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whisper

Transcribe and translate multilingual audio using OpenAI's Whisper speech recognition models.

2|Updated Aug 15, 2026
Search Tags:#OpenAI#Whisper#translation#speech recognition#transcription#audio processing#ASR
One-click install
npx skills add https://github.com/Jensen-Yao/agents-skills --skill whisper-jensen-yao
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: whisper
Source: https://github.com/Jensen-Yao/agents-skills/tree/main/skills/whisper
Command: npx skills add https://github.com/Jensen-Yao/agents-skills --skill whisper-jensen-yao

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? Converting audio recordings into text manually is slow and error-prone, especially across multiple languages. This Skill provides instructions for using OpenAI's Whisper to automate speech-to-text transcription, translation to English, and language identification across 99 languages. ## Core Features & Use Cases - Multilingual Transcription: Transcribe audio in 99 languages with six model sizes from tiny (39M) to large (1550M parameters), balancing speed and accuracy. - Translation & Language Detection: Translate any supported language's audio directly into English text and auto-detect the spoken language. - Flexible Output Formats: Generate plain text, SRT/VTT subtitles, 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 Use the whisper skill to transcribe the attached audio file 'meeting.mp3' into text with timestamps.

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?▼

Install openai-whisper and ffmpeg, then load a model with whisper.load_model("base") and call model.transcribe("audio.mp3"). The result dictionary contains the full text plus timestamped segments.

Which Whisper model size should I use for transcription?▼

Use turbo for the best speed and quality balance, or base for quick prototyping. The large model (1550M parameters) gives highest accuracy but needs about 10 GB VRAM, while tiny runs fastest with lower accuracy.

Can Whisper translate audio into English?▼

Yes, Whisper translates any of its 99 supported languages into English by passing task="translate" to the transcribe call. Input audio in Spanish, for example, produces English text output.

Does Whisper support real-time streaming transcription?▼

The standard openai-whisper package is not designed for live captioning due to latency. For streaming use cases, the documentation recommends faster-whisper, which runs about 4x faster with CTranslate2.

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. Quality also varies with accents, background noise, and low-resource languages.

How do I generate SRT subtitles from a video with Whisper?▼

Run the CLI command whisper video.mp4 --output_format srt --language English to produce a subtitle file. Whisper handles common media formats directly through its ffmpeg dependency.

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