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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()
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