# 🐸Coqui TTS
## News
- 📣 Fork of the [original, unmaintained repository](https://github.com/coqui-ai/TTS). New PyPI package: [coqui-tts](https://pypi.org/project/coqui-tts)
- 📣 [OpenVoice](https://github.com/myshell-ai/OpenVoice) models now available for voice conversion.
- 📣 Prebuilt wheels are now also published for Mac and Windows (in addition to Linux as before) for easier installation across platforms.
- 📣 XTTSv2 is here with 17 languages and better performance across the board. XTTS can stream with <200ms latency.
- 📣 XTTS fine-tuning code is out. Check the [example recipes](https://github.com/idiap/coqui-ai-TTS/tree/dev/recipes/ljspeech).
- 📣 You can use [Fairseq models in ~1100 languages](https://github.com/facebookresearch/fairseq/tree/main/examples/mms) with 🐸TTS.
## 
**🐸TTS is a library for advanced Text-to-Speech generation.**
🚀 Pretrained models in +1100 languages.
🛠️ Tools for training new models and fine-tuning existing models in any language.
📚 Utilities for dataset analysis and curation.
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[](https://discord.gg/5eXr5seRrv)
[![License]()](https://opensource.org/licenses/MPL-2.0)
[](https://badge.fury.io/py/coqui-tts)
[](https://pepy.tech/project/coqui-tts)
[](https://zenodo.org/badge/latestdoi/265612440)
[![Docs]()](https://coqui-tts.readthedocs.io/en/latest/)
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## 💬 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, Feature Requests & Ideas** | [GitHub Issue Tracker] |
| 👩💻 **Usage Questions** | [GitHub Discussions] |
| 🗯 **General Discussion** | [GitHub Discussions] or [Discord] |
[github issue tracker]: https://github.com/idiap/coqui-ai-TTS/issues
[github discussions]: https://github.com/idiap/coqui-ai-TTS/discussions
[discord]: https://discord.gg/5eXr5seRrv
[Tutorials and Examples]: https://github.com/coqui-ai/TTS/wiki/TTS-Notebooks-and-Tutorials
The [issues](https://github.com/coqui-ai/TTS/issues) and
[discussions](https://github.com/coqui-ai/TTS/discussions) in the original
repository are also still a useful source of information.
## 🔗 Links and Resources
| Type | Links |
| ------------------------------- | --------------------------------------- |
| 💼 **Documentation** | [ReadTheDocs](https://coqui-tts.readthedocs.io/en/latest/)
| 💾 **Installation** | [TTS/README.md](https://github.com/idiap/coqui-ai-TTS/tree/dev#installation)|
| 👩💻 **Contributing** | [CONTRIBUTING.md](https://github.com/idiap/coqui-ai-TTS/blob/main/CONTRIBUTING.md)|
| 🚀 **Released Models** | [Standard models](https://github.com/idiap/coqui-ai-TTS/blob/dev/TTS/.models.json) and [Fairseq models in ~1100 languages](https://github.com/idiap/coqui-ai-TTS#example-text-to-speech-using-fairseq-models-in-1100-languages-)|
## Features
- High-performance text-to-speech and voice conversion models, see list below.
- Fast and efficient model training with detailed training logs on the terminal and Tensorboard.
- Support for multi-speaker and multilingual TTS.
- Released and ready-to-use models.
- Tools to curate TTS datasets under ```dataset_analysis/```.
- Command line and Python APIs to use and test your models.
- Modular (but not too much) code base enabling easy implementation of new ideas.
## Model Implementations
### Spectrogram models
- [Tacotron](https://arxiv.org/abs/1703.10135), [Tacotron2](https://arxiv.org/abs/1712.05884)
- [Glow-TTS](https://arxiv.org/abs/2005.11129), [SC-GlowTTS](https://arxiv.org/abs/2104.05557)
- [Speedy-Speech](https://arxiv.org/abs/2008.03802)
- [Align-TTS](https://arxiv.org/abs/2003.01950)
- [FastPitch](https://arxiv.org/pdf/2006.06873.pdf)
- [FastSpeech](https://arxiv.org/abs/1905.09263), [FastSpeech2](https://arxiv.org/abs/2006.04558)
- [Capacitron](https://arxiv.org/abs/1906.03402)
- [OverFlow](https://arxiv.org/abs/2211.06892)
- [Neural HMM TTS](https://arxiv.org/abs/2108.13320)
- [Delightful TTS](https://arxiv.org/abs/2110.12612)
### End-to-End Models
- [XTTS](https://arxiv.org/abs/2406.04904)
- [VITS](https://arxiv.org/pdf/2106.06103)
- 🐸[YourTTS](https://arxiv.org/abs/2112.02418)
- 🐢[Tortoise](https://github.com/neonbjb/tortoise-tts)
- 🐶[Bark](https://github.com/suno-ai/bark)
### Vocoders
- [MelGAN](https://arxiv.org/abs/1910.06711)
- [MultiBandMelGAN](https://arxiv.org/abs/2005.05106)
- [ParallelWaveGAN](https://arxiv.org/abs/1910.11480)
- [GAN-TTS discriminators](https://arxiv.org/abs/1909.11646)
- [WaveRNN](https://github.com/fatchord/WaveRNN/)
- [WaveGrad](https://arxiv.org/abs/2009.00713)
- [HiFiGAN](https://arxiv.org/abs/2010.05646)
- [UnivNet](https://arxiv.org/abs/2106.07889)
### Voice Conversion
- [FreeVC](https://arxiv.org/abs/2210.15418)
- [OpenVoice](https://arxiv.org/abs/2312.01479)
### Others
- Attention methods: [Guided Attention](https://arxiv.org/abs/1710.08969),
[Forward Backward Decoding](https://arxiv.org/abs/1907.09006),
[Graves Attention](https://arxiv.org/abs/1910.10288),
[Double Decoder Consistency](https://erogol.com/solving-attention-problems-of-tts-models-with-double-decoder-consistency/),
[Dynamic Convolutional Attention](https://arxiv.org/pdf/1910.10288.pdf),
[Alignment Network](https://arxiv.org/abs/2108.10447)
- Speaker encoders: [GE2E](https://arxiv.org/abs/1710.10467),
[Angular Loss](https://arxiv.org/pdf/2003.11982.pdf)
You can also help us implement more models.
