ravdess.py

# 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