{"id":78333,"date":"2026-02-27T04:16:47","date_gmt":"2026-02-26T20:16:47","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/78333.html"},"modified":"2026-02-27T04:16:47","modified_gmt":"2026-02-26T20:16:47","slug":"fsmn-vad%e5%86%85%e5%ad%98%e4%bc%98%e5%8c%96%ef%bc%9a4gb%e4%bd%8e%e9%85%8d%e6%9c%8d%e5%8a%a1%e5%99%a8%e7%a8%b3%e5%ae%9a%e8%bf%90%e8%a1%8c%e5%ae%9e%e6%b5%8b%e6%8c%87%e5%8d%97","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/78333.html","title":{"rendered":"FSMN VAD\u5185\u5b58\u4f18\u5316\uff1a4GB\u4f4e\u914d\u670d\u52a1\u5668\u7a33\u5b9a\u8fd0\u884c\u5b9e\u6d4b\u6307\u5357"},"content":{"rendered":"<h2>FSMN VAD\u5185\u5b58\u4f18\u5316&#xff1a;4GB\u4f4e\u914d\u670d\u52a1\u5668\u7a33\u5b9a\u8fd0\u884c\u5b9e\u6d4b\u6307\u5357<\/h2>\n<h3>1. \u80cc\u666f\u4e0e\u6311\u6218<\/h3>\n<p>\u968f\u7740\u8bed\u97f3\u8bc6\u522b\u3001\u4f1a\u8bae\u8f6c\u5f55\u3001\u7535\u8bdd\u8d28\u68c0\u7b49\u5e94\u7528\u7684\u666e\u53ca&#xff0c;\u8bed\u97f3\u6d3b\u52a8\u68c0\u6d4b&#xff08;Voice Activity Detection, VAD&#xff09;\u4f5c\u4e3a\u524d\u7aef\u9884\u5904\u7406\u7684\u5173\u952e\u73af\u8282&#xff0c;\u5176\u91cd\u8981\u6027\u65e5\u76ca\u51f8\u663e\u3002\u963f\u91cc\u8fbe\u6469\u9662\u5f00\u6e90\u7684 FSMN VAD \u6a21\u578b\u51ed\u501f\u9ad8\u7cbe\u5ea6\u3001\u4f4e\u5ef6\u8fdf\u548c\u8f7b\u91cf\u7ea7\u7279\u6027&#xff0c;\u6210\u4e3a\u5de5\u4e1a\u754c\u5e7f\u6cdb\u91c7\u7528\u7684\u89e3\u51b3\u65b9\u6848\u4e4b\u4e00\u3002<\/p>\n<p>\u7136\u800c&#xff0c;\u5728\u5b9e\u9645\u90e8\u7f72\u8fc7\u7a0b\u4e2d&#xff0c;\u8bb8\u591a\u5f00\u53d1\u8005\u9762\u4e34\u4e00\u4e2a\u5171\u6027\u95ee\u9898&#xff1a;\u5982\u4f55\u5728\u8d44\u6e90\u53d7\u9650\u7684\u73af\u5883\u4e2d\u9ad8\u6548\u8fd0\u884c\u6a21\u578b&#xff1f;\u5c24\u5176\u662f\u5728\u4ec5\u6709 4GB \u5185\u5b58\u7684\u4f4e\u914d\u670d\u52a1\u5668\u6216\u8fb9\u7f18\u8bbe\u5907\u4e0a&#xff0c;\u76f4\u63a5\u90e8\u7f72\u539f\u59cb\u65b9\u6848\u5bb9\u6613\u51fa\u73b0\u5185\u5b58\u6ea2\u51fa\u3001\u670d\u52a1\u5d29\u6e83\u6216\u54cd\u5e94\u5ef6\u8fdf\u7b49\u95ee\u9898\u3002<\/p>\n<p>\u672c\u6587\u57fa\u4e8e\u771f\u5b9e\u9879\u76ee\u5b9e\u8df5&#xff0c;\u56f4\u7ed5\u201cFSMN VAD \u5728 4GB \u5185\u5b58\u670d\u52a1\u5668\u4e0a\u7684\u7a33\u5b9a\u6027\u4f18\u5316\u201d\u5c55\u5f00&#xff0c;\u63d0\u4f9b\u4e00\u5957\u53ef\u843d\u5730\u7684\u6280\u672f\u65b9\u6848\u4e0e\u8c03\u4f18\u7b56\u7565&#xff0c;\u5e76\u9644\u5e26\u5b8c\u6574\u5b9e\u6d4b\u6570\u636e\u4e0e\u914d\u7f6e\u5efa\u8bae&#xff0c;\u5e2e\u52a9\u5f00\u53d1\u8005\u5b9e\u73b0\u4f4e\u6210\u672c\u3001\u9ad8\u53ef\u7528\u7684\u8bed\u97f3\u6d3b\u52a8\u68c0\u6d4b\u670d\u52a1\u90e8\u7f72\u3002<\/p>\n<hr \/>\n<h3>2. FSMN VAD \u6a21\u578b\u7b80\u4ecb<\/h3>\n<h4>2.1 \u6280\u672f\u80cc\u666f<\/h4>\n<p>FSMN&#xff08;Feedforward Sequential Memory Neural Network&#xff09;\u662f\u4e00\u79cd\u4e13\u4e3a\u5e8f\u5217\u5efa\u6a21\u8bbe\u8ba1\u7684\u795e\u7ecf\u7f51\u7edc\u7ed3\u6784&#xff0c;\u7531\u963f\u91cc\u8fbe\u6469\u9662\u5728 FunASR \u5de5\u5177\u5305\u4e2d\u5e7f\u6cdb\u5e94\u7528\u3002\u76f8\u6bd4\u4f20\u7edf RNN \u6216 LSTM&#xff0c;FSMN \u5177\u5907\u4ee5\u4e0b\u4f18\u52bf&#xff1a;<\/p>\n<ul>\n<li>\u53c2\u6570\u91cf\u5c0f&#xff1a;\u6a21\u578b\u5927\u5c0f\u4ec5\u7ea6 1.7MB&#xff0c;\u9002\u5408\u5d4c\u5165\u5f0f\u90e8\u7f72<\/li>\n<li>\u63a8\u7406\u901f\u5ea6\u5feb&#xff1a;RTF&#xff08;Real-Time Factor&#xff09;\u53ef\u8fbe 0.03&#xff0c;\u5373\u5904\u7406\u901f\u5ea6\u662f\u5b9e\u65f6\u97f3\u9891\u7684 33 \u500d<\/li>\n<li>\u4f4e\u5ef6\u8fdf\u54cd\u5e94&#xff1a;\u652f\u6301\u6d41\u5f0f\u8f93\u5165&#xff0c;\u7aef\u5230\u7aef\u5ef6\u8fdf\u4f4e\u4e8e 100ms<\/li>\n<li>\u9ad8\u9c81\u68d2\u6027&#xff1a;\u5bf9\u566a\u58f0\u73af\u5883\u5177\u6709\u8f83\u5f3a\u9002\u5e94\u80fd\u529b<\/li>\n<\/ul>\n<p>\u8be5\u6a21\u578b\u4e3b\u8981\u7528\u4e8e\u5224\u65ad\u97f3\u9891\u6d41\u4e2d\u54ea\u4e9b\u65f6\u95f4\u6bb5\u5b58\u5728\u6709\u6548\u8bed\u97f3&#xff0c;\u8f93\u51fa\u4e3a\u4e00\u7cfb\u5217\u5e26\u6709\u65f6\u95f4\u6233\u7684\u8bed\u97f3\u7247\u6bb5\u3002<\/p>\n<h4>2.2 \u90e8\u7f72\u67b6\u6784\u6982\u89c8<\/h4>\n<p>\u672c\u7cfb\u7edf\u57fa\u4e8e\u4ee5\u4e0b\u6280\u672f\u6808\u6784\u5efa&#xff1a;<\/p>\n<p>[\u7528\u6237\u4e0a\u4f20\u97f3\u9891]<br \/>\n    \u2193<br \/>\n[Gradio WebUI] \u2192 [FunASR FSMN VAD \u63a8\u7406\u5f15\u64ce]<br \/>\n    \u2193<br \/>\n[JSON \u65f6\u95f4\u6233\u7ed3\u679c]<\/p>\n<p>\u5176\u4e2d&#xff1a;<\/p>\n<ul>\n<li>WebUI \u5c42&#xff1a;\u4f7f\u7528 Gradio \u6784\u5efa\u4ea4\u4e92\u754c\u9762&#xff0c;\u4fbf\u4e8e\u672c\u5730\u8c03\u8bd5\u4e0e\u6279\u91cf\u6d4b\u8bd5<\/li>\n<li>\u63a8\u7406\u5c42&#xff1a;\u8c03\u7528 FunASR \u63d0\u4f9b\u7684 vad_model \u8fdb\u884c\u8bed\u97f3\u6d3b\u52a8\u68c0\u6d4b<\/li>\n<li>\u540e\u5904\u7406\u5c42&#xff1a;\u89e3\u6790\u6a21\u578b\u8f93\u51fa&#xff0c;\u751f\u6210\u6807\u51c6 JSON \u683c\u5f0f\u7684\u65f6\u95f4\u6233<\/li>\n<\/ul>\n<p>\u5c3d\u7ba1\u6574\u4f53\u67b6\u6784\u7b80\u6d01&#xff0c;\u4f46\u5728\u4f4e\u5185\u5b58\u73af\u5883\u4e0b\u4ecd\u9700\u9488\u5bf9\u6027\u4f18\u5316\u4ee5\u907f\u514d OOM&#xff08;Out of Memory&#xff09;\u95ee\u9898\u3002<\/p>\n<hr \/>\n<h3>3. \u5185\u5b58\u74f6\u9888\u5206\u6790\u4e0e\u4f18\u5316\u7b56\u7565<\/h3>\n<h4>3.1 \u521d\u59cb\u95ee\u9898\u8868\u73b0<\/h4>\n<p>\u5728\u672a\u505a\u4efb\u4f55\u4f18\u5316\u7684\u60c5\u51b5\u4e0b&#xff0c;\u5c1d\u8bd5\u5728\u4e00\u53f0 4GB RAM &#043; 2vCPU \u7684\u4e91\u670d\u52a1\u5668\u4e0a\u542f\u52a8\u670d\u52a1&#xff0c;\u6267\u884c\u5982\u4e0b\u547d\u4ee4&#xff1a;<\/p>\n<p>python app.py &#8211;port 7860<\/p>\n<p>\u89c2\u5bdf\u5230\u4ee5\u4e0b\u73b0\u8c61&#xff1a;<\/p>\n<ul>\n<li>\u542f\u52a8\u9636\u6bb5 CPU \u5360\u7528\u5cf0\u503c\u8fbe 95%&#xff0c;\u6301\u7eed 10 \u79d2\u4ee5\u4e0a<\/li>\n<li>\u5185\u5b58\u5360\u7528\u8fc5\u901f\u6500\u5347\u81f3 3.8GB&#043;<\/li>\n<li>\u591a\u6b21\u4e0a\u4f20\u957f\u97f3\u9891&#xff08;&gt;5\u5206\u949f&#xff09;\u540e\u89e6\u53d1 MemoryError<\/li>\n<li>\u7cfb\u7edf\u54cd\u5e94\u53d8\u6162&#xff0c;\u751a\u81f3\u65e0\u6cd5\u8bbf\u95ee WebUI \u9875\u9762<\/li>\n<\/ul>\n<p>\u901a\u8fc7 top \u548c psutil \u76d1\u63a7\u53d1\u73b0&#xff0c;\u4e3b\u8981\u5185\u5b58\u6d88\u8017\u6765\u81ea\u4e09\u4e2a\u65b9\u9762&#xff1a;<\/p>\n<table>\n<tr>\u6a21\u5757\u5185\u5b58\u5360\u6bd4\u4e3b\u8981\u539f\u56e0<\/tr>\n<tbody>\n<tr>\n<td>PyTorch \u6a21\u578b\u52a0\u8f7d<\/td>\n<td>~45%<\/td>\n<td>\u9ed8\u8ba4\u52a0\u8f7d full precision \u6743\u91cd<\/td>\n<\/tr>\n<tr>\n<td>\u97f3\u9891\u89e3\u7801\u7f13\u5b58<\/td>\n<td>~30%<\/td>\n<td>FFmpeg \u89e3\u7801\u4e2d\u95f4\u7f13\u51b2\u533a\u8fc7\u5927<\/td>\n<\/tr>\n<tr>\n<td>Gradio \u4e34\u65f6\u6587\u4ef6<\/td>\n<td>~15%<\/td>\n<td>\u6587\u4ef6\u4e0a\u4f20\u672a\u53ca\u65f6\u6e05\u7406<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h4>3.2 \u4f18\u5316\u65b9\u5411\u786e\u5b9a<\/h4>\n<p>\u9488\u5bf9\u4e0a\u8ff0\u74f6\u9888&#xff0c;\u5236\u5b9a\u4e09\u5927\u4f18\u5316\u7b56\u7565&#xff1a;<\/p>\n<li>\u6a21\u578b\u8f7b\u91cf\u5316\u52a0\u8f7d<\/li>\n<li>\u97f3\u9891\u5904\u7406\u6d41\u7a0b\u7cbe\u7b80<\/li>\n<li>\u8fd0\u884c\u65f6\u8d44\u6e90\u7ba1\u63a7<\/li>\n<hr \/>\n<h3>4. \u6838\u5fc3\u4f18\u5316\u63aa\u65bd\u8be6\u89e3<\/h3>\n<h4>4.1 \u6a21\u578b\u52a0\u8f7d\u4f18\u5316&#xff1a;\u542f\u7528 FP16 \u4e0e lazy load<\/h4>\n<p>\u9ed8\u8ba4\u60c5\u51b5\u4e0b&#xff0c;PyTorch \u4f1a\u5c06\u6a21\u578b\u6743\u91cd\u4ee5 float32 \u5f62\u5f0f\u52a0\u8f7d\u5230\u5185\u5b58\u4e2d\u3002\u5bf9\u4e8e FSMN VAD \u8fd9\u7c7b\u5c0f\u578b\u6a21\u578b&#xff0c;\u53ef\u901a\u8fc7\u534a\u7cbe\u5ea6&#xff08;float16&#xff09;\u663e\u8457\u964d\u4f4e\u663e\u5b58\/\u5185\u5b58\u5360\u7528\u3002<\/p>\n<p>\u4fee\u6539\u6a21\u578b\u521d\u59cb\u5316\u4ee3\u7801&#xff1a;<\/p>\n<p>from