feat: standalone frontend uses production build instead of dev server

Override web service in docker-compose.standalone.yml to build from
www/Dockerfile (multi-stage: deps → build → standalone runner) instead
of running pnpm dev with bind-mounted source.

COMPOSE_STANDALONE_TODO.md

@@ -0,0 +1,10 @@
+# Standalone Compose: Remaining Production Work
+
+## Server/worker/beat: remove host network mode + bind mounts
+
+Currently `server` uses `network_mode: host` and all three services bind-mount `./server/:/app/`. For full standalone prod:
+
+- Remove `network_mode: host` from server
+- Remove bind-mount volumes from server, worker, beat (use built image only)
+- Update `compose_cmd` in `setup-standalone.sh` to not rely on host network
+- Change `SERVER_API_URL` from `http://host.docker.internal:1250` to `http://server:1250` (server reachable via Docker network once off host mode)

docker-compose.standalone.yml

@@ -0,0 +1,129 @@
+# Standalone services for fully local deployment (no external dependencies).
+# Usage: docker compose -f docker-compose.yml -f docker-compose.standalone.yml up -d
+#
+# On Linux with NVIDIA GPU, also pass: --profile ollama-gpu
+# On Linux without GPU:                --profile ollama-cpu
+# On Mac: Ollama runs natively (Metal GPU) — no profile needed, services here unused.
+
+services:
+  garage:
+    image: dxflrs/garage:v1.1.0
+    ports:
+      - "3900:3900"   # S3 API
+      - "3903:3903"   # Admin API
+    volumes:
+      - garage_data:/var/lib/garage/data
+      - garage_meta:/var/lib/garage/meta
+      - ./data/garage.toml:/etc/garage.toml:ro
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "/garage", "stats"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+      start_period: 5s
+
+  ollama:
+    image: ollama/ollama:latest
+    profiles: ["ollama-gpu"]
+    ports:
+      - "11434:11434"
+    volumes:
+      - ollama_data:/root/.ollama
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [gpu]
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:11434/api/tags"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+
+  ollama-cpu:
+    image: ollama/ollama:latest
+    profiles: ["ollama-cpu"]
+    ports:
+      - "11434:11434"
+    volumes:
+      - ollama_data:/root/.ollama
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:11434/api/tags"]
+      interval: 10s
+      timeout: 5s
+      retries: 5
+
+  # Override server/worker/beat to use self-hosted GPU service for transcription+diarization.
+  # compose `environment:` overrides values from `env_file:` — no need to edit server/.env.
+  server:
+    environment:
+      TRANSCRIPT_BACKEND: modal
+      TRANSCRIPT_URL: http://localhost:8100
+      TRANSCRIPT_MODAL_API_KEY: local
+      DIARIZATION_BACKEND: modal
+      DIARIZATION_URL: http://localhost:8100
+
+  worker:
+    environment:
+      TRANSCRIPT_BACKEND: modal
+      TRANSCRIPT_URL: http://cpu:8000
+      TRANSCRIPT_MODAL_API_KEY: local
+      DIARIZATION_BACKEND: modal
+      DIARIZATION_URL: http://cpu:8000
+
+  web:
+    image: reflector-frontend-standalone
+    build:
+      context: ./www
+    command: ["node", "server.js"]
+    volumes: !reset []
+    environment:
+      NODE_ENV: production
+
+  cpu:
+    build:
+      context: ./gpu/self_hosted
+      dockerfile: Dockerfile.cpu
+    ports:
+      - "8100:8000"
+    volumes:
+      - gpu_cache:/root/.cache
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8000/docs"]
+      interval: 15s
+      timeout: 5s
+      retries: 10
+      start_period: 120s
+
+  gpu-nvidia:
+    build:
+      context: ./gpu/self_hosted
+    profiles: ["gpu-nvidia"]
+    volumes:
+      - gpu_cache:/root/.cache
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [gpu]
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8000/docs"]
+      interval: 15s
+      timeout: 5s
+      retries: 10
+      start_period: 120s
+
+volumes:
+  garage_data:
+  garage_meta:
+  ollama_data:
+  gpu_cache:

docker-compose.yml

@@ -2,8 +2,7 @@ services:
   server:
     build:
       context: server
-    ports:
-      - 1250:1250
+    network_mode: host
     volumes:
       - ./server/:/app/
       - /app/.venv
@@ -11,6 +10,12 @@ services:
       - ./server/.env
     environment:
       ENTRYPOINT: server
+      DATABASE_URL: postgresql+asyncpg://reflector:reflector@localhost:5432/reflector
+      REDIS_HOST: localhost
+      CELERY_BROKER_URL: redis://localhost:6379/1
+      CELERY_RESULT_BACKEND: redis://localhost:6379/1
+      HATCHET_CLIENT_SERVER_URL: http://localhost:8889
+      HATCHET_CLIENT_HOST_PORT: localhost:7078
 
   worker:
     build:
@@ -22,6 +27,11 @@ services:
       - ./server/.env
     environment:
       ENTRYPOINT: worker
+      HATCHET_CLIENT_SERVER_URL: http://hatchet:8888
+      HATCHET_CLIENT_HOST_PORT: hatchet:7077
+    depends_on:
+      redis:
+        condition: service_started
 
   beat:
     build:
@@ -33,6 +43,9 @@ services:
       - ./server/.env
     environment:
       ENTRYPOINT: beat
+    depends_on:
+      redis:
+        condition: service_started
 
   hatchet-worker-cpu:
     build:
@@ -44,6 +57,8 @@ services:
       - ./server/.env
     environment:
       ENTRYPOINT: hatchet-worker-cpu
+      HATCHET_CLIENT_SERVER_URL: http://hatchet:8888
+      HATCHET_CLIENT_HOST_PORT: hatchet:7077
     depends_on:
       hatchet:
         condition: service_healthy
@@ -57,6 +72,8 @@ services:
       - ./server/.env
     environment:
       ENTRYPOINT: hatchet-worker-llm
+      HATCHET_CLIENT_SERVER_URL: http://hatchet:8888
+      HATCHET_CLIENT_HOST_PORT: hatchet:7077
     depends_on:
       hatchet:
         condition: service_healthy
@@ -75,10 +92,16 @@ services:
     volumes:
       - ./www:/app/
       - /app/node_modules
+      - next_cache:/app/.next
     env_file:
       - ./www/.env.local
     environment:
       - NODE_ENV=development
+      - SERVER_API_URL=http://host.docker.internal:1250
+    extra_hosts:
+      - "host.docker.internal:host-gateway"
+    depends_on:
+      - server
 
   postgres:
     image: postgres:17
@@ -94,13 +117,14 @@ services:
       - ./server/docker/init-hatchet-db.sql:/docker-entrypoint-initdb.d/init-hatchet-db.sql:ro
     healthcheck:
       test: ["CMD-SHELL", "pg_isready -d reflector -U reflector"]
-      interval: 10s
-      timeout: 10s
-      retries: 5
-      start_period: 10s
+      interval: 5s
+      timeout: 5s
+      retries: 10
+      start_period: 15s
 
   hatchet:
     image: ghcr.io/hatchet-dev/hatchet/hatchet-lite:latest
+    restart: on-failure
     ports:
       - "8889:8888"
       - "7078:7077"
@@ -108,7 +132,7 @@ services:
       postgres:
         condition: service_healthy
     environment:
-      DATABASE_URL: "postgresql://reflector:reflector@postgres:5432/hatchet?sslmode=disable"
+      DATABASE_URL: "postgresql://reflector:reflector@postgres:5432/hatchet?sslmode=disable&connect_timeout=30"
       SERVER_AUTH_COOKIE_DOMAIN: localhost
       SERVER_AUTH_COOKIE_INSECURE: "t"
       SERVER_GRPC_BIND_ADDRESS: "0.0.0.0"
@@ -128,6 +152,5 @@ services:
       retries: 5
       start_period: 30s
 
