💡 \u4e3e\u4e2a\u4f8b\u5b50：
\n\u8f93\u5165\u201c\u4eca\u5929\u771f\u662f\u4e2a\u597d\u65e5\u5b50\u5440！\u201d \u2192 \u8f93\u51fa\u5e26\u6709\u660e\u663e\u6b22\u5feb\u8bed\u6c14\u7684\u8bed\u97f3；
\n\u8f93\u5165\u201c\u5509\u2026\u2026\u6211\u4e5f\u4e0d\u77e5\u9053\u8be5\u600e\u4e48\u529e\u4e86\u3002\u201d \u2192 \u81ea\u52a8\u5207\u6362\u4e3a\u4f4e\u6c89\u3001\u7565\u5e26\u4f24\u611f\u7684\u8bed\u8c03\u3002<\/p>\n
\u8fd9\u79cd\u201c\u6709\u6e29\u5ea6\u201d\u7684\u8bed\u97f3\u80fd\u529b，\u6781\u5927\u63d0\u5347\u4e86\u7528\u6237\u4ea4\u4e92\u7684\u771f\u5b9e\u611f\u548c\u4eb2\u548c\u529b\u3002<\/p>\n
\n
🔧 \u57fa\u4e8eModelScope Sambert-Hifigan\u6a21\u578b，\u96c6\u6210Flask\u63a5\u53e3，\u5df2\u4fee\u590d\u6240\u6709\u4f9d\u8d56<\/h3>\n
\u5c3d\u7ba1 ModelScope \u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u9884\u8bad\u7ec3\u6a21\u578b，\u4f46\u76f4\u63a5\u90e8\u7f72\u4ecd\u9762\u4e34\u8bf8\u591a\u6311\u6218：Python \u7248\u672c\u51b2\u7a81\u3001\u4f9d\u8d56\u5305\u4e0d\u517c\u5bb9\u3001\u73af\u5883\u914d\u7f6e\u7e41\u7410\u3001Web\u670d\u52a1\u7f3a\u5931\u7b49\u3002\u4e3a\u6b64，\u6211\u4eec\u6784\u5efa\u4e86\u4e00\u4e2a\u9ad8\u5ea6\u7a33\u5b9a\u3001\u5f00\u7bb1\u5373\u7528\u7684Docker\u955c\u50cf，\u5f7b\u5e95\u89e3\u51b3\u8fd9\u4e9b\u95ee\u9898\u3002<\/p>\n
\u2705 \u955c\u50cf\u6838\u5fc3\u7279\u6027<\/h4>\n
| \u7279\u6027 | \u8bf4\u660e |
\n|——|——|
\n| \u57fa\u7840\u6846\u67b6 | Python 3.9 + PyTorch 1.13.1 + CUDA 11.7（\u53ef\u964d\u7ea7\u4e3aCPU\u6a21\u5f0f） |
\n| \u6a21\u578b\u6765\u6e90 | ModelScope \u5b98\u65b9 sambert-hifigan-tts-chinese \u6a21\u578b |
\n| \u670d\u52a1\u63a5\u53e3 | Flask \u6784\u5efa RESTful API \u4e0e WebUI \u53cc\u6a21\u5f0f |
\n| \u524d\u7aef\u754c\u9762 | \u652f\u6301\u54cd\u5e94\u5f0f\u7f51\u9875\u64cd\u4f5c，\u542b\u6587\u672c\u8f93\u5165\u6846\u3001\u8bed\u97f3\u64ad\u653e\u5668\u3001\u4e0b\u8f7d\u6309\u94ae |
\n| \u4f9d\u8d56\u7ba1\u7406 | \u5df2\u9501\u5b9a\u5e76\u9a8c\u8bc1 datasets==2.13.0, numpy==1.23.5, scipy<1.13 \u7b49\u5173\u952e\u7248\u672c，\u907f\u514d\u5e38\u89c1\u62a5\u9519 |<\/p>\n
🐍 \u5178\u578b\u9519\u8bef\u4fee\u590d\u793a\u4f8b<\/h4>\n
# \u5e38\u89c1\u95ee\u9898：huggingface\/datasets \u4e0e numpy \u4e0d\u517c\u5bb9\u5bfc\u81f4 import \u62a5\u9519
\n>>> ImportError: numpy.ndarray size changed, may indicate binary incompatibility<\/p>\n
