- ๐ฃ Fork of the original, unmaintained repository. New PyPI package: coqui-tts
- ๐ฃ โTTSv2 is here with 16 languages and better performance across the board.
- ๐ฃ โTTS fine-tuning code is out. Check the example recipes.
- ๐ฃ โTTS can now stream with <200ms latency.
- ๐ฃ โTTS, our production TTS model that can speak 13 languages, is released Blog Post, Demo, Docs
- ๐ฃ ๐ถBark is now available for inference with unconstrained voice cloning. Docs
- ๐ฃ You can use ~1100 Fairseq models with ๐ธTTS.
- ๐ฃ ๐ธTTS now supports ๐ขTortoise with faster inference. Docs
๐ธTTS is a library for advanced Text-to-Speech generation.
๐ Pretrained models in +1100 languages.
๐ ๏ธ Tools for training new models and fine-tuning existing models in any language.
๐ Utilities for dataset analysis and curation.
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 |
|---|---|
| ๐จ Bug Reports | GitHub Issue Tracker |
| ๐ Feature Requests & Ideas | GitHub Issue Tracker |
| ๐ฉโ๐ป Usage Questions | GitHub Discussions |
| ๐ฏ General Discussion | GitHub Discussions or Discord |
The issues and discussions in the original repository are also still a useful source of information.
| Type | Links |
|---|---|
| ๐ผ Documentation | ReadTheDocs |
| ๐พ Installation | TTS/README.md |
| ๐ฉโ๐ป Contributing | CONTRIBUTING.md |
| ๐ Road Map | Main Development Plans |
| ๐ Released Models | Standard models and Fairseq models in ~1100 languages |
| ๐ฐ Papers | TTS Papers |
- 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.
- 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
- Delightful TTS: paper
- โTTS: blog
- VITS: paper
- ๐ธ YourTTS: paper
- ๐ข Tortoise: orig. repo
- ๐ถ Bark: orig. repo
- Guided Attention: paper
- Forward Backward Decoding: paper
- Graves Attention: paper
- Double Decoder Consistency: blog
- Dynamic Convolutional Attention: paper
- Alignment Network: paper
- MelGAN: paper
- MultiBandMelGAN: paper
- ParallelWaveGAN: paper
- GAN-TTS discriminators: paper
- WaveRNN: origin
- WaveGrad: paper
- HiFiGAN: paper
- UnivNet: paper
- FreeVC: paper
You can also help us implement more models.
๐ธTTS is tested on Ubuntu 22.04 with python >= 3.9, < 3.13..
If you are only interested in synthesizing speech with the released ๐ธTTS models, installing from PyPI is the easiest option.
pip install coqui-ttsIf you plan to code or train models, clone ๐ธTTS and install it locally.
git clone https://github.com/idiap/coqui-ai-TTS
cd coqui-ai-TTS
pip install -e .The following extras allow the installation of optional dependencies:
| Name | Description |
|---|---|
all |
All optional dependencies, except dev and docs |
dev |
Development dependencies |
docs |
Dependencies for building the documentation |
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:
pip install coqui-tts[server,ja]
pip install -e .[server,ja]If you are on Ubuntu (Debian), you can also run following commands for installation.
$ make system-deps # intended to be used on Ubuntu (Debian). Let us know if you have a different OS.
$ make installIf you are on Windows, ๐@GuyPaddock wrote installation instructions here (note that these are out of date, e.g. you need to have at least Python 3.9).
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.
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 serverYou can then enjoy the TTS server here More details about the docker images (like GPU support) can be found here
import torch
from TTS.api import TTS
# Get device
device = "cuda" if torch.cuda.is_available() else "cpu"
# List available ๐ธTTS models
print(TTS().list_models())
# Init TTS
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2").to(device)
# Run TTS
# โ 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")# 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 รฉ clonagem de voz.", speaker_wav="my/cloning/audio.wav", language="pt-br", file_path="output.wav")Converting the voice in source_wav to the voice of target_wav
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")This way, you can clone voices by using any model in ๐ธTTS.
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"
)For Fairseq models, use the following name format: tts_models/<lang-iso_code>/fairseq/vits.
You can find the language ISO codes here
and learn about the Fairseq models here.
# 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"
)Synthesize speech on 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 LJSpeech based English model.
-
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 -
Query info for model info by full name:
$ tts --model_info_by_name "<model_type>/<language>/<dataset>/<model_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 "<model_type>/<language>/<dataset>/<model_name>" --out_path output/path/speech.wavFor 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.wavFor 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
-
List the available speakers and choose a <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>
$ tts --out_path output/path/speech.wav --model_name "<language>/<dataset>/<model_name>" --source_wav <path/to/speaker/wav> --target_wav <path/to/reference/wav>
|- 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)
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