File size: 5,047 Bytes
8d22b8d d0778db 8d22b8d 6656c43 2c844e5 6656c43 8d22b8d 6656c43 8d22b8d d0778db 8d22b8d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 |
# coding=utf-8
"""RAVDESS paralinguistics classification dataset."""
import os
import textwrap
import datasets
import itertools
import typing as tp
from pathlib import Path
from sklearn.model_selection import train_test_split
_COMPRESSED_FILENAME = 'ravdess.zip'
SAMPLE_RATE = 48_000
RAVDESS_EMOTIONS_MAPPING = {
'01': 'neutral',
'02': 'calm',
'03': 'happy',
'04': 'sad',
'05': 'angry',
'06': 'fearful',
'07': 'disgust',
'08': 'surprised',
}
RAVDESS_ACTOR_FOLD_MAPPING = {
'01': '5',
'02': '1',
'03': '2',
'04': '5',
'05': '1',
'06': '2',
'07': '2',
'08': '4',
'09': '5',
'10': '3',
'11': '3',
'12': '3',
'13': '2',
'14': '1',
'15': '1',
'16': '1',
'17': '4',
'18': '2',
'19': '3',
'20': '3',
'21': '4',
'22': '5',
'23': '4',
'24': '4',
}
CLASSES = list(RAVDESS_EMOTIONS_MAPPING.values())
class RavdessConfig(datasets.BuilderConfig):
"""BuilderConfig for RAVDESS."""
def __init__(self, features, **kwargs):
super(RavdessConfig, self).__init__(version=datasets.Version("0.0.1", ""), **kwargs)
self.features = features
class RAVDESS(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
RavdessConfig(
features=datasets.Features(
{
"file": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
"emotion": datasets.Value("string"),
"label": datasets.ClassLabel(names=CLASSES),
}
),
name=f"fold{f}",
description='',
) for f in range(1, 6)
]
def _info(self):
return datasets.DatasetInfo(
description="",
features=self.config.features,
supervised_keys=None,
homepage="",
citation="",
task_templates=None,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
archive_path = dl_manager.extract(_COMPRESSED_FILENAME)
extensions = ['.wav']
_, _walker = fast_scandir(archive_path, extensions, recursive=True)
_walker_with_fold = [(fileid, default_find_fold(fileid)) for fileid in _walker]
if self.config.name == 'fold1':
train_fold = ['2', '3', '4', '5']
test_fold = ['1']
elif self.config.name == 'fold2':
train_fold = ['1', '3', '4', '5']
test_fold = ['2']
elif self.config.name == 'fold3':
train_fold = ['1', '2', '4', '5']
test_fold = ['3']
elif self.config.name == 'fold4':
train_fold = ['1', '2', '3', '5']
test_fold = ['4']
elif self.config.name == 'fold5':
train_fold = ['1', '2', '3', '4']
test_fold = ['5']
train_walker = [fileid for fileid, fold in _walker_with_fold if fold in train_fold]
test_walker = [fileid for fileid, fold in _walker_with_fold if fold in test_fold]
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"audio_paths": train_walker, "split": "train"}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"audio_paths": test_walker, "split": "test"}
),
]
def _generate_examples(self, audio_paths, split=None):
for guid, audio_path in enumerate(audio_paths):
yield guid, {
"id": str(guid),
"file": audio_path,
"audio": audio_path,
"emotion": default_find_classes(audio_path),
"label": default_find_classes(audio_path),
}
def default_find_classes(audio_path):
return RAVDESS_EMOTIONS_MAPPING.get(Path(audio_path).name.split('-')[2])
def default_find_fold(audio_path):
actor_id = Path(audio_path).parent.stem.split('_')[1]
return RAVDESS_ACTOR_FOLD_MAPPING.get(actor_id)
def fast_scandir(path: str, exts: tp.List[str], recursive: bool = False):
# Scan files recursively faster than glob
# From github.com/drscotthawley/aeiou/blob/main/aeiou/core.py
subfolders, files = [], []
try: # hope to avoid 'permission denied' by this try
for f in os.scandir(path):
try: # 'hope to avoid too many levels of symbolic links' error
if f.is_dir():
subfolders.append(f.path)
elif f.is_file():
if os.path.splitext(f.name)[1].lower() in exts:
files.append(f.path)
except Exception:
pass
except Exception:
pass
if recursive:
for path in list(subfolders):
sf, f = fast_scandir(path, exts, recursive=recursive)
subfolders.extend(sf)
files.extend(f) # type: ignore
return subfolders, files |