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import gradio as gr
from transformers import pipeline
# Load the model using pipeline
pipe = pipeline("audio-classification", model="MelodyMachine/Deepfake-audio-detection-V2")
# Define the prediction function
def predict(audio):
print("Audio file received:", audio) # Debugging statement
try:
result = pipe(audio)
print("Raw prediction result:", result) # Debugging statement
# Convert the result to the expected format
output = {item['label']: item['score'] for item in result}
print("Formatted prediction result:", output) # Debugging statement
return output
except Exception as e:
print("Error during prediction:", e) # Debugging statement
return {"error": str(e)}
# Create the Gradio interface
iface = gr.Interface(
fn=predict,
inputs=gr.Audio(type="filepath"),
outputs=gr.Label(),
title="Testing Deepfake Audio Detection Simple Interface",
description="Upload an audio file or record your voice to detect if the audio is a deepfake."
)
# Launch the interface
iface.launch()
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