app.py

import gradio as gr
import numpy as np
import librosa
from tensorflow.keras.models import load_model

# Constants
MAX_TIME_STEPS = 109
SAMPLE_RATE = 16000
DURATION = 5
N_MELS = 128
MODEL_PATH = "audio_classifier.h5"  # Replace with the actual path to your saved model

# Load the pre-trained model
model = load_model(MODEL_PATH, compile=False)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

def classify_audio(audio):
    # Convert the audio data to NumPy array
    rate, ar = audio
    arone = ar.astype(np.float32)
    mel_spectrogram = librosa.feature.melspectrogram(y=arone, sr=SAMPLE_RATE, n_mels=N_MELS)
    mel_spectrogram = librosa.power_to_db(mel_spectrogram, ref=np.max)

    # Ensure all spectrograms have the same width (time steps)
    if mel_spectrogram.shape[1] < MAX_TIME_STEPS:
        mel_spectrogram = np.pad(mel_spectrogram, ((0, 0), (0, MAX_TIME_STEPS - mel_spectrogram.shape[1])), mode='constant')
    else:
        mel_spectrogram = mel_spectrogram[:, :MAX_TIME_STEPS]

    # Reshape for the model
    X_test = np.expand_dims(mel_spectrogram, axis=-1)
    X_test = np.expand_dims(X_test, axis=0)

    # Predict using the loaded model
    y_pred = model.predict(X_test)

    # Convert probabilities to predicted classes
    y_pred_classes = np.argmax(y_pred, axis=1)

    if(y_pred_classes[0]==1):
        return f"Prediction: {'Not spoof'}"
    else:
        return f"Prediction: {'Spoof'}"
    
title="Audios Spoof detection using CNN"
description="The model was trained on the ASVspoof 2015 dataset with an aim to detect spoof audios through deep learning.To use it please upload an audio file of suitable length."

iface = gr.Interface(classify_audio, inputs=["audio"], outputs=["text"],title=title,description=description)
iface.launch()