-networks:
-  default:
-    attachable: true
+volumes:
+  next_cache:

docs/docs/installation/setup-standalone.md

@@ -0,0 +1,214 @@
+---
+sidebar_position: 2
+title: Standalone Local Setup
+---
+
+# Standalone Local Setup
+
+**The goal**: a clueless user clones the repo, runs one script, and has a working Reflector instance locally. No cloud accounts, no API keys, no manual env file editing.
+
+```bash
+git clone https://github.com/monadical-sas/reflector.git
+cd reflector
+./scripts/setup-standalone.sh
+```
+
+The script is idempotent — safe to re-run at any time. It detects what's already set up and skips completed steps.
+
+## Prerequisites
+
+- Docker / OrbStack / Docker Desktop (any)
+- Mac (Apple Silicon) or Linux
+- 16GB+ RAM (32GB recommended for 14B LLM models)
+- **Mac only**: [Ollama](https://ollama.com/download) installed (`brew install ollama`)
+
+## What the script does
+
+### 1. LLM inference via Ollama
+
+**Mac**: starts Ollama natively (Metal GPU acceleration). Pulls the LLM model. Docker containers reach it via `host.docker.internal:11434`.
+
+**Linux**: starts containerized Ollama via `docker-compose.standalone.yml` profile (`ollama-gpu` with NVIDIA, `ollama-cpu` without). Pulls model inside the container.
+
+### 2. Environment files
+
+Generates `server/.env` and `www/.env.local` with standalone defaults:
+
+**`server/.env`** — key settings:
+
+| Variable | Value | Why |
+|----------|-------|-----|
+| `DATABASE_URL` | `postgresql+asyncpg://...@postgres:5432/reflector` | Docker-internal hostname |
+| `REDIS_HOST` | `redis` | Docker-internal hostname |
+| `CELERY_BROKER_URL` | `redis://redis:6379/1` | Docker-internal hostname |
+| `AUTH_BACKEND` | `none` | No Authentik in standalone |
+| `TRANSCRIPT_BACKEND` | `modal` | HTTP API to self-hosted CPU service |
+| `TRANSCRIPT_URL` | `http://cpu:8000` | Docker-internal CPU service |
+| `DIARIZATION_BACKEND` | `modal` | HTTP API to self-hosted CPU service |
+| `DIARIZATION_URL` | `http://cpu:8000` | Docker-internal CPU service |
+| `TRANSLATION_BACKEND` | `passthrough` | No Modal |
+| `LLM_URL` | `http://host.docker.internal:11434/v1` (Mac) | Ollama endpoint |
+
+**`www/.env.local`** — key settings:
+
+| Variable | Value |
+|----------|-------|
+| `API_URL` | `http://localhost:1250` |
+| `SERVER_API_URL` | `http://server:1250` |
+| `WEBSOCKET_URL` | `ws://localhost:1250` |
+| `FEATURE_REQUIRE_LOGIN` | `false` |
+| `NEXTAUTH_SECRET` | `standalone-dev-secret-not-for-production` |
+
+If env files already exist (including symlinks from worktree setup), the script resolves symlinks and ensures all standalone-critical vars are set. Existing vars not related to standalone are preserved.
+
+### 3. Object storage (Garage)
+
+Standalone uses [Garage](https://garagehq.deuxfleurs.fr/) — a lightweight S3-compatible object store running in Docker. The setup script starts Garage, initializes the layout, creates a bucket and access key, and writes the credentials to `server/.env`.
+
+**`server/.env`** — storage settings added by the script:
+
+| Variable | Value | Why |
+|----------|-------|-----|
+| `TRANSCRIPT_STORAGE_BACKEND` | `aws` | Uses the S3-compatible storage driver |
+| `TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL` | `http://garage:3900` | Docker-internal Garage S3 API |
+| `TRANSCRIPT_STORAGE_AWS_BUCKET_NAME` | `reflector-media` | Created by the script |
+| `TRANSCRIPT_STORAGE_AWS_REGION` | `garage` | Must match Garage config |
+| `TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID` | *(auto-generated)* | Created by `garage key create` |
+| `TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY` | *(auto-generated)* | Created by `garage key create` |
+
+The `TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL` setting enables S3-compatible backends. When set, the storage driver uses path-style addressing and routes all requests to the custom endpoint. When unset (production AWS), behavior is unchanged.
+
+Garage config template lives at `scripts/garage.toml`. The setup script generates `data/garage.toml` (gitignored) with a random RPC secret and mounts it read-only into the container. Single-node, `replication_factor=1`.
+
+> **Note**: Presigned URLs embed the Garage Docker hostname (`http://garage:3900`). This is fine — the server proxies S3 responses to the browser. Modal GPU workers cannot reach internal Garage, but standalone doesn't use Modal.
+
+### 4. Transcription and diarization
+
+Standalone runs the self-hosted ML service (`gpu/self_hosted/`) in a CPU-only Docker container named `cpu`. This is the same FastAPI service used for Modal.com GPU deployments, but built with `Dockerfile.cpu` (no NVIDIA CUDA dependencies). The compose service is named `cpu` (not `gpu`) to make clear it runs without GPU acceleration; the source code lives in `gpu/self_hosted/` because it's shared with the GPU deployment.
+
+The `modal` backend name is reused — it just means "HTTP API client". Setting `TRANSCRIPT_URL` / `DIARIZATION_URL` to `http://cpu:8000` routes requests to the local container instead of Modal.com.
+
+On first start, the service downloads pyannote speaker diarization models (~1GB) from a public S3 bundle. Models are cached in a Docker volume (`gpu_cache`) so subsequent starts are fast. No HuggingFace token or API key needed.
+
+> **Performance**: CPU-only transcription and diarization work but are slow (~15 min for a 3 min file). For faster processing on Linux with NVIDIA GPU, use `--profile gpu-nvidia` instead (see `docker-compose.standalone.yml`).
+
+### 5. Docker services
+
+```bash
+docker compose up -d postgres redis garage cpu server worker beat web
+```
+
+All services start in a single command. Garage and `cpu` are already started by earlier steps but included for idempotency. No Hatchet in standalone mode — LLM processing (summaries, topics, titles) runs via Celery tasks.
+
+### 6. Database migrations
+
+Run automatically by the `server` container on startup (`runserver.sh` calls `alembic upgrade head`). No manual step needed.
+
+### 7. Health check
+
+Verifies:
+- CPU service responds (transcription + diarization ready)
+- Server responds at `http://localhost:1250/health`
+- Frontend serves at `http://localhost:3000`
+- LLM endpoint reachable from inside containers
+
+## Services
+
+| Service | Port | Purpose |
+|---------|------|---------|
+| `server` | 1250 | FastAPI backend (runs migrations on start) |
+| `web` | 3000 | Next.js frontend |
+| `postgres` | 5432 | PostgreSQL database |
+| `redis` | 6379 | Cache + Celery broker |
+| `garage` | 3900, 3903 | S3-compatible object storage (S3 API + admin API) |
+| `cpu` | — | Self-hosted transcription + diarization (CPU-only) |
+| `worker` | — | Celery worker (live pipeline post-processing) |
+| `beat` | — | Celery beat (scheduled tasks) |
+
+## Testing programmatically
+
+After the setup script completes, verify the full pipeline (upload, transcription, diarization, LLM summary) via the API:
+
+```bash
+# 1. Create a transcript
+TRANSCRIPT_ID=$(curl -s -X POST 'http://localhost:1250/v1/transcripts' \
+  -H 'Content-Type: application/json' \
+  -d '{"name":"test-upload"}' | python3 -c "import sys,json; print(json.load(sys.stdin)['id'])")
+echo "Created: $TRANSCRIPT_ID"
+
+# 2. Upload an audio file (single-chunk upload)
+curl -s "http://localhost:1250/v1/transcripts/${TRANSCRIPT_ID}/record/upload?chunk_number=0&total_chunks=1" \
+  -X POST -F "chunk=@/path/to/audio.mp3"
+
+# 3. Poll until processing completes (status: ended or error)
+while true; do
+  STATUS=$(curl -s "http://localhost:1250/v1/transcripts/${TRANSCRIPT_ID}" \
+    | python3 -c "import sys,json; print(json.load(sys.stdin)['status'])")
+  echo "Status: $STATUS"
+  case "$STATUS" in ended|error) break;; esac
+  sleep 10
+done
+
+# 4. Check the result
+curl -s "http://localhost:1250/v1/transcripts/${TRANSCRIPT_ID}" | python3 -m json.tool
+```
+
+Expected result: status `ended`, auto-generated `title`, `short_summary`, `long_summary`, and `transcript` text with `Speaker 0` / `Speaker 1` labels.
+
+CPU-only processing is slow (~15 min for a 3 min audio file). Diarization finishes in ~3 min, transcription takes the rest.
+
+## Troubleshooting
+
+### Port conflicts (most common issue)
+
+If the frontend or backend behaves unexpectedly (e.g., env vars seem ignored, changes don't take effect), **check for port conflicts first**:
+
+```bash
+# Check what's listening on key ports
+lsof -i :3000   # frontend
+lsof -i :1250   # backend
+lsof -i :5432   # postgres
+lsof -i :3900   # Garage S3 API
+lsof -i :6379   # Redis
+
+# Kill stale processes on a port
+lsof -ti :3000 | xargs kill
+```
+
+Common causes:
+- A stale `next dev` or `pnpm dev` process from another terminal/worktree
+- Another Docker Compose project (different worktree) with containers on the same ports — the setup script only manages its own project; containers from other projects must be stopped manually (`docker ps` to find them, `docker stop` to kill them)
+
+The setup script checks ports 3000, 1250, 5432, 6379, 3900, 3903 for conflicts before starting services. It ignores OrbStack/Docker Desktop port forwarding processes (which always bind these ports but are not real conflicts).
+
+### OrbStack false port-conflict warnings (Mac)
+
+If you use OrbStack as your Docker runtime, `lsof` will show OrbStack binding ports like 3000, 1250, etc. even when no containers are running. This is OrbStack's port forwarding mechanism — not a real conflict. The setup script filters these out automatically.
+
+### Re-enabling authentication
+
+Standalone runs without authentication (`FEATURE_REQUIRE_LOGIN=false`, `AUTH_BACKEND=none`). To re-enable:
+
+1. In `www/.env.local`: set `FEATURE_REQUIRE_LOGIN=true`, uncomment `AUTHENTIK_ISSUER` and `AUTHENTIK_REFRESH_TOKEN_URL`
+2. In `server/.env`: set `AUTH_BACKEND=authentik` (or your backend), configure `AUTH_JWT_AUDIENCE`
+3. Restart: `docker compose -f docker-compose.yml -f docker-compose.standalone.yml up -d --force-recreate web server`
+
+## What's NOT covered
+
+These require external accounts and infrastructure that can't be scripted:
+
+- **Live meeting rooms** — requires Daily.co account, S3 bucket, IAM roles
+- **Authentication** — requires Authentik deployment and OAuth configuration
+- **Hatchet workflows** — requires separate Hatchet setup for multitrack processing
+- **Production deployment** — see [Deployment Guide](./overview)
+
+## Current status
+
+All steps implemented. The setup script handles everything end-to-end:
+
+- Step 1 (Ollama/LLM) — implemented
+- Step 2 (environment files) — implemented
+- Step 3 (object storage / Garage) — implemented
+- Step 4 (transcription/diarization) — implemented (self-hosted GPU service)
+- Steps 5-7 (Docker, migrations, health) — implemented
+- **Unified script**: `scripts/setup-standalone.sh`

