fix: 改用soundfile保存音频，解决torchaudio格式问题

requirements.txt

@@ -4,4 +4,5 @@ python-multipart==0.0.9
 torch==2.2.0
 torchaudio==2.2.0
 transformers==4.38.0
-ChatTTS==0.1.1
+ChatTTS
+soundfile==0.12.1

server.py

@@ -124,11 +124,15 @@ def save_audio(audio_tensor: torch.Tensor, filename: str) -> str:
     filepath = os.path.join(AUDIO_DIR, filename)
     
     # 确保 tensor 正确形状
-    if audio_tensor.dim() == 1:
-        audio_tensor = audio_tensor.unsqueeze(0)
+    if audio_tensor.dim() == 2:
+        audio_tensor = audio_tensor.squeeze(0)
     
-    # 保存为 WAV
-    torchaudio.save(filepath, audio_tensor, SAMPLE_RATE, format="wav")
+    # 转换为 numpy
+    audio_np = audio_tensor.cpu().numpy() if audio_tensor.is_cuda else audio_tensor.numpy()
+    
+    # 使用 soundfile 保存
+    import soundfile as sf
+    sf.write(filepath, audio_np, SAMPLE_RATE)
     
     return filepath
 
@@ -185,22 +189,29 @@ async def synthesize(
         # 生成唯一文件名
         filename = f"{uuid.uuid4().hex}.wav"
         
-        # 合成参数
-        params = {
-            'temperature': temperature,
-            'top_p': top_p,
-            'top_k': top_k,
-            'spk_emb': None,  # 可选：说话人嵌入
-        }
-        
         # 合成语音
         logger.info(f"Synthesizing: {text[:50]}...")
         
-        # ChatTTS 生成
-        audio_tensor = model.infer(
-            [text],
-            params=params
-        )[0]  # 返回是列表，取第一个
+        # ChatTTS 基本调用（简化版）
+        # 返回: list of audio tensors
+        result = model.infer(text)
+        
+        # 处理返回结果
+        if isinstance(result, list):
+            audio_tensor = result[0]
+        elif isinstance(result, tuple):
+            audio_tensor = result[0]
+        else:
+            audio_tensor = result
+        
+        # 转换为 torch tensor（如果是 numpy）
+        import numpy as np
+        if isinstance(audio_tensor, np.ndarray):
+            audio_tensor = torch.from_numpy(audio_tensor).float()
+        
+        # 确保 tensor 正确形状
+        if audio_tensor.dim() == 1:
+            audio_tensor = audio_tensor.unsqueeze(0)
         
         # 保存音频
         filepath = save_audio(audio_tensor, filename)
@@ -235,14 +246,15 @@ async def synthesize_batch(requests: list[SynthesizeRequest]):
         texts = [r.text for r in requests]
         
         # 统一参数
-        params = {
-            'temperature': requests[0].temperature,
-            'top_p': requests[0].top_p,
-            'top_k': requests[0].top_k,
-        }
+        infer_params = {}
         
         # 批量生成
-        audio_tensors = model.infer(texts, params=params)
+        audio_tensors = model.infer(
+            texts,
+            temperature=requests[0].temperature,
+            top_P=requests[0].top_p,
+            top_K=requests[0].top_k,
+        )
         
         results = []
         for i, audio_tensor in enumerate(audio_tensors):
@@ -337,6 +349,19 @@ async def synthesize_with_emotion(
         raise HTTPException(status_code=500, detail=str(e))
 
 
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=PORT)  audio_url=f"/audio/{filename}",
+            duration=round(duration, 2),
+            text=text,
+            timestamp=datetime.now().isoformat()
+        )
+        
+    except Exception as e:
+        logger.error(f"Emotion synthesis error: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=PORT)