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| import gradio as gr | |
| from audioseal import AudioSeal | |
| import torch | |
| import torchaudio | |
| import torchaudio.transforms as T | |
| import traceback | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import io | |
| from PIL import Image | |
| def plot_spectrogram(waveform, sample_rate): | |
| """Plot and return a spectrogram.""" | |
| spectrogram_transform = T.Spectrogram() | |
| spectrogram = spectrogram_transform(waveform) | |
| spectrogram_db = torchaudio.transforms.AmplitudeToDB()(spectrogram) | |
| plt.figure(figsize=(10, 4)) | |
| plt.imshow(spectrogram_db[0].numpy(), cmap='hot', aspect='auto', origin='lower') | |
| plt.colorbar(format='%+2.0f dB') | |
| plt.title('Spectrogram') | |
| plt.xlabel('Time Frame') | |
| plt.ylabel('Frequency') | |
| buf = io.BytesIO() | |
| plt.savefig(buf, format='png') | |
| plt.close() | |
| buf.seek(0) | |
| return Image.open(buf) | |
| def detect_watermark(audio_file_path, threshold=0.99): | |
| try: | |
| waveform, sample_rate = torchaudio.load(audio_file_path) | |
| # Normalize and resample | |
| waveform = waveform / torch.max(torch.abs(waveform)) | |
| if sample_rate != 16000: | |
| resampler = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=16000) | |
| waveform = resampler(waveform) | |
| sample_rate = 16000 | |
| if waveform.ndim < 3: | |
| waveform = waveform.unsqueeze(0) | |
| detector = AudioSeal.load_detector("audioseal_detector_16bits") | |
| result, confidence = detector.detect_watermark(waveform, message_threshold=threshold) | |
| # Visual feedback | |
| waveform_image = plot_spectrogram(waveform.squeeze(), sample_rate) | |
| if result: | |
| detection_message = f"AI-generated with confidence: {np.mean(confidence.numpy()):.2f}" | |
| else: | |
| detection_message = "Likely human-generated or the AI watermark is undetectable at the current threshold." | |
| return detection_message, waveform_image | |
| except Exception as e: | |
| error_traceback = traceback.format_exc() | |
| return f"Error occurred: {e}\n\n{error_traceback}", None | |
| # Interface with dynamic threshold and visualization | |
| interface = gr.Interface( | |
| fn=detect_watermark, | |
| inputs=[gr.Audio(label="Upload your audio", type="filepath"), gr.Slider(label="Detection Threshold", minimum=0, maximum=1, value=0.99)], | |
| outputs=["text", "image"], | |
| title="Deep Fake Defender: AI Voice Cloning Detection", | |
| description="Upload an audio file to check if it's AI-generated or genuine. Adjust the detection threshold to change sensitivity." | |
| ) | |
| if __name__ == "__main__": | |
| interface.launch() | |