o j@s\ddlZddlZddlmZmZddlmZddlmZddl m Z gZ Gddde Z dS) N)ListTuple) DATA_HOME)download)AudioClassificationDatasetc seZdZdZdddZgdZejdddZ e d d Z ejdd Z  ddededeffdd Zdee j fddZdededeeeeeffddZZS)ESC50a6 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 extract 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 [220500] 0 [220500] 14 [220500] 36 [220500] 36 [220500] 19 >>> 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 [40, 1723] 0 [40, 1723] 14 [40, 1723] 36 [40, 1723] 36 [40, 1723] 19 zr(typingrrZpaddle.dataset.commonrZ paddle.utilsrZdatasetr__all__rrrrr#s