## Installation
🐸TTS is tested on Ubuntu 24.04 with **python >= 3.9, < 3.13.**, but should also
work on Mac and Windows.
If you are only interested in [synthesizing speech](https://coqui-tts.readthedocs.io/en/latest/inference.html) with the pretrained 🐸TTS models, installing from PyPI is the easiest option.
```bash
pip install coqui-tts
```
If you plan to code or train models, clone 🐸TTS and install it locally.
```bash
git clone https://github.com/idiap/coqui-ai-TTS
cd coqui-ai-TTS
pip install -e .
```
### Optional dependencies
The following extras allow the installation of optional dependencies:
| Name | Description |
|------|-------------|
| `all` | All optional dependencies |
| `notebooks` | Dependencies only used in notebooks |
| `server` | Dependencies to run the TTS server |
| `bn` | Bangla G2P |
| `ja` | Japanese G2P |
| `ko` | Korean G2P |
| `zh` | Chinese G2P |
| `languages` | All language-specific dependencies |
You can install extras with one of the following commands:
```bash
pip install coqui-tts[server,ja]
pip install -e .[server,ja]
```
### Platforms
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
```
## Docker Image
You can also try out Coqui TTS without installation with the docker image.
Simply run the following command and you will be able to run TTS:
```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://coqui-tts.readthedocs.io/en/latest/docker_images.html)
## Synthesizing speech by 🐸TTS
### 🐍 Python API
#### Running a multi-speaker and multi-lingual model
```python
import torch
from TTS.api import TTS
# Get device
device = "cuda" if torch.cuda.is_available() else "cpu"
# List available 🐸TTS models
print(TTS().list_models())
# Init TTS
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(device)
# Run TTS
# ❗ Since this model is multi-lingual voice cloning model, we must set the target speaker_wav and language
# Text to speech list of amplitude values as output
wav = tts.tts(text="Hello world!", speaker_wav="my/cloning/audio.wav", language="en")
# Text to speech to a file
tts.tts_to_file(text="Hello world!", speaker_wav="my/cloning/audio.wav", language="en", file_path="output.wav")
```
#### Running a single speaker model
```python
# Init TTS with the target model name
tts = TTS(model_name="tts_models/de/thorsten/tacotron2-DDC", progress_bar=False).to(device)
# 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).to(device)
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 the voice in `source_wav` to the voice of `target_wav`
```python
tts = TTS(model_name="voice_conversion_models/multilingual/vctk/freevc24", progress_bar=False).to("cuda")
tts.voice_conversion_to_file(source_wav="my/source.wav", target_wav="my/target.wav", file_path="output.wav")
```
Other available voice conversion models:
- `voice_conversion_models/multilingual/multi-dataset/openvoice_v1`
- `voice_conversion_models/multilingual/multi-dataset/openvoice_v2`
#### Example voice cloning together with the default voice conversion model.
This way, you can clone voices by using any model in 🐸TTS. The FreeVC model is
used for voice conversion after synthesizing speech.
```python
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="output.wav"
)
```
#### Example text to speech using **Fairseq models in ~1100 languages** 🤯.
For Fairseq models, use the following name format: `tts_models//fairseq/vits`.
You can find the language ISO codes [here](https://dl.fbaipublicfiles.com/mms/tts/all-tts-languages.html)
and learn about the Fairseq models [here](https://github.com/facebookresearch/fairseq/tree/main/examples/mms).
```python
# TTS with fairseq models
api = TTS("tts_models/deu/fairseq/vits")
api.tts_to_file(
"Wie sage ich auf Italienisch, dass ich dich liebe?",
file_path="output.wav"
)
```
### Command-line `tts`
Synthesize speech on the command line.
You can either use your trained model or choose a model from the provided list.
If you don't specify any models, then it uses a Tacotron2 English model trained
on LJSpeech.
#### 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 "///"
```
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 "/"
```
For example:
```
$ tts --model_info_by_idx tts_models/3
```
- Query info for model info by full name:
```
$ tts --model_info_by_name "///"
```
- Run TTS with default models:
```
$ tts --text "Text for TTS" --out_path output/path/speech.wav
```
- Run TTS and pipe out the generated TTS wav file data:
```
$ tts --text "Text for TTS" --pipe_out --out_path output/path/speech.wav | aplay
```
- Run a TTS model with its default vocoder model:
```
$ tts --text "Text for TTS" --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 "///" --vocoder_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 a 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/model.pth --config_path path/to/config.json --speakers_file_path path/to/speaker.json --speaker_idx
```
### Voice Conversion Models
```
$ tts --out_path output/path/speech.wav --model_name "//" --source_wav --target_wav
```
## 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)
|- vc/ (Voice conversion models.)
|- (same)
```