feat: Qwen2-Audio语音模型API服务初始版本

README.md

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+# Qwen2-Audio 语音模型服务
+
+语音交互模型 API 服务，基于 Qwen2-Audio-7B-Instruct。
+
+## 端口
+
+- **服务端口**: 19018
+
+## 功能
+
+- 语音识别 + 对话生成
+- 多轮对话支持
+- 文本对话接口（可选）
+- 对话历史管理
+
+## 部署步骤
+
+### 1. 安装依赖
+
+```bash
+pip install -r requirements.txt
+```
+
+### 2. 启动服务
+
+```bash
+# 方式一：直接启动
+python server.py
+
+# 方式二：使用脚本（可指定端口）
+PORT=19018 ./start.sh
+```
+
+### 3. 验证服务
+
+```bash
+curl http://localhost:19018/
+```
+
+返回：
+```json
+{"status": "ok", "model": "Qwen/Qwen2-Audio-7B-Instruct", "conversations": 0}
+```
+
+## API 接口
+
+### 语音推理
+
+```bash
+POST /api/voice/inference
+Content-Type: multipart/form-data
+
+参数:
+- audio: 音频文件 (WAV/MP3/FLAC)
+- conversation_id: 对话ID（可选，不传则创建新对话）
+- max_length: 最大生成长度（默认256）
+
+返回:
+{
+  "reply": "你好，有什么可以帮助你的吗？",
+  "conversation_id": "xxx-xxx-xxx",
+  "timestamp": "2026-04-21T18:00:00"
+}
+```
+
+**示例**：
+```bash
+curl -X POST http://localhost:19018/api/voice/inference \
+  -F "audio=@test.wav"
+```
+
+### 文本推理（测试用）
+
+```bash
+POST /api/voice/text
+Content-Type: multipart/form-data
+
+参数:
+- text: 文本消息
+- conversation_id: 对话ID（可选）
+
+返回: 同上
+```
+
+### 获取对话历史
+
+```bash
+GET /api/voice/conversation/{conversation_id}
+```
+
+### 删除对话
+
+```bash
+DELETE /api/voice/conversation/{conversation_id}
+```
+
+## 多轮对话
+
+第一轮：
+```bash
+curl -X POST http://localhost:19018/api/voice/inference \
+  -F "audio=@audio1.wav"
+
+# 返回 conversation_id: "abc-123"
+```
+
+第二轮：
+```bash
+curl -X POST http://localhost:19018/api/voice/inference \
+  -F "audio=@audio2.wav" \
+  -F "conversation_id=abc-123"
+```
+
+## 环境变量
+
+| 变量 | 说明 | 默认值 |
+|------|------|--------|
+| MODEL_NAME | 模型名称 | Qwen/Qwen2-Audio-7B-Instruct |
+| MAX_HISTORY_TURNS | 最大历史轮数 | 10 |
+
+## 硬件要求
+
+- **GPU**: 推荐 NVIDIA GPU，显存 ≥ 16GB
+- **CPU**: 可运行，但速度较慢
+- **内存**: ≥ 32GB
+
+## 注意事项
+
+1. 模型首次加载需要下载约 15GB，请确保网络畅通
+2. 音频会自动转换为 16kHz 单声道格式
+3. 对话历史存储在内存中，重启服务会丢失

requirements.txt

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+fastapi==0.110.0
+uvicorn==0.27.1
+python-multipart==0.0.9
+librosa==0.10.1
+soundfile==0.12.1
+torch==2.2.0
+transformers==4.38.0
+modelscope==1.12.0

