format

Co-authored-by: pr-agent-monadical[bot] <198624643+pr-agent-monadical[bot]@users.noreply.github.com>

.gitignore

@@ -1,6 +1,5 @@
 .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

@@ -1,29 +1,5 @@
 # Changelog
 
-## [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

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

@@ -1,14 +1,9 @@
 # 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. If using Caddy: copy Caddyfile.example to Caddyfile and edit your domains
+# 2. Copy Caddyfile.example to Caddyfile and edit with your domains
 # 3. Deploy Modal GPU functions (see gpu/modal_deployments/deploy-all.sh)
 
 services:
@@ -89,8 +84,6 @@ 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 (optional; see [Caddy profile](#caddy-profile)) |
+| `caddy` | `caddy:2-alpine` | Reverse proxy with auto-SSL |
 
 ## Environment Files
 
@@ -30,7 +30,6 @@ 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
@@ -55,7 +54,6 @@ TRANSCRIPT_MODAL_API_KEY=...
 Used by: `web`
 
 Key variables:
-
 ```env
 # Domain configuration
 SITE_URL=https://app.example.com
@@ -72,42 +70,26 @@ 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
@@ -117,7 +99,6 @@ 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
@@ -127,32 +108,27 @@ 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
 ```
@@ -211,7 +187,6 @@ 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
@@ -60,17 +60,16 @@ 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
 
@@ -180,7 +179,6 @@ 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
 
@@ -192,7 +190,6 @@ nano server/.env
 ```
 
 **Required settings:**
-
 ```env
 # Database (defaults work with docker-compose.prod.yml)
 DATABASE_URL=postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
@@ -252,7 +249,6 @@ nano www/.env
 ```
 
 **Required settings:**
-
 ```env
 # Your domains
 SITE_URL=https://app.example.com
@@ -270,11 +266,7 @@ FEATURE_REQUIRE_LOGIN=false
 
 ---
 
-## Reverse proxy (Caddy or existing)
+## Configure Caddy
-
-**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
@@ -297,18 +289,10 @@ 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).
 
