fix: 修复 TTS 音频后处理中的多个缺陷 (#2753)

1. 修复音频超采样时 int16 双重转换导致整数溢出（CRITICAL）
   - audio_postprocess 中 `audio = (audio * 32768).astype(np.int16)` 位于
     if/else 块之外无条件执行，当 super_sampling=True 时音频已在分支内
     转为 int16，再次乘以 32768 导致溢出和音频完全失真
   - 同时修复 super_sampling=True 但超分模型不存在时 torch.Tensor 调用
     .astype() 的 AttributeError

2. 修复 batched vocoder 推理中 padding_len=0 导致音频丢失（HIGH）
   - 当 padding_len 恰好为 0 时，`-0 * upsample_rate == 0`，切片
     `audio[x:0]` 返回空张量，导致整段音频丢失

3. 修复文件不存在时错误地抛出 FileExistsError（LOW）
   - 应为 FileNotFoundError

Made-with: Cursor

GPT_SoVITS/TTS_infer_pack/TTS.py

@@ -499,7 +499,7 @@ class TTS:
 
         if if_lora_v3 == True and os.path.exists(path_sovits) == False:
             info = path_sovits + i18n("SoVITS %s 底模缺失，无法加载相应 LoRA 权重" % model_version)
-            raise FileExistsError(info)
+            raise FileNotFoundError(info)
 
         # dict_s2 = torch.load(weights_path, map_location=self.configs.device,weights_only=False)
         dict_s2 = load_sovits_new(weights_path)
@@ -1578,16 +1578,15 @@ class TTS:
                 max_audio = np.abs(audio).max()
                 if max_audio > 1:
                     audio /= max_audio
-            audio = (audio * 32768).astype(np.int16)
+                audio = (audio * 32768).astype(np.int16)
+            else:
+                audio = audio.cpu().numpy()
+                audio = (audio * 32768).astype(np.int16)
             t2 = time.perf_counter()
             print(f"超采样用时：{t2 - t1:.3f}s")
         else:
-            # audio = audio.float() * 32768
-            # audio = audio.to(dtype=torch.int16).clamp(-32768, 32767).cpu().numpy()
-
             audio = audio.cpu().numpy()
-
-        audio = (audio * 32768).astype(np.int16)
+            audio = (audio * 32768).astype(np.int16)
 
 
         # try:
@@ -1768,7 +1767,10 @@ class TTS:
             pos += chunk_len * upsample_rate
 
         audio = self.sola_algorithm(audio_fragments, overlapped_len * upsample_rate)
-        audio = audio[overlapped_len * upsample_rate : -padding_len * upsample_rate]
+        if padding_len > 0:
+            audio = audio[overlapped_len * upsample_rate : -padding_len * upsample_rate]
+        else:
+            audio = audio[overlapped_len * upsample_rate :]
 
         audio_fragments = []
         for feat_len in feat_lens: