Whisper

Convert audio into multilingual transcripts with automatic language detection and translation.

577|62|Updated May 15, 2026

Search Tags:#translation #whisper #multilingual #transcribe #audio-processing #speech-recognition

Ready to run

npx skills add https://github.com/agentic-in/elephant-agent --skill whisper-agentic-in

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Whisper
Source: https://github.com/agentic-in/elephant-agent/tree/main/packages/skills/builtin_packages/mlops/models/whisper
Command: npx skills add https://github.com/agentic-in/elephant-agent --skill whisper-agentic-in

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Whisper enables automated speech recognition and translation across 99 languages, turning audio into accurate transcripts and multilingual text for search, captioning, and accessibility.

Core Features & Use Cases

Multilingual transcription: Transcribe audio in many languages with high accuracy.
Language identification & translation: Detect language and translate to English when needed.
Flexible model sizes: Choose from tiny to large models to balance speed and accuracy for your workload.
Media captioning & accessibility: Generate captions for podcasts, videos, meetings, and lectures to improve accessibility and searchability.

Quick Start

Install Whisper in your Python environment and run a transcription on an audio file to generate text.

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 Python?▼

Multilingual speech recognition transcribes spoken audio into accurate text across 99 languages using the Whisper model. It supports auto language detection and translation to English, making it suitable for transcribing podcasts, lectures, and meetings.

How do I add captions to podcasts and videos using speech recognition?▼

Speech recognition generates captions for podcasts and videos by transcribing spoken audio into text. Whisper supports media captioning across many languages, improving accessibility and searchability with optional word-level timestamps for precise alignment.

Do I need ffmpeg installed to run Whisper for audio transcription?▼

Yes, ffmpeg is required to run Whisper for audio transcription. In addition to ffmpeg, you need Python and the Whisper model installed via pip to process audio files and generate multilingual transcripts successfully.

Can speech recognition automatically detect languages and translate audio to English?▼

Whisper offers flexible model sizes ranging from tiny to large, allowing you to balance transcription speed and accuracy. Smaller models process audio faster while larger models deliver higher accuracy for complex multilingual speech recognition workloads.

How do I get word-level timestamps for transcribed audio?▼

Word-level timestamps for transcribed audio are generated as an optional output during speech recognition. Whisper supports this feature, providing precise timing alignment for each word to facilitate media captioning and detailed transcript analysis.