 ---
@@ -316,21 +300,18 @@ 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
@@ -346,7 +327,6 @@ 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
@@ -378,7 +358,6 @@ 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
 ```
@@ -388,43 +367,35 @@ 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 (when using Caddy)
+### SSL certificate errors
-
 - Caddy auto-provisions Let's Encrypt certificates
-- Ensure ports 80 and 443 are open and not used by another proxy
+- Ensure ports 80 and 443 are open
 - 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
@@ -437,3 +408,4 @@ 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

@@ -131,15 +131,6 @@ 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 [ $? -ne 0 ]; then
-    echo "Error: Failed to deploy padding. Check Modal dashboard for details."
-    exit 1
-fi
-echo "  -> reflector-padding.pad_track (Modal SDK function)"
-
 # --- Output Configuration ---
 echo ""
 echo "=========================================="
@@ -156,6 +147,4 @@ echo ""
 echo "DIARIZATION_BACKEND=modal"
 echo "DIARIZATION_URL=$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

@@ -1,277 +0,0 @@
-"""
-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
-

server/pyproject.toml

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

server/reflector/hatchet/constants.py

@@ -8,7 +8,8 @@ from enum import StrEnum
 class TaskName(StrEnum):
     GET_RECORDING = "get_recording"
     GET_PARTICIPANTS = "get_participants"
-    PROCESS_TRACKS = "process_tracks"
+    PROCESS_PADDINGS = "process_paddings"
+    PROCESS_TRANSCRIPTIONS = "process_transcriptions"
     MIXDOWN_TRACKS = "mixdown_tracks"
     GENERATE_WAVEFORM = "generate_waveform"
     DETECT_TOPICS = "detect_topics"
@@ -35,9 +36,7 @@ LLM_RATE_LIMIT_PER_SECOND = 10
 
 # Task execution timeouts (seconds)
 TIMEOUT_SHORT = 60  # Quick operations: API calls, DB updates
-TIMEOUT_MEDIUM = (
+TIMEOUT_MEDIUM = 120  # Single LLM calls, waveform generation
-    300  # Single LLM calls, waveform generation (5m for slow LLM responses)
-)
 TIMEOUT_LONG = 180  # Action items (larger context LLM)
-TIMEOUT_AUDIO = 720  # Audio processing: padding, mixdown
+TIMEOUT_AUDIO = 300  # Audio processing: padding, mixdown
 TIMEOUT_HEAVY = 600  # Transcription, fan-out LLM tasks

server/reflector/hatchet/run_workers_cpu.py

@@ -1,9 +1,9 @@
 """
 CPU-heavy worker pool for audio processing tasks.
-Handles ONLY: mixdown_tracks
+Handles: mixdown_tracks only (serialized with max_runs=1)
 
 Configuration:
-- slots=1: Only mixdown (already serialized globally with max_runs=1)
+- slots=1: Only one mixdown at a time
 - Worker affinity: pool=cpu-heavy
 """
 
@@ -26,7 +26,7 @@ def main():
 
     cpu_worker = hatchet.worker(
         "cpu-worker-pool",
-        slots=1,  # Only 1 mixdown at a time (already serialized globally)
+        slots=1,
         labels={
             "pool": "cpu-heavy",
         },

server/reflector/hatchet/run_workers_llm.py

@@ -1,15 +1,16 @@
 """
 LLM/I/O worker pool for all non-CPU tasks.
-Handles: all tasks except mixdown_tracks (transcription, LLM inference, orchestration)
+Handles: all tasks except mixdown_tracks (padding, transcription, LLM inference, orchestration)
 """
 
 from reflector.hatchet.client import HatchetClientManager
 from reflector.hatchet.workflows.daily_multitrack_pipeline import (
     daily_multitrack_pipeline,
 )
+from reflector.hatchet.workflows.padding_workflow import padding_workflow
 from reflector.hatchet.workflows.subject_processing import subject_workflow
 from reflector.hatchet.workflows.topic_chunk_processing import topic_chunk_workflow
-from reflector.hatchet.workflows.track_processing import track_workflow
+from reflector.hatchet.workflows.transcription_workflow import transcription_workflow
 from reflector.logger import logger
 
 SLOTS = 10
@@ -29,7 +30,7 @@ def main():
 
     llm_worker = hatchet.worker(
         WORKER_NAME,
-        slots=SLOTS,  # not all slots are probably used
+        slots=SLOTS,
         labels={
             "pool": POOL,
         },
@@ -37,7 +38,8 @@ def main():
             daily_multitrack_pipeline,
             topic_chunk_workflow,
             subject_workflow,
-            track_workflow,
+            padding_workflow,
+            transcription_workflow,
         ],
     )
 

server/reflector/hatchet/workflows/__init__.py

@@ -4,6 +4,10 @@ from reflector.hatchet.workflows.daily_multitrack_pipeline import (
     PipelineInput,
     daily_multitrack_pipeline,
 )
+from reflector.hatchet.workflows.padding_workflow import (
