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import csv
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import datasets
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from datasets import BuilderConfig, GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split
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_PROSODIC_PROMPTS_URLS = {
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"validation": "prosodic/audios_dev_metadata.csv",
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"train": "prosodic/audios_train_metadata.csv",
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}
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_AUTOMATIC_PROMPTS_URLS = {
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"validation": "automatic/audios_dev_metadata.csv",
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"train": "automatic/audios_train_metadata.csv",
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}
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_ARCHIVES = {
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"validation_prosodic": "prosodic/audios_dev.zip",
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"train_prosodic": "prosodic/audios_train.zip",
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"validation_automatic": "automatic/audios_dev.zip",
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"train_automatic": "automatic/audios_train.zip",
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}
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_PATH_TO_CLIPS = {
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"validation_prosodic": "prosodic/audios_dev",
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"train_prosodic": "prosodic/audios_train",
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"validation_automatic": "automatic/audios_dev",
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"train_automatic": "automatic/audios_train",
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}
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class EntoaConfig(BuilderConfig):
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def __init__(self, prompts_type="prosodic", **kwargs):
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super().__init__(**kwargs)
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self.prompts_type = prompts_type
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class EntoaDataset(GeneratorBasedBuilder):
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BUILDER_CONFIGS = [
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EntoaConfig(name="prosodic", description="Prosodic audio prompts", prompts_type="prosodic"),
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EntoaConfig(name="automatic", description="Automatic audio prompts", prompts_type="automatic"),
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]
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def _info(self):
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if self.config.name == "prosodic":
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features = datasets.Features(
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{
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"path": datasets.Value("string"),
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"name": datasets.Value("string"),
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"speaker": datasets.Value("string"),
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"start_time": datasets.Value("string"),
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"end_time": datasets.Value("string"),
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"normalized_text": datasets.Value("string"),
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"text": datasets.Value("string"),
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"duration": datasets.Value("string"),
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"type": datasets.Value("string"),
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"year": datasets.Value("string"),
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"gender": datasets.Value("string"),
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"age_range": datasets.Value("string"),
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"total_duration": datasets.Value("string"),
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"quality": datasets.Value("string"),
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"theme": datasets.Value("string"),
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"audio": datasets.Audio(sampling_rate=16_000),
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}
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)
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else:
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features = datasets.Features(
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{
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"audio_name": datasets.Value("string"),
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"file_path": datasets.Value("string"),
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"text": datasets.Value("string"),
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"start_time": datasets.Value("string"),
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"end_time": datasets.Value("string"),
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"duration": datasets.Value("string"),
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"quality": datasets.Value("string"),
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"speech_genre": datasets.Value("string"),
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"speech_style": datasets.Value("string"),
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"variety": datasets.Value("string"),
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"accent": datasets.Value("string"),
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"sex": datasets.Value("string"),
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"age_range": datasets.Value("string"),
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"num_speakers": datasets.Value("string"),
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"speaker_id": datasets.Value("string"),
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"audio": datasets.Audio(sampling_rate=16_000),
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}
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)
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return DatasetInfo(features=features)
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def _split_generators(self, dl_manager):
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prompts_urls = _PROSODIC_PROMPTS_URLS if self.config.name == "prosodic" else _AUTOMATIC_PROMPTS_URLS
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path_to_clips = _PATH_TO_CLIPS
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archive = dl_manager.download(_ARCHIVES)
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prompts_path = dl_manager.download(prompts_urls)
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return [
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SplitGenerator(
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name=Split.VALIDATION,
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gen_kwargs={
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"prompts_path": prompts_path["validation"],
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"path_to_clips": path_to_clips[f"validation_{self.config.name}"],
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"audio_files": dl_manager.iter_archive(archive[f"validation_{self.config.name}"]),
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},
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),
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SplitGenerator(
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name=Split.TRAIN,
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gen_kwargs={
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"prompts_path": prompts_path["train"],
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"path_to_clips": path_to_clips[f"train_{self.config.name}"],
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"audio_files": dl_manager.iter_archive(archive[f"train_{self.config.name}"]),
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},
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),
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]
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def _generate_examples(self, prompts_path, path_to_clips, audio_files):
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examples = {}
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with open(prompts_path, "r") as f:
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csv_reader = csv.DictReader(f)
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for row in csv_reader:
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if self.config.name == "prosodic":
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examples[row['path']] = {
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"path": row['path'],
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"name": row['name'],
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"speaker": row['speaker'],
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"start_time": row['start_time'],
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"end_time": row['end_time'],
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"normalized_text": row['normalized_text'],
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"text": row['text'],
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"duration": row['duration'],
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"type": row['type'],
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"year": row['year'],
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"gender": row['gender'],
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"age_range": row['age_range'],
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"total_duration": row['total_duration'],
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"quality": row['quality'],
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"theme": row['theme'],
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}
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else:
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examples[row['file_path']] = {
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"audio_name": row['audio_name'],
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"file_path": row['file_path'],
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"text": row['text'],
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"start_time": row['start_time'],
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"end_time": row['end_time'],
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"duration": row['duration'],
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"quality": row['quality'],
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"speech_genre": row['speech_genre'],
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"speech_style": row['speech_style'],
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"variety": row['variety'],
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"accent": row['accent'],
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"sex": row['sex'],
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"age_range": row['age_range'],
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"num_speakers": row['num_speakers'],
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"speaker_id": row['speaker_id'],
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}
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id_ = 0
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inside_clips_dir = False
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for path, f in audio_files:
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if path.startswith(path_to_clips):
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inside_clips_dir = True
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if path in examples:
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audio = {"path": path, "bytes": f.read()}
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yield id_, {**examples[path], "audio": audio}
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id_ += 1
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elif inside_clips_dir:
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break
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