Update app.py

app.py

@@ -6,7 +6,7 @@ import os
 import numpy as np  
 
 token=os.environ.get("key_")
-tokenizer = AutoTokenizer.from_pretrained("wasmdashai/vtk",token=token)
+tokenizers={}
 models= {}
 
 import noisereduce as nr
@@ -107,15 +107,32 @@ def _inference_forward_stream(
 def  get_model(name_model):
     global models
     if name_model in   models:
-        return models[name_model]
+        if  name_model=='wasmdashai/vits-en-v1':
+            tokenizer = AutoTokenizer.from_pretrained("wasmdashai/vits-en-v1",token=token)
+        else: 
+            tokenizer = AutoTokenizer.from_pretrained("wasmdashai/vtk",token=token)
+
+        
+
+        
+        return models[name_model],tokenizer
     models[name_model]=VitsModel.from_pretrained(name_model,token=token)
+    
+    
+   
     models[name_model].decoder.apply_weight_norm()
     # torch.nn.utils.weight_norm(self.decoder.conv_pre)
     # torch.nn.utils.weight_norm(self.decoder.conv_post)
     for flow in models[name_model].flow.flows:
         torch.nn.utils.weight_norm(flow.conv_pre)
         torch.nn.utils.weight_norm(flow.conv_post)
-    return models[name_model]
+    
+    if  name_model=='wasmdashai/vits-en-v1':
+            tokenizer = AutoTokenizer.from_pretrained("wasmdashai/vits-en-v1",token=token)
+        else: 
+            tokenizer = AutoTokenizer.from_pretrained("wasmdashai/vtk",token=token)
+
+    return models[name_model],tokenizer
 
 
 
@@ -144,7 +161,8 @@ model_choices = gr.Dropdown(
                                  
                                  "wasmdashai/vits-ar-sa-A",
                                 "wasmdashai/vits-ar-ye-sa",
-                                "wasmdashai/vits-ar-sa-M-v1"
+                                "wasmdashai/vits-ar-sa-M-v1",
+                                "wasmdashai/vits-en-v1"
                                 
                                   
                             ],