Feat/joint diarization and embedding with prepared data #1583
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BREAKING(model): get rid of (flaky) `Model.introspection`
…o feat/joint-diarization-and-embedding
- fixes the dimension error between files id and probabilties arrays - changes the way of how chunks for the embedding task are sampled - creates two functions to draw chunks, one for each subtask Tests are required to ensure that there are no bugs
For now this is a copy past from methods in segmentation task.
as computing this loss probably does not make sense in powerset mode because first class (empty set of labels) does exactly this
as this instance attribute was not used
…` pipeline Co-authored-by: Hervé BREDIN <[email protected]>
as these loop could break gradient flow and to optimize the code
for now do the trick only for the diarization subtask
added 4 commits
December 8, 2023 13:47
Now, the first `num_dia_samples` samples in a batch are dedicated to the diarization substak, and the remaining sample are for the embedding subtask
... and fix some bugs
…ding-with-prepared-data
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I just pushed a (possibly buggy) pipeline that seems to work with a joint model from pyannote.audio.pipelines.speaker_diarization import SpeakerDiarizationV2
import torch
device = torch.device('cuda')
pipeline = SpeakerDiarizationV2('/path/to/joint.ckpt', batch_size=1, step=0.2).to(device)
# parameters obviously need to be optimized
pipeline.instantiate({'clustering': {'threshold': 0.75, 'method': 'centroid', 'min_cluster_size': 1}})
diarization = pipeline('/path/to/audio.wav') |
…' of https://github.com/clement-pages/pyannote-audio into feat/joint-diarization-and-embedding-with-prepared-data
This is done to use the same metrics as for other pyannote's tasks, and to benefit from lightning advantages (parallelization...)
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