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Commit
·
da764f1
0
Parent(s):
Initial commit for Hugging Face Space
Browse files- .space/app-entrypoint.sh +19 -0
- .space/config.json +11 -0
- README.md +27 -0
- app.py +653 -0
- download_model.py +128 -0
- requirements.txt +28 -0
.space/app-entrypoint.sh
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#!/bin/bash
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# Check if we're running on Hugging Face Space
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if [ -n "$SPACE_ID" ]; then
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echo "Running on Hugging Face Space: $SPACE_ID"
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else
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echo "Running locally"
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fi
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# Create necessary directories
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mkdir -p models/CRNN
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# Run model download script to ensure model is available
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echo "Checking for model files..."
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python src/download_model.py
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# Start the Streamlit app
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echo "Starting Streamlit app..."
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streamlit run src/app.py --server.address=0.0.0.0 --server.port=7860 --server.enableCORS=false --server.enableXsrfProtection=false
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.space/config.json
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{
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"app_file": "src/app.py",
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"docker_build_args": {
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"MODEL_HF_REPO": "dennisvdang/chorus-detection"
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},
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"sdk": "streamlit",
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"python_requirements": "requirements.txt",
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"suggested_hardware": "t4-small",
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"suggested_cuda": "11.8",
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"app_entrypoint": ".space/app-entrypoint.sh"
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}
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README.md
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---
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title: Chorus Detection
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emoji: 🎵
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colorFrom: purple
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colorTo: green
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sdk: streamlit
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sdk_version: "1.26.0"
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app_file: app.py
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pinned: false
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---
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# Chorus Detection App
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This Streamlit app uses a Convolutional Recurrent Neural Network (CRNN) to automatically detect chorus sections in music tracks.
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## Features
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- Detect and extract chorus sections in songs
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- Upload audio files or provide YouTube URLs for analysis
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- Display waveform visualization with highlighted chorus sections
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- Create playable snippets of detected choruses
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## About the Model
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The model was trained on a dataset of 332 manually labeled songs from various genres using a CRNN architecture. It achieved an F1 score of 0.864 (Precision: 0.831, Recall: 0.900) on an unseen test set.
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For more information, visit the [GitHub repository](https://github.com/dennisvdang/chorus-detection).
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app.py
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""Streamlit web app for chorus detection in audio files.
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This module provides a web-based interface for the chorus detection system,
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allowing users to upload audio files or provide YouTube URLs for analysis.
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"""
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import os
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# Configure TensorFlow logging before importing TensorFlow
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logs
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# Import model downloader to ensure model is available
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try:
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from download_model import ensure_model_exists
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except ImportError:
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from src.download_model import ensure_model_exists
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import base64
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import tempfile
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import warnings
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from typing import Optional, Tuple, List
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import time
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import io
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import matplotlib.pyplot as plt
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import streamlit as st
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import tensorflow as tf
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import librosa
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import soundfile as sf
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import numpy as np
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from pydub import AudioSegment
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# Suppress warnings
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warnings.filterwarnings("ignore") # Suppress all warnings
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tf.get_logger().setLevel('ERROR') # Suppress TensorFlow ERROR logs
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from chorus_detection.audio.data_processing import process_audio
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from chorus_detection.audio.processor import extract_audio
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from chorus_detection.config import MODEL_PATH
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from chorus_detection.models.crnn import load_CRNN_model, make_predictions
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from chorus_detection.utils.cli import is_youtube_url
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from chorus_detection.utils.logging import logger
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# Ensure the model is downloaded before proceeding
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MODEL_PATH = ensure_model_exists()
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# Define color scheme
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THEME_COLORS = {
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'background': '#121212',
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'card_bg': '#181818',
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'primary': '#1DB954',
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'secondary': '#1ED760',
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'text': '#FFFFFF',
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'subtext': '#B3B3B3',
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'highlight': '#1DB954',
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'border': '#333333',
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}
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def get_binary_file_downloader_html(bin_file: str, file_label: str = 'File') -> str:
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"""Generate HTML for file download link.
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Args:
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bin_file: Path to the binary file
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file_label: Label for the download link
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Returns:
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HTML string for the download link
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"""
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with open(bin_file, 'rb') as f:
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data = f.read()
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b64 = base64.b64encode(data).decode()
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return f'<a href="data:application/octet-stream;base64,{b64}" download="{os.path.basename(bin_file)}">{file_label}</a>'
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def set_custom_theme() -> None:
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"""Apply custom Spotify-inspired theme to Streamlit UI."""
