chore(main): release 0.33.0

* feat: set Daily as default video platform

Daily.co has been battle-tested and is ready to be the default.
Whereby remains available for rooms that explicitly set it.

* feat: enforce Hatchet for all multitrack processing

Remove use_celery option from rooms - multitrack (Daily) recordings
now always use Hatchet workflows. Celery remains for single-track
(Whereby) file processing only.

- Remove use_celery column from room table
- Simplify dispatch logic to always use Hatchet for multitracks
- Update tests to mock Hatchet instead of Celery

* fix: update whereby test to patch Hatchet instead of removed Celery import

---------

Co-authored-by: Igor Loskutov <igor.loskutoff@gmail.com>

.gitignore

@@ -1,5 +1,6 @@
 .DS_Store
 server/.env
+server/.env.production
 .env
 Caddyfile
 server/exportdanswer

.gitleaksignore

@@ -4,3 +4,4 @@ docs/docs/installation/daily-setup.md:curl-auth-header:277
 gpu/self_hosted/DEV_SETUP.md:curl-auth-header:74
 gpu/self_hosted/DEV_SETUP.md:curl-auth-header:83
 server/reflector/worker/process.py:generic-api-key:465
+server/reflector/worker/process.py:generic-api-key:594

CHANGELOG.md

@@ -1,5 +1,41 @@
 # Changelog
 
+## [0.33.0](https://github.com/Monadical-SAS/reflector/compare/v0.32.2...v0.33.0) (2026-02-05)
+
+
+### Features
+
+* Daily+hatchet default ([#846](https://github.com/Monadical-SAS/reflector/issues/846)) ([15ab2e3](https://github.com/Monadical-SAS/reflector/commit/15ab2e306eacf575494b4b5d2b2ad779d44a1c7f))
+
+
+### Bug Fixes
+
+* websocket tests ([#825](https://github.com/Monadical-SAS/reflector/issues/825)) ([1ce1c7a](https://github.com/Monadical-SAS/reflector/commit/1ce1c7a910b6c374115d2437b17f9d288ef094dc))
+
+## [0.32.2](https://github.com/Monadical-SAS/reflector/compare/v0.32.1...v0.32.2) (2026-02-03)
+
+
+### Bug Fixes
+
+* increase TIMEOUT_MEDIUM from 2m to 5m for LLM tasks ([#843](https://github.com/Monadical-SAS/reflector/issues/843)) ([4acde4b](https://github.com/Monadical-SAS/reflector/commit/4acde4b7fdef88cc02ca12cf38c9020b05ed96ac))
+* make caddy optional ([#841](https://github.com/Monadical-SAS/reflector/issues/841)) ([a2ed7d6](https://github.com/Monadical-SAS/reflector/commit/a2ed7d60d557b551a5b64e4dfd909b63a791d9fc))
+* use Daily API recording.duration as master source for transcript duration ([#844](https://github.com/Monadical-SAS/reflector/issues/844)) ([8707c66](https://github.com/Monadical-SAS/reflector/commit/8707c6694a80c939b6214bbc13331741f192e082))
+
+## [0.32.1](https://github.com/Monadical-SAS/reflector/compare/v0.32.0...v0.32.1) (2026-01-30)
+
+
+### Bug Fixes
+
+* daily multitrack pipeline finalze dependency fix ([23eb137](https://github.com/Monadical-SAS/reflector/commit/23eb1371cb9348c4b81eb12ad506b582f8a4799e))
+* match httpx pad with hatchet audio timeout ([c05d1f0](https://github.com/Monadical-SAS/reflector/commit/c05d1f03cd8369fc06efd455527e50246887efd0))
+
+## [0.32.0](https://github.com/Monadical-SAS/reflector/compare/v0.31.0...v0.32.0) (2026-01-30)
+
+
+### Features
+
+* modal padding ([#837](https://github.com/Monadical-SAS/reflector/issues/837)) ([7fde64e](https://github.com/Monadical-SAS/reflector/commit/7fde64e2529a1d37b0f7507c62d983a7bd0b5b89))
+
 ## [0.31.0](https://github.com/Monadical-SAS/reflector/compare/v0.30.0...v0.31.0) (2026-01-23)
 
 

Caddyfile.example

@@ -1,6 +1,8 @@
-# Reflector Caddyfile
+# Reflector Caddyfile (optional reverse proxy)
-# Replace example.com with your actual domains
+# Use this only when you run Caddy via: docker compose -f docker-compose.prod.yml --profile caddy up -d
-# CORS is handled by the backend - Caddy just proxies
+# If Coolify, Traefik, or nginx already use ports 80/443, do NOT start Caddy; point your proxy at web:3000 and server:1250.
+#
+# Replace example.com with your actual domains. CORS is handled by the backend - Caddy just proxies.
 #
 # For environment variable substitution, set:
 #   FRONTEND_DOMAIN=app.example.com

docker-compose.prod.yml

@@ -1,9 +1,14 @@
 # Production Docker Compose configuration
 # Usage: docker compose -f docker-compose.prod.yml up -d
 #
+# Caddy (reverse proxy on ports 80/443) is OPTIONAL and behind the "caddy" profile:
+# - With Caddy (self-hosted, you manage SSL): docker compose -f docker-compose.prod.yml --profile caddy up -d
+# - Without Caddy (Coolify/Traefik/nginx already on 80/443): docker compose -f docker-compose.prod.yml up -d
+#   Then point your proxy at web:3000 (frontend) and server:1250 (API).
+#
 # Prerequisites:
 # 1. Copy .env.example to .env and configure for both server/ and www/
-# 2. Copy Caddyfile.example to Caddyfile and edit with your domains
+# 2. If using Caddy: copy Caddyfile.example to Caddyfile and edit your domains
 # 3. Deploy Modal GPU functions (see gpu/modal_deployments/deploy-all.sh)
 
 services:
@@ -84,6 +89,8 @@ services:
       retries: 3
 
   caddy:
+    profiles:
+      - caddy
     image: caddy:2-alpine
     restart: unless-stopped
     ports:

docs/docs/installation/docker-setup.md

@@ -11,15 +11,15 @@ This page documents the Docker Compose configuration for Reflector. For the comp
 
 The `docker-compose.prod.yml` includes these services:
 
-| Service | Image | Purpose |
+| Service    | Image                             | Purpose                                                                     |
-|---------|-------|---------|
+| ---------- | --------------------------------- | --------------------------------------------------------------------------- |
-| `web` | `monadicalsas/reflector-frontend` | Next.js frontend |
+| `web`      | `monadicalsas/reflector-frontend` | Next.js frontend                                                            |
-| `server` | `monadicalsas/reflector-backend` | FastAPI backend |
+| `server`   | `monadicalsas/reflector-backend`  | FastAPI backend                                                             |
-| `worker` | `monadicalsas/reflector-backend` | Celery worker for background tasks |
+| `worker`   | `monadicalsas/reflector-backend`  | Celery worker for background tasks                                          |
-| `beat` | `monadicalsas/reflector-backend` | Celery beat scheduler |
+| `beat`     | `monadicalsas/reflector-backend`  | Celery beat scheduler                                                       |
-| `redis` | `redis:7.2-alpine` | Message broker and cache |
+| `redis`    | `redis:7.2-alpine`                | Message broker and cache                                                    |
-| `postgres` | `postgres:17-alpine` | Primary database |
+| `postgres` | `postgres:17-alpine`              | Primary database                                                            |
-| `caddy` | `caddy:2-alpine` | Reverse proxy with auto-SSL |
+| `caddy`    | `caddy:2-alpine`                  | Reverse proxy with auto-SSL (optional; see [Caddy profile](#caddy-profile)) |
 
 ## Environment Files
 
@@ -30,6 +30,7 @@ Reflector uses two separate environment files:
 Used by: `server`, `worker`, `beat`
 
 Key variables:
+
 ```env
 # Database connection
 DATABASE_URL=postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
@@ -54,6 +55,7 @@ TRANSCRIPT_MODAL_API_KEY=...
 Used by: `web`
 
 Key variables:
+
 ```env
 # Domain configuration
 SITE_URL=https://app.example.com
@@ -70,26 +72,42 @@ Note: `API_URL` is used client-side (browser), `SERVER_API_URL` is used server-s
 
 ## Volumes
 
-| Volume | Purpose |
+| Volume          | Purpose                       |
-|--------|---------|
+| --------------- | ----------------------------- |
-| `redis_data` | Redis persistence |
+| `redis_data`    | Redis persistence             |
-| `postgres_data` | PostgreSQL data |
+| `postgres_data` | PostgreSQL data               |
-| `server_data` | Uploaded files, local storage |
+| `server_data`   | Uploaded files, local storage |
-| `caddy_data` | SSL certificates |
+| `caddy_data`    | SSL certificates              |
-| `caddy_config` | Caddy configuration |
+| `caddy_config`  | Caddy configuration           |
 
 ## Network
 
 All services share the default network. The network is marked `attachable: true` to allow external containers (like Authentik) to join.
 
+## Caddy profile
+
+Caddy (ports 80 and 443) is **optional** and behind the `caddy` profile so it does not conflict with an existing reverse proxy (e.g. Coolify, Traefik, nginx).
+
+- **With Caddy** (you want Reflector to handle SSL):
+  `docker compose -f docker-compose.prod.yml --profile caddy up -d`
+- **Without Caddy** (Coolify or another proxy already on 80/443):
+  `docker compose -f docker-compose.prod.yml up -d`
+  Then configure your proxy to send traffic to `web:3000` (frontend) and `server:1250` (API).
+
 ## Common Commands
 
 ### Start all services
+
 ```bash
+# Without Caddy (e.g. when using Coolify)
 docker compose -f docker-compose.prod.yml up -d
+
+# With Caddy as reverse proxy
+docker compose -f docker-compose.prod.yml --profile caddy up -d
 ```
 
 ### View logs
+
 ```bash
 # All services
 docker compose -f docker-compose.prod.yml logs -f
@@ -99,6 +117,7 @@ docker compose -f docker-compose.prod.yml logs server --tail 50
 ```
 
 ### Restart a service
+
 ```bash
 # Quick restart (doesn't reload .env changes)
 docker compose -f docker-compose.prod.yml restart server
@@ -108,27 +127,32 @@ docker compose -f docker-compose.prod.yml up -d server
 ```
 
 ### Run database migrations
+
 ```bash
 docker compose -f docker-compose.prod.yml exec server uv run alembic upgrade head
 ```
 
 ### Access database
+
 ```bash
 docker compose -f docker-compose.prod.yml exec postgres psql -U reflector
 ```
 
 ### Pull latest images
+
 ```bash
 docker compose -f docker-compose.prod.yml pull
 docker compose -f docker-compose.prod.yml up -d
 ```
 
 ### Stop all services
+
 ```bash
 docker compose -f docker-compose.prod.yml down
 ```
 
 ### Full reset (WARNING: deletes data)
+
 ```bash
 docker compose -f docker-compose.prod.yml down -v
 ```
@@ -187,6 +211,7 @@ The Caddyfile supports environment variable substitution:
 Set `FRONTEND_DOMAIN` and `API_DOMAIN` environment variables, or edit the file directly.
 
