Open source · Apache 2.0 · v0.1.9

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 Install from PyPI

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

Latest verified release: v0.1.9 · User manual

textflowkit / terminal

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

✓ talk-<job-id>.json
✓ talk-<job-id>.srt
✓ talk-<job-id>.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.

Point it at media

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

Extract audio

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

Transcribe

The default speech engine, Whistle, produces timed segments on CPU with no PyTorch; openai-whisper is opt-in. Translation and speaker labels are optional.

Use the result

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

One shared core, four thin doors. Whistle is the default engine; openai-whisper is opt-in.

Whistle bounded block run and durable resume: a decoded PCM WAV is split into 26-second cores with up to two seconds of context per side so every clip stays at or under the native CLI's 30-second limit, each core runs as one owned child process with a per-clip timeout and forced telemetry-off flags, a partial checkpoint is written to the durable SQLite store after each finished core, and a resume validates the source and decoded-WAV hashes and the window policy then re-runs only the unfinished cores. — A Whistle run is 26-second cores with up to 2 seconds of context per side, so each clip handed to the native CLI stays at or under its 30-second limit. Every core is one owned child process with forced telemetry-off flags — those flags are set, not physically stripped from the binary. Durable resume needs a SQLite store configured with `TEXTFLOWKIT_DB`, because a checkpoint cannot outlive a process that kept it in memory; a resume then re-runs only the cores that never finished.

Start building

Bring your own media. Keep your workflow.

Python 3.10+ and ffmpeg are the starting point. Install from PyPI; the repository has GPU-specific 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 — this applies to the optional openai-whisper engine and to diarization.

Read install guide ↗

New here? Follow the v0.1.9 user manual for CLI, MCP, HTTP, export, and troubleshooting examples, or the developer and integration manual for harness configuration and production settings.

$ python -m pip install "textflowkit[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.