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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 contains transcripts from audio of Vietnamese speakers.
"""
_HOMEPAGE = "https://github.com/duytran1332002/vlr"
_MAIN_REPO_PATH = "datasets/phdkhanh2507/transcribed-vietnamese-audio"
_REPO_URL = "https://huggingface.co/{}/resolve/main"
_URLS = {
"meta": f"{_REPO_URL}/metadata/".format(_MAIN_REPO_PATH) + "{channel}.parquet",
"transcript": f"{_REPO_URL}/transcript/".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 TranscribedVietnameseAudioConfig(datasets.BuilderConfig):
"""Transcribed Vietnamese Audio configuration."""
def __init__(self, name, **kwargs):
"""
:param name: Name of subset.
:param kwargs: Arguments.
"""
super().__init__(
name=name,
version=datasets.Version("1.0.0"),
description=_DESCRIPTION,
**kwargs,
)
class TranscribedVietnameseAudio(datasets.GeneratorBasedBuilder):
"""Transcribed Vietnamese Audio dataset."""
BUILDER_CONFIGS = [TranscribedVietnameseAudioConfig(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("binary"),
"sampling_rate": datasets.Value("int64"),
"transcript": datasets.Value("string"),
})
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]
)
transcript_dirs = dl_manager.download_and_extract(
[_URLS["transcript"].format(channel=channel) for channel in config_names]
)
transcript_dict = {
channel: transcript_dir
for channel, transcript_dir in zip(config_names, transcript_dirs)
}
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"metadata_paths": metadata_paths,
"transcript_dict": transcript_dict,
},
),
]
def _generate_examples(
self, metadata_paths: List[str],
visual_dict: dict,
audio_dict: dict,
transcript_dict: dict,
) -> Tuple[int, dict]:
"""
Generate examples from metadata.
:param metadata_paths: Paths to metadata.
:param audio_dict: Paths to directory containing audios.
:param transcript_dict: Paths to directory containing transcripts.
:yield: Example.
"""
dataset = datasets.load_dataset(
"parquet",
data_files=metadata_paths,
split="train",
)
for i, sample in enumerate(dataset):
channel = sample["channel"]
transcript_path = os.path.join(
transcript_dict[channel], channel, sample["id"] + ".txt"
)
yield i, {
"id": sample["id"],
"channel": channel,
"duration": sample["duration"],
"fps": sample["fps"],
"sampling_rate": sample["sampling_rate"],
"transcript": self.__get_text_data(transcript_path),
}
def __get_binary_data(self, path: str) -> bytes:
"""
Get binary data from path.
:param path: Path to file.
:return: Binary data.
"""
with open(path, "rb") as f:
return f.read()
def __get_text_data(self, path: str) -> str:
"""
Get transcript from path.
:param path: Path to transcript.
:return: Transcript.
"""
with open(path, "r", encoding="utf-8") as f:
return f.read().strip()
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