Introduction to TTS: Text-to-Speech for All
TTS (Text-to-Speech) is an advanced library that facilitates the generation of speech from written text. It is created based on the latest research with a focus on achieving an optimal balance between training ease, processing speed, and output quality. The TTS library is highly versatile, offering pre-trained models and tools to assess the quality of speech datasets, and it has been utilized in over 20 languages for both commercial and research purposes.
Features of TTS
- High Performance Models: The library includes deep learning models for converting text to speech, such as Tacotron, Glow-TTS, and SpeedySpeech. It also supports multiple speakers and can train efficiently using multiple GPUs.
- Vocoder Models: These models, such as MelGAN and WaveRNN, are used to produce audio waves from spectrograms, ensuring high-quality, natural-sounding speech.
- Cross-Platform Compatibility: Models can be converted between PyTorch and TensorFlow/TFLite for easy deployment in various environments.
- Dataset Tools: TTS offers tools for curating and analyzing datasets to ensure quality outputs, as well as a demo server for testing models.
Implemented Models
- Text-to-Spectrogram Models: These include Tacotron, Tacotron2, Glow-TTS, and SpeedySpeech, which convert text to a visual representation called a spectrogram.
- Attention Methods: Techniques like Guided Attention and Graves Attention help align the text and speech outputs better.
- Speaker Encoder: Models such as GE2E facilitate efficient speaker embedding for multi-speaker scenarios.
- Vocoder Models: This includes MelGAN and its variants, ensuring the final audio output is realistic.
Installation and Usage
TTS supports Python versions 3.6 to 3.8. For those interested in synthesizing speech using pre-built models, installing TTS from the Python Package Index (PyPI) is straightforward:
pip install TTS
For developers wishing to customize or train their models, cloning the repository and installing it locally is recommended:
git clone https://github.com/mozilla/TTS
pip install -e .
Training and Fine-tuning
TTS provides flexibility for training models on custom datasets. By using a configuration file (config.json), users can define model parameters and training settings. Existing guides and notebooks aid in training models from scratch or fine-tuning existing ones.
Community and Contribution
The TTS project welcomes collaboration and feedback. It adheres to Mozilla’s community guidelines and encourages contributions through pull requests. Developers can propose improvements, share results openly, and engage with the community through various communication channels, ensuring the project continues evolving.
Ongoing and Future Goals
TTS aims to continually refine its models, introduce new enhancements, and expand language support. It also seeks to improve training efficiency and quality, potentially adding more models to the library.
Overall, TTS stands as a comprehensive and versatile tool in the field of text-to-speech technology, accessible to researchers, developers, and businesses aiming to create high-quality speech applications.