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| import streamlit as st | |
| import numpy as np | |
| from backend import sample_preparer, load_model | |
| def process_file(file, model): | |
| prediction = model.predict(np.array(sample_preparer(file))) | |
| type_num = np.argmax(prediction, axis=1) | |
| drum_types = ['Clap', 'Closed Hat', 'Kick', 'Open Hat', 'Snare'] | |
| return drum_types[int(type_num)] | |
| def main_page(): | |
| st.set_page_config(page_title="Drum Classifier", | |
| page_icon="🥁") | |
| st.markdown("# Drum Classifier 🥁") | |
| st.markdown("Classify Drum audio samples through the use of Artificial Intelligence / Machine Learning. The Drum " | |
| "Audio Classifier, uses a Convolutional Neural Network to predict the most likely drum type of a " | |
| "audio file. The dataset used to create this model was 2,700+ of my freelance music production audio " | |
| "samples.") | |
| st.markdown("Currently supported drums: Clap, Closed Hat, Kick, Open Hat, Snare.") | |
| st.markdown("Drag and Drop a WAV or Mp3 audio File to classify.") | |
| if "model" not in st.session_state: | |
| with st.spinner("Loading Database..."): | |
| st.session_state.model = load_model() | |
| with open("samples.zip", "rb") as file: | |
| st.download_button( | |
| label="Download Sample Files", | |
| data=file, | |
| file_name="samples.zip", | |
| mime="application/zip" | |
| ) | |
| file = st.file_uploader( | |
| "Upload an Audio File", | |
| accept_multiple_files=False, | |
| type=['wav', 'mp3'], | |
| label_visibility="hidden" | |
| ) | |
| if st.session_state.model and file: | |
| st.audio(file) | |
| with st.spinner("Processing..."): | |
| drum_type = process_file(file, st.session_state.model) | |
| st.markdown(f"\"{file.name}\" is a {drum_type}.") | |
| if __name__ == '__main__': | |
| main_page() | |