whisper

Transcribe audio from 99 languages into searchable text using OpenAI Whisper.

Updated Mar 18, 2026
One-click install
npx skills add https://github.com/tadod12/fraud-detection-research --skill whisper-tadod12
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: whisper
Source: https://github.com/tadod12/fraud-detection-research/tree/main/.agent/skills/18-multimodal/whisper
Command: npx skills add https://github.com/tadod12/fraud-detection-research --skill whisper-tadod12

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires openai-whisper, transformers, torch, and includes references (resource) components.

What problem does it solve?

Whisper enables accurate, scalable speech-to-text across 99 languages, simplifying transcription, captioning, and multilingual audio workflows without manual typing.

Core Features & Use Cases

  • Multilingual transcription: Transcribe audio in dozens of languages with high accuracy.
  • Translation to English: Convert non-English audio into English text for quick sharing.
  • Flexible deployment: Supports small to large model sizes to balance latency and quality for research, prototyping, or production.

Quick Start

Install the OpenAI Whisper package, load a model with whisper.load_model, and call transcribe on your audio file to obtain text.

Frequently Asked Questions about whisper

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I transcribe multilingual audio files into searchable text?▼

Multilingual transcription converts spoken language into accurate text by loading a Whisper model and calling the transcribe function on your audio file to process speech across 99 languages.

Can I translate non-English audio into English text using Whisper?▼

Yes, Whisper supports translation to English by processing non-English audio inputs and outputting translated English text, enabling quick sharing of multilingual content like podcasts and lectures.

How do I balance latency and accuracy when transcribing meeting recordings?▼

Balance transcription latency and accuracy by selecting among multiple Whisper model sizes, choosing smaller models for faster processing or larger ones for higher precision based on your hardware compatibility.

Do I need PyTorch and Transformers to run OpenAI Whisper for transcription?▼

Yes, running OpenAI Whisper requires installing the openai-whisper package along with PyTorch and Transformers dependencies to execute multilingual speech-to-text processing.

What are the limitations of using Whisper for video caption generation?▼

Whisper video caption generation limitations include balancing model size against hardware constraints, where larger models demand more compute resources, potentially increasing transcription latency on standard devices.