![\"\u56fe\u7247\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260114045020-6967208ccbfb8.png\")
<\/p>\n\u5728\u667a\u80fd\u8bed\u97f3\u4ea4\u4e92\u3001\u6709\u58f0\u5185\u5bb9\u751f\u6210\u3001\u8f85\u52a9\u9605\u8bfb\u7b49\u573a\u666f\u4e2d，\u9ad8\u8d28\u91cf\u7684\u4e2d\u6587\u8bed\u97f3\u5408\u6210（TTS）\u80fd\u529b\u6b63\u53d8\u5f97\u4e0d\u53ef\u6216\u7f3a\u3002\u7136\u800c，\u8bb8\u591a\u73b0\u6210\u7684\u8bed\u97f3\u5408\u6210\u670d\u52a1\u4f9d\u8d56\u4e91\u7aefAPI，\u5b58\u5728\u6570\u636e\u5916\u6cc4\u98ce\u9669\u3001\u7f51\u7edc\u5ef6\u8fdf\u9ad8\u3001\u65e0\u6cd5\u79bb\u7ebf\u4f7f\u7528\u7b49\u95ee\u9898\u3002\u4e3a\u6b64，\u6211\u4eec\u63a8\u51fa\u57fa\u4e8e ModelScope \u7684 Sambert-Hifigan \u591a\u60c5\u611f\u4e2d\u6587\u8bed\u97f3\u5408\u6210\u6a21\u578b \u7684\u672c\u5730\u5316\u90e8\u7f72\u89e3\u51b3\u65b9\u6848\u2014\u2014\u5b8c\u5168\u652f\u6301\u5185\u7f51\u8fd0\u884c，\u65e0\u9700\u8054\u7f51，\u4e0d\u8c03\u7528\u4efb\u4f55\u5916\u90e8\u670d\u52a1\u5668\u3002<\/p>\n
\u672c\u9879\u76ee\u5c01\u88c5\u4e86 ModelScope \u5e73\u53f0\u4e0a\u7684\u7ecf\u5178 Sambert-Hifigan（\u4e2d\u6587\u591a\u60c5\u611f） \u6a21\u578b，\u7ed3\u5408 Flask \u6784\u5efa\u4e86\u8f7b\u91cf\u7ea7 Web \u670d\u52a1，\u96c6\u6210\u53ef\u89c6\u5316\u754c\u9762\u4e0e\u6807\u51c6 HTTP API，\u5b9e\u73b0\u201c\u5f00\u7bb1\u5373\u7528\u201d\u7684\u672c\u5730\u8bed\u97f3\u5408\u6210\u80fd\u529b\u3002\u65e0\u8bba\u662f\u4f01\u4e1a\u5185\u90e8\u7cfb\u7edf\u96c6\u6210\u3001\u6559\u80b2\u4ea7\u54c1\u5d4c\u5165，\u8fd8\u662f\u9690\u79c1\u654f\u611f\u573a\u666f\u4e0b\u7684\u8bed\u97f3\u64ad\u62a5，\u5747\u53ef\u5b89\u5168\u3001\u9ad8\u6548\u5730\u8fd0\u884c\u4e8e\u9694\u79bb\u7f51\u7edc\u73af\u5883\u3002<\/p>\n
💡 \u6838\u5fc3\u4eae\u70b9：
\n– 100% \u79bb\u7ebf\u8fd0\u884c：\u6240\u6709\u6a21\u578b\u4e0e\u4f9d\u8d56\u5747\u6253\u5305\u5728\u955c\u50cf\u4e2d，\u65e0\u9700\u8bbf\u95ee\u4e92\u8054\u7f51\u3002
\n– \u591a\u60c5\u611f\u8868\u8fbe：\u652f\u6301\u559c\u3001\u6012\u3001\u54c0\u3001\u5e73\u7b49\u591a\u79cd\u8bed\u8c03，\u63d0\u5347\u8bed\u97f3\u81ea\u7136\u5ea6\u4e0e\u8868\u73b0\u529b\u3002
\n– \u53cc\u6a21\u8f93\u51fa：\u63d0\u4f9b\u56fe\u5f62\u5316 WebUI \u548c RESTful API \u63a5\u53e3，\u6ee1\u8db3\u5f00\u53d1\u4e0e\u6f14\u793a\u53cc\u91cd\u9700\u6c42\u3002
\n– \u73af\u5883\u7eaf\u51c0\u7a33\u5b9a：\u5df2\u4fee\u590d datasets(2.13.0)\u3001numpy(1.23.5) \u4e0e scipy(<1.13) \u7684\u7248\u672c\u51b2\u7a81\u95ee\u9898，\u675c\u7edd\u5e38\u89c1\u62a5\u9519\u3002
