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Update app.py
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app.py
CHANGED
@@ -12,18 +12,16 @@ local_model_path = "./"
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extractor = AutoFeatureExtractor.from_pretrained(local_model_path)
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model = AutoModelForAudioClassification.from_pretrained(local_model_path)
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def predict_voice(
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"""
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Predicts whether a voice is real or spoofed from an audio file.
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Args:
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-
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Returns:
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A string with the prediction and confidence level.
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"""
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# Gradio passes the audio file as a tuple (file_name, file_path). We only need the file_path.
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audio_file_path = audio_file[1]
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# Load the audio file. Adjust the loading mechanism based on your audio file format.
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waveform, sample_rate = sf.read(audio_file_path)
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@@ -49,11 +47,9 @@ def predict_voice(audio_file):
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result = f"The voice is classified as '{label}' with a confidence of {confidence:.2f}%."
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return result
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-
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# Setting up the Gradio interface
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iface = gr.Interface(
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fn=predict_voice,
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inputs=gr.Audio(type="filepath", label="Upload Audio File"), #
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outputs=gr.Textbox(label="Prediction"),
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title="Voice Authenticity Detection",
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description="Detects whether a voice is real or AI-generated. Upload an audio file to see the results.",
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extractor = AutoFeatureExtractor.from_pretrained(local_model_path)
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model = AutoModelForAudioClassification.from_pretrained(local_model_path)
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def predict_voice(audio_file_path):
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"""
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Predicts whether a voice is real or spoofed from an audio file.
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Args:
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audio_file_path: The file path of the uploaded audio file to be classified.
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Returns:
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A string with the prediction and confidence level.
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"""
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# Load the audio file. Adjust the loading mechanism based on your audio file format.
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waveform, sample_rate = sf.read(audio_file_path)
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result = f"The voice is classified as '{label}' with a confidence of {confidence:.2f}%."
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return result
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iface = gr.Interface(
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fn=predict_voice,
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inputs=gr.Audio(type="filepath", label="Upload Audio File"), # Ensure filepath is passed
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outputs=gr.Textbox(label="Prediction"),
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title="Voice Authenticity Detection",
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description="Detects whether a voice is real or AI-generated. Upload an audio file to see the results.",
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