🐸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.

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📢 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 <!-- Details... -->

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.

bash pip install TTS

If you plan to code or train models, clone 🐸TTS and install it locally.

bash 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.

bash $ 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 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)