\n– CPU \u53cb\u597d\u4f18\u5316：\u65e0\u9700 GPU \u5373\u53ef\u6d41\u7545\u63a8\u7406，\u9002\u7528\u4e8e\u666e\u901a\u670d\u52a1\u5668\u6216\u8fb9\u7f18\u8bbe\u5907\u3002<\/p>\n
\u8981\u7406\u89e3\u4e3a\u4f55\u8be5\u65b9\u6848\u80fd\u5b9e\u73b0\u9ad8\u8d28\u91cf\u4e14\u53ef\u672c\u5730\u90e8\u7f72\u7684\u8bed\u97f3\u5408\u6210，\u9700\u6df1\u5165\u5176\u6280\u672f\u67b6\u6784\u3002Sambert-Hifigan \u5b9e\u9645\u4e0a\u662f\u4e24\u4e2a\u5173\u952e\u6a21\u578b\u7684\u7ea7\u8054\u7ec4\u5408：Sambert（\u6587\u672c\u5230\u6885\u5c14\u8c31） \u4e0e Hifigan（\u6885\u5c14\u8c31\u5230\u6ce2\u5f62）\u3002<\/p>\n
Sambert \u662f\u4e00\u79cd\u57fa\u4e8e Transformer \u7ed3\u6784\u7684\u7aef\u5230\u7aef\u8bed\u97f3\u5408\u6210\u6a21\u578b，\u5168\u79f0\u4e3a Semantic-Aware Non-Attentive Background for End-to-End Text-to-Speech Synthesis\u3002\u5b83\u8d1f\u8d23\u5c06\u8f93\u5165\u6587\u672c\u8f6c\u6362\u4e3a\u4e2d\u95f4\u58f0\u5b66\u8868\u793a\u2014\u2014\u6885\u5c14\u9891\u8c31\u56fe（Mel-spectrogram）\u3002<\/p>\n
\u5176\u6838\u5fc3\u4f18\u52bf\u5728\u4e8e：
\n– \u975e\u6ce8\u610f\u529b\u673a\u5236（Non-Attentive）：\u907f\u514d\u4f20\u7edf Tacotron \u7c7b\u6a21\u578b\u56e0\u6ce8\u610f\u529b\u5bf9\u9f50\u5931\u8d25\u5bfc\u81f4\u7684\u91cd\u590d\u53d1\u97f3\u6216\u8df3\u5b57\u95ee\u9898\u3002
\n– \u8bed\u4e49\u611f\u77e5\u5efa\u6a21：\u901a\u8fc7\u663e\u5f0f\u5efa\u6a21\u97f3\u7d20\u6301\u7eed\u65f6\u95f4\u548c\u97f5\u5f8b\u8fb9\u754c，\u589e\u5f3a\u8bed\u97f3\u8282\u594f\u611f\u3002
\n– \u591a\u60c5\u611f\u63a7\u5236：\u652f\u6301\u901a\u8fc7\u60c5\u611f\u6807\u7b7e（emotion token）\u8c03\u8282\u8f93\u51fa\u8bed\u97f3\u7684\u60c5\u611f\u8272\u5f69，\u5982\u6b22\u5feb\u3001\u60b2\u4f24\u3001\u6124\u6012\u7b49\u3002<\/p>\n
# \u793a\u4f8b：Sambert \u6a21\u578b\u524d\u5411\u8fc7\u7a0b（\u7b80\u5316\u7248）
\ndef sambert_forward(text_input, emotion_label):
\n # \u6587\u672c\u7f16\u7801
\n text_emb = self.phoneme_encoder(text_input)
\n # \u52a0\u5165\u60c5\u611f\u5d4c\u5165
\n emotion_emb = self.emotion_embedding(emotion_label)
\n combined_emb = text_emb + emotion_emb
\n # \u751f\u6210\u6885\u5c14\u8c31\u4e0e\u6301\u7eed\u65f6\u95f4
\n mel_output, duration = self.decoder(combined_emb)
\n return mel_output<\/p>\n
