Update app.py

app.py

@@ -1,59 +1,59 @@
-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 = 'final_model.pth'
-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]
-    # need to pad
-    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))
-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)
-    
-    emotion = torch.argmax(validity_probs).item()
-    print(emotion)
-    validity = prediction_dict[emotion]
-    # validity as a dictionary of class probabilities
-    # validity = {prediction_dict[i]: float(validity_probs[0][i]) for i in range(2)}
-
-    return validity
-
-audio_1 = gr.Audio(type="filepath", label="Audio 1")
-# text_output = gr.Textbox(label="Prediction")
-text_output = gr.Textbox(label="Similarity Score")
-gr.Interface(
-    fn=Detection,
-    inputs=audio_1,
-    outputs=text_output,
-    title="Audio Deepfake Detection",
-    description="Audio Deepfake Detection using finetuned model on for-2seconds dataset.",
-).launch()
+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 = 'final_model.pth'
+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]
+    # need to pad
+    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))
+print("Model loaded : {}".format(model_path))
+
+model.eval()
+prediction_dict = {0: 'Fake', 1: 'Real'}
+def Detection(audio):
+    
+    x_inp,fs = load_data(audio)
+    print(x_inp.shape)
+    validity_probs = model(x_inp)
+    validity_probs = torch.nn.functional.softmax(validity_probs, dim=1)
+    
+    emotion = torch.argmax(validity_probs).item()
+    print(emotion)
+    validity = prediction_dict[emotion]
+    # validity as a dictionary of class probabilities
+    # validity = {prediction_dict[i]: float(validity_probs[0][i]) for i in range(2)}
+
+    return validity
+
+audio = gr.Audio(type="filepath", label="Audio")
+# text_output = gr.Textbox(label="Prediction")
+text_output = gr.Textbox(label="Real Or Fake")
+gr.Interface(
+    fn=Detection,
+    inputs=audio,
+    outputs=text_output,
+    title="Audio Deepfake Detection",
+    description="Audio Deepfake Detection.",
+).launch()