 ### Reload Caddy after changes
+
 ```bash
 docker compose -f docker-compose.prod.yml exec caddy caddy reload --config /etc/caddy/Caddyfile
 ```

docs/docs/installation/overview.md

@@ -26,7 +26,7 @@ flowchart LR
 
 Before starting, you need:
 
-- **Production server** -  4+ cores, 8GB+ RAM, public IP
+- **Production server** - 4+ cores, 8GB+ RAM, public IP
 - **Two domain names** - e.g., `app.example.com` (frontend) and `api.example.com` (backend)
 - **GPU processing** - Choose one:
   - Modal.com account, OR
@@ -60,16 +60,17 @@ Type: A    Name: api    Value: <your-server-ip>
 
 Reflector requires GPU processing for transcription and speaker diarization. Choose one option:
 
-| | **Modal.com (Cloud)** | **Self-Hosted GPU** |
+|              | **Modal.com (Cloud)**             | **Self-Hosted GPU**          |
-|---|---|---|
+| ------------ | --------------------------------- | ---------------------------- |
 | **Best for** | No GPU hardware, zero maintenance | Own GPU server, full control |
-| **Pricing** | Pay-per-use | Fixed infrastructure cost |
+| **Pricing**  | Pay-per-use                       | Fixed infrastructure cost    |
 
 ### Option A: Modal.com (Serverless Cloud GPU)
 
 #### Accept HuggingFace Licenses
 
 Visit both pages and click "Accept":
+
 - https://huggingface.co/pyannote/speaker-diarization-3.1
 - https://huggingface.co/pyannote/segmentation-3.0
 
@@ -179,6 +180,7 @@ Save these credentials - you'll need them in the next step.
 ## Configure Environment
 
 Reflector has two env files:
+
 - `server/.env` - Backend configuration
 - `www/.env` - Frontend configuration
 
@@ -190,6 +192,7 @@ nano server/.env
 ```
 
 **Required settings:**
+
 ```env
 # Database (defaults work with docker-compose.prod.yml)
 DATABASE_URL=postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
@@ -249,6 +252,7 @@ nano www/.env
 ```
 
 **Required settings:**
+
 ```env
 # Your domains
 SITE_URL=https://app.example.com
@@ -266,7 +270,11 @@ FEATURE_REQUIRE_LOGIN=false
 
 ---
 
-## Configure Caddy
+## Reverse proxy (Caddy or existing)
+
+**If Coolify, Traefik, or nginx already use ports 80/443** (e.g. Coolify on your host): skip Caddy. Start the stack without the Caddy profile (see [Start Services](#start-services) below), then point your proxy at `web:3000` (frontend) and `server:1250` (API).
+
+**If you want Reflector to provide the reverse proxy and SSL:**
 
 ```bash
 cp Caddyfile.example Caddyfile
@@ -289,10 +297,18 @@ Replace `example.com` with your domains. The `{$VAR:default}` syntax uses Caddy'
 
 ## Start Services
 
+**Without Caddy** (e.g. Coolify already on 80/443):
+
 ```bash
 docker compose -f docker-compose.prod.yml up -d
 ```
 
+**With Caddy** (Reflector handles SSL):
+
+```bash
+docker compose -f docker-compose.prod.yml --profile caddy up -d
+```
+
 Wait for containers to start (first run may take 1-2 minutes to pull images and initialize).
 
 ---
@@ -300,18 +316,21 @@ Wait for containers to start (first run may take 1-2 minutes to pull images and
 ## Verify Deployment
 
 ### Check services
+
 ```bash
 docker compose -f docker-compose.prod.yml ps
 # All should show "Up"
 ```
 
 ### Test API
+
 ```bash
 curl https://api.example.com/health
 # Should return: {"status":"healthy"}
 ```
 
 ### Test Frontend
+
 - Visit https://app.example.com
 - You should see the Reflector interface
 - Try uploading an audio file to test transcription
@@ -327,6 +346,7 @@ By default, Reflector is open (no login required). **Authentication is required
 See [Authentication Setup](./auth-setup) for full Authentik OAuth configuration.
 
 Quick summary:
+
 1. Deploy Authentik on your server
 2. Create OAuth provider in Authentik
 3. Extract public key for JWT verification
@@ -358,6 +378,7 @@ DAILYCO_STORAGE_AWS_ROLE_ARN=<arn:aws:iam::ACCOUNT:role/DailyCo>
 ```
 
 Reload env and restart:
+
 ```bash
 docker compose -f docker-compose.prod.yml up -d server worker
 ```
@@ -367,35 +388,43 @@ docker compose -f docker-compose.prod.yml up -d server worker
 ## Troubleshooting
 
 ### Check logs for errors
+
 ```bash
 docker compose -f docker-compose.prod.yml logs server --tail 20
 docker compose -f docker-compose.prod.yml logs worker --tail 20
 ```
 
 ### Services won't start
+
 ```bash
 docker compose -f docker-compose.prod.yml logs
 ```
 
 ### CORS errors in browser
+
 - Verify `CORS_ORIGIN` in `server/.env` matches your frontend domain exactly (including `https://`)
 - Reload env: `docker compose -f docker-compose.prod.yml up -d server`
 
-### SSL certificate errors
+### SSL certificate errors (when using Caddy)
+
 - Caddy auto-provisions Let's Encrypt certificates
-- Ensure ports 80 and 443 are open
+- Ensure ports 80 and 443 are open and not used by another proxy
 - Check: `docker compose -f docker-compose.prod.yml logs caddy`
+- If port 80 is already in use (e.g. by Coolify), run without Caddy: `docker compose -f docker-compose.prod.yml up -d` and use your existing proxy
 
 ### Transcription not working
+
 - Check Modal dashboard: https://modal.com/apps
 - Verify URLs in `server/.env` match deployed functions
 - Check worker logs: `docker compose -f docker-compose.prod.yml logs worker`
 
 ### "Login required" but auth not configured
+
 - Set `FEATURE_REQUIRE_LOGIN=false` in `www/.env`
 - Rebuild frontend: `docker compose -f docker-compose.prod.yml up -d --force-recreate web`
 
 ### Database migrations or connectivity issues
+
 Migrations run automatically on server startup. To check database connectivity or debug migration failures:
 
 ```bash
@@ -408,4 +437,3 @@ docker compose -f docker-compose.prod.yml exec server uv run python -c "from ref
 # Manually run migrations (if needed)
 docker compose -f docker-compose.prod.yml exec server uv run alembic upgrade head
 ```
-

gpu/modal_deployments/deploy-all.sh

@@ -131,6 +131,15 @@ if [ -z "$DIARIZER_URL" ]; then
 fi
 echo "  -> $DIARIZER_URL"
 
+echo ""
+echo "Deploying padding (CPU audio processing via Modal SDK)..."
+modal deploy reflector_padding.py
+if [ $? -ne 0 ]; then
+    echo "Error: Failed to deploy padding. Check Modal dashboard for details."
+    exit 1
+fi
+echo "  -> reflector-padding.pad_track (Modal SDK function)"
+
 # --- Output Configuration ---
 echo ""
 echo "=========================================="
@@ -147,4 +156,6 @@ echo ""
 echo "DIARIZATION_BACKEND=modal"
 echo "DIARIZATION_URL=$DIARIZER_URL"
 echo "DIARIZATION_MODAL_API_KEY=$API_KEY"
+echo ""
+echo "# Padding uses Modal SDK (requires MODAL_TOKEN_ID/SECRET in worker containers)"
 echo "# --- End Modal Configuration ---"

