from pyannote.audio import Model
model = Model.from_pretrained(
"pyannote/separation-ami-1.0",
use_auth_token="HUGGINGFACE_ACCESS_TOKEN_GOES_HERE")
Usage
# model ingests 5s of mono audio sampled at 16kHz...
duration = 5.0
num_channels = 1
sample_rate = 16000
waveforms = torch.randn(batch_size, num_channels, duration * sample_rate)
waveforms.shape
# (batch_size, num_channels = 1, num_samples = 80000)# ... and outputs both speaker diarization and separationwith torch.inference_mode():
diarization, sources = model(waveform)
diarization.shape
# (batch_size, num_frames = 624, max_num_speakers = 3)# with values between 0 (speaker inactive) and 1 (speaker active)
sources.shape
# (batch_size, num_samples = 80000, max_num_speakers = 3)
Limitations
This model cannot be used to perform speaker diarization and speech separation of full recordings on its own (it only processes 5s chunks): see
pyannote/speech-separation-ami-1.0
pipeline that uses an additional speaker embedding model to do that.
Citations
@inproceedings{Kalda24,
author={Joonas Kalda and Clément Pagés and Ricard Marxer and Tanel Alumäe and Hervé Bredin},
title={{PixIT: Joint Training of Speaker Diarization and Speech Separation from Real-world Multi-speaker Recordings}},
year=2024,
booktitle={Proc. Odyssey 2024},
}
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