Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import torch
+import torchaudio
+from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2Processor
+from sklearn.preprocessing import LabelEncoder
+import numpy as np
+
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load the fine-tuned model and processor
+model = Wav2Vec2ForSequenceClassification.from_pretrained("./fine_tuned_model").to(device)
+processor = Wav2Vec2Processor.from_pretrained("./fine_tuned_model")
+
+# Load the label encoder
+label_encoder = LabelEncoder()
+label_encoder.fit(pd.read_csv("dataset/train_wav.csv")["Common Name"])
+
+# Fixed audio length (e.g., 10 seconds)
+fixed_length = 10 * 16000  # 10 seconds * 16000 Hz
+
+# Prediction function
+def predict(file_path):
+    waveform, sample_rate = torchaudio.load(file_path)
+    
+    # Ensure the audio is mono
+    if waveform.size(0) > 1:
+        waveform = torch.mean(waveform, dim=0, keepdim=True)
+    
+    # Ensure the audio is exactly 10 seconds
+    if waveform.size(1) > fixed_length:
+        waveform = waveform[:, :fixed_length]
+    else:
+        padding = fixed_length - waveform.size(1)
+        waveform = torch.nn.functional.pad(waveform, (0, padding))
+    
+    inputs = processor(waveform.squeeze(0), sampling_rate=16000, return_tensors="pt", padding=True).to(device)
+    
+    with torch.no_grad():
+        logits = model(inputs.input_values).logits
+    
+    probabilities = torch.nn.functional.softmax(logits, dim=-1).cpu().numpy()[0]
+    top5_idx = np.argsort(probabilities)[-5:][::-1]
+    top5_probs = probabilities[top5_idx]
+    top5_labels = label_encoder.inverse_transform(top5_idx)
+    
+    return list(zip(top5_labels, top5_probs))
+
+# Streamlit interface
+st.title("Bird Sound Classification")
+
+uploaded_file = st.file_uploader("Upload an audio file", type=["wav"])
+
+if uploaded_file is not None:
+    # Save the uploaded file temporarily
+    file_path = f"temp/{uploaded_file.name}"
+    os.makedirs(os.path.dirname(file_path), exist_ok=True)
+    with open(file_path, "wb") as f:
+        f.write(uploaded_file.getbuffer())
+    
+    st.audio(file_path, format='audio/wav')
+    
+    if st.button("Predict"):
+        with st.spinner("Classifying..."):
+            top5_predictions = predict(file_path)
+            st.success("Top 5 Predicted Bird Species with Probabilities:")
+            for label, prob in top5_predictions:
+                st.write(f"{label}: {prob:.4f}")