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# Lint as: python3
"""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,
}
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