# dataset loading script # import os # import csv import json import random import datasets # from typing import List _DESCRIPTION = """\ A video-centric instruction-tuning dataset involving timestamps for Video Large Language Models """ # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "https://github.com/RenShuhuai-Andy/TimeChat" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "" _SEED = 1234 # for determinstic random sampling # TODO: Add link to the official dataset URLs here # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URLS = { "charades": { "train": "./data/temporal_video_grounding/charades/instruct_tvg_12.4k_charades.json", "instruction": "", }, "didemo": { "train": "./data/temporal_video_grounding/didemo/instruct_tvg_33.0k_didemo.json", "instruction": "", }, "queryd": { "train": "./data/temporal_video_grounding/queryd/instruct_tvg_14.6k_queryd.json", "instruction": "", }, "hirest_grounding": { "train": "./data/temporal_video_grounding/hirest/instruct_tvg_0.5k_hirest.json", "instruction": "", }, "qvhighlights": { "train": "./data/video_highlight_detection/qvhighlights/instruct_vhd_6.9k_qvhighlights.json", "instruction": "", }, "youcook2": { "train": "./data/dense_video_captioning/youcook2/instruct_dvc_1.2k_youcook2.json", "instruction": "", }, "anet": { "train": "./data/dense_video_captioning/anet/instruct_dvc_10.0k_anet.json", "instruction": "", }, "vitt": { "train": "./data/dense_video_captioning/vitt/instruct_dvc_5.1k_vitt.json", "instruction": "", }, "tvsum": { "train": "./data/video_summarization/tvsum/instruct_vhd_50_tvsum.json", "instruction": "", }, "summe": { "train": "./data/video_summarization/summe/instruct_vhd_50_tvsum.json", "instruction": "", }, "coin": { "train": "./data/step_localization/coin/instruct_action_9.0k_coin.json", "instruction": "", }, "hirest_step": { "train": "./data/step_localization/hirest_step/instruct_action_0.5k_hirest.json", "instruction": "", }, "yttemporal": { "train": "./data/transcribed_speech_generation/yttemporal/instruct_tsp_31.6k_yttemporal.json", "instruction": "", }, } _CITATION = "" class TimeITDataset(datasets.GeneratorBasedBuilder): """TODO: Short description of my dataset.""" VERSION = datasets.Version("1.0.1") # This is an example of a dataset with multiple configurations. # If you don't want/need to define several sub-sets in your dataset, # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. # If you need to make complex sub-parts in the datasets with configurable options # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig # BUILDER_CONFIG_CLASS = MyBuilderConfig # You will be able to load one or the other configurations in the following list with # data = datasets.load_dataset('my_dataset', 'first_domain') # data = datasets.load_dataset('my_dataset', 'second_domain') BUILDER_CONFIGS = [ datasets.BuilderConfig( name="charades", version=VERSION, description="Charades-STA dataset for Temporal Video Grounding" ), datasets.BuilderConfig( name="didemo", version=VERSION, description="DiDeMo dataset for Temporal Video Grounding" ), datasets.BuilderConfig( name="queryd", version=VERSION, description="QuerYD dataset for Temporal Video Grounding" ), datasets.BuilderConfig( name="hirest_grounding", version=VERSION, description="HiREST_grounding dataset for Temporal Video Grounding" ), datasets.BuilderConfig( name="qvhighlights", version=VERSION, description="QVHighlights dataset for Video Highlight Detection" ), datasets.BuilderConfig( name="youcook2", version=VERSION, description="YouCook2 dataset for Dense Video Captioning" ), datasets.BuilderConfig( name="anet", version=VERSION, description="ActivityNet Captions dataset for Dense Video Captioning" ), datasets.BuilderConfig( name="vitt", version=VERSION, description="ViTT dataset for Dense Video Captioning" ), datasets.BuilderConfig( name="tvsum", version=VERSION, description="TVSum dataset for Video Summarization" ), datasets.BuilderConfig( name="summe", version=VERSION, description="SumMe dataset for Video Summarization" ), datasets.BuilderConfig( name="coin", version=VERSION, description="COIN dataset for Step Localization" ), datasets.BuilderConfig( name="hirest_step", version=VERSION, description="HiREST_step dataset for Step Localization" ), datasets.BuilderConfig( name="yttemporal", version=VERSION, description="YT-Temporal dataset for Transcribed Speech Generation" ), ] DEFAULT_CONFIG_NAME = "youcook2" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): # unified schema features = datasets.Features( { "video": datasets.Value("string"), "QA": datasets.Sequence( { "q": datasets.Value("string"), "a": datasets.Value("string") }, ), } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive urls = _URLS[self.config.name] data_dir = dl_manager.download_and_extract(urls) # a dict string ret = [] # control the choice of the instruction random.seed(_SEED) ret.append( datasets.SplitGenerator( name=datasets.Split.TRAIN, # These kwargs will be passed to _generate_examples gen_kwargs={ "filepath": data_dir["train"], "split": "train", "instruction_path": data_dir["instruction"], "data_dir": data_dir, }, ) ) return ret # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath, split, instruction_path, data_dir=None): # print("instruction path: ", instruction_path) instructions = json.load(open(instruction_path)) # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. timeitdata = json.load(filepath) for i, d in enumerate(timeitdata): # yield i, { # "question": d["QA"][0]['q'], # "answer": d["QA"][0]['a'], # "video_path": d["video"], # } # # print(d) yield i, d