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| import gradio as gr | |
| from audioseal import AudioSeal | |
| import torch | |
| import numpy as np | |
| import traceback | |
| def detect_watermark(audio_data): | |
| try: | |
| # Ensure that audio_data is a tuple with two elements | |
| if not (isinstance(audio_data, tuple) and len(audio_data) == 2): | |
| return f"Invalid input: expected a tuple with two elements, got {type(audio_data)} with length {len(audio_data)}" | |
| # Ensure the first element of the tuple is a NumPy array | |
| audio_array, sample_rate = audio_data | |
| if not isinstance(audio_array, np.ndarray): | |
| return f"Invalid input: expected the first element of the tuple to be a np.ndarray, got {type(audio_array)}" | |
| # Ensure the second element of the tuple is an integer (sample rate) | |
| if not isinstance(sample_rate, int): | |
| return f"Invalid input: expected the second element of the tuple to be an int (sample rate), got {type(sample_rate)}" | |
| # Now we can proceed with the assurance that audio_array is an np.ndarray and sample_rate is an int | |
| # Ensure audio_array is 2D (channels, samples). If it's mono, add an axis. | |
| if audio_array.ndim == 1: | |
| audio_array = np.expand_dims(audio_array, axis=0) | |
| # Convert NumPy array to tensor | |
| waveform = torch.tensor(audio_array, dtype=torch.float32) | |
| # Ensure waveform is 3D (batch, channels, samples) for AudioSeal | |
| if waveform.ndim == 2: | |
| waveform = waveform.unsqueeze(0) | |
| # Initialize and use the AudioSeal detector | |
| detector = AudioSeal.load_detector("audioseal_detector_16bits") | |
| result, _ = detector.detect_watermark(waveform, message_threshold=0.5) | |
| # Interpret and return the detection result | |
| detection_result = "AI-generated" if result else "genuine" | |
| return f"This audio is likely {detection_result} based on watermark detection." | |
| except Exception as e: | |
| # Capture the full traceback in case of an error and return it as a string | |
| error_traceback = traceback.format_exc() | |
| return f"Error occurred: {str(e)}\n\n{error_traceback}" | |
| # Define Gradio interface | |
| interface = gr.Interface( | |
| fn=detect_watermark, | |
| inputs=gr.Audio(label="Upload your audio", type="numpy"), | |
| outputs="text", | |
| title="Deep Fake Defender: AI Voice Cloning Detection", | |
| description="Upload an audio file to check if it's AI-generated or genuine." | |
| ) | |
| if __name__ == "__main__": | |
| interface.launch() | |