yufan-aslp/AliMeeting
The project is associated with the recently-launched ICASSP 2022 Multi-channel Multi-party Meeting Transcription Challenge (M2MeT) to provide participants with baseline systems for speech recognition and speaker diarization in conference scenario.
This project offers baseline systems for transcribing multi-speaker, multi-channel meeting audio. It takes raw meeting recordings and outputs two key pieces of information: a detailed transcription of who said what and when (speaker diarization results in RTTM files) and the actual text of the speech (ASR results measured by Character Error Rate). This is ideal for researchers and engineers working on improving speech recognition and speaker separation in complex meeting environments.
135 stars. No commits in the last 6 months.
Use this if you are a speech researcher or engineer aiming to develop or benchmark advanced systems for transcribing spoken content from multi-participant conference calls or meetings.
Not ideal if you are looking for an out-of-the-box application to transcribe your meetings without significant technical expertise or further development.
Stars
135
Forks
18
Language
Python
License
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Category
Last pushed
Jun 10, 2022
Commits (30d)
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