Updata and Debug

webUI.py

@@ -15,6 +15,7 @@
 import asyncio
 from scipy.io import wavfile
 import librosa
+import torch
 
 logging.getLogger('numba').setLevel(logging.WARNING)
 logging.getLogger('markdown_it').setLevel(logging.WARNING)
@@ -24,6 +25,10 @@
 
 model = None
 spk = None
+cuda = []
+if torch.cuda.is_available():
+    for i in range(torch.cuda.device_count()):
+        cuda.append("cuda:{}".format(i))
 
 def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num):
     global model
@@ -38,7 +43,7 @@ def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise
         if len(audio.shape) > 1:
             audio = librosa.to_mono(audio.transpose(1, 0))
         temp_path = "temp.wav"
-        soundfile.write(temp_path, audio, model.target_sample, format="wav")
+        soundfile.write(temp_path, audio, sampling_rate, format="wav")
         _audio = model.slice_inference(temp_path, sid, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale,pad_seconds,cl_num,lg_num,lgr_num)
         model.clear_empty()
         os.remove(temp_path)
@@ -109,15 +114,15 @@ def vc_fn2(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, nois
                 <font size=3>下面是聚类模型文件选择，没有可以不填：</font>
                 """)
             cluster_model_path = gr.File(label="聚类模型文件")
-            device = gr.Dropdown(label="推理设备，留白则为自动选择cpu和gpu",choices=[None,"cuda","cpu"],value=None)
+            device = gr.Dropdown(label="推理设备，默认为自动选择cpu和gpu",choices=["Auto",*cuda,"cpu"],value="Auto")
             gr.Markdown(value="""
                 <font size=3>全部上传完毕后(全部文件模块显示download),点击模型解析进行解析：</font>
                 """)
             model_analysis_button = gr.Button(value="模型解析")
             sid = gr.Dropdown(label="音色（说话人）")
             sid_output = gr.Textbox(label="Output Message")
 
-            text2tts=gr.Textbox(label="在此输入要转译的文字")
+            text2tts=gr.Textbox(label="在此输入要转译的文字。注意，使用该功能建议打开F0预测，不然会跟怪")
             tts_rate = gr.Number(label="tts语速", value=0)
 
             vc_input3 = gr.Audio(label="上传音频")
@@ -136,16 +141,17 @@ def vc_fn2(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, nois
             vc_output2 = gr.Audio(label="Output Audio")
             def modelAnalysis(model_path,config_path,cluster_model_path,device):
                 global model
-                debug=False
+                debug=True
                 if debug:
-                    model = Svc(model_path.name, config_path.name,device=device if device!="" else None,cluster_model_path= cluster_model_path.name if cluster_model_path!=None else "")
+                    model = Svc(model_path.name, config_path.name,device=device if device!="Auto" else None,cluster_model_path= cluster_model_path.name if cluster_model_path!=None else "")
                     spks = list(model.spk2id.keys())
-                    return sid.update(choices = spks,value=spks[0]),"ok"
+                    return sid.update(choices = spks,value=spks[0]),"ok,模型被加载到了设备{}之上".format(device_name)
                 else:
                     try:
-                        model = Svc(model_path.name, config_path.name,device=device if device!="" else None,cluster_model_path= cluster_model_path.name if cluster_model_path!=None else "")
+                        model = Svc(model_path.name, config_path.name,device=device if device!="Auto" else None,cluster_model_path= cluster_model_path.name if cluster_model_path!=None else "")
                         spks = list(model.spk2id.keys())
-                        return sid.update(choices = spks,value=spks[0]),"ok"
+                        device_name = torch.cuda.get_device_properties(model.dev).name if "cuda" in str(model.dev) else str(model.dev)
+                        return sid.update(choices = spks,value=spks[0]),"ok,模型被加载到了设备{}之上".format(device_name)
                     except Exception as e:
                         return "","异常信息:"+str(e)+"\n请排障后重试"
         vc_submit.click(vc_fn, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num], [vc_output1, vc_output2])