azure-ai-transcription-py

DevOps & Cloud
v0.1.0
Benign

Azure AI Transcription SDK for Python.

11.7K downloads1.7K installsby @thegovind

Setup & Installation

Install command

clawhub install thegovind/azure-ai-transcription-py

If the CLI is not installed:

Install command

npx clawhub@latest install thegovind/azure-ai-transcription-py

Or install with OpenClaw CLI:

Install command

openclaw skills install thegovind/azure-ai-transcription-py

or paste the repo link into your assistant's chat

Install command

https://github.com/openclaw/skills/tree/main/skills/thegovind/azure-ai-transcription-py

What This Skill Does

Python client library for Microsoft Azure's speech-to-text service. Supports batch transcription of audio files stored in blob storage and real-time streaming transcription with per-event text output. Speaker diarization and timestamp capture are available in both modes.

Combines batch and real-time transcription with built-in diarization in a single client, removing the need to stitch together separate Azure services or third-party speaker separation tools.

When to Use It

  • Transcribing recorded meeting audio with speaker labels
  • Generating subtitle files from video recordings
  • Real-time captioning of live audio streams
  • Processing large call center recordings stored in blob storage
  • Converting interview audio to searchable text
View original SKILL.md file
# 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>
```

## Authentication

Use subscription key authentication (DefaultAzureCredential is not supported for this client):

```python
import os
from azure.ai.transcription import TranscriptionClient

client = TranscriptionClient(
    endpoint=os.environ["TRANSCRIPTION_ENDPOINT"],
    credential=os.environ["TRANSCRIPTION_KEY"]
)
```

## Transcription (Batch)

```python
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
stream = client.begin_stream_transcription(locale="en-US")
stream.send_audio_file("audio.wav")
for event in stream:
    print(event.text)
```

## Best Practices

1. **Enable diarization** when multiple speakers are present
2. **Use batch transcription** for long files stored in blob storage
3. **Capture timestamps** for subtitle generation
4. **Specify language** to improve recognition accuracy
5. **Handle streaming backpressure** for real-time transcription
6. **Close transcription sessions** when complete

Example Workflow

Here's how your AI assistant might use this skill in practice.

INPUT

User asks: Transcribing recorded meeting audio with speaker labels

AGENT
  1. 1Transcribing recorded meeting audio with speaker labels
  2. 2Generating subtitle files from video recordings
  3. 3Real-time captioning of live audio streams
  4. 4Processing large call center recordings stored in blob storage
  5. 5Converting interview audio to searchable text
OUTPUT
Azure AI Transcription SDK for Python.

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Last updatedFeb 27, 2026