Update app.py

app.py

@@ -330,26 +330,27 @@ import torch
 
 # if __name__ == "__main__":
 #     main()
-from transformers import Wav2Vec2FeatureExtractor, HubertForSequenceClassification
-import torch
-import soundfile as sf
-
-def load_audio(file_path):
-    # Load an audio file, return waveform and sampling rate
-    waveform, sample_rate = sf.read(file_path)
-    return waveform, sample_rate
-
-def prepare_dataset(data_paths):
-    # Dummy function to simulate loading and processing a dataset
-    # Replace this with actual data loading and processing logic
-    features = []
-    labels = []
-    for path, label in data_paths:
-        waveform, sr = load_audio(path)
-        input_values = feature_extractor(waveform, sampling_rate=sr, return_tensors="pt").input_values
-        features.append(input_values)
-        labels.append(label)
-    return torch.cat(features, dim=0), torch.tensor(labels)
+
+# from transformers import Wav2Vec2FeatureExtractor, HubertForSequenceClassification
+# import torch
+# import soundfile as sf
+
+# def load_audio(file_path):
+#     # Load an audio file, return waveform and sampling rate
+#     waveform, sample_rate = sf.read(file_path)
+#     return waveform, sample_rate
+
+# def prepare_dataset(data_paths):
+#     # Dummy function to simulate loading and processing a dataset
+#     # Replace this with actual data loading and processing logic
+#     features = []
+#     labels = []
+#     for path, label in data_paths:
+#         waveform, sr = load_audio(path)
+#         input_values = feature_extractor(waveform, sampling_rate=sr, return_tensors="pt").input_values
+#         features.append(input_values)
+#         labels.append(label)
+#     return torch.cat(features, dim=0), torch.tensor(labels)
 
     
 def main():
@@ -361,8 +362,8 @@ def main():
     # processor = Wav2Vec2Processor.from_pretrained(model_name)
     # net = HubertForSequenceClassification.from_pretrained(model_name, num_labels=2).to(DEVICE)
 
-    feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name)
-    net = HubertForSequenceClassification.from_pretrained(model_name, num_labels=2).to(DEVICE)
+    # feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained(model_name)
+    # net = HubertForSequenceClassification.from_pretrained(model_name, num_labels=2).to(DEVICE)
 
     NUM_CLIENTS = st.slider("Number of Clients", min_value=1, max_value=10, value=2)
     NUM_ROUNDS = st.slider("Number of Rounds", min_value=1, max_value=10, value=3)