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