gpu/self_hosted/Dockerfile.cpu

@@ -0,0 +1,39 @@
+FROM python:3.12-slim
+
+ENV PYTHONUNBUFFERED=1 \
+    UV_LINK_MODE=copy \
+    UV_NO_CACHE=1
+
+WORKDIR /tmp
+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
+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"
+
+RUN mkdir -p /app
+WORKDIR /app
+COPY pyproject.toml uv.lock /app/
+
+
+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/app/auth.py

@@ -3,14 +3,14 @@ import os
 from fastapi import Depends, HTTPException, status
 from fastapi.security import OAuth2PasswordBearer
 
-oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
+oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token", auto_error=False)
 
 
-def apikey_auth(apikey: str = Depends(oauth2_scheme)):
+def apikey_auth(apikey: str | None = Depends(oauth2_scheme)):
     required_key = os.environ.get("REFLECTOR_GPU_APIKEY")
     if not required_key:
         return
-    if apikey == required_key:
+    if apikey and apikey == required_key:
         return
     raise HTTPException(
         status_code=status.HTTP_401_UNAUTHORIZED,

gpu/self_hosted/app/services/diarizer.py

@@ -1,10 +1,65 @@
+import logging
 import os
+import tarfile
 import threading
+from pathlib import Path
+from urllib.request import urlopen
 
 import torch
 import torchaudio
+import yaml
 from pyannote.audio import Pipeline
 
+logger = logging.getLogger(__name__)
+
+S3_BUNDLE_URL = "https://reflector-public.s3.us-east-1.amazonaws.com/pyannote-speaker-diarization-3.1.tar.gz"
+BUNDLE_CACHE_DIR = Path("/root/.cache/pyannote-bundle")
+
+
+def _ensure_model(cache_dir: Path) -> str:
+    """Download and extract S3 model bundle if not cached."""
+    model_dir = cache_dir / "pyannote-speaker-diarization-3.1"
+    config_path = model_dir / "config.yaml"
+
+    if config_path.exists():
+        logger.info("Using cached model bundle at %s", model_dir)
+        return str(model_dir)
+
+    cache_dir.mkdir(parents=True, exist_ok=True)
+    tarball_path = cache_dir / "model.tar.gz"
+
+    logger.info("Downloading model bundle from %s", S3_BUNDLE_URL)
+    with urlopen(S3_BUNDLE_URL) as response, open(tarball_path, "wb") as f:
+        while chunk := response.read(8192):
+            f.write(chunk)
+
+    logger.info("Extracting model bundle")
+    with tarfile.open(tarball_path, "r:gz") as tar:
+        tar.extractall(path=cache_dir, filter="data")
+    tarball_path.unlink()
+
+    _patch_config(model_dir, cache_dir)
+    return str(model_dir)
+
+
+def _patch_config(model_dir: Path, cache_dir: Path) -> None:
+    """Rewrite config.yaml to reference local pytorch_model.bin paths."""
+    config_path = model_dir / "config.yaml"
+    with open(config_path) as f:
+        config = yaml.safe_load(f)
+
+    config["pipeline"]["params"]["segmentation"] = str(
+        cache_dir / "pyannote-segmentation-3.0" / "pytorch_model.bin"
+    )
+    config["pipeline"]["params"]["embedding"] = str(
+        cache_dir / "pyannote-wespeaker-voxceleb-resnet34-LM" / "pytorch_model.bin"
+    )
+
+    with open(config_path, "w") as f:
+        yaml.dump(config, f)
+
+    logger.info("Patched config.yaml with local model paths")
+
 
 class PyannoteDiarizationService:
     def __init__(self):
@@ -14,10 +69,20 @@ class PyannoteDiarizationService:
 
     def load(self):
         self._device = "cuda" if torch.cuda.is_available() else "cpu"
-        self._pipeline = Pipeline.from_pretrained(
-            "pyannote/speaker-diarization-3.1",
-            use_auth_token=os.environ.get("HF_TOKEN"),
-        )
+        hf_token = os.environ.get("HF_TOKEN")
+
+        if hf_token:
+            logger.info("Loading pyannote model from HuggingFace (HF_TOKEN set)")
+            self._pipeline = Pipeline.from_pretrained(
+                "pyannote/speaker-diarization-3.1",
+                use_auth_token=hf_token,
+            )
+        else:
+            logger.info("HF_TOKEN not set — loading model from S3 bundle")
+            model_path = _ensure_model(BUNDLE_CACHE_DIR)
+            config_path = Path(model_path) / "config.yaml"
+            self._pipeline = Pipeline.from_pretrained(str(config_path))
+
         self._pipeline.to(torch.device(self._device))
 
     def diarize_file(self, file_path: str, timestamp: float = 0.0) -> dict:

