π¬ 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 |
---|---|
GitHub Issue Tracker | |
π Feature Requests & Ideas | GitHub Issue Tracker |
Github Discussions | |
Github Discussions or Gitter Room |
π Links and Resources
Type | Links |
---|---|
ReadTheDocs | |
TTS/README.md | |
CONTRIBUTING.md | |
π Road Map | Main Development Plans |
TTS Releases and Experimental Models |
π₯ TTS Performance
Underlined "TTS*" and "Judy*" are πΈTTS 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
. - Ability to convert PyTorch models to Tensorflow 2.0 and TFLite for inference.
- Released and read-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
Text-to-Spectrogram
- Tacotron: paper
- Tacotron2: paper
- Glow-TTS: paper
- Speedy-Speech: paper
- Align-TTS: paper
- FastPitch: paper
- FastSpeech: paper
End-to-End Models
- VITS: 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
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
If you are only interested in synthesizing speech with the released
pip install TTS
By default, this only installs the requirements for PyTorch. To install the tensorflow dependencies as well, use the tf
extra.
pip install TTS[tf]
If you plan to code or train models, clone
git clone https://github.com/coqui-ai/TTS
pip install -e .[all,dev,notebooks,tf] # 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 diffent OS.
$ make install
If you are on Windows,
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.)
|- distribute.py (train your TTS model using Multiple GPUs.)
|- compute_statistics.py (compute dataset statistics for normalization.)
|- convert*.py (convert target torch model to TF.)
|- ...
|- tts/ (text to speech models)
|- layers/ (model layer definitions)
|- models/ (model definitions)
|- tf/ (Tensorflow 2 utilities and model implementations)
|- utils/ (model specific utilities.)
|- speaker_encoder/ (Speaker Encoder models.)
|- (same)
|- vocoder/ (Vocoder models.)
|- (same)