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Ahmed Rehan e7ae616385 refactor: flatten Microsoft skills from nested to flat directory structure

Rewrote sync_microsoft_skills.py (v4) to use each SKILL.md's frontmatter
'name' field as the flat directory name under skills/, replacing the nested
skills/official/microsoft/<lang>/<category>/<service>/ hierarchy.

This fixes CI failures caused by the indexing, validation, and catalog
scripts expecting skills/<id>/SKILL.md (depth 1).

Changes:
- Rewrite scripts/sync_microsoft_skills.py for flat output with collision detection
- Update scripts/tests/inspect_microsoft_repo.py for flat name mapping
- Update scripts/tests/test_comprehensive_coverage.py for name uniqueness checks
- Delete skills/official/ nested directory
- Add 129 Microsoft skills as flat directories (e.g. skills/azure-mgmt-botservice-dotnet/)
- Move attribution files to docs/ (LICENSE-MICROSOFT, microsoft-skills-attribution.json)
- Rebuild skills_index.json, CATALOG.md, README.md (845 total skills)

2026-02-12 00:17:38 +05:00

1.7 KiB

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name, description, package

name	description	package
azure-ai-transcription-py	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".	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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