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whisper

Transcribes multilingual audio files into timestamped text and generates SRT/VTT subtitles using OpenAI Whisper architecture.

Updated Jun 17, 2026

Search Tags:#transcription #whisper #audio #multilingual #speech-to-text #asr

Ready to run

npx skills add https://github.com/cxnaive/hermes-agent-llbot --skill whisper-cxnaive

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: whisper
Source: https://github.com/cxnaive/hermes-agent-llbot/tree/main/optional-skills/mlops/whisper
Command: npx skills add https://github.com/cxnaive/hermes-agent-llbot --skill whisper-cxnaive

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This skill solves the challenge of converting spoken audio into accurate, timestamped text across 99 different languages, eliminating the need for manual transcription or expensive third-party services.

Core Features & Use Cases

Multilingual Transcription: Supports 99 languages with high accuracy, including specialized models for different performance needs.
Translation & Subtitling: Automatically translates non-English audio to English text and generates standard subtitle formats like SRT and VTT.
Use Case: Use this to transcribe long-form meeting recordings or podcasts into searchable text documents or to generate subtitles for video content automatically.

Quick Start

Use the whisper skill to transcribe the audio file named meeting_recording.mp3 and save the output as a text file.

Frequently Asked Questions about whisper

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

FAQPage Schema

How do I transcribe an audio file into text with timestamps?▼

Audio transcription into timestamped text is handled by the Whisper model architecture, which converts spoken recordings into accurate text. It supports diverse audio processing tasks including podcast transcription and meeting note generation.

Can I generate SRT subtitles from a multilingual audio recording?▼

Generating SRT subtitles from multilingual audio is supported natively by this speech-to-text model. It processes the recording and outputs standard subtitle formats like SRT and VTT for video content.

Do I need Python and PyTorch installed for local speech-to-text processing?▼

Local speech-to-text processing requires a Python environment with torch, transformers, and openai-whisper dependencies installed. These frameworks provide the necessary execution environment for running the model locally on your machine.

What is the best way to transcribe a long podcast automatically?▼

Transcribing a long podcast automatically is best handled by the Whisper model architecture for high-accuracy speech-to-text conversion. It processes long-form audio recordings and outputs searchable text documents without manual transcription.

Does this transcription approach translate non-English audio to English text?▼

This transcription approach translates non-English audio to English text automatically during processing. It supports multilingual speech-to-text tasks across 99 languages and outputs the translated results as standard text or subtitle files.

Are there limitations when processing meeting notes with local ASR models?▼

Processing meeting notes with local ASR models requires sufficient hardware resources to run PyTorch and the transformers framework. You must ensure your local environment meets the computational demands of the openai-whisper architecture.