import csv import datasets from datasets import BuilderConfig, GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split from pathlib import Path import os _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), } ) ) 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), } ) ) def _split_generators(self, dl_manager): print("\n=== Configuration ===") print(f"Using prompts_type: {self.config.prompts_type}") 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: print("Invalid config") return print(f"Downloading prompts from: {prompts_urls}") prompts_path = dl_manager.download(prompts_urls) print(f"Downloaded prompts to: {prompts_path}") print(f"Downloading archives from: {archive_link}") archive = dl_manager.download(archive_link) print(f"Downloaded archives to: {archive}") 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): print(f"\n{'='*50}") print(f"Processing {split_name} split") print(f"{'='*50}") print(f"\nCSV Path: {prompts_path}") print(f"Expected clips directory: {path_to_clips}") examples = {} csv_paths = [] # Read CSV file print("\n=== Reading CSV ===") with open(prompts_path, "r") 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: examples[row['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'], } print(f"\nFound {len(csv_paths)} entries in CSV") print("\nFirst 3 CSV paths:") for path in csv_paths[:3]: print(f" CSV path: {path}") # Process archive print("\n=== Processing Archive ===") inside_clips_dir = False id_ = 0 matched_files = 0 archive_paths = [] for path, f in audio_files: path = Path(path).as_posix() archive_paths.append(path) if path.startswith(path_to_clips): inside_clips_dir = True if path in examples: audio = {"path": path, "bytes": f.read()} matched_files += 1 yield id_, {**examples[path], "audio": audio} id_ += 1 print("\n=== Path Analysis ===") print("\nFirst 3 archive paths:") for path in archive_paths[:3]: print(f" Archive path: {path}") # Try to find potential matches print("\nPotential matches in CSV:") for csv_path in csv_paths[:3]: print(f"\nComparing:") print(f" Archive: {path}") print(f" CSV: {csv_path}") print(f" Archive parts: {path.split('/')}") print(f" CSV parts: {csv_path.split('/')}") print(f"\n=== Summary for {split_name} split ===") print(f"Total paths in CSV: {len(csv_paths)}") print(f"Total paths found in archive: {len(archive_paths)}") print(f"Successfully matched files: {matched_files}") if matched_files == 0: print("\n!!! MATCHING FAILED !!!") print("No files were matched between CSV and archive") print("\nTroubleshooting:") print("1. Check if CSV paths start with the clip directory name") print("2. Check for case sensitivity issues") print("3. Check for extra/missing directory levels") print("4. Check path separator consistency")