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import json
import random
def seconds_to_mmss(seconds):
minutes = int(seconds // 60)
seconds = int(seconds % 60)
return f"{minutes:02d}:{seconds:02d}"
# Templates for speaker segment descriptions
SPEAKER_TEMPLATES = [
"Speaker {speaker} speaks during the following periods: {times}",
"Speaker {speaker}'s speaking segments occur at: {times}",
"Speaker {speaker} is active in the conversation at: {times}",
"The following time segments belong to Speaker {speaker}: {times}",
"Speaker {speaker} participates in the dialogue at: {times}",
"Speaker {speaker} contributes to the conversation during: {times}",
"Speaking turns for Speaker {speaker} are at: {times}",
"Speaker {speaker} takes the floor at: {times}",
"The voice of Speaker {speaker} is heard at: {times}",
"Speaker {speaker} engages in the discussion during: {times}"
]
file = "silence"
def process_speaker_segments():
# Read the overlap_5s_716.json file
with open(f'{file}.json', 'r', encoding='utf-8') as f:
data = json.load(f)
# List to store results for all conversations
results = []
# Process each conversation
for conversation_id, conversation in data.items():
segments = conversation.get('segments', [])
audio_path = conversation.get('stereo_audio', [])
# Dictionary to store speaking times for each speaker
speaker_times = {}
# Process each segment
for segment in segments:
speaker = segment['speaker']
start_time = segment['start_time'] # Keep as float for accurate conversion
end_time = segment['end_time'] # Keep as float for accurate conversion
# Initialize list for this speaker if not exists
if speaker not in speaker_times:
speaker_times[speaker] = []
# Add this speaking interval
speaker_times[speaker].append((start_time, end_time))
# Format the output string
output_lines = []
for speaker in sorted(speaker_times.keys()):
times = speaker_times[speaker]
time_ranges = [f"{seconds_to_mmss(start)}-{seconds_to_mmss(end)}" for start, end in times]
# Randomly select a template for each speaker
template = random.choice(SPEAKER_TEMPLATES)
output_lines.append(template.format(speaker=speaker, times=', '.join(time_ranges)))
# Create result entry
result = {
"key": conversation_id,
"audio_url": audio_path,
"model_output": "\n".join(output_lines)
}
results.append(result)
# Save the results to a JSON file
output_file = f'{file}_speaker.json'
with open(output_file, 'w', encoding='utf-8') as f:
json.dump(results, f, indent=2, ensure_ascii=False)
if __name__ == "__main__":
process_speaker_segments() |