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