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whisper

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

1|Updated Mar 12, 2026
Search Tags:#translation#speech recognition#transcription#whisper#audio processing#multilingual#asr
One-click install
npx skills add https://github.com/kaminocorp/hermes-alpha-hunter --skill whisper-kaminocorp
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: whisper
Source: https://github.com/kaminocorp/hermes-alpha-hunter/tree/main/skills/mlops/models/whisper
Command: npx skills add https://github.com/kaminocorp/hermes-alpha-hunter --skill whisper-kaminocorp

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? Converting spoken audio into text manually is slow and error-prone, especially across multiple languages. This Skill provides instructions for using OpenAI's Whisper model 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 (tiny to large) trading off speed and accuracy. - Translation & Language Detection: Translate any supported language's audio into English 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 batch of podcast episodes by loading the turbo model, running transcription over each MP3 file, and saving timestamped text or SRT subtitle files for publishing. ## Quick Start Ask the agent to transcribe an audio file such as 'meeting.mp3' using the Whisper turbo model and save the result as a text file 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 you can iterate over.

Which Whisper model size should I use for transcription?▼

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

Can Whisper translate audio from other languages to English?▼

Yes, Whisper supports translation to English from any of its 99 supported languages. Pass task="translate" to the transcribe call, and the model outputs English text regardless of the source audio language.

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 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. Quality also varies with accents, background noise, and lower-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 an SRT subtitle file. Whisper handles common media formats directly through its ffmpeg dependency.

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