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