server.py

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+"""
+Qwen2-Audio 模型服务
+语音转文字 + 多轮对话
+"""
+
+import os
+import io
+import uuid
+import tempfile
+import logging
+from typing import Optional, List, Dict, Any
+from datetime import datetime
+
+import librosa
+import soundfile as sf
+from fastapi import FastAPI, UploadFile, File, HTTPException, Form
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+
+# 模型加载（延迟加载）
+model = None
+processor = None
+
+# 对话历史存储（内存，可换成 Redis）
+conversations: Dict[str, List[Dict]] = {}
+
+# 配置
+MODEL_NAME = os.getenv("MODEL_NAME", "Qwen/Qwen2-Audio-7B-Instruct")
+MAX_HISTORY_TURNS = int(os.getenv("MAX_HISTORY_TURNS", "10"))  # 最多保留10轮对话
+SAMPLE_RATE = 16000  # Qwen2-Audio 采样率
+
+# 日志
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+app = FastAPI(
+    title="Qwen2-Audio Voice Service",
+    description="语音交互模型服务",
+    version="1.0.0"
+)
+
+# CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+class VoiceResponse(BaseModel):
+    """语音响应"""
+    reply: str
+    conversation_id: str
+    timestamp: str
+
+
+class ConversationHistory(BaseModel):
+    """对话历史"""
+    conversation_id: str
+    history: List[Dict[str, Any]]
+
+
+def load_model():
+    """加载模型（延迟加载）"""
+    global model, processor
+    if model is None:
+        logger.info(f"Loading model: {MODEL_NAME}")
+        from modelscope import Qwen2AudioForConditionalGeneration, AutoProcessor
+        processor = AutoProcessor.from_pretrained(MODEL_NAME)
+        model = Qwen2AudioForConditionalGeneration.from_pretrained(
+            MODEL_NAME, 
+            device_map="auto"
+        )
+        logger.info("Model loaded successfully")
+    return model, processor
+
+
+def process_audio(audio_bytes: bytes) -> tuple:
+    """
+    处理音频文件
+    返回: (audio_array, sample_rate)
+    """
+    # 使用 soundfile 读取音频
+    audio_io = io.BytesIO(audio_bytes)
+    audio, sr = sf.read(audio_io)
+    
+    # 转换为单声道
+    if len(audio.shape) > 1:
+        audio = audio.mean(axis=1)
+    
+    # 重采样到 16kHz
+    if sr != SAMPLE_RATE:
+        import resampy
+        audio = resampy.resample(audio, sr, SAMPLE_RATE)
+    
+    return audio, SAMPLE_RATE
+
+
+def build_conversation(
+    history: List[Dict], 
+    audio_array: Optional[Any] = None
+) -> List[Dict]:
+    """
+    构建对话格式
+    """
+    conversation = []
+    
+    for turn in history:
+        if turn["role"] == "user" and turn.get("audio"):
+            # 音频消息
+            conversation.append({
+                "role": "user",
+                "content": [{"type": "audio", "audio_url": turn["audio"]}]
+            })
+        elif turn["role"] == "user":
+            # 文本消息
+            conversation.append({
+                "role": "user", 
+                "content": [{"type": "text", "text": turn["content"]}]
+            })
+        else:
+            conversation.append({
+                "role": "assistant",
+                "content": turn["content"]
+            })
+    
+    # 添加当前音频
+    if audio_array is not None:
+        conversation.append({
+            "role": "user",
+            "content": [{"type": "audio", "audio": audio_array}]
+        })
+    
+    return conversation
+
+
+@app.on_event("startup")
+async def startup():
+    """启动时预加载模型"""
+    logger.info("Preloading model...")
+    load_model()
+    logger.info("Server ready!")
+
+
+@app.get("/")
+async def root():
+    """健康检查"""
+    return {
+        "status": "ok",
+        "model": MODEL_NAME,
+        "conversations": len(conversations)
+    }
+
+
+@app.post("/api/voice/inference", response_model=VoiceResponse)
+async def inference(
+    audio: UploadFile = File(..., description="音频文件"),
+    conversation_id: Optional[str] = Form(None, description="对话ID，不传则创建新对话"),
+    max_length: int = Form(256, description="最大生成长度")
+):
+    """
+    语音推理接口
+    
+    - 接收音频文件
+    - 返回模型回复文本
+    - 支持多轮对话
+    """
+    try:
+        # 加载模型
+        model, processor = load_model()
+        
+        # 读取音频
+        audio_bytes = await audio.read()
+        audio_array, sr = process_audio(audio_bytes)
+        
+        # 获取或创建对话
+        if conversation_id is None:
+            conversation_id = str(uuid.uuid4())
+            conversations[conversation_id] = []
+        
+        history = conversations.get(conversation_id, [])
+        
+        # 构建对话
+        conversation = []
+        for turn in history:
+            conversation.append(turn)
+        
+        # 添加当前音频
+        conversation.append({
+            "role": "user",
+            "content": [{"type": "audio"}]
+        })
+        
+        # 处理对话格式
+        text = processor.apply_chat_template(
+            conversation, 
+            add_generation_prompt=True, 
+            tokenize=False
+        )
+        
