first commit

asr_api.py

@@ -0,0 +1,113 @@
+import numpy as np
+import torch
+# server.py
+from fastapi import FastAPI, WebSocket
+from fastapi import Request
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import HTMLResponse
+from funasr import AutoModel
+from funasr.utils.postprocess_utils import rich_transcription_postprocess
+
+# 初始化FastAPI应用
+app = FastAPI(title="ASR API", description="语音识别API", version="1.0.0")
+
+# 添加跨域支持
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+model_dir = "iic/SenseVoiceSmall"
+
+# 检查是否有可用的CUDA设备
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model = AutoModel(
+    model=model_dir,
+    trust_remote_code=True,
+    vad_model="fsmn-vad",
+    vad_kwargs={"max_single_segment_time": 30000},
+    device=device,  # 根据实际环境选择设备
+    language="auto",  # 自动检测语言
+    use_itn=True,  # 启用逆文本归一化
+)
+
+
+def render_template(template_name: str, context: dict = None):
+    """简化模板渲染函数，避免依赖Jinja2"""
+    context = context or {}
+    if template_name == "3.html":
+        # 直接返回静态HTML内容
+        with open("3.html", "r", encoding="utf-8") as f:
+            content = f.read()
+        return HTMLResponse(content=content)
+    return HTMLResponse(content="Template not found")
+
+
+def bytes_to_float_array(audio_bytes: bytes) -> np.ndarray:
+    """Convert 16-bit PCM bytes to float32 array normalized to [-1, 1]"""
+    # 从 bytes 创建 int16 数组
+    audio_np = np.frombuffer(audio_bytes, dtype=np.int16)
+    # 转为 float32 并归一化
+    return audio_np.astype(np.float32) / 32768.0
+
+
+def convert_audio_data(audio_data: bytes) -> np.ndarray:
+    # 首先尝试作为 PCM 数据处理
+    try:
+        if len(audio_data) % 2 == 0 and len(audio_data) > 0:  # 16-bit数据长度应该是2的倍数
+            audio_array = bytes_to_float_array(audio_data)
+            # 确保数据是float32类型
+            return audio_array.astype(np.float32)
+    except Exception as e:
+        print(f"⚠️ 16-bit PCM处理失败: {e}")
+
+    return np.array([], dtype=np.float32)
+
+
+# chatbot = QwenChatbot()
+@app.websocket("/ws/asr")
+async def websocket_asr(websocket: WebSocket):
+    # 绕过origin检查，解决403 Forbidden问题
+    await websocket.accept()
+
+    while True:
+        # 🔁 接收前端发送的音频数据
+        audio_data = await websocket.receive_bytes()
+
+        # 转换音频数据为模型可处理的格式
+        audio_array = convert_audio_data(audio_data)
+
+        # 确保数据是正确的格式
+        if audio_array.dtype != np.float32:
+            audio_array = audio_array.astype(np.float32)
+
+        # ✅ 使用 SenseVoice 推理
+        result = model.generate(
+            audio_array,
+            sampling_rate=16000,
+            language="auto",  # 自动检测语言
+            use_itn=True,  # 启用逆文本归一化
+            batch_size_s=60
+        )
+        text = rich_transcription_postprocess(result[0]["text"])
+        # print(chatbot.generate_response(text))
+        print(f"🔍 Raw result type: {result}, content: {text}")
+        await websocket.send_text(text)
+
+
+@app.get("/", response_class=HTMLResponse)
+async def index(request: Request):
+    """
+    主页，提供Web界面
+    """
+    return render_template("3.html", {"request": request})
+
+
+if __name__ == '__main__':
+    import uvicorn
+
+    uvicorn.run(app, host="0.0.0.0", port=8000, log_level="info")