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# coding=utf-8
"""CREMA-D 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
SAMPLE_RATE = 16_000
_COMPRESSED_FILENAME = 'crema-d.zip'
CREMAD_EMOTIONS_MAPPING = {
'ANG': 'anger',
'DIS': 'disgust',
'FEA': 'fear',
'HAP': 'happy',
'NEU': 'neutral',
'SAD': 'sad',
}
CLASSES = list(sorted(CREMAD_EMOTIONS_MAPPING.values()))
class CremaDConfig(datasets.BuilderConfig):
"""BuilderConfig for CREMA-D."""
def __init__(self, features, **kwargs):
super(CremaDConfig, self).__init__(version=datasets.Version("0.0.1", ""), **kwargs)
self.features = features
class CREMAD(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
CremaDConfig(
features=datasets.Features(
{
"file": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
"emotion": datasets.Value("string"),
"label": datasets.ClassLabel(names=CLASSES),
}
),
name="crema-d",
description='',
),
]
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)
train_walker, val_test_walker = train_test_split(
_walker, test_size=0.3, random_state=914, stratify=[default_find_classes(f) for f in _walker]
)
val_walker, test_walker = train_test_split(
val_test_walker, test_size=0.5, random_state=914, stratify=[default_find_classes(f) for f in val_test_walker]
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"audio_paths": train_walker, "split": "train"}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"audio_paths": val_walker, "split": "validation"}
),
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 CREMAD_EMOTIONS_MAPPING.get(Path(audio_path).name.split('_')[2])
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 |