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custom_theme = f"""
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<style>
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82 |
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.stApp {{
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background-color: {THEME_COLORS['background']};
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color: {THEME_COLORS['text']};
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}}
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86 |
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.css-18e3th9 {{
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padding-top: 2rem;
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padding-bottom: 10rem;
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padding-left: 5rem;
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padding-right: 5rem;
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}}
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h1, h2, h3, h4, h5, h6 {{
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color: {THEME_COLORS['text']} !important;
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font-weight: 700 !important;
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}}
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96 |
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.stSidebar .sidebar-content {{
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97 |
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background-color: {THEME_COLORS['card_bg']};
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98 |
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}}
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99 |
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.stButton>button {{
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100 |
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background-color: {THEME_COLORS['primary']};
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101 |
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color: white;
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102 |
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border-radius: 500px;
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103 |
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padding: 8px 32px;
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104 |
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font-weight: 600;
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105 |
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border: none;
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transition: all 0.3s ease;
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}}
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108 |
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.stButton>button:hover {{
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109 |
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background-color: {THEME_COLORS['secondary']};
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110 |
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transform: scale(1.04);
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111 |
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}}
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112 |
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.stTextInput>div>div>input,
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113 |
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.stFileUploader>div>div {{
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114 |
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background-color: {THEME_COLORS['card_bg']};
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115 |
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color: {THEME_COLORS['text']};
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116 |
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border: 1px solid {THEME_COLORS['border']};
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117 |
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border-radius: 4px;
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}}
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119 |
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.stExpander {{
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120 |
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background-color: {THEME_COLORS['card_bg']};
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121 |
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border-radius: 8px;
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122 |
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margin-bottom: 10px;
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123 |
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border: 1px solid {THEME_COLORS['border']};
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124 |
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}}
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125 |
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.stExpander>div {{
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126 |
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border: none !important;
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127 |
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}}
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128 |
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.chorus-card {{
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129 |
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background-color: {THEME_COLORS['card_bg']};
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130 |
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border-radius: 8px;
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131 |
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padding: 20px;
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132 |
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margin-bottom: 15px;
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133 |
+
border: 1px solid {THEME_COLORS['border']};
|
134 |
+
}}
|
135 |
+
.result-container {{
|
136 |
+
padding: 20px;
|
137 |
+
border-radius: 8px;
|
138 |
+
background-color: {THEME_COLORS['card_bg']};
|
139 |
+
margin-bottom: 20px;
|
140 |
+
border: 1px solid {THEME_COLORS['border']};
|
141 |
+
}}
|
142 |
+
.song-title {{
|
143 |
+
font-size: 24px;
|
144 |
+
font-weight: 700;
|
145 |
+
color: {THEME_COLORS['text']};
|
146 |
+
margin-bottom: 10px;
|
147 |
+
}}
|
148 |
+
.time-stamp {{
|
149 |
+
color: {THEME_COLORS['primary']};
|
150 |
+
font-weight: 600;
|
151 |
+
}}
|
152 |
+
audio {{
|
153 |
+
width: 100%;
|
154 |
+
border-radius: 500px;
|
155 |
+
margin-top: 10px;
|
156 |
+
}}
|
157 |
+
.stAlert {{
|
158 |
+
background-color: {THEME_COLORS['card_bg']};
|
159 |
+
color: {THEME_COLORS['text']};
|
160 |
+
border: 1px solid {THEME_COLORS['border']};
|
161 |
+
}}
|
162 |
+
.stRadio > div {{
|
163 |
+
gap: 1rem;
|
164 |
+
}}
|
165 |
+
.stRadio label {{
|
166 |
+
background-color: {THEME_COLORS['card_bg']};
|
167 |
+
padding: 10px 20px;
|
168 |
+
border-radius: 500px;
|
169 |
+
margin-right: 10px;
|
170 |
+
border: 1px solid {THEME_COLORS['border']};
|
171 |
+
}}
|
172 |
+
.stRadio label:hover {{
|
173 |
+
border-color: {THEME_COLORS['primary']};
|
174 |
+
}}
|
175 |
+
.stRadio [data-baseweb="radio"] {{
|
176 |
+
margin-right: 20px;
|
177 |
+
}}
|
178 |
+
.subheader {{
|
179 |
+
color: {THEME_COLORS['subtext']};
|
180 |
+
font-size: 14px;
|
181 |
+
margin-bottom: 20px;
|
182 |
+
}}
|
183 |
+
.input-option {{
|
184 |
+
background-color: {THEME_COLORS['card_bg']};
|
185 |
+
border-radius: 10px;
|
186 |
+
padding: 25px;
|
187 |
+
margin-bottom: 20px;
|
188 |
+
border: 1px solid {THEME_COLORS['border']};
|
189 |
+
}}
|
190 |
+
.or-divider {{
|
191 |
+
text-align: center;
|
192 |
+
font-size: 18px;
|
193 |
+
font-weight: 600;
|
194 |
+
color: {THEME_COLORS['text']};
|
195 |
+
margin: 20px 0;
|
196 |
+
position: relative;
|
197 |
+
}}
|
198 |
+
.or-divider:before, .or-divider:after {{
|
199 |
+
content: "";
|
200 |
+
display: inline-block;
|
201 |
+
width: 40%;
|
202 |
+
margin: 0 10px;
|
203 |
+
vertical-align: middle;
|
204 |
+
border-top: 1px solid {THEME_COLORS['border']};
|
205 |
+
}}
|
206 |
+
</style>
|
207 |
+
"""
|
208 |
+
st.markdown(custom_theme, unsafe_allow_html=True)
|
209 |
+
|
210 |
+
|
211 |
+
def process_youtube(url: str) -> Tuple[Optional[str], Optional[str]]:
|
212 |
+
"""Process a YouTube URL and extract audio.