+        # 提取历史音频（如果有）
+        audios = []
+        for turn in history:
+            if turn.get("audio_array") is not None:
+                audios.append(turn["audio_array"])
+        audios.append(audio_array)
+        
+        # 推理
+        inputs = processor(
+            text=text, 
+            audios=audios if audios else None,
+            return_tensors="pt", 
+            padding=True
+        )
+        inputs.input_ids = inputs.input_ids.to("cuda")
+        
+        generate_ids = model.generate(**inputs, max_length=max_length)
+        generate_ids = generate_ids[:, inputs.input_ids.size(1):]
+        
+        reply = processor.batch_decode(
+            generate_ids, 
+            skip_special_tokens=True, 
+            clean_up_tokenization_spaces=False
+        )[0]
+        
+        # 保存对话历史（不保存音频数组，太大）
+        history.append({
+            "role": "user",
+            "content": "[audio]",
+            "audio_array": None  # 不保存，太占内存
+        })
+        history.append({
+            "role": "assistant",
+            "content": reply
+        })
+        
+        # 限制历史长度
+        if len(history) > MAX_HISTORY_TURNS * 2:
+            history = history[-MAX_HISTORY_TURNS * 2:]
+        
+        conversations[conversation_id] = history
+        
+        logger.info(f"Conversation {conversation_id}: {len(history)//2} turns")
+        
+        return VoiceResponse(
+            reply=reply,
+            conversation_id=conversation_id,
+            timestamp=datetime.now().isoformat()
+        )
+        
+    except Exception as e:
+        logger.error(f"Inference error: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/api/voice/text", response_model=VoiceResponse)
+async def text_inference(
+    text: str = Form(..., description="文本消息"),
+    conversation_id: Optional[str] = Form(None, description="对话ID")
+):
+    """
+    文本推理接口（可选，用于测试或纯文本对话）
+    """
+    try:
+        model, processor = load_model()
+        
+        if conversation_id is None:
+            conversation_id = str(uuid.uuid4())
+            conversations[conversation_id] = []
+        
+        history = conversations.get(conversation_id, [])
+        
+        # 构建对话
+        conversation = []
+        for turn in history:
+            conversation.append(turn)
+        conversation.append({
+            "role": "user",
+            "content": [{"type": "text", "text": text}]
+        })
+        
+        # 处理
+        prompt = processor.apply_chat_template(
+            conversation,
+            add_generation_prompt=True,
+            tokenize=False
+        )
+        
+        inputs = processor(text=prompt, return_tensors="pt", padding=True)
+        inputs.input_ids = inputs.input_ids.to("cuda")
+        
+        generate_ids = model.generate(**inputs, max_length=256)
+        generate_ids = generate_ids[:, inputs.input_ids.size(1):]
+        
+        reply = processor.batch_decode(
+            generate_ids,
+            skip_special_tokens=True,
+            clean_up_tokenization_tokens=False
+        )[0]
+        
+        # 保存历史
+        history.append({"role": "user", "content": text})
+        history.append({"role": "assistant", "content": reply})
+        conversations[conversation_id] = history
+        
+        return VoiceResponse(
+            reply=reply,
+            conversation_id=conversation_id,
+            timestamp=datetime.now().isoformat()
+        )
+        
+    except Exception as e:
+        logger.error(f"Text inference error: {e}", exc_info=True)
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.get("/api/voice/conversation/{conversation_id}", response_model=ConversationHistory)
+async def get_conversation(conversation_id: str):
+    """获取对话历史"""
+    if conversation_id not in conversations:
+        raise HTTPException(status_code=404, detail="Conversation not found")
+    
+    return ConversationHistory(
+        conversation_id=conversation_id,
+        history=conversations[conversation_id]
+    )
+
+
+@app.delete("/api/voice/conversation/{conversation_id}")
+async def delete_conversation(conversation_id: str):
+    """删除对话"""
+    if conversation_id in conversations:
+        del conversations[conversation_id]
+        return {"status": "deleted"}
+    return {"status": "not_found"}
+
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=19018)

start.sh

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+#!/bin/bash
+# Qwen2-Audio 模型服务启动脚本
+
+cd "$(dirname "$0")"
+
+# 默认端口
+PORT=${PORT:-19018}
+
+# 检查是否有 GPU
+if command -v nvidia-smi &> /dev/null; then
+    echo "GPU detected, using CUDA"
+else
+    echo "No GPU detected, using CPU (slower)"
+fi
+
+# 启动服务
+echo "Starting Qwen2-Audio server on port $PORT..."
+python server.py