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137 lines
5.2 KiB
137 lines
5.2 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import collections
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import os
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import random
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from typing import List
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from typing import Tuple
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from ..utils.download import download_and_decompress
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from ..utils.env import DATA_HOME
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from .dataset import AudioClassificationDataset
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__all__ = ['RAVDESS']
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class RAVDESS(AudioClassificationDataset):
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"""
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The RAVDESS contains 24 professional actors (12 female, 12 male), vocalizing two
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lexically-matched statements in a neutral North American accent. Speech emotions
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includes calm, happy, sad, angry, fearful, surprise, and disgust expressions.
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Each expression is produced at two levels of emotional intensity (normal, strong),
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with an additional neutral expression.
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Reference:
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The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS):
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A dynamic, multimodal set of facial and vocal expressions in North American English
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https://doi.org/10.1371/journal.pone.0196391
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"""
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archieves = [
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{
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'url':
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'https://zenodo.org/record/1188976/files/Audio_Song_Actors_01-24.zip',
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'md5':
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'5411230427d67a21e18aa4d466e6d1b9',
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},
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{
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'url':
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'https://zenodo.org/record/1188976/files/Audio_Speech_Actors_01-24.zip',
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'md5':
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'bc696df654c87fed845eb13823edef8a',
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},
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]
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label_list = [
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'neutral', 'calm', 'happy', 'sad', 'angry', 'fearful', 'disgust',
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'surprised'
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]
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meta_info = collections.namedtuple(
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'META_INFO', ('modality', 'vocal_channel', 'emotion',
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'emotion_intensity', 'statement', 'repitition', 'actor'))
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speech_path = os.path.join(DATA_HOME, 'Audio_Speech_Actors_01-24')
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song_path = os.path.join(DATA_HOME, 'Audio_Song_Actors_01-24')
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def __init__(self,
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mode='train',
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seed=0,
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n_folds=5,
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split=1,
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feat_type='raw',
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**kwargs):
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"""
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Ags:
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mode (:obj:`str`, `optional`, defaults to `train`):
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It identifies the dataset mode (train or dev).
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seed (:obj:`int`, `optional`, defaults to 0):
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Set the random seed to shuffle samples.
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n_folds (:obj:`int`, `optional`, defaults to 5):
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Split the dataset into n folds. 1 fold for dev dataset and n-1 for train dataset.
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split (:obj:`int`, `optional`, defaults to 1):
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It specify the fold of dev dataset.
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feat_type (:obj:`str`, `optional`, defaults to `raw`):
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It identifies the feature type that user wants to extrace of an audio file.
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"""
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assert split <= n_folds, f'The selected split should not be larger than n_fold, but got {split} > {n_folds}'
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files, labels = self._get_data(mode, seed, n_folds, split)
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super(RAVDESS, self).__init__(
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files=files, labels=labels, feat_type=feat_type, **kwargs)
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def _get_meta_info(self, files) -> List[collections.namedtuple]:
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ret = []
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for file in files:
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basename_without_extend = os.path.basename(file)[:-4]
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ret.append(self.meta_info(*basename_without_extend.split('-')))
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return ret
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def _get_data(self, mode, seed, n_folds,
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split) -> Tuple[List[str], List[int]]:
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if not os.path.isdir(self.speech_path) and not os.path.isdir(
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self.song_path):
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download_and_decompress(self.archieves, DATA_HOME)
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wav_files = []
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for root, _, files in os.walk(self.speech_path):
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for file in files:
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if file.endswith('.wav'):
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wav_files.append(os.path.join(root, file))
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for root, _, files in os.walk(self.song_path):
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for file in files:
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if file.endswith('.wav'):
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wav_files.append(os.path.join(root, file))
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random.seed(seed) # shuffle samples to split data
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random.shuffle(
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wav_files
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) # make sure using the same seed to create train and dev dataset
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meta_info = self._get_meta_info(wav_files)
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files = []
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labels = []
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n_samples_per_fold = len(meta_info) // n_folds
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for idx, sample in enumerate(meta_info):
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_, _, emotion, _, _, _, _ = sample
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target = int(emotion) - 1
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fold = idx // n_samples_per_fold + 1
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if mode == 'train' and int(fold) != split:
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files.append(wav_files[idx])
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labels.append(target)
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if mode != 'train' and int(fold) == split:
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files.append(wav_files[idx])
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labels.append(target)
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return files, labels
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