Ë çi@,ãó&—ddlmZGd„de«Zy)é)ÚSpeakerDiarizationProtocolcój—eZdZdZd„Zd„Zd„Zd„Zd„Zd„Z d„Z d „Z d „Z d „Z d „Zd „Zd„Zd„Zd„Zy)ÚSpeakerSpottingProtocola‹Speaker spotting protocol Parameters ---------- preprocessors : dict or (key, preprocessor) iterable When provided, each protocol item (dictionary) are preprocessed, such that item[key] = preprocessor(item). In case 'preprocessor' is not callable, it should be a string containing placeholder for item keys (e.g. {'audio': '/path/to/{uri}.wav'}) có—td«‚)Nz=Custom speaker spotting protocol should implement "trn_iter".©ÚNotImplementedError©Úselfs ú€/Volumes/fast/ai/experiments/voice-extract-mac/.venv/lib/python3.12/site-packages/pyannote/database/protocol/speaker_spotting.pyÚtrn_iterz SpeakerSpottingProtocol.trn_iter-ó€Ü!Ø Kó ð ócó—y©N©r s r Útrn_enrol_iterz&SpeakerSpottingProtocol.trn_enrol_iter2ó€Ø rcó—yrrr s r Ú trn_try_iterz$SpeakerSpottingProtocol.trn_try_iter5rrcó—td«‚)Nz=Custom speaker spotting protocol should implement "dev_iter".rr s r Údev_iterz SpeakerSpottingProtocol.dev_iter8r rcó—td«‚)NzCCustom speaker spotting protocol should implement "dev_enrol_iter".rr s r Údev_enrol_iterz&SpeakerSpottingProtocol.dev_enrol_iter=ó€Ü!Ø Qó ð rcó—td«‚)NzACustom speaker spotting protocol should implement "dev_try_iter".rr s r Ú dev_try_iterz$SpeakerSpottingProtocol.dev_try_iterBó€Ü!Ø Oó ð rcó—td«‚)Nz=Custom speaker spotting protocol should implement "tst_iter".rr s r Útst_iterz SpeakerSpottingProtocol.tst_iterGr rcó—td«‚)NzCCustom speaker spotting protocol should implement "tst_enrol_iter".rr s r Útst_enrol_iterz&SpeakerSpottingProtocol.tst_enrol_iterLrrcó—td«‚)NzACustom speaker spotting protocol should implement "tst_try_iter".rr s r Ú tst_try_iterz$SpeakerSpottingProtocol.tst_try_iterQrrc#ó`K—|j«}|D]}|j|«–—Œy­w)a;Iterate over the enrolments of the train set Yields dictionaries with the followings keys: * uri: str unique audio file identifier * database: str unique database identifier * model_id: str unique model identifier (the same speaker might have different models) * enrol_with: pyannote.core.Timeline parts of the audio file to use for enrolment as well as keys coming from the provided preprocessors (e.g. 'audio') Usage ----- >>> models = {} >>> for enrolment in protocol.train_enrolment(): ... # obtain path to audio file ... audio = enrolment['audio'] ... # obtain parts of the audio file to use for enrolment ... enrol_with = enrolment['enrol_with'] ... # this is where enrolment actually happens ... model = do_something(audio, enrol_with) ... # store models for later use ... model_id = enrolment['model_id'] ... models[model_id] = model N)rÚ preprocess©r Ú generatorÚcurrent_enrolments r Útrain_enrolmentz'SpeakerSpottingProtocol.train_enrolmentVó2èø€ð@×'Ñ'Ó)ˆ ã!*Ð Ø—/‘/Ð"3Ó4Ó 4ñ"+ùó‚,.c#ó`K—|j«}|D]}|j|«–—Œy­w)aQIterate over the trials of the train set Yields dictionaries with the followings keys: * uri: str unique audio file identifier * database: str unique database identifier * try_with: pyannote.core.Segment, optional parts of the audio file where to look for the target speaker. default is to use the whole audio file * model_id: str unique identifier of the target * reference: pyannote.core.Timeline parts of the audio file where the target actually speaks. it might be empty in case of impostor trials. in case of genuine trials, it should be contained in `try_with` as well as keys coming from the provided preprocessors (e.g. 'audio') Usage ----- >>> for trial in protocol.train_trial(): ... # obtain path to audio file ... audio = trial['audio'] ... # obtain parts of the audio file to use for trial ... try_with = trial['try_with'] ... # this is where the trial actually happens ... model_id = trial['model_id'] ... score = do_something(audio, try_with, model_id) ... # optionally perform evaluation ... reference = trial['reference'] ... metric(reference, score) N)rr%©r r'Ú current_trials r Ú train_trialz#SpeakerSpottingProtocol.train_trial{ó0èø€ðJ×%Ñ%Ó'ˆ ã&ˆMØ—/‘/ -Ó0Ó 0ñ'ùr+c#ó`K—|j«}|D]}|j|«–—Œy­w)aGIterate over the enrolments of the development set Yields dictionaries with the followings keys: * uri: str unique audio file identifier * database: str unique database identifier * model_id: str unique model identifier (the same speaker might have different models) * enrol_with: pyannote.core.Timeline parts of the audio file to use for enrolment as well as keys coming from the provided preprocessors (e.g. 