azure-ai-transcription-py
Azure AI Transcription SDK for Python.
Setup & Installation
Install command
clawhub install thegovind/azure-ai-transcription-pyIf the CLI is not installed:
Install command
npx clawhub@latest install thegovind/azure-ai-transcription-pyOr install with OpenClaw CLI:
Install command
openclaw skills install thegovind/azure-ai-transcription-pyor paste the repo link into your assistant's chat
Install command
https://github.com/openclaw/skills/tree/main/skills/thegovind/azure-ai-transcription-pyWhat 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.
User asks: Transcribing recorded meeting audio with speaker labels
- 1Transcribing recorded meeting audio with speaker labels
- 2Generating subtitle files from video recordings
- 3Real-time captioning of live audio streams
- 4Processing large call center recordings stored in blob storage
- 5Converting interview audio to searchable text
Azure AI Transcription SDK for Python.
Security Audits
These signals reflect official OpenClaw status values. A Suspicious status means the skill should be used with extra caution.