stt-transcription

Transcribe audio from files or microphones using multiple speech-to-text engines.

3|1|Updated Nov 5, 2025

Search Tags:#transcription #whisper #speech-to-text #stt #assemblyai #google-cloud-speech #azure-speech

Ready to run

npx skills add https://github.com/astoreyai/claude-skills --skill stt-transcription

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: stt-transcription
Source: https://github.com/astoreyai/claude-skills/tree/main/skills/utility/stt-transcription
Command: npx skills add https://github.com/astoreyai/claude-skills --skill stt-transcription

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires sounddevice, soundfile, numpy, whisper, and includes scripts (resource) components.

What problem does it solve?

Speech-to-text transcription across multiple engines to convert audio into accurate text for meetings, notes, captions, and search.

Core Features & Use Cases

Multi-engine STT: Whisper, Google Cloud Speech, Azure Speech, AssemblyAI for flexibility and reliability.
Record from microphone or transcribe audio files, with timestamps, diarization, and multilingual support.
Use cases include meeting minutes, podcast transcripts, video subtitling, and accessibility.

Quick Start

Record audio or load an existing file and transcribe it to text using your preferred engine.

Frequently Asked Questions about stt-transcription

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

FAQPage Schema

How do I transcribe audio files to text using multiple speech-to-text engines?▼

To transcribe audio to text, you can orchestrate engine selection using CLI scripts that support Whisper, Google Cloud Speech, Azure, and AssemblyAI. This multi-engine approach ensures flexibility and reliability for processing recordings or existing audio files.

Can I use Whisper for real-time streaming transcription from a microphone?▼

Yes, Whisper supports real-time streaming transcription directly from microphone input. The Skill uses sounddevice and soundfile to capture audio, allowing the Whisper engine to process speech-to-text continuously during live recording sessions.

Does Python speech-to-text transcription support speaker diarization and timestamps?▼

Speaker diarization and timestamps are supported features within the speech-to-text transcription workflow. These capabilities allow you to identify different speakers and map dialogue precisely, which is essential for generating accurate meeting minutes and podcast transcripts.

What is the best way to convert audio to text for multilingual meeting minutes?▼

The best way to convert audio to text for multilingual meeting minutes is to use a multi-engine STT approach. By leveraging engines like Whisper and Azure Speech, you can accurately transcribe audio content across multiple languages and varied acoustic scenarios.

Do I need Python 3.8+ and specific libraries to run speech-to-text transcription?▼

Yes, you need Python 3.8+ along with dependencies including sounddevice, soundfile, numpy, and whisper to run speech-to-text transcription. These libraries handle audio capture, numerical processing, and the core machine learning inference required for accurate text conversion.

Why choose a multi-engine STT script over a single speech-to-text API?▼

Choosing a multi-engine STT script over a single speech-to-text API provides flexibility and reliability. By supporting Whisper, Google Cloud Speech, Azure, and AssemblyAI, you can switch engines based on accuracy needs, cost, multilingual requirements, or specific platform constraints.