Modify

app.py

@@ -1,10 +1,21 @@
 import gradio as gr
+import process_data as PD
 
+model_master= {
+    "SSL-AASIST (Trained on ASV-Spoof5)": {"model_loaded": True,
+                                           "eer_threshold":3.3330237865448,
+                                           "data_process_func": "process_ssl_assist_input" ,
+                                           "note": "This model is trained on ASVSpoof 2024 training data."},
+    "AASIST": {"model_loaded": False,
+                                           "eer_threshold":1.8018419742584229,
+                                           "data_process_func": "process_assist_input" ,
+                                           "note": "This model is trained on ASVSpoof 2024 training data."}
+}
 def process(file, type):
-    return str(file)+str(type)
+    data = getattr(PD, model_master[type]["data_process_func"])
+    return str(file)+str(type)+model_master[type]["eer_threshold"]
 
 demo = gr.Blocks()
-
 file_proc = gr.Interface(
     fn=process,
     inputs=[

process_data.py

@@ -0,0 +1,20 @@
+import librosa
+import numpy as np
+from torch import Tensor
+
+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
+
+
+def process_ssl_assist_input(filepath):
+    X, fs = librosa.load(filepath, sr=16000)
+    X_pad = pad(X)
+    x_inp = Tensor(X_pad)
+    x_inp = x_inp.unsqueeze(0)
+    return x_inp

requirements.txt

@@ -1,3 +1,5 @@
 gradio==3.50.2
 torch==2.3.0
-fairseq
+fairseq
+librosa==0.10.1
+numpy==1.24.4