whisper

Name: whisper
Availability: InStock
Author: tangzheng202202

Transcribe and translate audio across 99 languages using openai-whisper.

1|Updated Apr 13, 2026

Search Tags:#transcription #whisper #multilingual #audio-processing #asr #speech-recognition

Ready to run

npx skills add https://github.com/tangzheng202202/hermes-skills --skill whisper-tangzheng202202

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: whisper
Source: https://github.com/tangzheng202202/hermes-skills/tree/main/03-mlops/mlops/models/whisper
Command: npx skills add https://github.com/tangzheng202202/hermes-skills --skill whisper-tangzheng202202

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Transcribes and translates audio across 99 languages using robust, model-based speech recognition.

Core Features & Use Cases

Multilingual transcription: convert audio to text in 99 languages with options for translation to English.
Model flexibility: supports tiny through large/turbo models with language detection or explicit language settings.
Practical use: podcast, interviews, meetings, and multilingual media analysis.

Quick Start

Install the Whisper package and run model.transcribe on your audio file to obtain text.

Frequently Asked Questions about whisper

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I transcribe multilingual audio recordings to text?▼

Multilingual audio transcription converts spoken words across 99 languages into text using automatic speech recognition. You can process podcasts, interviews, and meetings by running the Whisper model on your audio files to extract accurate text output.

Can I translate audio speech to English automatically?▼

You can translate audio speech to English by setting the task parameter to translate. This applies robust automatic speech recognition to transcribe the original audio and generate an English text translation of the spoken content.

How does automatic speech recognition handle language detection?▼

Automatic speech recognition handles language detection by automatically identifying the spoken language from the audio input. Alternatively, you can explicitly set the language parameter to bypass detection and force transcription for a specific known language.

Do I need Python to use Whisper for speech recognition?▼

You need Python with the openai-whisper library installed to run speech recognition. This environment provides the necessary dependencies to load models ranging from tiny to large and process your audio files into text.

What is the best way to transcribe long podcasts and meetings?▼

The best way to transcribe long podcasts and meetings is using a larger model for improved accuracy on extended audio. The system supports flexible model options from tiny to turbo, allowing you to balance processing speed and transcription quality.

What are the limitations of multilingual speech recognition with Whisper?▼

Limitations of multilingual speech recognition include potential accuracy decreases with low-resource languages or heavy background noise. Selecting smaller models trades accuracy for speed, while translation capabilities are currently restricted to outputting English text only.