(wip)debug

app.py

@@ -692,7 +692,7 @@ def generate_tts():
         # 清理临时参考音频文件
         if reference_audio_path and os.path.exists(reference_audio_path):
             os.remove(reference_audio_path)
-        return jsonify({"error": "Failed to generate TTS"}), 500
+        return jsonify({"error": f"Failed to generate TTS:{str(e)}"}), 500
     # --- End Cache Miss ---
 
 

templates/arena.html

@@ -869,13 +869,6 @@
                             return response.json();
                         })
                         .then(data => {
-                            if (data.error) {
-                                // 显示错误信息并重置界面
-                                loadingContainer.style.display = 'none';
-                                initialKeyboardHint.style.display = 'block';
-                                openToast(data.error, "error");
-                                return;
-                            }
                             currentSessionId = data.session_id;
 
                             // Load audio in waveplayers

tts.py

@@ -47,7 +47,7 @@ data = {"text": "string", "provider": "string", "model": "string"}
 
 def predict_index_tts(text, reference_audio_path=None):
     from gradio_client import Client, handle_file
-    client = Client("kemuriririn/IndexTTS",hf_token=os.getenv("HF_TOKEN"))
+    client = Client("kemuriririn/IndexTTS", hf_token=os.getenv("HF_TOKEN"))
     if reference_audio_path:
         prompt = handle_file(reference_audio_path)
     else:
@@ -65,7 +65,7 @@ def predict_index_tts(text, reference_audio_path=None):
 
 def predict_spark_tts(text, reference_audio_path=None):
     from gradio_client import Client, handle_file
-    client = Client("kemuriririn/SparkTTS",hf_token=os.getenv("HF_TOKEN"))
+    client = Client("kemuriririn/SparkTTS", hf_token=os.getenv("HF_TOKEN"))
     prompt_wav = None
     if reference_audio_path:
         prompt_wav = handle_file(reference_audio_path)
@@ -82,7 +82,7 @@ def predict_spark_tts(text, reference_audio_path=None):
 
 def predict_cosyvoice_tts(text, reference_audio_path=None):
     from gradio_client import Client, file, handle_file
-    client = Client("kemuriririn/CosyVoice2-0.5B",hf_token=os.getenv("HF_TOKEN"))
+    client = Client("kemuriririn/CosyVoice2-0.5B", hf_token=os.getenv("HF_TOKEN"))
     if not reference_audio_path:
         raise ValueError("cosyvoice-2.0 需要 reference_audio_path")
     prompt_wav = handle_file(reference_audio_path)
@@ -125,7 +125,7 @@ def predict_maskgct(text, reference_audio_path=None):
 
 def predict_gpt_sovits_v2(text, reference_audio_path=None):
     from gradio_client import Client, file
-    client = Client("kemuriririn/GPT-SoVITS-v2",hf_token=os.getenv("HF_TOKEN"))
+    client = Client("kemuriririn/GPT-SoVITS-v2", hf_token=os.getenv("HF_TOKEN"))
     if not reference_audio_path:
         raise ValueError("GPT-SoVITS-v2 需要 reference_audio_path")
     result = client.predict(
@@ -152,33 +152,19 @@ def predict_tts(text, model, reference_audio_path=None):
     global client
     print(f"Predicting TTS for {model}")
     # Exceptions: special models that shouldn't be passed to the router
-    try:
-        if model == "index-tts":
-            result = predict_index_tts(text, reference_audio_path)
-        elif model == "spark-tts":
-            result = predict_spark_tts(text, reference_audio_path)
-        elif model == "cosyvoice-2.0":
-            result = predict_cosyvoice_tts(text, reference_audio_path)
-        elif model == "maskgct":
-            result = predict_maskgct(text, reference_audio_path)
-        elif model == "gpt-sovits-v2":
-            result = predict_gpt_sovits_v2(text, reference_audio_path)
-        else:
-            raise ValueError(f"Model {model} not found")
-
-        if isinstance(result, dict) and "error" in result:
-            return result
-
-        return result
-    except AppError as e:
-        error_message = str(e)
-        print(f"Gradio客户端错误: {error_message}")
-        return {"error": error_message}
-    except Exception as e:
-        error_message = str(e)
-        print(f"生成失败: {error_message}")
-        return {"error": error_message}
-
+    if model == "index-tts":
+        result = predict_index_tts(text, reference_audio_path)
+    elif model == "spark-tts":
+        result = predict_spark_tts(text, reference_audio_path)
+    elif model == "cosyvoice-2.0":
+        result = predict_cosyvoice_tts(text, reference_audio_path)
+    elif model == "maskgct":
+        result = predict_maskgct(text, reference_audio_path)
+    elif model == "gpt-sovits-v2":
+        result = predict_gpt_sovits_v2(text, reference_audio_path)
+    else:
+        raise ValueError(f"Model {model} not found")
+    return result
 
 if __name__ == "__main__":
     pass