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 hicustomer/pyannote-speaker-diarization with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- pyannote.audio
How to use hicustomer/pyannote-speaker-diarization with pyannote.audio:
from pyannote.audio import Pipeline pipeline = Pipeline.from_pretrained("hicustomer/pyannote-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
File size: 503 Bytes
89a2e1b 369ac18 89a2e1b 5ccc152 369ac18 6b48cee 369ac18 89a2e1b 369ac18 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | pipeline:
name: pyannote.audio.pipelines.SpeakerDiarization
params:
clustering: AgglomerativeClustering
embedding: speechbrain/spkrec-ecapa-voxceleb
embedding_batch_size: 32
embedding_exclude_overlap: true
segmentation: hicustomer/pyannote-segmentation
segmentation_batch_size: 32
params:
clustering:
method: centroid
min_cluster_size: 15
threshold: 0.7153814381597874
segmentation:
min_duration_off: 0.5817029604921046
threshold: 0.4442333667381752
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