kowaalczyk/reformer-tts
An adaptation of Reformer: The Efficient Transformer for text-to-speech task.
This project offers tools to generate human-like speech from written text, focusing on efficiency for voice synthesis tasks. It takes text transcripts as input and produces audio speech. It is designed for researchers and engineers working on experimental speech synthesis, particularly those interested in exploring advanced, efficient deep learning architectures.
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Use this if you are an audio engineer or researcher exploring efficient text-to-speech models and need a flexible, PyTorch-based toolkit for experimentation, especially with vocoders.
Not ideal if you need a production-ready, highly accurate text-to-speech model with immediate high-quality output, as this project focuses on experimental efficiency rather than out-of-the-box performance.
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Language
Python
License
MIT
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Last pushed
Jun 22, 2020
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