o Me³ã@sdddlZddlZddlmZddlmZddlmZddlmZddl m Z gZ Gdd „d e ƒZ dS) éN)ÚList)ÚTuple)Údownload)Ú DATA_HOMEé)ÚAudioClassificationDatasetc s®eZdZdZdddœZgd¢Zej ddd¡Z e   d d ¡Z ej dd ¡Z  ddededef‡fdd„ Zdee j fdd„Zdededeeeeeffdd„Z‡ZS)ÚESC50aù The ESC-50 dataset is a labeled collection of 2000 environmental audio recordings suitable for benchmarking methods of environmental sound classification. The dataset consists of 5-second-long recordings organized into 50 semantical classes (with 40 examples per class) Reference: ESC: Dataset for Environmental Sound Classification http://dx.doi.org/10.1145/2733373.2806390 Args: mode (str, optional): It identifies the dataset mode (train or dev). Default:train. split (int, optional): It specify the fold of dev dataset. Default:1. feat_type (str, optional): It identifies the feature type that user wants to extrace of an audio file. Default:raw. archive(dict, optional): it tells where to download the audio archive. Default:None. Returns: :ref:`api_paddle_io_Dataset`. An instance of ESC50 dataset. Examples: .. code-block:: python import paddle mode = 'dev' esc50_dataset = paddle.audio.datasets.ESC50(mode=mode, feat_type='raw') for idx in range(5): audio, label = esc50_dataset[idx] # do something with audio, label print(audio.shape, label) # [audio_data_length] , label_id esc50_dataset = paddle.audio.datasets.ESC50(mode=mode, feat_type='mfcc', n_mfcc=40) for idx in range(5): audio, label = esc50_dataset[idx] # do something with mfcc feature, label print(audio.shape, label) # [feature_dim, length] , label_id zr(ÚtypingrrZ paddle.utilsrZpaddle.dataset.commonrZdatasetrÚ__all__rrrrr#Ús