Transcribe audio to text using Azure AI Speech (Fast Transcription REST API). Use when converting audio/video to text, generating subtitles, or processing spoken content in OpenMontage. Optional cloud STT provider — preferred when AZURE_SPEECH_KEY is configured; the local faster-whisper `transcriber

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](https://learn.microsoft.com/azure/ai services/speech service/fast transcription create) · [Speech service overview](https://learn.microsoft.com/azure/ai services/speech service/spx 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](https://portal.azure.com); copy the key and region from its Keys and Endpoint page. azure stt reports AVAILABLE once AZURE SPEECH KEY plus either AZURE SPEECH REGION or AZURE SPEECH ENDPOINT are set. Using it in a pipeline Prefer azure stt over transcriber unless the run must be offline. Its output matches the transcriber schema exactly, so it is a drop in for subtitle gen and any stage that consumes a transcript. If azure stt is unavailable (no key) or errors, fall back to transcriber (local whisper) — its execute signature and output are identical. Parameters that matter language — pass an ISO code ( "en" ) or a full locale ( "en US" ). Pin it when you know the language; it is faster and more accurate than auto ID. candidate locales — when language is omitted, Azure runs language identification across this shortlist. Narrow it to the languages you actually expect; a huge list slows detection and invites misclassification. diarize / max speakers — enable for multi speaker audio (interviews, podcasts). Set max speakers to the real upper bound. profanity filter — None Masked (default) Removed Tags . Response shape (mapped to the transcriber schema) The raw Azure response ( phrases[] with offsetMilliseconds / words[] ) is converted to seconds and the OpenMontage transcript schema: Note: Fast Transcription has no per word confidence, so each word carries the phrase confidence in probability . Limits & tips Single file up to ~2 hours / a few hundred MB per request. For longer or bulk jobs, use Azure Batch Transcription instead. Send clean audio (16 kHz+ mono is plenty). Transcode video to audio first if you only need speech — smaller upload, same result. Verify timing: word timestamps drive subtitle cues in subtitle gen . Spot check the first and last cues against the source audio.

azure-speech-to-text