Hifigan \u662f\u4e00\u79cd\u751f\u6210\u5bf9\u6297\u7f51\u7edc（GAN）\u7ed3\u6784\u7684\u58f0\u7801\u5668，\u4e13\u7528\u4e8e\u4ece\u6885\u5c14\u9891\u8c31\u56fe\u91cd\u5efa\u9ad8\u4fdd\u771f\u97f3\u9891\u6ce2\u5f62\u3002\u76f8\u6bd4\u4f20\u7edf\u7684 Griffin-Lim \u6216 WaveNet，Hifigan \u5728\u97f3\u8d28\u548c\u63a8\u7406\u901f\u5ea6\u4e4b\u95f4\u53d6\u5f97\u4e86\u6781\u4f73\u5e73\u8861\u3002<\/p>\n
\u5176\u8bbe\u8ba1\u7279\u70b9\u5305\u62ec：
\n– \u591a\u5468\u671f\u5224\u522b\u5668（MPD） + \u591a\u5c3a\u5ea6\u5224\u522b\u5668（MSD）：\u63d0\u5347\u7ec6\u8282\u771f\u5b9e\u611f\u3002
\n– \u9006\u77ed\u65f6\u5085\u91cc\u53f6\u53d8\u6362（iSTFT）\u5c42：\u76f4\u63a5\u5728\u7f51\u7edc\u4e2d\u5b8c\u6210\u9891\u57df\u5230\u65f6\u57df\u7684\u8f6c\u6362，\u7aef\u5230\u7aef\u8bad\u7ec3\u3002
\n– \u8f7b\u91cf\u5316\u8bbe\u8ba1：\u9002\u5408 CPU \u63a8\u7406，\u5355\u53e5\u5408\u6210\u65f6\u95f4\u63a7\u5236\u5728 1 \u79d2\u4ee5\u5185\u3002<\/p>\n
\u2705 \u4e3a\u4ec0\u4e48\u9009\u62e9 Sambert-Hifigan \u7ec4\u5408？
\n– \u97f3\u8d28\u63a5\u8fd1\u771f\u4eba\u5f55\u97f3，\u5c24\u5176\u5728\u4e2d\u6587\u8bed\u5883\u4e0b\u8868\u73b0\u4f18\u5f02\u3002
\n– \u6a21\u578b\u4f53\u79ef\u9002\u4e2d（\u5408\u8ba1\u7ea6 600MB），\u4fbf\u4e8e\u6253\u5305\u90e8\u7f72\u3002
\n– \u652f\u6301\u7ec6\u7c92\u5ea6\u63a7\u5236（\u5982\u8bed\u901f\u3001\u97f3\u8c03\u3001\u60c5\u611f），\u7075\u6d3b\u6027\u5f3a\u3002<\/p>\n
\u6211\u4eec\u5c06\u6574\u4e2a\u670d\u52a1\u5c01\u88c5\u4e3a Docker \u955c\u50cf，\u786e\u4fdd\u8de8\u5e73\u53f0\u4e00\u81f4\u6027\u4e0e\u90e8\u7f72\u4fbf\u6377\u6027\u3002\u4ee5\u4e0b\u662f\u5173\u952e\u5de5\u7a0b\u5b9e\u73b0\u6b65\u9aa4\u3002<\/p>\n
\u539f\u59cb ModelScope \u6a21\u578b\u4f9d\u8d56 transformers\u3001datasets\u3001numpy \u7b49\u5e93，\u4f46\u5728\u5b9e\u9645\u8fd0\u884c\u4e2d\u5e38\u51fa\u73b0\u4ee5\u4e0b\u95ee\u9898：<\/p>\n
| \u95ee\u9898 | \u539f\u56e0 | \u89e3\u51b3\u65b9\u6848 |
\n|——|——|———-|
\n| TypeError: expected str, bytes or os.PathLike object, not NoneType | datasets>=2.14.0 \u4fee\u6539\u4e86\u7f13\u5b58\u8def\u5f84\u903b\u8f91 | \u9501\u5b9a datasets==2.13.0 |
\n| AttributeError: module 'numpy' has no attribute 'int' | numpy>=1.24 \u79fb\u9664\u4e86\u65e7\u7c7b\u578b\u522b\u540d | \u56fa\u5b9a numpy==1.23.5 |
\n| scipy.optimize.minimize \u62a5\u9519 | scipy>=1.13 \u66f4\u6539\u4e86\u90e8\u5206\u63a5\u53e3 | \u4f7f\u7528 <1.13 \u7248\u672c |<\/p>\n
\u6700\u7ec8 requirements.txt \u5173\u952e\u6761\u76ee\u5982\u4e0b：<\/p>\n
numpy==1.23.5
\nscipy<1.13
\ntorch==1.13.1
\ntransformers==4.26.1
\ndatasets==2.13.0
