app.py

import gradio as gr
import librosa
import numpy as np
import torch
import matplotlib.pyplot as plt
from transformers import AutoModelForAudioClassification, ASTFeatureExtractor, Wav2Vec2Processor, Wav2Vec2ForCTC
import random
import tempfile

# Load Wav2Vec 2.0 models
wav2vec_processor = Wav2Vec2Processor.from_pretrained("facebook/wav2vec2-base-960h")
wav2vec_model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h")

# Original model and feature extractor loading
model = AutoModelForAudioClassification.from_pretrained("./")
feature_extractor = ASTFeatureExtractor.from_pretrained("./")

def plot_waveform(waveform, sr):
    plt.figure(figsize=(12, 4))
    plt.title('Waveform')
    plt.ylabel('Amplitude')
    plt.plot(np.linspace(0, len(waveform) / sr, len(waveform)), waveform)
    plt.xlabel('Time (s)')
    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png', dir='./')
    plt.savefig(temp_file.name)
    plt.close()
    return temp_file.name

def plot_spectrogram(waveform, sr):
    S = librosa.feature.melspectrogram(y=waveform, sr=sr, n_mels=128)
    S_DB = librosa.power_to_db(S, ref=np.max)
    plt.figure(figsize=(12, 6))
    librosa.display.specshow(S_DB, sr=sr, x_axis='time', y_axis='mel', cmap='inferno')
    plt.title('Mel Spectrogram')
    plt.colorbar(format='%+2.0f dB')
    plt.tight_layout()
    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png', dir='./')
    plt.savefig(temp_file.name)
    plt.close()
    return temp_file.name

def custom_feature_extraction(audio, sr=16000, target_length=1024):
    features = feature_extractor(audio, sampling_rate=sr, return_tensors="pt", padding="max_length", max_length=target_length)
    return features.input_values

def apply_time_shift(waveform, max_shift_fraction=0.1):
    shift = random.randint(-int(max_shift_fraction * len(waveform)), int(max_shift_fraction * len(waveform)))
    return np.roll(waveform, shift)

def transcribe_audio(audio_file_path):
    waveform, _ = librosa.load(audio_file_path, sr=wav2vec_processor.feature_extractor.sampling_rate, mono=True)
    input_values = wav2vec_processor(waveform, return_tensors="pt", padding="longest").input_values
    with torch.no_grad():
        logits = wav2vec_model(input_values).logits
    predicted_ids = torch.argmax(logits, dim=-1)
    transcription = wav2vec_processor.batch_decode(predicted_ids)
    return transcription

def predict_voice(audio_file_path):
    try:
        transcription = transcribe_audio(audio_file_path)
        
        waveform, sample_rate = librosa.load(audio_file_path, sr=feature_extractor.sampling_rate, mono=True)
        augmented_waveform = apply_time_shift(waveform)
        
        original_features = custom_feature_extraction(waveform, sr=sample_rate)
        augmented_features = custom_feature_extraction(augmented_waveform, sr=sample_rate)
        
        with torch.no_grad():
            outputs_original = model(original_features)
            outputs_augmented = model(augmented_features)
        
        logits = (outputs_original.logits + outputs_augmented.logits) / 2
        predicted_index = logits.argmax()
        original_label = model.config.id2label[predicted_index.item()]
        confidence = torch.softmax(logits, dim=1).max().item() * 100
        
        label_mapping = {
            "Spoof": "AI-generated Clone",
            "Bonafide": "Real Human Voice"
        }
        new_label = label_mapping.get(original_label, "Unknown")
        
        waveform_plot = plot_waveform(waveform, sample_rate)
        spectrogram_plot = plot_spectrogram(waveform, sample_rate)
        
        return (
            f"The voice is classified as '{new_label}' with a confidence of {confidence:.2f}%.",
            waveform_plot,
            spectrogram_plot,
            transcription[0]  # Assuming transcription returns a list with a single string
        )
    except Exception as e:
        return f"Error during processing: {e}", None, None, ""

with gr.Blocks(css="style.css") as demo:
    gr.Markdown("## Voice Clone Detection")
    gr.Markdown("Detects whether a voice is real or an AI-generated clone. Upload an audio file to see the results.")
    
    with gr.Row():
        audio_input = gr.Audio(label="Upload Audio File", type="filepath")

    with gr.Row():
        prediction_output = gr.Textbox(label="Prediction")
        transcription_output = gr.Textbox(label="Transcription")  # Fixed indentation
        waveform_output = gr.Image(label="Waveform")
        spectrogram_output = gr.Image(label="Spectrogram")

    detect_button = gr.Button("Detect Voice Clone")
    detect_button.click(
        fn=predict_voice,
        inputs=[audio_input],
        outputs=[prediction_output, waveform_output, spectrogram_output, transcription_output]
    )

# Launch the interface
demo.launch()