/coqui-ai/TTS/actions/workflows/zoo_tests2.yml/badge.svg)
[![Docs](<https://readthedocs.org/projects/tts/badge/?version=latest&style=plastic>)](https://tts.readthedocs.io/en/latest/)

📰 [**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)

<img src="https://static.scarf.sh/a.png?x-pxid=cf317fe7-2188-4721-bc01-124bb5d5dbb2" />

## 💬 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 [Discord]   |

[github issue tracker]: https://github.com/coqui-ai/tts/issues
[github discussions]: https://github.com/coqui-ai/TTS/discussions
[discord]: https://discord.gg/5eXr5seRrv
[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
<p align="center"><img src="https://raw.githubusercontent.com/coqui-ai/TTS/main/images/TTS-performance.png" width="800" /></p>

Underlined "TTS*" and "Judy*" are 🐸TTS models
<!-- [Details...](https://github.com/coqui-ai/TTS/wiki/Mean-Opinion-Score-Results) -->

## 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`.
- Released and ready-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
### Spectrogram models
- 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)
- FastSpeech2: [paper](https://arxiv.org/abs/2006.04558)
- SC-GlowTTS: [paper](https://arxiv.org/abs/2104.05557)
- Capacitron: [paper](https://arxiv.org/abs/1906.03402)
- OverFlow: [paper](https://arxiv.org/abs/2211.06892)
- Neural HMM TTS: [paper](https://arxiv.org/abs/2108.13320)

### End-to-End Models
- VITS: [paper](https://arxiv.org/pdf/2106.06103)
- YourTTS: [paper](https://arxiv.org/abs/2112.02418)

### 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 >= 3.7, < 3.11.**.

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
```

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]  # 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 different 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).


## Docker Image
You can also try TTS without install with the docker image.
Simply run the following command and you will be able to run TTS without installing it.

```bash
docker run --rm -it -p 5002:5002 --entrypoint /bin/bash ghcr.io/coqui-ai/tts-cpu
python3 TTS/server/server.py --list_models #To get the list of available models
python3 TTS/server/server.py --model_name tts_models/en/vctk/vits # To start a server
```

You can then enjoy the TTS server [here](http://[::1]:5002/)
More details about the docker images (like GPU support) can be found [here](https://tts.readthedocs.io/en/latest/docker_images.html)


## Synthesizing speech by 🐸TTS

### 🐍 Python API

```python
from TTS.api import TTS

# Running a multi-speaker and multi-lingual model

# List available 🐸TTS models and choose the first one
model_name = TTS.list_models()[0]
# Init TTS
tts = TTS(model_name)
# Run TTS
# ❗ Since this model is multi-speaker and multi-lingual, we must set the target speaker and the language
# Text to speech with a numpy output
wav = tts.tts("This is a test! This is also a test!!", speaker=tts.speakers[0], language=tts.languages[0])
# Text to speech to a file
tts.tts_to_file(text="Hello world!", speaker=tts.speakers[0], language=tts.languages[0], file_path="output.wav")

# Running a single speaker model

# Init TTS with the target model name
tts = TTS(model_name="tts_models/de/thorsten/tacotron2-DDC", progress_bar=False, gpu=False)
# Run TTS
tts.tts_to_file(text="Ich bin eine Testnachricht.", file_path=OUTPUT_PATH)

# Example voice cloning with YourTTS in English, French and Portuguese:
tts = TTS(model_name="tts_models/multilingual/multi-dataset/your_tts", progress_bar=False, gpu=True)
tts.tts_to_file("This is voice cloning.", speaker_wav="my/cloning/audio.wav", language="en", file_path="output.wav")
tts.tts_to_file("C'est le clonage de la voix.", speaker_wav="my/cloning/audio.wav", language="fr-fr", file_path="output.wav")
tts.tts_to_file("Isso é clonagem de voz.", speaker_wav="my/cloning/audio.wav", language="pt-br", file_path="output.wav")


# Example voice conversion converting speaker of the `source_wav` to the speaker of the `target_wav`

tts = TTS(model_name="voice_conversion_models/multilingual/vctk/freevc24", progress_bar=False, gpu=True)
tts.voice_conversion_to_file(source_wav="my/source.wav", target_wav="my/target.wav", file_path="output.wav")

