lucasnewman/best-rq-pytorch
Implementation of BEST-RQ - a model for self-supervised learning of speech signals using a random projection quantizer, in Pytorch.
This tool helps researchers and developers working on speech technology to create discrete 'semantic tokens' from raw audio. It takes unlabeled speech recordings as input and produces meaningful, quantized representations of the audio content. This is particularly useful for those building advanced speech synthesis or recognition systems who need to process audio efficiently.
133 stars. No commits in the last 6 months. Available on PyPI.
Use this if you need to transform continuous speech signals into a sequence of discrete, semantically rich tokens for tasks like text-to-speech or automatic speech recognition.
Not ideal if you are looking for a pre-trained, ready-to-use speech recognition or synthesis model without needing to work with intermediate speech representations.
Stars
133
Forks
12
Language
Python
License
MIT
Category
Last pushed
Sep 25, 2023
Commits (30d)
0
Dependencies
7
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