import csv import datasets from datasets import BuilderConfig, GeneratorBasedBuilder, DatasetInfo, SplitGenerator, Split _PROSODIC_PROMPTS_URLS = { "validation": "prosodic/validation.csv", "train": "prosodic/train.csv", } _AUTOMATIC_PROMPTS_URLS = { "validation": "automatic/validation.csv", "train": "automatic/train.csv", } _ARCHIVES = { "prosodic": "prosodic/audios.tar.gz", "automatic": "automatic/audios.tar.gz", } _PATH_TO_CLIPS = { "validation_prosodic": "audios", "train_prosodic": "audios", "validation_automatic": "audios/validation", "train_automatic": "audios/train", } def debug_path_matching(csv_path, archive_files): """ Debug utility to compare paths between CSV and archive files """ import csv from collections import defaultdict # Store CSV paths csv_paths = set() with open(csv_path, "r") as f: reader = csv.DictReader(f) for row in reader: # Store both the full path and filename path = row.get("path") or row.get("file_path") csv_paths.add(path) csv_paths.add(path.split("/")[-1]) # Compare with archive paths archive_paths = set() matches = defaultdict(list) for path, _ in archive_files: archive_paths.add(path) archive_paths.add(path.split("/")[-1]) # Check for matches for csv_path in csv_paths: if path.endswith(csv_path) or csv_path.endswith(path): matches[path].append(csv_path) print("=== Debug Report ===") print(f"CSV Paths: {len(csv_paths)}") print(f"Archive Paths: {len(archive_paths)}") print(f"Matched Paths: {len(matches)}") print("\nSample CSV paths:") for path in list(csv_paths)[:5]: print(f" {path}") print("\nSample Archive paths:") for path in list(archive_paths)[:5]: print(f" {path}") print("\nSample Matches:") for archive_path, csv_paths in list(matches.items())[:5]: print(f" Archive: {archive_path}") print(f" CSV: {csv_paths}") print() return csv_paths, archive_paths, matches class EntoaConfig(BuilderConfig): def __init__(self, prompts_type="prosodic", **kwargs): super().__init__(**kwargs) self.prompts_type = prompts_type class EntoaDataset(GeneratorBasedBuilder): BUILDER_CONFIGS = [ EntoaConfig(name="prosodic", description="Prosodic audio prompts", prompts_type="prosodic"), EntoaConfig(name="automatic", description="Automatic audio prompts", prompts_type="automatic"), ] def _info(self): if self.config.name == "prosodic": 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), } ) else: # automatic 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), } ) return DatasetInfo(features=features) def _split_generators(self, dl_manager): prompts_urls = _PROSODIC_PROMPTS_URLS if self.config.name == "prosodic" else _AUTOMATIC_PROMPTS_URLS archive = dl_manager.download(_ARCHIVES[self.config.name]) prompts_path = dl_manager.download(prompts_urls) # Debug prints for downloaded paths print(f"Downloaded prompts: {prompts_path}") print(f"Downloaded archive: {archive}") return [ SplitGenerator( name=Split.VALIDATION, gen_kwargs={ "prompts_path": prompts_path["validation"], "path_to_clips": _PATH_TO_CLIPS[f"validation_{self.config.name}"], "audio_files": dl_manager.iter_archive(archive), }, ), SplitGenerator( name=Split.TRAIN, gen_kwargs={ "prompts_path": prompts_path["train"], "path_to_clips": _PATH_TO_CLIPS[f"train_{self.config.name}"], "audio_files": dl_manager.iter_archive(archive), }, ), ] def _generate_examples(self, prompts_path, path_to_clips, audio_files): csv_paths, archive_paths, matches = debug_path_matching(prompts_path, audio_files) examples = {} with open(prompts_path, "r") as f: csv_reader = csv.DictReader(f) for row in csv_reader: if self.config.name == "prosodic": examples[row["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"], } else: # automatic 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"], } id_ = 0 inside_clips_dir = False for path, f in audio_files: if path.startswith(path_to_clips): inside_clips_dir = True if path in examples: # Debug: Match found print(f"Match found for: {path}") audio = {"path": path, "bytes": f.read()} yield id_, {**examples[path], "audio": audio} id_ += 1 else: # Debug: No match for this file print(f"No match for: {path}") elif inside_clips_dir: break # Debug: Print total examples generated print(f"Completed generating examples. Total examples: {id_}")