Create app.py

app.py

@@ -0,0 +1,36 @@
+import torch
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForTextToWaveform
+def install_model(namemodel,tokenn,namemodelonxx):
+     
+    model = AutoModelForTextToWaveform.from_pretrained(namemodel,token=tokenn)
+    namemodelonxxx=convert_to_onnx(model,namemodelonxx)
+    return namemodelonxxx
+def convert_to_onnx(model,namemodelonxx):
+      vocab_size = model.text_encoder.embed_tokens.weight.size(0)
+      example_input = torch.randint(0, vocab_size, (1, 100), dtype=torch.long)
+      torch.onnx.export(
+          model,  # The model to be exported
+          example_input,  # Example input for the model
+          namemodelonxx,  # The filename for the exported ONNX model
+          opset_version=11,  # Use an appropriate ONNX opset version
+          input_names=['input'],  # Name of the input layer
+          output_names=['output'],  # Name of the output layer
+          dynamic_axes={
+              'input': {0: 'batch_size', 1: 'sequence_length'},  # Dynamic axes for variable-length inputs
+              'output': {0: 'batch_size'}
+          }
+      )
+      return namemodelonxx
+with gr.Blocks() as demo:
+     with gr.Row():
+          with gr.Column():
+            text_n_model=gr.Textbox(label="name model")
+            text_n_token=gr.Textbox(label="token")
+            text_n_onxx=gr.Textbox(label="name model onxx")
+          with gr.Column():
+
+            btn=gr.Button("convert")
+            label=gr.Label("return  name model onxx")
+            btn.click(install_model,[text_n_model,text_n_token,text_n_onxx],[label])
+demo.launch()