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# import gradio as gr
# def greet(name):
# return "Hello " + name + "!!"
# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
# iface.launch()
import torch
import gradio as gr
from transformers import AutoModelForSequenceClassification
# Load your generator model checkpoint
generator_checkpoint_path = "/home/linux/Documents/Ravi_PHD_Data/hifi-gan/cp_hifigan/date_elevan_feb_twozerotwofour/g_00375000"
# Define your inference function
def generate_deepfake(wave_file):
# Load generator model
generator_model = AutoModelForSequenceClassification.from_pretrained(generator_checkpoint_path)
# Process input wave file (e.g., convert to spectrogram, extract features)
# Perform deepfake generation using the loaded model
# Replace the following lines with your actual deepfake generation logic
# For demonstration purposes, we'll just return the input wave file as-is.
deepfake_wave_file = wave_file
# Return the deepfake wave file
return deepfake_wave_file
# Create a Gradio interface
inputs = gr.inputs.Audio(label="Upload a wave file")
outputs = gr.outputs.Audio(label="Deepfake wave file")
gr.Interface(fn=generate_deepfake, inputs=inputs, outputs=outputs).launch()
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