96 lines
2.7 KiB
Python
96 lines
2.7 KiB
Python
"""
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测试模型加载（不使用多进程）
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用于诊断是否是模型本身的问题
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"""
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import sys
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import torch
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print("=" * 60)
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print("模型加载测试（单进程模式）")
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print("=" * 60)
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print(f"Python 版本：{sys.version}")
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print(f"PyTorch 版本：{torch.__version__}")
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print(f"CUDA 可用：{torch.cuda.is_available()}")
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if torch.cuda.is_available():
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print(f"GPU: {torch.cuda.get_device_name(0)}")
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print(f"CUDA 版本：{torch.version.cuda}")
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print()
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try:
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# 测试 ASR 模型
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print("=" * 60)
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print("测试 1: 加载 ASR 模型 (Paraformer)")
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print("=" * 60)
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from asr_service import ASRService
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asr_service = ASRService(model_name="paraformer-zh", device="auto")
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print("✓ ASRService 初始化完成")
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asr_service._load_model()
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print("✓ ASR 模型加载完成")
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print(f" 模型类型：{type(asr_service._model)}")
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print()
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# 测试说话人分离模型
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print("=" * 60)
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print("测试 2: 加载说话人分离模型 (3D-Speaker)")
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print("=" * 60)
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from diarization_service import DiarizationService
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diar_service = DiarizationService(
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embedding_model="eres2netv2",
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device="auto",
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cluster_threshold=0.5,
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min_cluster_size=10
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)
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print("✓ DiarizationService 初始化完成")
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diar_service._load_model()
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print("✓ 说话人分离模型加载完成")
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print(f" 模型类型：{type(diar_service.model)}")
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print()
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# 测试音频处理
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print("=" * 60)
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print("测试 3: 测试音频处理")
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print("=" * 60)
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# 检查是否有测试音频
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from pathlib import Path
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test_audio = Path("test.wav")
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if test_audio.exists():
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print(f"找到测试音频：{test_audio}")
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print("执行 ASR 识别...")
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sentences = asr_service.recognize(str(test_audio))
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print(f"✓ ASR 识别完成，共 {len(sentences)} 句")
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if sentences:
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print(f" 第一句：{sentences[0]}")
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print("执行说话人分离...")
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segments = diar_service.diarize(str(test_audio))
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print(f"✓ 说话人分离完成，共 {len(segments)} 个片段")
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if segments:
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print(f" 第一个片段：{segments[0]}")
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else:
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print("⚠️ 未找到测试音频 (test.wav)，跳过处理测试")
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print()
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print("=" * 60)
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print("✓ 所有测试通过！模型工作正常")
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print("=" * 60)
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except Exception as e:
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print()
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print("=" * 60)
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print("✗ 测试失败！")
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print("=" * 60)
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print(f"错误：{e}")
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print()
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import traceback
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traceback.print_exc()
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sys.exit(1)
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