An Azure service that integrates speech processing into apps and services.
Hello @Labatt
Thanks for the detailed reproduction. Based on the results you posted, I don't think additional client-side timeout or file-size troubleshooting will explain this.
Speaker diarization is a supported MAI-Transcribe-2 capability, enabled with: diarization.enabled = true
The current MAI-Transcribe documentation also specifies an input file size of less than 300 MB, but I cannot find a published MAI-Transcribe-2 diarization-specific duration limit around 15 minutes.
Your A/B tests are particularly useful here:
- 30-minute recording + diarization → fails
- same 30-minute recording without diarization → succeeds
- 73-minute recording without diarization → succeeds
- 17.5 MB re-encoded recording + diarization → fails
- 70.2 MB original recording without diarization → succeeds
That makes the general audio-duration limit, upload size, and transcription itself unlikely explanations. The 503 diarization_unavailable response containing “Diarization service returned error code 400” also points toward the downstream diarization component rather than the Speech request upload.
Microsoft also describes MAI-Transcribe-2 as supporting diarization for multi-party/long-form scenarios, and the model is currently in public preview.
At this point, I recommend opening an Azure Support request for Azure Speech in Foundry Tools and asking them to escalate it to the MAI-Transcribe/Speech engineering team.
Include your existing test matrix, region (eastus), API version (2025-10-15), resource ID, UTC timestamps for the failed requests, and especially the trace/detail ID from the HTTP 500 response: d582ccb307fe4e5c93857111650d7224
Specifically ask Microsoft engineering to confirm whether:
- There is an undocumented duration/processing limit for MAI-Transcribe-2 native diarization;
- The 408/500/503 responses represent a known preview-service issue; and
- The conflicting limits in the MAI, Fast Transcription, and REST documentation need to be reconciled.
I don't recommend further re-encoding or increasing the HTTP client timeout at this stage. Your results already show that substantially larger/longer audio succeeds when diarization.enabled is removed, and the service returns failures after upload.
Your current workaround, which is performing the MAI transcription without native diarization and applying diarization separately, is reasonable until Microsoft confirms the backend behavior.
References:
MAI-Transcribe-2 documentation - Microsoft Learn
MAI-Transcribe-2 model catalog - Microsoft Foundry
MAI-Transcribe-2 announcement - Microsoft Azure AI Foundry Blog
Help make this community better for everyone: if this answer assisted you/resolved your issue, please accept it or upvote it. If not, share more details in a comment so we can continue the discussion and find the right solution.