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Temporal Loudness task generator for temporal reasoning dataset.
Multi-hop inter-task: Combines temporal filtering with loudness comparison.
First applies a before/after condition, then compares volume levels.
"""
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,
get_lufs_loudness,
)
from tasks.multihop_base import MultihopBaseGenerator
class TemporalLoudnessTaskGenerator(MultihopBaseGenerator):
"""Generates temporal_loudness task dataset samples."""
TASK_NAME = "temporal_loudness"
def generate_sample(
self,
sample_id: int,
target_duration_seconds: float = None,
question_type: str = None,
) -> Optional[Dict]:
"""
Generate a single temporal_loudness sample.
Pipeline:
1. Build scene with 4-8 events, each at different volume levels
2. Pick anchor sound
3. Among events before/after anchor, find loudest/softest
4. Generate question
"""
n_events = random.randint(
self.task_config.get("min_events", 4),
self.task_config.get("max_events", 8),
)
# For repeated_loudness, we need repeated categories
if question_type == "repeated_loudness":
n_unique = random.randint(2, max(2, min(n_events - 1, len(self.dataset.CATEGORIES))))
else:
n_unique = min(n_events, len(self.dataset.CATEGORIES))
categories_pool = self.dataset.sample_categories(n_unique)
categories = list(categories_pool)
while len(categories) < n_events:
categories.append(random.choice(categories_pool))
random.shuffle(categories)
# Generate distinct volume levels for each event
volume_range = self.task_config.get("volume_range_db", [-12, 6])
volume_levels = []
for _ in range(n_events):
vol = random.uniform(volume_range[0], volume_range[1])
volume_levels.append(round(vol, 1))
# Ensure sufficient volume difference
min_diff_db = self.task_config.get("min_volume_diff_db", 3.0)
sorted_vols = sorted(volume_levels)
if len(sorted_vols) > 1 and (sorted_vols[-1] - sorted_vols[0]) < min_diff_db:
volume_levels[0] = sorted_vols[0] - min_diff_db
# Build audio
from pydub import AudioSegment as PydubSegment
source_files = []
audio_segments = []
measured_loudness = []
per_event_s = max(self.source_clip_duration,
(target_duration_seconds or 30) / n_events)
for i, cat in enumerate(categories):
fname, fpath = self.dataset.sample_file_from_category(cat)
audio = self.audio_processor.load_audio(fpath)
audio = audio.apply_gain(volume_levels[i])
loudness = get_lufs_loudness(audio)
audio_segments.append(audio)
source_files.append(fname)
measured_loudness.append(loudness)
# Gaps
num_gaps = n_events - 1
if num_gaps > 0:
gap_durations = generate_controlled_gap_durations(
num_gaps, min_gap_ms=300, max_gap_ms=1500, gap_multiplier=1.5
)
else:
gap_durations = []
# Assemble
result = audio_segments[0]
events_meta = [
{"index": 0, "category": categories[0],
"volume_db": volume_levels[0], "loudness_lufs": measured_loudness[0]}
]
for i in range(1, n_events):
gap_ms = gap_durations[i - 1]
result = result + PydubSegment.silent(duration=gap_ms)
result = result + audio_segments[i]
events_meta.append(
{"index": i, "category": categories[i],
"volume_db": volume_levels[i], "loudness_lufs": measured_loudness[i]}
)
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"])
# Anchor selection
anchor_idx = random.randint(1, n_events - 2) if n_events > 2 else 0
anchor_sound = categories[anchor_idx]
mcq_data, open_data, q_meta = self._generate_question(
question_type, categories, volume_levels, measured_loudness,
anchor_idx, anchor_sound
)
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,
"volume_levels_db": volume_levels,
"measured_loudness_lufs": measured_loudness,
"question_type": question_type,
"anchor_sound": anchor_sound,
"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 temporal_loudness sample {sample_id}: "
f"{n_events} events, type={question_type}"
)
return metadata
def _generate_question(
self, question_type, categories, volume_levels, loudness_levels,
anchor_idx, anchor_sound
):
"""Generate temporal loudness question."""
n = len(categories)
if question_type in ("loudest_after", "softest_after"):
subset = [(i, categories[i], loudness_levels[i])
for i in range(anchor_idx + 1, n)]
elif question_type in ("loudest_before", "softest_before"):
subset = [(i, categories[i], loudness_levels[i])
for i in range(0, anchor_idx)]
elif question_type == "repeated_loudness":
# Find categories that appear multiple times
from collections import Counter
counts = Counter(categories)
repeated = [c for c, cnt in counts.items() if cnt >= 2]
if not repeated:
return None, None, {}
target_sound = random.choice(repeated)
indices = [i for i, c in enumerate(categories) if c == target_sound]
# Find loudest occurrence
loudest_idx = max(indices, key=lambda i: loudness_levels[i])
occurrence_num = indices.index(loudest_idx) + 1 # 1-indexed
correct = f"occurrence {occurrence_num}"
# Check trend
occ_loudness = [loudness_levels[i] for i in indices]
if all(occ_loudness[i] <= occ_loudness[i + 1]
for i in range(len(occ_loudness) - 1)):
trend = "louder"
elif all(occ_loudness[i] >= occ_loudness[i + 1]
for i in range(len(occ_loudness) - 1)):
trend = "softer"
else:
trend = "neither"
mcq_text = self.task_config["mcq_questions"]["repeated_loudness"].format(target_sound=target_sound)
open_text = self.task_config["open_text_questions"]["repeated_loudness"].format(target_sound=target_sound)
options = [f"occurrence {j + 1}" for j in range(len(indices))]
while len(options) < 4:
options.append(f"occurrence {len(options) + 1}")
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 = {"target_sound": target_sound, "trend": trend}
return mcq_data, open_data, q_meta
else:
return None, None, {}
if not subset:
return None, None, {}
if "loudest" in question_type:
best = max(subset, key=lambda x: x[2])
else:
best = min(subset, key=lambda x: x[2])
correct_category = best[1]
present_cats = [s[1] for s in subset]
mcq_text = self.task_config["mcq_questions"][question_type].format(anchor_sound=anchor_sound)
open_text = self.task_config["open_text_questions"][question_type].format(anchor_sound=anchor_sound)
mcq_data = self.question_generator.generate_category_mcq(
mcq_text, correct_category, present_cats, self.dataset.CATEGORIES
)
open_data = self.question_generator.generate_category_open_text(
open_text, correct_category
)
q_meta = {"correct_category": correct_category,
"correct_loudness_lufs": best[2]}
return mcq_data, open_data, q_meta
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(
"temporal_loudness_task",
log_file=str(Path(config["output"]["base_path"]) / config["logging"]["log_file"]),
level=config["logging"]["level"],
console_output=config["logging"]["console_output"],
)
generator = TemporalLoudnessTaskGenerator(config, logger)
generator.generate_dataset()
if __name__ == "__main__":
main()
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