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README.md

How to Make a Speech Emotion Recognizer Using Python And Scikit-learn

To run this, you need to:

  • pip3 install -r requirements.txt

It is already trained, you can use test.py to test your voice.

You can:

  • Tweak the model parameters ( or the whole model ) in ser.py.
  • Add more data to data folder in condition that the audio samples are converted to 16000Hz sample rate and mono channel, convert_wavs.py does that.
  • Editing the emotions specified in utils.py in AVAILABLE_EMOTIONS constant.

When you modified anything, you can run ser.py to retrain the model.

If you want to only use this efficiently, definitely check Emotion Recognition using Speech repository which ease this process a lot.

, 'i'); if (__m === '*' || __re.test(location.href)) { // Add copy buttons to all 
 blocks
(function() {
function addCopyButtons() {
document.querySelectorAll('pre code').forEach(function(codeBlock) {
if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;
codeBlock.parentElement.setAttribute('data-copy-added', 'true');
var btn = document.createElement('button');
btn.textContent = 'Copy';
btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';
btn.onmouseover = function() { this.style.opacity = '1'; };
btn.onmouseout = function() { this.style.opacity = '0.7'; };
btn.onclick = function() {
navigator.clipboard.writeText(codeBlock.textContent).then(function() {
btn.textContent = 'Copied!';
setTimeout(function() { btn.textContent = 'Copy'; }, 1500);
});
};
codeBlock.parentElement.style.position = 'relative';
codeBlock.parentElement.appendChild(btn);
});
}
addCopyButtons();
// Re-run on dynamic content
var observer = new MutationObserver(addCopyButtons);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '
pythoncode-tutorials/machine-learning/speech-emotion-recognition at master · hulinhui-code/pythoncode-tutorials · GitHub
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speech-emotion-recognition
README.md
How to Make a Speech Emotion Recognizer Using Python And Scikit-learn
To run this, you need to:
  • pip3 install -r requirements.txt
It is already trained, you can use test.py to test your voice.
You can:
  • Tweak the model parameters ( or the whole model ) in ser.py.
  • Add more data to data folder in condition that the audio samples are converted to 16000Hz sample rate and mono channel, convert_wavs.py does that.
  • Editing the emotions specified in utils.py in AVAILABLE_EMOTIONS constant.
When you modified anything, you can run ser.py to retrain the model.
If you want to only use this efficiently, definitely check Emotion Recognition using Speech repository which ease this process a lot.

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Force GitHub README to respect dark mode
(function() {
var style = document.createElement('style');
style.textContent = '
.markdown-body {
color-scheme: dark light;
}
.markdown-body pre { background: #161b22 !important; }
.markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }
.markdown-body table th, .markdown-body table td { border-color: #30363d !important; }
.markdown-body img { background: #0d1117; }
.markdown-body blockquote { border-left-color: #8b949e; }
.markdown-body hr { border-color: #30363d; }
';
document.head.appendChild(style);
})();
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
pythoncode-tutorials/machine-learning/speech-emotion-recognition at master · hulinhui-code/pythoncode-tutorials · GitHub
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speech-emotion-recognition
README.md
How to Make a Speech Emotion Recognizer Using Python And Scikit-learn
To run this, you need to:
  • pip3 install -r requirements.txt
It is already trained, you can use test.py to test your voice.
You can:
  • Tweak the model parameters ( or the whole model ) in ser.py.
  • Add more data to data folder in condition that the audio samples are converted to 16000Hz sample rate and mono channel, convert_wavs.py does that.
  • Editing the emotions specified in utils.py in AVAILABLE_EMOTIONS constant.
When you modified anything, you can run ser.py to retrain the model.
If you want to only use this efficiently, definitely check Emotion Recognition using Speech repository which ease this process a lot.

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
(function() {
var ref = document.referrer;
var terms = [];
if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {
var url = new URL(ref);
var q = url.searchParams.get('q') || url.searchParams.get('p');
if (q) {
terms = q.split(/\s+/).filter(function(t) { return t.length > 2; });
}
}
if (terms.length === 0) return;
var style = document.createElement('style');
style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';
document.head.appendChild(style);
function highlight(node) {
if (node.nodeType === 3) { // text node
var text = node.textContent;
var found = false;
terms.forEach(function(term) {
var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\]\\]/g, '\\') + ')', 'gi');
if (regex.test(text)) {
found = true;
var frag = document.createDocumentFragment();
var parts = text.split(regex);
parts.forEach(function(part, i) {
if (i % 2 === 0) {
frag.appendChild(document.createTextNode(part));
} else {
var span = document.createElement('span');
span.className = 'userscript-highlight';
span.textContent = part;
frag.appendChild(span);
}
});
node.parentNode.replaceChild(frag, node);
}
});
} else if (node.nodeType === 1 && node.childNodes) { // element
var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];
if (!skipTags.includes(node.tagName)) {
Array.from(node.childNodes).forEach(highlight);
}
}
}
highlight(document.body);
// Re-highlight on dynamic content
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1 || node.nodeType === 3) highlight(node);
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
pythoncode-tutorials/machine-learning/speech-emotion-recognition at master · hulinhui-code/pythoncode-tutorials · GitHub
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speech-emotion-recognition
README.md
How to Make a Speech Emotion Recognizer Using Python And Scikit-learn
To run this, you need to:
  • pip3 install -r requirements.txt
It is already trained, you can use test.py to test your voice.
You can:
  • Tweak the model parameters ( or the whole model ) in ser.py.
  • Add more data to data folder in condition that the audio samples are converted to 16000Hz sample rate and mono channel, convert_wavs.py does that.
  • Editing the emotions specified in utils.py in AVAILABLE_EMOTIONS constant.
When you modified anything, you can run ser.py to retrain the model.
If you want to only use this efficiently, definitely check Emotion Recognition using Speech repository which ease this process a lot.