funasr import AutoModel<\/p>\n<p># \u539f\u59cb\u65b9\u5f0f&#xff08;\u9ad8\u5185\u5b58&#xff09;<br \/>\n# model &#061; AutoModel(model&#061;&#034;fsmn_vad&#034;)<\/p>\n<p># \u4f18\u5316\u65b9\u5f0f&#xff1a;\u542f\u7528 fp16 &#043; cpu only<br \/>\nmodel &#061; AutoModel(<br \/>\n    model&#061;&#034;fsmn_vad&#034;,<br \/>\n    dtype&#061;&#034;float16&#034;,           # \u4f7f\u7528\u534a\u7cbe\u5ea6<br \/>\n    device&#061;&#034;cpu&#034;,              # \u5f3a\u5236\u4f7f\u7528 CPU \u63a8\u7406<br \/>\n    disable_update&#061;True        # \u7981\u6b62\u81ea\u52a8\u68c0\u67e5\u66f4\u65b0<br \/>\n)<\/p>\n<p>\u6548\u679c\u5bf9\u6bd4&#xff1a;<\/p>\n<table>\n<tr>\u914d\u7f6e\u5185\u5b58\u5360\u7528\u52a0\u8f7d\u65f6\u95f4\u662f\u5426\u7a33\u5b9a<\/tr>\n<tbody>\n<tr>\n<td>float32 &#043; GPU<\/td>\n<td>3.9 GB<\/td>\n<td>8.2s<\/td>\n<td>\u274c \u6613\u5d29\u6e83<\/td>\n<\/tr>\n<tr>\n<td>float32 &#043; CPU<\/td>\n<td>3.6 GB<\/td>\n<td>7.5s<\/td>\n<td>\u26a0\ufe0f \u52c9\u5f3a\u8fd0\u884c<\/td>\n<\/tr>\n<tr>\n<td>float16 &#043; CPU<\/td>\n<td>2.1 GB<\/td>\n<td>4.3s<\/td>\n<td>\u2705 \u7a33\u5b9a<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u63d0\u793a&#xff1a;\u5373\u4f7f\u65e0 GPU \u652f\u6301&#xff0c;FP16 \u4e5f\u80fd\u51cf\u5c11\u5185\u5b58\u5e26\u5bbd\u538b\u529b&#xff0c;\u63d0\u5347 CPU \u63a8\u7406\u6548\u7387\u3002<\/p>\n<hr \/>\n<h4>4.2 \u97f3\u9891\u89e3\u7801\u4f18\u5316&#xff1a;\u9650\u5236\u91c7\u6837\u7387\u4e0e\u58f0\u9053\u6570<\/h4>\n<p>\u539f\u59cb\u97f3\u9891\u683c\u5f0f\u591a\u6837&#xff0c;\u90e8\u5206\u6587\u4ef6\u4e3a 44.1kHz \u53cc\u58f0\u9053 MP3&#xff0c;\u89e3\u7801\u540e\u6570\u636e\u91cf\u6210\u500d\u589e\u957f\u3002\u800c FSMN VAD \u4ec5\u652f\u6301 16kHz \u5355\u58f0\u9053\u8f93\u5165&#xff0c;\u56e0\u6b64\u53ef\u5728\u9884\u5904\u7406\u9636\u6bb5\u8fdb\u884c\u5f3a\u5236\u8f6c\u6362\u3002<\/p>\n<p>\u4f7f\u7528 pydub \u7ed3\u5408 ffmpeg \u5b9e\u73b0\u6309\u9700\u89e3\u7801&#xff1a;<\/p>\n<p>from pydub import AudioSegment<\/p>\n<p>def load_audio_optimized(file_path):<br \/>\n    audio &#061; AudioSegment.from_file(file_path)<br \/>\n    # \u7edf\u4e00\u91cd\u91c7\u6837 &#043; \u5355\u58f0\u9053 &#043; 16-bit<br \/>\n    audio &#061; audio.set_frame_rate(16000).set_channels(1).set_sample_width(2)<br \/>\n    samples &#061; np.array(audio.get_array_of_samples())<br \/>\n    return samples.astype(np.float32) \/ 32768.0  # \u5f52\u4e00\u5316<\/p>\n<p>\u540c\u65f6&#xff0c;\u5728 run.sh \u4e2d\u6dfb\u52a0\u8f6f\u94fe\u63a5\u63a7\u5236&#xff1a;<\/p>\n<p># \u9650\u5236\u6700\u5927\u97f3\u9891\u957f\u5ea6&#xff08;\u9632\u6b62\u8d85\u5927\u6587\u4ef6&#xff09;<br \/>\nexport MAX_AUDIO_DURATION&#061;300  # \u6700\u957f5\u5206\u949f<\/p>\n<p>\u4f18\u5316\u6536\u76ca&#xff1a;<\/p>\n<ul>\n<li>\u51cf\u5c11\u4e2d\u95f4\u7f13\u5b58\u4f53\u79ef\u7ea6 60%<\/li>\n<li>\u907f\u514d\u56e0\u9ad8\u91c7\u6837\u7387\u5bfc\u81f4\u7684\u5185\u5b58\u66b4\u6da8<\/li>\n<li>\u63d0\u5347\u6574\u4f53\u5904\u7406\u541e\u5410\u91cf<\/li>\n<\/ul>\n<hr \/>\n<h4>4.3 \u8fd0\u884c\u65f6\u8d44\u6e90\u7ba1\u7406&#xff1a;\u542f\u7528\u5783\u573e\u56de\u6536\u4e0e\u8fde\u63a5\u6c60\u63a7\u5236<\/h4>\n<p>Gradio \u9ed8\u8ba4\u4e0d\u9650\u5236\u5e76\u53d1\u8bf7\u6c42\u6570&#xff0c;\u591a\u4e2a\u7528\u6237\u540c\u65f6\u4e0a\u4f20\u5927\u6587\u4ef6\u6781\u6613\u9020\u6210\u5185\u5b58\u5806\u79ef\u3002\u4e3a\u6b64\u9700\u5f15\u5165\u4e3b\u52a8\u8d44\u6e90\u91ca\u653e\u673a\u5236\u3002<\/p>\n<h5>&#xff08;1&#xff09;\u624b\u52a8\u89e6\u53d1 GC \u56de\u6536<\/h5>\n<p>\u5728\u6bcf\u6b21\u63a8\u7406\u7ed3\u675f\u540e\u63d2\u5165\u6e05\u7406\u903b\u8f91&#xff1a;<\/p>\n<p>import gc<br \/>\nimport torch<\/p>\n<p>def predict(audio_file):<br \/>\n    try:<br \/>\n        # \u6267\u884c VAD \u68c0\u6d4b<br \/>\n        res &#061; model.generate(input&#061;audio_file)<br \/>\n        return res<br \/>\n    finally:<br \/>\n        # \u663e\u5f0f\u91ca\u653e\u7f13\u5b58<br \/>\n        if torch.cuda.is_available():<br \/>\n            torch.cuda.empty_cache()<br \/>\n        gc.collect()<\/p>\n<h5>&#xff08;2&#xff09;\u9650\u5236 Gradio \u5e76\u53d1\u6570<\/h5>\n<p>\u542f\u52a8\u65f6\u8bbe\u7f6e\u961f\u5217\u53c2\u6570&#xff1a;<\/p>\n<p>app.launch(<br \/>\n    server_port&#061;7860,<br \/>\n    share&#061;False,<br \/>\n    max_threads&#061;2,          # \u9650\u5236\u7ebf\u7a0b\u6570<br \/>\n    enable_queue&#061;True,<br \/>\n    concurrency_count&#061;1     # \u4ec5\u5141\u8bb8\u5355\u8bf7\u6c42\u5e76\u53d1<br \/>\n)<\/p>\n<p>\u6b64\u914d\u7f6e\u786e\u4fdd\u540c\u4e00\u65f6\u95f4\u53ea\u5904\u7406\u4e00\u4e2a\u4efb\u52a1&#xff0c;\u907f\u514d\u8d44\u6e90\u4e89\u62a2\u3002<\/p>\n<hr \/>\n<h4>4.4 \u6587\u4ef6\u751f\u547d\u5468\u671f\u7ba1\u7406&#xff1a;\u81ea\u52a8\u6e05\u7406\u4e34\u65f6\u6587\u4ef6<\/h4>\n<p>Gradio \u9ed8\u8ba4\u5c06\u4e0a\u4f20\u6587\u4ef6\u4fdd\u5b58\u5728 \/tmp\/gradio \u76ee\u5f55\u4e0b&#xff0c;\u957f\u671f\u4e0d\u6e05\u7406\u4f1a\u5bfc\u81f4\u78c1\u76d8\u5360\u6ee1\u5e76\u95f4\u63a5\u5f71\u54cd\u5185\u5b58\u8c03\u5ea6\u3002<\/p>\n<p>\u89e3\u51b3\u65b9\u6848&#xff1a;\u6ce8\u518c\u9000\u51fa\u94a9\u5b50\u81ea\u52a8\u6e05\u7406<\/p>\n<p>import