azure-speech-to-text-rest-py

azure-speech-to-text-rest-py is a skill for Claude Code, Codex from benjaminasterA/antigravity-awesome-skills. It costs 0 tokens per session (2,700 once invoked), scanned A, original, MIT.

A Python guide for calling Azure's speech-to-text REST API, which turns spoken audio into written text. It is intended for audio files lasting up to 60 seconds and uses ordinary HTTP requests.

In plain words
What is it for?
Use it to transcribe short WAV audio files in Python applications.
Why use it?
It provides a small integration path without requiring an Azure speech SDK or a larger client library.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to transcribe short WAV audio files in Python applications.

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Install with agentmods
npx agentmods add skills/benjaminastera/antigravity-awesome-skills/azure-speech-to-text-rest-py
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add benjaminasterA/antigravity-awesome-skills --skill azure-speech-to-text-rest-py
Clone the repo
git clone --depth 1 https://github.com/benjaminasterA/antigravity-awesome-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

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README.md
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Your own site · 80×15
<a href="https://agentmods.dev/skills/benjaminastera/antigravity-awesome-skills/azure-speech-to-text-rest-py"><img src="https://agentmods.dev/badge/skills/benjaminastera/antigravity-awesome-skills/azure-speech-to-text-rest-py.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,700 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 2 findings, up to high

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • high Privilege Escalation · line 193
    Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.
    Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
  • high Privilege Escalation · line 354
    Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.
    Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00000 $0.02700
Opus 5 $0.00000 $0.01350
Sonnet 5 $0.00000 $0.00540
Haiku 4.5 $0.00000 $0.00270

Measured 9d ago against content hash 0a24beea0c6b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

azure-speech-to-text-rest-py scanned grade A with 1 finding 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 9d 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.

Makes network callslowCapability

Not a fault in itself. Listed so you know the mod talks to something, and to what.

response = requests.post(url, headers=headers, params=params, data=audio_file)
skills/azure-speech-to-text-rest-py/SKILL.md · 378 lines

How it starts

The opening of the file, as written. The whole thing — 378 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Azure Speech to Text REST API for Short Audio

Simple REST API for speech-to-text transcription of short audio files (up to 60 seconds). No SDK required - just HTTP requests.

Prerequisites

  1. Azure subscription - Create one free
  2. Speech resource - Create in Azure Portal
  3. Get credentials - After deployment, go to resource > Keys and Endpoint

Environment Variables

# Required
AZURE_SPEECH_KEY=<your-speech-resource-key>
AZURE_SPEECH_REGION=<region>  # e.g., eastus, westus2, westeurope

# Alternative: Use endpoint directly
AZURE_SPEECH_ENDPOINT=https://<region>.stt.speech.microsoft.com

Installation

pip install requests

Quick Start

import os
import requests

def transcribe_audio(audio_file_path: str, language: str = "en-US") -> dict:
    """Transcribe short audio file (max 60 seconds) using REST API."""
    region = os.environ["AZURE_SPEECH_REGION"]
    api_key = os.environ["AZURE_SPEECH_KEY"]
    
    url = f"https://{region}.stt.speech.microsoft.com/speech/recognition/conversation/cognitiveservices/v1"
    
    headers = {
        "Ocp-Apim-Subscription-Key": api_key,
        "Content-Type": "audio/wav; codecs=audio/pcm; samplerate=16000",
        "Accept": "application/json"
    }
    
    params = {
        "language": language,
        "format": "detailed"  # or "simple"
    }
    
    with open(audio_file_path, "rb") as audio_file:
        response = requests.post(url, headers=headers, params=params, data=audio_file)
    
    response.raise_for_status()
    return response.json()

# Usage
result = transcribe_audio("audio.wav", "en-US")
print(result["DisplayText"])

Audio Requirements

Format Codec Sample Rate Notes
WAV PCM 16 kHz, mono Recommended
OGG OPUS 16 kHz, mono Smaller file size

Limitations:

  • Maximum 60 seconds of audio
  • For pronunciation assessment: maximum 30 seconds
  • No partial/interim results (final only)

Read the full file on GitHub · 378 lines

Changes

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.

  1. 9d ago First seen · 378 lines · 0 tokens per session scan A 0a24beea0c6b

Subscribe to this mod's changes

azure-speech-to-text-rest-py is a skill published in the GitHub repository benjaminasterA/antigravity-awesome-skills (277 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 2,700 tokens. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.

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