# Whisper RTX2080 — Lessons Learned & Improvement Notes

## Quality Baseline (as of 2026-05-06)

Audio: 101-minute YouTube conference talk (Unblocked — Peter Werry)
Model: ggml-large-v3, chunking at 60s on silence boundaries

| Metric            | Score  |
|-------------------|--------|
| WER               | 9.3%   |
| Word coverage     | 93.1%  |
| 1-gram F1         | 94.9%  |
| 3-gram F1         | 84.7%  |
| 5-gram F1         | 77.5%  |

---

## 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
- **Correct API**: `fp.set_language(None)` → passes `language = NULL` to whisper.cpp, which auto-detects AND transcribes
- `set_detect_language(true)` is only for language identification workflows, not transcription
- This caused 0-segment regressions on every job submitted without an explicit `language=` param

### VAD filter causes hallucinations
- `vad_filter=true` silences quiet audience speech → whisper fills the void with "Okay." hallucinations at ~1s intervals
- **Fix**: Remove `vad_filter` entirely

---

## Remaining Known Issues

### 1. Short-token hallucination loops (unfixable by entropy_thold)
- `entropy_thold` is only evaluated when `result_len > 32` output tokens
- Short loops like `kas`, `sick`, `Bye.` (each 1 token) are **never caught**, no matter how low you set the threshold
- Current occurrences: 'kas' ×12 at ~2801s, 'sick' ×4 at ~4540s, 'Bye.' ×10 at ~6070s
- **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

### 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
- **Possible future fix**: retry failed chunks at smaller scope (30s), detect by low-confidence score or segment density

### 3. CUDA device ordering inversion
- `nvidia-smi`: GPU0=RTX 2080 SUPER, GPU1=RTX 3060
- `whisper.cpp` on host: Device 0=RTX 3060, Device 1=RTX 2080 SUPER (inverted vs nvidia-smi)
- Inside Docker: matches nvidia-smi order
- Health endpoint uses nvml (nvidia-smi ordering) → reports wrong GPU name when running on host
- **Workaround**: `CUDA_DEVICE=1` on host to target RTX 2080 SUPER

---

## Whisper Parameter Tuning Notes

Current values in `src/transcriber.rs`:

```
beam_size = 5, patience = 1.0
entropy_thold = 3.5        (catches ~9-word phrase loops, theoretical entropy ≈ log₂(9) ≈ 3.17)
logprob_thold = -1.0       (rejects very low confidence segments)
temperature_inc = 0.2      (fallback temperature increment on failure)
no_context = true          (prevents context from one chunk poisoning the next)
suppress_non_speech_tokens = true
suppress_blank = true
language = None            (auto-detect + transcribe)
```

**What NOT to set:**
- `vad_filter=true` → hallucination loops on quiet speech
- `detect_language=true` → returns 0 segments, transcription never runs

---

## Audio Pre-Processing Pipeline

1. **Download**: yt-dlp → MP3
2. **Convert**: ffmpeg → 16kHz mono WAV (whisper native format)
3. **Silence detection**: ffmpeg `silencedetect` filter at -35dB / 0.4s min duration
4. **Chunking**: target 60s, snap to nearest silence midpoint within ±30s window, fallback to hard cut
5. **Trim trailing silence** per chunk: -35dB threshold, 0.5s padding (applied before whisper)
6. **Transcribe** each chunk independently, offset timestamps, concatenate

**Why chunking helps:** Whisper hallucinations compound over time. Starting each chunk fresh limits how far a bad segment can spread.

**Chunk size trade-off:**
- Smaller (60s): less hallucination spread, but short isolated sections (e.g. someone spelling a name) lose context
- Larger (180s): more context, handles short sections better, but hallucinations can corrupt more content
- Current sweet spot: 60s. If 'KAS'-type issues are a priority, try 90-120s.

---

## Potential Future Improvements (Prioritized)

1. **Retry bad chunks at smaller scope** — detect low-quality output (by segment density or avg logprob) and re-run the chunk at 30s windows
2. **Increase chunk size to 90-120s** — better context for short proper nouns / name spelling; test if hallucination spread stays acceptable
3. **compression_ratio_thold** — may catch short-token loops that entropy_thold misses; test values around 2.0-2.4
4. **Adaptive snap window** — if no silence in ±30s, try ±45s before hard-cutting; reduces long unbroken speech chunks
5. **Per-segment confidence scoring** — expose avg_logprob per segment in the JSON output for downstream filtering
6. **Multiple model support** — medium model for speed, large-v3 for quality; selectable per job