Datasets:
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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}")
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