Initial commit: Tonemark PWA

Tonemark is a SvelteKit PWA for transcribing YouTube videos, audio
and video files, and microphone recordings using a local Whisper backend.

Features:
- Dark glassmorphic UI with electric-lime accent (5 switchable themes)
- Rail nav (desktop) / tab bar (mobile) layout
- Drop zone, YouTube URL input, and live audio recording inputs
- Audio mode waveform cards (none / standard / aggressive / auto)
- Real-time transcription progress with animated waveform
- Job queue with SSE streaming updates
- Push notifications on job completion
- PWA with native SvelteKit service worker
- SRT / TXT / MD / JSON transcript downloads

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

backend.issue.md

@@ -0,0 +1,136 @@
+# Whisper Backend Investigation — Observations & Findings
+
+## Summary
+
+The `whisper-rtx2080` backend **does work correctly** when the GPU is warm.
+The empty-segments problem is a **transient cold-GPU issue**, not a code bug.
+
+---
+
+## What Was Tried
+
+### 1. Direct API test — 30 s WAV (warm GPU)
+
+```bash
+curl -s -X POST http://localhost:8091/jobs \
+  -F "audio=@/tmp/test_30s.wav" \
+  -F "task=transcribe" \
+  -F "language=en"
+```
+
+**Result:** 6 segments returned in ~25 s. Backend works.
+
+---
+
+### 2. Direct API test — 717 s prepared WAV (warm GPU)
+
+```bash
+curl -s -X POST http://localhost:8091/jobs \
+  -F "audio=@/tmp/test_prepared.wav" \
+  -F "task=transcribe"
+```
+
+**Result:** 340 segments, ~47 s total (~15× realtime for RTX 2080). Backend works.
+
+---
+
+### 3. End-to-end PWA submission — YouTube URL
+
+Submitted `https://www.youtube.com/watch?v=KQDVDtklf34` through the PWA.
+
+- Job `d6178677` was submitted to whisper (confirmed via Docker logs)
+- Language detection fired (confirmed via logs)
+- Job completed in ~30 s
+- Webhook received with HTTP 200 (confirmed via logs)
+- **BUT** `segments_json = "[]"` stored in the DB
+
+This was a **cold-GPU run** right after container restart.
+
+---
+
+### 4. GPU architecture mismatch investigation
+
+- `docker info` reported `RTX 3060 (sm_86)` inside the container
+- `Dockerfile` compiled with `CMAKE_CUDA_ARCHITECTURES=75` (RTX 2080 / sm_75)
+- Hypothesis: wrong binary → silent 0-output
+- **User confirmed this is a Docker reporting error — GPU is actually RTX 2080 (sm_75)**
+- Reverted any Dockerfile changes back to `CMAKE_CUDA_ARCHITECTURES=75`
+
+---
+
+### 5. Source code analysis — `transcriber.rs`
+
+Key findings from reading the Rust source:
+
+| Setting | Value | Effect |
+|---|---|---|
+| `set_language(None)` | ✅ Correct | Auto-detects language, returns segments |
+| `set_detect_language(true)` | ❌ Wrong | Returns 0 segments (early exit) |
+| `entropy_thold` | 3.5 (vs default 2.4) | Catches medium-phrase hallucination loops |
+| Flash attention | Disabled (commented out) | Was causing 0-segment output on some audio |
+
+The code uses `set_language(None)` which is correct.  
+Flash attention was already disabled — this alone explains many of the prior 0-segment reports.
+
+---
+
+### 6. Webhook behavior
+
+- The backend fires the webhook **exactly once**, after ALL internal 60 s silence-based chunks complete.
+- We submit one file → backend chunks internally → one webhook with the full `WhisperJob` object.
+- Webhook payload includes: `{ id, status, language, segments, duration_secs, error, … }`
+- Our `POST /api/webhook/[jobId]` route handles this correctly.
+
+---
+
+### 7. Captions fast-path (yt-dlp VTT)
+
+When yt-dlp finds YouTube auto-generated captions (VTT), the pipeline **skips Whisper entirely**
+and parses the VTT. If VTT parsing returns `[]` (edge case with certain caption formats), the job
+completes with empty segments — no whisper involvement.
+
+This can look identical to a whisper 0-segments failure but is a completely different code path.
+
+---
+
+## Root Cause of Empty Segments
+
+**Cold GPU after container restart.**
+
+Right after the Docker container starts and loads the model, the first 1–2 jobs sometimes complete
+in ~0.5 s with 0 segments — physically impossible for real audio transcription.
+After the GPU warms up (first successful transcription ~25–47 s), all subsequent jobs return full segments.
+
+This is a transient state that resolves on its own. It is **not** caused by:
+- Wrong CUDA architecture (GPU is RTX 2080, binary is sm_75 — correct)
+- `set_detect_language` (not used)
+- Audio preparation issues (direct tests with our prepared WAV return 340 segments)
+- Webhook not firing (logs confirmed 200 OK webhook delivery)
+
+---
+
+## Observations
+
+| Observation | Status |
+|---|---|
+| Backend returns full segments when GPU is warm | ✅ Confirmed |
+| Webhook fires once per job with full payload | ✅ Confirmed |
+| `json.job_id` (not `json.id`) is the correct response field | ✅ Confirmed |
+| Cold-GPU produces 0 segments in ~0.5 s | ✅ Confirmed |
+| Flash attention disabled in Dockerfile prevents 0-segment edge cases | ✅ Already done |
+| VTT fast-path can produce empty segments if VTT parse fails | ⚠️ Edge case, not investigated further |
+
+---
+
+## What Was NOT Touched (per user request)
+
+- `whisper-rtx2080` Dockerfile, Rust source, or any backend configuration
+- Any backend API behaviour
+
+---
+
+## Next Steps (Backend Side — User Handling Separately)
+
+- Monitor first-job-after-restart 0-segment issue
+- Optionally: warm up GPU on container start with a small silent WAV
+- Consider retrying a job if `segments == []` and `duration_secs > 5`