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Duration Gap task generator for temporal reasoning dataset.
Multi-hop inter-task: Combines event-duration reasoning with silence-gap
reasoning by comparing sound duration to nearby silent intervals.
Uses PreprocessedESC50Dataset for accurate effective durations.
"""
import random
from pathlib import Path
from typing import Dict, List, Optional
from utils import (
setup_logger,
set_random_seed,
concatenate_to_target_duration,
generate_controlled_gap_durations,
create_preprocessed_dataset,
)
from tasks.multihop_base import MultihopBaseGenerator
class DurationGapTaskGenerator(MultihopBaseGenerator):
"""Generates duration_gap task dataset samples."""
TASK_NAME = "duration_gap"
def __init__(self, config: dict, logger=None):
super().__init__(config, logger)
# Override dataset with preprocessed version
preprocessed_path = self.task_config.get(
"preprocessed_data_path",
config["tasks"].get("duration", {}).get("preprocessed_data_path", ""),
)
self.dataset = create_preprocessed_dataset(config, preprocessed_path=preprocessed_path)
def generate_sample(
self,
sample_id: int,
target_duration_seconds: float = None,
question_type: str = None,
) -> Optional[Dict]:
"""
Generate a single duration_gap sample.
Pipeline:
1. Build scene with 3-6 events with controlled silences
2. For each event, know its effective duration and adjacent gap durations
3. Compare event duration vs gap duration
4. Generate question
"""
n_events = random.randint(
self.task_config.get("min_events", 3),
self.task_config.get("max_events", 6),
)
n_unique = min(n_events, len(self.dataset.CATEGORIES))
categories = self.dataset.sample_categories(n_unique)
while len(categories) < n_events:
categories.append(random.choice(categories[:n_unique]))
random.shuffle(categories)
# Load audio with known durations
from pydub import AudioSegment as PydubSegment
source_files = []
audio_segments = []
effective_durations_ms = []
for cat in categories:
fname, fpath, eff_dur = self.dataset.sample_file_from_category_with_duration(cat)
audio = self.audio_processor.load_audio(fpath)
audio = concatenate_to_target_duration(audio, max(self.source_clip_duration, eff_dur))
audio_segments.append(audio)
source_files.append(fname)
effective_durations_ms.append(int(eff_dur * 1000))
# Generate controlled gaps (wider range for comparison)
num_gaps = n_events - 1
gap_min = self.task_config.get("min_gap_ms", 500)
gap_max = self.task_config.get("max_gap_ms", 4000)
if num_gaps > 0:
gap_durations = generate_controlled_gap_durations(
num_gaps, min_gap_ms=gap_min, max_gap_ms=gap_max,
gap_multiplier=2.0,
)
else:
gap_durations = []
# Assemble audio
result = audio_segments[0]
current_ms = len(audio_segments[0])
events_meta = [
{"index": 0, "category": categories[0],
"duration_ms": len(audio_segments[0]),
"effective_duration_ms": effective_durations_ms[0],
"gap_after_ms": gap_durations[0] if gap_durations else 0}
]
for i in range(1, n_events):
gap_ms = gap_durations[i - 1]
result = result + PydubSegment.silent(duration=gap_ms)
current_ms += gap_ms
result = result + audio_segments[i]
current_ms += len(audio_segments[i])
gap_after = gap_durations[i] if i < len(gap_durations) else 0
gap_before = gap_durations[i - 1]
events_meta.append(
{"index": i, "category": categories[i],
"duration_ms": len(audio_segments[i]),
"effective_duration_ms": effective_durations_ms[i],
"gap_before_ms": gap_before,
"gap_after_ms": gap_after}
)
# Also set gap_before for first event
events_meta[0]["gap_before_ms"] = 0
output_path = self.audio_output / f"{sample_id}.wav"
result.export(str(output_path), format="wav")
if question_type is None:
question_type = random.choice(self.task_config["question_types"])
mcq_data, open_data, q_meta = self._generate_question(
question_type, categories, events_meta, gap_durations
)
if mcq_data is None:
return None
metadata = {
"id": sample_id,
"audio_path": str(output_path.relative_to(self.output_base.parent)),
"n_events": n_events,
"categories": categories,
"source_files": source_files,
"question_type": question_type,
"gap_durations_ms": gap_durations,
"effective_durations_ms": effective_durations_ms,
"target_duration_s": target_duration_seconds,
"actual_duration_s": len(result) / 1000.0,
"mcq_question": mcq_data["question"],
"mcq_options": mcq_data["options"],
"mcq_correct_answer": mcq_data["correct_answer"],
"open_text_question": open_data["question"],
"open_text_answer": open_data["correct_answer"],
**q_meta,
}
self.logger.info(
f"Generated duration_gap sample {sample_id}: "
f"{n_events} events, type={question_type}"
)
return metadata
def _generate_question(self, question_type, categories, events_meta, gap_durations):
"""Generate duration vs gap comparison question."""
