Automatic Speech Recognition
pyannote.audio
pyannote
pyannote-audio-pipeline
audio
voice
speech
speaker
speaker-diarization
speaker-change-detection
voice-activity-detection
overlapped-speech-detection
Instructions to use eek/speaker-diarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- pyannote.audio
How to use eek/speaker-diarization with pyannote.audio:
from pyannote.audio import Pipeline pipeline = Pipeline.from_pretrained("eek/speaker-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
Radu-Sebastian Amarie commited on
Commit ·
a6d3c7b
1
Parent(s): a3288b2
Update: revert segmentation, update embedding
Browse files- config.yaml +2 -2
config.yaml
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@@ -4,10 +4,10 @@ pipeline:
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name: pyannote.audio.pipelines.SpeakerDiarization
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params:
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clustering: AgglomerativeClustering
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embedding:
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embedding_batch_size: 32
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embedding_exclude_overlap: false
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segmentation:
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segmentation_batch_size: 32
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params:
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name: pyannote.audio.pipelines.SpeakerDiarization
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params:
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clustering: AgglomerativeClustering
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embedding: eek/wespeaker-voxceleb-resnet293-LM
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embedding_batch_size: 32
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embedding_exclude_overlap: false
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segmentation: pyannote/segmentation-3.0
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segmentation_batch_size: 32
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params:
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