app.py

import gradio as gr
from transformers import AutoFeatureExtractor, AutoModelForAudioClassification
import numpy as np
import torch
from torch.nn.functional import softmax
import soundfile as sf

# 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.
    """
    # Gradio passes the audio file as a tuple (file_name, file_path). We only need the file_path.
    audio_file_path = audio_file[1]
    
    # Load the audio file. Adjust the loading mechanism based on your audio file format.
    waveform, sample_rate = sf.read(audio_file_path)

    # Convert the input audio file to model's expected format.
    inputs = extractor(waveform, return_tensors="pt", sampling_rate=sample_rate)
    
    # Generate predictions from the model.
    with torch.no_grad():  # Ensure no gradients are calculated
        outputs = model(**inputs)
    
    # Extract logits and compute the class with the highest score.
    logits = outputs.logits
    predicted_index = logits.argmax()

    # Translate index to label
    label = model.config.id2label[predicted_index.item()]
    
    # Calculate the confidence of the prediction using softmax.
    confidence = softmax(logits, dim=1).max().item() * 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,
    inputs=gr.Audio(type="filepath", label="Upload Audio File"),  # Corrected usage
    outputs=gr.Textbox(label="Prediction"),
    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()