audioku/meta-transfer-learning
Implementation of meta-transfer-learning for ASR and LM (ACL 2020)
This project helps create automated speech recognition (ASR) systems that can accurately transcribe audio where speakers frequently switch between multiple languages in a single conversation, often called code-switching. It takes recordings of mixed-language speech and produces transcribed text. This is designed for AI/ML researchers or engineers who are building robust ASR systems for multilingual environments, especially in low-resource settings.
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Use this if you need to develop an ASR system capable of handling code-switched speech efficiently, particularly when you have limited mixed-language data but access to more abundant single-language datasets.
Not ideal if you are looking for a ready-to-use application for transcribing audio or if your speech recognition needs are solely for a single language.
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Language
Python
License
MIT
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Last pushed
Jul 30, 2020
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