# Example voice cloning by a single speaker TTS model combining with the voice conversion model. This way, you can
# clone voices by using any model in 🐸TTS.

tts = TTS("tts_models/de/thorsten/tacotron2-DDC")
tts.tts_with_vc_to_file(
    "Wie sage ich auf Italienisch, dass ich dich liebe?",
    speaker_wav="target/speaker.wav",
    file_path="ouptut.wav"
)

# Example text to speech using [🐸Coqui Studio](https://coqui.ai) models. You can use all of your available speakers in the studio.
# [🐸Coqui Studio](https://coqui.ai) API token is required. You can get it from the [account page](https://coqui.ai/account).
# You should set the `COQUI_STUDIO_TOKEN` environment variable to use the API token.

# If you have a valid API token set you will see the studio speakers as separate models in the list.
# The name format is coqui_studio/en/<studio_speaker_name>/coqui_studio
models = TTS().list_models()
# Init TTS with the target studio speaker
tts = TTS(model_name="coqui_studio/en/Torcull Diarmuid/coqui_studio", progress_bar=False, gpu=False)
# Run TTS
tts.tts_to_file(text="This is a test.", file_path=OUTPUT_PATH)
# Run TTS with emotion and speed control
tts.tts_to_file(text="This is a test.", file_path=OUTPUT_PATH, emotion="Happy", speed=1.5)
```

### Command line `tts`
#### Single Speaker Models

- List provided models:

    ```
    $ tts --list_models
    ```
- Get model info (for both tts_models and vocoder_models):
    - Query by type/name:
        The model_info_by_name uses the name as it from the --list_models.
        ```
        $ tts --model_info_by_name "<model_type>/<language>/<dataset>/<model_name>"
        ```
        For example:

        ```
        $ tts --model_info_by_name tts_models/tr/common-voice/glow-tts
        ```
        ```
        $ tts --model_info_by_name vocoder_models/en/ljspeech/hifigan_v2
        ```
    - Query by type/idx:
        The model_query_idx uses the corresponding idx from --list_models.
        ```
        $ tts --model_info_by_idx "<model_type>/<model_query_idx>"
        ```
        For example:

        ```
        $ tts --model_info_by_idx tts_models/3
        ```

- Run TTS with default models:

    ```
    $ tts --text "Text for TTS" --out_path output/path/speech.wav
    ```

- Run a TTS model with its default vocoder model:

    ```
    $ tts --text "Text for TTS" --model_name "<model_type>/<language>/<dataset>/<model_name>" --out_path output/path/speech.wav
    ```
  For example:

    ```
    $ tts --text "Text for TTS" --model_name "tts_models/en/ljspeech/glow-tts" --out_path output/path/speech.wav
    ```

- Run with specific TTS and vocoder models from the list:

    ```
    $ tts --text "Text for TTS" --model_name "<model_type>/<language>/<dataset>/<model_name>" --vocoder_name "<model_type>/<language>/<dataset>/<model_name>" --out_path output/path/speech.wav
    ```

  For example:

    ```
    $ tts --text "Text for TTS" --model_name "tts_models/en/ljspeech/glow-tts" --vocoder_name "vocoder_models/en/ljspeech/univnet" --out_path output/path/speech.wav
    ```


- Run your own TTS model (Using Griffin-Lim Vocoder):

    ```
    $ tts --text "Text for TTS" --model_path path/to/model.pth --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/model.pth --config_path path/to/config.json --out_path output/path/speech.wav
        --vocoder_path path/to/vocoder.pth --vocoder_config_path path/to/vocoder_config.json
    ```

#### Multi-speaker Models

- List the available speakers and choose as <speaker_id> among them:

    ```
    $ tts --model_name "<language>/<dataset>/<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 "<language>/<dataset>/<model_name>"  --speaker_idx <speaker_id>
    ```

- Run your own multi-speaker TTS model:

    ```
    $ tts --text "Text for TTS" --out_path output/path/speech.wav --model_path path/to/model.pth --config_path path/to/config.json --speakers_file_path path/to/speaker.json --speaker_idx <speaker_id>
    ```

## 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.)
      |- ...
    |- tts/             (text to speech models)
        |- layers/          (model layer definitions)
        |- models/          (model definitions)
        |- utils/           (model specific utilities.)
    |- speaker_encoder/ (Speaker Encoder models.)
        |- (same)
    |- vocoder/         (Vocoder models.)
        |- (same)
```
{"language": ["en", "fr"], "license": "openrail", "datasets": ["arxiv_dataset"], "metrics": ["accuracy"], "pipeline_tag": "text-to-speech"}
text-to-speech en fr dataset:arxiv_dataset arxiv:1703.10135 arxiv:1712.05884 arxiv:2005.11129 arxiv:2008.03802 arxiv:2003.01950 arxiv:2006.06873 arxiv:1905.09263 arxiv:2006.04558 arxiv:2104.05557 arxiv:1906.03402 arxiv:2211.06892 arxiv:2108.13320 arxiv:2106.06103 arxiv:2112.02418 arxiv:1710.08969 arxiv:1907.09006 arxiv:1910.10288 arxiv:2108.10447 arxiv:1710.10467 arxiv:2003.11982 arxiv:1910.06711 arxiv:2005.05106 arxiv:1910.11480 arxiv:1909.11646 arxiv:2009.00713 arxiv:2010.05646 arxiv:2106.07889 doi:10.57967/hf/0523 license:openrail region:us
TAGS
#text-to-speech #en #fr #dataset-arxiv_dataset #arxiv-1703.10135 #arxiv-1712.05884 #arxiv-2005.11129 #arxiv-2008.03802 #arxiv-2003.01950 #arxiv-2006.06873 #arxiv-1905.09263 #arxiv-2006.04558 #arxiv-2104.05557 #arxiv-1906.03402 #arxiv-2211.06892 #arxiv-2108.13320 #arxiv-2106.06103 #arxiv-2112.02418 #arxiv-1710.08969 #arxiv-1907.09006 #arxiv-1910.10288 #arxiv-2108.10447 #arxiv-1710.10467 #arxiv-2003.11982 #arxiv-1910.06711 #arxiv-2005.05106 #arxiv-1910.11480 #arxiv-1909.11646 #arxiv-2009.00713 #arxiv-2010.05646 #arxiv-2106.07889 #doi-10.57967/hf/0523 #license-openrail #region-us 

