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import streamlit as st
import time
from transformers import pipeline
import librosa
import numpy as np
import plotly.graph_objects as go
import tempfile
import os
import soundfile as sf
# Set page config
st.set_page_config(page_title="🎵 Music Genre Classifier", layout="wide")
# Custom CSS for UI
st.markdown("""
<style>
.main-title {
font-size: 3rem;
color: #1DB954;
text-align: center;
padding: 2rem 0;
text-shadow: 2px 2px 4px rgba(0,0,0,0.1);
}
.sub-title {
font-size: 1.5rem;
color: #191414;
text-align: center;
margin-bottom: 2rem;
}
.stAudio {
margin: 2rem auto;
display: block;
}
.genre-result {
font-size: 2rem;
font-weight: bold;
text-align: center;
color: #1DB954;
margin: 1rem 0;
}
.prediction-time {
font-size: 1.2rem;
color: #191414;
text-align: center;
}
</style>
""", unsafe_allow_html=True)
@st.cache_resource
def load_model():
return pipeline("audio-classification", model="juangtzi/wav2vec2-base-finetuned-gtzan")
pipe = load_model()
def convert_to_wav(audio_file):
"""Converts uploaded audio file to WAV format."""
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_wav:
# Use soundfile to load and save the audio file as WAV
audio_data, samplerate = sf.read(audio_file)
sf.write(tmp_wav.name, audio_data, samplerate)
return tmp_wav.name
def classify_audio(audio_file):
"""Classifies the audio file using the loaded model."""
start_time = time.time()
# Convert to WAV format before passing to the model
wav_file = convert_to_wav(audio_file)
try:
# Use the wav file with the model
preds = pipe(wav_file)
outputs = {p["label"]: p["score"] for p in preds}
end_time = time.time()
prediction_time = end_time - start_time
return outputs, prediction_time
finally:
os.unlink(wav_file) # Remove the temp file
# Page title and subtitle
st.markdown("<h1 class='main-title'>🎵 Music Genre Classifier</h1>", unsafe_allow_html=True)
st.markdown("<p class='sub-title'>Upload a music file and let AI detect its genre!</p>", unsafe_allow_html=True)
# Sidebar with model and dataset information
st.sidebar.title("About")
st.sidebar.info("""
This app uses a fine-tuned wav2vec2-base model to classify music genres.
Model: juangtzi/wav2vec2-base-finetuned-gtzan
Dataset: GTZAN
""")
# Upload file section
uploaded_file = st.file_uploader("Choose an audio file", type=["wav", "mp3", "ogg"])
if uploaded_file is not None:
# Display the uploaded audio file
st.audio(uploaded_file)
# Classify the uploaded audio
if st.button("Classify Genre"):
with st.spinner("Analyzing the music... 🎧"):
try:
results, pred_time = classify_audio(uploaded_file)
# Get the top predicted genre
top_genre = max(results, key=results.get)
# Display the top predicted genre
st.markdown(f"<h2 class='genre-result'>Detected Genre: {top_genre.capitalize()}</h2>", unsafe_allow_html=True)
st.markdown(f"<p class='prediction-time'>Prediction Time: {pred_time:.2f} seconds</p>", unsafe_allow_html=True)
# Plot the genre probabilities as a bar chart
fig = go.Figure(data=[go.Bar(
x=list(results.keys()),
y=list(results.values()),
marker_color='#1DB954'
)])
fig.update_layout(
title="Genre Probabilities",
xaxis_title="Genre",
yaxis_title="Probability",
paper_bgcolor='rgba(0,0,0,0)',
plot_bgcolor='rgba(0,0,0,0)'
)
st.plotly_chart(fig, use_container_width=True)
# # Load the audio for displaying waveform
# y, sr = librosa.load(uploaded_file, sr=None)
# # Plot the audio waveform
# st.subheader("Audio Waveform")
# fig_waveform = go.Figure(data=[go.Scatter(y=y, mode='lines', line=dict(color='#1DB954'))])
# fig_waveform.update_layout(
# title="Audio Waveform",
# xaxis_title="Time",
# yaxis_title="Amplitude",
# paper_bgcolor='rgba(0,0,0,0)',
# plot_bgcolor='rgba(0,0,0,0)'
# )
# st.plotly_chart(fig_waveform, use_container_width=True)
# 🎈 Show balloons after successfully displaying the results
st.balloons()
except Exception as e:
st.error(f"An error occurred while processing the audio: {str(e)}")
st.info("Please try uploading the file again or use a different audio file.")
# Footer
st.markdown("""
<div style='text-align: center; margin-top: 2rem;'>
<p>Created with ❤️ by AI. Powered by Streamlit and Hugging Face Transformers.</p>
</div>
""", unsafe_allow_html=True)
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