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🎹 PixIT / joint speaker diarization and speech separation
This pipeline ingests mono audio sampled at 16kHz and outputs speaker diarization as an
Annotation
instance and speech separation as a
SlidingWindowFeature
.
Audio files sampled at a different rate are resampled to 16kHz automatically upon loading.
# instantiate the pipelinefrom pyannote.audio import Pipeline
pipeline = Pipeline.from_pretrained(
"pyannote/speech-separation-ami-1.0",
use_auth_token="HUGGINGFACE_ACCESS_TOKEN_GOES_HERE")
# run the pipeline on an audio file
diarization, sources = pipeline("audio.wav")
# dump the diarization output to disk using RTTM formatwithopen("audio.rttm", "w") as rttm:
diarization.write_rttm(rttm)
# dump sources to disk as SPEAKER_XX.wav filesimport scipy.io.wavfile
for s, speaker inenumerate(diarization.labels()):
scipy.io.wavfile.write(f'{speaker}.wav', 16000, sources.data[:,s])
Processing on GPU
pyannote.audio
pipelines run on CPU by default.
You can send them to GPU with the following lines:
import torch
pipeline.to(torch.device("cuda"))
Processing from memory
Pre-loading audio files in memory may result in faster processing:
Hooks are available to monitor the progress of the pipeline:
from pyannote.audio.pipelines.utils.hook import ProgressHook
with ProgressHook() as hook:
diarization = pipeline("audio.wav", hook=hook)
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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