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Kabatubare
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1c53cc7
1
Parent(s):
5a8e50c
Update app.py
Browse files
app.py
CHANGED
@@ -1,19 +1,14 @@
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import gradio as gr
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import torchaudio
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from audioseal import AudioSeal
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import torch
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import numpy as np
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import traceback
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import logging
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# Set up logging to file in the current directory
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logging.basicConfig(filename='app.log', filemode='w', level=logging.DEBUG)
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# Function to handle audio data as NumPy arrays
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def detect_watermark(audio_data, sample_rate):
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try:
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# Extract the audio array from the tuple (audio_data, sample_rate)
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audio_array, _ = audio_data
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# Ensure audio_array is 2D (channels, samples). If it's mono, add an axis.
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if audio_array.ndim == 1:
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@@ -24,7 +19,7 @@ def detect_watermark(audio_data, sample_rate):
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# Ensure waveform is 2D (batch, channels, samples) for AudioSeal
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if waveform.ndim == 2:
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waveform = waveform.unsqueeze(0)
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# Initialize and use the AudioSeal detector
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detector = AudioSeal.load_detector("audioseal_detector_16bits")
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@@ -34,18 +29,17 @@ def detect_watermark(audio_data, sample_rate):
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detection_result = "AI-generated" if result else "genuine"
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return f"This audio is likely {detection_result} based on watermark detection."
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except Exception as e:
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traceback_str = ''.join(traceback.format_tb(e.__traceback__))
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return
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## Corrected Gradio interface definition
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interface = gr.Interface(fn=detect_watermark,
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inputs=[gr.Audio(label="Upload your audio", type="numpy"),
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gr.Number(label="Sample Rate", value=44100, visible=False)],
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outputs="text",
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title="Deep Fake Defender: AI Voice Cloning Detection",
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description="Upload an audio file to check if it's AI-generated or genuine.")
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if __name__ == "__main__":
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interface.launch()
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import gradio as gr
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from audioseal import AudioSeal
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import torch
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import numpy as np
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import traceback
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# Function to handle audio data as NumPy arrays
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def detect_watermark(audio_data, sample_rate):
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try:
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# Extract the audio array from the tuple (audio_data, sample_rate)
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audio_array, _ = audio_data
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# Ensure audio_array is 2D (channels, samples). If it's mono, add an axis.
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if audio_array.ndim == 1:
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# Ensure waveform is 2D (batch, channels, samples) for AudioSeal
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if waveform.ndim == 2:
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waveform = waveform.unsqueeze(0)
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# Initialize and use the AudioSeal detector
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detector = AudioSeal.load_detector("audioseal_detector_16bits")
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detection_result = "AI-generated" if result else "genuine"
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return f"This audio is likely {detection_result} based on watermark detection."
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except Exception as e:
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error_message = f"An error occurred: {str(e)}"
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traceback_str = ''.join(traceback.format_tb(e.__traceback__))
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full_error_message = f"{error_message}\n{traceback_str}"
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return full_error_message
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interface = gr.Interface(fn=detect_watermark,
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inputs=[gr.Audio(label="Upload your audio", type="numpy"),
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gr.Number(label="Sample Rate", value=44100, visible=False)],
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outputs="text",
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title="Deep Fake Defender: AI Voice Cloning Detection",
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description="Upload an audio file to check if it's AI-generated or genuine.")
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if __name__ == "__main__":
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interface.launch(debug=True)
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