import json import os import datasets from datasets import Features, Value, DatasetInfo, SplitGenerator, BuilderConfig, LargeList, Sequence TASKS = [ "word_localization", "advertisement_localization", "named_entity_localization", "speaker_number_estimation", "entire_duration", "event_duration", "emotion_ranking", "emotion_reasoning", ] _DOCUMENT_DATASET_VERSION = "1.0.0" # --- Main Dataset Builder Class --- class BLAB(datasets.GeneratorBasedBuilder): """class BLAB(object): A dataset builder supporting various audio QA tasks, each with its own specific data schema. """ BUILDER_CONFIGS = [ BuilderConfig( name=task, version=datasets.Version(_DOCUMENT_DATASET_VERSION), description=f"BLAB dataset for task: {task}", ) for task in TASKS ] def _info(self): """Defines the dataset schema (features) based on the selected task configuration.""" # --- Schema Definitions for each individual task --- if self.config.name == "word_localization": return DatasetInfo( features=Features({ "video_url": Value("string"), "audio": Value("string"), "question": Value("string"), "groundtruth": LargeList( feature=Features({ "word": Value("string"), "start": Value("float32"), "end": Value("float32"), }) ) }), description="Schema for the Word Localization task: segmenting and labeling words.", license="MIT", ) elif self.config.name == "advertisement_localization": return DatasetInfo( features=Features({ "video_url": Value("string"), "audio": Value("string"), "question": Value("string"), "groundtruth": Features({ "ads_segment": LargeList( feature=Features({ "text": Value("string"), "start": Value("float32"), "end": Value("float32"), }), ), "word_timestamp": LargeList( feature=Features({ "word": Value("string"), "start": Value("float32"), "end": Value("float32"), }), ), }) }), description="Schema for Advertisement Localization task: identifying ad segments and their transcripts.", # ... (other metadata) ) elif self.config.name == "named_entity_localization": return DatasetInfo( features=Features({ "video_url": Value("string"), "audio": Value("string"), "question": Value("string"), "groundtruth": Features({ "entities": LargeList( feature=Features({ "entity_type": Value("string"), "entity": Value("string"), "start": Value("float32"), "end": Value("float32"), }), ), "word_timestamp": LargeList( feature=Features({ "word": Value("string"), "start": Value("float32"), "end": Value("float32"), }), ), }) }), description="Schema for Named Entity Localization task: identifying specific entities and their timestamps.", # ... (other metadata) ) elif self.config.name == "speaker_number_estimation": return DatasetInfo( features=Features({ "video_url": Value("string"), "audio": Value("string"), "question": Value("string"), "groundtruth": Sequence(Value("int32")) }), description="Schema for Speaker Number Estimation task: counting speakers in a segment.", # ... (other metadata) ) elif self.config.name == "entire_duration": return DatasetInfo( features=Features({ "video_url": Value("string"), "audio": Value("string"), "question": Value("string"), "groundtruth": Value("float32") }), description="Schema for Entire Duration task: determining the total duration of an audio.", ) elif self.config.name == "event_duration": return DatasetInfo( features=Features({ "video_url": Value("string"), "audio": Value("string"), "question": Value("string"), "groundtruth": Value("float32"), "answer_type": Value("string"), }), description="Schema for Event Duration task: identifying and timing specific events.", # ... (other metadata) ) elif self.config.name == "emotion_ranking": return DatasetInfo( features=Features({ "video_url": Value("string"), "audio": Value("string"), "question": Value("string"), "type": Value("string"), "correct_option": Value("string"), "option_A": Value("string"), "option_B": Value("string"), "option_C": Value("string"), "option_D": Value("string"), "option_E": Value("string"), "correct_answer": Value("string"), # Stores the correct_answer string }), description="Schema for Emotion Ranking task: selecting the best emotion option.", # ... (other metadata) ) elif self.config.name == "emotion_reasoning": return DatasetInfo( features=Features({ "video_url": Value("string"), "audio": Value("string"), "question": Value("string"), "type": Value("string"), "correct_option": Value("string"), "option_A": Value("string"), "option_B": Value("string"), "option_C": Value("string"), "option_D": Value("string"), "correct_answer": Value("string"), # Stores the correct_answer string }), description="Schema for Emotion Reasoning task: explaining emotional context.", # ... (other metadata) ) else: raise ValueError(f"Unknown config name: {self.config.name}") def _split_generators(self, dl_manager): """Returns SplitGenerators based on the selected task configuration.""" data_files = {} if self.config.name == "word_localization": data_files = {"word_localization": "blab_long_audio/word_localization.json"} elif self.config.name == "advertisement_localization": data_files = {"advertisement_localization": "blab_long_audio/advertisement_localization.json"} elif self.config.name == "named_entity_localization": data_files = {"named_entity_localization": "blab_long_audio/named_entity_localization.json"} elif self.config.name == "speaker_number_estimation": data_files = {"speaker_number_estimation": "blab_long_audio/speaker_number_estimation.json"} elif