app.py

import gradio as gr
from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
import numpy as np

# Path to the local directory where the model files are stored within the Space
local_model_path = "./"

# Initialize the feature extractor and model from the local files
extractor = AutoFeatureExtractor.from_pretrained(local_model_path)
model = AutoModelForAudioClassification.from_pretrained(local_model_path)

def predict_voice(audio_file):
    """
    Predicts whether a voice is real or spoofed from an audio file.
    
    Args:
        audio_file: The input audio file to be classified.
    
    Returns:
        A string with the prediction and confidence level.
    """
    # Convert the input audio file to model's expected format.
    inputs = extractor(audio_file, return_tensors="pt")
    
    # Generate predictions from the model.
    outputs = model(**inputs)
    
    # Extract logits and compute the class with the highest score.
    logits = outputs.logits
    predicted_index = np.argmax(logits.detach().numpy())

    # Translate index to label
    label = model.config.id2label[predicted_index]
    
    # Calculate the confidence of the prediction.
    confidence = np.max(np.softmax(logits.detach().numpy(), axis=1)) * 100
    
    # Prepare the output string.
    result = f"The voice is classified as '{label}' with a confidence of {confidence:.2f}%."
    return result

# Setting up the Gradio interface
iface = gr.Interface(
    fn=predict_voice,  # Function to call
    inputs=gr.inputs.Audio(source="upload", type="file", label="Upload Audio File"),  # Audio input
    outputs="text",  # Text output
    title="Voice Authenticity Detection",
    description="Detects whether a voice is real or AI-generated. Upload an audio file to see the results.",
    theme="huggingface"
)

# Run the Gradio interface
iface.launch()