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whisper

Transcribe and translate speech audio into text across 99 languages using OpenAI Whisper.

Updated Sep 10, 2026
Search Tags:#Whisper#translation#speech recognition#transcription#audio processing#multilingual#ASR
One-click install
npx skills add https://github.com/loteiron/ZeusAgent --skill whisper-loteiron
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: whisper
Source: https://github.com/loteiron/ZeusAgent/tree/main/optional-skills/mlops/whisper
Command: npx skills add https://github.com/loteiron/ZeusAgent --skill whisper-loteiron

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? Converting spoken audio into written text manually is slow and error-prone, especially for multilingual content, long recordings, or noisy audio. This Skill automates speech-to-text transcription and translation to English using OpenAI's Whisper model. ## Core Features & Use Cases - Multilingual Transcription: Transcribe audio in 99 languages with automatic language detection or explicit language selection. - Translation to English: Convert speech from any supported language directly into English text using the translate task. - Flexible Output Formats: Generate plain text, SRT subtitles, WebVTT, or JSON with word-level timestamps via CLI or Python API. - Use Case: Transcribe a folder of podcast episodes by batch-processing MP3 files with the turbo model on GPU, then export SRT subtitle files for video publishing. ## Quick Start Ask the agent to transcribe an audio file such as 'meeting.mp3' into text with timestamps using the Whisper turbo model.

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 with Whisper in Python?▼

Load a model with whisper.load_model("base") and call model.transcribe("audio.mp3") to get the text and timestamped segments. Install openai-whisper via pip and ensure ffmpeg is available on your system.

Which Whisper model size should I use for transcription?▼

Use turbo for the best speed and quality balance, or base for quick prototyping. Larger models like medium and large improve accuracy, especially for low-resource languages, but require 5-10 GB of VRAM.

Can Whisper translate foreign language audio to English?▼

Yes, pass task="translate" to model.transcribe and Whisper converts speech from any of its 99 supported languages into English text. Translation only outputs English, not other target languages.

Does Whisper support real-time streaming transcription?▼

The standard openai-whisper package is not designed for live captioning due to latency. For streaming or faster processing, use faster-whisper, which runs roughly 4x faster with CTranslate2 optimization.

What are the limitations of Whisper speech recognition?▼

Whisper can hallucinate or repeat text, accuracy degrades on audio longer than 30 minutes, and it provides no speaker diarization. Background noise and strong accents also reduce transcription quality.

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