self.config.name == "entire_duration": data_files = {"entire_duration": "blab_long_audio/entire_duration.json"} elif self.config.name == "event_duration": data_files = {"event_duration": "blab_long_audio/event_duration.json"} elif self.config.name == "emotion_ranking": data_files = {"emotion_ranking": "blab_long_audio/emotion_ranking.json"} elif self.config.name == "emotion_reasoning": data_files = {"emotion_reasoning": "blab_long_audio/emotion_reasoning.json"} else: raise ValueError(f"Unknown config name: {self.config.name}") resolved_data_files = dl_manager.download_and_extract(data_files) generators = [] for split_name, filepath in resolved_data_files.items(): generators.append( SplitGenerator( name=split_name, gen_kwargs={"filepath": filepath} ) ) return generators def _generate_examples(self, filepath): """Yields examples from the dataset files, parsing data based on the active config.""" with open(filepath, 'r', encoding='utf-8') as f: all_data = json.load(f) # For .json files, load the entire array for id_, data in enumerate(all_data): try: # Common fields for all tasks (handle missing with .get) video_url = data.get("video_url", None) audio = data.get("audio", None) question = data.get("question", None) #answer_type = data.get("answer_type", None) example = { "video_url": video_url, "audio": audio, "question": question, #"answer_type": answer_type # Include as it's a common field in your schemas } # --- Task-specific groundtruth and other fields --- if self.config.name == "word_localization": raw_groundtruth = data.get("groundtruth", []) processed_groundtruth = [] for item in raw_groundtruth: if isinstance(item, dict): processed_groundtruth.append({ "word": item.get("word", None), "start": item.get("start", None), "end": item.get("end", None), }) example["groundtruth"] = processed_groundtruth elif self.config.name == "advertisement_localization": raw_groundtruth = data.get("groundtruth", {}) raw_ads_segments = raw_groundtruth.get("ads_segment", []) processed_ads_segments = [] for ad_item in raw_ads_segments: if isinstance(ad_item, dict): processed_ads_segments.append({ "text": ad_item.get("text", None), "start": ad_item.get("start", None), "end": ad_item.get("end", None), }) raw_word_timestamps = raw_groundtruth.get("word_timestamp", []) processed_word_timestamps = [] for word_item in raw_word_timestamps: if isinstance(word_item, dict): processed_word_timestamps.append({ "word": word_item.get("word", None), "start": word_item.get("start", None), "end": word_item.get("end", None), }) example["groundtruth"] = { "ads_segment": processed_ads_segments, "word_timestamp": processed_word_timestamps, } elif self.config.name == "named_entity_localization": raw_groundtruth = data.get("groundtruth", {}) raw_entities = raw_groundtruth.get("entities", []) processed_entities = [] for entity_item in raw_entities: if isinstance(entity_item, dict): processed_entities.append({ "entity_type": entity_item.get("entity_type", None), "entity": entity_item.get("entity", None), "start": entity_item.get("start", None), "end": entity_item.get("end", None), }) raw_word_timestamps = raw_groundtruth.get("word_timestamp", []) processed_word_timestamps = [] for word_item in raw_word_timestamps: if isinstance(word_item, dict): processed_word_timestamps.append({ "word": word_item.get("word", None), "start": word_item.get("start", None), "end": word_item.get("end", None), }) example["groundtruth"] = { "entities": processed_entities, "word_timestamp": processed_word_timestamps, } elif self.config.name == "speaker_number_estimation": raw_groundtruth = data.get("groundtruth", None) processed_groundtruth = [] if raw_groundtruth is not None: if isinstance(raw_groundtruth, list): processed_groundtruth = [int(x) for x in raw_groundtruth if isinstance(x, (int, float))] elif isinstance(raw_groundtruth, (int, float)): processed_groundtruth = [int(raw_groundtruth)] example["groundtruth"] = processed_groundtruth elif self.config.name == "entire_duration": example["groundtruth"] = data.get("groundtruth", None) # Assuming float elif self.config.name == "event_duration": example["groundtruth"] = data.get("groundtruth", None) example["answer_type"] = data.get("answer_type", None) elif self.config.name == "emotion_ranking": example["type"] = data.get("type", None) example["correct_option"] = data.get("correct_option", None) example["option_A"] = data.get("option_A", None) example["option_B"] = data.get("option_B", None) example["option_C"] = data.get("option_C", None) example["option_D"] = data.get("option_D", None) example["option_E"] = data.get("option_E", None) example["correct_answer"] = data.get("correct_answer", None) elif self.config.name == "emotion_reasoning": example["type"] = data.get("type", None) example["correct_option"] = data.get("correct_option", None) example["option_A"] = data.get("option_A", None) example["option_B"] = data.get("option_B", None) example["option_C"] = data.get("option_C", None) example["option_D"] = data.get("option_D", None) example["correct_answer"] = data.get("correct_answer", None) else: raise ValueError(f"Unknown config name: {self.config.name}. This should not happen if BUILDER_CONFIGS and _info are consistent.") yield id_, example except Exception as e: print(f"Error processing example {id_} in {filepath} for config {self.config.name}: {e}")