import datasets _DATA_URL = "data/kscg_small_20v50_16k.tar.gz" _PROMPTS_URLS = { "train": "data/prompts-train.txt.gz", "test": "data/prompts-test.txt.gz", } class KscgSmall25(datasets.GeneratorBasedBuilder): """KSCG Small 20v50""" def _info(self): features = datasets.Features( { 'path': datasets.Value('string'), 'audio': datasets.Audio(sampling_rate=16_000), 'gender': datasets.ClassLabel( num_classes=2, names=[ 'M', 'F', ] ), 'age': datasets.ClassLabel( num_classes=2, names=[ '20s', '50s' ] ) } ) return datasets.DatasetInfo( features=features, supervised_keys=None, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" prompts_paths = dl_manager.download_and_extract(_PROMPTS_URLS) archive = dl_manager.download(_DATA_URL) train_dir = "kscg_small_20v50/train" test_dir = "kscg_small_20v50/test" return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={ "prompts_path": prompts_paths["train"], "path_to_clips": train_dir, "audio_files": dl_manager.iter_archive(archive) }, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={ "prompts_path": prompts_paths["test"], "path_to_clips": test_dir, "audio_files": dl_manager.iter_archive(archive) }, ), ] def _generate_examples(self, prompts_path, path_to_clips, audio_files): examples = {} with open(prompts_path, encoding='utf-8') as f: for row in f: data = row.strip().split(",") audio_path = data[0] + ".wav" examples[audio_path] = { 'path': audio_path, # 'phone_transcription': data[1], 'gender': data[1], 'age': data[2] } inside_clips_dir = False id_ = 0 for path, f, in audio_files: if path.startswith(path_to_clips): inside_clips_dir = True if path in examples: audio = {"path": path, "bytes": f.read()} yield id_, {**examples[path], "audio": audio} id_ += 1 elif inside_clips_dir: break