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| import gradio as gr | |
| import librosa | |
| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| import logging | |
| from transformers import AutoModelForAudioClassification | |
| # Configure logging for debugging and information | |
| logging.basicConfig(level=logging.INFO) | |
| # Model loading from the specified local path | |
| local_model_path = "./" | |
| model = AutoModelForAudioClassification.from_pretrained(local_model_path) | |
| def custom_feature_extraction(audio_file_path, sr=16000, n_mels=128, n_fft=2048, hop_length=512, target_length=1024): | |
| """ | |
| Custom feature extraction using Mel spectrogram, tailored for models trained on datasets like AudioSet. | |
| Args: | |
| audio_file_path: Path to the audio file for prediction. | |
| sr: Target sampling rate for the audio file. | |
| n_mels: Number of Mel bands to generate. | |
| n_fft: Length of the FFT window. | |
| hop_length: Number of samples between successive frames. | |
| target_length: Expected length of the Mel spectrogram in the time dimension. | |
| Returns: | |
| A tensor representation of the Mel spectrogram features. | |
| """ | |
| waveform, sample_rate = librosa.load(audio_file_path, sr=sr) | |
| S = librosa.feature.melspectrogram(y=waveform, sr=sample_rate, n_mels=n_mels, n_fft=n_fft, hop_length=hop_length) | |
| S_DB = librosa.power_to_db(S, ref=np.max) | |
| mel_tensor = torch.tensor(S_DB).float() | |
| # Ensure the tensor matches the expected sequence length | |
| current_length = mel_tensor.shape[1] | |
| if current_length > target_length: | |
| mel_tensor = mel_tensor[:, :target_length] # Truncate if longer | |
| elif current_length < target_length: | |
| padding = target_length - current_length | |
| mel_tensor = F.pad(mel_tensor, (0, padding), "constant", 0) # Pad if shorter | |
| mel_tensor = mel_tensor.unsqueeze(0) # Add batch dimension for compatibility with model | |
| return mel_tensor | |
| def predict_voice(audio_file_path): | |
| """ | |
| Predicts the audio class using a pre-trained model and custom feature extraction. | |
| Args: | |
| audio_file_path: Path to the audio file for prediction. | |
| Returns: | |
| A string containing the predicted class and confidence level. | |
| """ | |
| try: | |
| features = custom_feature_extraction(audio_file_path) | |
| with torch.no_grad(): | |
| outputs = model(features) | |
| logits = outputs.logits | |
| predicted_index = logits.argmax() | |
| label = model.config.id2label[predicted_index.item()] | |
| confidence = torch.softmax(logits, dim=1).max().item() * 100 | |
| result = f"The voice is classified as '{label}' with a confidence of {confidence:.2f}%." | |
| logging.info("Prediction successful.") | |
| except Exception as e: | |
| result = f"Error during processing: {e}" | |
| logging.error(result) | |
| return result | |
| # Setting up the Gradio interface | |
| iface = gr.Interface( | |
| fn=predict_voice, | |
| inputs=gr.Audio(label="Upload Audio File", type="filepath"), | |
| outputs=gr.Textbox(label="Prediction"), | |
| title="Voice Authenticity Detection", | |
| description="Detects whether a voice is real or AI-generated. Upload an audio file to see the results." | |
| ) | |
| # Launching the interface | |
| iface.launch() | |