+    PaddingInput,
+    padding_workflow,
+)
 from reflector.hatchet.workflows.subject_processing import (
     SubjectInput,
     subject_workflow,
@@ -12,15 +16,20 @@ from reflector.hatchet.workflows.topic_chunk_processing import (
     TopicChunkInput,
     topic_chunk_workflow,
 )
-from reflector.hatchet.workflows.track_processing import TrackInput, track_workflow
+from reflector.hatchet.workflows.transcription_workflow import (
+    TranscriptionInput,
+    transcription_workflow,
+)
 
 __all__ = [
     "daily_multitrack_pipeline",
     "subject_workflow",
     "topic_chunk_workflow",
-    "track_workflow",
+    "padding_workflow",
+    "transcription_workflow",
     "PipelineInput",
     "SubjectInput",
     "TopicChunkInput",
-    "TrackInput",
+    "PaddingInput",
+    "TranscriptionInput",
 ]

server/reflector/hatchet/workflows/daily_multitrack_pipeline.py

@@ -54,8 +54,9 @@ from reflector.hatchet.workflows.models import (
     PadTrackResult,
     ParticipantInfo,
     ParticipantsResult,
+    ProcessPaddingsResult,
     ProcessSubjectsResult,
-    ProcessTracksResult,
+    ProcessTranscriptionsResult,
     RecapResult,
     RecordingResult,
     SubjectsResult,
@@ -68,6 +69,7 @@ from reflector.hatchet.workflows.models import (
     WebhookResult,
     ZulipResult,
 )
+from reflector.hatchet.workflows.padding_workflow import PaddingInput, padding_workflow
 from reflector.hatchet.workflows.subject_processing import (
     SubjectInput,
     subject_workflow,
@@ -76,7 +78,10 @@ from reflector.hatchet.workflows.topic_chunk_processing import (
     TopicChunkInput,
     topic_chunk_workflow,
 )
-from reflector.hatchet.workflows.track_processing import TrackInput, track_workflow
+from reflector.hatchet.workflows.transcription_workflow import (
+    TranscriptionInput,
+    transcription_workflow,
+)
 from reflector.logger import logger
 from reflector.pipelines import topic_processing
 from reflector.processors import AudioFileWriterProcessor
@@ -322,7 +327,6 @@ 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,
         )
@@ -331,26 +335,15 @@ 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"
         )
@@ -416,72 +409,115 @@ async def get_participants(input: PipelineInput, ctx: Context) -> ParticipantsRe
     execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
     retries=3,
 )
-@with_error_handling(TaskName.PROCESS_TRACKS)
+@with_error_handling(TaskName.PROCESS_PADDINGS)
-async def process_tracks(input: PipelineInput, ctx: Context) -> ProcessTracksResult:
+async def process_paddings(input: PipelineInput, ctx: Context) -> ProcessPaddingsResult:
-    """Spawn child workflows for each track (dynamic fan-out)."""
+    """Spawn child workflows for each track to apply padding (dynamic fan-out)."""
-    ctx.log(f"process_tracks: spawning {len(input.tracks)} track workflows")
+    ctx.log(f"process_paddings: spawning {len(input.tracks)} padding workflows")
-
-    participants_result = ctx.task_output(get_participants)
-    source_language = participants_result.source_language
 
     bulk_runs = [
-        track_workflow.create_bulk_run_item(
+        padding_workflow.create_bulk_run_item(
-            input=TrackInput(
+            input=PaddingInput(
                 track_index=i,
                 s3_key=track["s3_key"],
                 bucket_name=input.bucket_name,
                 transcript_id=input.transcript_id,
-                language=source_language,
             )
         )
         for i, track in enumerate(input.tracks)
     ]
 
-    results = await track_workflow.aio_run_many(bulk_runs)
+    results = await padding_workflow.aio_run_many(bulk_runs)
 
-    target_language = participants_result.target_language
-
-    track_words: list[list[Word]] = []
     padded_tracks = []
-    created_padded_files = set()
+    created_padded_files = []
 
     for result in results:
-        transcribe_result = TranscribeTrackResult(**result[TaskName.TRANSCRIBE_TRACK])
-        track_words.append(transcribe_result.words)
-
         pad_result = PadTrackResult(**result[TaskName.PAD_TRACK])
 
-        # Store S3 key info (not presigned URL) - consumer tasks presign on demand
+        padded_tracks.append(
-        if pad_result.padded_key:
+            PaddedTrackInfo(
-            padded_tracks.append(
+                key=pad_result.padded_key,
-                PaddedTrackInfo(
+                bucket_name=pad_result.bucket_name,
-                    key=pad_result.padded_key, bucket_name=pad_result.bucket_name