scripts/garage.toml

@@ -0,0 +1,14 @@
+metadata_dir = "/var/lib/garage/meta"
+data_dir = "/var/lib/garage/data"
+replication_factor = 1
+
+rpc_secret = "__GARAGE_RPC_SECRET__"
+rpc_bind_addr = "[::]:3901"
+
+[s3_api]
+api_bind_addr = "[::]:3900"
+s3_region = "garage"
+root_domain = ".s3.garage.localhost"
+
+[admin]
+api_bind_addr = "[::]:3903"

scripts/setup-standalone.sh

@@ -0,0 +1,417 @@
+#!/usr/bin/env bash
+#
+# Standalone local development setup for Reflector.
+# Takes a fresh clone to a working instance — no cloud accounts, no API keys.
+#
+# Usage:
+#   ./scripts/setup-standalone.sh
+#
+# Idempotent — safe to re-run at any time.
+#
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+ROOT_DIR="$(cd "$SCRIPT_DIR/.." && pwd)"
+
+SERVER_ENV="$ROOT_DIR/server/.env"
+WWW_ENV="$ROOT_DIR/www/.env.local"
+
+MODEL="${LLM_MODEL:-qwen2.5:14b}"
+OLLAMA_PORT="${OLLAMA_PORT:-11434}"
+
+OS="$(uname -s)"
+
+# --- Colors ---
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+CYAN='\033[0;36m'
+NC='\033[0m'
+
+info()  { echo -e "${CYAN}==>${NC} $*"; }
+ok()    { echo -e "${GREEN}  ✓${NC} $*"; }
+warn()  { echo -e "${YELLOW}  !${NC} $*"; }
+err()   { echo -e "${RED}  ✗${NC} $*" >&2; }
+
+# --- Helpers ---
+
+wait_for_url() {
+    local url="$1" label="$2" retries="${3:-30}" interval="${4:-2}"
+    for i in $(seq 1 "$retries"); do
+        if curl -sf "$url" > /dev/null 2>&1; then
+            return 0
+        fi
+        echo -ne "\r  Waiting for $label... ($i/$retries)"
+        sleep "$interval"
+    done
+    echo ""
+    err "$label not responding at $url after $retries attempts"
+    return 1
+}
+
+env_has_key() {
+    local file="$1" key="$2"
+    grep -q "^${key}=" "$file" 2>/dev/null
+}
+
+env_set() {
+    local file="$1" key="$2" value="$3"
+    if env_has_key "$file" "$key"; then
+        # Replace existing value (portable sed)
+        if [[ "$OS" == "Darwin" ]]; then
+            sed -i '' "s|^${key}=.*|${key}=${value}|" "$file"
+        else
+            sed -i "s|^${key}=.*|${key}=${value}|" "$file"
+        fi
+    else
+        echo "${key}=${value}" >> "$file"
+    fi
+}
+
+resolve_symlink() {
+    local file="$1"
+    if [[ -L "$file" ]]; then
+        warn "$(basename "$file") is a symlink — creating standalone copy"
+        cp -L "$file" "$file.tmp"
+        rm "$file"
+        mv "$file.tmp" "$file"
+    fi
+}
+
+compose_cmd() {
+    local compose_files="-f $ROOT_DIR/docker-compose.yml -f $ROOT_DIR/docker-compose.standalone.yml"
+    if [[ "$OS" == "Linux" ]] && [[ -n "${OLLAMA_PROFILE:-}" ]]; then
+        docker compose $compose_files --profile "$OLLAMA_PROFILE" "$@"
+    else
+        docker compose $compose_files "$@"
+    fi
+}
+
+# =========================================================
+# Step 1: LLM / Ollama
+# =========================================================
+step_llm() {
+    info "Step 1: LLM setup (Ollama + $MODEL)"
+
+    case "$OS" in
+        Darwin)
+            if ! command -v ollama &> /dev/null; then
+                err "Ollama not found. Install it:"
+                err "  brew install ollama"
+                err "  # or https://ollama.com/download"
+                exit 1
+            fi
+
+            # Start if not running
+            if ! curl -sf "http://localhost:$OLLAMA_PORT/api/tags" > /dev/null 2>&1; then
+                info "Starting Ollama..."
+                ollama serve &
+                disown
+            fi
+
+            wait_for_url "http://localhost:$OLLAMA_PORT/api/tags" "Ollama"
+            echo ""
+
+            # Pull model if not already present
+            if ollama list 2>/dev/null | awk '{print $1}' | grep -qx "$MODEL"; then
+                ok "Model $MODEL already pulled"
+            else
+                info "Pulling model $MODEL (this may take a while)..."
+                ollama pull "$MODEL"
+            fi
+
+            LLM_URL_VALUE="http://host.docker.internal:$OLLAMA_PORT/v1"
+            ;;
+
+        Linux)
+            if command -v nvidia-smi &> /dev/null && nvidia-smi > /dev/null 2>&1; then
+                ok "NVIDIA GPU detected — using ollama-gpu profile"
+                OLLAMA_PROFILE="ollama-gpu"
+                OLLAMA_SVC="ollama"
+                LLM_URL_VALUE="http://ollama:$OLLAMA_PORT/v1"
+            else
+                warn "No NVIDIA GPU — using ollama-cpu profile"
+                OLLAMA_PROFILE="ollama-cpu"
+                OLLAMA_SVC="ollama-cpu"
+                LLM_URL_VALUE="http://ollama-cpu:$OLLAMA_PORT/v1"
+            fi
+
+            info "Starting Ollama container..."
+            compose_cmd up -d
+
+            wait_for_url "http://localhost:$OLLAMA_PORT/api/tags" "Ollama"
+            echo ""
+
+            # Pull model inside container
+            if compose_cmd exec "$OLLAMA_SVC" ollama list 2>/dev/null | awk '{print $1}' | grep -qx "$MODEL"; then
+                ok "Model $MODEL already pulled"
+            else
+                info "Pulling model $MODEL inside container (this may take a while)..."
+                compose_cmd exec "$OLLAMA_SVC" ollama pull "$MODEL"
+            fi
+            ;;
+
+        *)
+            err "Unsupported OS: $OS"
+            exit 1
+            ;;
+    esac
+
+    ok "LLM ready ($MODEL via Ollama)"
+}
+
+# =========================================================
+# Step 2: Generate server/.env
+# =========================================================
+step_server_env() {
+    info "Step 2: Generating server/.env"
+
+    resolve_symlink "$SERVER_ENV"
+
+    if [[ -f "$SERVER_ENV" ]]; then
+        ok "server/.env already exists — ensuring standalone vars"
+    else
+        cat > "$SERVER_ENV" << 'ENVEOF'
+# Generated by setup-standalone.sh — standalone local development
+# Source of truth for settings: server/reflector/settings.py
+ENVEOF
+        ok "Created server/.env"
+    fi
+
+    # Ensure all standalone-critical vars (appends if missing, replaces if present)
+    env_set "$SERVER_ENV" "DATABASE_URL" "postgresql+asyncpg://reflector:reflector@postgres:5432/reflector"
+    env_set "$SERVER_ENV" "REDIS_HOST" "redis"
+    env_set "$SERVER_ENV" "CELERY_BROKER_URL" "redis://redis:6379/1"
+    env_set "$SERVER_ENV" "CELERY_RESULT_BACKEND" "redis://redis:6379/1"
+    env_set "$SERVER_ENV" "AUTH_BACKEND" "none"
+    env_set "$SERVER_ENV" "PUBLIC_MODE" "true"
+    # TRANSCRIPT_BACKEND, TRANSCRIPT_URL, DIARIZATION_BACKEND, DIARIZATION_URL
+    # are set via docker-compose.standalone.yml `environment:` overrides — not written here
+    # so we don't clobber the user's server/.env for non-standalone use.
+    env_set "$SERVER_ENV" "TRANSLATION_BACKEND" "passthrough"
+    env_set "$SERVER_ENV" "LLM_URL" "$LLM_URL_VALUE"
+    env_set "$SERVER_ENV" "LLM_MODEL" "$MODEL"
+    env_set "$SERVER_ENV" "LLM_API_KEY" "not-needed"
+
+    ok "Standalone vars set (LLM_URL=$LLM_URL_VALUE)"
+}
+
+# =========================================================
+# Step 3: Object storage (Garage)
+# =========================================================
+step_storage() {
+    info "Step 3: Object storage (Garage)"
+
+    # Generate garage.toml from template (fill in RPC secret)
+    GARAGE_TOML="$ROOT_DIR/scripts/garage.toml"
+    GARAGE_TOML_RUNTIME="$ROOT_DIR/data/garage.toml"
+    if [[ ! -f "$GARAGE_TOML_RUNTIME" ]]; then
+        mkdir -p "$ROOT_DIR/data"
+        RPC_SECRET=$(openssl rand -hex 32)
+        sed "s|__GARAGE_RPC_SECRET__|${RPC_SECRET}|" "$GARAGE_TOML" > "$GARAGE_TOML_RUNTIME"
+    fi
+
+    compose_cmd up -d garage
+
+    wait_for_url "http://localhost:3903/health" "Garage admin API"
+    echo ""
+
