Voice Activity Detection
pyannote.audio
PyTorch
pyannote
pyannote-audio-model
audio
voice
speech
speaker
speaker-diarization
speaker-change-detection
speaker-segmentation
overlapped-speech-detection
resegmentation
Instructions to use objects76/speaker-diarization-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- pyannote.audio
How to use objects76/speaker-diarization-v1 with pyannote.audio:
from pyannote.audio import Model, Inference model = Model.from_pretrained("objects76/speaker-diarization-v1") inference = Inference(model) # inference on the whole file inference("file.wav") # inference on an excerpt from pyannote.core import Segment excerpt = Segment(start=2.0, end=5.0) inference.crop("file.wav", excerpt) - Notebooks
- Google Colab
- Kaggle
File size: 399 Bytes
1b28d26 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | task:
_target_: pyannote.audio.tasks.SpeakerDiarization
duration: 10.0
max_speakers_per_chunk: 3
max_speakers_per_frame: 2
model:
_target_: pyannote.audio.models.segmentation.PyanNet
sample_rate: 16000
num_channels: 1
sincnet:
stride: 10
lstm:
hidden_size: 128
num_layers: 4
bidirectional: true
monolithic: true
linear:
hidden_size: 128
num_layers: 2
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