+                track_index=pad_result.track_index,
-                )
             )
+        )
 
         if pad_result.size > 0:
             storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{pad_result.track_index}.webm"
-            created_padded_files.add(storage_path)
+            created_padded_files.append(storage_path)
 
-    all_words = [word for words in track_words for word in words]
+    ctx.log(f"process_paddings complete: {len(padded_tracks)} padded tracks")
-    all_words.sort(key=lambda w: w.start)
 
-    ctx.log(
+    return ProcessPaddingsResult(
-        f"process_tracks complete: {len(all_words)} words from {len(input.tracks)} tracks"
-    )
-
-    return ProcessTracksResult(
-        all_words=all_words,
         padded_tracks=padded_tracks,
-        word_count=len(all_words),
         num_tracks=len(input.tracks),
-        target_language=target_language,
         created_padded_files=list(created_padded_files),
     )
 
 
 @daily_multitrack_pipeline.task(
-    parents=[process_tracks],
+    parents=[process_paddings],
+    execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
+    retries=3,
+)
+@with_error_handling(TaskName.PROCESS_TRANSCRIPTIONS)
+async def process_transcriptions(
+    input: PipelineInput, ctx: Context
+) -> ProcessTranscriptionsResult:
+    """Spawn child workflows for each padded track to transcribe (dynamic fan-out)."""
+    participants_result = ctx.task_output(get_participants)
+    paddings_result = ctx.task_output(process_paddings)
+
+    source_language = participants_result.source_language
+    if not source_language:
+        raise ValueError("source_language is required for transcription")
+
+    target_language = participants_result.target_language
+    padded_tracks = paddings_result.padded_tracks
+
+    if not padded_tracks:
+        raise ValueError("No padded tracks available for transcription")
+
+    ctx.log(
+        f"process_transcriptions: spawning {len(padded_tracks)} transcription workflows"
+    )
+
+    bulk_runs = [
+        transcription_workflow.create_bulk_run_item(
+            input=TranscriptionInput(
+                track_index=padded_track.track_index,
+                padded_key=padded_track.key,
+                bucket_name=padded_track.bucket_name,
+                language=source_language,
+            )
+        )
+        for padded_track in padded_tracks
+    ]
+
+    results = await transcription_workflow.aio_run_many(bulk_runs)
+
+    track_words: list[list[Word]] = []
+    for result in results:
+        transcribe_result = TranscribeTrackResult(**result[TaskName.TRANSCRIBE_TRACK])
+        track_words.append(transcribe_result.words)
+
+    all_words = [word for words in track_words for word in words]
+    all_words.sort(key=lambda w: w.start)
+
+    ctx.log(
+        f"process_transcriptions complete: {len(all_words)} words from {len(padded_tracks)} tracks"
+    )
+
+    return ProcessTranscriptionsResult(
+        all_words=all_words,
+        word_count=len(all_words),
+        num_tracks=len(input.tracks),
+        target_language=target_language,
+    )
+
+
+@daily_multitrack_pipeline.task(
+    parents=[process_paddings],
     execution_timeout=timedelta(seconds=TIMEOUT_AUDIO),
     retries=3,
     desired_worker_labels={
@@ -501,12 +537,12 @@ async def process_tracks(input: PipelineInput, ctx: Context) -> ProcessTracksRes
 )
 @with_error_handling(TaskName.MIXDOWN_TRACKS)
 async def mixdown_tracks(input: PipelineInput, ctx: Context) -> MixdownResult:
-    """Mix all padded tracks into single audio file using PyAV (same as Celery)."""
+    """Mix all padded tracks into single audio file using PyAV."""
     ctx.log("mixdown_tracks: mixing padded tracks into single audio file")
 
-    track_result = ctx.task_output(process_tracks)
+    paddings_result = ctx.task_output(process_paddings)
     recording_result = ctx.task_output(get_recording)
-    padded_tracks = track_result.padded_tracks
+    padded_tracks = paddings_result.padded_tracks
 
     # Dynamic timeout: scales with track count and recording duration
     # Base 300s + 60s per track + 1s per 10s of recording
@@ -660,7 +696,7 @@ async def generate_waveform(input: PipelineInput, ctx: Context) -> WaveformResul
 
 
 @daily_multitrack_pipeline.task(
-    parents=[process_tracks],
+    parents=[process_transcriptions],
     execution_timeout=timedelta(seconds=TIMEOUT_HEAVY),
     retries=3,
 )
@@ -669,8 +705,8 @@ async def detect_topics(input: PipelineInput, ctx: Context) -> TopicsResult:
     """Detect topics using parallel child workflows (one per chunk)."""
     ctx.log("detect_topics: analyzing transcript for topics")
 
-    track_result = ctx.task_output(process_tracks)
+    transcriptions_result = ctx.task_output(process_transcriptions)
-    words = track_result.all_words
+    words = transcriptions_result.all_words
 
     if not words:
         ctx.log("detect_topics: no words, returning empty topics")
@@ -1107,7 +1143,7 @@ async def identify_action_items(
 
 
 @daily_multitrack_pipeline.task(
-    parents=[process_tracks, generate_title, generate_recap, identify_action_items],
+    parents=[generate_title, generate_recap, identify_action_items],
     execution_timeout=timedelta(seconds=TIMEOUT_SHORT),
     retries=3,
 )
@@ -1120,10 +1156,15 @@ async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
     """
     ctx.log("finalize: saving transcript and setting status to 'ended'")
 
-    track_result = ctx.task_output(process_tracks)
+    mixdown_result = ctx.task_output(mixdown_tracks)
+    transcriptions_result = ctx.task_output(process_transcriptions)
+    paddings_result = ctx.task_output(process_paddings)
+
+    duration = mixdown_result.duration
+    all_words = transcriptions_result.all_words
 
     # Cleanup temporary padded S3 files (deferred until finalize for semantic parity with Celery)
-    created_padded_files = track_result.created_padded_files
+    created_padded_files = paddings_result.created_padded_files
     if created_padded_files:
         ctx.log(f"Cleaning up {len(created_padded_files)} temporary S3 files")
         storage = _spawn_storage()
@@ -1141,6 +1182,7 @@ async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
 
     async with fresh_db_connection():
         from reflector.db.transcripts import (  # noqa: PLC0415
+            TranscriptDuration,
             TranscriptText,
             transcripts_controller,
         )
@@ -1149,6 +1191,8 @@ 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,
@@ -1160,15 +1204,21 @@ async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
             logger=logger,
         )
 
-        # Clear workflow_run_id (workflow completed successfully)
+        # Save duration and clear workflow_run_id (workflow completed successfully)
-        # Note: title/long_summary/short_summary/duration already saved by their callbacks
+        # Note: title/long_summary/short_summary 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/models.py

@@ -21,12 +21,14 @@ class ParticipantInfo(BaseModel):
 
 
 class PadTrackResult(BaseModel):
-    """Result from pad_track task."""
+    """Result from pad_track task.
 
-    padded_key: NonEmptyString  # S3 key (not presigned URL) - presign on demand to avoid stale URLs on replay
+    If size=0, track required no padding and padded_key contains original S3 key.
-    bucket_name: (
+    If size>0, track was padded and padded_key contains new padded file S3 key.
-        NonEmptyString | None
+    """
-    )  # None means use default transcript storage bucket
+
+    padded_key: NonEmptyString
+    bucket_name: NonEmptyString | None
     size: int
     track_index: int
 
@@ -59,18 +61,25 @@ class PaddedTrackInfo(BaseModel):
     """Info for a padded track - S3 key + bucket for on-demand presigning."""
 
     key: NonEmptyString
-    bucket_name: NonEmptyString | None  # None = use default storage bucket
+    bucket_name: NonEmptyString | None
+    track_index: int
 
 
-class ProcessTracksResult(BaseModel):
+class ProcessPaddingsResult(BaseModel):
-    """Result from process_tracks task."""
+    """Result from process_paddings task."""
+
+    padded_tracks: list[PaddedTrackInfo]
+    num_tracks: int
+    created_padded_files: list[NonEmptyString]
+
+
+class ProcessTranscriptionsResult(BaseModel):
+    """Result from process_transcriptions task."""
 
     all_words: list[Word]
-    padded_tracks: list[PaddedTrackInfo]  # S3 keys, not presigned URLs
     word_count: int
     num_tracks: int
     target_language: NonEmptyString
-    created_padded_files: list[NonEmptyString]
 
 
 class MixdownResult(BaseModel):

server/reflector/hatchet/workflows/padding_workflow.py

@@ -1,9 +1,11 @@
 """
 Hatchet child workflow: PaddingWorkflow
-Handles individual audio track padding via Modal.com backend.
+Handles individual audio track padding only.
 """
 
+import tempfile
 from datetime import timedelta
+from pathlib import Path
 
 import av
 from hatchet_sdk import Context
@@ -14,7 +16,10 @@ 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
