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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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from pathlib import Path |
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import os |
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_PROMPTS_PROSODIC_URLS = { |
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"dev": "prosodic/validation.csv", |
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"train": "prosodic/train.csv", |
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} |
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_PROMPTS_AUTOMATIC_URLS = { |
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"dev": "automatic/validation.csv", |
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"train": "automatic/train.csv", |
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} |
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_ARCHIVES_PROSODIC = { |
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"dev": "prosodic/audios.tar.gz", |
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"train": "prosodic/audios.tar.gz", |
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} |
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_ARCHIVES_AUTOMATIC = { |
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"dev": "automatic/audios.tar.gz", |
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"train": "automatic/audios.tar.gz", |
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} |
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_PATH_TO_CLIPS = { |
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"dev": "", |
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"train": "", |
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} |
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class NurcSPConfig(BuilderConfig): |
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def __init__(self, prompts_type, **kwargs): |
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super().__init__(**kwargs) |
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self.prompts_type = prompts_type |
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class NurcSPDataset(GeneratorBasedBuilder): |
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BUILDER_CONFIGS = [ |
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NurcSPConfig(name="automatic", description="Automatic audio prompts", prompts_type="automatic"), |
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NurcSPConfig(name="prosodic", description="Prosodic audio prompts", prompts_type="prosodic"), |
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] |
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def _info(self): |
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if self.config.name == "prosodic": |
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return DatasetInfo( |
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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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) |
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elif self.config.name == "automatic": |
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return DatasetInfo( |
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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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) |
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def _split_generators(self, dl_manager): |
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print("\n=== Configuration ===") |
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print(f"Using prompts_type: {self.config.prompts_type}") |
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if self.config.prompts_type == "prosodic": |
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prompts_urls = _PROMPTS_PROSODIC_URLS |
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archive_link = _ARCHIVES_PROSODIC |
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elif self.config.prompts_type == "automatic": |
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prompts_urls = _PROMPTS_AUTOMATIC_URLS |
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archive_link = _ARCHIVES_AUTOMATIC |
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else: |
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print("Invalid config") |
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return |
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print(f"Downloading prompts from: {prompts_urls}") |
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prompts_path = dl_manager.download(prompts_urls) |
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print(f"Downloaded prompts to: {prompts_path}") |
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print(f"Downloading archives from: {archive_link}") |
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archive = dl_manager.download(archive_link) |
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print(f"Downloaded archives to: {archive}") |
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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["dev"], |
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"path_to_clips": _PATH_TO_CLIPS["dev"], |
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"audio_files": dl_manager.iter_archive(archive["dev"]), |
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"split_name": "validation" |
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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["train"], |
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"audio_files": dl_manager.iter_archive(archive["train"]), |
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"split_name": "train" |
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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, split_name): |
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print(f"\n{'='*50}") |
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print(f"Processing {split_name} split") |
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print(f"{'='*50}") |
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print(f"\nCSV Path: {prompts_path}") |
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print(f"Expected clips directory: {path_to_clips}") |
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examples = {} |
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csv_paths = [] |
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print("\n=== Reading CSV ===") |
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with open(prompts_path, "r") as f: |
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csv_reader = csv.DictReader(f) |
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if self.config.prompts_type == "prosodic": |
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for row in csv_reader: |
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file_path = Path(row['path']).as_posix() |
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examples[file_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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csv_paths.append(file_path) |
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elif self.config.prompts_type == "automatic": |
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for row in csv_reader: |
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file_path = Path(row['file_path']).as_posix() |
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examples[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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csv_paths.append(file_path) |
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print(f"\nFound {len(csv_paths)} entries in CSV") |
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print("\nFirst 3 CSV paths:") |
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for path in csv_paths[:3]: |
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print(f" CSV path: {path}") |
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print("\n=== Processing Archive ===") |
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inside_clips_dir = False |
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id_ = 0 |
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matched_files = 0 |
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archive_paths = [] |
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for path, f in audio_files: |
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path = Path(path).as_posix() |
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archive_paths.append(path) |
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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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matched_files += 1 |
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yield id_, {**examples[path], "audio": audio} |
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id_ += 1 |
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print("\n=== Path Analysis ===") |
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print("\nFirst 3 archive paths:") |
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for path in archive_paths[:3]: |
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print(f" Archive path: {path}") |
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print("\nPotential matches in CSV:") |
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for csv_path in csv_paths[:3]: |
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print(f"\nComparing:") |
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print(f" Archive: {path}") |
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print(f" CSV: {csv_path}") |
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print(f" Archive parts: {path.split('/')}") |
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print(f" CSV parts: {csv_path.split('/')}") |
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print(f"\n=== Summary for {split_name} split ===") |
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print(f"Total paths in CSV: {len(csv_paths)}") |
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print(f"Total paths found in archive: {len(archive_paths)}") |
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print(f"Successfully matched files: {matched_files}") |
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if matched_files == 0: |
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print("\n!!! MATCHING FAILED !!!") |
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print("No files were matched between CSV and archive") |
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print("\nTroubleshooting:") |
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print("1. Check if CSV paths start with the clip directory name") |
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print("2. Check for case sensitivity issues") |
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print("3. Check for extra/missing directory levels") |
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print("4. Check path separator consistency") |