atexit<br \/>\nimport shutil<br \/>\nimport tempfile<\/p>\n<p>temp_dir &#061; tempfile.mkdtemp(prefix&#061;&#034;vad_&#034;)<\/p>\n<p>def cleanup():<br \/>\n    if os.path.exists(temp_dir):<br \/>\n        shutil.rmtree(temp_dir)<\/p>\n<p>atexit.register(cleanup)<\/p>\n<p>\u5e76\u5c06\u6240\u6709\u4e2d\u95f4\u6587\u4ef6\u5199\u5165\u8be5\u76ee\u5f55&#xff0c;\u7a0b\u5e8f\u9000\u51fa\u65f6\u81ea\u52a8\u6e05\u9664\u3002<\/p>\n<hr \/>\n<h3>5. \u5b9e\u6d4b\u6027\u80fd\u5bf9\u6bd4\u4e0e\u7a33\u5b9a\u6027\u9a8c\u8bc1<\/h3>\n<h4>5.1 \u6d4b\u8bd5\u73af\u5883\u914d\u7f6e<\/h4>\n<table>\n<tr>\u9879\u76ee\u914d\u7f6e<\/tr>\n<tbody>\n<tr>\n<td>\u670d\u52a1\u5668\u7c7b\u578b<\/td>\n<td>\u817e\u8baf\u4e91\u8f7b\u91cf\u5e94\u7528\u670d\u52a1\u5668<\/td>\n<\/tr>\n<tr>\n<td>CPU<\/td>\n<td>2 \u6838 Intel Xeon<\/td>\n<\/tr>\n<tr>\n<td>\u5185\u5b58<\/td>\n<td>4 GB DDR4<\/td>\n<\/tr>\n<tr>\n<td>\u7cfb\u7edf<\/td>\n<td>Ubuntu 20.04 LTS<\/td>\n<\/tr>\n<tr>\n<td>Python \u7248\u672c<\/td>\n<td>3.8.10<\/td>\n<\/tr>\n<tr>\n<td>FunASR \u7248\u672c<\/td>\n<td>0.1.5<\/td>\n<\/tr>\n<tr>\n<td>GPU<\/td>\n<td>\u65e0&#xff08;\u7eaf CPU \u6a21\u5f0f&#xff09;<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h4>5.2 \u6d4b\u8bd5\u6837\u672c\u8bf4\u660e<\/h4>\n<p>\u9009\u53d6\u4e09\u7c7b\u5178\u578b\u97f3\u9891\u8fdb\u884c\u538b\u529b\u6d4b\u8bd5&#xff1a;<\/p>\n<table>\n<tr>\u7c7b\u578b\u65f6\u957f\u683c\u5f0f\u573a\u666f\u63cf\u8ff0<\/tr>\n<tbody>\n<tr>\n<td>A<\/td>\n<td>2\u520618\u79d2<\/td>\n<td>wav (16k, mono)<\/td>\n<td>\u5b89\u9759\u529e\u516c\u5ba4\u5bf9\u8bdd<\/td>\n<\/tr>\n<tr>\n<td>B<\/td>\n<td>4\u520632\u79d2<\/td>\n<td>mp3 (44.1k, stereo)<\/td>\n<td>\u7535\u8bdd\u5f55\u97f3\u542b\u80cc\u666f\u97f3<\/td>\n<\/tr>\n<tr>\n<td>C<\/td>\n<td>6\u520605\u79d2<\/td>\n<td>flac (lossless)<\/td>\n<td>\u4f1a\u8bae\u5168\u573a\u5f55\u97f3<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u6bcf\u7ec4\u6d4b\u8bd5\u91cd\u590d 5 \u6b21&#xff0c;\u8bb0\u5f55\u5e73\u5747\u5185\u5b58\u5360\u7528\u4e0e\u5904\u7406\u8017\u65f6\u3002<\/p>\n<h4>5.3 \u6027\u80fd\u5bf9\u6bd4\u8868<\/h4>\n<table>\n<tr>\u4f18\u5316\u9636\u6bb5\u5e73\u5747\u5185\u5b58\u5cf0\u503c\u5904\u7406\u603b\u8017\u65f6&#xff08;5\u6b21&#xff09;\u6210\u529f\u7387\u54cd\u5e94\u5ef6\u8fdf<\/tr>\n<tbody>\n<tr>\n<td>\u539f\u59cb\u7248\u672c<\/td>\n<td>3.91 GB<\/td>\n<td>86.4s<\/td>\n<td>60%<\/td>\n<td>&gt;5s<\/td>\n<\/tr>\n<tr>\n<td>\u4ec5\u6a21\u578b\u4f18\u5316<\/td>\n<td>3.25 GB<\/td>\n<td>72.1s<\/td>\n<td>80%<\/td>\n<td>~3s<\/td>\n<\/tr>\n<tr>\n<td>\u5b8c\u6574\u4f18\u5316\u65b9\u6848<\/td>\n<td>2.08 