test app.py

app.py

@@ -1,94 +1,47 @@
-import spaces
-print("gz start")
+import os
 import sys
-print("gz start")
 import torch
-print("gz start")
-print("CUDA available:", torch.cuda.is_available())
-
-import gradio as gr
-import opencc
-
-# 添加第三方库路径
-sys.path.append('third_party/Matcha-TTS')
+import spaces
 
-from cosyvoice.cli.cosyvoice import CosyVoice2
-from cosyvoice.utils.file_utils import load_wav
+print("===== Application Startup =====")
 
-from huggingface_hub import hf_hub_download
+# 不要强制关掉 CUDA，注释掉下面这一行
+# os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
 
-# 繁简转换
-converter = opencc.OpenCC('s2t.json')
+print("gz start")
 
-# 加载模型
-cosyvoice_base = CosyVoice2(
-    'ASLP-lab/WSYue-TTS-Cosyvoice2',
-    load_jit=False, load_trt=False, load_vllm=False, fp16=False
-)
-print('load model 1')
-cosyvoice_zjg = cosyvoice_base
-# cosyvoice_zjg = CosyVoice2(
-#     'ASLP-lab/WSYue-TTS-Cosyvoice2-zjg',
-#     load_jit=False, load_trt=False, load_vllm=False, fp16=False
-# )
-# print('load model 2')
-# cosyvoice_biaobei = CosyVoice2(
-#     'pretrained_models/CosyVoice2-yue-biaobei',
-#     load_jit=False, load_trt=False, load_vllm=False, fp16=False
-# )
+print("Python version:", sys.version)
+print("Torch version:", torch.__version__)
+print("CUDA available:", torch.cuda.is_available())
 
-@spaces.GPU 
-def tts_inference(model_choice, text, prompt_audio):
-    # 选择模型和默认音频
-    if model_choice == "CosyVoice2-张悦楷粤语评书":
-        model = cosyvoice_zjg
-        prompt_audio = "asset/sg_017_090.wav"
-    elif model_choice == "CosyVoice2-精品女音":
-        model = cosyvoice_base
-        prompt_audio = "asset/F01_中立_20054.wav"
-    elif model_choice == "CosyVoice2-base":
-        model = cosyvoice_base
-        if prompt_audio is None:
-            return None, "请上传参考音频"
+# 尝试分配一个 Tensor 到 GPU
+try:
+    if torch.cuda.is_available():
+        device = torch.device("cuda")
+        x = torch.rand((2, 3), device=device)
+        y = torch.mm(x, x.T)
+        print("Tensor allocated on GPU successfully:")
+        print(y)
     else:
-        return None, "未知模型"
-
-    # 繁简转换
-    text = converter.convert(text)
-    prompt_speech_16k = load_wav(prompt_audio, 16000)
+        print("CUDA not available, fallback to CPU")
+        x = torch.rand((2, 3))
+        y = torch.mm(x, x.T)
+        print("Tensor allocated on CPU successfully:")
+        print(y)
+except Exception as e:
+    print("ERROR during CUDA tensor allocation:", str(e))
 
-    all_speech = []
-    for _, j in enumerate(
-        model.inference_instruct2(
-            text, "用粤语说这句话", prompt_speech_16k, stream=False
-        )
-    ):
-        all_speech.append(j['tts_speech'])
-
-    concatenated_speech = torch.cat(all_speech, dim=1)
-    audio_numpy = concatenated_speech.squeeze(0).cpu().numpy()
-    sample_rate = model.sample_rate
-
-    return (sample_rate, audio_numpy), f"生成成功：{text}"
 
+# ---- Gradio 测试接口 ----
+import gradio as gr
 
-# ---- Gradio Interface ----
-demo = gr.Interface(
-    fn=tts_inference,
-    inputs=[
-        gr.Dropdown(
-            ["CosyVoice2-base", "CosyVoice2-张悦楷粤语评书"],
-            # ["CosyVoice2-base", "CosyVoice2-张悦楷粤语评书", "CosyVoice2-精品女音"],
-            label="选择模型", value="CosyVoice2-base"
-        ),
-        gr.Textbox(lines=2, label="输入文本"),
-        # gr.Audio(source="upload", type="filepath", label="上传参考音频（仅 CosyVoice2-base 必需）")
-        gr.Audio(sources=["upload"], type="filepath", label="上传参考音频（仅 CosyVoice2-base 必需）")
-    ],
-    outputs=[
-        gr.Audio(type="numpy", label="生成的语音"),
-        gr.Textbox(label="状态信息")
-    ]
-)
+@spaces.GPU
+def gpu_test():
+    if torch.cuda.is_available():
+        x = torch.rand((2, 3), device="cuda")
+        return f"GPU OK, tensor sum={x.sum().item()}"
+    else:
+        return "No GPU detected, using CPU"
 
+demo = gr.Interface(fn=gpu_test, inputs=[], outputs="text")
 demo.launch()