Update app.py

app.py

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+import gradio as gr
+from transformers import Wav2Vec2Processor
+import torch
+import librosa
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+class Wav2Vec2Classifier(torch.nn.Module):
+    def __init__(self, num_classes):
+        super().__init__()
+        from transformers import Wav2Vec2Model
+        self.wav2vec2 = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base")
+        self.dropout = torch.nn.Dropout(0.3)
+        self.classifier = torch.nn.Linear(self.wav2vec2.config.hidden_size, num_classes)
+    
+    def forward(self, input_values, attention_mask=None):
+        outputs = self.wav2vec2(input_values, attention_mask=attention_mask)
+        pooled_output = outputs.last_hidden_state.mean(dim=1)
+        pooled_output = self.dropout(pooled_output)
+        logits = self.classifier(pooled_output)
+        return logits
+
+processor = Wav2Vec2Processor.from_pretrained("hrid0yyy/BornoNet")
+num_classes = 50
+model = Wav2Vec2Classifier(num_classes=num_classes)
+model.load_state_dict(torch.load(hf_hub_download("hrid0yyy/BornoNet", "pytorch_model.bin"), map_location="cpu"))
+model.eval()
+le_classes = np.load(hf_hub_download("hrid0yyy/BornoNet", "label_encoder_classes.npy"), allow_pickle=True)
+
+def predict(audio):
+    try:
+        y, sr = librosa.load(audio, sr=16000)
+        inputs = processor(y, sampling_rate=sr, return_tensors="pt", padding=True)
+        with torch.no_grad():
+            logits = model(inputs.input_values)
+            predicted = le_classes[torch.argmax(logits, dim=1).item()]
+        return f"Predicted character: {predicted}"
+    except Exception as e:
+        return f"Error processing audio: {str(e)}"
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Audio(type="filepath", label="Upload an MP3 file (16kHz)"),
+    outputs=gr.Textbox(label="Prediction"),
+    title="BornoNet: Bengali Speech Recognition",
+    description="Upload a 16kHz MP3 file to classify Bengali speech into characters (e.g., ত, অ, ক)."
+)
+iface.launch()