|
213 |
+
|
214 |
+
Args:
|
215 |
+
url: YouTube URL to process
|
216 |
+
|
217 |
+
Returns:
|
218 |
+
Tuple containing the path to the extracted audio file and the video title
|
219 |
+
"""
|
220 |
+
progress_bar = st.progress(0)
|
221 |
+
status_text = st.empty()
|
222 |
+
|
223 |
+
try:
|
224 |
+
status_text.text("Getting video information...")
|
225 |
+
progress_bar.progress(10)
|
226 |
+
|
227 |
+
status_text.text("Downloading audio from YouTube...")
|
228 |
+
progress_bar.progress(30)
|
229 |
+
|
230 |
+
# Use yt-dlp to download the video
|
231 |
+
audio_path, video_name = extract_audio(url)
|
232 |
+
|
233 |
+
if not audio_path:
|
234 |
+
status_text.text("Download failed.")
|
235 |
+
progress_bar.progress(100)
|
236 |
+
|
237 |
+
st.error("Failed to extract audio from the provided URL.")
|
238 |
+
st.info("Try downloading the video manually and uploading it instead.")
|
239 |
+
return None, None
|
240 |
+
|
241 |
+
progress_bar.progress(90)
|
242 |
+
status_text.text(f"Successfully downloaded '{video_name}'")
|
243 |
+
progress_bar.progress(100)
|
244 |
+
return audio_path, video_name
|
245 |
+
|
246 |
+
except Exception as e:
|
247 |
+
import traceback
|
248 |
+
progress_bar.progress(100)
|
249 |
+
status_text.text("Download failed with an error.")
|
250 |
+
st.error(f"Failed to extract audio: {str(e)}")
|
251 |
+
st.code(traceback.format_exc())
|
252 |
+
return None, None
|
253 |
+
|
254 |
+
|
255 |
+
def process_uploaded_file(uploaded_file) -> Tuple[Optional[str], Optional[str]]:
|
256 |
+
"""Process an uploaded audio file.
|
257 |
+
|
258 |
+
Args:
|
259 |
+
uploaded_file: File uploaded through Streamlit
|
260 |
+
|
261 |
+
Returns:
|
262 |
+
Tuple containing the path to the saved file and the file name
|
263 |
+
"""
|
264 |
+
try:
|
265 |
+
with tempfile.NamedTemporaryFile(delete=False, suffix='.mp3') as tmp:
|
266 |
+
tmp.write(uploaded_file.getvalue())
|
267 |
+
audio_path = tmp.name
|
268 |
+
return audio_path, uploaded_file.name
|
269 |
+
except Exception as e:
|
270 |
+
st.error(f"Error processing uploaded file: {e}")
|
271 |
+
return None, None
|
272 |
+
|
273 |
+
|
274 |
+
def extract_chorus_segments(y: np.ndarray, sr: int, smoothed_predictions: np.ndarray,
|
275 |
+
meter_grid_times: np.ndarray) -> List[Tuple[float, float, np.ndarray]]:
|
276 |
+
"""Extract chorus segments from the audio array with 1 second before each chorus.
|
277 |
+
|
278 |
+
Args:
|
279 |
+
y: Audio array
|
280 |
+
sr: Sample rate
|
281 |
+
smoothed_predictions: Array of binary predictions
|
282 |
+
meter_grid_times: Array of meter grid times
|
283 |
+
|
284 |
+
Returns:
|
285 |
+
List of tuples (start_time, end_time, audio_segment)
|
286 |
+
"""
|
287 |
+
# Find continuous chorus segments
|
288 |
+
chorus_segments = []
|
289 |
+
start_idx = None
|
290 |
+
|
291 |
+
for i, pred in enumerate(smoothed_predictions):
|
292 |
+
if pred == 1 and (i == 0 or smoothed_predictions[i-1] == 0):
|
293 |
+
start_idx = i
|
294 |
+
elif pred == 0 and start_idx is not None:
|
295 |
+
# Found the end of a segment
|
296 |
+
start_time = meter_grid_times[start_idx]
|
297 |
+
end_time = meter_grid_times[i]
|
298 |
+
chorus_segments.append((start_idx, i, start_time, end_time))
|
299 |
+
start_idx = None
|
300 |
+
|
301 |
+
# Handle the case where the last segment extends to the end
|
302 |
+
if start_idx is not None:
|
303 |
+
start_time = meter_grid_times[start_idx]
|
304 |
+
end_time = meter_grid_times[-1] if len(meter_grid_times) > start_idx + 1 else len(y) / sr
|
305 |
+
chorus_segments.append((start_idx, len(smoothed_predictions), start_time, end_time))
|
306 |
+
|
307 |
+
# Extract the audio segments with 1 second before each chorus
|
308 |
+
extracted_segments = []
|
309 |
+
for _, _, start_time, end_time in chorus_segments:
|
310 |
+
# Add 1 second before the chorus starts
|
311 |
+
adjusted_start_time = max(0, start_time - 1.0)
|
312 |
+
# Convert times to samples
|
313 |
+
start_sample = int(adjusted_start_time * sr)
|
314 |
+
end_sample = min(len(y), int(end_time * sr))
|
315 |
+
# Extract the segment
|
316 |
+
segment = y[start_sample:end_sample]
|
317 |
+
extracted_segments.append((adjusted_start_time, end_time, segment))
|
318 |
+
|
319 |
+
return extracted_segments
|
320 |
+
|
321 |
+
|
322 |
+
def create_chorus_compilation(segments: List[Tuple[float, float, np.ndarray]],
|
323 |
+
sr: int, fade_duration: float = 0.3) -> Tuple[np.ndarray, str]:
|
324 |
+
"""Create a compilation of all chorus segments with fading between segments.