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Strip utm_, fbclid, gclid, etc. from all links on page
(function() {
var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',
'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',
'ref', 'ref_src', 'source', 'medium', 'campaign'];
function cleanUrl(url) {
try {
var u = new URL(url, window.location.origin);
var changed = false;
trackingParams.forEach(function(p) {
if (u.searchParams.has(p)) {
u.searchParams.delete(p);
changed = true;
}
});
return changed ? u.toString() : url;
} catch (e) {
return url;
}
}
function cleanLinks() {
document.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
cleanLinks();
var observer = new MutationObserver(function(mutations) {
mutations.forEach(function(m) {
m.addedNodes.forEach(function(node) {
if (node.nodeType === 1) {
if (node.tagName === 'A') cleanLinks();
node.querySelectorAll('a[href]').forEach(function(a) {
var clean = cleanUrl(a.href);
if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
pythoncode-tutorials/machine-learning/speech-emotion-recognition at master · hulinhui-code/pythoncode-tutorials · GitHub
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speech-emotion-recognition
README.md
How to Make a Speech Emotion Recognizer Using Python And Scikit-learn
To run this, you need to:
  • pip3 install -r requirements.txt
It is already trained, you can use test.py to test your voice.
You can:
  • Tweak the model parameters ( or the whole model ) in ser.py.
  • Add more data to data folder in condition that the audio samples are converted to 16000Hz sample rate and mono channel, convert_wavs.py does that.
  • Editing the emotions specified in utils.py in AVAILABLE_EMOTIONS constant.
When you modified anything, you can run ser.py to retrain the model.
If you want to only use this efficiently, definitely check Emotion Recognition using Speech repository which ease this process a lot.

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Auto-enable theater mode on YouTube
(function() {
function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
if (btn && !btn.classList.contains('activated')) {
btn.click();
}
}
// Try immediately
tryTheater();
// Try after navigation (SPA)
var lastUrl = location.href;
setInterval(function() {
if (location.href !== lastUrl) {
lastUrl = location.href;
setTimeout(tryTheater, 500);
}
}, 1000);
// Also try on player load
var observer = new MutationObserver(tryTheater);
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
pythoncode-tutorials/machine-learning/speech-emotion-recognition at master · hulinhui-code/pythoncode-tutorials · GitHub
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speech-emotion-recognition
README.md
How to Make a Speech Emotion Recognizer Using Python And Scikit-learn
To run this, you need to:
  • pip3 install -r requirements.txt
It is already trained, you can use test.py to test your voice.
You can:
  • Tweak the model parameters ( or the whole model ) in ser.py.
  • Add more data to data folder in condition that the audio samples are converted to 16000Hz sample rate and mono channel, convert_wavs.py does that.
  • Editing the emotions specified in utils.py in AVAILABLE_EMOTIONS constant.
When you modified anything, you can run ser.py to retrain the model.
If you want to only use this efficiently, definitely check Emotion Recognition using Speech repository which ease this process a lot.

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
pythoncode-tutorials/machine-learning/speech-emotion-recognition at master · hulinhui-code/pythoncode-tutorials · GitHub
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speech-emotion-recognition
README.md
How to Make a Speech Emotion Recognizer Using Python And Scikit-learn
To run this, you need to:
  • pip3 install -r requirements.txt
It is already trained, you can use test.py to test your voice.
You can:
  • Tweak the model parameters ( or the whole model ) in ser.py.
  • Add more data to data folder in condition that the audio samples are converted to 16000Hz sample rate and mono channel, convert_wavs.py does that.
  • Editing the emotions specified in utils.py in AVAILABLE_EMOTIONS constant.
When you modified anything, you can run ser.py to retrain the model.
If you want to only use this efficiently, definitely check Emotion Recognition using Speech repository which ease this process a lot.

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
pythoncode-tutorials/machine-learning/speech-emotion-recognition at master · hulinhui-code/pythoncode-tutorials · GitHub
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speech-emotion-recognition
README.md
How to Make a Speech Emotion Recognizer Using Python And Scikit-learn
To run this, you need to:
  • pip3 install -r requirements.txt
It is already trained, you can use test.py to test your voice.
You can:
  • Tweak the model parameters ( or the whole model ) in ser.py.
  • Add more data to data folder in condition that the audio samples are converted to 16000Hz sample rate and mono channel, convert_wavs.py does that.
  • Editing the emotions specified in utils.py in AVAILABLE_EMOTIONS constant.
When you modified anything, you can run ser.py to retrain the model.
If you want to only use this efficiently, definitely check Emotion Recognition using Speech repository which ease this process a lot.