OpenClaw · Skill

Azure AI Transcription Py

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

DevOps & Cloud
v0.1.0
VirusTotal: Benign

Install

Start with the primary install command. Alternate entrypoints are included below for ClawHub and OpenClaw CLI users.

Primary command

clawhub install thegovind/azure-ai-transcription-py

ClawHub installer

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

OpenClaw CLI

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

Direct OpenClaw install

openclaw install thegovind/azure-ai-transcription-py

What this skill does

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

Why it matters

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.

Typical use cases

  • 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

Source instructions

Azure AI Transcription SDK for Python

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

Installation

pip install azure-ai-transcription

Environment Variables

TRANSCRIPTION_ENDPOINT=https://<resource>.cognitiveservices.azure.com
TRANSCRIPTION_KEY=<your-key>

Authentication

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

import os
from azure.ai.transcription import TranscriptionClient

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

Transcription (Batch)

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)

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

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