|
325 |
+
|
326 |
+
Args:
|
327 |
+
segments: List of tuples (start_time, end_time, audio_segment)
|
328 |
+
sr: Sample rate
|
329 |
+
fade_duration: Duration of fade in/out in seconds
|
330 |
+
|
331 |
+
Returns:
|
332 |
+
Tuple containing the compiled audio array and a string with timing info
|
333 |
+
"""
|
334 |
+
if not segments:
|
335 |
+
return np.array([]), ""
|
336 |
+
|
337 |
+
# Create a compilation of all segments
|
338 |
+
compilation = np.array([])
|
339 |
+
timing_info = ""
|
340 |
+
current_position = 0
|
341 |
+
|
342 |
+
for i, (start_time, end_time, segment) in enumerate(segments):
|
343 |
+
# Add 0.5 seconds of silence between segments
|
344 |
+
if i > 0:
|
345 |
+
silence_samples = int(0.5 * sr)
|
346 |
+
compilation = np.concatenate([compilation, np.zeros(silence_samples)])
|
347 |
+
current_position += 0.5
|
348 |
+
|
349 |
+
# Add segment info to timing
|
350 |
+
minutes_start = int(current_position // 60)
|
351 |
+
seconds_start = int(current_position % 60)
|
352 |
+
|
353 |
+
# Add the segment
|
354 |
+
compilation = np.concatenate([compilation, segment])
|
355 |
+
|
356 |
+
# Update current position
|
357 |
+
segment_duration = len(segment) / sr
|
358 |
+
current_position += segment_duration
|
359 |
+
|
360 |
+
minutes_end = int(current_position // 60)
|
361 |
+
seconds_end = int(current_position % 60)
|
362 |
+
|
363 |
+
# Original times in the song
|
364 |
+
orig_min_start = int(start_time // 60)
|
365 |
+
orig_sec_start = int(start_time % 60)
|
366 |
+
orig_min_end = int(end_time // 60)
|
367 |
+
orig_sec_end = int(end_time % 60)
|
368 |
+
|
369 |
+
# Add timing info
|
370 |
+
timing_info += f"Chorus {i+1}: {minutes_start}:{seconds_start:02d} - {minutes_end}:{seconds_end:02d} "
|
371 |
+
timing_info += f"(Original: {orig_min_start}:{orig_sec_start:02d} - {orig_min_end}:{orig_sec_end:02d})\n"
|
372 |
+
|
373 |
+
return compilation, timing_info
|
374 |
+
|
375 |
+
|
376 |
+
def save_audio_for_streamlit(audio_data: np.ndarray, sr: int, file_format: str = 'mp3') -> bytes:
|
377 |
+
"""Save audio data to a BytesIO object for use with st.audio.
|
378 |
+
|
379 |
+
Args:
|
380 |
+
audio_data: Audio array
|
381 |
+
sr: Sample rate
|
382 |
+
file_format: Audio file format
|
383 |
+
|
384 |
+
Returns:
|
385 |
+
BytesIO object containing the audio data
|
386 |
+
"""
|
387 |
+
buffer = io.BytesIO()
|
388 |
+
sf.write(buffer, audio_data, sr, format=file_format)
|
389 |
+
buffer.seek(0)
|
390 |
+
return buffer
|
391 |
+
|
392 |
+
|
393 |
+
def format_time(seconds: float) -> str:
|
394 |
+
"""Format time in seconds to MM:SS format.
|
395 |
+
|
396 |
+
Args:
|
397 |
+
seconds: Time in seconds
|
398 |
+
|
399 |
+
Returns:
|
400 |
+
Formatted time string
|
401 |
+
"""
|
402 |
+
minutes = int(seconds // 60)
|
403 |
+
secs = int(seconds % 60)
|
404 |
+
return f"{minutes}:{secs:02d}"
|
405 |
+
|
406 |
+
|
407 |
+
def create_waveform_visualization(audio_features, smoothed_predictions, meter_grid_times):
|
408 |
+
"""Create waveform visualization with highlighted chorus sections.
