This AI model has several potential use cases for a technical audience. For example, it can be applied in the creation of transcription services that are capable of accurately transcribing audio files with multiple speakers. This could be useful for professionals such as journalists, researchers, and podcasters who need to convert interviews or discussions into written form. Another possible application could be in the development of voice-controlled virtual assistants or smart speakers. By leveraging this model, these devices could improve their ability to understand and respond to multiple speakers in real-time. Additionally, this technology could be used to develop automated note-taking or meeting recording tools that can generate detailed minutes by transcribing and differentiating between speakers. Overall, this AI model has the potential to enhance various products and practical solutions that rely on accurate transcription and speaker identification.
- Cost per run
- Avg run time
- Nvidia T4 GPU
You can use this area to play around with demo applications that incorporate the Speaker Transcription model. These demos are maintained and hosted externally by third-party creators. If you see an error, message me on Twitter.
Currently, there are no demos available for this model.
Summary of this model and related resources.
|Model Name||Speaker Transcription|
Whisper transcription plus speaker diarization
|Model Link||View on Replicate|
|API Spec||View on Replicate|
|Github Link||View on Github|
|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||$0.0198|
|Prediction Hardware||Nvidia T4 GPU|
|Average Completion Time||36 seconds|