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TTS: Text-to-Speech for all.

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.

:loudspeaker: English Voice Samples and SoundCloud playlist

:man_cook: TTS training recipes

:page_facing_up: 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 | | ❔ FAQ | TTS/Wiki | | 🎁 Feature Requests & Ideas | GitHub Issue Tracker | | 👩‍💻 Usage Questions | Discourse Forum | | 🗯 General Discussion | Discourse Forum and Matrix Channel |

🔗 Links and Resources

| Type | Links | | ------------------------------- | --------------------------------------- | | 💾 Installation | TTS/README.md| | 👩🏾‍🏫 Tutorials and Examples | TTS/Wiki | | 🚀 Released Models | TTS/Wiki| | 💻 Docker Image | Repository by @synesthesiam| | 🖥️ Demo Server | TTS/server| | 🤖 Running TTS on Terminal | TTS/README.md| | ✨ How to contribute |TTS/README.md|

🥇 TTS Performance

<p align="center"><img src="https://discourse-prod-uploads-81679984178418.s3.dualstack.us-west-2.amazonaws.com/optimized/3X/6/4/6428f980e9ec751c248e591460895f7881aec0c6_2_1035x591.png" width="800" /></p>

"Mozilla*" and "Judy*" are our 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 console and Tensorboard.
• Support for multi-speaker TTS.
• Efficient Multi-GPUs training.
• Ability to convert PyTorch models to Tensorflow 2.0 and TFLite for inference.
• Released models in PyTorch, Tensorflow and TFLite.
• Tools to curate Text2Speech datasets underdataset_analysis.
• Demo server for model testing.
• Notebooks for extensive model benchmarking.
• Modular (but not too much) code base enabling easy testing for new ideas.

Implemented Models

Text-to-Spectrogram

• Tacotron: paper
• Tacotron2: paper
• Glow-TTS: paper
• Speedy-Speech: paper

Attention Methods

• Guided Attention: paper
• Forward Backward Decoding: paper
• Graves Attention: paper
• Double Decoder Consistency: blog

Speaker Encoder

• GE2E: paper
• Angular Loss: paper

Vocoders

• MelGAN: paper
• MultiBandMelGAN: paper
• ParallelWaveGAN: paper
• GAN-TTS discriminators: paper
• WaveRNN: origin
• WaveGrad: paper

You can also help us implement more models. Some TTS related work can be found here.

Install TTS

TTS supports python >= 3.6, <3.9.

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/mozilla/TTS
pip install -e .

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)

Sample Model Output

Below you see Tacotron model state after 16K iterations with batch-size 32 with LJSpeech dataset.

"Recent research at Harvard has shown meditating for as little as 8 weeks can actually increase the grey matter in the parts of the brain responsible for emotional regulation and learning."

Audio examples: soundcloud

<img src="images/example_model_output.png?raw=true" alt="example_output" width="400"/>

Datasets and Data-Loading

TTS provides a generic dataloader easy to use for your custom dataset. You just need to write a simple function to format the dataset. Check datasets/preprocess.py to see some examples. After that, you need to set dataset fields in config.json.

Some of the public datasets that we successfully applied TTS:

• LJ Speech
• Nancy
• TWEB
• M-AI-Labs
• LibriTTS
• Spanish - thx! @carlfm01

Example: Synthesizing Speech on Terminal Using the Released Models.

After the installation, TTS provides a CLI interface for synthesizing speech using pre-trained models. You can either use your own model or the release models under the TTS project.

Listing released TTS models.

tts --list_models

Run a tts and a vocoder model from the released model list. (Simply copy and paste the full model names from the list as arguments for the command below.)

tts --text "Text for TTS" \
    --model_name "<type>/<language>/<dataset>/<model_name>" \
    --vocoder_name "<type>/<language>/<dataset>/<model_name>" \
    --out_path folder/to/save/output/

Run your own TTS model (Using Griffin-Lim Vocoder)

tts --text "Text for TTS" \
    --model_path path/to/model.pth.tar \
    --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.tar \
    --out_path output/path/speech.wav \
    --vocoder_path path/to/vocoder.pth.tar \
    --vocoder_config_path path/to/vocoder_config.json

Note: You can use ./TTS/bin/synthesize.py if you prefer running tts from the TTS project folder.

Example: Training and Fine-tuning LJ-Speech Dataset

Here you can find a CoLab notebook for a hands-on example, training LJSpeech. Or you can manually follow the g