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README.md
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setup.py

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ASP_Project

This project is for Audio signal class final project.

VAD part

The current VAD folder contains a pipline for TDNN-LSTM frame work and a Pyannote framework using AMI dataset which is not the main problem we are solving in this project. The pipeline here is just for comparing the result with our result. In order to reproduce the result of these pipelines, please first install the OVAD package using the following commands:

git clone https://github.com/desh2608/ovad.git

python setup.py install

Goal of this project

In this project we are trying to work on the Overlapped Voice Activity Detection problem as a Multiclass classification problem with three different classes: {Silence, Speech, Overlapped speech}. We are trying to work on this problem in a signal processing aspect. We will try to solve this problem using Energy Based, Format detector, and Pitch detector. We will also try to work on different speech augmentation methods to provide a better result.

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n 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;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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README.md
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requirements.txt
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setup.py
Repository files navigation
ASP_Project
This project is for Audio signal class final project.
VAD part
The current VAD folder contains a pipline for TDNN-LSTM frame work and a Pyannote framework using AMI dataset which is not the main problem we are solving in this project. The pipeline here is just for comparing the result with our result. In order to reproduce the result of these pipelines, please first install the OVAD package using the following commands:
git clone https://github.com/desh2608/ovad.git
python setup.py install
Goal of this project
In this project we are trying to work on the Overlapped Voice Activity Detection problem as a Multiclass classification problem with three different classes: {Silence, Speech, Overlapped speech}. We are trying to work on this problem in a signal processing aspect. We will try to solve this problem using Energy Based, Format detector, and Pitch detector. We will also try to work on different speech augmentation methods to provide a better result.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
2 watching
Forks
1 fork
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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README.md
requirements.txt
requirements.txt
setup.py
setup.py
Repository files navigation
ASP_Project
This project is for Audio signal class final project.
VAD part
The current VAD folder contains a pipline for TDNN-LSTM frame work and a Pyannote framework using AMI dataset which is not the main problem we are solving in this project. The pipeline here is just for comparing the result with our result. In order to reproduce the result of these pipelines, please first install the OVAD package using the following commands:
git clone https://github.com/desh2608/ovad.git
python setup.py install
Goal of this project
In this project we are trying to work on the Overlapped Voice Activity Detection problem as a Multiclass classification problem with three different classes: {Silence, Speech, Overlapped speech}. We are trying to work on this problem in a signal processing aspect. We will try to solve this problem using Energy Based, Format detector, and Pitch detector. We will also try to work on different speech augmentation methods to provide a better result.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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README.md
README.md
requirements.txt
requirements.txt
setup.py
setup.py
Repository files navigation
ASP_Project
This project is for Audio signal class final project.
VAD part
The current VAD folder contains a pipline for TDNN-LSTM frame work and a Pyannote framework using AMI dataset which is not the main problem we are solving in this project. The pipeline here is just for comparing the result with our result. In order to reproduce the result of these pipelines, please first install the OVAD package using the following commands:
git clone https://github.com/desh2608/ovad.git
python setup.py install
Goal of this project
In this project we are trying to work on the Overlapped Voice Activity Detection problem as a Multiclass classification problem with three different classes: {Silence, Speech, Overlapped speech}. We are trying to work on this problem in a signal processing aspect. We will try to solve this problem using Energy Based, Format detector, and Pitch detector. We will also try to work on different speech augmentation methods to provide a better result.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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11 Commits
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NameName
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VAD
VAD
data
data
dataprocess
dataprocess
LICENSE
LICENSE
README.md
README.md
requirements.txt
requirements.txt
setup.py
setup.py
Repository files navigation
ASP_Project
This project is for Audio signal class final project.
VAD part
The current VAD folder contains a pipline for TDNN-LSTM frame work and a Pyannote framework using AMI dataset which is not the main problem we are solving in this project. The pipeline here is just for comparing the result with our result. In order to reproduce the result of these pipelines, please first install the OVAD package using the following commands:
git clone https://github.com/desh2608/ovad.git
python setup.py install
Goal of this project
In this project we are trying to work on the Overlapped Voice Activity Detection problem as a Multiclass classification problem with three different classes: {Silence, Speech, Overlapped speech}. We are trying to work on this problem in a signal processing aspect. We will try to solve this problem using Energy Based, Format detector, and Pitch detector. We will also try to work on different speech augmentation methods to provide a better result.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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11 Commits
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VAD
data
data
dataprocess
dataprocess
LICENSE
LICENSE
README.md
README.md
requirements.txt
requirements.txt
setup.py
setup.py
Repository files navigation
ASP_Project
This project is for Audio signal class final project.
VAD part
The current VAD folder contains a pipline for TDNN-LSTM frame work and a Pyannote framework using AMI dataset which is not the main problem we are solving in this project. The pipeline here is just for comparing the result with our result. In order to reproduce the result of these pipelines, please first install the OVAD package using the following commands:
git clone https://github.com/desh2608/ovad.git
python setup.py install
Goal of this project
In this project we are trying to work on the Overlapped Voice Activity Detection problem as a Multiclass classification problem with three different classes: {Silence, Speech, Overlapped speech}. We are trying to work on this problem in a signal processing aspect. We will try to solve this problem using Energy Based, Format detector, and Pitch detector. We will also try to work on different speech augmentation methods to provide a better result.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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README.md
README.md
requirements.txt
requirements.txt
setup.py
setup.py
Repository files navigation
ASP_Project
This project is for Audio signal class final project.
VAD part
The current VAD folder contains a pipline for TDNN-LSTM frame work and a Pyannote framework using AMI dataset which is not the main problem we are solving in this project. The pipeline here is just for comparing the result with our result. In order to reproduce the result of these pipelines, please first install the OVAD package using the following commands:
git clone https://github.com/desh2608/ovad.git
python setup.py install
Goal of this project
In this project we are trying to work on the Overlapped Voice Activity Detection problem as a Multiclass classification problem with three different classes: {Silence, Speech, Overlapped speech}. We are trying to work on this problem in a signal processing aspect. We will try to solve this problem using Energy Based, Format detector, and Pitch detector. We will also try to work on different speech augmentation methods to provide a better result.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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VAD
VAD
data
data
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dataprocess
LICENSE
LICENSE
README.md
README.md
requirements.txt
requirements.txt
setup.py
setup.py
Repository files navigation
ASP_Project
This project is for Audio signal class final project.
VAD part
The current VAD folder contains a pipline for TDNN-LSTM frame work and a Pyannote framework using AMI dataset which is not the main problem we are solving in this project. The pipeline here is just for comparing the result with our result. In order to reproduce the result of these pipelines, please first install the OVAD package using the following commands:
git clone https://github.com/desh2608/ovad.git
python setup.py install
Goal of this project
In this project we are trying to work on the Overlapped Voice Activity Detection problem as a Multiclass classification problem with three different classes: {Silence, Speech, Overlapped speech}. We are trying to work on this problem in a signal processing aspect. We will try to solve this problem using Energy Based, Format detector, and Pitch detector. We will also try to work on different speech augmentation methods to provide a better result.
About
No description, website, or topics provided.
Resources
Readme
MIT license
Activity
Stars
2 stars
Watchers
2 watching
Forks
1 fork
Report repository
Releases
Packages
Contributors
Languages