Cog Whisper Diarization

Audio transcribing + diarization pipeline.

Models used

• Whisper Large v3 (CTranslate 2 version faster-whisper)
• Pyannote audio 3.1.1

Usage

• Used at Audiogest
• Or try at Replicate
• Or deploy yourself at Replicate (Make sure to add your own HuggingFace API key and accept the terms of use of the pyannote models used)

Input

• file_string: str: Either provide a Base64 encoded audio file.
• file_url: str: Or provide a direct audio file URL.
• file: Path: Or provide an audio file.
• group_segments: bool: Group segments of the same speaker shorter than 2 seconds apart. Default is True.
• num_speakers: int: Number of speakers. Leave empty to autodetect. Must be between 1 and 50.
• language: str: Language of the spoken words as a language code like 'en'. Leave empty to auto detect language.
• prompt: str: Vocabulary: provide names, acronyms, and loanwords in a list. Use punctuation for best accuracy.
• offset_seconds: int: Offset in seconds, used for chunked inputs. Default is 0.
• transcript_output_format: str: Specify the format of the transcript output: individual words with timestamps, full text of segments, or a combination of both.
  • Default is both.
  • Options are words_only, segments_only, both,

Output

• segments: List[Dict]: List of segments with speaker, start and end time.
  • Includes avg_logprob for each segment and probability for each word level segment.
• num_speakers: int: Number of speakers (detected, unless specified in input).
• language: str: Language of the spoken words as a language code like 'en' (detected, unless specified in input).

Thanks to

• pyannote
• faster-whisper
• whisper