# azure-ai-transcription-py

This skill guides Python development with Azure AI Transcription for batch jobs and live speech recognition. Use it when an app needs speaker labels or timestamps alongside the transcript.

- 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-ai-transcription-py/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/azure-ai-transcription-py

## What it does

The skill covers the `azure-ai-transcription` SDK in both batch and streaming workflows. A batch job can transcribe audio from a storage URL, while a live session sends audio through `TranscriptionClient` and reads text from emitted events. Speaker diarization helps distinguish people in a recording; timestamps can support subtitle generation.

Install the `azure-ai-transcription` Python package and provide the resource address through `TRANSCRIPTION_ENDPOINT`. Authentication uses either `TRANSCRIPTION_KEY` or Microsoft Entra ID credentials. Clients should run inside context managers, and sync and async clients should not be mixed in the same call path. Without a valid endpoint and credentials, the client cannot connect.

## Requirements
- Works with: Azure

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add microsoft/skills --skill azure-ai-transcription-py -a claude-code
```

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

### Codex

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

### Cursor

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

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install microsoft/skills azure-ai-transcription-py
```

2. **Manually: put the files in place**
- File: `.github/skills/azure-ai-transcription-py/SKILL.md`

### Gemini CLI

**Platform CLI**
```bash
gemini skills install https://github.com/microsoft/skills --path .github/plugins/azure-sdk-python/skills/azure-ai-transcription-py
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/azure-ai-transcription-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-ai-transcription-py/SKILL.md

## When to use it
- Transcribe a long recording as a batch job
- Read recognized text from a live audio stream
- Separate transcript segments by speaker
- Add timestamps for subtitle generation

## Example prompts
- Write Python code to transcribe `audio.wav` as an Azure AI Transcription batch job.
- Show how to stream `audio.wav` and read text from transcription events.
- Add speaker diarization and timestamps to the transcription example.

## 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: 19559b97443d9de6.

BEGIN THIRD-PARTY FILE 19559b97443d9de6
````text
---
name: azure-ai-transcription-py
description: |
  Azure AI Transcription SDK for Python. Use for real-time and batch speech-to-text transcription with timestamps and diarization.
  Triggers: "transcription", "speech to text", "Azure AI Transcription", "TranscriptionClient".
license: MIT
metadata:
  author: Microsoft
  version: "1.0.0"
  package: azure-ai-transcription
---

# Azure AI Transcription SDK for Python

Client library for Azure AI Transcription (speech-to-text) with real-time and batch transcription.

## Installation

```bash
pip install azure-ai-transcription
```

## Environment Variables

```bash
TRANSCRIPTION_ENDPOINT=https://<resource>.cognitiveservices.azure.com
TRANSCRIPTION_KEY=<your-key>  # For key auth; not needed when using DefaultAzureCredential/TokenCredential
```

## Authentication & Lifecycle

> **🔑 Two rules apply to every code sample below:**
>
> 1. **Two auth modes are supported:** `AzureKeyCredential(os.environ["TRANSCRIPTION_KEY"])` for key-based auth, or `DefaultAzureCredential()` / any `TokenCredential` for Entra ID. Prefer `DefaultAzureCredential` in production; never hardcode credentials in code.
> 2. **Wrap every client in a context manager** so HTTP transports and sockets are released deterministically:
>    - Sync: `with <Client>(...) as client:`
>    - Async: `async with <Client>(...) as client:`
>
> Snippets may abbreviate this setup, but production code should always follow both rules.

Use subscription key authentication:

```python
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.transcription import TranscriptionClient

with TranscriptionClient(
    endpoint=os.environ["TRANSCRIPTION_ENDPOINT"],
    credential=AzureKeyCredential(os.environ["TRANSCRIPTION_KEY"]),
) as client:
    transcriptions = list(client.list_transcriptions())
```

## Transcription (Batch)

```python
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.transcription import TranscriptionClient

with TranscriptionClient(
    endpoint=os.environ["TRANSCRIPTION_ENDPOINT"],
    credential=AzureKeyCredential(os.environ["TRANSCRIPTION_KEY"]),
) as client:
    job = client.begin_transcription(
        name="meeting-transcription",
        locale="en-US",
        content_urls=["https://<storage>/audio.wav"],
        diarization_enabled=True,
    )
    result = job.result()
    print(result.status)
```

## Transcription (Real-time)

```python
import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.transcription import TranscriptionClient

with TranscriptionClient(
    endpoint=os.environ["TRANSCRIPTION_ENDPOINT"],
    credential=AzureKeyCredential(os.environ["TRANSCRIPTION_KEY"]),
) as client:
    stream = client.begin_stream_transcription(locale="en-US")
    stream.send_audio_file("audio.wav")
    for event in stream:
        print(event.text)
```

## Best Practices

1. **Pick sync OR async and stay consistent.** Do not mix `azure.xxx` sync clients with `azure.xxx.aio` async clients in the same call path. Choose one mode per module.
````
END THIRD-PARTY FILE 19559b97443d9de6

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---
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