create yttemporal loading script

yttemporal180m.py

@@ -0,0 +1,112 @@
+import os
+import json
+import datasets
+import datetime
+
+_CITATION = """
+@inproceedings{zellersluhessel2021merlot,
+  title={MERLOT: Multimodal Neural Script Knowledge Models},
+  author={Zellers, Rowan and Lu, Ximing and Hessel, Jack and Yu, Youngjae and Park, Jae Sung and Cao, Jize and Farhadi, Ali and Choi, Yejin},
+  booktitle={Advances in Neural Information Processing Systems 34},
+  year={2021}
+}
+"""
+
+_DESCRIPTION = """\
+YT-Temporal-180M, a large and diverse dataset of 6 million videos (spanning 180M extracted frames)
+that covers diverse topics.
+"""
+
+_URL_BASE = "https://rowanzellers.com/merlot/#data"
+
+url_numbers = ["00" + str(i) if i<10 else "0"+ str(i) for i in range(100)]  
+_DL_URLS = ["https://storage.googleapis.com/merlot/yttemporal180m/yttemporal180m_{num}of100.jsonl.gz" for num in url_numbers]
+
+def json_serializer(o):
+    if isinstance(o, datetime):
+        return str(o)
+
+    raise TypeError(
+        f"Object of type {o.__class__.__name__} is not JSON serializable")
+
+class yttemporal180mConfig(datasets.BuilderConfig):
+    """BuilderConfig for ActivityNet Captions."""
+
+    def __init__(self, **kwargs):
+        super(yttemporal180mConfig, self).__init__(
+            version=datasets.Version("2.1.0", ""), **kwargs)
+
+
+class yttemporal180m(datasets.GeneratorBasedBuilder):
+
+    DEFAULT_CONFIG_NAME = "all"
+    BUILDER_CONFIGS = [
+        yttemporal180mConfig(
+            name="default", description="Default full yttemporal180m dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "video_id": datasets.Value("string"),
+                    "video_url": datasets.Value("string"),
+                    "asr": datasets.Value("string"),
+                    "title": datasets.Value("string"), 
+                    "quality": datasets.Value("int8"),
+                    "meta": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage=_URL_BASE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        archive_paths = [dl_manager.download_and_extract(url) for url in _DL_URLS]
+
+        train_split = [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "jsonl_files": archive_paths
+                },
+            )
+        ]
+        
+        return train_split
+
+    def _generate_examples(self, jsonl_files):
+        """This function returns the examples."""
+        idx = 0
+        for file in jsonl_files:
+            with open(file, encoding="utf-8") as jsonl_file:
+                json_list = list(jsonl_file)
+                for json_str in json_list:
+                    infos = json.loads(json_str)
+                    
+                    id = infos['info']['display_id']
+                    url = "https://www.youtube.com/watch?v=" + id[2:]
+                    asr = ""
+                    for example in infos['denoised']:
+                        asr += example["cleanasr"]
+                        
+                    metadata_dict = {
+                        "asr_info": infos["denoised"],
+                        "info": infos["info"],
+                        "subtitles": infos["subtitles"],                
+                    }
+                    yield idx, {
+                        "video_id": id,
+                        "video_url": url,
+                        "asr": asr,
+                        "title": infos['info']['title'], 
+                        "quality": infos['info']['quality'],
+                        "meta": json.dumps(
+                            metadata_dict,
+                            default=json_serializer,
+                            indent=2
+                        )
+                    }
+                    idx += 1