Whisper-diarization has several possible use cases for a technical audience. It can be used in speech recognition systems to accurately transcribe audio recordings with multiple speakers, making it useful for applications in transcription services and voice assistants. It can also facilitate the analysis of conversations in call center recordings or customer service interactions, enabling organizations to extract insights from customer interactions. Additionally, this model can be integrated into video editing software to automatically generate closed captions with speaker labels, improving accessibility for individuals with hearing impairments. Other potential products or practical uses of this model could include voice-controlled meeting transcription software, intelligent voice assistants that can distinguish between different users, and speech analytics tools for research or market analysis purposes.
- Cost per run
- Avg run time
- Nvidia T4 GPU
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Summary of this model and related resources.
|Model Name||Whisper Diarization|
Transcribes any audio file (file, base64 or url) with speaker diarization. ...Read more »
|Model Link||View on Replicate|
|API Spec||View on Replicate|
|Github Link||View on Github|
|Paper Link||View on Arxiv|
How popular is this model, by number of runs? How popular is the creator, by the sum of all their runs?
How much does it cost to run this model? How long, on average, does it take to complete a run?
|Cost per Run||$-|
|Prediction Hardware||Nvidia T4 GPU|
|Average Completion Time||-|