The whisper-subtitles model has numerous use cases in various industries. In media and entertainment, it can be used to generate subtitles for movies, TV shows, and online videos, improving accessibility for the hearing impaired and allowing for broader international distribution. In the education sector, the model can be utilized to create captions for online courses, enhancing the learning experience for students and making the content more accessible. The model can also find applications in the transcription industry, automating the process of converting audio recordings into text, saving time and effort for transcriptionists. Additionally, the whisper-subtitles model can be integrated into voice assistants, enabling them to accurately transcribe spoken commands or queries for improved user interactions. Overall, this powerful AI model opens up opportunities for creating a range of products and applications that leverage the conversion of audio to text, enhancing accessibility, convenience, and productivity.
- Cost per run
- Avg run time
- Nvidia T4 GPU
|Hello World Rust||$?||31|
|Safe Latent Diffusion||$0.3381||1,829|
|Stable Diffusion Rs||$?||55|
You can use this area to play around with demo applications that incorporate the Whisper Subtitles 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||Whisper Subtitles|
Generate subtitles from an audio file, using OpenAI's Whisper model.
|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||$0.00825|
|Prediction Hardware||Nvidia T4 GPU|
|Average Completion Time||15 seconds|