gpu/modal_deployments/reflector_padding.py

@@ -0,0 +1,277 @@
+"""
+Reflector GPU backend - audio padding
+======================================
+
+CPU-intensive audio padding service for adding silence to audio tracks.
+Uses PyAV filter graph (adelay) for precise track synchronization.
+
+IMPORTANT: This padding logic is duplicated from server/reflector/utils/audio_padding.py
+for Modal deployment isolation (Modal can't import from server/reflector/). If you modify
+the PyAV filter graph or padding algorithm, you MUST update both:
+  - gpu/modal_deployments/reflector_padding.py (this file)
+  - server/reflector/utils/audio_padding.py
+
+Constants duplicated from server/reflector/utils/audio_constants.py for same reason.
+"""
+
+import os
+import tempfile
+from fractions import Fraction
+import math
+import asyncio
+
+import modal
+
+S3_TIMEOUT = 60 # happens 2 times
+PADDING_TIMEOUT = 600 + (S3_TIMEOUT * 2)
+SCALEDOWN_WINDOW = 60  # The maximum duration (in seconds) that individual containers can remain idle when scaling down.
+DISCONNECT_CHECK_INTERVAL = 2  # Check for client disconnect
+
+
+app = modal.App("reflector-padding")
+
+# CPU-based image
+image = (
+    modal.Image.debian_slim(python_version="3.12")
+    .apt_install("ffmpeg")  # Required by PyAV
+    .pip_install(
+        "av==13.1.0",  # PyAV for audio processing
+        "requests==2.32.3",  # HTTP for presigned URL downloads/uploads
+        "fastapi==0.115.12",  # API framework
+    )
+)
+
+# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
+OPUS_STANDARD_SAMPLE_RATE = 48000
+# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
+OPUS_DEFAULT_BIT_RATE = 128000
+
+
+@app.function(
+    cpu=2.0,
+    timeout=PADDING_TIMEOUT,
+    scaledown_window=SCALEDOWN_WINDOW,
+    image=image,
+)
+@modal.asgi_app()
+def web():
+    from fastapi import FastAPI, Request, HTTPException
+    from pydantic import BaseModel
+
+    class PaddingRequest(BaseModel):
+        track_url: str
+        output_url: str
+        start_time_seconds: float
+        track_index: int
+
+    class PaddingResponse(BaseModel):
+        size: int
+        cancelled: bool = False
+
+    web_app = FastAPI()
+
+    @web_app.post("/pad")
+    async def pad_track_endpoint(request: Request, req: PaddingRequest) -> PaddingResponse:
+        """Modal web endpoint for padding audio tracks with disconnect detection.
+        """
+        import logging
+
+        logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
+        logger = logging.getLogger(__name__)
+
+        if not req.track_url:
+            raise HTTPException(status_code=400, detail="track_url cannot be empty")
+        if not req.output_url:
+            raise HTTPException(status_code=400, detail="output_url cannot be empty")
+        if req.start_time_seconds <= 0:
+            raise HTTPException(status_code=400, detail=f"start_time_seconds must be positive, got {req.start_time_seconds}")
+        if req.start_time_seconds > 18000:
+            raise HTTPException(status_code=400, detail=f"start_time_seconds exceeds maximum 18000s (5 hours)")
+
+        logger.info(f"Padding request: track {req.track_index}, delay={req.start_time_seconds}s")
+
+        # Thread-safe cancellation flag shared between async disconnect checker and blocking thread
+        import threading
+        cancelled = threading.Event()
+
+        async def check_disconnect():
+            """Background task to check for client disconnect every 2 seconds."""
+            while not cancelled.is_set():
+                await asyncio.sleep(DISCONNECT_CHECK_INTERVAL)
+                if await request.is_disconnected():
+                    logger.warning("Client disconnected, setting cancellation flag")
+                    cancelled.set()
+                    break
+
+        # Start disconnect checker in background
+        disconnect_task = asyncio.create_task(check_disconnect())
+
+        try:
+            result = await asyncio.get_event_loop().run_in_executor(
+                None, _pad_track_blocking, req, cancelled, logger
+            )
+            return PaddingResponse(**result)
+        finally:
+            cancelled.set()
+            disconnect_task.cancel()
+            try:
+                await disconnect_task
+            except asyncio.CancelledError:
+                pass
+
+    def _pad_track_blocking(req, cancelled, logger) -> dict:
+        """Blocking CPU-bound padding work with periodic cancellation checks.
+
+        Args:
+            cancelled: threading.Event for thread-safe cancellation signaling
+        """
+        import av
+        import requests
+        from av.audio.resampler import AudioResampler
+        import time
+
+        temp_dir = tempfile.mkdtemp()
+        input_path = None
+        output_path = None
+        last_check = time.time()
+
+        try:
+            logger.info("Downloading track for padding")
+            response = requests.get(req.track_url, stream=True, timeout=S3_TIMEOUT)
+            response.raise_for_status()
+
+            input_path = os.path.join(temp_dir, "track.webm")
+            total_bytes = 0
+            chunk_count = 0
+            with open(input_path, "wb") as f:
+                for chunk in response.iter_content(chunk_size=8192):
+                    if chunk:
+                        f.write(chunk)
+                        total_bytes += len(chunk)
+                        chunk_count += 1
+
+                        # Check for cancellation every arbitrary amount of chunks
+                        if chunk_count % 12 == 0:
+                            now = time.time()
+                            if now - last_check >= DISCONNECT_CHECK_INTERVAL:
+                                if cancelled.is_set():
+                                    logger.info("Cancelled during download, exiting early")
+                                    return {"size": 0, "cancelled": True}
+                                last_check = now
+            logger.info(f"Track downloaded: {total_bytes} bytes")
+
+            if cancelled.is_set():
+                logger.info("Cancelled after download, exiting early")
+                return {"size": 0, "cancelled": True}
+
+            # Apply padding using PyAV
+            output_path = os.path.join(temp_dir, "padded.webm")
+            delay_ms = math.floor(req.start_time_seconds * 1000)
+            logger.info(f"Padding track {req.track_index} with {delay_ms}ms delay using PyAV")
+
+            in_container = av.open(input_path)
+            in_stream = next((s for s in in_container.streams if s.type == "audio"), None)
+            if in_stream is None:
+                raise ValueError("No audio stream in input")
+
+            with av.open(output_path, "w", format="webm") as out_container:
+                out_stream = out_container.add_stream("libopus", rate=OPUS_STANDARD_SAMPLE_RATE)
+                out_stream.bit_rate = OPUS_DEFAULT_BIT_RATE
+                graph = av.filter.Graph()
+
+                abuf_args = (
+                    f"time_base=1/{OPUS_STANDARD_SAMPLE_RATE}:"
+                    f"sample_rate={OPUS_STANDARD_SAMPLE_RATE}:"
+                    f"sample_fmt=s16:"
+                    f"channel_layout=stereo"
+                )
+                src = graph.add("abuffer", args=abuf_args, name="src")
+                aresample_f = graph.add("aresample", args="async=1", name="ares")
+                delays_arg = f"{delay_ms}|{delay_ms}"
+                adelay_f = graph.add("adelay", args=f"delays={delays_arg}:all=1", name="delay")
+                sink = graph.add("abuffersink", name="sink")
+
+                src.link_to(aresample_f)
+                aresample_f.link_to(adelay_f)
+                adelay_f.link_to(sink)
+                graph.configure()
+
+                resampler = AudioResampler(
+                    format="s16", layout="stereo", rate=OPUS_STANDARD_SAMPLE_RATE
+                )
+
+                for frame in in_container.decode(in_stream):
+                    # Check for cancellation periodically
+                    now = time.time()
+                    if now - last_check >= DISCONNECT_CHECK_INTERVAL:
+                        if cancelled.is_set():
+                            logger.info("Cancelled during processing, exiting early")
+                            in_container.close()
+                            return {"size": 0, "cancelled": True}
+                        last_check = now
+
+                    out_frames = resampler.resample(frame) or []
+                    for rframe in out_frames:
+                        rframe.sample_rate = OPUS_STANDARD_SAMPLE_RATE
+                        rframe.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
+                        src.push(rframe)
+
+                        while True:
+                            try:
+                                f_out = sink.pull()
+                            except Exception:
+                                break
+                            f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
+                            f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
+                            for packet in out_stream.encode(f_out):
+                                out_container.mux(packet)
+
+                # Flush filter graph
+                src.push(None)
+                while True:
+                    try:
+                        f_out = sink.pull()
+                    except Exception:
+                        break
+                    f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
+                    f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
+                    for packet in out_stream.encode(f_out):
+                        out_container.mux(packet)
+
+                # Flush encoder
+                for packet in out_stream.encode(None):
+                    out_container.mux(packet)
+
+            in_container.close()
+
+            file_size = os.path.getsize(output_path)
+            logger.info(f"Padding complete: {file_size} bytes")
+
+            logger.info("Uploading padded track to S3")
+
+            with open(output_path, "rb") as f:
+                upload_response = requests.put(req.output_url, data=f, timeout=S3_TIMEOUT)
+
+            upload_response.raise_for_status()
+            logger.info(f"Upload complete: {file_size} bytes")
+
+            return {"size": file_size}
+
+        finally:
+            if input_path and os.path.exists(input_path):
+                try:
+                    os.unlink(input_path)
+                except Exception as e:
+                    logger.warning(f"Failed to cleanup input file: {e}")
+            if output_path and os.path.exists(output_path):
+                try:
+                    os.unlink(output_path)
+                except Exception as e:
+                    logger.warning(f"Failed to cleanup output file: {e}")
+            try:
+                os.rmdir(temp_dir)
+            except Exception as e:
+                logger.warning(f"Failed to cleanup temp directory: {e}")
+
+    return web_app
+

gpu/self_hosted/Dockerfile

@@ -4,27 +4,31 @@ ENV PYTHONUNBUFFERED=1 \
     UV_LINK_MODE=copy \
     UV_NO_CACHE=1
 
+# patch until nvidia updates the sha1 repo
+ADD sequoia.config /etc/crypto-policies/back-ends/sequoia.config
+
 WORKDIR /tmp
-RUN apt-get update \
+RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
+ --mount=type=cache,target=/var/lib/apt,sharing=locked \
+ apt-get update \
  && apt-get install -y \
     ffmpeg \
     curl \
     ca-certificates \
     gnupg \
-    wget \
+    wget
- && apt-get clean
 # Add NVIDIA CUDA repo for Debian 12 (bookworm) and install cuDNN 9 for CUDA 12
 ADD https://developer.download.nvidia.com/compute/cuda/repos/debian12/x86_64/cuda-keyring_1.1-1_all.deb /cuda-keyring.deb
-RUN dpkg -i /cuda-keyring.deb \
+RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
+ --mount=type=cache,target=/var/lib/apt,sharing=locked \
+ dpkg -i /cuda-keyring.deb \
  && rm /cuda-keyring.deb \
  && apt-get update \
  && apt-get install -y --no-install-recommends \
     cuda-cudart-12-6 \
     libcublas-12-6 \
     libcudnn9-cuda-12 \
-    libcudnn9-dev-cuda-12 \
+    libcudnn9-dev-cuda-12
- && apt-get clean \
- && rm -rf /var/lib/apt/lists/*
 ADD https://astral.sh/uv/install.sh /uv-installer.sh
 RUN sh /uv-installer.sh && rm /uv-installer.sh
 ENV PATH="/root/.local/bin/:$PATH"
@@ -39,6 +43,13 @@ COPY ./app /app/app
 COPY ./main.py /app/
 COPY ./runserver.sh /app/
 
+# prevent uv failing with too many open files on big cpus
+ENV UV_CONCURRENT_INSTALLS=16
+
+# first install
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv sync --compile-bytecode --locked
+
 EXPOSE 8000
 
 CMD ["sh", "/app/runserver.sh"]

gpu/self_hosted/sequoia.config

@@ -0,0 +1,2 @@
+[hash_algorithms]
+sha1 = "always"

server/migrations/versions/3aa20b96d963_drop_use_celery_column.py

@@ -0,0 +1,35 @@
+"""drop_use_celery_column
+
+Revision ID: 3aa20b96d963
+Revises: e69f08ead8ea
+Create Date: 2026-02-05 10:12:44.065279
+
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision: str = "3aa20b96d963"
+down_revision: Union[str, None] = "e69f08ead8ea"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("room", schema=None) as batch_op:
+        batch_op.drop_column("use_celery")
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("room", schema=None) as batch_op:
+        batch_op.add_column(
+            sa.Column(
+                "use_celery",
+                sa.Boolean(),
+                server_default=sa.text("false"),
+                nullable=False,
+            )
+        )

server/pyproject.toml

@@ -8,7 +8,7 @@ readme = "README.md"
 dependencies = [
     "aiohttp>=3.9.0",
     "aiohttp-cors>=0.7.0",
-    "av>=10.0.0",
+    "av>=15.0.0",
     "requests>=2.31.0",
     "aiortc>=1.5.0",
     "sortedcontainers>=2.4.0",

server/reflector/db/rooms.py

@@ -57,12 +57,6 @@ rooms = sqlalchemy.Table(
         sqlalchemy.String,
         nullable=False,
     ),
-    sqlalchemy.Column(
-        "use_celery",
-        sqlalchemy.Boolean,
-        nullable=False,
-        server_default=false(),
-    ),
     sqlalchemy.Column(
         "skip_consent",
         sqlalchemy.Boolean,
@@ -97,7 +91,6 @@ class Room(BaseModel):
     ics_last_sync: datetime | None = None
     ics_last_etag: str | None = None
     platform: Platform = Field(default_factory=lambda: settings.DEFAULT_VIDEO_PLATFORM)
-    use_celery: bool = False
     skip_consent: bool = False
 
 

server/reflector/hatchet/constants.py

@@ -35,7 +35,9 @@ LLM_RATE_LIMIT_PER_SECOND = 10
 
 # Task execution timeouts (seconds)
 TIMEOUT_SHORT = 60  # Quick operations: API calls, DB updates
-TIMEOUT_MEDIUM = 120  # Single LLM calls, waveform generation
+TIMEOUT_MEDIUM = (
+    300  # Single LLM calls, waveform generation (5m for slow LLM responses)
+)
 TIMEOUT_LONG = 180  # Action items (larger context LLM)
-TIMEOUT_AUDIO = 300  # Audio processing: padding, mixdown
+TIMEOUT_AUDIO = 720  # Audio processing: padding, mixdown
 TIMEOUT_HEAVY = 600  # Transcription, fan-out LLM tasks

server/reflector/hatchet/workflows/daily_multitrack_pipeline.py

@@ -322,6 +322,7 @@ async def get_participants(input: PipelineInput, ctx: Context) -> ParticipantsRe
     mtg_session_id = recording.mtg_session_id
     async with fresh_db_connection():
         from reflector.db.transcripts import (  # noqa: PLC0415
+            TranscriptDuration,
             TranscriptParticipant,
             transcripts_controller,
         )
@@ -330,15 +331,26 @@ async def get_participants(input: PipelineInput, ctx: Context) -> ParticipantsRe
         if not transcript:
             raise ValueError(f"Transcript {input.transcript_id} not found")
         # Note: title NOT cleared - preserves existing titles
+        # Duration from Daily API (seconds -> milliseconds) - master source
+        duration_ms = recording.duration * 1000 if recording.duration else 0
         await transcripts_controller.update(
             transcript,
             {
                 "events": [],
                 "topics": [],
                 "participants": [],
+                "duration": duration_ms,
             },
         )
 
