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"""EMOVO dataset.""" |
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import os |
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import re |
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import gdown |
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import textwrap |
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import datasets |
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import itertools |
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import typing as tp |
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from pathlib import Path |
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VERSION = "0.0.1" |
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SAMPLE_RATE = 48_000 |
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_DATASET_GOOGLE_DRIVE_ID = '1SUtaKeA-LYnKaD3qv87Y5wYgihJiNJAo' |
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EMOTIONS = [ |
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'dis', 'gio', 'neu', 'pau', 'rab', 'sor', 'tri' |
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] |
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DEFAULT_XDG_CACHE_HOME = "~/.cache" |
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XDG_CACHE_HOME = os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME) |
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DEFAULT_HF_CACHE_HOME = os.path.join(XDG_CACHE_HOME, "huggingface") |
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HF_CACHE_HOME = os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME)) |
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DEFAULT_HF_DATASETS_CACHE = os.path.join(HF_CACHE_HOME, "datasets") |
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HF_DATASETS_CACHE = Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE)) |
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class EMOVOConfig(datasets.BuilderConfig): |
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"""BuilderConfig for EMOVO.""" |
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def __init__(self, features, **kwargs): |
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super(EMOVOConfig, self).__init__(version=datasets.Version("0.0.1", ""), **kwargs) |
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self.features = features |
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class EMOVO(datasets.GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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EMOVOConfig( |
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features=datasets.Features( |
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{ |
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"file": datasets.Value("string"), |
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"audio": datasets.Audio(sampling_rate=SAMPLE_RATE), |
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"emotion": datasets.Value("string"), |
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"label": datasets.ClassLabel(names=EMOTIONS), |
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} |
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), |
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name=f"fold{i}", |
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description=textwrap.dedent( |
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"""\ |
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""" |
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), |
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) |
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for i in range(1, 6+1) |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description="", |
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features=self.config.features, |
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supervised_keys=None, |
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homepage="", |
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citation=""" |
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""", |
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task_templates=None, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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refine_url = f'http://drive.google.com/uc?id={_DATASET_GOOGLE_DRIVE_ID}&confirm=t' |
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output_filepath = os.path.join(HF_DATASETS_CACHE, 'confit___emovo', 'emovo.zip') |
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if not os.path.exists(output_filepath): |
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gdown.download(refine_url, output_filepath, quiet=True) |
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archive_path = dl_manager.extract(output_filepath) |
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extensions = ['.wav'] |
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_, _walker = fast_scandir(archive_path, extensions, recursive=True) |
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if self.config.name == 'fold1': |
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speaker = 'f1' |
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elif self.config.name == 'fold2': |
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speaker = 'f2' |
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elif self.config.name == 'fold3': |
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speaker = 'f3' |
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elif self.config.name == 'fold4': |
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speaker = 'm1' |
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elif self.config.name == 'fold5': |
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speaker = 'm2' |
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elif self.config.name == 'fold6': |
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speaker = 'm3' |
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_test_walker = [fileid for fileid in _walker if Path(fileid).parent.stem == speaker] |
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_train_walker = [fileid for fileid in _walker if fileid not in _test_walker] |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={"audio_filepaths": _train_walker, "split": "train"} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={"audio_filepaths": _test_walker, "split": "test"} |
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), |
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] |
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def _generate_examples(self, audio_filepaths, split=None): |
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for guid, audio_path in enumerate(audio_filepaths): |
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yield guid, { |
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"id": str(guid), |
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"file": audio_path, |
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"audio": audio_path, |
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"emotion": Path(audio_path).name.split('-')[0], |
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"label": Path(audio_path).name.split('-')[0] |
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} |
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def fast_scandir(path: str, exts: tp.List[str], recursive: bool = False): |
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subfolders, files = [], [] |
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try: |
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for f in os.scandir(path): |
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try: |
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if f.is_dir(): |
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subfolders.append(f.path) |
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elif f.is_file(): |
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if os.path.splitext(f.name)[1].lower() in exts: |
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files.append(f.path) |
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except Exception: |
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pass |
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except Exception: |
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pass |
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if recursive: |
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for path in list(subfolders): |
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sf, f = fast_scandir(path, exts, recursive=recursive) |
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subfolders.extend(sf) |
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files.extend(f) |
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return subfolders, files |