\nflask==2.3.3
\ngunicorn==21.2.0<\/p>\n
\u901a\u8fc7\u7cbe\u786e\u9501\u5b9a\u7248\u672c，\u5b9e\u73b0\u4e86 \u201c\u4e00\u6b21\u6784\u5efa，\u5904\u5904\u8fd0\u884c\u201d \u7684\u7a33\u5b9a\u6027\u4fdd\u969c\u3002<\/p>\n
\u524d\u7aef\u91c7\u7528 Bootstrap 5 \u6784\u5efa\u54cd\u5e94\u5f0f\u9875\u9762，\u540e\u7aef\u901a\u8fc7 Flask \u63d0\u4f9b \/tts \u63a5\u53e3\u5904\u7406\u8bf7\u6c42\u3002\u7528\u6237\u8f93\u5165\u6587\u672c\u540e，\u670d\u52a1\u81ea\u52a8\u5b8c\u6210\u5206\u8bcd\u3001\u60c5\u611f\u6807\u6ce8\u3001\u8bed\u97f3\u5408\u6210\u4e0e\u97f3\u9891\u8fd4\u56de\u3002<\/p>\n
\/sambert-hifigan-tts
\n\u251c\u2500\u2500 app.py # Flask \u4e3b\u7a0b\u5e8f
\n\u251c\u2500\u2500 static\/
\n\u2502 \u2514\u2500\u2500 style.css # \u81ea\u5b9a\u4e49\u6837\u5f0f
\n\u251c\u2500\u2500 templates\/
\n\u2502 \u2514\u2500\u2500 index.html # Web \u9875\u9762\u6a21\u677f
\n\u251c\u2500\u2500 models\/
\n\u2502 \u251c\u2500\u2500 sambert\/
\n\u2502 \u2514\u2500\u2500 hifigan\/ # \u9884\u52a0\u8f7d\u6a21\u578b
\n\u2514\u2500\u2500 core\/
\n \u251c\u2500\u2500 tokenizer.py # \u4e2d\u6587\u5206\u8bcd\u4e0e\u97f3\u7d20\u8f6c\u6362
\n \u2514\u2500\u2500 synthesizer.py # \u5408\u6210\u4e3b\u903b\u8f91<\/p>\n
# app.py
\nfrom flask import Flask, request, render_template, send_file
\nimport io
\nfrom core.synthesizer import Synthesizer<\/p>\n
app = Flask(__name__)
\nsynthesizer = Synthesizer()<\/p>\n
@app.route("\/", methods=["GET"])
\ndef index():
\n return render_template("index.html")<\/p>\n
@app.route("\/tts", methods=["POST"])
\ndef tts():
\n text = request.form.get("text", "").strip()
\n emotion = request.form.get("emotion", "neutral")<\/p>\n
if not text:
\n return {"error": "\u6587\u672c\u4e0d\u80fd\u4e3a\u7a7a"}, 400<\/p>\n
try:
\n audio_data = synthesizer.text_to_speech(text, emotion=emotion)
\n byte_io = io.BytesIO(audio_data)
\n byte_io.seek(0)
\n return send_file(
\n byte_io,
\n mimetype="audio\/wav",
\n as_attachment=True,
\n download_name="speech.wav"
\n )
\n except Exception as e:
\n return {"error": str(e)}, 500<\/p>\n
if __name__ == "__main__":
\n app.run(host="0.0.0.0", port=5000)<\/p>\n
<!– templates\/index.html –>
\n<form id="ttsForm">
\n <textarea name="text" placeholder="\u8bf7\u8f93\u5165\u8981\u5408\u6210\u7684\u4e2d\u6587\u6587\u672c…" required><\/textarea>
\n <select name="emotion">
\n <option value="happy">\u5f00\u5fc3<\/option>
\n <option value="sad">\u60b2\u4f24<\/option>
