添加耗时记录
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1e9906ef1a
commit
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92
main.py
92
main.py
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@ -135,7 +135,7 @@ def extract_wav(video_path: Path, temp_dir: Path) -> Optional[Path]:
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def process_batch_diarization(video_paths: List[Path], max_workers: int = 1):
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def process_batch_diarization(video_paths, max_workers=1):
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"""
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第一阶段：批量执行说话人分离（主进程顺序处理）
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@ -144,7 +144,7 @@ def process_batch_diarization(video_paths: List[Path], max_workers: int = 1):
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max_workers: 并发数（目前固定为 1）
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Returns:
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Dict[video_path -> diar_result_path]: 说话人分离结果映射
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字典：video_path -> diar_result_path
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"""
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print("=" * 60)
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print("第一阶段：批量说话人分离")
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@ -172,6 +172,7 @@ def process_batch_diarization(video_paths: List[Path], max_workers: int = 1):
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# 顺序处理每个视频
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for i, video_path in enumerate(video_paths, 1):
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video_start_time = time.time()
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try:
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print(f"\n[{i}/{len(video_paths)}] 处理：{video_path.name}")
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@ -179,6 +180,12 @@ def process_batch_diarization(video_paths: List[Path], max_workers: int = 1):
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wav_path = extract_wav(video_path, TEMP_DIR)
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if wav_path is None:
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print(f" ✗ 音频提取失败")
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results[video_path] = {
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"success": False,
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"diar_result": None,
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"error": "音频提取失败",
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"process_time": time.time() - video_start_time
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}
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continue
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# 2. 执行说话人分离
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@ -186,6 +193,12 @@ def process_batch_diarization(video_paths: List[Path], max_workers: int = 1):
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if not diar_segments:
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print(f" ✗ 说话人分离结果为空")
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results[video_path] = {
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"success": False,
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"diar_result": None,
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"error": "说话人分离结果为空",
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"process_time": time.time() - video_start_time
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}
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continue
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# 3. 保存说话人分离结果（临时文件）
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@ -196,16 +209,29 @@ def process_batch_diarization(video_paths: List[Path], max_workers: int = 1):
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from map_speaker import save_json
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save_json(temp_diar_path, diar_result)
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results[video_path] = str(temp_diar_path)
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print(f" ✓ 说话人分离完成")
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video_process_time = time.time() - video_start_time
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results[video_path] = {
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"success": True,
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"diar_result": str(temp_diar_path),
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"error": None,
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"process_time": video_process_time
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}
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print(f" ✓ 说话人分离完成 (耗时：{video_process_time:.1f}s)")
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# 4. 清理临时 WAV（保留用于后续 ASR）
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# 注意：这里不删除，ASR 阶段还需要
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except Exception as e:
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import traceback
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video_process_time = time.time() - video_start_time
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print(f" ✗ 处理失败：{e}")
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traceback.print_exc()
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results[video_path] = {
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"success": False,
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"diar_result": None,
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"error": str(e),
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"process_time": video_process_time
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}
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# 显示进度
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elapsed = time.time() - start_time
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@ -222,7 +248,7 @@ def process_batch_diarization(video_paths: List[Path], max_workers: int = 1):
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return results
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def process_batch_asr(video_paths: List[Path], diar_results: Dict, max_workers: int = 1):
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def process_batch_asr(video_paths, diar_results, max_workers=1):
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"""
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第二阶段：批量执行 ASR 识别并合并结果（主进程顺序处理）
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@ -232,7 +258,7 @@ def process_batch_asr(video_paths: List[Path], diar_results: Dict, max_workers:
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max_workers: 并发数（目前固定为 1）
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Returns:
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List[Dict]: 最终结果列表
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列表：最终结果列表
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"""
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print("=" * 60)
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print("第二阶段：批量语音识别 + 合并结果")
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@ -255,13 +281,39 @@ def process_batch_asr(video_paths: List[Path], diar_results: Dict, max_workers:
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# 顺序处理每个视频
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for i, video_path in enumerate(video_paths, 1):
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diar_path = diar_results.get(video_path)
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if not diar_path:
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video_start_time = time.time()
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diar_info = diar_results.get(video_path)
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# 检查第一阶段的结果（现在是字典结构）
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if not diar_info:
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print(f"\n[{i}/{len(video_paths)}] 跳过 {video_path.name}（无说话人分离结果）")
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results.append({
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"video": str(video_path),
