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