\n <option value="angry">\u6124\u6012<\/option>
\n <option value="neutral" selected>\u5e73\u9759<\/option>
\n <\/select>
\n <button type="submit">\u5f00\u59cb\u5408\u6210\u8bed\u97f3<\/button>
\n<\/form><\/p>\n
<audio id="player" controls><\/audio><\/p>\n
<script>
\ndocument.getElementById("ttsForm").onsubmit = async (e) => {
\n e.preventDefault();
\n const formData = new FormData(e.target);<\/p>\n
const res = await fetch("\/tts", {
\n method: "POST",
\n body: formData
\n });<\/p>\n
if (res.ok) {
\n const blob = await res.blob();
\n const url = URL.createObjectURL(blob);
\n document.getElementById("player").src = url;
\n } else {
\n alert("\u5408\u6210\u5931\u8d25：" + await res.text());
\n }
\n};
\n<\/script><\/p>\n
💡 \u4eae\u70b9\u8bf4\u660e：
\n– \u6240\u6709\u8d44\u6e90\u672c\u5730\u52a0\u8f7d，\u65e0 CDN \u4f9d\u8d56\u3002
\n– \u97f3\u9891\u901a\u8fc7 Blob URL \u5b9e\u73b0\u6d4f\u89c8\u5668\u5185\u5b9e\u65f6\u64ad\u653e\u3002
\n– \u652f\u6301\u957f\u6587\u672c\u81ea\u52a8\u5206\u6bb5\u5904\u7406，\u907f\u514d\u5185\u5b58\u6ea2\u51fa\u3002<\/p>\n
\u5047\u8bbe\u4f60\u5df2\u83b7\u53d6\u9884\u6784\u5efa\u7684 Docker \u955c\u50cf（\u5982 tts-sambert-hifigan:latest），\u6267\u884c\u4ee5\u4e0b\u547d\u4ee4：<\/p>\n
docker run -d -p 5000:5000 –name tts-service tts-sambert-hifigan:latest<\/p>\n
\u5bb9\u5668\u542f\u52a8\u540e，Flask \u670d\u52a1\u5c06\u5728 http:\/\/localhost:5000 \u76d1\u542c\u8bf7\u6c42\u3002<\/p>\n
\u6253\u5f00\u6d4f\u89c8\u5668，\u8fdb\u5165\u670d\u52a1\u5730\u5740：<\/p>\n
<\/p>\n
\u70b9\u51fb\u5e73\u53f0\u63d0\u4f9b\u7684 HTTP \u8bbf\u95ee\u6309\u94ae，\u5373\u53ef\u8fdb\u5165\u4ea4\u4e92\u754c\u9762\u3002<\/p>\n
\u9664 WebUI \u5916，\u8be5\u670d\u52a1\u8fd8\u63d0\u4f9b\u6807\u51c6 HTTP API，\u4fbf\u4e8e\u96c6\u6210\u81f3\u5176\u4ed6\u5e94\u7528\u3002<\/p>\n
POST \/tts
\nContent-Type: multipart\/form-data<\/p>\n
| \u5b57\u6bb5 | \u7c7b\u578b | \u5fc5\u586b | \u8bf4\u660e |
\n|——|——|——|——|
\n| text | string | \u662f | \u8981\u5408\u6210\u7684\u4e2d\u6587\u6587\u672c（UTF-8 \u7f16\u7801） |
\n| emotion | string | \u5426 | \u60c5\u611f\u7c7b\u578b：happy, sad, angry, neutral（\u9ed8\u8ba4） |<\/p>\n
import requests<\/p>\n
url = "http:\/\/localhost:5000\/tts"
\ndata = {
\n "text": "\u6b22\u8fce\u4f7f\u7528\u672c\u5730\u8bed\u97f3\u5408\u6210\u670d\u52a1，\u73b0\u5728\u652f\u6301\u591a\u60c5\u611f\u8868\u8fbe\u4e86！",
\n "emotion": "happy"
\n}<\/p>\n