# \u89e3\u51b3\u65b9\u6848：\u4e25\u683c\u6307\u5b9a\u7248\u672c\u7ec4\u5408
\npip install "numpy==1.23.5" "datasets==2.13.0" –no-cache-dir<\/p>\n
\u6211\u4eec\u5728\u6784\u5efa\u8fc7\u7a0b\u4e2d\u8fdb\u884c\u4e86\u6570\u5341\u6b21\u6d4b\u8bd5，\u6700\u7ec8\u786e\u5b9a\u4e86\u4e00\u7ec4\u96f6\u51b2\u7a81\u3001\u53ef\u590d\u73b0\u7684\u4f9d\u8d56\u7ec4\u5408，\u5e76\u901a\u8fc7 requirements.txt \u56fa\u5316，\u786e\u4fdd\u6bcf\u6b21\u90e8\u7f72\u90fd\u80fd\u6210\u529f\u542f\u52a8\u3002<\/p>\n
\n
📖 Sambert-HifiGan \u4e2d\u6587\u591a\u60c5\u611f\u8bed\u97f3\u5408\u6210\u670d\u52a1 (WebUI + API)<\/h3>\n🎯 \u9879\u76ee\u7b80\u4ecb<\/h4>\n
\u672c\u955c\u50cf\u57fa\u4e8e ModelScope \u7ecf\u5178\u7684 Sambert-HifiGan (\u4e2d\u6587\u591a\u60c5\u611f) \u6a21\u578b\u6784\u5efa，\u63d0\u4f9b\u9ad8\u8d28\u91cf\u7684\u7aef\u5230\u7aef\u4e2d\u6587\u8bed\u97f3\u5408\u6210\u80fd\u529b\u3002\u5df2\u96c6\u6210 Flask WebUI，\u7528\u6237\u53ef\u4ee5\u901a\u8fc7\u6d4f\u89c8\u5668\u76f4\u63a5\u8f93\u5165\u6587\u672c，\u5728\u7ebf\u5408\u6210\u5e76\u64ad\u653e\u8bed\u97f3\u3002<\/p>\n
💡 \u6838\u5fc3\u4eae\u70b9：
\n1. \u53ef\u89c6\u4ea4\u4e92：\u5185\u7f6e\u73b0\u4ee3\u5316 Web \u754c\u9762，\u652f\u6301\u6587\u5b57\u8f6c\u8bed\u97f3\u5b9e\u65f6\u64ad\u653e\u4e0e\u4e0b\u8f7d\u3002
\n2. \u6df1\u5ea6\u4f18\u5316：\u5df2\u4fee\u590d datasets(2.13.0)\u3001numpy(1.23.5) \u4e0e scipy(<1.13) \u7684\u7248\u672c\u51b2\u7a81，\u73af\u5883\u6781\u5ea6\u7a33\u5b9a，\u62d2\u7edd\u62a5\u9519\u3002
\n3. \u53cc\u6a21\u670d\u52a1：\u540c\u65f6\u63d0\u4f9b\u56fe\u5f62\u754c\u9762\u4e0e\u6807\u51c6 HTTP API \u63a5\u53e3，\u6ee1\u8db3\u4e0d\u540c\u573a\u666f\u9700\u6c42\u3002
\n4. \u8f7b\u91cf\u9ad8\u6548：\u9488\u5bf9 CPU \u63a8\u7406\u8fdb\u884c\u4e86\u4f18\u5316，\u54cd\u5e94\u901f\u5ea6\u5feb\u3002<\/p>\n
\n
🚀 \u4f7f\u7528\u8bf4\u660e<\/h3>\n\u6b65\u9aa4\u4e00：\u51c6\u5907\u8f7b\u91cf\u670d\u52a1\u5668（\u63a8\u8350\u914d\u7f6e）<\/h4>\n
| \u9879\u76ee | \u63a8\u8350\u914d\u7f6e |
\n|——|———-|
\n| \u4e91\u5382\u5546 | \u817e\u8baf\u4e91\u8f7b\u91cf\u5e94\u7528\u670d\u52a1\u5668 \/ \u963f\u91cc\u4e91ECS\u7a81\u53d1\u6027\u80fd\u5b9e\u4f8b |
\n| \u5730\u57df\u9009\u62e9 | \u534e\u5357\u5730\u533a（\u5e7f\u5dde）\u3001\u534e\u4e1c（\u4e0a\u6d77）\u4ee5\u964d\u4f4e\u5ef6\u8fdf |
