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# coding=utf-8
"""EmoDB paralinguistics dataset."""
import os
import textwrap
import datasets
import itertools
import typing as tp
from pathlib import Path
from ._emodb import OFFICIAL_TRAIN, OFFICIAL_TEST
SAMPLE_RATE = 16_000
_COMPRESSED_FILENAME = 'emo-db.tar.gz'
EMOTIONS_MAPPING = {
'A': 'anxiety',
'E': 'disgust',
'F': 'happiness',
'L': 'boredom',
'N': 'neutral',
'T': 'sadness',
'W': 'anger',
}
EMOTIONS = [
'anxiety', 'disgust', 'happiness', 'boredom', 'neutral', 'sadness', 'anger'
]
class EmodbConfig(datasets.BuilderConfig):
"""BuilderConfig for EmoDB."""
def __init__(self, features, **kwargs):
super(EmodbConfig, self).__init__(version=datasets.Version("0.0.1", ""), **kwargs)
self.features = features
class EmoDB(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
EmodbConfig(
features=datasets.Features(
{
"file": datasets.Value("string"),
"audio": datasets.Audio(sampling_rate=SAMPLE_RATE),
"emotion": datasets.Value("string"),
"label": datasets.ClassLabel(names=EMOTIONS),
}
),
name="emodb",
description=textwrap.dedent(
"""\
Paralinguistics classifies each audio for its emotion as a multi-class
classification, where emotions are in the same pre-defined set for both training and testing.
The evaluation metric is accuracy (ACC).
"""
),
),
]
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)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"archive_path": archive_path, "split": "train"}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": "test"}
),
]
def _generate_examples(self, archive_path, split=None):
extensions = ['.wav']
_, _walker = fast_scandir(archive_path, extensions, recursive=True)
if split == 'train':
_walker = [fileid for fileid in _walker if Path(fileid).stem in OFFICIAL_TRAIN]
elif split == 'test':
_walker = [fileid for fileid in _walker if Path(fileid).stem in OFFICIAL_TEST]
for guid, audio_path in enumerate(_walker):
yield guid, {
"id": str(guid),
"file": audio_path,
"audio": audio_path,
"emotion": EMOTIONS_MAPPING.get(Path(audio_path).stem[-2]),
"label": EMOTIONS_MAPPING.get(Path(audio_path).stem[-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 |