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