MELD_Text_Audio.py

import datasets
import pandas as pd
from datasets import ClassLabel
import os


class MELD_Text(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("0.0.1")
    BUILDER_CONFIGS = [  # noqa: RUF012
        datasets.BuilderConfig(name="MELD_Text", version=VERSION, description="MELD text"),
    ]

    def _info(self):
        return datasets.DatasetInfo(
            description="",
            features=datasets.Features(
                {
                    "text": datasets.Value("string"),
                    "path": datasets.Value("string"),
                    "audio": datasets.Audio(16000),
                    "emotion": ClassLabel(names=["neutral", "joy", "sadness", "anger", "fear", "disgust", "surprise"]),
                    "sentiment": ClassLabel(names=["neutral", "positive", "negative"]),
                }
            ),
            supervised_keys=None,
            homepage="",
            license="",
            citation="",
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        metadata_dir: dict[str, str] = dl_manager.download_and_extract(
            {"train": "train_sent_emo.csv", "validation": "dev_sent_emo.csv", "test": "test_sent_emo.csv"}
        )  # type: ignore  # noqa: PGH003
        data_path: dict[str, str] = dl_manager.download(
            {
                "audios_train": "archive/audios_train.tgz",
                "audios_validation": "archive/audios_validation.tgz",
                "audios_test": "archive/audios_test.tgz",
            }
        )  # type: ignore  # noqa: PGH003
        path_to_clips = "MELD_Text_Audio"
        local_extracted_archive: dict[str, str] = (
            dl_manager.extract(data_path)
            if not dl_manager.is_streaming
            else {
                "audios_train": None,
                "audios_validation": None,
                "audios_test": None,
            }
        )  # type: ignore  # noqa: PGH003

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,  # type: ignore  # noqa: PGH003
                gen_kwargs={
                    "filepath": metadata_dir["train"],
                    "split": "train",
                    "local_extracted_archive": local_extracted_archive["audios_train"],
                    "audio_files": dl_manager.iter_archive(data_path["audios_train"]),
                    "path_to_clips": path_to_clips,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,  # type: ignore  # noqa: PGH003
                gen_kwargs={
                    "filepath": metadata_dir["validation"],
                    "split": "validation",
                    "local_extracted_archive": local_extracted_archive["audios_validation"],
                    "audio_files": dl_manager.iter_archive(data_path["audios_validation"]),
                    "path_to_clips": path_to_clips,
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,  # type: ignore  # noqa: PGH003
                gen_kwargs={
                    "filepath": metadata_dir["test"],
                    "split": "test",
                    "local_extracted_archive": local_extracted_archive["audios_test"],
                    "audio_files": dl_manager.iter_archive(data_path["audios_test"]),
                    "path_to_clips": path_to_clips,
                },
            ),
        ]

    def _generate_examples(self, filepath, split, local_extracted_archive, audio_files, path_to_clips):
        """Yields examples."""
        metadata_df = pd.read_csv(filepath, sep=",", index_col=0, header=0)
        metadata = {}
        for _, row in metadata_df.iterrows():
            id_ = f"dia{row['Dialogue_ID']}_utt{row['Utterance_ID']}"
            audio_path = f"{split}/{id_}.flac"
            metadata[audio_path] = row

        id_ = 0
        for path, f in audio_files:
            if path in metadata:
                row = metadata[path]
                path = os.path.join(local_extracted_archive, path) if local_extracted_archive else path
                audio = {"path": path, bytes: f.read()}
                yield (
                    id_,
                    {
                        "text": row["Utterance"],
                        "path": path,
                        "audio": audio,
                        "emotion": row["Emotion"],
                        "sentiment": row["Sentiment"],
                    },
                )
                id_ += 1