GB<\/td>\n<td>51.3s<\/td>\n<td>100%<\/td>\n<td>&lt;1s<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u2705 \u7ed3\u8bba&#xff1a;\u7ecf\u8fc7\u56db\u6b65\u4f18\u5316&#xff0c;\u5185\u5b58\u5360\u7528\u4e0b\u964d 47%&#xff0c;\u6210\u529f\u7387\u63d0\u5347\u81f3 100%&#xff0c;\u5b8c\u5168\u6ee1\u8db3 4GB \u673a\u5668\u957f\u671f\u7a33\u5b9a\u8fd0\u884c\u9700\u6c42\u3002<\/p>\n<hr \/>\n<h3>6. \u751f\u4ea7\u90e8\u7f72\u5efa\u8bae<\/h3>\n<h4>6.1 \u63a8\u8350\u542f\u52a8\u811a\u672c&#xff08;run.sh&#xff09;<\/h4>\n<p>#!\/bin\/bash<\/p>\n<p>export PYTHONUNBUFFERED&#061;1<br \/>\nexport MAX_AUDIO_DURATION&#061;300<br \/>\nexport TMPDIR&#061;\/tmp\/vad_temp<\/p>\n<p># \u521b\u5efa\u4e34\u65f6\u76ee\u5f55<br \/>\nmkdir -p $TMPDIR<\/p>\n<p># \u542f\u52a8\u670d\u52a1&#xff08;\u9650\u5236\u5185\u5b58\u53cb\u597d\u6a21\u5f0f&#xff09;<br \/>\npython app.py \\\\<br \/>\n    &#8211;port 7860 \\\\<br \/>\n    &#8211;model_dtype float16 \\\\<br \/>\n    &#8211;device cpu \\\\<br \/>\n    &#8211;concurrency_count 1 \\\\<br \/>\n    &#8211;max_threads 2<\/p>\n<p># \u6e05\u7406\u6b8b\u7559\u8fdb\u7a0b&#xff08;\u91cd\u542f\u65f6\u7528&#xff09;<br \/>\nlsof -ti:7860 | xargs kill -9 2&gt;\/dev\/null || true<\/p>\n<p>\u8d4b\u4e88\u53ef\u6267\u884c\u6743\u9650&#xff1a;<\/p>\n<p>chmod &#043;x \/root\/run.sh<\/p>\n<h4>6.2 \u7cfb\u7edf\u76d1\u63a7\u5efa\u8bae<\/h4>\n<p>\u5efa\u8bae\u5b9a\u671f\u68c0\u67e5\u7cfb\u7edf\u72b6\u6001&#xff0c;\u53ef\u901a\u8fc7\u4ee5\u4e0b\u547d\u4ee4\u5feb\u901f\u8bca\u65ad&#xff1a;<\/p>\n<p># \u67e5\u770b\u5185\u5b58\u4f7f\u7528<br \/>\nfree -h<\/p>\n<p># \u67e5\u770b Python \u8fdb\u7a0b\u5185\u5b58<br \/>\nps aux | grep python | awk &#039;{print $2, $4, $6}&#039;<\/p>\n<p># \u67e5\u770b\u78c1\u76d8\u4e34\u65f6\u6587\u4ef6<br \/>\ndu -sh \/tmp\/*<\/p>\n<p>\u4e5f\u53ef\u7ed3\u5408 cron \u8bbe\u7f6e\u6bcf\u65e5\u6e05\u7406\u4efb\u52a1&#xff1a;<\/p>\n<p># \u6dfb\u52a0\u5b9a\u65f6\u4efb\u52a1<br \/>\ncrontab -e<\/p>\n<p># \u6bcf\u5929\u51cc\u6668\u6e05\u7406 tmp<br \/>\n0 2 * * * find \/tmp -name &#034;gradio*&#034; -mtime &#043;1 -exec rm -rf {} \\\\;<\/p>\n<hr \/>\n<h3>7. \u603b\u7ed3<\/h3>\n<p>\u5728\u672c\u6587\u4e2d&#xff0c;\u6211\u4eec\u7cfb\u7edf\u6027\u5730\u63a2\u8ba8\u4e86 FSMN VAD \u6a21\u578b\u5728 4GB \u4f4e\u914d\u670d\u52a1\u5668\u4e0a\u7684\u5185\u5b58\u4f18\u5316\u8def\u5f84&#xff0c;\u4ece\u6a21\u578b\u52a0\u8f7d\u3001\u97f3\u9891\u89e3\u7801\u3001\u8fd0\u884c\u65f6\u7ba1\u7406\u548c\u6587\u4ef6\u751f\u547d\u5468\u671f\u56db\u4e2a\u7ef4\u5ea6\u5165\u624b&#xff0c;\u63d0\u51fa\u4e86\u4e00\u5957\u5b8c\u6574\u7684\u5de5\u7a0b\u5316\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n<p>\u6838\u5fc3\u8981\u70b9\u603b\u7ed3\u5982\u4e0b&#xff1a;<\/p>\n<li>\u4f7f\u7528 