fix: GPU warmup on startup + fix test_all.sh + document cold-GPU finding

GPU warmup (src/transcriber.rs):
  After creating WhisperState, run a 1s silent inference pass in load().
  CUDA JIT-compiles device kernels on the first whisper_full_with_state call.
  On a cold GPU this compilation disrupts the decode pipeline mid-inference,
  returning 0 segments in ~0.5s. The warmup forces all kernel compilation at
  startup so the first real job runs on fully compiled kernels.

test_all.sh:
  - Fix submit response field: 'id' → 'job_id' (was breaking all downstream steps)
  - Remove language=auto: not a valid ISO 639-1 code; omit field for auto-detect
  - Make BASE and AUDIO configurable via env vars (WHISPER_BASE_URL, TEST_AUDIO)
  - Fix DELETE assertion: completed jobs return 409 Conflict, not 204
  - Add explicit zero-segments failure check in quality inspection (step 9)
  - Add progress reporting to poll loop

docs/FINDINGS.md + KNOWLEDGE.md:
  Document cold GPU warmup issue, root cause, and fix.
  Document language=auto as invalid API usage.

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>

KNOWLEDGE.md

@@ -15,7 +15,27 @@ Model: ggml-large-v3, chunking at 60s on silence boundaries
 
 ---
 
-## Critical Bugs Found & Fixed
+## Cold GPU Warmup — First Job Returns 0 Segments in ~0.5s
+
+**Severity: Critical (production issue, intermittent, hard to diagnose)**
+
+**Symptom:** After a container restart, the very first submitted job completes in ~0.5 seconds and returns 0 segments. Subsequent jobs work correctly.
+
+**Root cause:** CUDA JIT-compiles its kernels on the **first** call to `whisper_full_with_state`. On a cold GPU, this compilation happens mid-inference and blocks/disrupts the decode pipeline, causing whisper to return immediately with 0 segments.
+
+**Why language detection can still succeed:** Language detection uses only a small mel-spectrogram + encoder pass on the first 30 seconds of audio. Some of these kernels may already be compiled or cached from a prior session. The full decoder kernels (the heavier ones) are what get JIT-compiled on the first full inference.
+
+**Fix:** In `Transcriber::load()`, after creating the state, run a 1-second silent inference pass:
+```rust
+let silence = vec![0.0f32; 16_000]; // 1s @ 16 kHz
+let mut wp = FullParams::new(SamplingStrategy::Greedy { best_of: 1 });
+wp.set_language(Some("en"));
+let _ = state.full(wp, &silence); // forces CUDA JIT — 0 segments expected
+tracing::info!("GPU warmup complete");
+```
+This forces all CUDA kernel compilation at startup. The first real job then runs on fully compiled kernels. Startup takes a few seconds longer but every job is reliable.
+
+---
 
 ### `set_detect_language(true)` is NOT "auto-detect and transcribe"
 - `whisper.cpp` source: `if (params.detect_language) { return 0; }` — it exits immediately after language detection, returns 0 segments
@@ -38,7 +58,14 @@ Model: ggml-large-v3, chunking at 60s on silence boundaries
 - **Possible future fix**: post-process to collapse consecutive identical segments (user declined this for now — raw output only)
 - `compression_ratio_thold` may also help but wasn't tested
 
-### 2. Five significant content gaps (~1600 words total)
+### 4. Cold GPU: first job returns 0 segments in ~0.5s (intermittent, after container restart)
+
+CUDA JIT-compiles kernels on the first call to `whisper_full_with_state`. On a cold GPU this compilation blocks/disrupts the decode pipeline mid-inference, causing an immediate return with 0 segments.
+
+**Fix**: Run a 1-second silent warmup inference in `Transcriber::load()`. This forces JIT compilation at startup so the first real job runs on fully compiled kernels.
+
+---
+
 - Largest: 439 words at ~68 min, 328 words at ~80 min, then 3 × ~293-250 word gaps
 - These are chunks where whisper produced off-topic or repetitive output instead of real content
 - Likely caused by: speaker overlap, audience noise, or poor audio quality in those windows