🐸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.
💬 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.
🥇 TTS Performance#
Underlined “TTS*” and “Judy*” are 🐸TTS models
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
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.
GAN-TTS discriminators: paper
You can also help us implement more models.
🐸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.
pip install TTS
If you plan to code or train models, clone 🐸TTS and install it locally.
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.
$ 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.
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 server
Synthesizing speech by 🐸TTS#
🐍 Python API#
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() # 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, language=tts.languages) # Text to speech to a file tts.tts_to_file(text="Hello world!", speaker=tts.speakers, language=tts.languages, 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", file_path="output.wav") tts.tts_to_file("Isso é clonagem de voz.", speaker_wav="my/cloning/audio.wav", language="pt", file_path="output.wav")
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>"
$ 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>"
$ 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
$ 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
$ 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 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>
|- 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)
- Tutorial For Nervous Beginners
- Humble FAQ
- Errors with a pre-trained model. How can I resolve this?
- What are the requirements of a good 🐸TTS dataset?
- How should I choose the right model?
- How can I train my own
- How can I train in a different language?
- How can I train multi-GPUs?
- How can I check model performance?
- How do I know when to stop training?
- My model does not learn. How can I debug?
- Attention does not align. How can I make it work?
- How can I test a trained model?
- My Tacotron model does not stop - I see “Decoder stopped with ‘max_decoder_steps” - Stopnet does not work.
- Contribution guidelines
- Synthesizing Speech
- Docker images
- Basic inference
- Start a server
- Implementing a Model
- Template 🐸TTS Model implementation
- Training a Model
- Multi-speaker Training
- Fine-tuning a 🐸 TTS model
- Formatting Your Dataset
- What makes a good TTS dataset
- TTS Datasets
- Glow TTS
- Forward TTS model(s)
- 🌮 Tacotron 1 and 2