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/azure-ai-transcription-py

Azure AI Transcription SDK for Python. Use for real-time and batch speech-to-text transcription with timestamps and diarization.

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$ npx -y skills add sickn33/antigravity-awesome-skills --skill azure-ai-transcription-py --agent claude-code

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  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/azure-ai-transcription-py

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Azure AI Transcription SDK for Python. Use for real-time and batch speech-to-text transcription with timestamps and diarization.

SKILL.md

azure-ai-transcription-py.SKILL.md
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.
risk: critical
source: community
date_added: '2026-02-27'

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

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
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