Matcha-TTS: A Fast Speech Synthesis Tool
Introduction
Matcha-TTS is an innovative text-to-speech (TTS) technology developed by a team of experts, including Shivam Mehta, Ruibo Tu, Jonas Beskow, Éva Székely, and Gustav Eje Henter. This tool introduces a new approach to speech synthesis by utilizing a technique known as conditional flow matching. This technique is designed to enhance the speed and efficiency of creating speech from text, making the synthesis process faster and more natural-sounding.
Key Features
Matcha-TTS is built with several notable features that set it apart from traditional TTS systems:
- Probabilistic Nature: The system is able to generate speech with variations, creating a more natural tone.
- Compact Memory Usage: It operates with a smaller memory footprint, making it suitable for devices with limited resources.
- Natural Sound: The synthesized speech sounds very realistic, thanks to the advanced techniques employed.
- Fast Synthesis: It is designed to convert text into speech rapidly, which is particularly beneficial for real-time applications.
Demonstrations and Resources
For those interested in seeing Matcha-TTS in action, a demo page is available, showcasing its capabilities. Additionally, there is an academic paper from ICASSP 2024 that further details the technical aspects of the project.
Pre-trained models are readily available for download and can be accessed through Matcha-TTS's command-line interface (CLI) or through a browser-based application on platforms like Hugging Face.
Installation
To get started with Matcha-TTS, users can install it using Python's package manager, pip. Here is a summary of the installation process:
-
Create a Python Environment (optional but recommended):
conda create -n matcha-tts python=3.10 -y conda activate matcha-tts -
Install via pip:
pip install matcha-tts -
Run the Application: Users can utilize the CLI, a gradio app, or a Jupyter notebook to synthesize speech from text using various options to adjust speech traits like speaking rate and temperature.
Training with Custom Data
Matcha-TTS supports training with custom datasets. For instance, users can train the model using the LJ Speech dataset by configuring file paths and setting up training scripts. This customizable nature allows users to adapt the tool to better suit specific voice requirements or datasets.
Export and Inference with ONNX
Thanks to contributions from developers like @mush42, Matcha-TTS can be exported to the ONNX format, allowing for streamlined inference processes. Whether by CPU or GPU, ONNX makes it simpler to integrate TTS in various applications and environments.
Extracting Phoneme Alignments
For detailed linguistic analysis, Matcha-TTS can extract phoneme alignments from trained models. This feature adds a level of granularity that can be useful for developers needing precise control over speech output.
Conclusion
Matcha-TTS is an advanced tool for fast and natural speech synthesis, making it an excellent choice for developers and researchers working with voice applications. With its probabilistic approach, efficient memory usage, and high-speed performance, it stands out as a robust solution in the TTS landscape.
Acknowledgements
This project benefits from various open-source resources and libraries, such as Lightning-Hydra-Template, Coqui-TTS, Hugging Face Diffusers, and others. These collaborations have significantly contributed to the success and functionality of Matcha-TTS.
For more technical details or to contribute, explore their GitHub repository or read their paper for reference and citation information.