\n| \u5b9e\u4f8b\u89c4\u683c | 2\u6838CPU \/ 4GB\u5185\u5b58 \/ 80GB SSD |
\n| \u64cd\u4f5c\u7cfb\u7edf | Ubuntu 20.04 LTS |
\n| \u6708\u6210\u672c\u4f30\u7b97 | \u7ea6 150~180\u5143\/\u6708（\u65b0\u7528\u6237\u9996\u5e74\u5e38\u4eab\u6298\u6263） |<\/p>\n
\u26a0\ufe0f \u6ce8\u610f：\u867d\u7136\u6a21\u578b\u53ef\u5728CPU\u4e0a\u8fd0\u884c，\u4f46\u5efa\u8bae\u81f3\u5c11\u4f7f\u75282\u68384G\u914d\u7f6e，\u4fdd\u8bc1\u5e76\u53d1\u54cd\u5e94\u901f\u5ea6\u3002<\/p>\n
\u6b65\u9aa4\u4e8c：\u62c9\u53d6\u5e76\u8fd0\u884cDocker\u955c\u50cf<\/h4>\n
# 1. \u5b89\u88c5Docker（\u82e5\u672a\u5b89\u88c5）
\ncurl -fsSL https:\/\/get.docker.com | bash<\/p>\n
# 2. \u62c9\u53d6\u955c\u50cf（\u5047\u8bbe\u955c\u50cf\u5df2\u53d1\u5e03\u81f3\u516c\u5f00\u4ed3\u5e93）
\ndocker pull your-repo\/sambert-hifigan-chinese:latest<\/p>\n
# 3. \u542f\u52a8\u5bb9\u5668，\u6620\u5c04\u7aef\u53e38080
\ndocker run -d -p 8080:8080 –name tts-service your-repo\/sambert-hifigan-chinese:latest<\/p>\n
\u670d\u52a1\u542f\u52a8\u540e，Flask \u5e94\u7528\u5c06\u5728 http:\/\/<your-server-ip>:8080 \u63d0\u4f9b\u8bbf\u95ee\u5165\u53e3\u3002<\/p>\n
\u6b65\u9aa4\u4e09：\u901a\u8fc7WebUI\u4f7f\u7528\u8bed\u97f3\u5408\u6210\u529f\u80fd<\/h4>\n
\u955c\u50cf\u542f\u52a8\u540e，\u70b9\u51fb\u5e73\u53f0\u63d0\u4f9b\u7684 HTTP \u8bbf\u95ee\u6309\u94ae（\u90e8\u5206\u4e91\u5e73\u53f0\u4f1a\u81ea\u52a8\u751f\u6210\u9884\u89c8\u94fe\u63a5）\u3002
\n![\"\u56fe\u7247\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260113044055-6965ccd7961f6.png\")
<\/p>\n<\/li>\n
\u5728\u7f51\u9875\u6587\u672c\u6846\u4e2d\u8f93\u5165\u60f3\u8981\u5408\u6210\u7684\u4e2d\u6587\u5185\u5bb9（\u652f\u6301\u957f\u6587\u672c，\u6700\u957f\u53ef\u8fbe500\u5b57）\u3002<\/p>\n<\/li>\n
\u70b9\u51fb \u201c\u5f00\u59cb\u5408\u6210\u8bed\u97f3\u201d，\u7cfb\u7edf\u5c06\u8c03\u7528 Sambert-Hifigan \u6a21\u578b\u8fdb\u884c\u63a8\u7406，\u901a\u5e38\u8017\u65f63~8\u79d2（\u53d6\u51b3\u4e8e\u6587\u672c\u957f\u5ea6\u548c\u670d\u52a1\u5668\u6027\u80fd）\u3002<\/p>\n<\/li>\n
\u5408\u6210\u5b8c\u6210\u540e，\u9875\u9762\u5c06\u663e\u793a\u97f3\u9891\u64ad\u653e\u63a7\u4ef6，\u53ef\u5728\u7ebf\u8bd5\u542c\u6216\u70b9\u51fb \u201c\u4e0b\u8f7dWAV\u201d \u4fdd\u5b58\u81f3\u672c\u5730\u3002<\/p>\n<\/li>\n
\n
🔄 API \u63a5\u53e3\u8c03\u7528\u65b9\u5f0f（\u9002\u7528\u4e8e\u7a0b\u5e8f\u96c6\u6210）<\/h3>\n