+        await append_event_and_broadcast(
+            input.transcript_id,
+            transcript,
+            "DURATION",
+            TranscriptDuration(duration=duration_ms),
+            logger=logger,
+        )
+
         mtg_session_id = assert_non_none_and_non_empty(
             mtg_session_id, "mtg_session_id is required"
         )
@@ -1095,7 +1107,7 @@ async def identify_action_items(
 
 
 @daily_multitrack_pipeline.task(
-    parents=[generate_title, generate_recap, identify_action_items],
+    parents=[process_tracks, generate_title, generate_recap, identify_action_items],
     execution_timeout=timedelta(seconds=TIMEOUT_SHORT),
     retries=3,
 )
@@ -1108,12 +1120,8 @@ async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
     """
     ctx.log("finalize: saving transcript and setting status to 'ended'")
 
-    mixdown_result = ctx.task_output(mixdown_tracks)
     track_result = ctx.task_output(process_tracks)
 
-    duration = mixdown_result.duration
-    all_words = track_result.all_words
-
     # Cleanup temporary padded S3 files (deferred until finalize for semantic parity with Celery)
     created_padded_files = track_result.created_padded_files
     if created_padded_files:
@@ -1133,7 +1141,6 @@ async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
 
     async with fresh_db_connection():
         from reflector.db.transcripts import (  # noqa: PLC0415
-            TranscriptDuration,
             TranscriptText,
             transcripts_controller,
         )
@@ -1142,8 +1149,6 @@ async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
         if transcript is None:
             raise ValueError(f"Transcript {input.transcript_id} not found in database")
 
-        merged_transcript = TranscriptType(words=all_words, translation=None)
-
         await append_event_and_broadcast(
             input.transcript_id,
             transcript,
@@ -1155,21 +1160,15 @@ async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
             logger=logger,
         )
 
-        # Save duration and clear workflow_run_id (workflow completed successfully)
+        # Clear workflow_run_id (workflow completed successfully)
-        # Note: title/long_summary/short_summary already saved by their callbacks
+        # Note: title/long_summary/short_summary/duration already saved by their callbacks
         await transcripts_controller.update(
             transcript,
             {
-                "duration": duration,
                 "workflow_run_id": None,  # Clear on success - no need to resume
             },
         )
 
-        duration_data = TranscriptDuration(duration=duration)
-        await append_event_and_broadcast(
-            input.transcript_id, transcript, "DURATION", duration_data, logger=logger
-        )
-
         await set_status_and_broadcast(input.transcript_id, "ended", logger=logger)
 
         ctx.log(

server/reflector/hatchet/workflows/padding_workflow.py

@@ -0,0 +1,165 @@
+"""
+Hatchet child workflow: PaddingWorkflow
+Handles individual audio track padding via Modal.com backend.
+"""
+
+from datetime import timedelta
+
+import av
+from hatchet_sdk import Context
+from pydantic import BaseModel
+
+from reflector.hatchet.client import HatchetClientManager
+from reflector.hatchet.constants import TIMEOUT_AUDIO
+from reflector.hatchet.workflows.models import PadTrackResult
+from reflector.logger import logger
+from reflector.utils.audio_constants import PRESIGNED_URL_EXPIRATION_SECONDS
+from reflector.utils.audio_padding import extract_stream_start_time_from_container
+
+
+class PaddingInput(BaseModel):
+    """Input for individual track padding."""
+
+    track_index: int
+    s3_key: str
+    bucket_name: str
+    transcript_id: str
+
+
+hatchet = HatchetClientManager.get_client()
+
+padding_workflow = hatchet.workflow(
+    name="PaddingWorkflow", input_validator=PaddingInput
+)
+
+
+@padding_workflow.task(execution_timeout=timedelta(seconds=TIMEOUT_AUDIO), retries=3)
+async def pad_track(input: PaddingInput, ctx: Context) -> PadTrackResult:
+    """Pad audio track with silence based on WebM container start_time."""
+    ctx.log(f"pad_track: track {input.track_index}, s3_key={input.s3_key}")
+    logger.info(
+        "[Hatchet] pad_track",
+        track_index=input.track_index,
+        s3_key=input.s3_key,
+        transcript_id=input.transcript_id,
+    )
+
+    try:
+        # Create fresh storage instance to avoid aioboto3 fork issues
+        from reflector.settings import settings  # noqa: PLC0415
+        from reflector.storage.storage_aws import AwsStorage  # noqa: PLC0415
+
+        storage = AwsStorage(
+            aws_bucket_name=settings.TRANSCRIPT_STORAGE_AWS_BUCKET_NAME,
+            aws_region=settings.TRANSCRIPT_STORAGE_AWS_REGION,
+            aws_access_key_id=settings.TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID,
+            aws_secret_access_key=settings.TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY,
+        )
+
+        source_url = await storage.get_file_url(
+            input.s3_key,
+            operation="get_object",
+            expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
+            bucket=input.bucket_name,
+        )
+
+        # Extract start_time to determine if padding needed
+        with av.open(source_url) as in_container:
+            if in_container.duration:
+                try:
+                    duration = timedelta(seconds=in_container.duration // 1_000_000)
+                    ctx.log(
+                        f"pad_track: track {input.track_index}, duration={duration}"
+                    )
+                except (ValueError, TypeError, OverflowError) as e:
+                    ctx.log(
+                        f"pad_track: track {input.track_index}, duration error: {str(e)}"
+                    )
+
+            start_time_seconds = extract_stream_start_time_from_container(
+                in_container, input.track_index, logger=logger
+            )
+
+        if start_time_seconds <= 0:
+            logger.info(
+                f"Track {input.track_index} requires no padding",
+                track_index=input.track_index,
+            )
+            return PadTrackResult(
+                padded_key=input.s3_key,
+                bucket_name=input.bucket_name,
+                size=0,
+                track_index=input.track_index,
+            )
+
+        storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
+
+        # Presign PUT URL for output (Modal will upload directly)
+        output_url = await storage.get_file_url(
+            storage_path,
+            operation="put_object",
+            expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
+        )
+
+        import httpx  # noqa: PLC0415
+
+        from reflector.processors.audio_padding_modal import (  # noqa: PLC0415
+            AudioPaddingModalProcessor,
+        )
+
+        try:
+            processor = AudioPaddingModalProcessor()
+            result = await processor.pad_track(
+                track_url=source_url,
+                output_url=output_url,
+                start_time_seconds=start_time_seconds,
+                track_index=input.track_index,
+            )
+            file_size = result.size
+
+            ctx.log(f"pad_track: Modal returned size={file_size}")
+        except httpx.HTTPStatusError as e:
+            error_detail = e.response.text if hasattr(e.response, "text") else str(e)
+            logger.error(
+                "[Hatchet] Modal padding HTTP error",
+                transcript_id=input.transcript_id,
+                track_index=input.track_index,
+                status_code=e.response.status_code if hasattr(e, "response") else None,
+                error=error_detail,
+                exc_info=True,
+            )
+            raise Exception(
+                f"Modal padding failed: HTTP {e.response.status_code}"
+            ) from e
+        except httpx.TimeoutException as e:
+            logger.error(
+                "[Hatchet] Modal padding timeout",
+                transcript_id=input.transcript_id,
+                track_index=input.track_index,
+                error=str(e),
+                exc_info=True,
+            )
+            raise Exception("Modal padding timeout") from e
+
+        logger.info(
+            "[Hatchet] pad_track complete",
+            track_index=input.track_index,
+            padded_key=storage_path,
+        )
+
+        return PadTrackResult(
+            padded_key=storage_path,
+            bucket_name=None,  # None = use default transcript storage bucket
+            size=file_size,
+            track_index=input.track_index,
+        )
+
+    except Exception as e:
+        logger.error(
+            "[Hatchet] pad_track failed",
+            transcript_id=input.transcript_id,
+            track_index=input.track_index,
+            error=str(e),
+            exc_info=True,
+        )
+        raise

server/reflector/hatchet/workflows/track_processing.py

@@ -14,9 +14,7 @@ Hatchet workers run in forked processes; fresh imports per task ensure
 storage/DB connections are not shared across forks.
 """
 
-import tempfile
 from datetime import timedelta
-from pathlib import Path
 
 import av
 from hatchet_sdk import Context
@@ -27,10 +25,7 @@ from reflector.hatchet.constants import TIMEOUT_AUDIO, TIMEOUT_HEAVY
 from reflector.hatchet.workflows.models import PadTrackResult, TranscribeTrackResult
 from reflector.logger import logger
 from reflector.utils.audio_constants import PRESIGNED_URL_EXPIRATION_SECONDS
-from reflector.utils.audio_padding import (
+from reflector.utils.audio_padding import extract_stream_start_time_from_container
-    apply_audio_padding_to_file,
-    extract_stream_start_time_from_container,
-)
 
 
 class TrackInput(BaseModel):
@@ -83,63 +78,44 @@ async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
         )
 
         with av.open(source_url) as in_container:
-            if in_container.duration:
-                try:
-                    duration = timedelta(seconds=in_container.duration // 1_000_000)
-                    ctx.log(
-                        f"pad_track: track {input.track_index}, duration={duration}"
-                    )
-                except Exception:
-                    ctx.log(f"pad_track: track {input.track_index}, duration=ERROR")
-
             start_time_seconds = extract_stream_start_time_from_container(
                 in_container, input.track_index, logger=logger
             )
 
-            # If no padding needed, return original S3 key
+        # If no padding needed, return original S3 key
-            if start_time_seconds <= 0:
+        if start_time_seconds <= 0:
-                logger.info(
+            logger.info(
-                    f"Track {input.track_index} requires no padding",
+                f"Track {input.track_index} requires no padding",
-                    track_index=input.track_index,
+                track_index=input.track_index,
-                )
+            )
-                return PadTrackResult(
+            return PadTrackResult(
-                    padded_key=input.s3_key,
+                padded_key=input.s3_key,
-                    bucket_name=input.bucket_name,
+                bucket_name=input.bucket_name,
-                    size=0,
+                size=0,
-                    track_index=input.track_index,
+                track_index=input.track_index,
-                )
+            )
 
-            with tempfile.NamedTemporaryFile(suffix=".webm", delete=False) as temp_file:
+        storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
-                temp_path = temp_file.name
 
-            try:
+        # Presign PUT URL for output (Modal uploads directly)
-                apply_audio_padding_to_file(
+        output_url = await storage.get_file_url(
-                    in_container,
+            storage_path,
-                    temp_path,
+            operation="put_object",
-                    start_time_seconds,
+            expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
-                    input.track_index,
+        )
-                    logger=logger,
-                )
 
