Spaces:
Sleeping
Sleeping
File size: 1,842 Bytes
c5de5c7 20b2533 c5de5c7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 |
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()
|