\u9664\u4e86 WebUI，\u8be5\u670d\u52a1\u8fd8\u66b4\u9732\u4e86\u6807\u51c6\u7684 HTTP API \u63a5\u53e3，\u4fbf\u4e8e\u4f01\u4e1a\u5c06\u5176\u5d4c\u5165\u81ea\u6709\u7cfb\u7edf\u3002<\/p>\n
POST \/tts \u2014\u2014 \u6587\u672c\u8f6c\u8bed\u97f3\u63a5\u53e3<\/h4>\n
\u8bf7\u6c42\u793a\u4f8b（Python）：<\/p>\n
import requests<\/p>\n
url = "http:\/\/<your-server-ip>:8080\/tts"
\ndata = {
\n "text": "\u6b22\u8fce\u4f7f\u7528AI\u8bed\u97f3\u5408\u6210\u670d\u52a1，\u795d\u60a8\u5de5\u4f5c\u6109\u5feb！",
\n "emotion": "happy" # \u53ef\u9009：happy, sad, angry, neutral, tender
\n}<\/p>\n
response = requests.post(url, json=data)<\/p>\n
if response.status_code == 200:
\n with open("output.wav", "wb") as f:
\n f.write(response.content)
\n print("\u8bed\u97f3\u5df2\u4fdd\u5b58\u4e3a output.wav")
\nelse:
\n print("\u5408\u6210\u5931\u8d25:", response.json())<\/p>\n
\u8fd4\u56de\u7ed3\u679c：
\n\u6210\u529f\u65f6\u8fd4\u56de .wav \u6587\u4ef6\u7684\u4e8c\u8fdb\u5236\u6d41，HTTP\u72b6\u6001\u7801 200；\u5931\u8d25\u65f6\u8fd4\u56de JSON \u9519\u8bef\u4fe1\u606f\u3002<\/p>\n
\u652f\u6301\u7684\u60c5\u611f\u53c2\u6570：
\n– happy：\u6b22\u5feb\u3001\u79ef\u6781
\n– sad：\u4f4e\u843d\u3001\u5fe7\u4f24
\n– angry：\u6025\u4fc3\u3001\u6124\u6012
\n– neutral：\u6807\u51c6\u3001\u65e0\u611f\u60c5
\n– tender：\u6e29\u67d4\u3001\u4eb2\u5207<\/p>\n
💡 \u4f01\u4e1a\u53ef\u7ed3\u5408 NLP \u60c5\u611f\u5206\u6790\u6a21\u5757，\u81ea\u52a8\u5224\u65ad\u7528\u6237\u8f93\u5165\u6587\u672c\u7684\u60c5\u7eea\u503e\u5411，\u5e76\u4f20\u5165\u5bf9\u5e94 emotion \u53c2\u6570，\u5b9e\u73b0\u201c\u667a\u80fd\u60c5\u611f\u5339\u914d\u201d\u3002<\/p>\n
\n
💡 \u5b9e\u9645\u5e94\u7528\u573a\u666f\u4e3e\u4f8b<\/h3>\n
| \u573a\u666f | \u5e94\u7528\u65b9\u5f0f | \u6210\u672c\u6536\u76ca |
\n|——|———-|———|
\n| \u667a\u80fd\u5ba2\u670dIVR | \u5c06FAQ\u6587\u672c\u8f6c\u4e3a\u8bed\u97f3，\u66ff\u4ee3\u4eba\u5de5\u5f55\u97f3 | \u8282\u7701\u5f55\u97f3\u6210\u672c，\u5feb\u901f\u66f4\u65b0\u8bdd\u672f |
\n| \u6709\u58f0\u4e66\/\u77e5\u8bc6\u4ed8\u8d39 | \u6279\u91cf\u751f\u6210\u8bfe\u7a0b\u8bb2\u89e3\u97f3\u9891 | \u63d0\u5347\u5185\u5bb9\u751f\u4ea7\u6548\u73875\u500d\u4ee5\u4e0a |
\n| \u6821\u56ed\u5e7f\u64ad\u7cfb\u7edf | \u81ea\u52a8\u64ad\u62a5\u901a\u77e5\u3001\u5929\u6c14\u3001\u65b0\u95fb | \u51cf\u5c11\u4eba\u5de5\u64ad\u97f3\u8d1f\u62c5 |