|
409 |
+
|
410 |
+
Args:
|
411 |
+
audio_features: Audio features
|
412 |
+
smoothed_predictions: Array of binary predictions
|
413 |
+
meter_grid_times: Array of meter grid times
|
414 |
+
|
415 |
+
Returns:
|
416 |
+
Matplotlib figure with visualization
|
417 |
+
"""
|
418 |
+
from chorus_detection.visualization.plotter import plot_meter_lines
|
419 |
+
|
420 |
+
# Set Matplotlib style to be dark and minimal
|
421 |
+
plt.style.use('dark_background')
|
422 |
+
|
423 |
+
fig, ax = plt.subplots(figsize=(12, 4), dpi=120)
|
424 |
+
|
425 |
+
# Display harmonic and percussive components
|
426 |
+
librosa.display.waveshow(audio_features.y_harm, sr=audio_features.sr,
|
427 |
+
alpha=0.8, ax=ax, color='#1DB954') # Primary color
|
428 |
+
librosa.display.waveshow(audio_features.y_perc, sr=audio_features.sr,
|
429 |
+
alpha=0.7, ax=ax, color='#B3B3B3') # Light gray
|
430 |
+
plot_meter_lines(ax, meter_grid_times)
|
431 |
+
|
432 |
+
# Highlight chorus sections
|
433 |
+
for i, prediction in enumerate(smoothed_predictions):
|
434 |
+
start_time = meter_grid_times[i]
|
435 |
+
end_time = meter_grid_times[i + 1] if i < len(
|
436 |
+
meter_grid_times) - 1 else len(audio_features.y) / audio_features.sr
|
437 |
+
if prediction == 1:
|
438 |
+
ax.axvspan(start_time, end_time, color='#1DB954', alpha=0.3,
|
439 |
+
label='Predicted Chorus' if i == 0 else None)
|
440 |
+
|
441 |
+
# Set plot limits and labels
|
442 |
+
ax.set_xlim([0, len(audio_features.y) / audio_features.sr])
|
443 |
+
ax.set_ylabel('Amplitude', color='#FFFFFF')
|
444 |
+
|
445 |
+
# Add legend
|
446 |
+
chorus_patch = plt.Rectangle((0, 0), 1, 1, fc='#1DB954', alpha=0.3)
|
447 |
+
handles, labels = ax.get_legend_handles_labels()
|
448 |
+
handles.append(chorus_patch)
|
449 |
+
labels.append('Chorus')
|
450 |
+
ax.legend(handles=handles, labels=labels)
|
451 |
+
|
452 |
+
# Set x-tick labels in minutes:seconds format
|
453 |
+
duration = len(audio_features.y) / audio_features.sr
|
454 |
+
xticks = [i for i in range(0, int(duration) + 10, 30)] # Every 30 seconds
|
455 |
+
xlabels = [f"{int(tick // 60)}:{int(tick % 60):02d}" for tick in xticks]
|
456 |
+
ax.set_xticks(xticks)
|
457 |
+
ax.set_xticklabels(xlabels, color='#FFFFFF')
|
458 |
+
ax.tick_params(axis='y', colors='#FFFFFF')
|
459 |
+
|
460 |
+
# Style the plot
|
461 |
+
ax.spines['top'].set_visible(False)
|
462 |
+
ax.spines['right'].set_visible(False)
|
463 |
+
ax.spines['bottom'].set_color('#333333')
|
464 |
+
ax.spines['left'].set_color('#333333')
|
465 |
+
ax.set_facecolor('#121212')
|
466 |
+
fig.patch.set_facecolor('#121212')
|
467 |
+
|
468 |
+
plt.tight_layout()
|
469 |
+
return fig
|
470 |
+
|
471 |
+
|
472 |
+
def analyze_audio(audio_path: str, video_name: str, model_path: str = str(MODEL_PATH)) -> None:
|
473 |
+
"""Analyze audio file and display predictions.
|
474 |
+
|
475 |
+
Args:
|
476 |
+
audio_path: Path to the audio file
|
477 |
+
video_name: Name of the video or audio file
|
478 |
+
model_path: Path to the model file
|
479 |
+
"""
|
480 |
+
try:
|
481 |
+
# Process audio
|
482 |
+
with st.spinner("Processing audio..."):
|
483 |
+
processed_audio, audio_features = process_audio(audio_path)
|
484 |
+
|
485 |
+
if processed_audio is None:
|
486 |
+
st.error("Failed to process audio. Please try a different file.")
