alexsander532/azure-ai-transcription-py
skills_antigravity/skills/azure-ai-transcription-py/SKILL.md
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".
tools
Updated Apr 3, 2026
$ install --global
skillsauthnpx skillsauth add alexsander532/atlas azure-ai-transcription-pyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
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9
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Clean
TrivyContainer and dependency vulnerability scanner
95%
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SemgrepStatic code analysis for vulnerabilities
95%
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mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
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Socket.devSupply chain security analysis
50%
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VirusTotalMulti-engine malware detection
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CrowdStrikeAdvanced threat intelligence
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OSV-ScannerOpen Source Vulnerability database check
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OWASP Dep-Check
50%
Last scanned: Mar 31, 2026, 7:18 PM56.6s1 file scanned
SKILL.md
- name:
- azure-ai-transcription-py
- description:
- |
- Triggers:
- transcription", "speech to text", "Azure AI Transcription", "TranscriptionClient".
- 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
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
- Enable diarization when multiple speakers are present
- Use batch transcription for long files stored in blob storage
- Capture timestamps for subtitle generation
- Specify language to improve recognition accuracy
- Handle streaming backpressure for real-time transcription
- Close transcription sessions when complete
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