whisperx

Maintainer: carnifexer

Total Score

13

Last updated 6/13/2024
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Paper LinkView on Arxiv

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Model overview

whisperx is an AI model that provides accelerated audio transcription capabilities by building upon the popular Whisper speech recognition model. Developed by Replicate creator carnifexer, whisperx aims to improve the speed and efficiency of transcribing audio files compared to the original Whisper model. It achieves this through batch processing and other optimizations, while still maintaining the high-quality transcription results that Whisper is known for. whisperx can be a powerful tool for a variety of use cases that require fast and accurate speech-to-text conversion, such as podcast production, video subtitling, and meeting transcription. It is one of several Whisper-based models available on the AIModels.fyi platform, including whisperx by daanelson and whisperx by victor-upmeet.

Model inputs and outputs

whisperx takes an audio file as input and produces a text transcript as output. The model supports additional options to control the behavior, such as whether to include word-level timing information, and the batch size for parallelizing the transcription process. The output can be in either plain text or a format that includes the transcript along with segment-level metadata.

Inputs

  • audio: The audio file to be transcribed, provided as a URI
  • batch_size: The number of audio segments to process in parallel, defaulting to 32
  • align_output: A boolean flag to control whether word-level timing information is included in the output
  • only_text: A boolean flag to control whether only the text transcript is returned, or if segment-level metadata is also included

Outputs

  • Output: The transcribed text, either as a plain string or with additional metadata depending on the input options

Capabilities

whisperx is capable of rapidly transcribing audio files with high accuracy, thanks to the underlying Whisper model. It can handle a wide range of audio content, including speech in multiple languages, and can provide word-level timing information if desired. The batch processing capabilities of whisperx make it particularly well-suited for handling large volumes of audio data, such as podcast episodes or video recordings.

What can I use it for?

whisperx can be a valuable tool for a variety of applications that require fast and accurate speech-to-text conversion. Some potential use cases include:

  • Podcast production: Quickly transcribe podcast episodes to generate captions, subtitles, or show notes.
  • Video subtitling: Add captions to videos by transcribing the audio, potentially with word-level timing information.
  • Meeting transcription: Transcribe audio recordings of meetings, interviews, or conversations to create searchable text records.
  • Media accessibility: Improve the accessibility of audio and video content by providing transcripts and captions.
  • Language learning: Use the transcripts generated by whisperx to help language learners improve their listening comprehension.

Things to try

One interesting aspect of whisperx is its ability to perform word-level alignment, which can be particularly useful for applications like video subtitling or language learning. By enabling the align_output option, you can generate transcripts that include the start and end times for each word, allowing for precise synchronization with the audio or video.

Another feature worth exploring is the batch processing capability of whisperx. By adjusting the batch_size parameter, you can experiment with finding the optimal balance between transcription speed and accuracy for your specific use case. This can be especially helpful when working with large volumes of audio data, as it allows for more efficient processing.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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