Borrowing it
Nothing to install: this file belongs to Vadiml1024/whisper-speech-to-text. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Vadiml1024/whisper-speech-to-text/main/CLAUDE.mdgit clone --depth 1 https://github.com/Vadiml1024/whisper-speech-to-textWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/vadiml1024/whisper-speech-to-text/claude-md)<a href="https://agentmods.dev/instructions/vadiml1024/whisper-speech-to-text/claude-md"><img src="https://agentmods.dev/badge/instructions/vadiml1024/whisper-speech-to-text/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/vadiml1024/whisper-speech-to-text/claude-md"><img src="https://agentmods.dev/badge/instructions/vadiml1024/whisper-speech-to-text/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00993 | $0.00993 |
| Opus 5 | $0.00496 | $0.00496 |
| Sonnet 5 | $0.00199 | $0.00199 |
| Haiku 4.5 | $0.00099 | $0.00099 |
Grade A, and why
whisper-speech-to-text CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Whisper Speech-to-Text with Speaker Diarization
Project Overview
This is a Python-based audio transcription project that combines MLX Whisper for speech-to-text with pyannote for speaker diarization. The project creates transcripts that identify different speakers in audio files.
Architecture
Core Components
- speech-to-text.py - Main production script that combines MLX Whisper transcription with pyannote speaker diarization
- test-mlx.py - Testing script for validating MLX Whisper API functionality
- .env - Environment configuration file containing HuggingFace token
Technology Stack
- Python 3 - Core runtime
- mlx_whisper - Apple MLX-optimized Whisper for fast speech transcription on Apple Silicon
- pyannote.audio - Speaker diarization pipeline from HuggingFace
- HuggingFace Hub - Model hosting and authentication
Data Flow
- Audio file input
- MLX Whisper transcription (segments with timestamps)
- Pyannote speaker diarization (speaker segments with timestamps)
- Overlap analysis to assign speakers to transcript segments
- Output formatting (txt, srt, or json)
Key Features
- Multi-format output: Support for plain text, SRT subtitles, and JSON
- Speaker identification: Assigns speaker labels to transcript segments
- Timestamp alignment: Matches speakers to text based on time overlap
- Error handling: Graceful handling of API failures and file issues
Dependencies
The project requires these key Python packages:
mlx_whisper- MLX-optimized Whisper implementationpyannote.audio- Speaker diarization toolkit- Standard library:
json,sys,os
Configuration
Environment Variables
HF_TOKEN- HuggingFace authentication token (stored in .env)- Required for accessing pyannote speaker diarization models
- Format:
hf_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
Models Used
- MLX Whisper: Default model or
mlx-community/whisper-large-v3-mlx - Pyannote:
pyannote/speaker-diarization-3.1
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 134 lines · 993 tokens per session scan A 34038506a57c
whisper-speech-to-text CLAUDE.md is an instructions file published in the GitHub repository Vadiml1024/whisper-speech-to-text (10 stars, last pushed 1y ago), licensed MIT. It adds 993 tokens to every session, about $0.0050 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other instructions, from other repositories
spec-kit AGENTS.md
AGENTS.md instructions for github/spec-kit, covering agents.md, about spec kit and specify, quickstart — add a new integration in 5 steps, integration architecture and integrationmanifest — file tracking.
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).