---
language:
- "fr"
tags:
- "audio"
- "speech"
- "speaker-diarization"
- "medkit"
- "pyannote-audio"
datasets:
- "common_voice"
- "pxcorpus"
- "simsamu"
metrics:
- "der"
---

# Simsamu diarization pipeline

This repository contains a pretrained
[pyannote-audio](https://github.com/pyannote/pyannote-audio) diarization
pipeline that was fine-tuned on the
[Simsamu](https://huggingface.co/datasets/medkit/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](https://github.com/medkit-lib/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(
    model="medkit/simsamu-diarization",
    device=0,
    segmentation_batch_size=10,
    embedding_batch_size=10,
)

# 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](https://huggingface.co/medkit/simsamu-transcription)