Create app.py

app.py

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+import numpy as np
+import torch
+import gradio as gr
+
+# Placeholder for model loading and voice cloning logic
+class VoiceCloner:
+    def __init__(self):
+        self.model = None
+    
+    def load_model(self, npz_file):
+        data = np.load(npz_file)
+        # Load your model parameters from the npz file
+        # Initialize your model here with the loaded parameters
+        self.model = data  # Example; replace with your actual model loading code
+
+    def clone_voice(self, audio_file, text=None):
+        # Implement the logic to clone voice from the uploaded audio file
+        # and possibly from the text if provided
+        return audio_file  # Placeholder; return processed audio
+
+# Create the Gradio interface
+def create_interface():
+    cloner = VoiceCloner()
+    
+    with gr.Blocks() as demo:
+        gr.Markdown("## Voice Cloning Application")
+
+        # User uploads their .npz file
+        npz_file = gr.File(label="Upload Your .npz Voice Model")
+        audio_input = gr.Audio(source="upload", type="filepath", label="Upload Original Audio")
+        text_input = gr.Textbox(label="Text Input for TTS (Optional)")
+        output_audio = gr.Audio(label="Cloned Voice Output")
+
+        upload_button = gr.Button("Load Model")
+        
+        # Button to clone voice
+        clone_button = gr.Button("Clone Voice")
+
+        # Load the model when the user uploads the .npz file
+        def load_and_initialize(npz):
+            cloner.load_model(npz.name)  # Use the file path to load the model
+            return "Model Loaded!"
+
+        upload_button.click(fn=load_and_initialize, inputs=npz_file, outputs="text")
+
+        # Clone the voice when the button is pressed
+        clone_button.click(fn=cloner.clone_voice, inputs=[audio_input, text_input], outputs=output_audio)
+
+    return demo
+
+if __name__ == "__main__":
+    demo = create_interface()
+    demo.launch()