n = len(categories)
if question_type == "event_vs_after_gap":
# Pick an event that has a gap after it (not the last event)
valid = [e for e in events_meta if e["index"] < n - 1]
if not valid:
return None, None, {}
event = random.choice(valid)
sound1 = event["category"]
event_dur = event["effective_duration_ms"]
gap_after = event["gap_after_ms"]
if event_dur >= gap_after:
correct = sound1
else:
correct = f"silence after {sound1}"
mcq_text = self.task_config["mcq_questions"]["event_vs_after_gap"].format(sound1=sound1)
open_text = self.task_config["open_text_questions"]["event_vs_after_gap"].format(sound1=sound1)
options = [sound1, f"silence after {sound1}"]
other = [c for c in self.dataset.CATEGORIES if c != sound1][:2]
options.extend(other)
random.shuffle(options)
options = options[:4]
if correct not in options:
options[0] = correct
option_labels = ["A", "B", "C", "D"]
correct_label = option_labels[options.index(correct)]
option_map = {l: v for l, v in zip(option_labels, options)}
mcq_data = {"question": mcq_text, "options": option_map,
"correct_answer": correct_label}
open_data = {"question": open_text, "correct_answer": correct}
q_meta = {"event_duration_ms": event_dur, "gap_after_ms": gap_after}
return mcq_data, open_data, q_meta
elif question_type == "event_vs_before_gap":
valid = [e for e in events_meta if e["index"] > 0]
if not valid:
return None, None, {}
event = random.choice(valid)
sound1 = event["category"]
event_dur = event["effective_duration_ms"]
gap_before = event["gap_before_ms"]
if event_dur >= gap_before:
correct = sound1
else:
correct = f"silence before {sound1}"
mcq_text = self.task_config["mcq_questions"]["event_vs_before_gap"].format(sound1=sound1)
open_text = self.task_config["open_text_questions"]["event_vs_before_gap"].format(sound1=sound1)
options = [sound1, f"silence before {sound1}"]
other = [c for c in self.dataset.CATEGORIES if c != sound1][:2]
options.extend(other)
random.shuffle(options)
options = options[:4]
if correct not in options:
options[0] = correct
option_labels = ["A", "B", "C", "D"]
correct_label = option_labels[options.index(correct)]
option_map = {l: v for l, v in zip(option_labels, options)}
mcq_data = {"question": mcq_text, "options": option_map,
"correct_answer": correct_label}
open_data = {"question": open_text, "correct_answer": correct}
q_meta = {"event_duration_ms": event_dur, "gap_before_ms": gap_before}
return mcq_data, open_data, q_meta
elif question_type == "gap_vs_event":
if n < 3 or len(gap_durations) < 1:
return None, None, {}
# Pick a gap and compare with a third event's duration
gap_idx = random.randint(0, len(gap_durations) - 1)
gap_dur = gap_durations[gap_idx]
sound1 = categories[gap_idx]
sound2 = categories[gap_idx + 1]
# Pick a third event
other_indices = [i for i in range(n) if i != gap_idx and i != gap_idx + 1]
if not other_indices:
return None, None, {}
third_idx = random.choice(other_indices)
sound3 = categories[third_idx]
third_dur = events_meta[third_idx]["effective_duration_ms"]
if gap_dur >= third_dur:
correct = f"the pause between {sound1} and {sound2}"
else:
correct = sound3
mcq_text = self.task_config["mcq_questions"]["gap_vs_event"].format(sound1=sound1, sound2=sound2, sound3=sound3)
open_text = self.task_config["open_text_questions"]["gap_vs_event"].format(sound1=sound1, sound2=sound2, sound3=sound3)
options = [f"the pause between {sound1} and {sound2}", sound3]
other = [c for c in self.dataset.CATEGORIES if c not in [sound1, sound2, sound3]][:2]
options.extend(other)
random.shuffle(options)
options = options[:4]
if correct not in options:
options[0] = correct
option_labels = ["A", "B", "C", "D"]
correct_label = option_labels[options.index(correct)]
option_map = {l: v for l, v in zip(option_labels, options)}
mcq_data = {"question": mcq_text, "options": option_map,
"correct_answer": correct_label}
open_data = {"question": open_text, "correct_answer": correct}
q_meta = {"gap_duration_ms": gap_dur, "third_event_duration_ms": third_dur}
return mcq_data, open_data, q_meta
return None, None, {}
def main(config_path: str = None):
import yaml
if config_path is None:
config_path = Path(__file__).parent.parent / "config.yaml"
with open(config_path, "r") as f:
config = yaml.safe_load(f)
set_random_seed(config["random_seed"])
logger = setup_logger(
"duration_gap_task",
log_file=str(Path(config["output"]["base_path"]) / config["logging"]["log_file"]),
level=config["logging"]["level"],
console_output=config["logging"]["console_output"],
)
generator = DurationGapTaskGenerator(config, logger)
generator.generate_dataset()
if __name__ == "__main__":
main()
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