Inconsistent result when use different embeddin config

[leohuang2013]

I used following code to do speaker diarization,

from pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization@2.1",
                                    use_auth_token="ACCESS_TOKEN_GOES_HERE")


# apply the pipeline to an audio file
diarization = pipeline("audio.wav")

After reading source code of pyannote/audio/core/pipeline.py, I noticed that embedding model name is read from config.yaml file which in local model folder, and there are 2 embedding models can be configured,

1. pyannote/embedding
2. speechbrain/spkrec-ecapa-voxceleb

--- pyannote/embedding

=================================================
 1 pipeline:
  2   name: pyannote.audio.pipelines.SpeakerDiarization
  3   params:
  4     clustering: AgglomerativeClustering
  5     embedding: pyannote/embedding
  6     embedding_batch_size: 32
  7     embedding_exclude_overlap: true
  8     segmentation: pyannote/segmentation@2022.07
  9     segmentation_batch_size: 32
 10 
 11 params:
 12   clustering:
 13     method: centroid
 14     min_cluster_size: 15
 15     threshold: 0.7153814381597874
 16   segmentation:
 17     min_duration_off: 0.5817029604921046
 18     threshold: 0.4442333667381752
=================================================

--- speechbrain/spkrec-ecapa-voxceleb

=================================================
 1 pipeline:
  2   name: pyannote.audio.pipelines.SpeakerDiarization
  3   params:
  4     clustering: AgglomerativeClustering
  5     embedding: speechbrain/spkrec-ecapa-voxceleb
  6     embedding_batch_size: 32
  7     embedding_exclude_overlap: true
  8     segmentation: pyannote/segmentation@2022.07
  9     segmentation_batch_size: 32
 10 
 11 params:
 12   clustering:
 13     method: centroid
 14     min_cluster_size: 15
 15     threshold: 0.7153814381597874
 16   segmentation:
 17     min_duration_off: 0.5817029604921046
 18     threshold: 0.4442333667381752
=================================================

So I changed embedding in config.yaml to above value and tested with wav file, I found second model: speechbrain/spkrec-ecapa-voxceleb gave more accurate result. My question is what else I can change to make diarization result of pyannote/embedding is same accurate as speechbrain/spkrec-ecapa-voxceleb. The reason why I want to use pyannote/embedding, because it is much faster than speechbrain/spkrec-ecapa-voxceleb, 6s vs 15s for audio.wav file( in attachment audio.wav.zip) on my machine.

[github-actions]

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This is an automated reply, generated by FAQtory

[hbredin]

• pyannote/embedding will give you worse results and that is expected because the speechbrain model is better (it might change in the future -- no ETA, though).
• the clustering/threshold you are using is suited for speechbrain but not for pyannote/embedding: you need to optimize this threshold specifically for pyannote/embedding.

[leohuang2013]

• ... it might change in the future
  Does this mean pyannote/embedding will be optimized? or there will be better model than speechbrain/spkrec-ecapa-voxceleb?
• ... you need to optimize this threshold specifically for pyannote/embedding
  Thanks for tip, any rough idea/direction to adjust its value, like range or something?

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