|
487 |
+
return
|
488 |
+
|
489 |
+
# Load model
|
490 |
+
with st.spinner("Loading model..."):
|
491 |
+
model = load_CRNN_model(model_path=model_path)
|
492 |
+
|
493 |
+
# Make predictions
|
494 |
+
with st.spinner("Generating predictions..."):
|
495 |
+
smoothed_predictions = make_predictions(model, processed_audio, audio_features, None, None)
|
496 |
+
|
497 |
+
# Get chorus start times
|
498 |
+
meter_grid_times = librosa.frames_to_time(
|
499 |
+
audio_features.meter_grid, sr=audio_features.sr, hop_length=audio_features.hop_length)
|
500 |
+
chorus_start_times = [
|
501 |
+
meter_grid_times[i] for i in range(len(smoothed_predictions))
|
502 |
+
if smoothed_predictions[i] == 1 and (i == 0 or smoothed_predictions[i - 1] == 0)
|
503 |
+
]
|
504 |
+
|
505 |
+
# Extract chorus segments
|
506 |
+
chorus_segments = []
|
507 |
+
chorus_audio = None
|
508 |
+
|
509 |
+
if chorus_start_times:
|
510 |
+
with st.spinner("Extracting chorus segments..."):
|
511 |
+
chorus_segments = extract_chorus_segments(
|
512 |
+
audio_features.y, audio_features.sr, smoothed_predictions, meter_grid_times)
|
513 |
+
|
514 |
+
compilation, _ = create_chorus_compilation(
|
515 |
+
chorus_segments, audio_features.sr)
|
516 |
+
|
517 |
+
if len(compilation) > 0:
|
518 |
+
chorus_audio = save_audio_for_streamlit(compilation, audio_features.sr)
|
519 |
+
|
520 |
+
# Create waveform visualization
|
521 |
+
waveform_fig = create_waveform_visualization(audio_features, smoothed_predictions, meter_grid_times)
|
522 |
+
|
523 |
+
# Display results in custom-style container
|
524 |
+
st.markdown('<div class="result-container">', unsafe_allow_html=True)
|
525 |
+
st.subheader("Results")
|
526 |
+
st.markdown(f'<div class="song-title">{video_name}</div>', unsafe_allow_html=True)
|
527 |
+
|
528 |
+
# Display waveform
|
529 |
+
st.pyplot(waveform_fig)
|
530 |
+
|
531 |
+
if chorus_start_times:
|
532 |
+
# Create chorus compilation section
|
533 |
+
st.markdown("### Chorus Compilation")
|
534 |
+
st.markdown('<div class="subheader">All choruses with 1-second lead-in</div>', unsafe_allow_html=True)
|
535 |
+
st.audio(chorus_audio, format="audio/mp3")
|
536 |
+
|
537 |
+
# Display individual chorus segments
|
538 |
+
st.markdown("### Chorus Segments")
|
539 |
+
|
540 |
+
# Create columns for each chorus segment
|
541 |
+
for i, (start_time, end_time, segment) in enumerate(chorus_segments):
|
542 |
+
segment_audio = save_audio_for_streamlit(segment, audio_features.sr)
|
543 |
+
|
544 |
+
st.markdown(f"""
|
545 |
+
<div class="chorus-card">
|
546 |
+
<span style="font-weight: 700;">Chorus {i+1}:</span>
|
547 |
+
<span class="time-stamp">{format_time(start_time)} - {format_time(end_time)}</span>
|
548 |
+
</div>
|
549 |
+
""", unsafe_allow_html=True)
|
550 |
+
|
551 |
+
st.audio(segment_audio, format="audio/mp3")
|
552 |
+
else:
|
553 |
+
st.warning("No choruses were identified in this song.")
|
554 |
+
|
555 |
+
st.markdown('</div>', unsafe_allow_html=True)
|
556 |
+
|
557 |
+
except Exception as e:
|
558 |
+
st.error(f"An error occurred: {e}")
|
559 |
+
import traceback
|
560 |
+
st.error(traceback.format_exc())
|
561 |
+
|
562 |
+
|
563 |
+
def main() -> None:
|
564 |
+
"""Main function for the Streamlit app."""
|
565 |
+
st.set_page_config(
|
566 |
+
page_title="Automated Chorus Detection",
|
567 |
+
page_icon="🎵",
|
568 |
+
layout="wide",
|
569 |
+
initial_sidebar_state="expanded",
|
570 |
+
)
|
571 |
+
|
572 |
+
# Apply custom theme
|
573 |
+
set_custom_theme()
|
574 |
+
|
575 |
+
# Header
|
576 |
+
col1, col2 = st.columns([1, 5])
|
577 |
+
with col2:
|
578 |
+
st.title("Automated Chorus Detection")
|
579 |
+
st.markdown('<div class="subheader">Analyze songs and identify chorus sections using AI</div>', unsafe_allow_html=True)
|
580 |
+
|
581 |
+
# Sidebar
|
582 |
+
st.sidebar.markdown("## About")
|
583 |
+
st.sidebar.markdown("""
|
584 |
+
This app uses a deep learning model trained on over 300 annotated songs
|
585 |
+
to identify chorus sections in music.
|
586 |
+
|
587 |
+
**Features:**
|
588 |
+
- Detects chorus sections in songs
|
589 |
+
- Creates playable audio snippets of choruses
|
590 |
+
- Visualizes audio waveform with highlighted choruses
|
591 |
+
|
592 |
+
For more information, visit the [GitHub repository](https://github.com/dennisvdang/chorus-detection).
