def task(self): if self.regression: return Task(type=TaskType.REGRESSION, output=TaskOutput.SEQUENCE) else: return Task(type=TaskType.MULTI_CLASS_CLASSIFICATION, output=TaskOutput.SEQUENCE)
def task(self): if self.allow_overlap: # when allowing overlap, multiple speakers can be active at the # same time (hence multi-label classification task) return Task(type=TaskType.MULTI_LABEL_CLASSIFICATION, output=TaskOutput.SEQUENCE) else: # when overlap is not allowed, only one speaker can be active at # a particular time (hence: multi-class classification) return Task(type=TaskType.MULTI_CLASS_CLASSIFICATION, output=TaskOutput.SEQUENCE)
def specifications(self): if self.regression: return { 'task': Task(type=TaskType.REGRESSION, output=TaskOutput.SEQUENCE), 'X': {'dimension': self.feature_extraction.dimension}, 'y': {'classes': ['change', ]}, } else: return { 'task': Task(type=TaskType.MULTI_CLASS_CLASSIFICATION, output=TaskOutput.SEQUENCE), 'X': {'dimension': self.feature_extraction.dimension}, 'y': {'classes': ['non_change', 'change']}, }
def specifications(self): return { 'task': Task(type=TaskType.MULTI_CLASS_CLASSIFICATION, output=TaskOutput.SEQUENCE), 'X': {'dimension': self.feature_extraction.dimension}, 'y': {'classes': ['non_overlap', 'overlap']}, }
def specifications(self): return { 'X': { 'dimension': self.feature_extraction.dimension }, 'y': { 'classes': self.segment_labels_ }, 'task': Task(type=TaskType.REPRESENTATION_LEARNING, output=TaskOutput.VECTOR), }
def specifications(self): return { "X": { "dimension": self.feature_extraction.dimension }, "y": { "classes": self.segment_labels_ }, "task": Task(type=TaskType.REPRESENTATION_LEARNING, output=TaskOutput.VECTOR), }
def specifications(self): return { 'task': Task(type=TaskType.MULTI_CLASS_CLASSIFICATION, output=TaskOutput.VECTOR), 'X': { 'dimension': self.feature_extraction.dimension }, 'y': { 'classes': self.file_labels_[self.domain] }, }
def load_specs(specs_yml: Path) -> Dict: """ Returns ------- specs : Dict ['task'] [and others] """ with open(specs_yml, "r") as fp: specifications = yaml.load(fp, Loader=yaml.SafeLoader) specifications["task"] = Task.from_str(specifications["task"]) return specifications
def specifications(self): """Task & sample specifications Returns ------- specs : `dict` ['task'] (`pyannote.audio.train.Task`) : task ['X']['dimension'] (`int`) : features dimension ['y']['classes'] (`list`) : list of classes """ specs = { 'task': Task(type=TaskType.MULTI_CLASS_CLASSIFICATION, output=TaskOutput.SEQUENCE), 'X': {'dimension': self.feature_extraction.dimension}, 'y': {'classes': self.segment_labels_}, } return specs
def task(self): return Task( type=TaskType.MULTI_CLASS_CLASSIFICATION, output=TaskOutput.SEQUENCE )
def task(self): return Task(type=TaskType.REPRESENTATION_LEARNING, output=TaskOutput.VECTOR)