+from reflector.utils.audio_padding import (
+    apply_audio_padding_to_file,
+    extract_stream_start_time_from_container,
+)
 
 
 class PaddingInput(BaseModel):
@@ -63,83 +68,61 @@ async def pad_track(input: PaddingInput, ctx: Context) -> PadTrackResult:
             bucket=input.bucket_name,
         )
 
-        # Extract start_time to determine if padding needed
         with av.open(source_url) as in_container:
-            if in_container.duration:
+            with av.open(source_url) as in_container:
-                try:
+                if in_container.duration:
-                    duration = timedelta(seconds=in_container.duration // 1_000_000)
+                    try:
-                    ctx.log(
+                        duration = timedelta(seconds=in_container.duration // 1_000_000)
-                        f"pad_track: track {input.track_index}, duration={duration}"
+                        ctx.log(
-                    )
+                            f"pad_track: track {input.track_index}, duration={duration}"
-                except (ValueError, TypeError, OverflowError) as e:
+                        )
-                    ctx.log(
+                    except (ValueError, TypeError, OverflowError) as e:
-                        f"pad_track: track {input.track_index}, duration error: {str(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:
+            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,
-            )
+                )
 
-        storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
+            storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
 
-        # Presign PUT URL for output (Modal will upload directly)
+            with tempfile.NamedTemporaryFile(suffix=".webm", delete=False) as temp_file:
-        output_url = await storage.get_file_url(
+                temp_path = temp_file.name
-            storage_path,
-            operation="put_object",
-            expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
-        )
 
-        import httpx  # noqa: PLC0415
+            try:
+                apply_audio_padding_to_file(
+                    in_container,
+                    temp_path,
+                    start_time_seconds,
+                    input.track_index,
+                    logger=logger,
+                )
 
-        from reflector.processors.audio_padding_modal import (  # noqa: PLC0415
+                file_size = Path(temp_path).stat().st_size
-            AudioPaddingModalProcessor,
-        )
 
-        try:
+                with open(temp_path, "rb") as padded_file:
-            processor = AudioPaddingModalProcessor()
+                    await storage.put_file(storage_path, padded_file)
-            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}")
+                logger.info(
-        except httpx.HTTPStatusError as e:
+                    f"Uploaded padded track to S3",
-            error_detail = e.response.text if hasattr(e.response, "text") else str(e)
+                    key=storage_path,
-            logger.error(
+                    size=file_size,
-                "[Hatchet] Modal padding HTTP error",
+                )
-                transcript_id=input.transcript_id,
+            finally:
-                track_index=input.track_index,
+                Path(temp_path).unlink(missing_ok=True)
-                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",

server/reflector/hatchet/workflows/track_processing.py

@@ -1,205 +0,0 @@
-"""
-Hatchet child workflow: TrackProcessing
-
-Handles individual audio track processing: padding and transcription.
-Spawned dynamically by the main diarization pipeline for each track.
-
-Architecture note: This is a separate workflow (not inline tasks in DailyMultitrackPipeline)
-because Hatchet workflow DAGs are defined statically, but the number of tracks varies
-at runtime. Child workflow spawning via `aio_run()` + `asyncio.gather()` is the
-standard pattern for dynamic fan-out. See `process_tracks` in daily_multitrack_pipeline.py.
-
-Note: This file uses deferred imports (inside tasks) intentionally.
-Hatchet workers run in forked processes; fresh imports per task ensure
-storage/DB connections are not shared across forks.
-"""
-
-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, 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 extract_stream_start_time_from_container
-
-
-class TrackInput(BaseModel):
-    """Input for individual track processing."""
-
-    track_index: int
