Thomasmol

whisper-diarization

thomasmol

229

whisper-diarization is a fast audio transcription model that combines the powerful Whisper Large v3 model with speaker diarization from the Pyannote audio library. This model provides accurate transcription with word-level timestamps and the ability to identify different speakers in the audio. Similar models like whisperx and voicecraft also offer advanced speech-to-text capabilities, but whisper-diarization stands out with its speed and ease of use. Model inputs and outputs whisper-diarization takes in audio data in various formats, including a direct file URL, a Base64 encoded audio file, or a local audio file path. Users can also provide a prompt containing relevant vocabulary to improve transcription accuracy. The model outputs a list of speaker segments with start and end times, the detected number of speakers, and the language of the spoken words. Inputs file_string: Base64 encoded audio file file_url: Direct URL to an audio file file: Local audio file path prompt: Vocabulary to improve transcription accuracy group_segments: Option to group short segments from the same speaker num_speakers: Specify the number of speakers (leave empty to autodetect) language: Language of the spoken words (leave empty to autodetect) offset_seconds: Offset in seconds for chunked inputs Outputs segments: List of speaker segments with start/end times, average log probability, and word-level probabilities num_speakers: Number of detected speakers language: Detected language of the spoken words Capabilities whisper-diarization excels at fast and accurate audio transcription, even in noisy or multilingual environments. The model's ability to identify different speakers and provide word-level timestamps makes it a powerful tool for a variety of applications, from meeting recordings to podcast production. What can I use it for? whisper-diarization can be used in many industries and applications that require accurate speech-to-text conversion and speaker identification. Some potential use cases include: Meeting and interview transcription**: Quickly generate transcripts with speaker attribution for remote or in-person meetings, interviews, and conferences. Podcast and audio production**: Streamline the podcast production workflow by automatically generating transcripts and identifying different speakers. Accessibility and subtitling**: Provide accurate, time-stamped captions for videos and audio content to improve accessibility. Market research and customer service**: Analyze audio recordings of customer calls or focus groups to extract insights and improve product or service offerings. Things to try One interesting aspect of whisper-diarization is its ability to handle multiple speakers and provide word-level timestamps. This can be particularly useful for applications that require speaker segmentation, such as conversation analysis or audio captioning. You could experiment with the group_segments and num_speakers parameters to see how they affect the model's performance on different types of audio content. Another area to explore is the use of the prompt parameter to improve transcription accuracy. By providing relevant vocabulary, acronyms, or proper names, you can potentially boost the model's performance on domain-specific content, such as technical jargon or industry-specific terminology.

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