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import json
import os
import random
def seconds_to_mmss(seconds):
minutes = int(seconds // 60)
seconds = int(seconds % 60)
return f"{minutes:02d}:{seconds:02d}"
# Templates for overlap descriptions
OVERLAP_TEMPLATES = [
"An overlap where multiple speakers talk simultaneously for more than 3 seconds starts at {start} and ends at {end}.",
"The overlap starts at {start} and ends at {end}.",
"Multiple speakers talk simultaneously from {start} to {end}, an overlap lasting over three seconds.",
"A conversation overlap occurs between {start} and {end}, with multiple people speaking at once.",
"There is a significant overlap in the conversation from {start} to {end}, where speakers talk simultaneously.",
"During the period {start}-{end}, multiple participants speak at the same time.",
"A 3+ second overlap is detected between {start} and {end}, with concurrent speakers.",
"The conversation features overlapping speech from {start} to {end}.",
"Several speakers talk over each other between {start} and {end}.",
"An overlapping segment is identified from {start} to {end}, lasting more than 3 seconds."
]
# Templates for no overlap case
NO_OVERLAP_TEMPLATES = [
"No significant overlaps found in this conversation.",
"The conversation shows no overlapping speech segments longer than 3 seconds.",
"No instances of multiple speakers talking simultaneously for more than 3 seconds were detected.",
"The dialogue proceeds without any significant overlaps between speakers.",
"No overlapping segments exceeding 3 seconds were identified in this conversation.",
"The speakers maintain clear turn-taking without significant overlaps.",
"This conversation features no substantial overlapping speech periods.",
"No overlapping speech segments of 3 seconds or longer were found.",
"The conversation flows smoothly without any significant speaker overlaps.",
"All speakers maintain clear speaking turns without substantial overlap."
]
# Templates for correct overlap case
CORRECT_OVERLAP_TEMPLATES = [
"The conversation contains overlapping speech segments longer than 3 seconds.",
"The dialogue features instances where speakers overlap for more than 3 seconds.",
"The recording includes multiple overlapping exchanges exceeding 3 seconds.",
"There are clear overlaps in speech lasting beyond 3 seconds.",
"Speaker interruptions or overlaps exceed 3 seconds in duration.",
"Clear evidence shows speech overlaps extending beyond 3 seconds in duration.",
"Speaker overlap durations consistently breach the 3-second threshold.",
"Recorded overlaps between speakers routinely last longer than 3 seconds.",
"The interaction contains several instances where voices overlap for over 3 seconds."
]
file = "silence"
def process_overlap_segments():
# Read the overlap_5s_716.json file
with open(f'{file}.json', 'r', encoding='utf-8') as f:
overlap_data = json.load(f)
# List to store results for all conversations
results = []
# Process each conversation
for conversation_id, conversation in overlap_data.items():
segments = conversation.get('segments', [])
overlap_periods = []
audio_path = conversation.get('stereo_audio', [])
# Find overlaps > 5s between segments
for i in range(len(segments) - 1):
current = segments[i]
next_segment = segments[i + 1]
# Calculate overlap
overlap_start = max(current['start_time'], next_segment['start_time'])
overlap_end = min(current['end_time'], next_segment['end_time'])
overlap_duration = overlap_end - overlap_start
# If overlap is more than 5 seconds
if overlap_duration >= 3:
overlap_periods.append(f"{seconds_to_mmss(overlap_start)}-{seconds_to_mmss(overlap_end)}")
# Create result entry with random template
if overlap_periods:
# For each overlap period, select a random template
overlap_descriptions = []
for period in overlap_periods:
start, end = period.split('-')
template = random.choice(OVERLAP_TEMPLATES)
overlap_descriptions.append(template.format(start=start, end=end))
model_output = " ".join(overlap_descriptions)
else:
model_output = random.choice(NO_OVERLAP_TEMPLATES)
result = {
"key": conversation_id,
"audio_url": audio_path,
"model_output": model_output
}
results.append(result)
# Save the results to a JSON file
output_file = f'{file}_overlapgap.json'
with open(output_file, 'w', encoding='utf-8') as f:
json.dump(results, f, indent=2, ensure_ascii=False)
print(f"Processed {len(results)} conversations")
print(f"Results written to {output_file}")
if __name__ == "__main__":
process_overlap_segments() |