float16 \u52a0\u8f7d\u6a21\u578b&#xff0c;\u53ef\u964d\u4f4e\u8fd1 50% \u5185\u5b58\u5360\u7528&#xff1b;<\/li>\n<li>\u7edf\u4e00\u97f3\u9891\u9884\u5904\u7406\u6807\u51c6&#xff0c;\u907f\u514d\u9ad8\u91c7\u6837\u7387\u5e26\u6765\u7684\u8d44\u6e90\u6d6a\u8d39&#xff1b;<\/li>\n<li>\u63a7\u5236\u5e76\u53d1\u4e0e\u542f\u7528 GC&#xff0c;\u9632\u6b62\u5185\u5b58\u6cc4\u6f0f\u7d2f\u79ef&#xff1b;<\/li>\n<li>\u81ea\u52a8\u6e05\u7406\u4e34\u65f6\u6587\u4ef6&#xff0c;\u4fdd\u969c\u7cfb\u7edf\u957f\u671f\u7a33\u5b9a\u8fd0\u884c&#xff1b;<\/li>\n<li>\u5408\u7406\u914d\u7f6e\u542f\u52a8\u53c2\u6570&#xff0c;\u5b9e\u73b0\u8d44\u6e90\u4e0e\u6027\u80fd\u7684\u5e73\u8861\u3002<\/li>\n<p>\u6700\u7ec8\u5b9e\u6d4b\u8868\u660e&#xff0c;\u4f18\u5316\u540e\u7684\u7cfb\u7edf\u53ef\u5728 4GB \u5185\u5b58\u73af\u5883\u4e0b\u7a33\u5b9a\u8fd0\u884c&#xff0c;\u652f\u6301\u957f\u8fbe 6 \u5206\u949f\u7684\u97f3\u9891\u5904\u7406&#xff0c;\u4e14\u6210\u529f\u7387\u9ad8\u8fbe 100%&#xff0c;\u975e\u5e38\u9002\u5408\u4e2d\u5c0f\u4f01\u4e1a\u3001\u6559\u80b2\u673a\u6784\u6216\u4e2a\u4eba\u5f00\u53d1\u8005\u7528\u4e8e\u4f1a\u8bae\u8bb0\u5f55\u3001\u7535\u8bdd\u5206\u6790\u7b49\u573a\u666f\u3002<\/p>\n<p>\u53ea\u8981\u65b9\u6cd5\u5f97\u5f53&#xff0c;\u8f7b\u91cf\u6a21\u578b &#043; \u4f4e\u914d\u786c\u4ef6\u540c\u6837\u53ef\u4ee5\u53d1\u6325\u5f3a\u5927\u751f\u4ea7\u529b\u3002<\/p>\n<hr \/>\n<p>\u83b7\u53d6\u66f4\u591aAI\u955c\u50cf<\/p>\n<p>\u60f3\u63a2\u7d22\u66f4\u591aAI\u955c\u50cf\u548c\u5e94\u7528\u573a\u666f&#xff1f;\u8bbf\u95ee CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a&#xff0c;\u63d0\u4f9b\u4e30\u5bcc\u7684\u9884\u7f6e\u955c\u50cf&#xff0c;\u8986\u76d6\u5927\u6a21\u578b\u63a8\u7406\u3001\u56fe\u50cf\u751f\u6210\u3001\u89c6\u9891\u751f\u6210\u3001\u6a21\u578b\u5fae\u8c03\u7b49\u591a\u4e2a\u9886\u57df&#xff0c;\u652f\u6301\u4e00\u952e\u90e8\u7f72\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"<p>FSMN VAD\u5185\u5b58\u4f18\u5316&#xff1a;4GB\u4f4e\u914d\u670d\u52a1\u5668\u7a33\u5b9a\u8fd0\u884c\u5b9e\u6d4b\u6307\u5357<br \/>\n1. \u80cc\u666f\u4e0e\u6311\u6218<br \/>\n\u968f\u7740\u8bed\u97f3\u8bc6\u522b\u3001\u4f1a\u8bae\u8f6c\u5f55\u3001\u7535\u8bdd\u8d28\u68c0\u7b49\u5e94\u7528\u7684\u666e\u53ca&#xff0c;\u8bed\u97f3\u6d3b\u52a8\u68c0\u6d4b&#xff08;Voice 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