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Simsamu diarization pipeline

This repository contains a pretrained pyannote-audio diarization pipeline that was fine-tuned on the Simsamu dataset.

The pipeline uses a fine-tuned segmentation model based on https://huggingface.co/pyannote/segmentation-3.0 and pretrained embeddings from https://huggingface.co/pyannote/wespeaker-voxceleb-resnet34-LM. The pipeline hyperparameters were optimized.

The pipeline can be used in medkit the following way:

from medkit.core.audio import AudioDocument
from medkit.audio.segmentation.pa_speaker_detector import PASpeakerDetector

# init speaker detector operation
speaker_detector = PASpeakerDetector(

# create audio document
audio_doc = AudioDocument.from_file("path/to/audio.wav")

# apply operation on audio document
speech_segments = speaker_detector.run([audio_doc.raw_segment])

# display each speech turn and corresponding speaker
for speech_seg in speech_segments:
    speaker_attr = speech_seg.attrs.get(label="speaker")[0]
    print(speech_seg.span.start, speech_seg.span.end, speaker_attr.value)

More info at https://medkit.readthedocs.io/

See also: Simsamu transcription model

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Dataset used to train medkit/simsamu-diarization