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"""semantic and acoustic codes dataset with text. |
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""" |
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import glob |
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import os |
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import datasets |
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import torch |
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class TextSpeechCodesDatasetConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Text-SpeechCodes dataset.""" |
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def __init__(self, **kwargs): |
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super(TextSpeechCodesDatasetConfig, self).__init__(**kwargs) |
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class TextSpeechCodesDataset(datasets.GeneratorBasedBuilder): |
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"""Codes dataset.""" |
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BUILDER_CONFIGS = [ |
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TextSpeechCodesDatasetConfig(name="all", description="TextSpeechCodes dataset"), |
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] |
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@property |
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def manual_download_instructions(self): |
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return ( |
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"Codes should be computed before using this dataset. " |
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"`datasets.load_dataset('/path/to/this/script', name=all, data_dir='path/to/folder/folder_name/of/codes')`" |
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) |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"length": datasets.Value("int32"), |
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"transcription": datasets.Value("string"), |
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"acoustic_tokens": datasets.Array2D(shape=(None, 12), dtype="int16"), |
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"semantic_tokens": datasets.Array2D(shape=(None, 1), dtype="int16"), |
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"transcription_bytes": datasets.Sequence(datasets.Value("uint8")), |
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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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) |
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def _split_generators(self, dl_manager): |
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base_data_dir = os.path.abspath(os.path.expanduser(dl_manager.manual_dir)) |
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if not os.path.exists(base_data_dir): |
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raise FileNotFoundError( |
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f"{base_data_dir} does not exist. Make sure you insert a manual dir via " |
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f"`datasets.load_dataset('/this/script', data_dir=...)` " |
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f"that includes code files .pt files " |
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f"dataset. Manual download instructions: {self.manual_download_instructions}" |
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) |
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train_data_dirs = glob.glob(os.path.join(base_data_dir, "**", "*.pt"), recursive=True) |
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print(f"Found {len(train_data_dirs)} files") |
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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={"data_dirs": train_data_dirs}, |
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), |
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] |
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def _generate_examples(self, data_dirs): |
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for key, path in enumerate(data_dirs): |
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id_ = path.split("/")[-1].replace(".pt", "") |
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data = torch.load(path, map_location="cpu", weights_only=False) |
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for i, (k, v) in enumerate(data.items()): |
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acoustic_tokens = v["acoustic_codes"] |
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semantic_tokens = v["semantic_codes"] |
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if acoustic_tokens.ndim == 3: |
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acoustic_tokens = acoustic_tokens.squeeze(0).transpose(0, 1) |
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else: |
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acoustic_tokens = acoustic_tokens.transpose(0, 1) |
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if semantic_tokens.ndim == 2: |
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semantic_tokens = semantic_tokens.transpose(0, 1) |
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else: |
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semantic_tokens = semantic_tokens.unsqueeze(1) |
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transcription = v["transcription"] |
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transcription_bytes = list(transcription.encode("utf-8")) |
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yield f"{id_}_{i}", { |
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"id": f"{id_}_{i}", |
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"length": semantic_tokens.shape[0] + len(transcription_bytes), |
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"transcription": transcription, |
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"transcription_bytes": transcription_bytes, |
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"acoustic_tokens": acoustic_tokens, |
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"semantic_tokens": semantic_tokens, |
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} |
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