179 lines
4.9 KiB
Python
179 lines
4.9 KiB
Python
"""
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FunASR 语音识别测试脚本
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测试功能：句级时间戳、说话人分离
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"""
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import os
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import sys
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import argparse
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from pathlib import Path
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def print_banner():
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"""打印欢迎信息"""
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print("=" * 70)
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print(" FunASR 语音识别测试工具")
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print("=" * 70)
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print("功能特性:")
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print(" • 句级时间戳（开始时间 - 结束时间）")
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print(" • 说话人分离（自动区分不同说话人）")
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print(" • 抗噪处理（VAD 语音活动检测）")
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print(" • 支持中文、方言、多语言")
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print("=" * 70)
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print()
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def test_single_audio(audio_path: str, model_name: str = "paraformer-zh"):
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"""测试单个音频文件"""
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from asr_service import ASRService
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# 检查文件
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if not os.path.exists(audio_path):
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print(f"❌ 错误: 文件不存在 - {audio_path}")
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return
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# 初始化服务
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print(f"🔄 正在初始化模型: {model_name}")
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print(f"📝 音频文件: {audio_path}")
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print("-" * 70)
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service = ASRService(model_name=model_name)
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# 执行识别
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try:
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sentences = service.recognize(audio_path)
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except Exception as e:
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print(f"❌ 识别失败: {e}")
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return
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# 显示结果
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print("\n✅ 识别完成！")
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print("=" * 70)
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print(f"共识别出 {len(sentences)} 句话\n")
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for i, sent in enumerate(sentences, 1):
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print(f"[{i}] {sent}")
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# 导出结果
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base_name = Path(audio_path).stem
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# 导出 JSON
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json_path = f"output/{base_name}_result.json"
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service.export_to_json(sentences, json_path) # type: ignore
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# 导出 SRT 字幕
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srt_path = f"output/{base_name}_result.srt"
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service.export_to_srt(sentences, srt_path) # type: ignore
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print("\n" + "=" * 70)
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print("📁 输出文件:")
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print(f" • JSON: {json_path}")
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print(f" • SRT: {srt_path}")
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print("=" * 70)
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def test_batch(audio_dir: str, model_name: str = "paraformer-zh"):
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"""批量测试目录中的音频文件"""
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from asr_service import ASRService
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# 支持的音频格式
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audio_extensions = {".wav", ".mp3", ".m4a", ".flac", ".ogg", ".wma"}
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# 扫描音频文件
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audio_files = []
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for ext in audio_extensions:
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audio_files.extend(Path(audio_dir).glob(f"*{ext}"))
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if not audio_files:
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print(f"❌ 未找到音频文件（支持格式: {', '.join(audio_extensions)}）")
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return
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print(f"🔄 找到 {len(audio_files)} 个音频文件")
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print("-" * 70)
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# 初始化服务
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service = ASRService(model_name=model_name)
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# 批量识别
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for audio_path in audio_files:
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print(f"\n处理: {audio_path.name}")
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try:
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sentences = service.recognize(audio_path)
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print(f" ✓ 识别出 {len(sentences)} 句话")
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# 导出
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base_name = audio_path.stem
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service.export_to_json(sentences, f"output/{base_name}_result.json") # type: ignore
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except Exception as e:
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print(f" ✗ 失败: {e}")
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print("\n✅ 批量处理完成！")
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def download_test_audio():
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"""下载测试音频（示例）"""
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print("📝 请准备测试音频文件")
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print("支持的格式: wav, mp3, m4a, flac, ogg, wma")
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print("\n示例音频来源:")
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print(" • 自行录制会议/对话音频")
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print(" • AISHELL 开源数据集: https://www.openslr.org/33/")
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print(" • 魔搭社区示例: https://modelscope.cn/models/iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch")
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def main():
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parser = argparse.ArgumentParser(
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description="FunASR 语音识别测试工具",
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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示例用法:
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# 识别单个文件
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python test_asr.py -f your_audio.wav
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# 使用 SenseVoice 模型（多语言）
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python test_asr.py -f your_audio.wav -m SenseVoice
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# 批量识别目录
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python test_asr.py -d ./audio_files/
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"""
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)
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parser.add_argument(
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"-f", "--file",
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help="要识别的音频文件路径"
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)
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parser.add_argument(
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"-d", "--directory",
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help="要批量识别的音频目录"
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)
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parser.add_argument(
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"-m", "--model",
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default="paraformer-zh",
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choices=["paraformer-zh", "SenseVoice"],
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help="选择模型 (默认: paraformer-zh)"
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)
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parser.add_argument(
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"--download-sample",
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action="store_true",
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help="显示测试音频下载信息"
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)
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args = parser.parse_args()
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print_banner()
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if args.download_sample:
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download_test_audio()
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elif args.file:
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test_single_audio(args.file, args.model)
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elif args.directory:
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test_batch(args.directory, args.model)
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else:
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parser.print_help()
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print("\n" + "=" * 70)
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print("提示: 使用 -f 指定音频文件，或 -d 指定音频目录")
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print("=" * 70)
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if __name__ == "__main__":
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main()
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