victor-upmeet / whisperx-a40-large

Accelerated transcription, word-level timestamps and diarization with whisperX large-v3 for large audio files

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This model is a clone of victor-upmeet/whisperx.

The purpose of this model is to offer the possibility to transcribe large audio files (a few 100 MB / a few hours long), which victor-upmeet/whisperx is not able to do due to its available RAM. Please try victor-upmeet/whisperx first, and if the model fails due to an unknown error and if you have a large audio file, you can use this model instead.

Model Information

WhisperX provides fast automatic speech recognition (70x realtime with large-v3) with word-level timestamps and speaker diarization.

Whisper is an ASR model developed by OpenAI, trained on a large dataset of diverse audio. Whilst it does produces highly accurate transcriptions, the corresponding timestamps are at the utterance-level, not per word, and can be inaccurate by several seconds. OpenAI’s whisper does not natively support batching, but WhisperX does.

Model used is for transcription is large-v3 from faster-whisper.

For more information about WhisperX, including implementation details, see the WhisperX github repo.


      title={WhisperX: Time-Accurate Speech Transcription of Long-Form Audio}, 
      author={Max Bain and Jaesung Huh and Tengda Han and Andrew Zisserman},