response = requests.post(url, data=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("\u9519\u8bef:", response.json())<\/p>\n
| \u7ef4\u5ea6 | \u672c\u5730\u90e8\u7f72（\u672c\u65b9\u6848） | \u516c\u6709\u4e91 TTS \u670d\u52a1 |
\n|——|——————|—————-|
\n| \u6570\u636e\u5b89\u5168\u6027 | \u2705 \u5b8c\u5168\u79c1\u6709，\u6570\u636e\u4e0d\u51fa\u5185\u7f51 | \u274c \u6587\u672c\u4e0a\u4f20\u81f3\u7b2c\u4e09\u65b9\u670d\u52a1\u5668 |
\n| \u7f51\u7edc\u4f9d\u8d56 | \u274c \u65e0\u9700\u8054\u7f51 | \u2705 \u5fc5\u987b\u7a33\u5b9a\u7f51\u7edc |
\n| \u5ef6\u8fdf\u8868\u73b0 | \u2705 \u5c40\u57df\u7f51\u6beb\u79d2\u7ea7\u54cd\u5e94 | \u26a0\ufe0f \u53d7\u516c\u7f51\u5e26\u5bbd\u5f71\u54cd |
\n| \u6210\u672c\u63a7\u5236 | \u2705 \u4e00\u6b21\u6027\u90e8\u7f72，\u957f\u671f\u514d\u8d39 | \u2705 \u6309\u8c03\u7528\u91cf\u8ba1\u8d39 |
\n| \u5b9a\u5236\u80fd\u529b | \u2705 \u53ef\u4fee\u6539\u6a21\u578b\u3001\u6dfb\u52a0\u65b0\u97f3\u8272 | \u274c \u9ed1\u76d2\u670d\u52a1，\u6269\u5c55\u53d7\u9650 |
\n| \u7ef4\u62a4\u96be\u5ea6 | \u26a0\ufe0f \u9700\u4e00\u5b9a\u8fd0\u7ef4\u80fd\u529b | \u2705 \u5b8c\u5168\u6258\u7ba1 |<\/p>\n
📌 \u9009\u578b\u5efa\u8bae：
\n– \u82e5\u6d89\u53ca\u91d1\u878d\u3001\u533b\u7597\u3001\u653f\u52a1\u7b49\u654f\u611f\u9886\u57df \u2192 \u9996\u9009\u672c\u5730\u90e8\u7f72
\n– \u82e5\u8ffd\u6c42\u5feb\u901f\u4e0a\u7ebf\u3001\u4f4e\u7ef4\u62a4\u6210\u672c \u2192 \u53ef\u8003\u8651\u4e91\u670d\u52a1
\n– \u82e5\u9700\u591a\u8bed\u8a00\u3001\u591a\u65b9\u8a00\u652f\u6301 \u2192 \u53ef\u7ed3\u5408\u4e24\u8005\u6df7\u5408\u67b6\u6784<\/p>\n
\u667a\u80fd\u5ba2\u670d\u64ad\u62a5\u7cfb\u7edf
\n \u5728\u94f6\u884c ATM \u6216\u533b\u9662\u5bfc\u8bca\u673a\u4e2d，\u4f7f\u7528\u672c\u5730 TTS \u5b9e\u73b0\u8bed\u97f3\u63d0\u793a，\u907f\u514d\u56e0\u65ad\u7f51\u5bfc\u81f4\u670d\u52a1\u4e2d\u65ad\u3002<\/p>\n<\/li>\n
\u65e0\u969c\u788d\u9605\u8bfb\u5de5\u5177
\n \u4e3a\u89c6\u969c\u4eba\u58eb\u63d0\u4f9b\u79bb\u7ebf\u6717\u8bfb\u529f\u80fd，\u4fdd\u62a4\u4e2a\u4eba\u9605\u8bfb\u9690\u79c1\u3002<\/p>\n<\/li>\n
\u6559\u80b2\u7c7b\u8f6f\u4ef6\u914d\u97f3
\n \u5c06\u6559\u6750\u6587\u5b57\u81ea\u52a8\u8f6c\u4e3a\u5e26\u60c5\u611f\u7684\u8bed\u97f3\u8bb2\u89e3，\u63d0\u5347\u5b66\u4e60\u8da3\u5473\u6027\u3002<\/p>\n<\/li>\n
\u5de5\u4e1a\u81ea\u52a8\u5316\u8bed\u97f3\u62a5\u8b66
\n \u5728\u5de5\u5382\u63a7\u5236\u7cfb\u7edf\u4e2d，\u96c6\u6210\u5f02\u5e38\u72b6\u6001\u8bed\u97f3\u64ad\u62a5\u6a21\u5757，\u5b9e\u73b0\u5b9e\u65f6\u63d0\u9192\u3002<\/p>\n<\/li>\n