+    # Layout: get node ID, assign, apply (skip if already applied)
+    NODE_ID=$(compose_cmd exec -T garage /garage node id -q 2>/dev/null | tr -d '[:space:]')
+    LAYOUT_STATUS=$(compose_cmd exec -T garage /garage layout show 2>&1 || true)
+    if echo "$LAYOUT_STATUS" | grep -q "No nodes"; then
+        compose_cmd exec -T garage /garage layout assign "$NODE_ID" -c 1G -z dc1
+        compose_cmd exec -T garage /garage layout apply --version 1
+    fi
+
+    # Create bucket (idempotent — skip if exists)
+    if ! compose_cmd exec -T garage /garage bucket info reflector-media &>/dev/null; then
+        compose_cmd exec -T garage /garage bucket create reflector-media
+    fi
+
+    # Create key (idempotent — skip if exists)
+    CREATED_KEY=false
+    if compose_cmd exec -T garage /garage key info reflector &>/dev/null; then
+        ok "Key 'reflector' already exists"
+    else
+        KEY_OUTPUT=$(compose_cmd exec -T garage /garage key create reflector)
+        CREATED_KEY=true
+    fi
+
+    # Grant bucket permissions (idempotent)
+    compose_cmd exec -T garage /garage bucket allow reflector-media --read --write --key reflector
+
+    # Set env vars (only parse key on first create — key info redacts the secret)
+    env_set "$SERVER_ENV" "TRANSCRIPT_STORAGE_BACKEND" "aws"
+    env_set "$SERVER_ENV" "TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL" "http://garage:3900"
+    env_set "$SERVER_ENV" "TRANSCRIPT_STORAGE_AWS_BUCKET_NAME" "reflector-media"
+    env_set "$SERVER_ENV" "TRANSCRIPT_STORAGE_AWS_REGION" "garage"
+    if [[ "$CREATED_KEY" == "true" ]]; then
+        KEY_ID=$(echo "$KEY_OUTPUT" | grep -i "key id" | awk '{print $NF}')
+        KEY_SECRET=$(echo "$KEY_OUTPUT" | grep -i "secret key" | awk '{print $NF}')
+        env_set "$SERVER_ENV" "TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID" "$KEY_ID"
+        env_set "$SERVER_ENV" "TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY" "$KEY_SECRET"
+    fi
+
+    ok "Object storage ready (Garage)"
+}
+
+# =========================================================
+# Step 4: Generate www/.env.local
+# =========================================================
+step_www_env() {
+    info "Step 4: Generating www/.env.local"
+
+    resolve_symlink "$WWW_ENV"
+
+    if [[ -f "$WWW_ENV" ]]; then
+        ok "www/.env.local already exists — ensuring standalone vars"
+    else
+        cat > "$WWW_ENV" << 'ENVEOF'
+# Generated by setup-standalone.sh — standalone local development
+ENVEOF
+        ok "Created www/.env.local"
+    fi
+
+    env_set "$WWW_ENV" "SITE_URL" "http://localhost:3000"
+    env_set "$WWW_ENV" "NEXTAUTH_URL" "http://localhost:3000"
+    env_set "$WWW_ENV" "NEXTAUTH_SECRET" "standalone-dev-secret-not-for-production"
+    env_set "$WWW_ENV" "API_URL" "http://localhost:1250"
+    env_set "$WWW_ENV" "WEBSOCKET_URL" "ws://localhost:1250"
+    env_set "$WWW_ENV" "SERVER_API_URL" "http://server:1250"
+    env_set "$WWW_ENV" "FEATURE_REQUIRE_LOGIN" "false"
+
+    ok "Standalone www vars set"
+}
+
+# =========================================================
+# Step 5: Start all services
+# =========================================================
+step_services() {
+    info "Step 5: Starting Docker services"
+
+    # Check for port conflicts — stale processes silently shadow Docker port mappings.
+    # OrbStack/Docker Desktop bind ports for forwarding; ignore those PIDs.
+    local ports_ok=true
+    for port in 3000 1250 5432 6379 3900 3903; do
+        local pids
+        pids=$(lsof -ti :"$port" 2>/dev/null || true)
+        for pid in $pids; do
+            local pname
+            pname=$(ps -p "$pid" -o comm= 2>/dev/null || true)
+            # OrbStack and Docker Desktop own port forwarding — not real conflicts
+            if [[ "$pname" == *"OrbStack"* ]] || [[ "$pname" == *"com.docker"* ]] || [[ "$pname" == *"vpnkit"* ]]; then
+                continue
+            fi
+            warn "Port $port already in use by PID $pid ($pname)"
+            warn "Kill it with: lsof -ti :$port | xargs kill"
+            ports_ok=false
+        done
+    done
+    if [[ "$ports_ok" == "false" ]]; then
+        warn "Port conflicts detected — Docker containers may not be reachable"
+        warn "Continuing anyway (services will start but may be shadowed)"
+    fi
+
+    # server runs alembic migrations on startup automatically (see runserver.sh)
+    compose_cmd up -d postgres redis garage cpu server worker beat web
+    ok "Containers started"
+    info "Server is running migrations (alembic upgrade head)..."
+}
+
+# =========================================================
+# Step 6: Health checks
+# =========================================================
+step_health() {
+    info "Step 6: Health checks"
+
+    # CPU service may take a while on first start (model download + load).
+    # No host port exposed — check via docker exec.
+    info "Waiting for CPU service (first start downloads ~1GB of models)..."
+    local cpu_ok=false
+    for i in $(seq 1 120); do
+        if compose_cmd exec -T cpu curl -sf http://localhost:8000/docs > /dev/null 2>&1; then
+            cpu_ok=true
+            break
+        fi
+        echo -ne "\r  Waiting for CPU service... ($i/120)"
+        sleep 5
+    done
+    echo ""
+    if [[ "$cpu_ok" == "true" ]]; then
+        ok "CPU service healthy (transcription + diarization)"
+    else
+        warn "CPU service not ready yet — it will keep loading in the background"
+        warn "Check with: docker compose logs cpu"
+    fi
+
+    wait_for_url "http://localhost:1250/health" "Server API" 60 3
+    echo ""
+    ok "Server API healthy"
+
+    wait_for_url "http://localhost:3000" "Frontend" 90 3
+    echo ""
+    ok "Frontend responding"
+
+    # Check LLM reachability from inside a container
+    if compose_cmd exec -T server \
+        curl -sf "$LLM_URL_VALUE/models" > /dev/null 2>&1; then
+        ok "LLM reachable from containers"
+    else
+        warn "LLM not reachable from containers at $LLM_URL_VALUE"
+        warn "Summaries/topics/titles won't work until LLM is accessible"
+    fi
+}
+
+# =========================================================
+# Main
+# =========================================================
+main() {
+    echo ""
+    echo "=========================================="
+    echo " Reflector — Standalone Local Setup"
+    echo "=========================================="
+    echo ""
+
+    # Ensure we're in the repo root
+    if [[ ! -f "$ROOT_DIR/docker-compose.yml" ]]; then
+        err "docker-compose.yml not found in $ROOT_DIR"
+        err "Run this script from the repo root: ./scripts/setup-standalone.sh"
+        exit 1
+    fi
+
+
+    # LLM_URL_VALUE is set by step_llm, used by later steps
+    LLM_URL_VALUE=""
+    OLLAMA_PROFILE=""
+
+    # docker-compose.yml may reference env_files that don't exist yet;
+    # touch them so compose_cmd works before the steps that populate them.
+    touch "$SERVER_ENV" "$WWW_ENV"
+
+    step_llm
+    echo ""
+    step_server_env
+    echo ""
+    step_storage
+    echo ""
+    step_www_env
+    echo ""
+    step_services
+    echo ""
+    step_health
+
+    echo ""
+    echo "=========================================="
+    echo -e " ${GREEN}Reflector is running!${NC}"
+    echo "=========================================="
+    echo ""
+    echo "  Frontend:  http://localhost:3000"
+    echo "  API:       http://localhost:1250"
+    echo ""
+    echo "  To stop:   docker compose down"
+    echo "  To re-run: ./scripts/setup-standalone.sh"
+    echo ""
+}
+
+main "$@"

