The Whisper AI model with channel separation and speaker diarization has a wide range of potential use cases for a technical audience. One interesting application would be in the field of transcription services, where accurate transcription of multi-speaker audio is crucial. This model could be utilized to automate the transcription process, saving time and effort for transcriptionists. Additionally, the ability to separate audio channels and identify speakers could be used in fields such as telecommunications, call centers, and voice assistants, where understanding and responding to different speakers' requests is essential. This model also has potential in the media industry, where it could be used for subtitling, closed captioning, and transcription services for video content. Overall, the Whisper AI model with channel separation and speaker diarization opens up possibilities for improved speech recognition and transcription in various industries, enabling the development of innovative products and services.
- Cost per run
- Avg run time
- Nvidia T4 GPU
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Summary of this model and related resources.
|Model Name||Sabuhi Model|
Whisper AI with channel separation and speaker diarization
|Model Link||View on Replicate|
|API Spec||View on Replicate|
|Github Link||No Github link provided|
|Paper Link||No paper link provided|
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||-|