TTS: A Python repository from samiptimalsena

🐸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 different 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" --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

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 "<model_type>/<language>/<dataset>/<model_name>" --vocoder_name "<model_type>/<language>/<dataset>/<model_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/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)

samiptimalsena/TTS