CORAA-NURC-SP-Audio-Corpus.py

import csv
import datasets
from datasets import BuilderConfig, GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split



_PROMPTS_URLS = {
    "dev": "original/audios_dev_metadata.csv",
    "test": "original/audios_test_metadata.csv",
    "train": "original/audios_train_metadata.csv",
}

_PROMPTS_FILTERED_URLS = {
    "dev": "filtered/audios_dev_metadata.csv",
    "test": "filtered/audios_test_metadata.csv",
    "train": "filtered/audios_train_metadata.csv",
}

_ARCHIVES = {
    "dev": "dev.tar.gz",
    "test": "test.tar.gz",
    "train": "train.tar.gz",
}

_PATH_TO_CLIPS = {
    "dev": "dev",
    "test": "test",
    "train": "train",
}


class NurcSPConfig(BuilderConfig):
    def __init__(self, prompts_type="original", **kwargs):
        super().__init__(**kwargs)
        self.prompts_type = prompts_type


class NurcSPDataset(GeneratorBasedBuilder):
    BUILDER_CONFIGS = [
        NurcSPConfig(name="original", description="Original audio prompts", prompts_type="original"),
        NurcSPConfig(name="filtered", description="Filtered audio prompts", prompts_type="filtered"),
    ]

    def _info(self):
        return DatasetInfo(
            features=datasets.Features(
                {
                    "audio_name": datasets.Value("string"),
                    "file_path": datasets.Value("string"),
                    "text": datasets.Value("string"),
                    "start_time": datasets.Value("string"),
                    "end_time": datasets.Value("string"),
                    "duration": datasets.Value("string"),
                    "quality": datasets.Value("string"),
                    "speech_genre": datasets.Value("string"),
                    "speech_style": datasets.Value("string"),
                    "variety": datasets.Value("string"),
                    "accent": datasets.Value("string"),
                    "sex": datasets.Value("string"),
                    "age_range": datasets.Value("string"),
                    "num_speakers": datasets.Value("string"),
                    "speaker_id": datasets.Value("string"),
                    "audio": datasets.Audio(sampling_rate=16_000),
                }
            )
        )

    def _split_generators(self, dl_manager):
        prompts_urls = _PROMPTS_URLS  # Default to original prompts URLs

        if self.config.prompts_type == "filtered":
            prompts_urls = _PROMPTS_FILTERED_URLS

        prompts_path = dl_manager.download(prompts_urls)
        archive = dl_manager.download(_ARCHIVES)

        return [
            SplitGenerator(
                name=Split.VALIDATION,
                gen_kwargs={
                    "prompts_path": prompts_path["dev"],
                    "path_to_clips": _PATH_TO_CLIPS["dev"],
                    "audio_files": dl_manager.iter_archive(archive["dev"]),
                }
            ),
            SplitGenerator(
                name=Split.TEST,
                gen_kwargs={
                    "prompts_path": prompts_path["test"],
                    "path_to_clips": _PATH_TO_CLIPS["test"],
                    "audio_files": dl_manager.iter_archive(archive["test"]),
                }
            ),
            SplitGenerator(
                name=Split.TRAIN,
                gen_kwargs={
                    "prompts_path": prompts_path["train"],
                    "path_to_clips": _PATH_TO_CLIPS["train"],
                    "audio_files": dl_manager.iter_archive(archive["train"]),
                }
            ),
        ]

    def _generate_examples(self, prompts_path, path_to_clips, audio_files):
        examples = {}
        with open(prompts_path, "r") as f:
            csv_reader = csv.DictReader(f)
            for row in csv_reader:
                audio_name = row['audio_name']
                file_path = row['file_path']
                text = row['text']
                start_time = row['start_time']
                end_time = row['end_time']
                duration = row['duration']
                quality = row['quality']
                speech_genre = row['speech_genre']
                speech_style = row['speech_style']
                variety = row['variety']
                accent = row['accent']
                sex = row['sex']
                age_range = row['age_range']
                num_speakers = row['num_speakers']
                speaker_id = row['speaker_id']
                examples[file_path] = {
                    "audio_name": audio_name,
                    "file_path": file_path,
                    "text": text,
                    "start_time": start_time,
                    "end_time": end_time,
                    "duration": duration,
                    "quality": quality,
                    "speech_genre": speech_genre,
                    "speech_style": speech_style,
                    "variety": variety,
                    "accent": accent,
                    "sex": sex,
                    "age_range": age_range,
                    "num_speakers": num_speakers,
                    "speaker_id": speaker_id,
                }
        inside_clips_dir = False
        id_ = 0
        for path, f in audio_files:
            if path.startswith(path_to_clips):
                inside_clips_dir = True
                if path in examples:
                    audio = {"path": path, "bytes": f.read()}
                    yield id_, {**examples[path], "audio": audio}
                    id_ += 1
            elif inside_clips_dir:
                break