Datasets:
Tasks:
Automatic Speech Recognition
Formats:
webdataset
Languages:
Uzbek
Size:
10K - 100K
Tags:
audio
License:
File size: 4,647 Bytes
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import os
import tarfile
import csv
import datasets
from datasets.utils.py_utils import size_str
from tqdm import tqdm
class STTUzbekConfig(datasets.BuilderConfig):
"""BuilderConfig for the STT Uzbek Dataset."""
def __init__(self, **kwargs):
description = (
"Speech-to-Text dataset for the Uzbek language. "
"The dataset contains audio files stored in different folders: "
"`wavs`, `uz_other_dataset`, `uz_validated_dataset`, `uz_train_dataset`. "
"The corresponding transcriptions are provided in the `metadata.csv` file."
)
super(STTUzbekConfig, self).__init__(description=description, **kwargs)
class STTUzbek(datasets.GeneratorBasedBuilder):
DEFAULT_WRITER_BATCH_SIZE = 1000
BUILDER_CONFIGS = [
STTUzbekConfig(
name="stt_uzbek",
version=datasets.Version("1.0.0"),
),
]
def _info(self):
features = datasets.Features(
{
"file_name": datasets.Value("string"),
"audio": datasets.features.Audio(sampling_rate=48_000),
"transcription": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=self.config.description,
features=features,
supervised_keys=None,
homepage="https://huggingface.co/datasets/Beehzod/STT_uz",
license="Apache License 2.0",
citation="""@misc{uzbek_stt_dataset,
author = {Beehzod},
title = {Uzbek Speech-to-Text Dataset},
year = {2024},
howpublished = {https://huggingface.co/datasets/Beehzod/STT_uz},
note = {Dataset for Uzbek language speech-to-text tasks.}
}""",
version=self.config.version,
)
def _split_generators(self, dl_manager):
# Adjust the paths according to your setup
wavs_dir = "audio/wavs"
uz_other_dir = "audio/uz_other_dataset"
uz_validated_dir = "audio/uz_validated_dataset"
uz_train_dir = "audio/uz_train_dataset"
metadata_file = "metadata.csv"
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"wavs_dir": wavs_dir,
"uz_other_dir": uz_other_dir,
"uz_validated_dir": uz_validated_dir,
"uz_train_dir": uz_train_dir,
"metadata_file": metadata_file,
},
),
]
def _generate_examples(self, wavs_dir, uz_other_dir, uz_validated_dir, uz_train_dir, metadata_file):
with open(metadata_file, encoding="utf-8") as f:
reader = csv.DictReader(f)
for row in tqdm(reader, desc="Processing metadata..."):
file_name = row["file_name"]
transcription = row["transcription"]
# Determine the file's location based on the path prefix
if file_name.startswith("audio/wavs"):
audio_path = os.path.join(wavs_dir, os.path.basename(file_name))
elif file_name.startswith("audio/uz_other_dataset"):
audio_path = self._extract_from_tar(uz_other_dir, file_name)
elif file_name.startswith("audio/uz_validated_dataset"):
audio_path = self._extract_from_tar(uz_validated_dir, file_name)
elif file_name.startswith("audio/uz_train_dataset"):
audio_path = self._extract_from_tar(uz_train_dir, file_name)
else:
raise ValueError(f"Unknown path prefix in file_name: {file_name}")
# Yield the example
yield file_name, {
"file_name": file_name,
"audio": audio_path,
"transcription": transcription,
}
def _extract_from_tar(self, tar_dir, file_name):
# Extract the specific file from the tar archives
for tar_file in os.listdir(tar_dir):
tar_path = os.path.join(tar_dir, tar_file)
with tarfile.open(tar_path, "r") as tar:
try:
file_path = file_name.split("/")[-1]
extracted_file = tar.extractfile(file_path)
if extracted_file:
return {"path": file_path, "bytes": extracted_file.read()}
except KeyError:
continue
raise FileNotFoundError(f"File {file_name} not found in any tar archives in {tar_dir}.")
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