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
* use npz archive instead pickle to save task data * improve code readability * improve(task): update numpy array dtypes In order to use types whose size better machtes the contents of the arrays * remove `end` entry from `annotated_regions` numpy array This entry was redundant with the start and duration entries, since `end` = `start` + `duration`. * fix: allow data preparation to be finished when task has no validation * improve: clear data lists after assignation to `self.prepared_data` This is to avoid data redundancy in the `prepare_data` method --------- Co-authored-by: clement-pages <[email protected]>
Now the joint task uses `prepare_data` and `setup` from core `Task` and `SpeakerDiarization` task.
…' of github.com:clement-pages/pyannote-audio into feat/joint-diarization-and-embedding-with-prepared-data
…embedding-with-prepared-data
…ddins This new model is based on a `WeSpeakerResnet34` for the speaker embeddings extraction part, and on `PyanNet` for (local) segmentation.
…embedding-with-prepared-data
…embedding-with-prepared-data
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
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