<img src="URL height="56"/>




---


### Clone your voice with a single click on URL




---


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.


![Dicord](URL
![License](URL
![PyPI version](URL
![Covenant](URL
![Downloads](URL
![DOI](URL


!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
![Docs](URL


Subscribe to URL Newsletter


English Voice Samples and SoundCloud playlist


Text-to-Speech paper collection


<img src="URL />


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.



Links and Resources
-------------------



TTS Performance
---------------


![](URL width=)


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'.
* Released and ready-to-use models.
* Tools to curate Text2Speech datasets under.
* Utilities to use and test your models.
* Modular (but not too much) code base enabling easy implementation of new ideas.


Implemented Models
------------------


### Spectrogram models


* Tacotron: paper
* Tacotron2: paper
* Glow-TTS: paper
* Speedy-Speech: paper
* Align-TTS: paper
* FastPitch: paper
* FastSpeech: paper
* FastSpeech2: paper
* SC-GlowTTS: paper
* Capacitron: paper
* OverFlow: paper
* Neural HMM TTS: paper


### End-to-End Models


* VITS: paper
* YourTTS: paper


### Attention Methods


* Guided Attention: paper
* Forward Backward Decoding: paper
* Graves Attention: paper
* Double Decoder Consistency: blog
* Dynamic Convolutional Attention: paper
* Alignment Network: paper


### Speaker Encoder


* GE2E: paper
* Angular Loss: paper


### Vocoders


* MelGAN: paper
* MultiBandMelGAN: paper
* ParallelWaveGAN: paper
* GAN-TTS discriminators: paper
* WaveRNN: origin
* WaveGrad: paper
* HiFiGAN: paper
* UnivNet: paper


You can also help us implement more models.


Install TTS
-----------


TTS is tested on Ubuntu 18.04 with python >= 3.7, < 3.11..