\n| \u7535\u5546\u76f4\u64ad\u811a\u672c\u914d\u97f3 | \u4e3a\u77ed\u89c6\u9891\u811a\u672c\u751f\u6210\u80cc\u666f\u8bed\u97f3 | \u5feb\u901f\u5236\u4f5c\u591a\u7248\u672c\u8425\u9500\u7d20\u6750 |
\n| \u65e0\u969c\u788d\u9605\u8bfb | \u4e3a\u89c6\u969c\u7528\u6237\u63d0\u4f9b\u7f51\u9875\u6717\u8bfb\u529f\u80fd | \u63d0\u5347\u4ea7\u54c1\u5305\u5bb9\u6027\u4e0e\u5408\u89c4\u6027 |<\/p>\n
\n
🛠\ufe0f \u6027\u80fd\u4f18\u5316\u5efa\u8bae（\u63d0\u5347CPU\u63a8\u7406\u6548\u7387）<\/h3>\n
\u7531\u4e8e\u8be5\u65b9\u6848\u4e3b\u6253\u201c\u4f4e\u6210\u672c\u201d，\u591a\u6570\u60c5\u51b5\u4e0b\u8fd0\u884c\u5728\u65e0GPU\u7684\u73af\u5883\u4e2d，\u56e0\u6b64\u9700\u6ce8\u610f\u4ee5\u4e0b\u4f18\u5316\u63aa\u65bd：<\/p>\n
1. \u5f00\u542fJIT\u7f16\u8bd1\u52a0\u901f（PyTorch）<\/h4>\n
# \u5728\u6a21\u578b\u52a0\u8f7d\u65f6\u542f\u7528trace\u4f18\u5316
\nmodel = torch.jit.script(model) # \u6216 trace<\/p>\n
2. \u4f7f\u7528ONNX Runtime（\u53ef\u9009\u8fdb\u9636）<\/h4>\n
\u5c06 Sambert \u548c Hifigan \u5206\u522b\u5bfc\u51fa\u4e3a ONNX \u6a21\u578b，\u5229\u7528 ONNX Runtime \u7684 CPU \u4f18\u5316\u540e\u7aef（\u5982OpenMP）\u8fdb\u4e00\u6b65\u63d0\u901f\u3002<\/p>\n
3. \u542f\u7528Gunicorn\u591aWorker\u6a21\u5f0f<\/h4>\n
# \u66ff\u4ee3\u9ed8\u8ba4Flask\u5355\u7ebf\u7a0b，\u63d0\u5347\u5e76\u53d1\u5904\u7406\u80fd\u529b
\ngunicorn -w 4 -b 0.0.0.0:8080 app:app<\/p>\n
\u5efa\u8bae\u8bbe\u7f6e worker \u6570\u91cf\u4e3a CPU \u6838\u5fc3\u6570\u7684 1~2 \u500d\u3002<\/p>\n
4. \u7f13\u5b58\u9ad8\u9891\u6587\u672c\u8bed\u97f3<\/h4>\n
\u5bf9\u4e8e\u56fa\u5b9a\u8bdd\u672f（\u5982\u201c\u60a8\u597d，\u8bf7\u95ee\u6709\u4ec0\u4e48\u53ef\u4ee5\u5e2e\u60a8？\u201d），\u53ef\u9884\u5148\u5408\u6210\u5e76\u7f13\u5b58 .wav \u6587\u4ef6，\u907f\u514d\u91cd\u590d\u63a8\u7406\u3002<\/p>\n
import os
\nfrom hashlib import md5<\/p>\n
def get_cache_path(text, emotion):
\n key = md5(f"{text}_{emotion}".encode()).hexdigest()
\n return f".\/cache\/{key}.wav"<\/p>\n
\n
📊 \u6210\u672c\u5bf9\u6bd4：\u81ea\u5efa vs \u5546\u4e1aAPI<\/h3>\n
| \u65b9\u6848 | \u521d\u671f\u6295\u5165 | \u6708\u5747\u6210\u672c（5\u4e07\u5b57\u7b26） | \u662f\u5426\u53ef\u63a7 | \u60c5\u611f\u652f\u6301 | \u6570\u636e\u5b89\u5168 |
\n|——|——–|——————|———-|———-|———-|
