refactor(tts): use numpy and proper temp file cleanup for WAV encoding

Update WAV encoding logic to convert audio to a NumPy array, employ a
temporary file for safe write with soundfile, and ensure cleanup in a
finally block. This resolves the BytesIO limitation and improves the
reliability of the TTS endpoint.

backend/tts_asr.py

@@ -2,12 +2,13 @@ import asyncio
 import base64
 import logging
 import os
-from io import BytesIO
+import tempfile
 from typing import Optional
 
 # 设置 Hugging Face 镜像源为国内镜像
 os.environ.setdefault("HF_ENDPOINT", "https://hf-mirror.com")
 
+import numpy as np
 import torch
 from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel
@@ -193,11 +194,11 @@ async def tts_endpoint(req: TTSRequest):
 
     text = req.text
     instruct = req.instruct or ""
-    speaker = req.speaker or "Vivian"
 
     try:
         # VoiceDesign 模型使用 generate_voice_design 方法
-        wavs_sr = model.generate_voice_design(
+        # 返回 (wavs, sr)，其中 wavs 是列表，wavs[0] 是第一个音频数据
+        wavs, sr = model.generate_voice_design(
             text=text,
             language="Chinese",
             instruct=instruct,
@@ -206,28 +207,36 @@ async def tts_endpoint(req: TTSRequest):
         logger.exception("TTS 推理失败")
         raise HTTPException(status_code=500, detail=f"TTS 推理失败: {e}")
 
-    # Normalize output
-    if isinstance(wavs_sr, tuple) and len(wavs_sr) == 2:
-        wav_data, sr = wavs_sr
-    else:
-        wav_data, sr = wavs_sr[0], wavs_sr[1]  # type: ignore
+    # 获取第一个音频数据
+    wav_data = wavs[0] if isinstance(wavs, (list, tuple)) else wavs
+
+    # 转换为 numpy 数组
+    if hasattr(wav_data, 'numpy'):
+        wav_data = wav_data.cpu().numpy()
+    wav_data = np.asarray(wav_data, dtype=np.float32)
+
+    logger.debug("wav_data shape: %s, dtype: %s, sr: %s", wav_data.shape, wav_data.dtype, sr)
 
     # 编码 WAV 到内存
+    tmp_path = None
     try:
-        import tempfile
         import soundfile as sf
-        # soundfile 不支持直接写入 BytesIO，需要用临时文件
-        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
-            tmp_path = tmp.name
-        sf.write(tmp_path, wav_data, sr, format="WAV")
+        # 创建临时文件
+        fd, tmp_path = tempfile.mkstemp(suffix=".wav")
+        os.close(fd)
+        sf.write(tmp_path, wav_data, sr)
         with open(tmp_path, "rb") as f:
             audio_bytes = f.read()
-        # 清理临时文件
-        import os as _os
-        _os.unlink(tmp_path)
     except Exception as e:
         logger.exception("音频编码失败")
         raise HTTPException(status_code=500, detail=f"音频编码失败: {e}")
+    finally:
+        # 清理临时文件
+        if tmp_path and os.path.exists(tmp_path):
+            try:
+                os.unlink(tmp_path)
+            except Exception:
+                pass
 
     # 计算时长（毫秒）
     duration_ms = int(len(wav_data) / sr * 1000) if sr > 0 else 0