If you are only interested in synthesizing speech with the released TTS models, installing from PyPI is the easiest option.


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


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


If you are on Windows, @GuyPaddock wrote installation instructions here.


Docker Image
------------


You can also try TTS without install with the docker image.
Simply run the following command and you will be able to run TTS without installing it.


You can then enjoy the TTS server here
More details about the docker images (like GPU support) can be found here


Synthesizing speech by TTS
--------------------------


### Python API


### Command line 'tts'


#### Single Speaker Models


* List provided models:
* Get model info (for both tts\_models and vocoder\_models):


	+ Query by type/name:
	The model\_info\_by\_name uses the name as it from the --list\_models.
	
	
	For example:
	+ Query by type/idx:
	The model\_query\_idx uses the corresponding idx from --list\_models.
	
	
	For example:
* Run TTS with default models:
* Run a TTS model with its default vocoder model:


For example:
* Run with specific TTS and vocoder models from the list:


For example:
* Run your own TTS model (Using Griffin-Lim Vocoder):
* Run your own TTS and Vocoder models:


#### Multi-speaker Models


* List the available speakers and choose as <speaker\_id> among them:
* Run the multi-speaker TTS model with the target speaker ID:
* Run your own multi-speaker TTS model:


Directory Structure
-------------------



### Clone your voice with a single click on URL




---


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.


![Dicord](URL
![License](URL
![PyPI version](URL
![Covenant](URL
![Downloads](URL
![DOI](URL


!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
![Docs](URL


Subscribe to URL Newsletter


English Voice Samples and SoundCloud playlist


Text-to-Speech paper collection


<img src="URL />


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.



Links and Resources
-------------------



TTS Performance
---------------


![](URL width=)


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'.
* Released and ready-to-use models.
* Tools to curate Text2Speech datasets under.
* Utilities to use and test your models.
* Modular (but not too much) code base enabling easy implementation of new ideas.


Implemented Models
------------------ ### Spectrogram models


* Tacotron: paper
* Tacotron2: paper
* Glow-TTS: paper
* Speedy-Speech: paper
* Align-TTS: paper
* FastPitch: paper
* FastSpeech: paper
* FastSpeech2: paper
* SC-GlowTTS: paper
* Capacitron: paper
* OverFlow: paper
* Neural HMM TTS: paper ### End-to-End Models


* VITS: paper
* YourTTS: paper ### Attention Methods


* Guided Attention: paper
* Forward Backward Decoding: paper
* Graves Attention: paper
* Double Decoder Consistency: blog
* Dynamic Convolutional Attention: paper
* Alignment Network: paper ### Speaker Encoder


* GE2E: paper
* Angular Loss: paper ### Vocoders


* MelGAN: paper
* MultiBandMelGAN: paper
* ParallelWaveGAN: paper
* GAN-TTS discriminators: paper
* WaveRNN: origin
* WaveGrad: paper
* HiFiGAN: paper
* UnivNet: paper


You can also help us implement more models.


Install TTS
-----------


TTS is tested on Ubuntu 18.04 with python >= 3.7, < 3.11..


If you are only interested in synthesizing speech with the released TTS models, installing from PyPI is the easiest option.


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


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


If you are on Windows, @GuyPaddock wrote installation instructions here.


Docker Image
------------


You can also try TTS without install with the docker image.
Simply run the following command and you will be able to run TTS without installing it.


You can then enjoy the TTS server here
More details about the docker images (like GPU support) can be found here


Synthesizing speech by TTS
-------------------------- ### Python API ### Command line 'tts' #### Single Speaker Models


* List provided models:
* Get model info (for both tts\_models and vocoder\_models):


	+ Query by type/name:
	The model\_info\_by\_name uses the name as it from the --list\_models.
	
	
	For example:
	+ Query by type/idx:
	The model\_query\_idx uses the corresponding idx from --list\_models.
	
