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| from pyannote.audio import Pipeline | |
| pipeline = Pipeline.from_pretrained( | |
| "pyannote/speaker-diarization-3.1") | |
| # send pipeline to GPU (when available) | |
| import os | |
| from pydub import AudioSegment | |
| import torch | |
| pipeline.to(torch.device("cuda")) | |
| from pathlib import Path | |
| def run_diarization(input_file): | |
| # apply pretrained pipeline | |
| diarization = pipeline(input_file) | |
| rttm_out=diarization.to_rttm() | |
| rttm_file= open(Path(input_file).stem+'.rttm','w') | |
| rttm_file.write(rttm_out) | |
| rttm_file.close | |
| diarization | |
| diarization_result= [] | |
| # print the result | |
| for turn, _, speaker in diarization.itertracks(yield_label=True): | |
| #diarization_results.append([{turn.start:.1f},{turn.end:.1f},{speaker}]) | |
| print_out=f"{turn.start:.1f} {turn.end:.1f} {speaker}" | |
| diarization_result.append(print_out.split(' ')) | |
| print(f"start={turn.start:.1f}s stop={turn.end:.1f}s speaker_{speaker}") | |
| audio_segments=[] | |
| for segment in diarization_result: | |
| start= float(segment[0]) | |
| end= float(segment[1]) | |
| speaker= segment[2] | |
| start_time= start*1000 | |
| end_time= end*1000 | |
| name=speaker+'_'+'['+str(start)+'_'+str(end)+']' | |
| audio_segments.append([name+'.wav',start_time,end_time]) | |
| sound= AudioSegment.from_wav(input_file) | |
| output_directory=Path(input_file).stem+"_segments" | |
| os.mkdir(output_directory) | |
| counter=1 | |
| for interval in audio_segments: | |
| extract= sound[interval[1]:interval[2]] | |
| segment_name=output_directory+'/'+str(counter)+'_'+interval[0] | |
| extract.export(segment_name, format='wav') | |
| counter=counter+1 |