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import gradio as gr |
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import librosa |
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import numpy as np |
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import torch |
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import matplotlib.pyplot as plt |
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from transformers import AutoModelForAudioClassification, ASTFeatureExtractor |
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import random |
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import tempfile |
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import logging |
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import os |
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logging.basicConfig(level=logging.DEBUG, filename='app.log', filemode='w', format='%(name)s - %(levelname)s - %(message)s') |
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logger = logging.getLogger(__name__) |
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model = AutoModelForAudioClassification.from_pretrained("./") |
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feature_extractor = ASTFeatureExtractor.from_pretrained("./") |
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def plot_waveform(waveform, sr): |
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plt.figure(figsize=(24, 8)) |
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plt.title('Waveform') |
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plt.ylabel('Amplitude') |
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plt.plot(np.linspace(0, len(waveform) / sr, len(waveform)), waveform) |
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plt.xlabel('Time (s)') |
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temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png', dir='./') |
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plt.savefig(temp_file.name) |
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plt.close() |
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logger.debug(f"Waveform image generated: {temp_file.name}, Size: {os.path.getsize(temp_file.name)} bytes") |
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return temp_file.name |
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def plot_spectrogram(waveform, sr): |
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S = librosa.feature.melspectrogram(y=waveform, sr=sr, n_mels=128) |
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S_DB = librosa.power_to_db(S, ref=np.max) |
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plt.figure(figsize=(24, 12)) |
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librosa.display.specshow(S_DB, sr=sr, x_axis='time', y_axis='mel') |
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plt.title('Mel Spectrogram') |
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plt.colorbar(format='%+2.0f dB') |
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plt.tight_layout() |
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temp_file = tempfile.NamedTemporaryFile(delete=False, suffix='.png', dir='./') |
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plt.savefig(temp_file.name) |
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plt.close() |
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logger.debug(f"Spectrogram image generated: {temp_file.name}, Size: {os.path.getsize(temp_file.name)} bytes") |
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return temp_file.name |
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def custom_feature_extraction(audio, sr=16000, target_length=1024): |
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features = feature_extractor(audio, sampling_rate=sr, return_tensors="pt", padding="max_length", max_length=target_length) |
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return features.input_values |
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def apply_time_shift(waveform, max_shift_fraction=0.1): |
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shift = random.randint(-int(max_shift_fraction * len(waveform)), int(max_shift_fraction * len(waveform))) |
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return np.roll(waveform, shift) |
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def predict_voice(audio_file_path): |
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waveform, _ = librosa.load(audio_file_path, sr=16000, mono=True) |
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augmented_waveform = apply_time_shift(waveform) |
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original_features = custom_feature_extraction(waveform, sr=16000) |
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augmented_features = custom_feature_extraction(augmented_waveform, sr=16000) |
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with torch.no_grad(): |
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outputs_original = model(original_features) |
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outputs_augmented = model(augmented_features) |
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logits = (outputs_original.logits + outputs_augmented.logits) / 2 |
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predicted_index = logits.argmax() |
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original_label = model.config.id2label[predicted_index.item()] |
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confidence = torch.softmax(logits, dim=1).max().item() * 100 |
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label_mapping = {"Spoof": "AI-generated Clone", "Bonafide": "Real Human Voice"} |
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new_label = label_mapping.get(original_label, "Unknown") |
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waveform_plot = plot_waveform(waveform, 16000) |
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spectrogram_plot = plot_spectrogram(waveform, 16000) |
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return (f"The voice is classified as '{new_label}' with a confidence of {confidence:.2f}%.", |
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waveform_plot, |
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spectrogram_plot) |
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with gr.Blocks(css="style.css") as demo: |
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gr.Markdown("## Voice Clone Detection") |
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gr.Markdown("Detects whether a voice is real or an AI-generated clone. Upload an audio file to see the results.") |
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with gr.Row(): |
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audio_input = gr.Audio(label="Upload Audio File", type="filepath") |
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detect_button = gr.Button("Detect Voice Clone") |
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with gr.Row(): |
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prediction_output = gr.Textbox(label="Prediction") |
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with gr.Row(): |
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waveform_output = gr.Image(label="Waveform") |
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spectrogram_output = gr.Image(label="Spectrogram") |
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detect_button.click(fn=predict_voice, inputs=[audio_input], outputs=[prediction_output, waveform_output, spectrogram_output]) |
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demo.launch() |
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