v6.1.1 · open-source · GPL-3.0

Turn your meetings, lectures and training sessions into shareable notes.

Upload a recording to get a full transcript, identify speakers and generate a report in the format your audience needs.

$ curl -fsSL https://raw.githubusercontent.com/Lesur-ai/transkryptor/v6.1.1/scripts/install.sh | bash
View on GitHub Other install methods

macOS / Linux · self-hosted on your machine · works offline once installed

Notes for meetings, courses and interviews

Choose a report format for your audience. Transkryptor transcribes the recording and organizes its content into meeting minutes, study notes or interview summaries.

Meeting minutes

Standups, steering committees, client calls. Get a structured recap with decisions, owners, and next steps, in the same format your team already reads.

Study notes from your recordings.

Lectures, workshops, onboarding sessions. Get notes organized by topic and a full searchable transcript.

Interview transcripts

User interviews, podcasts, field recordings. Get an edited transcript with speaker labels, recurring themes and quotes for your reports.

Five report formats, plus your own.

Choose a preset report format, or select custom to use your own template.

Executive summary

A brief for leadership covering the main findings and decisions.

Meeting minutes

Sectioned by topic, attendees, decisions, open questions.

Actions & decisions

A flat list of commitments, owners, and dependencies.

Cleaned verbatim

Filler words removed; meaning and structure preserved.

Thematic analysis

Recurring themes across the recording, with supporting quotes.

Your own prompt

Use your own template in the sidebar’s prompt editor.

Built for long recordings.

Parallel processing, optional speaker detection and separate input and output languages support long recordings.

An hour of audio in a few minutes.

Recordings get processed in parallel instead of one long queue. You watch progress live, then download the report.

Identify speakers

Optional speaker detection labels each turn of speech so you can follow who said what.

15+ source languages, any target.

Record in French, send the report in English. Or the other way around. The two languages are picked independently.

From a single audio file to a finished report.

The four stages run in sequence, with per-chunk progress, throughput, and live logs visible throughout.

01
chunk· MP3 · WAV · M4A

The file is split into overlap-safe windows so long recordings can be processed in parallel.

02
transcribe· parallel · 15+ languages

Chunks are transcribed in parallel and reassembled in order, with optional speaker detection.

03
analyse· semantic batches

The transcript is split into semantic batches, analysed in parallel by your chosen model.

04
synthesise· structured Markdown

The analyses are combined into one report in your chosen format, available to download as Markdown.

Choose your provider and model.

Transkryptor is optimised for the Cloud Temple LLMaaS (SecNumCloud, EU-bound) — and works out of the box with any OpenAI-compatible endpoint. Use the provider that meets your security and budget requirements.

Configure your inference provider

A self-hosted vLLM, an EU-sovereign API, a US frontier-model API, an on-prem appliance — if it speaks the OpenAI chat / transcription protocol, Transkryptor connects to it. One URL, one key, in your .env.

Cloud Temple vLLM self-hosted Ollama LiteLLM gateway Azure OpenAI any compatible API

Cloud Temple configuration

Default settings, prompts, and retry policy are tuned against Cloud Temple's SecNumCloud-qualified LLMaaS. Enter your API key to use this configuration. Audio, transcripts and prompts stay within the EU.

SecNumCloud qualification, no third-party LLM in the data path.
Curated model whitelist, exposed in the sidebar.
The API key lives server-side; the browser never sees it.

Install with one command.

Prerequisites: Node.js 18+ and an API key from your provider of choice. The installer places the app in ~/Applications/Transkryptor, sets up the config, starts the service on localhost:3000, and opens your browser.

one-liner installer
        $ curl -fsSL https://raw.githubusercontent.com/Lesur-ai/transkryptor/v6.1.1/scripts/install.sh | bash
# installs to ~/Applications/Transkryptor (macOS)
# or ${XDG_DATA_HOME:-~/.local/share}/transkryptor (Linux)
# starts the service, opens http://localhost:3000$ git clone https://github.com/Lesur-ai/transkryptor.git
$ cd transkryptor
$ npm install
$ cp .env.example .env
# edit .env — point it at any OpenAI-compatible endpoint
$ npm start
# → open http://localhost:3000$ git clone https://github.com/Lesur-ai/transkryptor.git
$ cd transkryptor
$ cp .env.example .env
# edit .env — point it at any OpenAI-compatible endpoint
$ docker compose up --buildPS> wsl bash -lc "curl -fsSL https://raw.githubusercontent.com/Lesur-ai/transkryptor/v6.1.1/scripts/install.sh | bash"
# a native Windows installer is on the roadmap
# for now, WSL is the supported path
      
Edit .env before you start

Set the API endpoint and key of the OpenAI-compatible provider you want to use, plus the comma-separated list of models the UI should expose. The first model becomes the default.

Open source · GPL-3.0

Self-hostable, vanilla stack, contributions on GitHub.

Bring your own provider

Any OpenAI-compatible endpoint — sovereign, self-hosted, or commercial.

Server-side API key

The API key lives on your host. Audio never leaves your provider.

Processing logs

Per-chunk progress, throughput, server logs streamed live to the UI.

Try it with your next recording.

Transkryptor is live and open-source. Report bugs or suggest improvements on GitHub.

View on GitHub Read the docs