Use Case · transcription · multilingual audio

OpenClaw OpenRouter Transcription: Multi-Lingual Audio to Text Workflow

The Showcase features an OpenRouter-based transcription skill on ClawHub for multi-lingual audio processing inside OpenClaw workflows.

Last updated: 2026-03-10 · Language: English

0) TL;DR (3-minute launch)

Teams that rely on voice notes, interviews, or call recordings often spend too much time on manual transcription.
Workflow in short: Audio file or voice note → OpenClaw sends file to OpenRouter transcription skill → skill returns transcript text → optional cleanup pass (speaker labels, punctuation, summaries) → publish transcript to docs, chat, or downstream task systems
Start fast: Install the OpenRouter transcription skill from ClawHub.
Guardrail: Do not auto-trigger high-impact actions from transcripts without human verification.

1) What problem this solves

Teams that rely on voice notes, interviews, or call recordings often spend too much time on manual transcription. This skill turns audio into text that can be searched, summarized, and routed to follow-up actions in the same OpenClaw flow.

2) Who this is for

Operators receiving frequent voice-note updates across languages
Builders creating transcript-first workflows for support or research
Teams that need a reusable transcription step before summarization and task extraction

3) Workflow map

Audio file or voice note
   -> OpenClaw sends file to OpenRouter transcription skill
   -> skill returns transcript text
   -> optional cleanup pass (speaker labels, punctuation, summaries)
   -> publish transcript to docs, chat, or downstream task systems

4) MVP setup

Install the OpenRouter transcription skill from ClawHub
Configure OpenRouter credentials and model choices according to skill docs
Start with one input channel (for example Telegram voice notes)
Add a transcript validation step before triggering external automations
Store original audio plus transcript for QA sampling during early rollout

5) Prompt template

You are a transcription post-processor.

Input: raw transcript from OpenRouter skill.
Output requirements:
1) keep original meaning unchanged
2) fix obvious punctuation and casing
3) if speaker turns are explicit, preserve them
4) list any unclear segments in a separate "uncertain" section
5) do not invent missing words or facts

Return plain text plus an "uncertain" bullet list.

6) Cost and payoff

Cost

Audio processing usage costs plus QA checks for language and domain-specific vocabulary.

Payoff

Faster documentation and easier search across voice-heavy communication.

Scale

Chain transcript output into summaries, action extraction, and multilingual reporting.

7) Risk boundaries

Do not auto-trigger high-impact actions from transcripts without human verification
Apply retention and redaction rules for sensitive voice data
Mark uncertain transcript segments clearly so downstream users can review