'audio') Usage ----- >>> models = {} >>> for enrolment in protocol.development_enrolment(): ... # obtain path to audio file ... audio = enrolment['audio'] ... # obtain parts of the audio file to use for enrolment ... enrol_with = enrolment['enrol_with'] ... # this is where enrolment actually happens ... model = do_something(audio, enrol_with) ... # store models for later use ... model_id = enrolment['model_id'] ... models[model_id] = model N)rr%r&s r Údevelopment_enrolmentz-SpeakerSpottingProtocol.development_enrolment¥r*r+c#ó`K—|j«}|D]}|j|«–—Œy­w)a]Iterate over the trials of the development set Yields dictionaries with the followings keys: * uri: str unique audio file identifier * database: str unique database identifier * try_with: pyannote.core.Segment, optional parts of the audio file where to look for the target speaker. default is to use the whole audio file * model_id: str unique identifier of the target * reference: pyannote.core.Timeline parts of the audio file where the target actually speaks. it might be empty in case of impostor trials. in case of genuine trials, it should be contained in `try_with` as well as keys coming from the provided preprocessors (e.g. 'audio') Usage ----- >>> for trial in protocol.development_trial(): ... # obtain path to audio file ... audio = trial['audio'] ... # obtain parts of the audio file to use for trial ... try_with = trial['try_with'] ... # this is where the trial actually happens ... model_id = trial['model_id'] ... score = do_something(audio, try_with, model_id) ... # optionally perform evaluation ... reference = trial['reference'] ... metric(reference, score) N)rr%r-s r Údevelopment_trialz)SpeakerSpottingProtocol.development_trialÊr0r+c#ó`K—|j«}|D]}|j|«–—Œy­w)a9Iterate over the enrolments of the test set Yields dictionaries with the followings keys: * uri: str unique audio file identifier * database: str unique database identifier * model_id: str unique model identifier (the same speaker might have different models) * enrol_with: pyannote.core.Timeline parts of the audio file to use for enrolment as well as keys coming from the provided preprocessors (e.g. 'audio') Usage ----- >>> models = {} >>> for enrolment in protocol.test_enrolment(): ... # obtain path to audio file ... audio = enrolment['audio'] ... # obtain parts of the audio file to use for enrolment ... enrol_with = enrolment['enrol_with'] ... # this is where enrolment actually happens ... model = do_something(audio, enrol_with) ... # store models for later use ... model_id = enrolment['model_id'] ... models[model_id] = model N)r!r%r&s r Útest_enrolmentz&SpeakerSpottingProtocol.test_enrolmentôr*r+c#ó`K—|j«}|D]}|j|«–—Œy­w)aOIterate over the trials of the test set Yields dictionaries with the followings keys: * uri: str unique audio file identifier * database: str unique database identifier * try_with: pyannote.core.Segment, optional parts of the audio file where to look for the target speaker. default is to use the whole audio file * model_id: str unique identifier of the target * reference: pyannote.core.Timeline parts of the audio file where the target actually speaks. it might be empty in case of impostor trials. in case of genuine trials, it should be contained in `try_with` as well as keys coming from the provided preprocessors (e.g. 'audio') Usage ----- >>> for trial in protocol.test_trial(): ... # obtain path to audio file ... audio = trial['audio'] ... # obtain parts of the audio file to use for trial ... try_with = trial['try_with'] ... # this is where the trial actually happens ... model_id = trial['model_id'] ... score = do_something(audio, try_with, model_id) ... # optionally perform evaluation ... reference = trial['reference'] ... metric(reference, score) N)r#r%r-s r Ú test_trialz"SpeakerSpottingProtocol.test_trialr0r+N)Ú__name__Ú __module__Ú __qualname__Ú__doc__r rrrrrrr!r#r)r/r2r4r6r8rrr rr!sW„ñ ò ò  ò ò ò  ò  ò  ò  ò  ò #5òJ(1òT#5òJ(1òT#5óJ(1rrN)Úspeaker_diarizationrrrrr Úr>sðõ<<ô`1Ð8õ`1r