whisperx
Maintainer: erium
9.8K
| Property | Value |
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| Model Link | View on Replicate |
| API Spec | View on Replicate |
| Github Link | View on Github |
| Paper Link | View on Arxiv |
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Model overview
WhisperX is an automatic speech recognition (ASR) model that builds upon OpenAI's Whisper model, providing improved timestamp accuracy and speaker diarization capabilities. Developed by Replicate's maintainer erium, WhisperX incorporates forced phoneme alignment and voice activity detection (VAD) to produce transcripts with accurate word-level timestamps. It also includes speaker diarization, which identifies different speakers within the audio.
Compared to similar models like whisper-diarization, whisperx and whisperx, WhisperX offers faster inference speed (up to 70x real-time) and improved accuracy for long-form audio transcription tasks. It is particularly useful for applications that require precise word timing and speaker identification, such as video subtitling, meeting transcription, and audio indexing.
Model inputs and outputs
WhisperX takes an audio file as input and produces a transcript with word-level timestamps and speaker labels. The model supports a variety of input audio formats and can handle multiple languages, with default models provided for languages like English, German, French, and more.
Inputs
- Audio file: The audio file to be transcribed, in a supported format (e.g., WAV, MP3, FLAC).
- Language: The language of the audio file, which is automatically detected if not provided. Supported languages include English, German, French, Spanish, Italian, Japanese, and Chinese, among others.
- Diarization: An optional flag to enable speaker diarization, which will identify and label different speakers in the audio.
Outputs
- Transcript: The transcribed text of the audio, with word-level timestamps and optional speaker labels.
- Alignment information: Details about the alignment of the transcript to the audio, including the start and end times of each word.
- Diarization information: If enabled, the speaker labels for each word in the transcript.
Capabilities
WhisperX excels at transcribing long-form audio with high accuracy and precise word timing. The model's forced alignment and VAD-based preprocessing result in significantly improved timestamp accuracy compared to the original Whisper model, which can be crucial for applications like video subtitling and meeting transcription.
The speaker diarization capabilities of WhisperX allow it to identify different speakers within the audio, making it useful for multi-speaker scenarios, such as interviews or panel discussions. This added functionality can simplify the post-processing and analysis of transcripts, especially in complex audio environments.
What can I use it for?
WhisperX is well-suited for a variety of applications that require accurate speech-to-text transcription, precise word timing, and speaker identification. Some potential use cases include:
- Video subtitling and captioning: The accurate word-level timestamps and speaker labels generated by
WhisperXcan streamline the process of creating subtitles and captions for video content. - Meeting and lecture transcription:
WhisperXcan capture the discussions in meetings, lectures, and webinars, with speaker identification to help organize the transcript. - Audio indexing and search: The detailed transcript and timing information can enable more advanced indexing and search capabilities for audio archives and podcasts.
- Assistive technology: The speaker diarization and word-level timestamps can aid in applications like real-time captioning for the deaf and hard of hearing.
Things to try
One interesting aspect of WhisperX is its ability to handle long-form audio efficiently, thanks to its batched inference and VAD-based preprocessing. This makes it well-suited for transcribing lengthy recordings, such as interviews, podcasts, or webinars, without sacrificing accuracy or speed.
Another key feature to explore is the speaker diarization functionality. By identifying different speakers within the audio, WhisperX can provide valuable insights for applications like meeting transcription, where knowing who said what is crucial for understanding the context and flow of the conversation.
Finally, the model's multilingual capabilities allow you to transcribe audio in a variety of languages, making it a versatile tool for international or diverse audio content. Experimenting with different languages and benchmarking the performance can help you determine the best fit for your specific use case.
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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