docs(scriberr): Parakeet memory table, local-attention OOM, scripts are rewritten from the embedded copy

stacks/scriberr/README.md

@@ -133,3 +133,33 @@ paid vendor. In the UI under the AI provider settings:
 
 ⚠ That key also reaches **paid** passthrough models (GLM, Kimi) on a shared
 tab. Keep the configured model on a free local seat.
+
+## Parakeet memory and slicing (measured 2026-09-30)
+
+Scriberr shares fv-ml1 GPU 1 with intern-decision (~10.3 GB cap). The budget left
+for Scriberr is about 5.8 GB, so the compose file sets
+`PARAKEET_CHUNK_THRESHOLD_SECS=120` and
+`PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True`. The numbers behind those
+settings are in the compose comments.
+
+Peak GPU memory on a 35-minute file:
+
+| setting | peak |
+|---|---|
+| 300 s slices | 9,384 MiB |
+| 300 s slices + expandable_segments | 6,962 MiB |
+| 120 s slices + expandable_segments | 5,496 MiB (live setting) |
+
+- **Whole file in one pass with local attention** (`--context-left/right 255`,
+  standard script): **CUDA OOM at >16 GB**. It asked for another 6.46 GiB at
+  9.8 GiB in use. It is not viable on this card budget.
+- ⚠ **Hand-editing the Parakeet scripts does not persist.** Scriberr's
+  `PrepareEnvironment` rewrites `parakeet_transcribe.py` and
+  `parakeet_transcribe_buffered.py` into `whisperx-env/parakeet/` from the copies
+  embedded in the Go binary every time it prepares the environment. A change to the
+  slicer has to go into the source checkout
+  (`internal/transcription/adapters/py/nvidia/parakeet_transcribe_buffered.py`,
+  embedded at build) and be rebuilt. Or it goes upstream (MIT).
+- The two env knobs are read by upstream's Go code (`parakeet_adapter.go`), so
+  they survive image upgrades for as long as upstream keeps them. Re-measure the
+  peak after any upgrade.