Datasets:
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import os
import json
import csv
import datasets
from datasets.utils.py_utils import size_str
from tqdm import tqdm
from .languages import LANGUAGES
from .release_stats import STATS
_CITATION = """\
@inproceedings{Trans,
title={fj11: A Massively Speech Corpus},
author={fj11},
year={2025}
}
"""
_HOMEPAGE = "https://huggingface.co/datasets/DataLabX"
_LICENSE = "CC-BY-SA-4.0"
_BASE_URL = "https://huggingface.co/datasets/DataLabX/ScreenTalk-XS/resolve/main/"
_AUDIO_URL = _BASE_URL + "audio/{lang}/{split}/{lang}_{split}_{shard_idx}.tar"
_TRANSCRIPT_URL = _BASE_URL + "transcript/{lang}/{split}.tsv"
_N_SHARDS_URL = _BASE_URL + "n_shards.json"
def is_valid_token(token):
return True
class TransConfig(datasets.BuilderConfig):
"""BuilderConfig for TransConfig."""
def __init__(self, name, version, **kwargs):
self.language = kwargs.pop("language", None)
self.release_date = kwargs.pop("release_date", None)
self.total_hr = kwargs.pop("total_hr", None)
self.validated_hr = kwargs.pop("validated_hr", None)
self.num_clips = kwargs.pop("num_clips", None)
self.size_bytes = kwargs.pop("size_bytes", None)
self.size_human = size_str(self.size_bytes)
description = (
f"This dataset consists of transcribed speech data from TV series and movies across various genres, "
f"including action, drama, sci-fi, and romance. It was released on {self.release_date} and contains "
f"{self.total_hr} hours of transcribed speech data. "
f"The dataset includes {self.num_clips} audio clips in {self.language}, with a total size of {self.size_human}, "
f"and is designed for automatic speech recognition (ASR) model training and fine-tuning, providing diverse and "
f"natural conversational speech from real-world entertainment media."
)
super(TransConfig, self).__init__(
name=name,
version=version,
description=description,
**kwargs
)
class ScreenTalk(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
TransConfig(
name=lang,
version=STATS["version"],
language=LANGUAGES[lang],
release_date=STATS["date"],
num_clips=lang_stats["clips"],
total_hr=float(lang_stats["totalHrs"]) if lang_stats["totalHrs"] else None,
size_bytes=int(lang_stats["size"]) if lang_stats["size"] else None,
)
for lang, lang_stats in STATS["locales"].items()
]
def _info(self):
total_languages = len(STATS["locales"])
total_valid_hours = STATS["totalValidHrs"]
description = (
f"This dataset consists of {total_valid_hours} hours of validated speech data "
f"in {total_languages} languages, with more content being continuously added. "
"It is designed for training and fine-tuning automatic speech recognition (ASR) models, "
"providing a diverse and realistic representation of spoken language."
)
features = datasets.Features(
{
"audio": datasets.Audio(), # Use datasets.Audio() instead of string
"sentence": datasets.Value("string"),
}
)
return datasets.DatasetInfo(
description=description,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
version=self.config.version,
)
def _split_generators(self, dl_manager):
lang = self.config.name
n_shards_path = dl_manager.download_and_extract(_N_SHARDS_URL)
with open(n_shards_path, encoding="utf-8") as f:
n_shards = json.load(f)
user_token = dl_manager.download_config.token
has_valid_token = is_valid_token(user_token)
audio_urls = {}
splits = ["xs"]
for split in splits:
audio_urls[split] = [
_AUDIO_URL.format(lang=lang, split=split, shard_idx=i) for i in range(n_shards[lang][split])
]
archive_paths = dl_manager.download(audio_urls)
local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
meta_urls = {split: _TRANSCRIPT_URL.format(lang=lang, split=split) for split in splits}
meta_paths = dl_manager.download_and_extract(meta_urls)
split_generators = []
split_names = {
"xs": datasets.Split("xs"),
}
for split in splits:
split_generators.append(
datasets.SplitGenerator(
name=split_names.get(split, split),
gen_kwargs={
"local_extracted_archive_paths": local_extracted_archive_paths.get(split),
"archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
"meta_path": meta_paths[split],
},
),
)
return split_generators
def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
metadata = {}
with open(meta_path, encoding="utf-8") as f:
reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
for row in tqdm(reader, desc="Reading metadata..."):
if not isinstance(row, dict):
continue
path = row.get("audio")
_, filename = os.path.split(path)
metadata[filename] = row
for i, audio_archive in enumerate(archives):
for path, file in audio_archive:
_, filename = os.path.split(path)
if filename in metadata:
result = dict(metadata[filename])
# set the audio feature and the path to the extracted file
path = os.path.join(local_extracted_archive_paths[i], path) if local_extracted_archive_paths else path
# print("path: ", path)
result["audio"] = path
# result["path"] = path
yield path, result |