textflowkit
Open source · Apache 2.0

Media in.
Useful text out.

Transcribe video and audio into timestamped, searchable text. One shared core for your command line, AI harness, and application.

Get started Latest release

Self-hosted toolkit · Native Windows, macOS, Linux · No account required

textflowkit / terminal
# A URL or a file. Your machine does the work.
$ textflowkit transcribe talk.mp4 \
    --formats json,srt,txt

✓ transcript.json
✓ transcript.srt
✓ transcript.txt

# Or run it under an AI harness
$ textflowkit-mcp
CLIMCP stdio + HTTPJSON API
One coreShared transcript and job model
Three doorsCLI · MCP · HTTP API
13 sourcesRecognized through yt-dlp
Apache 2.0Free to use and build on
Built to plug in

A transcription engine, not another silo.

Use the interface your product already speaks. The pipeline and transcript format stay the same underneath.

01 / SCRIPT

Command line

Transcribe a local file or URL, run batches, resume saved work, and export subtitles or documents.

Explore CLI →
02 / AGENT

MCP server

Connect AI harnesses over stdio or Streamable HTTP. Submit jobs, poll status, search, and page through results.

Explore MCP →
03 / PRODUCT

HTTP API

Give software a job-based JSON interface. Keep long transcriptions off the request-response clock.

Explore API →
How it works

From source to something you can use.

01

Point it at media

Use a local file, a direct media URL, or a recognized platform link.

02

Extract audio

yt-dlp acquires supported URLs; ffmpeg prepares audio for transcription.

03

Transcribe

Whisper produces timed segments. Translation and speaker labels are optional.

04

Use the result

Read, search, page, or export JSON, TXT, Markdown, SRT, or VTT. DOCX and PDF use an optional extra.

Start building

Bring your own media. Keep your workflow.

Python 3.10+ and ffmpeg are the starting point. The repository is the home for install notes, integration examples, and releases.

  • Run locally on Windows without WSL, or on macOS and Linux.
  • Use durable SQLite jobs and bounded concurrency when you need them.
  • Find the right setup for AMD ROCm in the install guide before installing dependencies.
Read install guide ↗ 
$ git clone https://github.com/scottconverse/textflowkit
$ cd textflowkit
$ python -m pip install -e ".[mcp,http]"
$ textflowkit doctor

For a GPU-specific or Windows-native ROCm setup, follow the install guide instead of using this generic CPU-oriented quickstart.