whisper

Convert spoken language into text using local Whisper transcription.

Updated Apr 16, 2026

Search Tags:#translation #transcription #whisper #multilingual #asr #speech-recognition

Ready to run

npx skills add https://github.com/jacardl/New-Radar --skill whisper-jacardl

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: whisper
Source: https://github.com/jacardl/New-Radar/tree/main/backend/frameworks/hermes-agent/skills/mlops/models/whisper
Command: npx skills add https://github.com/jacardl/New-Radar --skill whisper-jacardl

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Whisper provides robust, multilingual speech recognition to convert audio to text and identify language, enabling automated transcription, captioning, and data labeling across languages.

Core Features & Use Cases

High-accuracy transcription across 99 languages
Translation to English and language identification for multilingual workflows
Use cases include podcast transcription, video captioning, meeting notes, and multilingual audio analytics.

Quick Start

Transcribe an audio file using whisper to obtain a text transcript in seconds.

Frequently Asked Questions about whisper

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

FAQPage Schema

How do I transcribe audio to text offline?▼

Transcribe audio to text offline using local speech recognition models. This Skill loads Python packages like openai-whisper, transformers, and torch to perform speech-to-text conversion directly on your machine without requiring internet connectivity.

Can I use Whisper for podcast transcription and video captioning?▼

Yes, Whisper handles podcast transcription and video captioning by converting spoken language into text. It provides high-accuracy transcription across 99 languages, making it suitable for automated captioning workflows.

Does Whisper support multilingual speech recognition and translation?▼

Whisper supports multilingual speech recognition and translation by identifying the spoken language and translating audio content to English. It enables automated transcription across 99 languages for multilingual analytics workflows.

What Python packages do I need to run local speech recognition?▼

Running local speech recognition requires loading models with Python packages such as openai-whisper, transformers, and torch. These dependencies enable offline speech-to-text and translation tasks without external API calls.

Is offline transcription suitable for meeting notes and multilingual analytics?▼

Offline transcription is suitable for meeting notes and multilingual analytics workflows. The Skill processes audio locally to generate text transcripts, enabling data labeling and language identification across various languages.

What are the limitations of running Whisper locally for speech-to-text?▼

Running Whisper locally for speech-to-text requires sufficient computational resources to load models via torch and transformers. Processing large audio files offline may demand significant hardware capacity compared to cloud-based alternatives.