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from pyannote.audio import Pipeline
from pydub import AudioSegment
from tool.file_name import *
import torch
import json
import gc
import os
gc.collect()
torch.cuda.empty_cache()
hugging_face_token = os.environ["HUGGING_FACE_TOKEN"]
pipeline = Pipeline.from_pretrained(
'pyannote/speaker-diarization', use_auth_token=hugging_face_token)
use_device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
pipeline.to(use_device)
def start_diarization(input_file):
diarization = pipeline(input_file)
sample_groups = []
speaker_groups = {}
for turn, _, speaker in diarization.itertracks(yield_label=True):
if (speaker not in sample_groups):
sample_groups.append(str(speaker))
suffix = 1
file_name = f"{speaker}-{suffix}"
while file_name in speaker_groups:
suffix += 1
file_name = f"{speaker}-{suffix}"
speaker_groups[file_name] = [turn.start, turn.end]
print(f"speaker_groups {file_name}: {speaker_groups[file_name]}")
print(
f"start={turn.start:.3f}s stop={turn.end:.3f}s speaker_{speaker}")
save_groups_json(input_file, sample_groups, speaker_groups)
audio_segmentation(input_file, speaker_groups)
print(str(speaker_groups))
return str(speaker_groups)
def audio_segmentation(input_file, speaker_groups_dict):
audioSegment = AudioSegment.from_wav(input_file)
for speaker in speaker_groups_dict:
time = speaker_groups_dict[speaker]
audioSegment[time[0]*1000: time[1] *
1000].export(f"{speaker}.wav", format='wav')
print(f"group {speaker}: {time[0]*1000}--{time[1]*1000}")
def save_groups_json(input_file, sample_groups_list: list, speaker_groups_dict: dict):
with open(dir_sample_groups_json, "w", encoding="utf-8") as json_file_sample:
json.dump(sample_groups_list, json_file_sample)
with open(dir_speaker_groups_json, "w", encoding="utf-8") as json_file_speaker:
json.dump(speaker_groups_dict, json_file_speaker)
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