-                file_size = Path(temp_path).stat().st_size
+        from reflector.processors.audio_padding_modal import (  # noqa: PLC0415
-                storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
+            AudioPaddingModalProcessor,
+        )
 
-                logger.info(
+        processor = AudioPaddingModalProcessor()
-                    f"About to upload padded track",
+        result = await processor.pad_track(
-                    key=storage_path,
+            track_url=source_url,
-                    size=file_size,
+            output_url=output_url,
-                )
+            start_time_seconds=start_time_seconds,
-
+            track_index=input.track_index,
-                with open(temp_path, "rb") as padded_file:
+        )
-                    await storage.put_file(storage_path, padded_file)
+        file_size = result.size
-
-                logger.info(
-                    f"Uploaded padded track to S3",
-                    key=storage_path,
-                    size=file_size,
-                )
-            finally:
-                Path(temp_path).unlink(missing_ok=True)
 
         ctx.log(f"pad_track complete: track {input.track_index} -> {storage_path}")
         logger.info(

server/reflector/processors/audio_padding_modal.py

@@ -0,0 +1,113 @@
+"""
+Modal.com backend for audio padding.
+"""
+
+import asyncio
+import os
+
+import httpx
+from pydantic import BaseModel
+
+from reflector.hatchet.constants import TIMEOUT_AUDIO
+from reflector.logger import logger
+
+
+class PaddingResponse(BaseModel):
+    size: int
+    cancelled: bool = False
+
+
+class AudioPaddingModalProcessor:
+    """Audio padding processor using Modal.com CPU backend via HTTP."""
+
+    def __init__(
+        self, padding_url: str | None = None, modal_api_key: str | None = None
+    ):
+        self.padding_url = padding_url or os.getenv("PADDING_URL")
+        if not self.padding_url:
+            raise ValueError(
+                "PADDING_URL required to use AudioPaddingModalProcessor. "
+                "Set PADDING_URL environment variable or pass padding_url parameter."
+            )
+
+        self.modal_api_key = modal_api_key or os.getenv("MODAL_API_KEY")
+
+    async def pad_track(
+        self,
+        track_url: str,
+        output_url: str,
+        start_time_seconds: float,
+        track_index: int,
+    ) -> PaddingResponse:
+        """Pad audio track with silence via Modal backend.
+
+        Args:
+            track_url: Presigned GET URL for source audio track
+            output_url: Presigned PUT URL for output WebM
+            start_time_seconds: Amount of silence to prepend
+            track_index: Track index for logging
+        """
+        if not track_url:
+            raise ValueError("track_url cannot be empty")
+        if start_time_seconds <= 0:
+            raise ValueError(
+                f"start_time_seconds must be positive, got {start_time_seconds}"
+            )
+
+        log = logger.bind(track_index=track_index, padding_seconds=start_time_seconds)
+        log.info("Sending Modal padding HTTP request")
+
+        url = f"{self.padding_url}/pad"
+
+        headers = {}
+        if self.modal_api_key:
+            headers["Authorization"] = f"Bearer {self.modal_api_key}"
+
+        try:
+            async with httpx.AsyncClient(timeout=TIMEOUT_AUDIO) as client:
+                response = await client.post(
+                    url,
+                    headers=headers,
+                    json={
+                        "track_url": track_url,
+                        "output_url": output_url,
+                        "start_time_seconds": start_time_seconds,
+                        "track_index": track_index,
+                    },
+                    follow_redirects=True,
+                )
+
+                if response.status_code != 200:
+                    error_body = response.text
+                    log.error(
+                        "Modal padding API error",
+                        status_code=response.status_code,
+                        error_body=error_body,
+                    )
+
+                response.raise_for_status()
+                result = response.json()
+
+            # Check if work was cancelled
+            if result.get("cancelled"):
+                log.warning("Modal padding was cancelled by disconnect detection")
+                raise asyncio.CancelledError(
+                    "Padding cancelled due to client disconnect"
+                )
+
+            log.info("Modal padding complete", size=result["size"])
+            return PaddingResponse(**result)
+        except asyncio.CancelledError:
+            log.warning(
+                "Modal padding cancelled (Hatchet timeout, disconnect detected on Modal side)"
+            )
+            raise
+        except httpx.TimeoutException as e:
+            log.error("Modal padding timeout", error=str(e), exc_info=True)
+            raise Exception(f"Modal padding timeout: {e}") from e
+        except httpx.HTTPStatusError as e:
+            log.error("Modal padding HTTP error", error=str(e), exc_info=True)
+            raise Exception(f"Modal padding HTTP error: {e}") from e
+        except Exception as e:
+            log.error("Modal padding unexpected error", error=str(e), exc_info=True)
+            raise

server/reflector/services/transcript_process.py

@@ -15,14 +15,10 @@ from hatchet_sdk.clients.rest.exceptions import ApiException, NotFoundException
 from hatchet_sdk.clients.rest.models import V1TaskStatus
 
 from reflector.db.recordings import recordings_controller
-from reflector.db.rooms import rooms_controller
 from reflector.db.transcripts import Transcript, transcripts_controller
 from reflector.hatchet.client import HatchetClientManager
 from reflector.logger import logger
 from reflector.pipelines.main_file_pipeline import task_pipeline_file_process
-from reflector.pipelines.main_multitrack_pipeline import (
-    task_pipeline_multitrack_process,
-)
 from reflector.utils.string import NonEmptyString
 
 
@@ -181,124 +177,98 @@ async def dispatch_transcript_processing(
     Returns AsyncResult for Celery tasks, None for Hatchet workflows.
     """
     if isinstance(config, MultitrackProcessingConfig):
-        use_celery = False
+        # Multitrack processing always uses Hatchet (no Celery fallback)
-        if config.room_id:
+        # First check if we can replay (outside transaction since it's read-only)
-            room = await rooms_controller.get_by_id(config.room_id)
+        transcript = await transcripts_controller.get_by_id(config.transcript_id)
-            use_celery = room.use_celery if room else False
+        if transcript and transcript.workflow_run_id and not force:
-
+            can_replay = await HatchetClientManager.can_replay(
-        use_hatchet = not use_celery
+                transcript.workflow_run_id
-
-        if use_celery:
-            logger.info(
-                "Room uses legacy Celery processing",
-                room_id=config.room_id,
-                transcript_id=config.transcript_id,
             )
-
+            if can_replay:
-        if use_hatchet:
+                await HatchetClientManager.replay_workflow(transcript.workflow_run_id)
-            # First check if we can replay (outside transaction since it's read-only)
+                logger.info(
-            transcript = await transcripts_controller.get_by_id(config.transcript_id)
+                    "Replaying Hatchet workflow",
-            if transcript and transcript.workflow_run_id and not force:
+                    workflow_id=transcript.workflow_run_id,
-                can_replay = await HatchetClientManager.can_replay(
-                    transcript.workflow_run_id
                 )
-                if can_replay:
+                return None
-                    await HatchetClientManager.replay_workflow(
+            else:
-                        transcript.workflow_run_id
+                # Workflow can't replay (CANCELLED, COMPLETED, or 404 deleted)
-                    )
+                # Log and proceed to start new workflow
-                    logger.info(
-                        "Replaying Hatchet workflow",
-                        workflow_id=transcript.workflow_run_id,
-                    )
-                    return None
-                else:
-                    # Workflow can't replay (CANCELLED, COMPLETED, or 404 deleted)
-                    # Log and proceed to start new workflow
-                    try:
-                        status = await HatchetClientManager.get_workflow_run_status(
-                            transcript.workflow_run_id
-                        )
-                        logger.info(
-                            "Old workflow not replayable, starting new",
-                            old_workflow_id=transcript.workflow_run_id,
-                            old_status=status.value,
-                        )
-                    except NotFoundException:
-                        # Workflow deleted from Hatchet but ID still in DB
-                        logger.info(
-                            "Old workflow not found in Hatchet, starting new",
-                            old_workflow_id=transcript.workflow_run_id,
-                        )
-
-            # Force: cancel old workflow if exists
-            if force and transcript and transcript.workflow_run_id:
-                try:
-                    await HatchetClientManager.cancel_workflow(
-                        transcript.workflow_run_id
-                    )
-                    logger.info(
-                        "Cancelled old workflow (--force)",
-                        workflow_id=transcript.workflow_run_id,
-                    )
-                except NotFoundException:
-                    logger.info(
-                        "Old workflow already deleted (--force)",
-                        workflow_id=transcript.workflow_run_id,
-                    )
-                await transcripts_controller.update(
-                    transcript, {"workflow_run_id": None}
-                )
-
-            # Re-fetch and check for concurrent dispatch (optimistic approach).
-            # No database lock - worst case is duplicate dispatch, but Hatchet
-            # workflows are idempotent so this is acceptable.
-            transcript = await transcripts_controller.get_by_id(config.transcript_id)
-            if transcript and transcript.workflow_run_id:
-                # Another process started a workflow between validation and now
                 try:
                     status = await HatchetClientManager.get_workflow_run_status(
                         transcript.workflow_run_id
                     )
-                    if status in (V1TaskStatus.RUNNING, V1TaskStatus.QUEUED):
+                    logger.info(
-                        logger.info(
+                        "Old workflow not replayable, starting new",
-                            "Concurrent workflow detected, skipping dispatch",
+                        old_workflow_id=transcript.workflow_run_id,
-                            workflow_id=transcript.workflow_run_id,
+                        old_status=status.value,
-                        )
+                    )
-                        return None
+                except NotFoundException:
-                except ApiException:
+                    # Workflow deleted from Hatchet but ID still in DB
-                    # Workflow might be gone (404) or API issue - proceed with new workflow
+                    logger.info(
-                    pass
+                        "Old workflow not found in Hatchet, starting new",
+                        old_workflow_id=transcript.workflow_run_id,
+                    )
 
