transcription-analysis

---

name: transcription-analysis description: Use this skill for tasks involving transcription providers, caption streaming, or the audio pipeline. Reference: portal/transcription/, TRANSCRIPTION_MAP.md.

Skill: Transcription Analysis

Use this skill for tasks involving transcription providers, caption streaming, or the audio pipeline. Reference: portal/transcription/, TRANSCRIPTION_MAP.md.

---

Audio Pipeline (end-to-end)

Interpreter browser
  └── getUserMedia (mic, 16-bit PCM via WebRTC/WHIP)
        └── MediaMTX (RTSP port 8554, path: {event_slug}/{language_code})
              └── ffmpeg (spawned by worker.py)
                    -rtsp_transport tcp -i rtsp://mediamtx:8554/{path}
                    -f s16le -acodec pcm_s16le -ar 16000 -ac 1 -
                        └── TranscriptionProvider.run_stream()
                              └── 3-second PCM chunks
                                    └── process_chunk() → text
                                          └── CaptionAggregator
                                                └── broadcast_transcription()
                                                      ├── /ws/booth/{booth_id}  (interpreters)
                                                      └── /ws/captions/{booth_id}  (listeners)

---

Worker State

• active_workers: dict[str, dict] — keyed by booth_id; value has {task, provider, stderr_task}
• active_processes: dict[str, asyncio.subprocess.Process] — the ffmpeg process per booth
• Both protected by active_workers_lock: asyncio.Lock
• MAX_TOTAL_WORKERS = 10 — process-global hard limit

---

Adding a New Provider (checklist)

1. Create portal/transcription/providers/{name}.py
2. Implement class inheriting TranscriptionProvider from providers/base.py
3. Implement process_chunk(chunk, language_code, model_variant, config, booth_state) -> str
4. For streaming (WebSocket-based): override run_stream(process, language_code, model_variant, config, broadcast_callback, booth_id) — see deepgram.py and openai.py as references
5. Add ProviderEnum.{NAME} = "{name}" in constants.py
6. Add {name}: {allowed_models_set} to ALLOWED_MODELS
7. Add to PROVIDERS dict in worker.py
8. If API key needed: add encrypted column to Event model (migration 008 pattern) + update get_api_key key_map in base.py
9. Update admin_event_api_settings_post in portal/routers/admin/settings.py to handle new key
10. Update templates/admin/api_settings.html

---

CaptionAggregator Usage

Providers call aggregator methods, not broadcast_callback directly:

aggregator = CaptionAggregator(broadcast_callback)
await aggregator.handle_partial(booth_id, partial_text)   # streaming partial
await aggregator.handle_final(booth_id, final_text)       # completed utterance
await aggregator.handle_chunk(booth_id, whisper_chunk)    # Whisper-style chunks
await aggregator.handle_clear(booth_id)                   # silence endpoint

Forced finalization: if an utterance exceeds 50 words or 15 seconds, it auto-finalizes regardless of provider signal. This prevents endlessly growing partials.

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Local Provider Details

• Model loading: get_model(model_size) — thread-safe lazy loading. Model is loaded once and reused.
• Overlap buffer: 1 second (32 000 bytes) of previous chunk is prepended to current chunk → 4-second effective window per inference call.
• Thread pool: asyncio.to_thread(self._run_inference, ...) — each chunk is inferred in a thread.
• VAD filter: vad_filter=True in faster-whisper — reduces empty segment noise.
• Model eviction: eviction_loop checks every 15 min; evicts model if no active booths and last used > 1 hour ago.

---

OpenAI Provider Details

• whisper-1: uses REST POST /v1/audio/transcriptions (WAV conversion via pcm_to_wav)
• gpt-4o-realtime-*: uses wss://api.openai.com/v1/realtime WebSocket; streams raw PCM
• Retry: tenacity with exponential backoff (2–10s, 3 attempts) on 429/5xx
• HTTP client: portal.transcription.shared_http_client — created in lifespan in fastapi_app.py

---

Deepgram Provider Details

• Connects to wss://api.deepgram.com/v1/listen?model=nova-2&...&interim_results=true
• Runs concurrent sender (pipes PCM) and receiver (gets transcripts) tasks
• Handles KeepAlive (every 5s timeout to prevent WS close)
• speech_final=True or is_final=True → handle_final; otherwise → handle_partial

---

NVIDIA Provider Details

• Uses Riva gRPC (requires nvidia-riva-client optional dependency: uv add eventyay-interpretation-portal[nvidia])
• Models: parakeet-rnnt, parakeet-ctc
• Requires NVIDIA_FUNCTION_ID setting in portal/config.py

---

Transcription Trigger (Admin → Worker)

1. Admin sets transcription_enabled=True, provider, model via booth detail form.
2. POST /admin/.../booths/{id}/transcription-settings handler:
   • Validates provider + model combination.
   • Verifies API key exists if non-local provider.
   • Saves settings to DB.
   • If booth is currently live (has active_interpreter_id): stops old worker, starts new worker.
3. On interpreter Go Live: worker is started if transcription_enabled=True on the DBBooth.
   • Look for this logic in portal/routers/api.py (search for start_transcription_worker).

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Debugging Transcription

1. Check active_workers dict for the booth_id.
2. Check ffmpeg process is alive: active_processes[booth_id].returncode is None.
3. Check MediaMTX RTSP is reachable: rtsp://mediamtx:8554/{event_slug}/{language_code}.
4. Check API key: get_api_key(event, ProviderEnum.X) — returns None if not set.
5. Check MAX_TOTAL_WORKERS not exceeded.
6. Check event.transcription_api_enabled is True for external providers.