azure-speech-to-text
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 AZURESPEECHKEY is configured; the local faster-whisper transcriber is the default offline path.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/calesthio/openmontage --skill azure-speech-to-text- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads azure-speech-to-text automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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.
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
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.
from tools.tool_registry import registry
registry.discover()
stt = registry._tools["azure_stt"]
result = stt.execute({
"input_path": "projects/my-video/assets/audio/narration.mp3",
# "language": "en", # ISO 639-1 or BCP-47 ("en-US"); omit for auto-ID
# "diarize": True, # speaker labels, no HuggingFace token needed
# "max_speakers": 4,
"output_dir": "projects/my-video/artifacts",
})
if result.success:
segs = result.data["segments"] # [{id,start,end,text,words:[...]}]
words = result.data["word_timestamps"] # flat [{word,start,end,probability}]
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— whenlanguageis 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). Setmax_speakersto 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:
{
"segments": [
{"id": 0, "start": 0.0, "end": 2.4, "text": "Hello world",
"speaker": 1,
"words": [{"word": "Hello", "start": 0.0, "end": 0.5, "probability": 0.98}]}
],
"word_timestamps": [{"word": "Hello", "start": 0.0, "end": 0.5, "probability": 0.98}],
"language": "en-US",
"duration_seconds": 2.4,
"provider": "azure"
}
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.
FAQ
What does the azure-speech-to-text skill do?
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 AZURESPEECHKEY is configured; the local faster-whisper transcriber is the default offline path.
How do I install azure-speech-to-text?
Run npx skills add https://github.com/calesthio/openmontage --skill azure-speech-to-text in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/azure-speech-to-text/ (personal) or .claude/skills/azure-speech-to-text/ (project).
Which AI agents can use azure-speech-to-text?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is azure-speech-to-text safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains azure-speech-to-text?
It is published by calesthio in calesthio/openmontage. It has 787 recorded installs.
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