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"success": False,
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"error": "无说话人分离结果"
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"error": "无说话人分离结果",
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"process_time": 0.0
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})
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continue
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# 如果第一阶段失败，跳过该视频
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if not diar_info.get("success"):
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print(f"\n[{i}/{len(video_paths)}] 跳过 {video_path.name}（第一阶段失败：{diar_info.get('error')})")
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results.append({
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"video": str(video_path),
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"success": False,
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"error": f"说话人分离失败：{diar_info.get('error')}",
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"process_time": diar_info.get("process_time", 0.0)
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})
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continue
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diar_path = diar_info.get("diar_result")
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if not diar_path:
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print(f"\n[{i}/{len(video_paths)}] 跳过 {video_path.name}（无说话人分离结果文件）")
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results.append({
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"video": str(video_path),
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"success": False,
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"error": "说话人分离结果文件不存在",
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"process_time": 0.0
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})
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continue
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@ -279,7 +331,8 @@ def process_batch_asr(video_paths: List[Path], diar_results: Dict, max_workers:
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results.append({
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"video": str(video_path),
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"success": False,
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"error": "音频提取失败"
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"error": "音频提取失败",
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"process_time": time.time() - video_start_time
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})
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continue
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@ -295,7 +348,8 @@ def process_batch_asr(video_paths: List[Path], diar_results: Dict, max_workers:
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results.append({
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"video": str(video_path),
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"success": False,
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"error": "ASR 识别结果为空"
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"error": "ASR 识别结果为空",
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"process_time": time.time() - video_start_time
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})
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continue
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@ -328,7 +382,7 @@ def process_batch_asr(video_paths: List[Path], diar_results: Dict, max_workers:
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# 5. 保存最终结果
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output_file = OUTPUT_DIR / f"{video_path.stem}_result.json"
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# 确保 asr_sentences 是 List[Sentence] 类型
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# 确保 asr_sentences 是列表类型
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if isinstance(asr_sentences, dict):
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# 如果是字典，尝试获取 sentences 键或转换为空列表
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asr_sentences = asr_sentences.get("sentences", [])
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@ -341,27 +395,31 @@ def process_batch_asr(video_paths: List[Path], diar_results: Dict, max_workers:
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speaker = sentence.speaker
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speaker_counts[speaker] = speaker_counts.get(speaker, 0) + 1
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video_process_time = time.time() - video_start_time
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results.append({
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"video": str(video_path),
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"success": True,
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"asr_result": [s.to_dict() for s in asr_sentences],
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"merged_result": str(output_file),
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"speaker_counts": speaker_counts,
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"total_sentences": len(asr_sentences)
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"total_sentences": len(asr_sentences),
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"process_time": video_process_time
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})
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print(f" ✓ 处理完成")
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print(f" ✓ 处理完成 (耗时：{video_process_time:.1f}s)")
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print(f" - 句子数：{len(asr_sentences)}")
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print(f" - 说话人：{speaker_counts}")
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except Exception as e:
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import traceback
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video_process_time = time.time() - video_start_time
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print(f" ✗ 处理失败：{e}")
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traceback.print_exc()
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results.append({
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"video": str(video_path),
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"success": False,
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"error": str(e)
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"error": str(e),
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"process_time": video_process_time
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})
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finally:
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@ -390,10 +448,7 @@ def process_batch_asr(video_paths: List[Path], diar_results: Dict, max_workers:
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print(f"\n✓ 第二阶段完成，耗时：{total_time:.1f}s")
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print()
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return results
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# 汇总报告
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total_time = time.time() - start_time
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success_count = sum(1 for r in results if r["success"])
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print("\n" + "=" * 60)
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@ -417,6 +472,7 @@ def process_batch_asr(video_paths: List[Path], diar_results: Dict, max_workers:
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"output": r.get("merged_result"),
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"total_sentences": r.get("total_sentences", 0),
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"speaker_counts": r.get("speaker_counts", {}),
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"process_time_seconds": round(r.get("process_time", 0.0), 2),
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"error": r.get("error")
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}
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for r in results
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