-    s3_key: str
-    bucket_name: str
-    transcript_id: str
-    language: str = "en"
-
-
-hatchet = HatchetClientManager.get_client()
-
-track_workflow = hatchet.workflow(name="TrackProcessing", input_validator=TrackInput)
-
-
-@track_workflow.task(execution_timeout=timedelta(seconds=TIMEOUT_AUDIO), retries=3)
-async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
-    """Pad single audio track with silence for alignment.
-
-    Extracts stream.start_time from WebM container metadata and applies
-    silence padding using PyAV filter graph (adelay).
-    """
-    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,
-        )
-
-        with av.open(source_url) as in_container:
-            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 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 uploads directly)
-        output_url = await storage.get_file_url(
-            storage_path,
-            operation="put_object",
-            expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
-        )
-
-        from reflector.processors.audio_padding_modal import (  # noqa: PLC0415
-            AudioPaddingModalProcessor,
-        )
-
-        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 complete: track {input.track_index} -> {storage_path}")
-        logger.info(
-            "[Hatchet] pad_track complete",
-            track_index=input.track_index,
-            padded_key=storage_path,
-        )
-
-        # Return S3 key (not presigned URL) - consumer tasks presign on demand
-        # This avoids stale URLs when workflow is replayed
-        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", error=str(e), exc_info=True)
-        raise
-
-
-@track_workflow.task(
-    parents=[pad_track], execution_timeout=timedelta(seconds=TIMEOUT_HEAVY), retries=3
-)
-async def transcribe_track(input: TrackInput, ctx: Context) -> TranscribeTrackResult:
-    """Transcribe audio track using GPU (Modal.com) or local Whisper."""
-    ctx.log(f"transcribe_track: track {input.track_index}, language={input.language}")
-    logger.info(
-        "[Hatchet] transcribe_track",
-        track_index=input.track_index,
-        language=input.language,
-    )
-
-    try:
-        pad_result = ctx.task_output(pad_track)
-        padded_key = pad_result.padded_key
-        bucket_name = pad_result.bucket_name
-
-        if not padded_key:
-            raise ValueError("Missing padded_key from pad_track")
-
-        # Presign URL on demand (avoids stale URLs on workflow replay)
-        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,
-        )
-
-        audio_url = await storage.get_file_url(
-            padded_key,
-            operation="get_object",
-            expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
-            bucket=bucket_name,
-        )
-
-        from reflector.pipelines.transcription_helpers import (  # noqa: PLC0415
-            transcribe_file_with_processor,
-        )
-
-        transcript = await transcribe_file_with_processor(audio_url, input.language)
-
-        # Tag all words with speaker index
-        for word in transcript.words:
-            word.speaker = input.track_index
-
-        ctx.log(
-            f"transcribe_track complete: track {input.track_index}, {len(transcript.words)} words"
-        )
-        logger.info(
-            "[Hatchet] transcribe_track complete",
-            track_index=input.track_index,
-            word_count=len(transcript.words),
-        )
-
-        return TranscribeTrackResult(
-            words=transcript.words,
-            track_index=input.track_index,
-        )
-
-    except Exception as e:
-        logger.error("[Hatchet] transcribe_track failed", error=str(e), exc_info=True)
-        raise

server/reflector/hatchet/workflows/transcription_workflow.py

@@ -0,0 +1,98 @@
+"""
+Hatchet child workflow: TranscriptionWorkflow
+Handles individual audio track transcription only.
+"""
+
+from datetime import timedelta
+
+from hatchet_sdk import Context
+from pydantic import BaseModel
+
+from reflector.hatchet.client import HatchetClientManager
+from reflector.hatchet.constants import TIMEOUT_HEAVY
+from reflector.hatchet.workflows.models import TranscribeTrackResult
+from reflector.logger import logger
+from reflector.utils.audio_constants import PRESIGNED_URL_EXPIRATION_SECONDS
+
+
+class TranscriptionInput(BaseModel):
+    """Input for individual track transcription."""
+
+    track_index: int
+    padded_key: str  # S3 key from padding step
