victor-upmeet / whisperx-a40-large

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

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Input

Output

Run time and cost

This model runs on Nvidia A40 (Large) GPU hardware. Predictions typically complete within 5 minutes. The predict time for this model varies significantly based on the inputs.

Readme

General

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.

Citation

@misc{bain2023whisperx,
      title={WhisperX: Time-Accurate Speech Transcription of Long-Form Audio}, 
      author={Max Bain and Jaesung Huh and Tengda Han and Andrew Zisserman},
      year={2023},
      eprint={2303.00747},
      archivePrefix={arXiv},
      primaryClass={cs.SD}
}