114 lines
3.5 KiB
Python
114 lines
3.5 KiB
Python
import numpy as np
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import torch
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# server.py
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from fastapi import FastAPI, WebSocket
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from fastapi import Request
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import HTMLResponse
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from funasr import AutoModel
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from funasr.utils.postprocess_utils import rich_transcription_postprocess
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# 初始化FastAPI应用
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app = FastAPI(title="ASR API", description="语音识别API", version="1.0.0")
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# 添加跨域支持
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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model_dir = "iic/SenseVoiceSmall"
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# 检查是否有可用的CUDA设备
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = AutoModel(
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model=model_dir,
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trust_remote_code=True,
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vad_model="fsmn-vad",
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vad_kwargs={"max_single_segment_time": 30000},
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device=device, # 根据实际环境选择设备
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language="auto", # 自动检测语言
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use_itn=True, # 启用逆文本归一化
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)
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def render_template(template_name: str, context: dict = None):
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"""简化模板渲染函数，避免依赖Jinja2"""
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context = context or {}
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if template_name == "3.html":
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# 直接返回静态HTML内容
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with open("3.html", "r", encoding="utf-8") as f:
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content = f.read()
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return HTMLResponse(content=content)
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return HTMLResponse(content="Template not found")
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def bytes_to_float_array(audio_bytes: bytes) -> np.ndarray:
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"""Convert 16-bit PCM bytes to float32 array normalized to [-1, 1]"""
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# 从 bytes 创建 int16 数组
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audio_np = np.frombuffer(audio_bytes, dtype=np.int16)
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# 转为 float32 并归一化
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return audio_np.astype(np.float32) / 32768.0
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def convert_audio_data(audio_data: bytes) -> np.ndarray:
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# 首先尝试作为 PCM 数据处理
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try:
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if len(audio_data) % 2 == 0 and len(audio_data) > 0: # 16-bit数据长度应该是2的倍数
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audio_array = bytes_to_float_array(audio_data)
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# 确保数据是float32类型
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return audio_array.astype(np.float32)
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except Exception as e:
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print(f"⚠️ 16-bit PCM处理失败: {e}")
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return np.array([], dtype=np.float32)
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# chatbot = QwenChatbot()
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@app.websocket("/ws/asr")
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async def websocket_asr(websocket: WebSocket):
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# 绕过origin检查，解决403 Forbidden问题
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await websocket.accept()
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while True:
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# 🔁 接收前端发送的音频数据
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audio_data = await websocket.receive_bytes()
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# 转换音频数据为模型可处理的格式
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audio_array = convert_audio_data(audio_data)
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# 确保数据是正确的格式
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if audio_array.dtype != np.float32:
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audio_array = audio_array.astype(np.float32)
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# ✅ 使用 SenseVoice 推理
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result = model.generate(
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audio_array,
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sampling_rate=16000,
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language="auto", # 自动检测语言
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use_itn=True, # 启用逆文本归一化
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batch_size_s=60
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)
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text = rich_transcription_postprocess(result[0]["text"])
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# print(chatbot.generate_response(text))
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print(f"🔍 Raw result type: {result}, content: {text}")
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await websocket.send_text(text)
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@app.get("/", response_class=HTMLResponse)
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async def index(request: Request):
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"""
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主页，提供Web界面
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"""
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return render_template("3.html", {"request": request})
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if __name__ == '__main__':
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8000, log_level="info")
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