Transcribe videos from 1000+ platforms or local files offline, and search across every transcript
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io.github.nhatvu148/video-transcriber-mcp is an MCP server that transcribe videos from 1000+ platforms or local files offline, and search across every transcript. Its tool list has not been published yet over stdio, sse and http, requires no API key, and scores 88/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients — only the file and path differ.
{
"mcpServers": {
"video-transcriber-mcp": {
"url": "http://localhost:8080/mcp"
}
}
}Are you the author?
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High-performance video transcription MCP server using whisper.cpp (Rust)
Run this in your terminal to verify the server starts. Then let us know if it worked — your result helps other developers.
uvx 'yt-dlp' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories — click any category to see the underlying evidence.
yt-dlp: Downstream command injection via improper sanitization of yt-dlp --write-link output
### Summary If the `--write-link`, `--write-url-link` or `--write-desktop-link` options are used with yt-dlp, it may produce output that can lead to downstream remote code execution. An attacker can craft a malicious metadata payload to achieve arbitrary command injection in the `.url` and `.desktop` shortcut files written by yt-dlp. This allows for malicious shell commands or malicious remote executables to run on the user's system if the user executes the generated `.url` or `.desktop` files.
yt-dlp: Arbitrary code execution via manifest downloads with aria2c
### Summary If aria2c is used as an external downloader for a fragmented manifest format (such as an HLS/DASH stream), yt-dlp passes insufficiently sanitized input to aria2c that allows an attacker to perform an arbitrary file write. On Windows platforms, this can lead to immediate arbitrary code execution. On non-Windows platforms, this can lead to arbitrary code execution upon the next invocation of yt-dlp. ### Details When downloading a fragmented manifest format such as an HLS or DASH strea
yt-dlp: Dangerous file type creation via insufficient filename sanitization (Bypass of CVE-2024-38519)
### Summary A vulnerability exists in yt-dlp that allows a remote attacker to write arbitrary OS-shortcut files (such as `.desktop`, `.url`, `.webloc`) to the user's filesystem, bypassing the remediation for `CVE-2024-38519`. ### Details The fix for `CVE-2024-38519` enforced an allowlist for file extensions, in order to prevent writing files with unsafe extensions (such as `.exe` or `.sh`) during file downloads. However, this allowlist explicitly included the unsafe extensions `.desktop`, `.ur
yt-dlp: File Downloader cookie leak with curl
### Summary If curl is used an external downloader for yt-dlp, cookies may be leaked to an unintended host upon HTTP redirect or when the host for download fragments differs from their parent manifest's. This is the equivalent to [GHSA-v8mc-9377-rwjj](<https://github.com/yt-dlp/yt-dlp/security/advisories/GHSA-v8mc-9377-rwjj>) for the `curl` downloader. The vulnerable behavior is present in [yt-dlp](https://github.com/yt-dlp/yt-dlp) released since 2023.09.24. ### Details At the file download st
yt-dlp: Arbitrary Command Injection when using the `--netrc-cmd` option
### Summary When yt-dlp's `--netrc-cmd` command-line option (or `netrc_cmd` Python API parameter) is used, an attacker could achieve arbitrary command injection on the user's system with a maliciously crafted URL. ### Impact yt-dlp maintainers assume the impact of this vulnerability to be high for anyone who uses `--netrc-cmd` in their command/configuration or `netrc_cmd` in their Python scripts. Even though the maliciously crafted URL itself will look very suspicious to many users, it would be
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High-performance video transcription MCP server using whisper.cpp (Rust)
A Model Context Protocol (MCP) server that transcribes videos from 1000+ platforms using whisper.cpp. Built with Rust for maximum performance and efficiency.
The easiest way to install with all dependencies:
brew install nhatvu148/tap/video-transcriber-mcp
This automatically installs the binary along with required dependencies (cmake, yt-dlp, ffmpeg).
If you have Rust installed:
cargo install video-transcriber-mcp
Note: You'll need to manually install dependencies: yt-dlp, ffmpeg, cmake
Download from GitHub Releases:
# macOS (Intel)
curl -L https://github.com/nhatvu148/video-transcriber-mcp-rs/releases/latest/download/video-transcriber-mcp-x86_64-apple-darwin.tar.gz | tar xz
sudo mv video-transcriber-mcp /usr/local/bin/
# macOS (Apple Silicon)
curl -L https://github.com/nhatvu148/video-transcriber-mcp-rs/releases/latest/download/video-transcriber-mcp-aarch64-apple-darwin.tar.gz | tar xz
sudo mv video-transcriber-mcp /usr/local/bin/
# Linux (x86_64) — no ARM64 Linux build, see issue #13; use `cargo install`
curl -L https://github.com/nhatvu148/video-transcriber-mcp-rs/releases/latest/download/video-transcriber-mcp-x86_64-unknown-linux-gnu.tar.gz | tar xz
sudo mv video-transcriber-mcp /usr/local/bin/
# Windows: Download .zip from releases page
Note: You'll need to manually install dependencies: yt-dlp, ffmpeg
Installs the MCP server and a /transcribe skill in one step:
/plugin marketplace add nhatvu148/video-transcriber-mcp-rs
/plugin install video-transcriber@nhatvu148-tools
The plugin registers the MCP server for you, but it does not install the binary — run one of the install commands above first, so video-transcriber-mcp is on your PATH.
This version uses whisper.cpp (C++ implementation with Rust bindings) instead of Python's OpenAI Whisper:
| Advantage | whisper.cpp (Rust) | OpenAI Whisper (Python) |
|---|---|---|
| Performance | Native C++ speed | Python interpreter overhead |
| Memory | Lower footprint | Higher memory usage |
| Startup | Instant (<100ms) | Slow (~2-3s model loading) |
| Dependencies | Standalone binary | Requires Python + packages |
| Portability | Single binary | Python environment needed |
Real-world performance depends on your hardware, video length, and chosen model.
The fastest way to get started:
# 1. Install Task (if not already installed)
brew install go-task/tap/go-task
# 2. Complete setup (build + download model)
task setup
# 3. Run a quick test
task test:quick
# Done! 🎉
**Available Comma