76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
import logging
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import os
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import tempfile
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from datetime import datetime
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from fastapi import APIRouter, Request, File, UploadFile
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from fastapi.responses import JSONResponse
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# 创建路由路由器
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router = APIRouter(prefix="/api", tags=["学伴"])
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# 配置日志
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logger = logging.getLogger(__name__)
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@router.post("/xueban/upload-audio")
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async def upload_audio(file: UploadFile = File(...)):
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"""
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上传音频文件并进行ASR处理
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- 参数: file - 音频文件
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- 返回: JSON包含识别结果
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"""
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try:
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# 记录日志
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logger.info(f"接收到音频文件: {file.filename}")
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# 保存临时文件
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timestamp = datetime.now().strftime("%Y%m%d%H%M%S")
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file_ext = os.path.splitext(file.filename)[1]
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temp_file_name = f"temp_audio_{timestamp}{file_ext}"
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temp_file_path = os.path.join(tempfile.gettempdir(), temp_file_name)
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with open(temp_file_path, "wb") as f:
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content = await file.read()
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f.write(content)
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logger.info(f"音频文件已保存至临时目录: {temp_file_path}")
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# 这里应该调用ASR服务进行处理
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# 示例：asr_result = await process_asr(temp_file_path)
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# 为了演示，这里返回模拟结果
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asr_result = {
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"text": "这是一段测试音频的识别结果",
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"confidence": 0.95,
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"audio_duration": len(content) / 1024 / 16 # 估算音频时长
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}
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# 删除临时文件
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os.remove(temp_file_path)
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logger.info(f"临时文件已删除: {temp_file_path}")
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# 返回识别结果
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return JSONResponse(content={
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"success": True,
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"message": "音频识别成功",
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"data": asr_result
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})
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except Exception as e:
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logger.error(f"音频处理失败: {str(e)}")
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return JSONResponse(content={
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"success": False,
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"message": f"音频处理失败: {str(e)}"
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}, status_code=500)
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# 实际应用中，您需要实现ASR处理函数
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async def process_asr(audio_path: str) -> dict:
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"""
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调用ASR服务处理音频文件
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:param audio_path: 音频文件路径
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:return: 识别结果字典
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"""
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# 这里应该集成实际的ASR服务
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# 例如百度AI、阿里云、讯飞等ASR服务
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# 或者本地的ASR模型
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pass |