-            workflow_id = await HatchetClientManager.start_workflow(
+        # Force: cancel old workflow if exists
-                workflow_name="DiarizationPipeline",
+        if force and transcript and transcript.workflow_run_id:
-                input_data={
+            try:
-                    "recording_id": config.recording_id,
+                await HatchetClientManager.cancel_workflow(transcript.workflow_run_id)
-                    "tracks": [{"s3_key": k} for k in config.track_keys],
+                logger.info(
-                    "bucket_name": config.bucket_name,
+                    "Cancelled old workflow (--force)",
-                    "transcript_id": config.transcript_id,
+                    workflow_id=transcript.workflow_run_id,
-                    "room_id": config.room_id,
+                )
-                },
+            except NotFoundException:
-                additional_metadata={
+                logger.info(
-                    "transcript_id": config.transcript_id,
+                    "Old workflow already deleted (--force)",
-                    "recording_id": config.recording_id,
+                    workflow_id=transcript.workflow_run_id,
-                    "daily_recording_id": config.recording_id,
+                )
-                },
+            await transcripts_controller.update(transcript, {"workflow_run_id": None})
+
+        # Re-fetch and check for concurrent dispatch (optimistic approach).
+        # No database lock - worst case is duplicate dispatch, but Hatchet
+        # workflows are idempotent so this is acceptable.
+        transcript = await transcripts_controller.get_by_id(config.transcript_id)
+        if transcript and transcript.workflow_run_id:
+            # Another process started a workflow between validation and now
+            try:
+                status = await HatchetClientManager.get_workflow_run_status(
+                    transcript.workflow_run_id
+                )
+                if status in (V1TaskStatus.RUNNING, V1TaskStatus.QUEUED):
+                    logger.info(
+                        "Concurrent workflow detected, skipping dispatch",
+                        workflow_id=transcript.workflow_run_id,
+                    )
+                    return None
+            except ApiException:
+                # Workflow might be gone (404) or API issue - proceed with new workflow
+                pass
+
+        workflow_id = await HatchetClientManager.start_workflow(
+            workflow_name="DiarizationPipeline",
+            input_data={
+                "recording_id": config.recording_id,
+                "tracks": [{"s3_key": k} for k in config.track_keys],
+                "bucket_name": config.bucket_name,
+                "transcript_id": config.transcript_id,
+                "room_id": config.room_id,
+            },
+            additional_metadata={
+                "transcript_id": config.transcript_id,
+                "recording_id": config.recording_id,
+                "daily_recording_id": config.recording_id,
+            },
+        )
+
+        if transcript:
+            await transcripts_controller.update(
+                transcript, {"workflow_run_id": workflow_id}
             )
 
-            if transcript:
+        logger.info("Hatchet workflow dispatched", workflow_id=workflow_id)
-                await transcripts_controller.update(
+        return None
-                    transcript, {"workflow_run_id": workflow_id}
-                )
 
-            logger.info("Hatchet workflow dispatched", workflow_id=workflow_id)
-            return None
-
-        # Celery pipeline (durable workflows disabled)
-        return task_pipeline_multitrack_process.delay(
-            transcript_id=config.transcript_id,
-            bucket_name=config.bucket_name,
-            track_keys=config.track_keys,
-        )
     elif isinstance(config, FileProcessingConfig):
         return task_pipeline_file_process.delay(transcript_id=config.transcript_id)
     else:

server/reflector/settings.py

@@ -1,7 +1,7 @@
 from pydantic.types import PositiveInt
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
-from reflector.schemas.platform import WHEREBY_PLATFORM, Platform
+from reflector.schemas.platform import DAILY_PLATFORM, Platform
 from reflector.utils.string import NonEmptyString
 
 
@@ -98,6 +98,10 @@ class Settings(BaseSettings):
     # Diarization: local pyannote.audio
     DIARIZATION_PYANNOTE_AUTH_TOKEN: str | None = None
 
+    # Audio Padding (Modal.com backend)
+    PADDING_URL: str | None = None
+    PADDING_MODAL_API_KEY: str | None = None
+
     # Sentry
     SENTRY_DSN: str | None = None
 
@@ -151,7 +155,7 @@ class Settings(BaseSettings):
         None  # Webhook UUID for this environment. Not used by production code
     )
     # Platform Configuration
-    DEFAULT_VIDEO_PLATFORM: Platform = WHEREBY_PLATFORM
+    DEFAULT_VIDEO_PLATFORM: Platform = DAILY_PLATFORM
 
     # Zulip integration
     ZULIP_REALM: str | None = None

server/reflector/utils/audio_constants.py

@@ -5,7 +5,9 @@ Used by both Hatchet workflows and Celery pipelines for consistent audio encodin
 """
 
 # Opus codec settings
+# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
 OPUS_STANDARD_SAMPLE_RATE = 48000
+# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
 OPUS_DEFAULT_BIT_RATE = 128000  # 128kbps for good speech quality
 
 # S3 presigned URL expiration

server/reflector/worker/process.py

@@ -27,9 +27,6 @@ from reflector.db.transcripts import (
 from reflector.hatchet.client import HatchetClientManager
 from reflector.pipelines.main_file_pipeline import task_pipeline_file_process
 from reflector.pipelines.main_live_pipeline import asynctask
-from reflector.pipelines.main_multitrack_pipeline import (
-    task_pipeline_multitrack_process,
-)
 from reflector.pipelines.topic_processing import EmptyPipeline
 from reflector.processors import AudioFileWriterProcessor
 from reflector.processors.audio_waveform_processor import AudioWaveformProcessor
@@ -351,49 +348,29 @@ async def _process_multitrack_recording_inner(
             room_id=room.id,
         )
 
-    use_celery = room and room.use_celery
+    # Multitrack processing always uses Hatchet (no Celery fallback)
-    use_hatchet = not use_celery
+    workflow_id = await HatchetClientManager.start_workflow(
-
+        workflow_name="DiarizationPipeline",
-    if use_celery:
+        input_data={
-        logger.info(
+            "recording_id": recording_id,
-            "Room uses legacy Celery processing",
+            "tracks": [{"s3_key": k} for k in filter_cam_audio_tracks(track_keys)],
-            room_id=room.id,
+            "bucket_name": bucket_name,
-            transcript_id=transcript.id,
+            "transcript_id": transcript.id,
-        )
+            "room_id": room.id,
-
+        },
-    if use_hatchet:
+        additional_metadata={
-        workflow_id = await HatchetClientManager.start_workflow(
+            "transcript_id": transcript.id,
-            workflow_name="DiarizationPipeline",
+            "recording_id": recording_id,
-            input_data={
+            "daily_recording_id": recording_id,
-                "recording_id": recording_id,
+        },
-                "tracks": [{"s3_key": k} for k in filter_cam_audio_tracks(track_keys)],
-                "bucket_name": bucket_name,
-                "transcript_id": transcript.id,
-                "room_id": room.id,
-            },
-            additional_metadata={
-                "transcript_id": transcript.id,
-                "recording_id": recording_id,
-                "daily_recording_id": recording_id,
-            },
-        )
-        logger.info(
-            "Started Hatchet workflow",
-            workflow_id=workflow_id,
-            transcript_id=transcript.id,
-        )
-
-        await transcripts_controller.update(
-            transcript, {"workflow_run_id": workflow_id}
-        )
-        return
-
-    # Celery pipeline (runs when durable workflows disabled)
-    task_pipeline_multitrack_process.delay(
-        transcript_id=transcript.id,
-        bucket_name=bucket_name,
-        track_keys=filter_cam_audio_tracks(track_keys),
     )
+    logger.info(
+        "Started Hatchet workflow",
+        workflow_id=workflow_id,
+        transcript_id=transcript.id,
+    )
+
+    await transcripts_controller.update(transcript, {"workflow_run_id": workflow_id})
 
 
 @shared_task
@@ -1072,66 +1049,43 @@ async def reprocess_failed_daily_recordings():
                 )
                 continue
 
-            use_celery = room and room.use_celery
+            # Multitrack reprocessing always uses Hatchet (no Celery fallback)
-            use_hatchet = not use_celery
+            if not transcript:
-
+                logger.warning(
-            if use_hatchet:
+                    "No transcript for Hatchet reprocessing, skipping",
-                if not transcript:
-                    logger.warning(
-                        "No transcript for Hatchet reprocessing, skipping",
-                        recording_id=recording.id,
-                    )
-                    continue
-
-                workflow_id = await HatchetClientManager.start_workflow(
-                    workflow_name="DiarizationPipeline",
-                    input_data={
-                        "recording_id": recording.id,
-                        "tracks": [
-                            {"s3_key": k}
-                            for k in filter_cam_audio_tracks(recording.track_keys)
-                        ],
-                        "bucket_name": bucket_name,
-                        "transcript_id": transcript.id,
-                        "room_id": room.id if room else None,
-                    },
-                    additional_metadata={
-                        "transcript_id": transcript.id,
-                        "recording_id": recording.id,
-                        "reprocess": True,
-                    },
-                )
-                await transcripts_controller.update(
-                    transcript, {"workflow_run_id": workflow_id}
-                )
-
-                logger.info(
-                    "Queued Daily recording for Hatchet reprocessing",
                     recording_id=recording.id,
-                    workflow_id=workflow_id,
-                    room_name=meeting.room_name,
-                    track_count=len(recording.track_keys),
-                )
-            else:
-                logger.info(
-                    "Queueing Daily recording for Celery reprocessing",
-                    recording_id=recording.id,
-                    room_name=meeting.room_name,
-                    track_count=len(recording.track_keys),
-                    transcript_status=transcript.status if transcript else None,
                 )
+                continue
 
-                # For reprocessing, pass actual recording time (though it's ignored - see _process_multitrack_recording_inner)
+            workflow_id = await HatchetClientManager.start_workflow(
-                # Reprocessing uses recording.meeting_id directly instead of time-based matching
+                workflow_name="DiarizationPipeline",
-                recording_start_ts = int(recording.recorded_at.timestamp())
+                input_data={
+                    "recording_id": recording.id,
+                    "tracks": [
+                        {"s3_key": k}
+                        for k in filter_cam_audio_tracks(recording.track_keys)
+                    ],
+                    "bucket_name": bucket_name,
+                    "transcript_id": transcript.id,
+                    "room_id": room.id if room else None,
+                },
+                additional_metadata={
+                    "transcript_id": transcript.id,
+                    "recording_id": recording.id,
+                    "reprocess": True,
+                },
+            )
+            await transcripts_controller.update(
+                transcript, {"workflow_run_id": workflow_id}
+            )
 
-                process_multitrack_recording.delay(
+            logger.info(
-                    bucket_name=bucket_name,
+                "Queued Daily recording for Hatchet reprocessing",
-                    daily_room_name=meeting.room_name,
+                recording_id=recording.id,
-                    recording_id=recording.id,
+                workflow_id=workflow_id,
-                    track_keys=recording.track_keys,
+                room_name=meeting.room_name,
-                    recording_start_ts=recording_start_ts,
+                track_count=len(recording.track_keys),
-                )
+            )
 
             reprocessed_count += 1
 

server/reflector/ws_manager.py

@@ -11,7 +11,6 @@ broadcast messages to all connected websockets.
 
 import asyncio
 import json
-import threading
 
 import redis.asyncio as redis
 from fastapi import WebSocket
@@ -98,6 +97,7 @@ class WebsocketManager:
 
     async def _pubsub_data_reader(self, pubsub_subscriber):
         while True:
+            # timeout=1.0 prevents tight CPU loop when no messages available
             message = await pubsub_subscriber.get_message(
                 ignore_subscribe_messages=True
             )
@@ -109,29 +109,38 @@ class WebsocketManager:
                     await socket.send_json(data)
 
 
+# Process-global singleton to ensure only one WebsocketManager instance exists.
+# Multiple instances would cause resource leaks and CPU issues.
+_ws_manager: WebsocketManager | None = None
+
+
 def get_ws_manager() -> WebsocketManager:
     """
-    Returns the WebsocketManager instance for managing websockets.
+    Returns the global WebsocketManager singleton.
 
-    This function initializes and returns the WebsocketManager instance,
+    Creates instance on first call, subsequent calls return cached instance.
-    which is responsible for managing websockets and handling websocket
+    Thread-safe via GIL. Concurrent initialization may create duplicate
-    connections.
+    instances but last write wins (acceptable for this use case).
 
