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This is atorch.utils.data.Dataset classfor the Deep Evaluation of Acoustic Representations (DEAR) dataset.The correspondingpaper is on the arXiv and thedata is on Zenodo.
Copy the dear directory to the source repository,then spawn aDataset for an evaluation task using the desired class.
environment_eval_dataset=EnvironmentDEARDataset(base_path=Path("/data/evaluation/dear"),split=DatasetType.TRAIN,target_variable_type=TargetVariableType.DISCRETE,)
indoor_or_outdoor_eval_dataset=IndoorOutdoorDEARDataset(base_path=Path("/data/evaluation/dear"),split=DatasetType.TRAIN,target_variable_type=TargetVariableType.DISCRETE,)
noise_eval_dataset=StationaryTransientNoiseDEARDataset(base_path=Path("/data/evaluation/dear"),split=DatasetType.TRAIN,target_variable_type=TargetVariableType.DISCRETE,)
snr_eval_dataset=SNRDEARDataset(base_path=Path("/data/evaluation/dear"),split=DatasetType.TRAIN,target_variable_type=TargetVariableType.CONTINUOUS,)
speech_present_eval_dataset=SpeechDEARDataset(base_path=Path("/data/evaluation/dear"),split=DatasetType.TRAIN,speech_present=True,target_variable_type=TargetVariableType.DISCRETE,)
speakers_active_eval_dataset=SpeechDEARDataset(base_path=Path("/data/evaluation/dear"),split=DatasetType.TRAIN,speech_present=False,target_variable_type=TargetVariableType.CONTINUOUS,)
drr_eval_dataset=DRRDEARDataset(base_path=Path("/data/evaluation/dear"),split=DatasetType.TRAIN,target_variable_type=TargetVariableType.CONTINUOUS,)
rt60_eval_dataset=RT60DEARDataset(base_path=Path("/data/evaluation/dear"),split=DatasetType.TRAIN,target_variable_type=TargetVariableType.CONTINUOUS,)
Use the standard PyTorch pattern to run the evaluation, e.g.
model=Wav2Vec2Model()forsegments,labelsinrt60_eval_dataset:predicted_labels=model(segments)score=metric(labels,predicted_labels)
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