Convert dataset to Parquet (#1)

- Convert dataset to Parquet (a4990a48408216f00f0f49dc8ea984a576359100)
- Delete loading script auxiliary file (e5d46afa133aaaabd89370f60a4eae077210e5d2)
- Delete loading script auxiliary file (628cfa90134d8179017de36a34c2d34e200d3a19)
- Delete loading script (eb0327e64eb8fee7edcb514252b55844ad7ab804)
- Delete data file (c6eb7446b39be87a134d2785b794b26c56068460)
- Delete data file (43af9b7ab852b4ad3b96dfc7cf05b7ebd2650380)
- Delete data file (219858cba27fefea8cd66ff004c160cd441d7d5f)
- Delete data file (8184f948199a4bc464c403439fa62c95de6105f9)

README.md

@@ -6,6 +6,25 @@ language:
 - en
 size_categories:
 - 1K<n<10K
+dataset_info:
+  config_name: en
+  features:
+  - name: audio
+    dtype: audio
+  - name: sentence
+    dtype: string
+  splits:
+  - name: xs
+    num_bytes: 653140044.0
+    num_examples: 10000
+  download_size: 647516502
+  dataset_size: 653140044.0
+configs:
+- config_name: en
+  data_files:
+  - split: xs
+    path: en/xs-*
+  default: true
 ---
 
 # 🎬 ScreenTalk-XS: Sample Speech Dataset from Screen Content 🖥️

ScreenTalk-XS.py

@@ -1,167 +0,0 @@
-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

languages.py

@@ -1 +0,0 @@
-LANGUAGES = {"en": "English",}

n_shards.json

@@ -1,5 +0,0 @@
-{
-  "en": {
-    "xs": 2
-  }
-}

release_stats.py

@@ -1,17 +0,0 @@
-STATS = {
-    "version": "1.0.0",
-    "date": "2025-02-24",
-    "totalValidHrs": 0,
-    "locales": {
-        "en": {
-            "buckets": {
-                "xs": 1,
-            },
-            "duration": 0,
-            "clips": 10,
-            "totalHrs": 0,
-            "size": 10
-            
-        }
-    }
-}