     Returns:
-        WebsocketManager: The initialized WebsocketManager instance.
+        WebsocketManager: The global WebsocketManager instance.
-
-    Raises:
-        ImportError: If the 'reflector.settings' module cannot be imported.
-        RedisConnectionError: If there is an error connecting to the Redis server.
     """
-    local = threading.local()
+    global _ws_manager
-    if hasattr(local, "ws_manager"):
-        return local.ws_manager
 
+    if _ws_manager is not None:
+        return _ws_manager
+
+    # No lock needed - GIL makes this safe enough
+    # Worst case: race creates two instances, last assignment wins
     pubsub_client = RedisPubSubManager(
         host=settings.REDIS_HOST,
         port=settings.REDIS_PORT,
     )
-    ws_manager = WebsocketManager(pubsub_client=pubsub_client)
+    _ws_manager = WebsocketManager(pubsub_client=pubsub_client)
-    local.ws_manager = ws_manager
+    return _ws_manager
-    return ws_manager
+
+
+def reset_ws_manager() -> None:
+    """Reset singleton for testing. DO NOT use in production."""
+    global _ws_manager
+    _ws_manager = None

server/tests/conftest.py

@@ -1,11 +1,10 @@
 import os
 from contextlib import asynccontextmanager
-from tempfile import NamedTemporaryFile
 from unittest.mock import patch
 
 import pytest
 
-from reflector.schemas.platform import WHEREBY_PLATFORM
+from reflector.schemas.platform import DAILY_PLATFORM, WHEREBY_PLATFORM
 
 
 @pytest.fixture(scope="session", autouse=True)
@@ -15,6 +14,7 @@ def register_mock_platform():
     from reflector.video_platforms.registry import register_platform
 
     register_platform(WHEREBY_PLATFORM, MockPlatformClient)
+    register_platform(DAILY_PLATFORM, MockPlatformClient)
     yield
 
 
@@ -333,11 +333,14 @@ def celery_enable_logging():
 
 @pytest.fixture(scope="session")
 def celery_config():
-    with NamedTemporaryFile() as f:
+    redis_host = os.environ.get("REDIS_HOST", "localhost")
-        yield {
+    redis_port = os.environ.get("REDIS_PORT", "6379")
-            "broker_url": "memory://",
+    # Use db 2 to avoid conflicts with main app
-            "result_backend": f"db+sqlite:///{f.name}",
+    redis_url = f"redis://{redis_host}:{redis_port}/2"
-        }
+    yield {
+        "broker_url": redis_url,
+        "result_backend": redis_url,
+    }
 
 
 @pytest.fixture(scope="session")
@@ -370,9 +373,12 @@ async def ws_manager_in_memory(monkeypatch):
         def __init__(self, queue: asyncio.Queue):
             self.queue = queue
 
-        async def get_message(self, ignore_subscribe_messages: bool = True):
+        async def get_message(
+            self, ignore_subscribe_messages: bool = True, timeout: float | None = None
+        ):
+            wait_timeout = timeout if timeout is not None else 0.05
             try:
-                return await asyncio.wait_for(self.queue.get(), timeout=0.05)
+                return await asyncio.wait_for(self.queue.get(), timeout=wait_timeout)
             except Exception:
                 return None
 

server/tests/test_transcripts_process.py

@@ -1,6 +1,6 @@
 import asyncio
 import time
-from unittest.mock import patch
+from unittest.mock import AsyncMock, patch
 
 import pytest
 from httpx import ASGITransport, AsyncClient
@@ -142,17 +142,17 @@ async def test_whereby_recording_uses_file_pipeline(client):
             "reflector.services.transcript_process.task_pipeline_file_process"
         ) as mock_file_pipeline,
         patch(
-            "reflector.services.transcript_process.task_pipeline_multitrack_process"
+            "reflector.services.transcript_process.HatchetClientManager"
-        ) as mock_multitrack_pipeline,
+        ) as mock_hatchet,
     ):
         response = await client.post(f"/transcripts/{transcript.id}/process")
 
         assert response.status_code == 200
         assert response.json()["status"] == "ok"
 
-        # Whereby recordings should use file pipeline
+        # Whereby recordings should use file pipeline, not Hatchet
         mock_file_pipeline.delay.assert_called_once_with(transcript_id=transcript.id)
-        mock_multitrack_pipeline.delay.assert_not_called()
+        mock_hatchet.start_workflow.assert_not_called()
 
 
 @pytest.mark.usefixtures("setup_database")
@@ -177,8 +177,6 @@ async def test_dailyco_recording_uses_multitrack_pipeline(client):
         recording_trigger="automatic-2nd-participant",
         is_shared=False,
     )
-    # Force Celery backend for test
-    await rooms_controller.update(room, {"use_celery": True})
 
     transcript = await transcripts_controller.add(
         "",
@@ -213,18 +211,23 @@ async def test_dailyco_recording_uses_multitrack_pipeline(client):
             "reflector.services.transcript_process.task_pipeline_file_process"
         ) as mock_file_pipeline,
         patch(
-            "reflector.services.transcript_process.task_pipeline_multitrack_process"
+            "reflector.services.transcript_process.HatchetClientManager"
-        ) as mock_multitrack_pipeline,
+        ) as mock_hatchet,
     ):
+        mock_hatchet.start_workflow = AsyncMock(return_value="test-workflow-id")
+
         response = await client.post(f"/transcripts/{transcript.id}/process")
 
         assert response.status_code == 200
         assert response.json()["status"] == "ok"
 
-        # Daily.co multitrack recordings should use multitrack pipeline
+        # Daily.co multitrack recordings should use Hatchet workflow
-        mock_multitrack_pipeline.delay.assert_called_once_with(
+        mock_hatchet.start_workflow.assert_called_once()
-            transcript_id=transcript.id,
+        call_kwargs = mock_hatchet.start_workflow.call_args.kwargs
-            bucket_name="daily-bucket",
+        assert call_kwargs["workflow_name"] == "DiarizationPipeline"
-            track_keys=track_keys,
+        assert call_kwargs["input_data"]["transcript_id"] == transcript.id
-        )
+        assert call_kwargs["input_data"]["bucket_name"] == "daily-bucket"
+        assert call_kwargs["input_data"]["tracks"] == [
+            {"s3_key": k} for k in track_keys
+        ]
         mock_file_pipeline.delay.assert_not_called()

server/tests/test_transcripts_rtc_ws.py

@@ -115,9 +115,7 @@ def appserver(tmpdir, setup_database, celery_session_app, celery_session_worker)
     settings.DATA_DIR = DATA_DIR
 
 
-@pytest.fixture(scope="session")
+# Using celery_includes from conftest.py which includes both pipelines
-def celery_includes():
-    return ["reflector.pipelines.main_live_pipeline"]
 
 
 @pytest.mark.usefixtures("setup_database")

server/tests/test_user_websocket_auth.py

@@ -56,7 +56,12 @@ def appserver_ws_user(setup_database):
 
     if server_instance:
         server_instance.should_exit = True
-        server_thread.join(timeout=30)
+        server_thread.join(timeout=2.0)
+
+    # Reset global singleton for test isolation
+    from reflector.ws_manager import reset_ws_manager
+
+    reset_ws_manager()
 
 
 @pytest.fixture(autouse=True)
@@ -133,6 +138,8 @@ async def test_user_ws_accepts_valid_token_and_receives_events(appserver_ws_user
 
     # Connect and then trigger an event via HTTP create
     async with aconnect_ws(base_ws, subprotocols=subprotocols) as ws:
+        await asyncio.sleep(0.2)
+
         # Emit an event to the user's room via a standard HTTP action
         from httpx import AsyncClient
 
@@ -150,6 +157,7 @@ async def test_user_ws_accepts_valid_token_and_receives_events(appserver_ws_user
             "email": "user-abc@example.com",
         }
 
+        # Use in-memory client (global singleton makes it share ws_manager)
         async with AsyncClient(app=app, base_url=f"http://{host}:{port}/v1") as ac:
             # Create a transcript as this user so that the server publishes TRANSCRIPT_CREATED to user room
             resp = await ac.post("/transcripts", json={"name": "WS Test"})

server/uv.lock

@@ -159,21 +159,20 @@ wheels = [
 
 [[package]]
 name = "aiortc"
-version = "1.13.0"
+version = "1.14.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "aioice" },
     { name = "av" },
-    { name = "cffi" },
     { name = "cryptography" },
     { name = "google-crc32c" },
     { name = "pyee" },
     { name = "pylibsrtp" },
     { name = "pyopenssl" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/62/03/bc947d74c548e0c17cf94e5d5bdacaed0ee9e5b2bb7b8b8cf1ac7a7c01ec/aiortc-1.13.0.tar.gz", hash = "sha256:5d209975c22d0910fb5a0f0e2caa828f2da966c53580f7c7170ac3a16a871620", size = 1179894 }
+sdist = { url = "https://files.pythonhosted.org/packages/51/9c/4e027bfe0195de0442da301e2389329496745d40ae44d2d7c4571c4290ce/aiortc-1.14.0.tar.gz", hash = "sha256:adc8a67ace10a085721e588e06a00358ed8eaf5f6b62f0a95358ff45628dd762", size = 1180864 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/87/29/765633cab5f1888890f5f172d1d53009b9b14e079cdfa01a62d9896a9ea9/aiortc-1.13.0-py3-none-any.whl", hash = "sha256:9ccccec98796f6a96bd1c3dd437a06da7e0f57521c96bd56e4b965a91b03a0a0", size = 92910 },
+    { url = "https://files.pythonhosted.org/packages/57/ab/31646a49209568cde3b97eeade0d28bb78b400e6645c56422c101df68932/aiortc-1.14.0-py3-none-any.whl", hash = "sha256:4b244d7e482f4e1f67e685b3468269628eca1ec91fa5b329ab517738cfca086e", size = 93183 },
 ]
 
