ttslearn: Exploring Text-to-Speech with Python
The ttslearn project is a comprehensive library designed to help users learn about text-to-speech (TTS) synthesis using Python. Developed primarily for Japanese text-to-speech, this library provides valuable resources for anyone interested in TTS, even if they are working with other languages.
Installation
Installing ttslearn is straightforward with Python's package manager:
pip install ttslearn
Repository Structure
The ttslearn project is well-structured to offer users a clear pathway to learning and utilizing text-to-speech technology:
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ttslearn: This is the core library created for the book "音声合成 (Text-to-Speech with Python)" and can be installed via
pip install ttslearn. It serves as a general-purpose TTS library, useful beyond the book's sample codes. -
notebooks: This directory contains Jupyter notebook format source codes from chapters 4 to 10, providing interactive learning and practice materials.
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hydra: Sample codes for Hydra, explained in chapter 6, are located here. Hydra is a popular framework for managing complex configurations, and the examples help demystify its usage within TTS setups.
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recipes: This section includes Japanese TTS recipes explained in chapters 6, 8, and 10. It includes implementations for Japanese TTS systems using the JSUT corpus.
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extra_recipes: Though not covered in the book, this directory offers additional advanced TTS recipes using the JSUT and JVS corpora, showcasing more extensive usage of the ttslearn library.
Documentation and License
The complete documentation for the ttslearn project can be found online at ttslearn documentation. The source code is licensed under the MIT License, which allows for both commercial and non-commercial use. For more details, users can check the LICENSE file within the repository.
Pre-trained Models and Usage Guidelines
The repository also provides pre-trained models developed using the JSUT and JVS corpora, available for non-commercial use only. Users need to adhere to the corpus usage policies, and the author disclaims any responsibility for liabilities arising from their use.
Additional Resources
For those interested, an appendix summarizing the full context label specification for Japanese TTS is available in docs/appendix.pdf.
Support and Corrections
Queries related to the book or source code can be addressed through GitHub issues, where responses will be provided as much as possible. A list of known errors and corrections is maintained at Errata sheet. In case of any unlisted errors or typos, readers are encouraged to report them via GitHub issues.
Acknowledgments
The project has benefited from various contributions, particularly:
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Portions of the Tacotron 2 codebase were adapted from ESPnet, thanks to @kan-bayashi.
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Most advanced recipe implementations utilized kan-bayashi/ParallelWaveGAN.
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For Japanese text processing, Open JTalk and its Python wrapper were employed.
Further Information
Those interested in the accompanying book can find it on Amazon and other platforms:
- Amazon: Amazon Link
- Impress Books: Impress Book Information
Through ttslearn, Python enthusiasts and researchers have a valuable tool for delving into the realm of text-to-speech, with resources tailored to educate and facilitate the development of TTS systems, primarily in Japanese.