frank-luongt/skills/codex/azure-ai-transcription-py
skills/codex/azure-ai-transcription-py/SKILL.md
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: azure-ai-transcription-py description: "Azure AI Transcription SDK for Python" --- # 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> ``` ## A
$ install --global
skillsauthnpx skillsauth add frank-luongt/faos-skills-marketplace skills/codex/azure-ai-transcription-pyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
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mcp-scan (Snyk)Model Context Protocol security validation
95%
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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: Apr 20, 2026, 3:04 PM5.2s3 files scanned
SKILL.md
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT -->
name: azure-ai-transcription-py description: "Azure AI Transcription SDK for Python"
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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