azure-speech-to-text Checked
Installation
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⬇ Download v1.0.0 · 3 KBNot live-tested yet — this skill is rated on static analysis only.
About this skill
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 is the default offline path.
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
Documentation
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 is the default offline path.
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
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"]
View full documentation
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`** — 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:
{
"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.
Score breakdown
Assessment methodology
Version history
Imported from calesthio/OpenMontage@main