"FFmpeg" matching MCP servers:

Enables AI clients to watch local video files or YouTube/Bilibili and other supported URLs, receiving timestamped transcripts, subtitles, searchable text and keyframe contact sheets. Runs fully offline with local speech recognition and bundled ffmpeg, so no API key or cloud upload is needed.

Enables downloading videos from platforms like YouTube and converting them to text using OpenAI Whisper and ffmpeg. It supports multiple output formats including TXT, JSON, SRT, and VTT for transcriptions.

This MCP server enables local-first video subtitle processing on Windows, offering tools for transcription, subtitle formatting, SRT/ASS export, FFmpeg rendering, and Kdenlive project creation without cloud APIs.

Enables podcast producers and agents to manage multitrack projects, run per-track transcription, perform transcript-driven edits, process audio via FFmpeg, and inspect a read-only DAW timeline over MCP.

An MCP server enabling video processing via natural language: transcription with Whisper, segment cutting with FFmpeg, and file management.

Enables an assistant to turn any video ffmpeg can read into a text transcript by extracting the audio, splitting it on silence, and transcribing each piece on-device through utt. Exposes tools to start a transcription job with optional hints and timestamps, and to poll for a transcript that is still being worked on.

vidlizer pulls frames out of any video, image, or PDF using ffmpeg, sends them to a vision LLM, and returns a flow array — one entry per scene. Each entry tells you what happened, who was on screen, what text was visible, and what changed. If the video has audio, it transcribes it with Apple MLX Whisper and merges the speech into each step.

Enables MCP clients to run project-scoped video editing workflows: propose and approve editing strategies, apply validated plans, review immutable versions, and export final renders via FFmpeg, with durable persistence and approval gates.

Enables transcribing speech from public HTTPS video URLs using OpenAI Speech-to-Text, with audio extraction via FFmpeg and structured JSON results returned through an MCP tool.