 [[package]]
@@ -327,28 +326,24 @@ wheels = [
 
 [[package]]
 name = "av"
-version = "14.4.0"
+version = "16.1.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/86/f6/0b473dab52dfdea05f28f3578b1c56b6c796ce85e76951bab7c4e38d5a74/av-14.4.0.tar.gz", hash = "sha256:3ecbf803a7fdf67229c0edada0830d6bfaea4d10bfb24f0c3f4e607cd1064b42", size = 3892203 }
+sdist = { url = "https://files.pythonhosted.org/packages/78/cd/3a83ffbc3cc25b39721d174487fb0d51a76582f4a1703f98e46170ce83d4/av-16.1.0.tar.gz", hash = "sha256:a094b4fd87a3721dacf02794d3d2c82b8d712c85b9534437e82a8a978c175ffd", size = 4285203 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/18/8a/d57418b686ffd05fabd5a0a9cfa97e63b38c35d7101af00e87c51c8cc43c/av-14.4.0-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:5b21d5586a88b9fce0ab78e26bd1c38f8642f8e2aad5b35e619f4d202217c701", size = 19965048 },
+    { url = "https://files.pythonhosted.org/packages/48/d0/b71b65d1b36520dcb8291a2307d98b7fc12329a45614a303ff92ada4d723/av-16.1.0-cp311-cp311-macosx_11_0_x86_64.whl", hash = "sha256:e88ad64ee9d2b9c4c5d891f16c22ae78e725188b8926eb88187538d9dd0b232f", size = 26927747 },
-    { url = "https://files.pythonhosted.org/packages/f5/aa/3f878b0301efe587e9b07bb773dd6b47ef44ca09a3cffb4af50c08a170f3/av-14.4.0-cp311-cp311-macosx_12_0_x86_64.whl", hash = "sha256:cf8762d90b0f94a20c9f6e25a94f1757db5a256707964dfd0b1d4403e7a16835", size = 23750064 },
+    { url = "https://files.pythonhosted.org/packages/2f/79/720a5a6ccdee06eafa211b945b0a450e3a0b8fc3d12922f0f3c454d870d2/av-16.1.0-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:cb296073fa6935724de72593800ba86ae49ed48af03960a4aee34f8a611f442b", size = 21492232 },
-    { url = "https://files.pythonhosted.org/packages/9a/b4/6fe94a31f9ed3a927daa72df67c7151968587106f30f9f8fcd792b186633/av-14.4.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c0ac9f08920c7bbe0795319689d901e27cb3d7870b9a0acae3f26fc9daa801a6", size = 33648775 },
+    { url = "https://files.pythonhosted.org/packages/8e/4f/a1ba8d922f2f6d1a3d52419463ef26dd6c4d43ee364164a71b424b5ae204/av-16.1.0-cp311-cp311-manylinux_2_28_aarch64.whl", hash = "sha256:720edd4d25aa73723c1532bb0597806d7b9af5ee34fc02358782c358cfe2f879", size = 39291737 },
-    { url = "https://files.pythonhosted.org/packages/6c/f3/7f3130753521d779450c935aec3f4beefc8d4645471159f27b54e896470c/av-14.4.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a56d9ad2afdb638ec0404e962dc570960aae7e08ae331ad7ff70fbe99a6cf40e", size = 32216915 },
+    { url = "https://files.pythonhosted.org/packages/1a/31/fc62b9fe8738d2693e18d99f040b219e26e8df894c10d065f27c6b4f07e3/av-16.1.0-cp311-cp311-manylinux_2_28_x86_64.whl", hash = "sha256:c7f2bc703d0df260a1fdf4de4253c7f5500ca9fc57772ea241b0cb241bcf972e", size = 40846822 },
-    { url = "https://files.pythonhosted.org/packages/f8/9a/8ffabfcafb42154b4b3a67d63f9b69e68fa8c34cb39ddd5cb813dd049ed4/av-14.4.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6bed513cbcb3437d0ae47743edc1f5b4a113c0b66cdd4e1aafc533abf5b2fbf2", size = 35287279 },
+    { url = "https://files.pythonhosted.org/packages/53/10/ab446583dbce730000e8e6beec6ec3c2753e628c7f78f334a35cad0317f4/av-16.1.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d69c393809babada7d54964d56099e4b30a3e1f8b5736ca5e27bd7be0e0f3c83", size = 40675604 },
-    { url = "https://files.pythonhosted.org/packages/ad/11/7023ba0a2ca94a57aedf3114ab8cfcecb0819b50c30982a4c5be4d31df41/av-14.4.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d030c2d3647931e53d51f2f6e0fcf465263e7acf9ec6e4faa8dbfc77975318c3", size = 36294683 },
+    { url = "https://files.pythonhosted.org/packages/31/d7/1003be685277005f6d63fd9e64904ee222fe1f7a0ea70af313468bb597db/av-16.1.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:441892be28582356d53f282873c5a951592daaf71642c7f20165e3ddcb0b4c63", size = 42015955 },
-    { url = "https://files.pythonhosted.org/packages/3d/fa/b8ac9636bd5034e2b899354468bef9f4dadb067420a16d8a493a514b7817/av-14.4.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:1cc21582a4f606271d8c2036ec7a6247df0831050306c55cf8a905701d0f0474", size = 34552391 },
+    { url = "https://files.pythonhosted.org/packages/2f/4a/fa2a38ee9306bf4579f556f94ecbc757520652eb91294d2a99c7cf7623b9/av-16.1.0-cp311-cp311-win_amd64.whl", hash = "sha256:273a3e32de64819e4a1cd96341824299fe06f70c46f2288b5dc4173944f0fd62", size = 31750339 },
-    { url = "https://files.pythonhosted.org/packages/fb/29/0db48079c207d1cba7a2783896db5aec3816e17de55942262c244dffbc0f/av-14.4.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:ce7c9cd452153d36f1b1478f904ed5f9ab191d76db873bdd3a597193290805d4", size = 37265250 },
+    { url = "https://files.pythonhosted.org/packages/9c/84/2535f55edcd426cebec02eb37b811b1b0c163f26b8d3f53b059e2ec32665/av-16.1.0-cp312-cp312-macosx_11_0_x86_64.whl", hash = "sha256:640f57b93f927fba8689f6966c956737ee95388a91bd0b8c8b5e0481f73513d6", size = 26945785 },
-    { url = "https://files.pythonhosted.org/packages/1c/55/715858c3feb7efa4d667ce83a829c8e6ee3862e297fb2b568da3f968639d/av-14.4.0-cp311-cp311-win_amd64.whl", hash = "sha256:fd261e31cc6b43ca722f80656c39934199d8f2eb391e0147e704b6226acebc29", size = 27925845 },
+    { url = "https://files.pythonhosted.org/packages/b6/17/ffb940c9e490bf42e86db4db1ff426ee1559cd355a69609ec1efe4d3a9eb/av-16.1.0-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:ae3fb658eec00852ebd7412fdc141f17f3ddce8afee2d2e1cf366263ad2a3b35", size = 21481147 },
-    { url = "https://files.pythonhosted.org/packages/a6/75/b8641653780336c90ba89e5352cac0afa6256a86a150c7703c0b38851c6d/av-14.4.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:a53e682b239dd23b4e3bc9568cfb1168fc629ab01925fdb2e7556eb426339e94", size = 19954125 },
+    { url = "https://files.pythonhosted.org/packages/15/c1/e0d58003d2d83c3921887d5c8c9b8f5f7de9b58dc2194356a2656a45cfdc/av-16.1.0-cp312-cp312-manylinux_2_28_aarch64.whl", hash = "sha256:27ee558d9c02a142eebcbe55578a6d817fedfde42ff5676275504e16d07a7f86", size = 39517197 },
-    { url = "https://files.pythonhosted.org/packages/99/e6/37fe6fa5853a48d54d749526365780a63a4bc530be6abf2115e3a21e292a/av-14.4.0-cp312-cp312-macosx_12_0_x86_64.whl", hash = "sha256:5aa0b901751a32703fa938d2155d56ce3faf3630e4a48d238b35d2f7e49e5395", size = 23751479 },
+    { url = "https://files.pythonhosted.org/packages/32/77/787797b43475d1b90626af76f80bfb0c12cfec5e11eafcfc4151b8c80218/av-16.1.0-cp312-cp312-manylinux_2_28_x86_64.whl", hash = "sha256:7ae547f6d5fa31763f73900d43901e8c5fa6367bb9a9840978d57b5a7ae14ed2", size = 41174337 },
-    { url = "https://files.pythonhosted.org/packages/f7/75/9a5f0e6bda5f513b62bafd1cff2b495441a8b07ab7fb7b8e62f0c0d1683f/av-14.4.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a3b316fed3597675fe2aacfed34e25fc9d5bb0196dc8c0b014ae5ed4adda48de", size = 33801401 },
+    { url = "https://files.pythonhosted.org/packages/8e/ac/d90df7f1e3b97fc5554cf45076df5045f1e0a6adf13899e10121229b826c/av-16.1.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:8cf065f9d438e1921dc31fc7aa045790b58aee71736897866420d80b5450f62a", size = 40817720 },
-    { url = "https://files.pythonhosted.org/packages/6a/c9/e4df32a2ad1cb7f3a112d0ed610c5e43c89da80b63c60d60e3dc23793ec0/av-14.4.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a587b5c5014c3c0e16143a0f8d99874e46b5d0c50db6111aa0b54206b5687c81", size = 32364330 },
+    { url = "https://files.pythonhosted.org/packages/80/6f/13c3a35f9dbcebafd03fe0c4cbd075d71ac8968ec849a3cfce406c35a9d2/av-16.1.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:a345877a9d3cc0f08e2bc4ec163ee83176864b92587afb9d08dff50f37a9a829", size = 42267396 },
-    { url = "https://files.pythonhosted.org/packages/ca/f0/64e7444a41817fde49a07d0239c033f7e9280bec4a4bb4784f5c79af95e6/av-14.4.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:10d53f75e8ac1ec8877a551c0db32a83c0aaeae719d05285281eaaba211bbc30", size = 35519508 },
+    { url = "https://files.pythonhosted.org/packages/c8/b9/275df9607f7fb44317ccb1d4be74827185c0d410f52b6e2cd770fe209118/av-16.1.0-cp312-cp312-win_amd64.whl", hash = "sha256:f49243b1d27c91cd8c66fdba90a674e344eb8eb917264f36117bf2b6879118fd", size = 31752045 },
-    { url = "https://files.pythonhosted.org/packages/c2/a8/a370099daa9033a3b6f9b9bd815304b3d8396907a14d09845f27467ba138/av-14.4.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:c8558cfde79dd8fc92d97c70e0f0fa8c94c7a66f68ae73afdf58598f0fe5e10d", size = 36448593 },
-    { url = "https://files.pythonhosted.org/packages/27/bb/edb6ceff8fa7259cb6330c51dbfbc98dd1912bd6eb5f7bc05a4bb14a9d6e/av-14.4.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:455b6410dea0ab2d30234ffb28df7d62ca3cdf10708528e247bec3a4cdcced09", size = 34701485 },
-    { url = "https://files.pythonhosted.org/packages/a7/8a/957da1f581aa1faa9a5dfa8b47ca955edb47f2b76b949950933b457bfa1d/av-14.4.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:1661efbe9d975f927b8512d654704223d936f39016fad2ddab00aee7c40f412c", size = 37521981 },
-    { url = "https://files.pythonhosted.org/packages/28/76/3f1cf0568592f100fd68eb40ed8c491ce95ca3c1378cc2d4c1f6d1bd295d/av-14.4.0-cp312-cp312-win_amd64.whl", hash = "sha256:fbbeef1f421a3461086853d6464ad5526b56ffe8ccb0ab3fd0a1f121dfbf26ad", size = 27925944 },
 ]
 
 [[package]]
@@ -3267,7 +3262,7 @@ requires-dist = [
     { name = "aiohttp-cors", specifier = ">=0.7.0" },
     { name = "aiortc", specifier = ">=1.5.0" },
     { name = "alembic", specifier = ">=1.11.3" },
-    { name = "av", specifier = ">=10.0.0" },
+    { name = "av", specifier = ">=15.0.0" },
     { name = "celery", specifier = ">=5.3.4" },
     { name = "databases", extras = ["aiosqlite", "asyncpg"], specifier = ">=0.7.0" },
     { name = "fastapi", extras = ["standard"], specifier = ">=0.100.1" },