| \u6587\u672c\u957f\u5ea6 | \u5e73\u5747\u5408\u6210\u65f6\u95f4 | \u8f93\u51fa\u91c7\u6837\u7387 | \u6587\u4ef6\u5927\u5c0f |
\n|———|————–|————|———-|
\n| 50 \u5b57 | 0.8s | 24kHz | ~120KB |
\n| 100 \u5b57 | 1.5s | 24kHz | ~230KB |
\n| 500 \u5b57 | 7.2s | 24kHz | ~1.1MB |<\/p>\n
\u2705 \u652f\u6301\u5e76\u53d1\u8bf7\u6c42（Gunicorn + 4 workers），QPS \u8fbe 8+（\u60c5\u611f\u6a21\u5f0f\u56fa\u5b9a）<\/p>\n
📢 \u7ed3\u8bed：
\n\u5728 AI \u6a21\u578b\u65e5\u76ca\u5f3a\u5927\u7684\u4eca\u5929，\u201c\u53ef\u63a7\u3001\u53ef\u89e3\u91ca\u3001\u53ef\u79c1\u6709\u5316\u201d \u7684\u672c\u5730\u5316\u90e8\u7f72\u65b9\u6848\u6b63\u6210\u4e3a\u4f01\u4e1a\u7ea7\u5e94\u7528\u7684\u521a\u9700\u3002Sambert-Hifigan \u4e2d\u6587\u591a\u60c5\u611f\u8bed\u97f3\u5408\u6210\u670d\u52a1，\u4e0d\u4ec5\u662f\u4e00\u5957\u6280\u672f\u5b9e\u73b0，\u66f4\u662f\u4e00\u79cd\u5bf9\u6570\u636e\u4e3b\u6743\u4e0e\u7528\u6237\u4f53\u9a8c\u7684\u5c0a\u91cd\u3002
\n\u65e0\u8bba\u4f60\u662f\u5f00\u53d1\u8005\u3001\u4ea7\u54c1\u7ecf\u7406\u8fd8\u662f\u7cfb\u7edf\u67b6\u6784\u5e08，\u90fd\u53ef\u4ee5\u57fa\u4e8e\u6b64\u65b9\u6848，\u5feb\u901f\u6784\u5efa\u5c5e\u4e8e\u81ea\u5df1\u7684\u5b89\u5168\u3001\u7a33\u5b9a\u3001\u667a\u80fd\u7684\u8bed\u97f3\u4ea4\u4e92\u7cfb\u7edf\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
\u80fd\u5426\u79bb\u7ebf\u4f7f\u7528？\u5b8c\u5168\u652f\u6301\u5185\u7f51\u90e8\u7f72，\u65e0\u9700\u8fde\u63a5\u5916\u90e8\u670d\u52a1\u5668
\n🎙\ufe0f Sambert-HifiGan \u4e2d\u6587\u591a\u60c5\u611f\u8bed\u97f3\u5408\u6210\u670d\u52a1 (WebUI API)
\n📖 \u9879\u76ee\u7b80\u4ecb
\n\u5728\u667a\u80fd\u8bed\u97f3\u4ea4\u4e92\u3001\u6709\u58f0\u5185\u5bb9\u751f\u6210\u3001\u8f85\u52a9\u9605\u8bfb\u7b49\u573a\u666f\u4e2d，\u9ad8\u8d28\u91cf\u7684\u4e2d\u6587\u8bed\u97f3\u5408\u6210（TTS）\u80fd\u529b\u6b63\u53d8\u5f97\u4e0d\u53ef\u6216\u7f3a\u3002\u7136\u800c，\u8bb8\u591a\u73b0\u6210\u7684\u8bed\u97f3\u5408\u6210\u670d\u52a1\u4f9d\u8d56\u4e91\u7aefAPI，\u5b58\u5728\u6570\u636e\u5916\u6cc4\u98ce\u9669\u3001\u7f51\u7edc\u5ef6\u8fdf\u9ad8\u3001\u65e0\u6cd5\u79bb\u7ebf\u4f7f\u7528\u7b49\u95ee\u9898\u3002\u4e3a\u6b64<\/p>\n","protected":false},"author":2,"featured_media":59759,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"topic":[],"class_list":["post-59760","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server"],"yoast_head":"\n