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whisper

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

14|5|Updated Apr 9, 2026
Search Tags:#translation#speech recognition#transcription#whisper#openai#audio processing#asr
One-click install
npx skills add https://github.com/MLT-OSS/hermes-agent-go --skill whisper-mlt-oss
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: whisper
Source: https://github.com/MLT-OSS/hermes-agent-go/tree/main/skills/mlops/models/whisper
Command: npx skills add https://github.com/MLT-OSS/hermes-agent-go --skill whisper-mlt-oss

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 39M to large 1550M parameters) to balance 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, iterating over MP3 files, and saving each transcript with timestamps as SRT subtitle files. ## Quick Start Transcribe the attached audio file 'meeting.mp3' to 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?▼

Install openai-whisper with pip, load a model with whisper.load_model("base"), then 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-to-quality balance, base for quick prototyping, and large for maximum accuracy on multilingual audio. Tiny and base models need about 1 GB VRAM, while large requires around 10 GB.

Can Whisper translate audio from other languages to English?▼

Yes, pass task="translate" to model.transcribe() to translate any of the 99 supported languages into English text. The default task="transcribe" keeps the original 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 about 4x faster with lower memory.

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.

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