andybi7676/reborn-uasr
REBORN: Reinforcement-Learned Boundary Segmentation with Iterative Training for Unsupervised ASR
This project helps speech researchers and language model developers transcribe speech into phoneme sequences without needing extensive labeled audio data. It takes raw audio recordings, such as those from the LibriSpeech datasets in English or various other languages, and outputs the underlying phonemic structure of the speech. This is useful for building speech recognition systems or analyzing speech sounds.
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Use this if you need to analyze or transcribe audio data in a language where labeled speech data for traditional ASR training is scarce or unavailable.
Not ideal if you already have a large, labeled dataset for your target language and can train a supervised ASR model.
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Language
Python
License
MIT
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Last pushed
Dec 11, 2024
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