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azure-ai-transcription-py

Transcribe Azure Blob Storage audio with speaker attribution and timestamps.

Updated Apr 12, 2026
Search Tags:#azure#streaming#transcription#batch#timestamps#speech-to-text#diarization
One-click install
npx skills add https://github.com/davidrrowley/CortexYouV3 --skill azure-ai-transcription-py-davidrrowley
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: azure-ai-transcription-py
Source: https://github.com/davidrrowley/CortexYouV3/tree/main/.agents/skills/azure-ai-transcription-py
Command: npx skills add https://github.com/davidrrowley/CortexYouV3 --skill azure-ai-transcription-py-davidrrowley

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Converts audio into reliable, timestamped text and speaker-attributed transcripts so teams can index, search, subtitle, and analyze recorded meetings and calls without manual transcription.

Core Features & Use Cases

  • Batch transcription for long recordings stored in Azure Blob Storage with job status checks and result retrieval.
  • Real-time streaming for live captioning and low-latency transcription consumption.
  • Diarization and timings to identify speakers, produce phrase- and word-level timestamps, and generate subtitles or meeting summaries.
  • Python SDK integration with environment-variable driven subscription key authentication and guidance for resource cleanup and error handling.
  • Use Case: Transcribe a multi-speaker meeting, attribute text to speakers, and export SRT subtitles with accurate timing.

Quick Start

Set TRANSCRIPTION_ENDPOINT and TRANSCRIPTION_KEY, then call begin_transcription with locale and content_urls for batch jobs or begin_stream_transcription for live streams.

Frequently Asked Questions about azure-ai-transcription-py

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I transcribe audio files into timestamped text with Python?▼

You can transcribe audio files into timestamped text using Python by calling begin_transcription with locale and content_urls for batch jobs, or begin_stream_transcription for live streams.

Can I identify speakers in meeting recordings using Azure speech-to-text?▼

Yes, Azure speech-to-text supports diarization to identify speakers in meeting recordings, producing speaker-attributed transcripts with phrase- and word-level timestamps for accurate subtitle generation.

Does this batch transcription approach work with audio stored in Azure Blob Storage?▼

Batch transcription works with audio stored in Azure Blob Storage by submitting content URLs to the transcription job and polling job status checks until results are retrieved.

What do I need to set up before authenticating Azure speech-to-text in Python?▼

You need to set the TRANSCRIPTION_ENDPOINT and TRANSCRIPTION_KEY environment variables for subscription key authentication before initializing the Python SDK client.

How do I generate SRT subtitles from a multi-speaker recording?▼

You generate SRT subtitles by transcribing the recording with diarization enabled, extracting phrase- and word-level timestamps, and exporting the timed text with speaker attribution.

What are the limitations of streaming transcription for real-time audio processing?▼

Streaming transcription requires proper stream and client cleanup to maintain robust workflows, and you must handle low-latency consumption and error handling carefully to avoid resource leaks.

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