faster-whisper

Name: faster-whisper
Availability: InStock
Author: sunchendd

Transcribe local audio files to text with faster-whisper and optional word-level timestamps.

3|1|Updated Feb 2, 2026

Search Tags:#python #transcription #ffmpeg #speech-to-text #gpu-acceleration #local-processing #faster-whisper

Ready to run

npx skills add https://github.com/sunchendd/good_skills --skill faster-whisper

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: faster-whisper
Source: https://github.com/sunchendd/good_skills/tree/main/theplasmak/faster-whisper
Command: npx skills add https://github.com/sunchendd/good_skills --skill faster-whisper

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires faster-whisper, and includes scripts (resource) components.

What problem does it solve?

Transcribe audio locally to text using faster-whisper, enabling fast, offline speech-to-text with optional word-level timestamps.

Core Features & Use Cases

Local transcription with GPU acceleration for high-speed results
Multilingual support and offline processing for meetings, lectures, podcasts, and video content
Word-level timestamps for subtitles and precise transcripts

Quick Start

Transcribe a local audio file by running the provided script to produce a transcript.

Frequently Asked Questions about faster-whisper

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

FAQPage Schema

How do I transcribe audio to text locally with GPU acceleration?▼

To transcribe audio locally with GPU acceleration, use faster-whisper to process meetings, lectures, and podcasts offline at 4-6x speeds. It requires a Python environment and ffmpeg, leveraging CUDA-enabled PyTorch for GPU processing.

Do I need ffmpeg to run speech-to-text transcription locally?▼

Yes, ffmpeg is required to run speech-to-text transcription locally with faster-whisper. You also need a Python environment, and optionally CUDA-enabled PyTorch to leverage GPU acceleration for high-speed offline processing.

Can I generate word-level timestamps for subtitles from audio files?▼

Yes, you can generate word-level timestamps for subtitles from audio files. The local speech-to-text processing supports optional word-level timestamps, enabling precise transcripts and subtitle alignment for meetings and video content.

Does faster-whisper support multilingual offline transcription for podcasts?▼

Yes, faster-whisper supports multilingual offline transcription for podcasts. It processes audio locally without internet connectivity, handling multilingual input for meetings, lectures, and video content using CPU-to-GPU acceleration.

What is the best way to run speech-to-text without sending audio to the cloud?▼

The best way to run speech-to-text without sending audio to the cloud is local offline processing using faster-whisper. It enables fast, private transcription on your machine via included scripts, utilizing GPU acceleration for high-speed results.

How do I set up a Python environment for local transcription with faster-whisper?▼

To set up a Python environment for local transcription with faster-whisper, install the faster-whisper dependency and ffmpeg. For high-speed GPU acceleration, ensure you have CUDA-enabled PyTorch installed, then load models via the included scripts.