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import streamlit as st
import pandas as pd
import torch
import torchaudio
from transformers import Wav2Vec2ForSequenceClassification, Wav2Vec2Processor, safetensors
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", from_safetensors=True).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
# Function to get top 5 predictions with probabilities
def get_top_5_predictions(logits, label_encoder):
probabilities = torch.nn.functional.softmax(logits, dim=-1).cpu().numpy()
top5_idx = np.argsort(probabilities, axis=-1)[:, -5:][:, ::-1] # Top 5 indices
top5_probs = np.take_along_axis(probabilities, top5_idx, axis=-1)
top5_labels = label_encoder.inverse_transform(top5_idx[0])
return list(zip(top5_labels, top5_probs[0]))
# 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
return get_top_5_predictions(logits, label_encoder)
# 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}") |