| import csv | |
| import datasets | |
| from datasets import BuilderConfig, GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split | |
| from pathlib import Path | |
| _PROMPTS_PROSODIC_URLS = { | |
| "dev": "prosodic/validation.csv", | |
| "train": "prosodic/train.csv", | |
| } | |
| _PROMPTS_AUTOMATIC_URLS = { | |
| "dev": "automatic/validation.csv", | |
| "train": "automatic/train.csv", | |
| } | |
| _ARCHIVES_PROSODIC = { | |
| "dev": "prosodic/audios.tar.gz", | |
| "train": "prosodic/audios.tar.gz", | |
| } | |
| _ARCHIVES_AUTOMATIC = { | |
| "dev": "automatic/audios.tar.gz", | |
| "train": "automatic/audios.tar.gz", | |
| } | |
| _PATH_TO_CLIPS = { | |
| "dev": "", | |
| "train": "", | |
| } | |
| class NurcSPConfig(BuilderConfig): | |
| def __init__(self, prompts_type, **kwargs): | |
| super().__init__(**kwargs) | |
| self.prompts_type = prompts_type | |
| class NurcSPDataset(GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = [ | |
| NurcSPConfig(name="automatic", description="Automatic audio prompts", prompts_type="automatic"), | |
| NurcSPConfig(name="prosodic", description="Prosodic audio prompts", prompts_type="prosodic"), | |
| ] | |
| def _info(self): | |
| if self.config.name == "prosodic": | |
| return DatasetInfo( | |
| features=datasets.Features( | |
| { | |
| "path": datasets.Value("string"), | |
| "name": datasets.Value("string"), | |
| "speaker": datasets.Value("string"), | |
| "start_time": datasets.Value("string"), | |
| "end_time": datasets.Value("string"), | |
| "normalized_text": datasets.Value("string"), | |
| "text": datasets.Value("string"), | |
| "duration": datasets.Value("string"), | |
| "type": datasets.Value("string"), | |
| "year": datasets.Value("string"), | |
| "gender": datasets.Value("string"), | |
| "age_range": datasets.Value("string"), | |
| "total_duration": datasets.Value("string"), | |
| "quality": datasets.Value("string"), | |
| "theme": datasets.Value("string"), | |
| "audio": datasets.Audio(sampling_rate=16_000, mono=True), | |
| } | |
| ) | |
| ) | |
| elif self.config.name == "automatic": | |
| 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, mono=True), | |
| } | |
| ) | |
| ) | |
| def _split_generators(self, dl_manager): | |
| if self.config.prompts_type == "prosodic": | |
| prompts_urls = _PROMPTS_PROSODIC_URLS | |
| archive_link = _ARCHIVES_PROSODIC | |
| elif self.config.prompts_type == "automatic": | |
| prompts_urls = _PROMPTS_AUTOMATIC_URLS | |
| archive_link = _ARCHIVES_AUTOMATIC | |
| else: | |
| return | |
| prompts_path = dl_manager.download(prompts_urls) | |
| archive = dl_manager.download(archive_link) | |
| 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"]), | |
| "split_name": "validation" | |
| } | |
| ), | |
| 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"]), | |
| "split_name": "train" | |
| } | |
| ), | |
| ] | |
| def _generate_examples(self, prompts_path, path_to_clips, audio_files, split_name): | |
| examples = {} | |
| csv_paths = [] | |
| with open(prompts_path, "r", encoding="utf-8") as f: | |
| csv_reader = csv.DictReader(f) | |
| if self.config.prompts_type == "prosodic": | |
| for row in csv_reader: | |
| file_path = Path(row['path']).as_posix() | |
| examples[file_path] = { | |
| "path": row['path'], | |
| "name": row['name'], | |
| "speaker": row['speaker'], | |
| "start_time": row['start_time'], | |
| "end_time": row['end_time'], | |
| "normalized_text": row['normalized_text'], | |
| "text": row['text'], | |
| "duration": row['duration'], | |
| "type": row['type'], | |
| "year": row['year'], | |
| "gender": row['gender'], | |
| "age_range": row['age_range'], | |
| "total_duration": row['total_duration'], | |
| "quality": row['quality'], | |
| "theme": row['theme'], | |
| } | |
| csv_paths.append(file_path) | |
| elif self.config.prompts_type == "automatic": | |
| for row in csv_reader: | |
| file_path = Path(row['file_path']).as_posix() | |
| examples[file_path] = { | |
| "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'], | |
| } | |
| csv_paths.append(file_path) | |
| id_ = 0 | |
| for path, f in audio_files: | |
| path = Path(path).as_posix() | |
| if path.startswith(path_to_clips) and path in examples: | |
| audio = {"path": path, "bytes": f.read()} | |
| yield id_, {**examples[path], "audio": audio} | |
| id_ += 1 | |