- class paddle.audio.datasets. TESS ( mode: str = 'train', n_folds: int = 5, split: int = 1, feat_type: str = 'raw', archive=None, **kwargs ) [source]
TESS is a set of 200 target words were spoken in the carrier phrase “Say the word _____’ by two actresses (aged 26 and 64 years) and recordings were made of the set portraying each of seven emotions(anger, disgust, fear, happiness, pleasant surprise, sadness, and neutral). There are 2800 stimuli in total.
Toronto emotional speech set (TESS) https://tspace.library.utoronto.ca/handle/1807/24487 https://doi.org/10.5683/SP2/E8H2MF
mode (str, optional) – It identifies the dataset mode (train or dev). Defaults to train.
n_folds (int, optional) – Split the dataset into n folds. 1 fold for dev dataset and n-1 for train dataset. Defaults to 5.
split (int, optional) – It specify the fold of dev dataset. Defaults to 1.
feat_type (str, optional) – It identifies the feature type that user wants to extrace of an audio file. Defaults to raw.
archive (dict) – it tells where to download the audio archive. Defaults to None.
Dataset. An instance of TESS dataset.
import paddle mode = 'dev' tess_dataset = paddle.audio.datasets.TESS(mode=mode, feat_type='raw') for idx in range(5): audio, label = tess_dataset[idx] # do something with audio, label print(audio.shape, label) # [audio_data_length] , label_id tess_dataset = paddle.audio.datasets.TESS(mode=mode, feat_type='mfcc', n_mfcc=40) for idx in range(5): audio, label = tess_dataset[idx] # do something with mfcc feature, label print(audio.shape, label) # [feature_dim, num_frames] , label_id