server/.env.example

@@ -66,15 +66,22 @@ TRANSLATE_URL=https://monadical-sas--reflector-translator-web.modal.run
 ## LLM backend (Required)
 ##
 ## Responsible for generating titles, summaries, and topic detection
-## Requires OpenAI API key
+## Supports any OpenAI-compatible endpoint.
 ## =======================================================
 
-## OpenAI API key - get from https://platform.openai.com/account/api-keys
-LLM_API_KEY=sk-your-openai-api-key
-LLM_MODEL=gpt-4o-mini
+## --- Option A: Local LLM via Ollama (recommended for dev) ---
+## Setup: ./scripts/setup-standalone.sh
+## Mac: Ollama runs natively (Metal GPU). Containers reach it via host.docker.internal.
+## Linux: docker compose --profile ollama-gpu up -d (or ollama-cpu for no GPU)
+LLM_URL=http://host.docker.internal:11434/v1
+LLM_MODEL=qwen2.5:14b
+LLM_API_KEY=not-needed
+## Linux with containerized Ollama: LLM_URL=http://ollama:11434/v1
 
-## Optional: Custom endpoint (defaults to OpenAI)
-# LLM_URL=https://api.openai.com/v1
+## --- Option B: Remote/cloud LLM ---
+#LLM_API_KEY=sk-your-openai-api-key
+#LLM_MODEL=gpt-4o-mini
+## LLM_URL defaults to OpenAI when unset
 
 ## Context size for summary generation (tokens)
 LLM_CONTEXT_WINDOW=16000

server/pyproject.toml

@@ -68,7 +68,6 @@ evaluation = [
     "pydantic>=2.1.1",
 ]
 local = [
-    "pyannote-audio>=3.3.2",
     "faster-whisper>=0.10.0",
 ]
 silero-vad = [

server/reflector/asynctask.py

@@ -22,6 +22,8 @@ def asynctask(f):
                 await database.disconnect()
 
         coro = run_with_db()
+        if current_task:
+            return asyncio.run(coro)
         try:
             loop = asyncio.get_running_loop()
         except RuntimeError:

server/reflector/auth/auth_none.py

@@ -1,11 +1,5 @@
-from typing import Annotated
-
-from fastapi import Depends
-from fastapi.security import OAuth2PasswordBearer
 from pydantic import BaseModel
 
-oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token", auto_error=False)
-
 
 class UserInfo(BaseModel):
     sub: str
@@ -15,13 +9,13 @@ class AccessTokenInfo(BaseModel):
     pass
 
 
-def authenticated(token: Annotated[str, Depends(oauth2_scheme)]):
+def authenticated():
     return None
 
 
-def current_user(token: Annotated[str, Depends(oauth2_scheme)]):
+def current_user():
     return None
 
 
-def current_user_optional(token: Annotated[str, Depends(oauth2_scheme)]):
+def current_user_optional():
     return None

server/reflector/dailyco_api/client.py

@@ -146,6 +146,8 @@ class DailyApiClient:
             )
             raise DailyApiError(operation, response)
 
+        if not response.content:
+            return {}
         return response.json()
 
     # ============================================================================

server/reflector/dailyco_api/webhook_utils.py

@@ -99,7 +99,7 @@ def extract_room_name(event: DailyWebhookEvent) -> str | None:
         >>> event = DailyWebhookEvent(**webhook_payload)
         >>> room_name = extract_room_name(event)
     """
-    room = event.payload.get("room_name")
+    room = event.payload.get("room_name") or event.payload.get("room")
     # Ensure we return a string, not any falsy value that might be in payload
     return room if isinstance(room, str) else None
 

server/reflector/dailyco_api/webhooks.py

@@ -6,7 +6,7 @@ Reference: https://docs.daily.co/reference/rest-api/webhooks
 
 from typing import Annotated, Any, Dict, Literal, Union
 
-from pydantic import BaseModel, Field, field_validator
+from pydantic import AliasChoices, BaseModel, ConfigDict, Field, field_validator
 
 from reflector.utils.string import NonEmptyString
 
@@ -41,6 +41,8 @@ class DailyTrack(BaseModel):
     Reference: https://docs.daily.co/reference/rest-api/recordings
     """
 
+    model_config = ConfigDict(extra="ignore")
+
     type: Literal["audio", "video"]
     s3Key: NonEmptyString = Field(description="S3 object key for the track file")
     size: int = Field(description="File size in bytes")
@@ -54,6 +56,8 @@ class DailyWebhookEvent(BaseModel):
     Reference: https://docs.daily.co/reference/rest-api/webhooks
     """
 
+    model_config = ConfigDict(extra="ignore")
+
     version: NonEmptyString = Field(
         description="Represents the version of the event. This uses semantic versioning to inform a consumer if the payload has introduced any breaking changes"
     )
@@ -82,7 +86,13 @@ class ParticipantJoinedPayload(BaseModel):
     Reference: https://docs.daily.co/reference/rest-api/webhooks/events/participant-joined
     """
 
-    room_name: NonEmptyString | None = Field(None, description="Daily.co room name")
+    model_config = ConfigDict(extra="ignore")
+
+    room_name: NonEmptyString | None = Field(
+        None,
+        description="Daily.co room name",
+        validation_alias=AliasChoices("room_name", "room"),
+    )
     session_id: NonEmptyString = Field(description="Daily.co session identifier")
     user_id: NonEmptyString = Field(description="User identifier (may be encoded)")
     user_name: NonEmptyString | None = Field(None, description="User display name")
@@ -100,7 +110,13 @@ class ParticipantLeftPayload(BaseModel):
     Reference: https://docs.daily.co/reference/rest-api/webhooks/events/participant-left
     """
 
-    room_name: NonEmptyString | None = Field(None, description="Daily.co room name")
+    model_config = ConfigDict(extra="ignore")
+
+    room_name: NonEmptyString | None = Field(
+        None,
+        description="Daily.co room name",
+        validation_alias=AliasChoices("room_name", "room"),
+    )
     session_id: NonEmptyString = Field(description="Daily.co session identifier")
     user_id: NonEmptyString = Field(description="User identifier (may be encoded)")
     user_name: NonEmptyString | None = Field(None, description="User display name")
@@ -112,6 +128,9 @@ class ParticipantLeftPayload(BaseModel):
     _normalize_joined_at = field_validator("joined_at", mode="before")(
         normalize_timestamp_to_int
     )
+    _normalize_duration = field_validator("duration", mode="before")(
+        normalize_timestamp_to_int
+    )
 
 
 class RecordingStartedPayload(BaseModel):
@@ -121,6 +140,8 @@ class RecordingStartedPayload(BaseModel):
     Reference: https://docs.daily.co/reference/rest-api/webhooks/events/recording-started
     """
 
+    model_config = ConfigDict(extra="ignore")
+
     room_name: NonEmptyString | None = Field(None, description="Daily.co room name")
     recording_id: NonEmptyString = Field(description="Recording identifier")
     start_ts: int | None = Field(None, description="Recording start timestamp")
@@ -138,7 +159,9 @@ class RecordingReadyToDownloadPayload(BaseModel):
     Reference: https://docs.daily.co/reference/rest-api/webhooks/events/recording-ready-to-download
     """
 
