i(ddlmZmZddlmZddlmZddlmZddlmZddlmZddl m Z dd l m Z dd l m Z dd l Z dd ed eede fdZ dd ed eede fdZGddeZy ))DictOptional)Protocol) ProtocolFile)Subset) Preprocessor) Preprocessors) Annotation)Timeline)SegmentN current_fileexisting_preprocessorreturnc||jdd}n||}|y|jdd}||Std|}|r%|j|vr|j|dS|S)aPreprocessor that crops 'annotated' according to 'duration' Returns 'annotated' unchanged if 'duration' is not available Parameters ---------- current_file : ProtocolFile Protocol file. existing_preprocessor : Preprocessor, optional When provided, this preprocessor must be used to get the initial 'annotated' instead of getting it from 'current_file["annotated"]' Returns ------- cropped_annotated : Timeline "annotated" cropped by "duration". N annotatedduration intersectionmode)getr extentcrop)rrrrs |/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/pyannote/database/protocol/segmentation.pycrop_annotatedr*s*$ $$[$7 ),7  D1HsH%H))+x7~~h^~<< c||jdd}n||}|y|jdd}||S|r2|j|js|j|dS|S)aPreprocessor that crops 'annotation' by 'annotated' Returns 'annotation' unchanged if 'annotated' is not available Parameters ---------- current_file : ProtocolFile Protocol file. existing_preprocessor : Preprocessor, optional When provided, this preprocessor must be used to get the initial 'annotation' instead of getting it from 'current_file["annotation"]' Returns ------- cropped_annotation : Annotation "annotation" cropped by "annotated". N annotationrrr)rcovers get_timeliner)rrrrs rcrop_annotationr"Ts*$!%%lD9 *<8   d3I))**A*A*CDy~>> rcBeZdZdZddeeffd ZddedefdZ xZ S) SegmentationProtocolaA protocol for segmentation experiments A segmentation protocol can be defined programmatically by creating a class that inherits from SegmentationProtocol and implements at least one of `train_iter`, `development_iter` and `test_iter` methods: >>> class MySegmentationProtocol(SegmentationProtocol): ... def train_iter(self) -> Iterator[Dict]: ... yield {"uri": "filename1", ... "annotation": Annotation(...), ... "annotated": Timeline(...)} ... yield {"uri": "filename2", ... "annotation": Annotation(...), ... "annotated": Timeline(...)} `{subset}_iter` should return an iterator of dictionnaries with - "uri" key (mandatory) that provides a unique file identifier (usually the filename), - "annotation" key (mandatory for train and development subsets) that provides reference segmentation as a `pyannote.core.Annotation` instance, - "annotated" key (recommended) that describes which part of the file has been annotated, as a `pyannote.core.Timeline` instance. Any part of "annotation" that lives outside of the provided "annotated" will be removed. This is also used by `pyannote.metrics` to remove un-annotated regions from its evaluation report, and by `pyannote.audio` to not consider empty un-annotated regions as non-speech. - any other key that the protocol may provide. It can then be used in Python like this: >>> protocol = MySegmentationProtocol() >>> for file in protocol.train(): ... print(file["uri"]) filename1 filename2 A segmentation protocol can also be defined using `pyannote.database` configuration file, whose (configurable) path defaults to "~/database.yml". ~~~ Content of ~/database.yml ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Protocols: MyDatabase: Segmentation: MyProtocol: train: uri: /path/to/collection.lst annotation: /path/to/reference.rttm annotated: /path/to/reference.uem any_other_key: ... # see custom loader documentation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ where "/path/to/collection.lst" contains the list of identifiers of the files in the collection: ~~~ Content of "/path/to/collection.lst ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ filename1 filename2 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ "/path/to/reference.rttm" contains the reference segmentation using RTTM format: ~~~ Content of "/path/to/reference.rttm ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ SPEAKER filename1 1 3.168 0.800 music SPEAKER filename1 1 5.463 0.640 music SPEAKER filename1 1 5.496 0.574 speech SPEAKER filename1 1 10.454 0.499 speech SPEAKER filename2 1 2.977 0.391 noise SPEAKER filename2 1 18.705 0.964 noise SPEAKER filename2 1 22.269 0.457 music SPEAKER filename2 1 28.474 1.526 music ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ "/path/to/reference.uem" describes the annotated regions using UEM format: ~~~ Content of "/path/to/reference.uem ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ filename1 NA 0.000 30.000 filename2 NA 0.000 30.000 filename2 NA 40.000 70.000 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ It can then be used in Python like this: >>> from pyannote.database import registry >>> protocol = registry.get_protocol('MyDatabase.SpeakerDiarization.MyProtocol') >>> for file in protocol.train(): ... print(file["uri"]) filename1 filename2 preprocessorsc| t}tjt|j dd|d<tjt |j dd|d<t ||y)Nr)rr)r%)dict functoolspartialrrr"super__init__)selfr% __class__s rr+zSegmentationProtocol.__init__sv   FM&/%6%6 -2C2CKQU2V& k" '0&7&7 =3D3D\SW3X' l# }5rsubsetrcDddlm}d}d}d}i}t||D]v}||}||jz }|d} || j jz }| j D]\} } | |vrd|| <|| xx| z cc<|dz }x||||d} | S)abObtain global statistics on a given subset Parameters ---------- subset : {'train', 'development', 'test'} Returns ------- stats : dict Dictionary with the followings keys: * annotated: float total duration (in seconds) of the parts that were manually annotated * annotation: float total duration (in seconds) of actual annotations * n_files: int number of files in the subset * labels: dict maps classes with their total duration (in seconds) ) get_annotatedrrrr)rrn_fileslabels)utilr1getattrrr!chart) r,r.r1annotated_durationannotation_durationr2r3itemrrlabelrstatss rr;zSegmentationProtocol.statss* ) !)GD&)+D%d+I )"4"4"6 6 l+J :#:#:#<#E#E#G G #-#3#3#5x&$'F5Mu ) $6 qLG, ,-   rN)train) __name__ __module__ __qualname____doc__rr r+rrr; __classcell__)r-s@rr$r$|s.[z6h}&=6&2F22rr$r<)typingrrprotocolrrrr r pyannote.corer r r r(rr"r$rrrGs<"""#$"!QU''7? 7M' 'VQU%%7? 7M%%Pc8cr