\n| \u672c\u65b9\u6848（\u81ea\u5efa） | ~300\u5143（\u670d\u52a1\u5668\u62bc\u91d1） | <200\u5143 | \u2705 \u5b8c\u5168\u81ea\u4e3b | \u2705 \u591a\u60c5\u611f | \u2705 \u5185\u7f51\u90e8\u7f72 |
\n| \u767e\u5ea6\u8bed\u97f3\u5408\u6210 | 0 | 150\u5143（\u514d\u8d39\u989d\u5ea6\u540e） | \u274c \u4f9d\u8d56\u7f51\u7edc | \u26a0\ufe0f \u6709\u9650\u60c5\u611f | \u274c \u6570\u636e\u5916\u4f20 |
\n| \u963f\u91cc\u4e91\u667a\u80fd\u8bed\u97f3\u4ea4\u4e92 | 0 | 200\u5143\u8d77 | \u274c \u7528\u91cf\u8ba1\u8d39 | \u2705 \u652f\u6301\u60c5\u611f | \u26a0\ufe0f \u9700\u6388\u6743 |
\n| \u817e\u8baf\u4e91TTS | 0 | 180\u5143（\u8d85\u51fa\u514d\u8d39\u989d\u5ea6） | \u274c \u52a8\u6001\u5b9a\u4ef7 | \u2705 \u652f\u6301\u60c5\u611f | \u26a0\ufe0f \u5916\u90e8\u8c03\u7528 |<\/p>\n
\u2705 \u7ed3\u8bba：\u5bf9\u4e8e\u6bcf\u6708\u8c03\u7528\u91cf\u7a33\u5b9a\u57285\u4e07\u5b57\u7b26\u4ee5\u4e0a\u7684\u4e2d\u5c0f\u4f01\u4e1a，\u81ea\u5efa\u65b9\u6848\u66f4\u5177\u6027\u4ef7\u6bd4\u548c\u957f\u671f\u53ef\u63a7\u6027\u3002<\/p>\n
\n
🎯 \u603b\u7ed3：\u4e3a\u4ec0\u4e48\u8fd9\u662f\u4e2d\u5c0f\u4f01\u4e1a\u7684\u6700\u4f73\u9009\u62e9？<\/h3>\n
\n
📌 \u4e0b\u4e00\u6b65\u5efa\u8bae<\/h3>\n\n- \u7acb\u5373\u5c1d\u8bd5：\u8d2d\u4e70\u4e00\u53f0\u8f7b\u91cf\u670d\u52a1\u5668，\u62c9\u53d6\u955c\u50cf\u9a8c\u8bc1\u6548\u679c\u3002<\/li>\n
- \u96c6\u6210\u8fdb\u7cfb\u7edf：\u901a\u8fc7API\u5c06\u8bed\u97f3\u5408\u6210\u529f\u80fd\u5d4c\u5165CRM\u3001\u5ba2\u670d\u7cfb\u7edf\u6216\u5c0f\u7a0b\u5e8f\u3002<\/li>\n
- \u8fdb\u9636\u4f18\u5316：\u5c1d\u8bd5\u5fae\u8c03\u6a21\u578b\u52a0\u5165\u4f01\u4e1a\u4e13\u5c5e\u97f3\u8272（\u9700\u5c11\u91cf\u6807\u6ce8\u6570\u636e）\u3002<\/li>\n
- \u5173\u6ce8\u66f4\u65b0：ModelScope \u793e\u533a\u6301\u7eed\u63a8\u51fa\u65b0\u6a21\u578b（\u5982\u66f4\u5feb\u7684FastSpeech2\u7248\u672c），\u53ef\u5b9a\u671f\u5347\u7ea7\u3002<\/li>\n<\/ul>\n
🔗 \u8d44\u6e90\u83b7\u53d6\u63d0\u793a：\u672c\u6587\u6240\u8ff0\u955c\u50cf\u53ef\u901a\u8fc7 GitHub \u6216 Gitee \u83b7\u53d6\u6784\u5efa\u811a\u672c（Dockerfile + requirements.txt + Flask \u670d\u52a1\u4ee3\u7801），\u6b22\u8fce Star\/Fork\u3002<\/p>\n
\u8ba9\u6bcf\u4e00\u4e2a\u4e2d\u5c0f\u4f01\u4e1a，\u90fd\u80fd\u62e5\u6709\u5c5e\u4e8e\u81ea\u5df1\u7684\u201cAI\u4e3b\u64ad\u201d\u3002🎙\ufe0f\u2728<\/p>\n","protected":false},"excerpt":{"rendered":"
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