Description:

TTS2 0.7.0.1

🐸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.
















📰 Subscribe to 🐸Coqui.ai Newsletter
📢 English Voice Samples and SoundCloud playlist
📄 Text-to-Speech paper collection

💬 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 Gitter Room



🔗 Links and Resources



Type
Links




💼 Documentation
ReadTheDocs


💾 Installation
TTS/README.md


👩‍💻 Contributing
CONTRIBUTING.md


📌 Road Map
Main Development Plans


🚀 Released Models
TTS Releases and Experimental Models



🥇 TTS Performance

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 underdataset_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

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.
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 diffent OS.
$ make install

If you are on Windows, 👑@GuyPaddock wrote installation instructions here.
Use TTS
Single Speaker Models


List provided models:
$ tts --list_models



Run TTS with default models:
$ tts --text "Text for TTS"



Run a TTS model with its default vocoder model:
$ tts --text "Text for TTS" --model_name "<language>/<dataset>/<model_name>



Run with specific TTS and vocoder models from the list:
$ tts --text "Text for TTS" --model_name "<language>/<dataset>/<model_name>" --vocoder_name "<language>/<dataset>/<model_name>" --output_path



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/config.json --config_path path/to/model.pth --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/config.json --config_path path/to/model.pth --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.)
|- distribute.py (train your TTS model using Multiple GPUs.)
|- compute_statistics.py (compute dataset statistics for normalization.)
|- ...
|- 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)

License

For personal and professional use. You cannot resell or redistribute these repositories in their original state.

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