	
	For example:
* Run TTS with default models:
* Run a TTS model with its default vocoder model:


For example:
* Run with specific TTS and vocoder models from the list:


For example:
* Run your own TTS model (Using Griffin-Lim Vocoder):
* Run your own TTS and Vocoder models: #### Multi-speaker Models


* List the available speakers and choose as <speaker\_id> among them:
* Run the multi-speaker TTS model with the target speaker ID:
* Run your own multi-speaker TTS model:


Directory Structure
-------------------
TAGS
#text-to-speech #en #fr #dataset-arxiv_dataset #arxiv-1703.10135 #arxiv-1712.05884 #arxiv-2005.11129 #arxiv-2008.03802 #arxiv-2003.01950 #arxiv-2006.06873 #arxiv-1905.09263 #arxiv-2006.04558 #arxiv-2104.05557 #arxiv-1906.03402 #arxiv-2211.06892 #arxiv-2108.13320 #arxiv-2106.06103 #arxiv-2112.02418 #arxiv-1710.08969 #arxiv-1907.09006 #arxiv-1910.10288 #arxiv-2108.10447 #arxiv-1710.10467 #arxiv-2003.11982 #arxiv-1910.06711 #arxiv-2005.05106 #arxiv-1910.11480 #arxiv-1909.11646 #arxiv-2009.00713 #arxiv-2010.05646 #arxiv-2106.07889 #doi-10.57967/hf/0523 #license-openrail #region-us 
 ### Clone your voice with a single click on URL




---


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.


![Dicord](URL
![License](URL
![PyPI version](URL
![Covenant](URL
![Downloads](URL
![DOI](URL


!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
![Docs](URL


Subscribe to URL Newsletter


English Voice Samples and SoundCloud playlist


Text-to-Speech paper collection


<img src="URL />


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.



Links and Resources
-------------------



TTS Performance
---------------


![](URL width=)


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'.
* Released and ready-to-use models.
* Tools to curate Text2Speech datasets under.
* Utilities to use and test your models.
* Modular (but not too much) code base enabling easy implementation of new ideas.


Implemented Models
------------------ ### Spectrogram models


* Tacotron: paper
* Tacotron2: paper
* Glow-TTS: paper
* Speedy-Speech: paper
* Align-TTS: paper
* FastPitch: paper
* FastSpeech: paper
* FastSpeech2: paper
* SC-GlowTTS: paper
* Capacitron: paper
* OverFlow: paper
* Neural HMM TTS: paper ### End-to-End Models


* VITS: paper
* YourTTS: paper ### Attention Methods


* Guided Attention: paper
* Forward Backward Decoding: paper
* Graves Attention: paper
* Double Decoder Consistency: blog
* Dynamic Convolutional Attention: paper
* Alignment Network: paper ### Speaker Encoder


* GE2E: paper
* Angular Loss: paper ### Vocoders


* MelGAN: paper
* MultiBandMelGAN: paper
* ParallelWaveGAN: paper
* GAN-TTS discriminators: paper
* WaveRNN: origin
* WaveGrad: paper
* HiFiGAN: paper
* UnivNet: paper


You can also help us implement more models.


Install TTS
-----------


TTS is tested on Ubuntu 18.04 with python >= 3.7, < 3.11..


If you are only interested in synthesizing speech with the released TTS models, installing from PyPI is the easiest option.


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


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


If you are on Windows, @GuyPaddock wrote installation instructions here.


Docker Image
------------


You can also try TTS without install with the docker image.
Simply run the following command and you will be able to run TTS without installing it.


You can then enjoy the TTS server here
More details about the docker images (like GPU support) can be found here


Synthesizing speech by TTS
-------------------------- ### Python API ### Command line 'tts' #### Single Speaker Models


* List provided models:
* Get model info (for both tts\_models and vocoder\_models):


	+ Query by type/name:
	The model\_info\_by\_name uses the name as it from the --list\_models.
	
	
	For example:
	+ Query by type/idx:
	The model\_query\_idx uses the corresponding idx from --list\_models.
	
