Azure AI Speech, Speech-to-Text

Transcribe audio to text with Azure Fast Transcription, synchronous, word-level timestamps, speaker diarization, and multi-language identification. In OpenMontage this is exposed through the azure_stt tool (capability=analysis, provider=azure). It is an optional cloud STT provider, when AZURE_SPEECH_KEY is configured, prefer it for cloud transcription. The local transcriber tool (faster-whisper) remains the default offline path and the fallback when Azure is unavailable.

Docs: Fast Transcription · Speech service overview

Why Fast Transcription (not Batch)

Azure exposes three STT surfaces. OpenMontage uses Fast Transcription because the pipeline transcribes local audio files:

Surface	Input	Latency	Needs
Fast Transcription (used here)	local file, multipart POST	synchronous, sub-real-time	key + region
Batch Transcription	audio at a URL (Blob + SAS)	async job + polling	Blob storage plumbing
Speech SDK (spx)	mic / stream / file	streaming	native azure-cognitiveservices-speech package

Fast Transcription needs no Blob storage, no SAS URLs, and no native SDK, just requests and the two env vars.

Setup

Create a Speech resource in the Azure portal; copy the key and region from its Keys and Endpoint page.

export AZURE_SPEECH_KEY=your_speech_resource_key
export AZURE_SPEECH_REGION=eastus          # your resource's region
# export AZURE_SPEECH_ENDPOINT=https://...  # optional: overrides region

azure_stt reports AVAILABLE once AZURE_SPEECH_KEY plus either AZURE_SPEECH_REGION or AZURE_SPEECH_ENDPOINT are set.

Using it in a pipeline