whisper

Transcribe audio into text using Whisper multilingual speech recognition models.

1.2k|116|Updated Mar 19, 2026
One-click install
npx skills add https://github.com/math-inc/OpenGauss --skill whisper-math-inc
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: whisper
Source: https://github.com/math-inc/OpenGauss/tree/main/skills/mlops/models/whisper
Command: npx skills add https://github.com/math-inc/OpenGauss --skill whisper-math-inc

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Transcribing audio and video across many languages is time-consuming and error-prone; Whisper provides robust, multilingual speech recognition and optional translation to English to streamline content creation, accessibility, and data extraction.

Core Features & Use Cases

  • Multilingual transcription across 99 languages with language identification
  • Flexible model sizes to balance speed and accuracy for transcripts and captions
  • Use cases include podcast transcription, meeting notes, video captions, and archival transcription

Quick Start

Load a Whisper model and run transcribe on your audio file to obtain the transcript.

Frequently Asked Questions about whisper

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

FAQPage Schema
How do I transcribe audio files into text using multilingual speech recognition?

You can transcribe audio files into text by loading a Whisper speech recognition model and running the transcribe function on your input. This handles multilingual audio processing across 99 languages to generate accurate transcripts.

Does multilingual speech recognition support automatic language detection for transcriptions?

Yes, multilingual speech recognition supports automatic language detection for transcriptions. The models identify the spoken language among 99 supported options, streamlining multilingual audio processing without requiring manual language selection.

Can I use Whisper for podcast transcription and generating video captions?

Yes, you can use Whisper for podcast transcription and generating video captions. The models support flexible sizes to balance processing speed and accuracy, making them suitable for meeting notes and archival transcription.

Do I need Python dependencies to run multilingual speech recognition models?

Yes, you need Python dependencies to run multilingual speech recognition models. The setup requires installing packages like openai-whisper, transformers, and torch to execute the transcription and optional English translation features.

What is the best way to process large volumes of multilingual audio?

The best way to process large volumes of multilingual audio is selecting flexible Whisper model sizes that balance speed and accuracy. This approach ensures efficient multilingual transcription across 99 languages for various content formats.

Can speech recognition models translate foreign language audio to English text?

Yes, speech recognition models can translate foreign language audio to English text. Whisper provides optional translation to English alongside its core multilingual transcription, streamlining accessibility and content extraction from diverse audio sources.