-    type: Literal["cloud", "raw-tracks"] = Field(
+    model_config = ConfigDict(extra="ignore")
+
+    type: Literal["cloud", "cloud-audio-only", "raw-tracks"] = Field(
         description="The type of recording that was generated"
     )
     recording_id: NonEmptyString = Field(
@@ -153,8 +176,9 @@ class RecordingReadyToDownloadPayload(BaseModel):
     status: Literal["finished"] = Field(
         description="The status of the given recording (always 'finished' in ready-to-download webhook, see RecordingStatus in responses.py for full API statuses)"
     )
-    max_participants: int = Field(
-        description="The number of participants on the call that were recorded"
+    max_participants: int | None = Field(
+        None,
+        description="The number of participants on the call that were recorded (optional; Daily may omit it in some webhook versions)",
     )
     duration: int = Field(description="The duration in seconds of the call")
     s3_key: NonEmptyString = Field(
@@ -180,6 +204,8 @@ class RecordingErrorPayload(BaseModel):
     Reference: https://docs.daily.co/reference/rest-api/webhooks/events/recording-error
     """
 
+    model_config = ConfigDict(extra="ignore")
+
     action: Literal["clourd-recording-err", "cloud-recording-error"] = Field(
         description="A string describing the event that was emitted (both variants are documented)"
     )
@@ -200,6 +226,8 @@ class RecordingErrorPayload(BaseModel):
 
 
 class ParticipantJoinedEvent(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
     version: NonEmptyString
     type: Literal["participant.joined"]
     id: NonEmptyString
@@ -212,6 +240,8 @@ class ParticipantJoinedEvent(BaseModel):
 
 
 class ParticipantLeftEvent(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
     version: NonEmptyString
     type: Literal["participant.left"]
     id: NonEmptyString
@@ -224,6 +254,8 @@ class ParticipantLeftEvent(BaseModel):
 
 
 class RecordingStartedEvent(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
     version: NonEmptyString
     type: Literal["recording.started"]
     id: NonEmptyString
@@ -236,6 +268,8 @@ class RecordingStartedEvent(BaseModel):
 
 
 class RecordingReadyEvent(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
     version: NonEmptyString
     type: Literal["recording.ready-to-download"]
     id: NonEmptyString
@@ -248,6 +282,8 @@ class RecordingReadyEvent(BaseModel):
 
 
 class RecordingErrorEvent(BaseModel):
+    model_config = ConfigDict(extra="ignore")
+
     version: NonEmptyString
     type: Literal["recording.error"]
     id: NonEmptyString

server/reflector/db/search.py

@@ -26,6 +26,7 @@ from reflector.db.rooms import rooms
 from reflector.db.transcripts import SourceKind, TranscriptStatus, transcripts
 from reflector.db.utils import is_postgresql
 from reflector.logger import logger
+from reflector.settings import settings
 from reflector.utils.string import NonEmptyString, try_parse_non_empty_string
 
 DEFAULT_SEARCH_LIMIT = 20
@@ -396,7 +397,7 @@ class SearchController:
                     transcripts.c.user_id == params.user_id, rooms.c.is_shared
                 )
             )
-        else:
+        elif not settings.PUBLIC_MODE:
             base_query = base_query.where(rooms.c.is_shared)
         if params.room_id:
             base_query = base_query.where(transcripts.c.room_id == params.room_id)

server/reflector/db/transcripts.py

@@ -406,7 +406,7 @@ class TranscriptController:
             query = query.where(
                 or_(transcripts.c.user_id == user_id, rooms.c.is_shared)
             )
-        else:
+        elif not settings.PUBLIC_MODE:
             query = query.where(rooms.c.is_shared)
 
         if source_kind:

server/reflector/hatchet/client.py

@@ -12,7 +12,9 @@ import threading
 
 from hatchet_sdk import ClientConfig, Hatchet
 from hatchet_sdk.clients.rest.models import V1TaskStatus
+from hatchet_sdk.rate_limit import RateLimitDuration
 
+from reflector.hatchet.constants import LLM_RATE_LIMIT_KEY, LLM_RATE_LIMIT_PER_SECOND
 from reflector.logger import logger
 from reflector.settings import settings
 
@@ -113,3 +115,26 @@ class HatchetClientManager:
         """Reset the client instance (for testing)."""
         with cls._lock:
             cls._instance = None
+
+    @classmethod
+    async def ensure_rate_limit(cls) -> None:
+        """Ensure the LLM rate limit exists in Hatchet.
+
+        Uses the Hatchet SDK rate_limits client (aio_put). See:
+        https://docs.hatchet.run/sdks/python/feature-clients/rate_limits
+        """
+        logger.info(
+            "[Hatchet] Ensuring rate limit exists",
+            rate_limit_key=LLM_RATE_LIMIT_KEY,
+            limit=LLM_RATE_LIMIT_PER_SECOND,
+        )
+        client = cls.get_client()
+        await client.rate_limits.aio_put(
+            key=LLM_RATE_LIMIT_KEY,
+            limit=LLM_RATE_LIMIT_PER_SECOND,
+            duration=RateLimitDuration.SECOND,
+        )
+        logger.info(
+            "[Hatchet] Rate limit put successfully",
+            rate_limit_key=LLM_RATE_LIMIT_KEY,
+        )

server/reflector/hatchet/run_workers_llm.py

@@ -3,6 +3,8 @@ LLM/I/O worker pool for all non-CPU tasks.
 Handles: all tasks except mixdown_tracks (transcription, LLM inference, orchestration)
 """
 
+import asyncio
+
 from reflector.hatchet.client import HatchetClientManager
 from reflector.hatchet.workflows.daily_multitrack_pipeline import (
     daily_multitrack_pipeline,
@@ -20,6 +22,15 @@ POOL = "llm-io"
 def main():
     hatchet = HatchetClientManager.get_client()
 
+    try:
+        asyncio.run(HatchetClientManager.ensure_rate_limit())
+    except Exception as e:
+        logger.warning(
+            "[Hatchet] Rate limit initialization failed, but continuing. "
+            "If workflows fail to register, rate limits may need to be created manually.",
+            error=str(e),
+        )
+
     logger.info(
         "Starting Hatchet LLM worker pool (all tasks except mixdown)",
         worker_name=WORKER_NAME,

server/reflector/hatchet/workflows/daily_multitrack_pipeline.py

@@ -171,11 +171,13 @@ async def set_workflow_error_status(transcript_id: NonEmptyString) -> bool:
 
 def _spawn_storage():
     """Create fresh storage instance."""
+    # TODO: replace direct AwsStorage construction with get_transcripts_storage() factory
     return 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,
+        aws_endpoint_url=settings.TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL,
     )
 
 

server/reflector/hatchet/workflows/padding_workflow.py

@@ -49,11 +49,13 @@ async def pad_track(input: PaddingInput, ctx: Context) -> PadTrackResult:
         from reflector.settings import settings  # noqa: PLC0415
         from reflector.storage.storage_aws import AwsStorage  # noqa: PLC0415
 
+        # TODO: replace direct AwsStorage construction with get_transcripts_storage() factory
         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,
+            aws_endpoint_url=settings.TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL,
         )
 
         source_url = await storage.get_file_url(

server/reflector/hatchet/workflows/track_processing.py

@@ -60,6 +60,7 @@ async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
 
     try:
         # Create fresh storage instance to avoid aioboto3 fork issues
+        # TODO: replace direct AwsStorage construction with get_transcripts_storage() factory
         from reflector.settings import settings  # noqa: PLC0415
         from reflector.storage.storage_aws import AwsStorage  # noqa: PLC0415
 
@@ -68,6 +69,7 @@ async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
             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,
+            aws_endpoint_url=settings.TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL,
         )
 
         source_url = await storage.get_file_url(
@@ -159,6 +161,7 @@ async def transcribe_track(input: TrackInput, ctx: Context) -> TranscribeTrackRe
             raise ValueError("Missing padded_key from pad_track")
 
