Duplicate from HuggingFaceM4/yttemporal180m

Co-authored-by: naruto bruto <narutoelbruto@users.noreply.huggingface.co>

.gitattributes

@@ -0,0 +1,37 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zstandard filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,3 @@
+---
+license: other
+---

yttemporal180m.py

@@ -0,0 +1,136 @@
+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 = [
+    f"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 = "default"
+    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"),
+                    "caption": datasets.Value("string"),
+                    "timestamp_start": datasets.Value("float32"),
+                    "timestamp_stop": datasets.Value("float32"),
+                    "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
+
+                    # Divide video by segments of 15 sec
+                    max_sec_per_segment = 15
+                    last_caption_timestamp = infos["subtitles"][-1]["time"]
+                    num_chunks = (
+                        int(divmod(last_caption_timestamp, max_sec_per_segment)[0]) + 1
+                    )
+                    time_chunks = [
+                        i * max_sec_per_segment for i in range(num_chunks + 1)
+                    ]
+                    time_chunk_idx = 0
+                    caption = ""
+                    for el in infos["subtitles"]:
+                        if (
+                            el["time"] > time_chunks[time_chunk_idx + 1]
+                            or el["time"] == last_caption_timestamp
+                        ):
+                            timestamp_start = float(time_chunks[time_chunk_idx])
+                            timestamp_stop = float(time_chunks[time_chunk_idx + 1])
+                            time_chunk_idx += 1
+
+                            metadata_dict = {
+                                "asr_info": infos["denoised"],
+                                "info": infos["info"],
+                                "subtitles": infos["subtitles"],
+                                "title": infos["info"]["title"],
+                            }
+                            yield idx, {
+                                "video_id": id,
+                                "video_url": url,
+                                "caption": caption,
+                                "timestamp_start": timestamp_start,
+                                "timestamp_stop": timestamp_stop
+                                if el["time"] != last_caption_timestamp
+                                else last_caption_timestamp,
+                                "meta": json.dumps(
+                                    metadata_dict, default=json_serializer, indent=2
+                                ),
+                            }
+                            idx += 1
+                            caption = ""
+                        else:
+                            caption += el["word"] + " "