|
593 |
+
""")
|
594 |
+
|
595 |
+
# Main content with vertically stacked input methods
|
596 |
+
st.markdown("## Select Input Method")
|
597 |
+
|
598 |
+
# File upload option (now first)
|
599 |
+
st.markdown("### Upload Audio File")
|
600 |
+
uploaded_file = st.file_uploader(
|
601 |
+
"",
|
602 |
+
type=["mp3", "wav", "ogg", "flac", "m4a"],
|
603 |
+
help="Upload an audio file in MP3, WAV, OGG, FLAC, or M4A format",
|
604 |
+
key="file_upload"
|
605 |
+
)
|
606 |
+
|
607 |
+
if uploaded_file is not None:
|
608 |
+
st.audio(uploaded_file, format="audio/mp3")
|
609 |
+
|
610 |
+
upload_process_button = st.button("Process Uploaded Audio")
|
611 |
+
|
612 |
+
# OR divider
|
613 |
+
st.markdown('<div class="or-divider">OR</div>', unsafe_allow_html=True)
|
614 |
+
|
615 |
+
# YouTube URL input (now second)
|
616 |
+
st.markdown("### YouTube URL")
|
617 |
+
url = st.text_input(
|
618 |
+
"",
|
619 |
+
placeholder="Paste a YouTube video URL here...",
|
620 |
+
help="Enter the URL of a YouTube video to analyze",
|
621 |
+
key="youtube_url"
|
622 |
+
)
|
623 |
+
|
624 |
+
youtube_process_button = st.button("Process YouTube Video")
|
625 |
+
|
626 |
+
# Process uploaded file if selected
|
627 |
+
if uploaded_file is not None and upload_process_button:
|
628 |
+
audio_path, file_name = process_uploaded_file(uploaded_file)
|
629 |
+
if audio_path:
|
630 |
+
analyze_audio(audio_path, file_name)
|
631 |
+
# Clean up the temporary file
|
632 |
+
try:
|
633 |
+
os.remove(audio_path)
|
634 |
+
except:
|
635 |
+
pass
|
636 |
+
|
637 |
+
# Process YouTube URL if selected
|
638 |
+
if youtube_process_button and url:
|
639 |
+
if not is_youtube_url(url):
|
640 |
+
st.error("Please enter a valid YouTube URL.")
|
641 |
+
else:
|
642 |
+
audio_path, video_name = process_youtube(url)
|
643 |
+
if audio_path:
|
644 |
+
analyze_audio(audio_path, video_name)
|
645 |
+
# Clean up the temporary file
|
646 |
+
try:
|
647 |
+
os.remove(audio_path)
|
648 |
+
except:
|
649 |
+
pass
|
650 |
+
|
651 |
+
|
652 |
+
if __name__ == "__main__":
|
653 |
+
main()
|
download_model.py
ADDED
@@ -0,0 +1,128 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#!/usr/bin/env python3
|
2 |
+
# -*- coding: utf-8 -*-
|
3 |
+
|
4 |
+
"""Script to download the chorus detection model from HuggingFace.
|
5 |
+
|
6 |
+
This script checks if the model file exists locally, and if not, downloads it
|
7 |
+
from the specified HuggingFace repository.
|
8 |
+
"""
|
9 |
+
|
10 |
+
import os
|
11 |
+
import sys
|
12 |
+
from pathlib import Path
|
13 |
+
import logging
|
14 |
+
|
15 |
+
# Use huggingface_hub for better integration with HF ecosystem
|
16 |
+
try:
|
17 |
+
from huggingface_hub import hf_hub_download
|
18 |
+
HF_HUB_AVAILABLE = True
|
19 |
+
except ImportError:
|
20 |
+
HF_HUB_AVAILABLE = False
|
21 |
+
import requests
|
22 |
+
from tqdm import tqdm
|
23 |
+
|
24 |
+
# Configure logging
|
25 |
+
logging.basicConfig(
|
26 |
+
level=logging.INFO,
|
27 |
+
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
|
28 |
+
)
|
29 |
+
logger = logging.getLogger("model-downloader")
|
30 |
+
|
31 |
+
def download_file_with_progress(url: str, destination: Path) -> None:
|
32 |
+
"""Download a file with a progress bar.
|
33 |
+
|
34 |
+
Args:
|
35 |
+
url: URL to download from
|
36 |
+
destination: Path to save the file to
|
37 |
+
"""
|
38 |
+
# Create parent directories if they don't exist
|
39 |
+
destination.parent.mkdir(parents=True, exist_ok=True)
|
40 |
+
|
41 |
+
# Stream the download with progress bar
|
42 |
+
response = requests.get(url, stream=True)
|
43 |
+
response.raise_for_status()
|
44 |
+
|
45 |
+
total_size = int(response.headers.get('content-length', 0))
|
46 |
+
block_size = 1024 # 1 Kibibyte
|
47 |
+
|
48 |
+
logger.info(f"Downloading model from {url}")
|
49 |
+
logger.info(f"File size: {total_size / (1024*1024):.1f} MB")
|
50 |
+
|
51 |
+
with open(destination, 'wb') as file, tqdm(
|
52 |
+
desc=destination.name,
|
53 |
+
total=total_size,
|
54 |
+
unit='iB',
|
55 |
+
unit_scale=True,
|
56 |
+
unit_divisor=1024,
|
57 |
+
) as bar:
|
58 |
+
for data in response.iter_content(block_size):
|
59 |
+
size = file.write(data)
|
60 |
+
bar.update(size)
|
61 |
+
|
62 |
+
def ensure_model_exists(
|
63 |
+
model_filename: str = "best_model_V3.h5",
|
64 |
+
repo_id: str = "dennisvdang/chorus-detection",
|
65 |
+
model_dir: Path = Path("models/CRNN"),
|
66 |
+
hf_model_filename: str = "chorus_detection_crnn.h5"
|
67 |
+
) -> Path:
|
68 |
+
"""Ensure the model file exists, downloading it if necessary.
