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#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""
简单的说话人标注示例程序
从本地文件读取音频并进行说话人分离
"""
import os
import sys
from pathlib import Path
# 添加项目根目录到Python路径
sys.path.insert(0, str(Path(__file__).parent.parent))
from src.podcast_transcribe.audio import load_audio
from src.podcast_transcribe.diarization.diarization_pyannote_mlx import diarize_audio as diarize_audio_mlx
from src.podcast_transcribe.diarization.diarization_pyannote_transformers import diarize_audio as diarize_audio_transformers
def main():
"""主函数"""
audio_file = Path.joinpath(Path(__file__).parent, "input", "lex_ai_john_carmack_1.wav") # 播客音频文件路径
# audio_file = "/Users/konie/Desktop/voices/history_in_the_baking.mp3" # 播客音频文件路径
model_name = "pyannote/speaker-diarization-3.1" # 说话人分离模型名称
device = "mps" # 设备类型
# 检查文件是否存在
if not os.path.exists(audio_file):
print(f"错误：文件 '{audio_file}' 不存在")
return 1
try:
print(f"正在加载音频文件: {audio_file}")
# 加载音频文件
audio, _ = load_audio(audio_file)
print(f"音频信息: 时长={audio.duration_seconds:.2f}秒, 通道数={audio.channels}, 采样率={audio.frame_rate}Hz")
# 根据model_name选择合适的实现
if "pyannote/speaker-diarization" in model_name:
# 使用transformers版本进行说话人分离
print(f"使用transformers版本处理模型: {model_name}")
result = diarize_audio_transformers(audio, model_name=model_name, device=device, segmentation_batch_size=128)
version_name = "Transformers"
else:
# 使用MLX版本进行说话人分离
print(f"使用MLX版本处理模型: {model_name}")
result = diarize_audio_mlx(audio, model_name=model_name, device=device, segmentation_batch_size=128)
version_name = "MLX"
# 输出结果
print(f"\n{version_name}版本说话人分离结果:")
print("-" * 50)
print(f"检测到的说话人数量: {result.num_speakers}")
print(f"分段总数: {len(result.segments)}")
print("\n分段详情:")
for i, segment in enumerate(result.segments, 1):
start = segment["start"]
end = segment["end"]
speaker = segment["speaker"]
duration = end - start
print(f"分段 {i}: [{start:.2f}s - {end:.2f}s] (时长: {duration:.2f}s) 说话人: {speaker}")
return 0
except Exception as e:
print(f"错误: {str(e)}")
return 1
if __name__ == "__main__":
sys.exit(main())
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