WhisperX, an AI model for accelerated transcription of audio, has numerous use cases that can greatly benefit various industries. For instance, in the field of speech recognition, WhisperX can be used to convert spoken words into written text, making it easier for voice-based applications to understand and respond to user commands. In the media industry, WhisperX can enable quick and efficient transcription of audio files, facilitating the creation of subtitles for videos, podcast transcripts, and more. Additionally, WhisperX can be applied in audio indexing, allowing for better organization and searchability of large volumes of audio data. This AI model also has the potential to be integrated into products that require real-time transcription, such as automated closed captioning systems, voice assistants, and transcription services. Overall, WhisperX offers the promise of streamlined and accurate audio-to-text conversion, opening up opportunities for a range of practical uses and innovative products.
- Cost per run
- Avg run time
- Nvidia T4 GPU
|Stable Diffusion Speed Lab||$0.0069||3,121|
|Whisper Jax Hindi||$0.0184||62|
|Speedy Stable Diffusion Inpainting||$0.2668||309|
You can use this area to play around with demo applications that incorporate the Whisperx 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.
Accelerated transcription of audio using WhisperX
|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||$0.0022|
|Prediction Hardware||Nvidia T4 GPU|
|Average Completion Time||4 seconds|