# azure-speech-to-text-rest-py

A skill for short audio transcription via the Azure Speech REST API in Python. It handles speech recognition under 60 seconds without installing the Speech SDK.

- skill · `type=skill` · Claude Code, GitHub Copilot · 2 installs · ★ 3149
- Author: Microsoft
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-speech-to-text-rest-py/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/azure-speech-to-text-rest-py

## What it does

This skill provides instructions for transcribing brief audio clips with Python HTTP requests. It targets the Azure Speech REST endpoint, bypassing the heavy Speech SDK dependency. Supported inputs include 16 kHz mono audio in WAV PCM or OGG OPUS formats.

Working with the API requires an active Azure Speech resource, plus `AZURE_SPEECH_KEY` and `AZURE_SPEECH_REGION` set in your environment. Calls use the standard `requests` library. You can authenticate via the `Ocp-Apim-Subscription-Key` header directly or request an ephemeral bearer token.

Audio length is strictly capped at 60 seconds, dropped to 30 seconds for pronunciation assessment. For real-time continuous streaming or long recordings, the REST endpoint fails and Microsoft directs users to the Speech SDK or Batch Transcription API instead.

## Requirements
- Works with: Azure

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add microsoft/skills --skill azure-speech-to-text-rest-py -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/azure-speech-to-text-rest-py/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/azure-speech-to-text-rest-py/

### Codex

**Manually: put the files in place**
- File: `.agents/skills/azure-speech-to-text-rest-py/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

**Manually: put the files in place**
- File: `.cursor/skills/azure-speech-to-text-rest-py/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install microsoft/skills azure-speech-to-text-rest-py
```

2. **Manually: put the files in place**
- File: `.github/skills/azure-speech-to-text-rest-py/SKILL.md`

### Gemini CLI

**Platform CLI**
```bash
gemini skills install https://github.com/microsoft/skills --path .github/plugins/azure-sdk-python/skills/azure-speech-to-text-rest-py
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/azure-speech-to-text-rest-py/SKILL.md` — The legacy .windsurf/skills/ path still works

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-speech-to-text-rest-py/SKILL.md

## When to use it
- transcribe short WAV or OGG audio files without the Speech SDK
- configure chunked audio transfer to cut request latency
- authenticate Azure Speech REST requests with subscription keys or bearer tokens
- fetch detailed transcription output containing confidence scores

## Example prompts
- Write a Python script to transcribe `sample.wav` using the Azure Speech REST API.
- How do I stream audio chunks to Azure STT via chunked transfer encoding in Python?
- Create an Azure speech recognition function with bearer token authentication.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 1be7f36788467c6a.

BEGIN THIRD-PARTY FILE 1be7f36788467c6a
````text
---
name: azure-speech-to-text-rest-py
description: |-
  Azure Speech to Text REST API for short audio (Python). Use for simple speech recognition of audio files up to 60 seconds without the Speech SDK.
  Triggers: "speech to text REST", "short audio transcription", "speech recognition REST API", "STT REST", "recognize speech REST".
  DO NOT USE FOR: Long audio (>60 seconds), real-time streaming, batch transcription, custom speech models, speech translation. Use Speech SDK or Batch Transcription API instead.
license: MIT
metadata:
  author: Microsoft
  version: "1.0.0"
---

# 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](https://azure.microsoft.com/free/)
2. **Speech resource** - Create in [Azure Portal](https://portal.azure.com/#create/Microsoft.CognitiveServicesSpeechServices)
3. **Get credentials** - After deployment, go to resource > Keys and Endpoint

## Environment Variables

```bash
# 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

```bash
pip install requests
```

## Authentication & Lifecycle

> **🔑 Two rules apply to every code sample below:**
>
> 1. **Two auth modes are supported.** Use a subscription key (`Ocp-Apim-Subscription-Key` header) for quick access, or a Microsoft Entra token (including one acquired with `DefaultAzureCredential`) via the `Authorization` request header (see "Option 2" below). Never hardcode credentials in source.
> 2. **Use context managers for files and HTTP resources** so file handles and network connections are released deterministically:
>    - Sync: `with open(...) as f:` and (when reusing connections) `with requests.Session() as session:`
>    - Async: `async with aiohttp.ClientSession() as session:`
>
> Snippets may abbreviate this setup, but production code should always follow both rules.

## Quick Start

```python
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
````
END THIRD-PARTY FILE 1be7f36788467c6a

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