This content originally appeared on DEV Community and was authored by aimodels-fyi
This is a simplified guide to an AI model called Whisperx maintained by Victor-Upmeet. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.
Model overview
whisperx is a speech transcription model developed by researchers at Upmeet. It builds upon OpenAI's Whisper model, adding features like accelerated transcription, word-level timestamps, and speaker diarization. Unlike the original Whisper, whisperx supports batching for faster processing of long-form audio. It also offers several model variants optimized for different hardware setups, including the victor-upmeet/whisperx-a40-large and victor-upmeet/whisperx-a100-80gb models.
Model inputs and outputs
whisperx takes an audio file as input and generates a transcript with word-level timestamps and optional speaker diarization. It can handle a variety of audio formats and supports language detection and automatic transcription of multiple languages.
Inputs
- Audio File: The audio file to be transcribed
- Language: The ISO code of the language spoken in the audio (optional, can be automatically detected)
- VAD Onset/Offset: Parameters for voice activity detection
- Diarization: Whether to assign speaker ID labels
- Alignment: Whether to align the transcript to get accurate word-level timestamps
- Speaker Limits: Minimum and maximum number of speakers for diarization
Outputs
- Detected Language: The ISO code of the detected language
- Segments: The transcribed text, with word-level timestamps and optional speaker IDs
Capabilities
whisperx provides fast and accurate ...
Click here to read the full guide to Whisperx
This content originally appeared on DEV Community and was authored by aimodels-fyi
aimodels-fyi | Sciencx (2025-11-21T01:45:09+00:00) A beginner’s guide to the Whisperx model by Victor-Upmeet on Replicate. Retrieved from https://www.scien.cx/2025/11/21/a-beginners-guide-to-the-whisperx-model-by-victor-upmeet-on-replicate/
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