+    bucket_name: str | None  # None = use default bucket
+    language: str = "en"
+
+
+hatchet = HatchetClientManager.get_client()
+
+transcription_workflow = hatchet.workflow(
+    name="TranscriptionWorkflow", input_validator=TranscriptionInput
+)
+
+
+@transcription_workflow.task(
+    execution_timeout=timedelta(seconds=TIMEOUT_HEAVY), retries=3
+)
+async def transcribe_track(
+    input: TranscriptionInput, ctx: Context
+) -> TranscribeTrackResult:
+    """Transcribe audio track using GPU (Modal.com) or local Whisper."""
+    ctx.log(f"transcribe_track: track {input.track_index}, language={input.language}")
+    logger.info(
+        "[Hatchet] transcribe_track",
+        track_index=input.track_index,
+        language=input.language,
+    )
+
+    try:
+        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,
+        )
+
+        audio_url = await storage.get_file_url(
+            input.padded_key,
+            operation="get_object",
+            expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
+            bucket=input.bucket_name,
+        )
+
+        from reflector.pipelines.transcription_helpers import (  # noqa: PLC0415
+            transcribe_file_with_processor,
+        )
+
+        transcript = await transcribe_file_with_processor(audio_url, input.language)
+
+        for word in transcript.words:
+            word.speaker = input.track_index
+
+        ctx.log(
+            f"transcribe_track complete: track {input.track_index}, {len(transcript.words)} words"
+        )
+        logger.info(
+            "[Hatchet] transcribe_track complete",
+            track_index=input.track_index,
+            word_count=len(transcript.words),
+        )
+
+        return TranscribeTrackResult(
+            words=transcript.words,
+            track_index=input.track_index,
+        )
+
+    except Exception as e:
+        logger.error(
+            "[Hatchet] transcribe_track failed",
+            track_index=input.track_index,
+            padded_key=input.padded_key,
+            language=input.language,
+            error=str(e),
+            exc_info=True,
+        )
+        raise

server/reflector/processors/audio_padding_modal.py

@@ -1,113 +0,0 @@
-"""
-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/settings.py

@@ -98,10 +98,6 @@ 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
 

server/reflector/utils/audio_constants.py

@@ -5,9 +5,7 @@ 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/uv.lock

@@ -159,20 +159,21 @@ wheels = [
 
 [[package]]
 name = "aiortc"
-version = "1.14.0"
+version = "1.13.0"
 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/51/9c/4e027bfe0195de0442da301e2389329496745d40ae44d2d7c4571c4290ce/aiortc-1.14.0.tar.gz", hash = "sha256:adc8a67ace10a085721e588e06a00358ed8eaf5f6b62f0a95358ff45628dd762", size = 1180864 }
+sdist = { url = "https://files.pythonhosted.org/packages/62/03/bc947d74c548e0c17cf94e5d5bdacaed0ee9e5b2bb7b8b8cf1ac7a7c01ec/aiortc-1.13.0.tar.gz", hash = "sha256:5d209975c22d0910fb5a0f0e2caa828f2da966c53580f7c7170ac3a16a871620", size = 1179894 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/57/ab/31646a49209568cde3b97eeade0d28bb78b400e6645c56422c101df68932/aiortc-1.14.0-py3-none-any.whl", hash = "sha256:4b244d7e482f4e1f67e685b3468269628eca1ec91fa5b329ab517738cfca086e", size = 93183 },
+    { url = "https://files.pythonhosted.org/packages/87/29/765633cab5f1888890f5f172d1d53009b9b14e079cdfa01a62d9896a9ea9/aiortc-1.13.0-py3-none-any.whl", hash = "sha256:9ccccec98796f6a96bd1c3dd437a06da7e0f57521c96bd56e4b965a91b03a0a0", size = 92910 },
 ]
 
 [[package]]
@@ -326,24 +327,28 @@ wheels = [
 
 [[package]]
 name = "av"
-version = "16.1.0"
+version = "14.4.0"
 source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/78/cd/3a83ffbc3cc25b39721d174487fb0d51a76582f4a1703f98e46170ce83d4/av-16.1.0.tar.gz", hash = "sha256:a094b4fd87a3721dacf02794d3d2c82b8d712c85b9534437e82a8a978c175ffd", size = 4285203 }
+sdist = { url = "https://files.pythonhosted.org/packages/86/f6/0b473dab52dfdea05f28f3578b1c56b6c796ce85e76951bab7c4e38d5a74/av-14.4.0.tar.gz", hash = "sha256:3ecbf803a7fdf67229c0edada0830d6bfaea4d10bfb24f0c3f4e607cd1064b42", size = 3892203 }
 wheels = [
-    { 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/18/8a/d57418b686ffd05fabd5a0a9cfa97e63b38c35d7101af00e87c51c8cc43c/av-14.4.0-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:5b21d5586a88b9fce0ab78e26bd1c38f8642f8e2aad5b35e619f4d202217c701", size = 19965048 },
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