update with hubert classification

app.py

@@ -309,13 +309,36 @@ def test(net, testloader):
     return loss, accuracy
 
 
+
+
+
+from transformers import Wav2Vec2Processor, HubertForSequenceClassification
+import torch
+
+# def main():
+#     st.write("## Audio Classification with HuBERT")
+#     dataset_name = st.selectbox("Dataset", ["librispeech", "your_audio_dataset"])
+#     model_name = "facebook/hubert-base-ls960"
+
+#     processor = Wav2Vec2Processor.from_pretrained(model_name)
+#     net = HubertForSequenceClassification.from_pretrained(model_name, num_labels=2).to(DEVICE)
+
+#     train_dataset, test_dataset = load_data(dataset_name)
+#     # Further implementation needed for actual data preparation and training loops
+
+#     st.write("Details of further steps would be filled in based on specific requirements and dataset structure.")
+
+# if __name__ == "__main__":
+#     main()
+
 def main():
     st.write("## Federated Learning with dynamic models and datasets for mobile devices")
     dataset_name = st.selectbox("Dataset", ["audio_instruction_task","imdb", "amazon_polarity", "ag_news"])
     model_name = st.selectbox("Model", ["facebook/hubert-base-ls960","bert-base-uncased", "distilbert-base-uncased"])
 
-    net = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2).to(DEVICE)
-
+    # net = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2).to(DEVICE)
+    processor = Wav2Vec2Processor.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)