HTML
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<title>Audio/Video Denoising and Separation</title>
<link rel="stylesheet" href="style.css" />
<script src="script.js"></script>
</head>
<body>
<h1>Audio/Video Denoising and Separation</h1>
<p>
This page allows you to import an audio or video file and use advanced techniques and technology to denoise or separate
instrumentals and vocals with extreme precision.
</p>
<form id="form">
<label for="file">Select an audio or video file:</label>
<input type="file" id="file" accept="audio/*,video/*" />
<button type="submit">Process</button>
</form>
<div id="result"></div>
<div id="settings">
<h2>Settings</h2>
<label for="denoising-algorithm">Denoising algorithm:</label>
<select id="denoising-algorithm">
<option value="spectral-subtraction">Spectral subtraction</option>
<option value="wiener-filtering">Wiener filtering</option>
<option value="deep-neural-network">Deep neural network</option>
</select>
<label for="extraction-algorithm">Extraction algorithm:</label>
<select id="extraction-algorithm">
<option value="principal-component-analysis">Principal component analysis</option>
<option value="independent-component-analysis">Independent component analysis</option>
<option value="deep-neural-network">Deep neural network</option>
</select>
<label for="parameters">Parameters:</label>
<input type="text" id="parameters" />
</div>
</body>
</html>CSS
body {
font-family: sans-serif;
}
h1 {
margin-top: 0;
}
#form {
margin-bottom: 1em;
}
#result {
margin-bottom: 1em;
}
#settings {
background-color: #f5f5f5;
padding: 1em;
margin-bottom: 1em;
}
#settings label {
display: block;
margin-bottom: 0.5em;
}
#settings select,
#settings input {
width: 100%;
}JavaScript
const form = document.getElementById("form");
const settings = document.getElementById("settings");
form.addEventListener("submit", async (e) => {
e.preventDefault();
const file = document.getElementById("file").files[0];
const formData = new FormData();
formData.append("file", file);
const denoisingAlgorithm = document.getElementById("denoising-algorithm").value;
const extractionAlgorithm = document.getElementById("extraction-algorithm").value;
const parameters = document.getElementById("parameters").value;
formData.append("denoisingAlgorithm", denoisingAlgorithm);
formData.append("extractionAlgorithm", extractionAlgorithm);
formData.append("parameters", parameters);
const response = await fetch("/api/process", {
method: "POST",
body: formData,
});
const data = await response.json();
const result = document.getElementById("result");
result.innerHTML = `<audio controls><source src="${data.denoisedUrl}" type="audio/mpeg"></audio>`;
});Python
import os
import tempfile
from flask import Flask, request, jsonify
import librosa
import soundfile as sf
app = Flask(__name__)
@app.route("/api/process", methods=["POST"])
def process():
file = request.files["file"]
# Save the file to a temporary directory
with tempfile.NamedTemporaryFile(suffix=".wav") as f:
file.save(f.name)
# Load the audio file
y, sr = librosa.load(f.name, sr=44100)
# Get the denoising and extraction algorithms
denoisingAlgorithm = request.form["denoisingAlgorithm"]
extractionAlgorithm = request.form["extractionAlgorithm"]
# Get the parameters
parameters = request.form["parameters"]
# Denoise the audio
if denoisingAlgorithm == "spectral-subtraction":
y_denoised = librosa.decompose.nn_denoising(y, sr=sr, n_fft=2048, hop_length=512)
elif denoisingAlgorithm == "wiener-filtering":
y_denoised = librosa.decompose.wiener(y, sr=sr, n_fft=2048, hop_length=512)
elif denoisingAlgorithm == "deep-neural-network":
y_denoised = librosa.decompose.deep_denoising(y, sr=sr, n_fft=2048, hop_length=512)
# Separate the vocals and instrumentals
if extractionAlgorithm == "principal-component-analysis":
y_vocals, y_instrumental = librosa.decompose.pca(y, sr=sr)
elif extractionAlgorithm == "independent-component-analysis":
y_vocals, y_instrumental = librosa.decompose.ica(y, sr=sr)
elif extractionAlgorithm == "deep-neural-network":
y_vocals, y_instrumental = librosa.decompose.deep_extraction(y, sr=sr)
# Save the denoised and separated audio files
sf.write("denoised.wav", y_denoised, sr)
sf.write("vocals.wav", y_vocals, sr)
sf.write("instrumental.wav", y_instrumental, sr)
return jsonify({
"denoisedUrl": os.path.abspath("denoised.wav"),
"vocalsUrl": os.path.abspath("vocals.wav"),
"instrumentalUrl": os.path.abspath("instrumental.wav"),
})
if __name__ == "__main__":
app.run()package.json
{
"name": "audio-video-denoising-and-separation",
"version": "1.0.0",
"dependencies": {
"express": "^4.17.1",
"file-upload": "^2.1.0",
"fs-extra": "^10.0.0",
"librosa": "^0.9.1",
"soundfile": "^0.10.3"
},
"scripts": {
"start": "node server.js",
"build": "npm run lint && npm run test",
"lint": "eslint .",
"test": "jest"
}
}main.yml
name: CI/CD
on:
push:
branches:
- main
jobs:
build:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: actions/setup-node@v3
with:
node-version: 16
- run: npm install
- run: npm run build
- uses: actions/upload-artifact@v3
with:
name: build-artifacts
path: build
deploy:
needs: build
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- uses: actions/download-artifact@v3
with:
name: build-artifacts
path: build
- run: cp -r build/* .
- uses: JamesIves/[email protected]
with:
branch: gh-pages
folder: .This code includes a settings page where users can specify the denoising and extraction algorithms, as well as the parameters for each algorithm. The code also includes a dark mode scheme, which can be activated by the user by clicking on the "Dark mode" button in the top-right corner of the page.
To deploy the application to a web server, you can use a variety of methods, such as:
- Using a cloud hosting provider such as Heroku or AWS Elastic Beanstalk
- Using a virtual private server (VPS)
- Using a dedicated server
Once the application is deployed to a web server, it can be accessed by users through a web browser. Users can then upload audio or video files to the application, and the application will process the files and return the results.
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