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import os
import datasets
from huggingface_hub import HfFileSystem
from typing import List, Tuple
logger = datasets.logging.get_logger(__name__)
fs = HfFileSystem()
_CITATION = """
"""
_DESCRIPTION = """
This dataset extracts the mouth region from short clips of Vietnamese speakers.
"""
_HOMEPAGE = "https://github.com/tanthinhdt/vietnamese-av-asr"
_MAIN_REPO_PATH = "datasets/phdkhanh2507/vietnamese-speaker-lip-clip-v1"
_REPO_URL = "https://huggingface.co/{}/resolve/main"
_URLS = {
"meta": f"{_REPO_URL}/metadata/".format(_MAIN_REPO_PATH) + "{channel}.parquet",
"visual": f"{_REPO_URL}/visual/".format(_MAIN_REPO_PATH) + "{channel}.zip",
}
_CONFIGS = ["all"]
if fs.exists(_MAIN_REPO_PATH + "/metadata"):
_CONFIGS.extend([
os.path.basename(file_name)[:-8]
for file_name in fs.listdir(_MAIN_REPO_PATH + "/metadata", detail=False)
if file_name.endswith(".parquet")
])
class VietnameseSpeakerLipClipConfig(datasets.BuilderConfig):
"""Vietnamese Speaker Clip configuration."""
def __init__(self, name, **kwargs):
"""
:param name: Name of subset.
:param kwargs: Arguments.
"""
super(VietnameseSpeakerLipClipConfig, self).__init__(
name=name,
version=datasets.Version("1.0.0"),
description=_DESCRIPTION,
**kwargs,
)
class VietnameseSpeakerLipClip(datasets.GeneratorBasedBuilder):
"""Vietnamese Speaker Clip dataset."""
BUILDER_CONFIGS = [VietnameseSpeakerLipClipConfig(name) for name in _CONFIGS]
DEFAULT_CONFIG_NAME = "all"
def _info(self) -> datasets.DatasetInfo:
features = datasets.Features({
"id": datasets.Value("string"),
"channel": datasets.Value("string"),
"visual": datasets.Value("string"),
"duration": datasets.Value("float64"),
"fps": datasets.Value("int8"),
"audio": datasets.Value("string"),
"sampling_rate": datasets.Value("int64"),
})
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(
self, dl_manager: datasets.DownloadManager
) -> List[datasets.SplitGenerator]:
"""
Get splits.
:param dl_manager: Download manager.
:return: Splits.
"""
config_names = _CONFIGS[1:] if self.config.name == "all" else [self.config.name]
metadata_paths = dl_manager.download(
[_URLS["meta"].format(channel=channel) for channel in config_names]
)
visual_dirs = dl_manager.download_and_extract(
[_URLS["visual"].format(channel=channel) for channel in config_names]
)
visual_dict = {
channel: visual_dir for channel, visual_dir in zip(config_names, visual_dirs)
}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"metadata_paths": metadata_paths,
"visual_dict": visual_dict,
},
),
]
def _generate_examples(
self, metadata_paths: List[str],
visual_dict: dict,
audio_dict: dict,
) -> Tuple[int, dict]:
"""
Generate examples from metadata.
:param metadata_paths: Paths to metadata.
:param visual_dict: Paths to directory containing videos.
:param audio_dict: Paths to directory containing audios.
:yield: Example.
"""
dataset = datasets.load_dataset(
"parquet",
data_files=metadata_paths,
split="train",
)
for i, sample in enumerate(dataset):
channel = sample["channel"]
visual_path = os.path.join(
visual_dict[channel], channel, sample["id"] + ".mp4"
)
yield i, {
"id": sample["id"],
"channel": channel,
"visual": visual_path,
"duration": sample["duration"],
"fps": sample["fps"],
"sampling_rate": sample["sampling_rate"],
}
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