whisperx
Maintainer: daanelson
48
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
whisperx is a Cog implementation of the WhisperX library, which adds batch processing on top of the popular Whisper speech recognition model. This allows for very fast audio transcription compared to the original Whisper model. whisperx is developed and maintained by daanelson.
Similar models include whisperx-victor-upmeet, which provides accelerated transcription, word-level timestamps, and diarization with the Whisper large-v3 model, and whisper-diarization-thomasmol, which offers fast audio transcription, speaker diarization, and word-level timestamps.
Model inputs and outputs
whisperx takes an audio file as input, along with optional parameters to control the batch size, whether to output only the transcribed text or include segment metadata, and whether to print out memory usage information for debugging purposes.
Inputs
- audio: The audio file to be transcribed
- batch_size: The number of audio segments to process in parallel for faster transcription
- only_text: A boolean flag to return only the transcribed text, without segment metadata
- align_output: A boolean flag to generate word-level timestamps (currently only works for English)
- debug: A boolean flag to print out memory usage information
Outputs
- The transcribed text, optionally with segment-level metadata
Capabilities
whisperx builds on the strong speech recognition capabilities of the Whisper model, providing accelerated transcription through batch processing. This can be particularly useful for transcribing long audio files or processing multiple audio files in parallel.
What can I use it for?
whisperx can be used for a variety of applications that require fast and accurate speech-to-text transcription, such as podcast production, video captioning, or meeting minutes generation. The ability to process audio in batches and the option to output only the transcribed text can make the model well-suited for high-volume or real-time transcription scenarios.
Things to try
One interesting aspect of whisperx is the ability to generate word-level timestamps, which can be useful for applications like video editing or language learning. You can experiment with the align_output parameter to see how this feature performs on your audio files.
Another thing to try is leveraging the batch processing capabilities of whisperx to transcribe multiple audio files in parallel, which can significantly reduce the overall processing time for large-scale transcription tasks.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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