	
	For example:
* Run TTS with default models:
* Run a TTS model with its default vocoder model:


For example:
* Run with specific TTS and vocoder models from the list:


For example:
* Run your own TTS model (Using Griffin-Lim Vocoder):
* Run your own TTS and Vocoder models: #### Multi-speaker Models


* List the available speakers and choose as <speaker\_id> among them:
* Run the multi-speaker TTS model with the target speaker ID:
* Run your own multi-speaker TTS model:


Directory Structure
-------------------
TAGS
#text-to-speech #en #fr #dataset-arxiv_dataset #arxiv-1703.10135 #arxiv-1712.05884 #arxiv-2005.11129 #arxiv-2008.03802 #arxiv-2003.01950 #arxiv-2006.06873 #arxiv-1905.09263 #arxiv-2006.04558 #arxiv-2104.05557 #arxiv-1906.03402 #arxiv-2211.06892 #arxiv-2108.13320 #arxiv-2106.06103 #arxiv-2112.02418 #arxiv-1710.08969 #arxiv-1907.09006 #arxiv-1910.10288 #arxiv-2108.10447 #arxiv-1710.10467 #arxiv-2003.11982 #arxiv-1910.06711 #arxiv-2005.05106 #arxiv-1910.11480 #arxiv-1909.11646 #arxiv-2009.00713 #arxiv-2010.05646 #arxiv-2106.07889 #doi-10.57967/hf/0523 #license-openrail #region-us 
### Clone your voice with a single click on URL




---


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.


![Dicord](URL
![License](URL
![PyPI version](URL
![Covenant](URL
![Downloads](URL
![DOI](URL


!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
!GithubActions
![Docs](URL


Subscribe to URL Newsletter


English Voice Samples and SoundCloud playlist


Text-to-Speech paper collection


<img src="URL />


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.



Links and Resources
-------------------



TTS Performance
---------------


![](URL width=)


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'.
* Released and ready-to-use models.
* Tools to curate Text2Speech datasets under.
* Utilities to use and test your models.
* Modular (but not too much) code base enabling easy implementation of new ideas.


Implemented Models
------------------### Spectrogram models


* Tacotron: paper
* Tacotron2: paper
* Glow-TTS: paper
* Speedy-Speech: paper
* Align-TTS: paper
* FastPitch: paper
* FastSpeech: paper
* FastSpeech2: paper
* SC-GlowTTS: paper
* Capacitron: paper
* OverFlow: paper
* Neural HMM TTS: paper### End-to-End Models


* VITS: paper
* YourTTS: paper### Attention Methods


* Guided Attention: paper
* Forward Backward Decoding: paper
* Graves Attention: paper
* Double Decoder Consistency: blog
* Dynamic Convolutional Attention: paper
* Alignment Network: paper### Speaker Encoder


* GE2E: paper
* Angular Loss: paper### Vocoders


* MelGAN: paper
* MultiBandMelGAN: paper
* ParallelWaveGAN: paper
* GAN-TTS discriminators: paper
* WaveRNN: origin
* WaveGrad: paper
* HiFiGAN: paper
* UnivNet: paper


You can also help us implement more models.


Install TTS
-----------


TTS is tested on Ubuntu 18.04 with python >= 3.7, < 3.11..


If you are only interested in synthesizing speech with the released TTS models, installing from PyPI is the easiest option.


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


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


If you are on Windows, @GuyPaddock wrote installation instructions here.


Docker Image
------------


You can also try TTS without install with the docker image.
Simply run the following command and you will be able to run TTS without installing it.


You can then enjoy the TTS server here
More details about the docker images (like GPU support) can be found here


Synthesizing speech by TTS
--------------------------### Python API### Command line 'tts'#### Single Speaker Models


* List provided models:
* Get model info (for both tts\_models and vocoder\_models):


	+ Query by type/name:
	The model\_info\_by\_name uses the name as it from the --list\_models.
	
	
	For example:
	+ Query by type/idx:
	The model\_query\_idx uses the corresponding idx from --list\_models.
	
	
	For example:
* Run TTS with default models:
* Run a TTS model with its default vocoder model:


For example:
* Run with specific TTS and vocoder models from the list:


For example:
* Run your own TTS model (Using Griffin-Lim Vocoder):
* Run your own TTS and Vocoder models:#### Multi-speaker Models


* List the available speakers and choose as <speaker\_id> among them:
* Run the multi-speaker TTS model with the target speaker ID:
* Run your own multi-speaker TTS model:


Directory Structure
-------------------