[binyangzhu000-sudo]

Summary

• keep OpenAI as the default transcription provider and add Atlas Cloud as an explicit opt-in
• map local audio to the live xai/stt-v1 schema, including optional diarization
• submit Atlas jobs once and poll prediction status with bounded exponential backoff
• document provider-specific keys, capabilities, and unsupported options

Testing

• python3 -m py_compile skills/transcribe/scripts/transcribe_diarize.py
• OpenAI default dry-run and Atlas diarized dry-run
• focused mock harness confirming one POST, bounded GET polling, and text/JSON formatting
• workflow-equivalent frontmatter, category, injection-pattern, and length checks

No paid transcription request was sent; the integration was validated against the live Atlas model catalog and OpenAPI schema plus local request/response mocks.

[greptile-apps]

Greptile Summary

This change adds an opt-in Atlas Cloud transcription path with Base64 audio submission and asynchronous prediction polling. Offline execution reproduced two reliability failures in skills/transcribe/scripts/transcribe_diarize.py: supported large audio files create several simultaneous in-memory representations before submission, and polling can issue a prediction request after its configured timeout has elapsed. The change should not merge until the large-input memory behavior is addressed.

Confidence Score: 4/5

The Atlas transcription implementation is not safe to merge as written because a documented supported input size can exhaust the invoking process's memory.

Focused offline executions exercised the production payload, serialization, and polling paths. They measured the duplicated audio representations and reproduced a request occurring after the polling deadline.

Files Needing Attention: skills/transcribe/scripts/transcribe_diarize.py needs changes to avoid retaining multiple full-size audio representations and to prevent polling requests after the deadline.

T-Rex Logs

What T-Rex did

• Ran the offline Atlas audio payload serialization probe and executed both the Base64-omitted control path and the encoded serialization path to validate the P1 finding.
• Executed the focused offline Atlas polling boundary test, reproduced the boundary output, and confirmed Python compilation for the test source.
• Validated the Atlas audio serialization probe contract by inspecting the authored probe script and reviewing the before- and after-path outputs.
• Ran the atlas_poll_deadline_boundary_test and performed a Python compile check for the related scripts to verify the deadline boundary behavior and script syntax.

_{Ran code and verified through T-Rex}

Comments Outside Diff (2)

1. General comment

   Atlas payload construction duplicates large audio representations in memory

   • Bug
     • For inputs allowed up to 500 MiB, _build_atlas_payload reads the whole file then Base64-encodes it into a Python string; _atlas_json_request then creates and retains a separate UTF-8 JSON bytes body. The executed 64 MiB sparse control/encoded pair shows an 85.33 MiB Base64 string and 85.33 MiB request body, with 256.00 MiB traced peak (4.00× the input) and 170.67 MiB retained after serialization. This supports a severe memory-pressure/OOM consequence at the 500 MiB limit.
   • Cause
     • audio_path.read_bytes() creates a complete binary buffer, base64.b64encode(...).decode('ascii') materializes Base64 bytes then a text string, and json.dumps(payload).encode('utf-8') materializes the serialized request bytes while the payload remains referenced.
   • Fix
     • Avoid inline Base64 JSON for large audio: use a streaming/multipart upload or a provider upload/reference flow. If inline JSON is unavoidable, enforce a substantially lower safe limit and minimize simultaneous representations.

   _{Ran code and verified through T-Rex}

2. General comment

   Atlas polling issues a GET after its polling deadline

   • Bug
     • For a pending Atlas prediction, a sleep can cross the deadline and the implementation still invokes the prediction GET before the loop condition is evaluated again. The focused injected-clock run set the deadline to 2.0 seconds, advanced the clock to 3.0 during sleep, and recorded the GET at 3.0 before receiving the timeout error.
   • Cause
     • _run_atlas_one evaluates monotonic() < deadline only at the start of the while body (line 294). It sleeps at line 295 and performs request_json(..., "GET", ...) at lines 296-298 without checking whether that sleep exhausted the deadline.
   • Fix
     • Re-check monotonic() < deadline immediately after sleep and before the GET, or cap sleep duration to remaining deadline time and exit when no time remains.

   _{Ran code and verified through T-Rex}

_{Reviews (1): Last reviewed commit: "feat(transcribe): add optional Atlas Clo..." | Re-trigger Greptile}

[greptile-apps]

Large Atlas inputs exhaust memory

Atlas accepts audio files up to 500 MB, but this path reads the entire file into memory, materializes a Base64 string, and then serializes that string into a separate JSON byte buffer. The exercised 64 MiB input reached a 256 MiB traced allocation peak; at the documented limit this can require roughly 2 GiB of process memory before the request is sent, causing avoidable memory pressure or termination. Use an upload/reference flow or streaming transport, or lower the accepted size to one that is safe for the simultaneous representations.

Note: If this suggestion doesn't match your team's coding style, reply to this and let me know. I'll remember it for next time!

Artifacts

Offline Atlas audio payload and serialization probe source

• The authored executable invokes the real payload and request-serialization functions against a sparse audio file with an offline HTTP capture, showing the measured representations.

Control run with Base64 audio omitted

• The executed 64 MiB sparse-file control captured a 58-byte request and negligible traced allocation, establishing the non-encoded baseline.

Encoded Atlas audio serialization run

• The executed 64 MiB sparse-file encoded run captured an 85.33 MiB Base64 payload and JSON body with a 256.00 MiB traced peak, supporting the memory-pressure finding.

_{Ran code and verified through T-Rex}

[greptile-apps]

Polling exceeds its deadline

A polling sleep can cross the deadline, but the code issues the prediction GET before evaluating the loop condition again. The deadline-boundary run advanced the injected clock from 0 to 3 seconds during the first sleep for a 2-second timeout and recorded a GET at time 3 before the timeout was raised. Re-check the deadline after sleeping and before requesting the prediction, or cap the sleep interval to the remaining time.

Artifacts

Focused offline Atlas polling deadline boundary test source

• The exact authored Python test injects a clock, sleep function, and Atlas request function to cross the deadline during polling, demonstrating the boundary condition.

Atlas polling boundary reproduction output

• The executed offline test records a GET at simulated time 3.0 after a 2.0-second deadline and then records the timeout error, proving the extra request occurs.

Python compilation validation output

• The executed byte-compilation command exits successfully for both the focused test and the target transcription module, confirming they are syntactically executable.

Captured exact Atlas polling boundary test source

• The executed source capture prints the complete authored test file used for the offline reproduction, preserving the exact injected test setup.

_{Ran code and verified through T-Rex}