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| import gradio as gr | |
| import librosa | |
| import numpy as np | |
| import torch | |
| import matplotlib.pyplot as plt | |
| from transformers import AutoModelForAudioClassification, ASTFeatureExtractor | |
| import random | |
| # Model and feature extractor loading | |
| model = AutoModelForAudioClassification.from_pretrained("./") | |
| feature_extractor = ASTFeatureExtractor.from_pretrained("./") | |
| def plot_waveform(waveform, sr): | |
| plt.figure(figsize=(12, 3)) # Larger figure size | |
| plt.title('Waveform') | |
| plt.ylabel('Amplitude') | |
| plt.plot(np.linspace(0, len(waveform) / sr, len(waveform)), waveform) | |
| plt.xlabel('Time (s)') | |
| return plt.gcf() | |
| def plot_spectrogram(waveform, sr): | |
| S = librosa.feature.melspectrogram(y=waveform, sr=sr, n_mels=128) | |
| S_DB = librosa.power_to_db(S, ref=np.max) | |
| plt.figure(figsize=(12, 4)) # Larger figure size | |
| librosa.display.specshow(S_DB, sr=sr, x_axis='time', y_axis='mel') | |
| plt.title('Mel Spectrogram') | |
| plt.colorbar(format='%+2.0f dB') | |
| plt.tight_layout() | |
| return plt.gcf() | |
| def custom_feature_extraction(audio, sr=16000, target_length=1024): | |
| features = feature_extractor(audio, sampling_rate=sr, return_tensors="pt", padding="max_length", max_length=target_length) | |
| return features.input_values | |
| def apply_time_shift(waveform, max_shift_fraction=0.1): | |
| shift = random.randint(-int(max_shift_fraction * len(waveform)), int(max_shift_fraction * len(waveform))) | |
| return np.roll(waveform, shift) | |
| def predict_voice(audio_file_path): | |
| try: | |
| waveform, sample_rate = librosa.load(audio_file_path, sr=feature_extractor.sampling_rate, mono=True) | |
| augmented_waveform = apply_time_shift(waveform) | |
| original_features = custom_feature_extraction(waveform, sr=sample_rate) | |
| augmented_features = custom_feature_extraction(augmented_waveform, sr=sample_rate) | |
| with torch.no_grad(): | |
| outputs_original = model(original_features) | |
| outputs_augmented = model(augmented_features) | |
| logits = (outputs_original.logits + outputs_augmented.logits) / 2 | |
| predicted_index = logits.argmax() | |
| label = model.config.id2label[predicted_index.item()] | |
| confidence = torch.softmax(logits, dim=1).max().item() * 100 | |
| prediction_text = (f"The model predicts the voice as '{label}'. " | |
| f"Confidence level: {confidence:.2f}%") | |
| waveform_plot = plot_waveform(waveform, sample_rate) | |
| spectrogram_plot = plot_spectrogram(waveform, sample_rate) | |
| return prediction_text, waveform_plot, spectrogram_plot | |
| except Exception as e: | |
| return f"Error during processing: {e}", None, None | |
| # Define the Gradio app layout | |
| iface = gr.Interface( | |
| fn=predict_voice, | |
| inputs=gr.Audio(label="Upload Audio File", type="filepath"), | |
| outputs=[ | |
| gr.Textbox(label="Analysis", type="auto"), | |
| gr.Plot(label="Waveform"), | |
| gr.Plot(label="Spectrogram") | |
| ], | |
| layout="vertical", | |
| title="Voice Clone Detection", | |
| description="This tool determines whether a voice is real or an AI-generated clone. Audio files judged to be authentic and produced by humans are classified as 'Bonafide'. In contrast, those perceived to be synthetically generated are labeled as 'Spoof'. Upload an audio file for analysis." | |
| ) | |
| # Customize the CSS to adjust the layout and component sizes | |
| css = """ | |
| .gradio-container { | |
| max-width: 960px; /* Adjust the maximum width as needed */ | |
| } | |
| .input-container { | |
| width: 25%; /* Smaller input area */ | |
| } | |
| .output-container { | |
| width: 74%; /* Larger output area */ | |
| } | |
| """ | |
| iface.launch(css=css) | |