|
69 |
+
|
70 |
+
Args:
|
71 |
+
model_filename: Local filename for the model
|
72 |
+
repo_id: HuggingFace repository ID
|
73 |
+
model_dir: Directory to save the model to
|
74 |
+
hf_model_filename: Filename of the model in the HuggingFace repo
|
75 |
+
|
76 |
+
Returns:
|
77 |
+
Path to the model file
|
78 |
+
"""
|
79 |
+
model_path = model_dir / model_filename
|
80 |
+
|
81 |
+
# Check if the model already exists
|
82 |
+
if model_path.exists():
|
83 |
+
logger.info(f"Model already exists at {model_path}")
|
84 |
+
return model_path
|
85 |
+
|
86 |
+
# Create model directory if it doesn't exist
|
87 |
+
model_dir.mkdir(parents=True, exist_ok=True)
|
88 |
+
|
89 |
+
logger.info(f"Model not found at {model_path}. Downloading...")
|
90 |
+
|
91 |
+
try:
|
92 |
+
if HF_HUB_AVAILABLE:
|
93 |
+
# Use huggingface_hub to download the model
|
94 |
+
logger.info(f"Downloading model from {repo_id}/{hf_model_filename} using huggingface_hub")
|
95 |
+
downloaded_path = hf_hub_download(
|
96 |
+
repo_id=repo_id,
|
97 |
+
filename=hf_model_filename,
|
98 |
+
local_dir=model_dir,
|
99 |
+
local_dir_use_symlinks=False
|
100 |
+
)
|
101 |
+
|
102 |
+
# Rename if necessary
|
103 |
+
if os.path.basename(downloaded_path) != model_filename:
|
104 |
+
downloaded_path_obj = Path(downloaded_path)
|
105 |
+
model_path.parent.mkdir(parents=True, exist_ok=True)
|
106 |
+
if model_path.exists():
|
107 |
+
model_path.unlink()
|
108 |
+
downloaded_path_obj.rename(model_path)
|
109 |
+
logger.info(f"Renamed {downloaded_path} to {model_path}")
|
110 |
+
else:
|
111 |
+
# Fallback to direct download if huggingface_hub is not available
|
112 |
+
huggingface_url = f"https://huggingface.co/{repo_id}/resolve/main/{hf_model_filename}"
|
113 |
+
download_file_with_progress(huggingface_url, model_path)
|
114 |
+
|
115 |
+
logger.info(f"Successfully downloaded model to {model_path}")
|
116 |
+
return model_path
|
117 |
+
except Exception as e:
|
118 |
+
logger.error(f"Failed to download model: {e}")
|
119 |
+
sys.exit(1)
|
120 |
+
|
121 |
+
if __name__ == "__main__":
|
122 |
+
# Allow overriding the repository via environment variable
|
123 |
+
repo_id = os.environ.get("MODEL_HF_REPO", "dennisvdang/chorus-detection")
|
124 |
+
|
125 |
+
# Check if an alternative model filename was provided
|
126 |
+
hf_model_filename = os.environ.get("HF_MODEL_FILENAME", "chorus_detection_crnn.h5")
|
127 |
+
|
128 |
+
ensure_model_exists(repo_id=repo_id, hf_model_filename=hf_model_filename)
|
requirements.txt
ADDED
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Core dependencies
|
2 |
+
numpy>=1.24.4
|
3 |
+
scipy>=1.10.1
|
4 |
+
tqdm>=4.66.1
|
5 |
+
|
6 |
+
# Machine learning
|
7 |
+
tensorflow>=2.15.0
|
8 |
+
keras>=2.15.0
|
9 |
+
scikit-learn>=1.3.0
|
10 |
+
|
11 |
+
# Audio processing
|
12 |
+
librosa>=0.10.1
|
13 |
+
soundfile>=0.12.1
|
14 |
+
pydub>=0.25.1
|
15 |
+
ffmpeg-python>=0.2.0
|
16 |
+
|
17 |
+
# Video/data acquisition
|
18 |
+
yt-dlp>=2023.10.7
|
19 |
+
requests>=2.31.0
|
20 |
+
|
21 |
+
# Visualization
|
22 |
+
matplotlib>=3.7.2
|
23 |
+
|
24 |
+
# Web app
|
25 |
+
streamlit>=1.26.0
|
26 |
+
|
27 |
+
# For model downloading
|
28 |
+
huggingface_hub>=0.16.4
|