app.py

import gradio as gr
import librosa
import numpy as np
import torch
import logging
from transformers import AutoModelForAudioClassification

logging.basicConfig(level=logging.INFO)

model_path = "./"
model = AutoModelForAudioClassification.from_pretrained(model_path)

def augment_and_extract_features(audio_path, sr=16000, n_mfcc=40, n_fft=2048, hop_length=512, target_length=512):
    y, sr = librosa.load(audio_path, sr=sr)
    y_pitch_shifted = librosa.effects.pitch_shift(y, sr=sr, n_steps=4)
    y_time_stretched = librosa.effects.time_stretch(y_pitch_shifted, rate=1.2)

    mfcc = librosa.feature.mfcc(y=y_time_stretched, sr=sr, n_mfcc=n_mfcc, n_fft=n_fft, hop_length=hop_length)
    chroma = librosa.feature.chroma_stft(y=y_time_stretched, sr=sr, n_fft=n_fft, hop_length=hop_length)
    mel = librosa.feature.melspectrogram(y=y_time_stretched, sr=sr, n_fft=n_fft, hop_length=hop_length)
    contrast = librosa.feature.spectral_contrast(y=y_time_stretched, sr=sr, n_fft=n_fft, hop_length=hop_length)
    tonnetz = librosa.feature.tonnetz(y=librosa.effects.harmonic(y_time_stretched), sr=sr)

    features = np.concatenate((mfcc, chroma, mel, contrast, tonnetz), axis=0)
    features_normalized = (features - np.mean(features, axis=1, keepdims=True)) / np.std(features, axis=1, keepdims=True)

    if features_normalized.shape[1] > target_length:
        features_normalized = features_normalized[:, :target_length]
    else:
        padding = target_length - features_normalized.shape[1]
        features_normalized = np.pad(features_normalized, ((0, 0), (0, padding)), 'constant')

    features_tensor = torch.tensor(features_normalized).float().unsqueeze(0)  # Add batch dimension
    return features_tensor

def predict_voice(audio_file_path):
    try:
        features_tensor = augment_and_extract_features(audio_file_path)
        with torch.no_grad():
            outputs = model(features_tensor)

        logits = outputs.logits
        predicted_index = logits.argmax()
        label = model.config.id2label[predicted_index.item()]
        confidence = torch.softmax(logits, dim=1).max().item() * 100

        result = f"The voice is classified as '{label}' with a confidence of {confidence:.2f}%."
        logging.info("Prediction successful.")
    except Exception as e:
        result = f"Error during processing: {e}"
        logging.error(result)
    return result

iface = gr.Interface(
    fn=predict_voice,
    inputs=gr.Audio(label="Upload Audio File", type="filepath"),
    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."
)

iface.launch()