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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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from torch import Tensor |
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import torch.nn as nn |
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from model import Model |
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model_path = 'DF_FOR_finetune_best.pt' |
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def load_data(path): |
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X, fs = librosa.load(path) |
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X_pad = pad(X,64600) |
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x_inp = Tensor(X_pad).unsqueeze(0) |
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return x_inp,fs |
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def pad(x, max_len=64600): |
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x_len = x.shape[0] |
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if x_len >= max_len: |
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return x[:max_len] |
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num_repeats = int(max_len / x_len)+1 |
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padded_x = np.tile(x, (1, num_repeats))[:, :max_len][0] |
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return padded_x |
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device = 'cuda' if torch.cuda.is_available() else 'cpu' |
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model = Model(None, device) |
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nb_params = sum([param.view(-1).size()[0] for param in model.parameters()]) |
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model =nn.DataParallel(model).to(device) |
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model.load_state_dict(torch.load(model_path, map_location=device)['model']) |
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print("Model loaded : {}".format(model_path)) |
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model.eval() |
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prediction_dict = {0: 'Fake', 1: 'Real'} |
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def Detection(audio_1): |
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x_inp,fs = load_data(audio_1) |
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print(x_inp.shape) |
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validity_probs = model(x_inp) |
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validity_probs = torch.nn.functional.softmax(validity_probs, dim=1) |
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validity = {prediction_dict[i]: float(validity_probs[0][i]) for i in range(2)} |
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return validity |
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audio_1 = gr.Audio(sources="upload", type="filepath", label="Audio 1") |
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gr.Interface( |
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fn=Detection, |
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inputs=audio_1, |
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outputs='label', |
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title="Audio Deepfake Detection", |
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description="Audio Deepfake Detection using finetuned model on for-2seconds dataset.", |
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).launch() |
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