Instructions to use hbredin/utter-project-diarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- pyannote.audio
How to use hbredin/utter-project-diarization with pyannote.audio:
from pyannote.audio import Pipeline pipeline = Pipeline.from_pretrained("hbredin/utter-project-diarization") # inference on the whole file pipeline("file.wav") # inference on an excerpt from pyannote.core import Segment excerpt = Segment(start=2.0, end=5.0) from pyannote.audio import Audio waveform, sample_rate = Audio().crop("file.wav", excerpt) pipeline({"waveform": waveform, "sample_rate": sample_rate}) - Notebooks
- Google Colab
- Kaggle
Update config.yaml
Browse files- config.yaml +2 -2
config.yaml
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@@ -7,8 +7,8 @@ pipeline:
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embedding: pyannote/embedding
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embedding_batch_size: 32
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embedding_exclude_overlap: true
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segmentation:
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segmentation_step: 0.
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segmentation_batch_size: 32
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params:
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embedding: pyannote/embedding
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embedding_batch_size: 32
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embedding_exclude_overlap: true
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segmentation: hbredin/utter-project-segmentation
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segmentation_step: 0.19909120129072105
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segmentation_batch_size: 32
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params:
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