
Speech To Text
This handles local speech-to-text using Faster Whisper, built specifically for the JARVIS voice assistant. It's a complete TDD-based implementation that prioritizes privacy (processes locally, deletes audio immediately) and real-time performance with VAD filtering and streaming transcription. The guide walks through model selection from tiny to large-v3, includes concrete latency targets (under 300ms for short audio), and shows optimization patterns like int8 quantization for CPU and chunked processing to avoid waiting for complete recordings. The medium risk rating reflects audio processing and privacy concerns, but the approach is solid if you need offline voice recognition without cloud dependencies.
- skills.sh installs
- 282
- GitHub stars
- 48
- last updated
- Aug 21, 2026
Install
$ npx -y skills add martinholovsky/claude-skills-generator --skill speech-to-text --agent claude-codeInstalls into .claude/skills of the current project.
Files
Speech-to-Text Skill
File Organization: Split structure. See
references/for detailed implementations.
1. Overview
Risk Level: MEDIUM - Processes audio input, potential privacy concerns, resource-intensive
You are an expert in speech-to-text systems with deep expertise in Faster Whisper, audio processing, and transcription optimization. Your mastery spans model selection, audio preprocessing, real-time transcription, and privacy protection for voice data.
You excel at:
- Faster Whisper deployment and optimization
- Audio preprocessing and noise reduction
- Real-time streaming transcription
- Privacy-preserving voice processing
- Multi-language and accent handling
Primary Use Cases:
- JARVIS voice command recognition
- Real-time transcription with low latency
- Offline speech recognition (no cloud dependency)
- Multi-language support for accessibility
2. Core Principles
Sponsors
Details
- Repository
- github.com/martinholovsky/claude-skills-generator
- Categories
- Automation & Workflows, Generative Media, Video & Audio Editing
- First seen
- Jun 3, 2026
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