         # Presign URL on demand (avoids stale URLs on workflow replay)
+        # TODO: replace direct AwsStorage construction with get_transcripts_storage() factory
         from reflector.settings import settings  # noqa: PLC0415
         from reflector.storage.storage_aws import AwsStorage  # noqa: PLC0415
 
@@ -167,6 +170,7 @@ async def transcribe_track(input: TrackInput, ctx: Context) -> TranscribeTrackRe
             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,
+            aws_endpoint_url=settings.TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL,
         )
 
         audio_url = await storage.get_file_url(

server/reflector/llm.py

@@ -144,7 +144,18 @@ class StructuredOutputWorkflow(Workflow, Generic[OutputT]):
         )
 
         # Network retries handled by OpenAILike (max_retries=3)
-        response = await Settings.llm.acomplete(json_prompt)
+        # response_format enables grammar-based constrained decoding on backends
+        # that support it (DMR/llama.cpp, vLLM, Ollama, OpenAI).
+        response = await Settings.llm.acomplete(
+            json_prompt,
+            response_format={
+                "type": "json_schema",
+                "json_schema": {
+                    "name": self.output_cls.__name__,
+                    "schema": self.output_cls.model_json_schema(),
+                },
+            },
+        )
         return ExtractionDone(output=response.text)
 
     @step
@@ -206,12 +217,6 @@ class LLM:
         """Configure llamaindex Settings with OpenAILike LLM"""
         session_id = llm_session_id.get() or f"fallback-{uuid4().hex}"
 
-        extra_body: dict = {"litellm_session_id": session_id}
-        # Only send enable_thinking when explicitly set (not None/unset).
-        # Models that don't support it will ignore the param.
-        if self.settings_obj.LLM_ENABLE_THINKING is not None:
-            extra_body["enable_thinking"] = self.settings_obj.LLM_ENABLE_THINKING
-
         Settings.llm = OpenAILike(
             model=self.model_name,
             api_base=self.url,
@@ -221,7 +226,7 @@ class LLM:
             is_function_calling_model=False,
             temperature=self.temperature,
             max_tokens=self.max_tokens,
-            additional_kwargs={"extra_body": extra_body},
+            additional_kwargs={"extra_body": {"litellm_session_id": session_id}},
         )
 
     async def get_response(

server/reflector/processors/audio_diarization_pyannote.py

@@ -1,74 +0,0 @@
-import os
-
-import torch
-import torchaudio
-from pyannote.audio import Pipeline
-
-from reflector.processors.audio_diarization import AudioDiarizationProcessor
-from reflector.processors.audio_diarization_auto import AudioDiarizationAutoProcessor
-from reflector.processors.types import AudioDiarizationInput, DiarizationSegment
-
-
-class AudioDiarizationPyannoteProcessor(AudioDiarizationProcessor):
-    """Local diarization processor using pyannote.audio library"""
-
-    def __init__(
-        self,
-        model_name: str = "pyannote/speaker-diarization-3.1",
-        pyannote_auth_token: str | None = None,
-        device: str | None = None,
-        **kwargs,
-    ):
-        super().__init__(**kwargs)
-        self.model_name = model_name
-        self.auth_token = pyannote_auth_token or os.environ.get("HF_TOKEN")
-        self.device = device
-
-        if device is None:
-            self.device = "cuda" if torch.cuda.is_available() else "cpu"
-
-        self.logger.info(f"Loading pyannote diarization model: {self.model_name}")
-        self.diarization_pipeline = Pipeline.from_pretrained(
-            self.model_name, use_auth_token=self.auth_token
-        )
-        self.diarization_pipeline.to(torch.device(self.device))
-        self.logger.info(f"Diarization model loaded on device: {self.device}")
-
-    async def _diarize(self, data: AudioDiarizationInput) -> list[DiarizationSegment]:
-        try:
-            # Load audio file (audio_url is assumed to be a local file path)
-            self.logger.info(f"Loading local audio file: {data.audio_url}")
-            waveform, sample_rate = torchaudio.load(data.audio_url)
-            audio_input = {"waveform": waveform, "sample_rate": sample_rate}
-            self.logger.info("Running speaker diarization")
-            diarization = self.diarization_pipeline(audio_input)
-
-            # Convert pyannote diarization output to our format
-            segments = []
-            for segment, _, speaker in diarization.itertracks(yield_label=True):
-                # Extract speaker number from label (e.g., "SPEAKER_00" -> 0)
-                speaker_id = 0
-                if speaker.startswith("SPEAKER_"):
-                    try:
-                        speaker_id = int(speaker.split("_")[-1])
-                    except (ValueError, IndexError):
-                        # Fallback to hash-based ID if parsing fails
-                        speaker_id = hash(speaker) % 1000
-
-                segments.append(
-                    {
-                        "start": round(segment.start, 3),
-                        "end": round(segment.end, 3),
-                        "speaker": speaker_id,
-                    }
-                )
-
-            self.logger.info(f"Diarization completed with {len(segments)} segments")
-            return segments
-
-        except Exception as e:
-            self.logger.exception(f"Diarization failed: {e}")
-            raise
-
-
-AudioDiarizationAutoProcessor.register("pyannote", AudioDiarizationPyannoteProcessor)

server/reflector/services/transcript_process.py

@@ -97,8 +97,11 @@ async def validate_transcript_for_processing(
     if transcript.locked:
         return ValidationLocked(detail="Recording is locked")
 
-    # Check if recording is ready for processing
-    if transcript.status == "idle" and not transcript.workflow_run_id:
+    if (
+        transcript.status == "idle"
+        and not transcript.workflow_run_id
+        and not transcript.recording_id
+    ):
         return ValidationNotReady(detail="Recording is not ready for processing")
 
     # Check Celery tasks

server/reflector/settings.py

@@ -49,6 +49,7 @@ class Settings(BaseSettings):
     TRANSCRIPT_STORAGE_AWS_REGION: str = "us-east-1"
     TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID: str | None = None
     TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY: str | None = None
+    TRANSCRIPT_STORAGE_AWS_ENDPOINT_URL: str | None = None
 
     # Platform-specific recording storage (follows {PREFIX}_STORAGE_AWS_{CREDENTIAL} pattern)
     # Whereby storage configuration
@@ -75,7 +76,6 @@ class Settings(BaseSettings):
     LLM_URL: str | None = None
     LLM_API_KEY: str | None = None
     LLM_CONTEXT_WINDOW: int = 16000
-    LLM_ENABLE_THINKING: bool | None = None
 
     LLM_PARSE_MAX_RETRIES: int = (
         3  # Max retries for JSON/validation errors (total attempts = retries + 1)
@@ -85,9 +85,7 @@ class Settings(BaseSettings):
     )
 
     # Diarization
-    # backends:
-    #   - pyannote: in-process model loading (no HTTP, runs in same process)
-    #   - modal: HTTP API client (works with Modal.com OR self-hosted gpu/self_hosted/)
+    # backend: modal — HTTP API client (works with Modal.com OR self-hosted gpu/self_hosted/)
     DIARIZATION_ENABLED: bool = True
     DIARIZATION_BACKEND: str = "modal"
     DIARIZATION_URL: str | None = None
@@ -96,9 +94,6 @@ class Settings(BaseSettings):
     # Diarization: modal backend
     DIARIZATION_MODAL_API_KEY: str | None = None
 
-    # 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

server/reflector/storage/storage_aws.py

@@ -53,6 +53,7 @@ class AwsStorage(Storage):
         aws_access_key_id: str | None = None,
         aws_secret_access_key: str | None = None,
         aws_role_arn: str | None = None,
+        aws_endpoint_url: str | None = None,
     ):
         if not aws_bucket_name:
             raise ValueError("Storage `aws_storage` require `aws_bucket_name`")
@@ -73,17 +74,26 @@ class AwsStorage(Storage):
         self._access_key_id = aws_access_key_id
         self._secret_access_key = aws_secret_access_key
         self._role_arn = aws_role_arn
+        self._endpoint_url = aws_endpoint_url
 
         self.aws_folder = ""
         if "/" in aws_bucket_name:
             self._bucket_name, self.aws_folder = aws_bucket_name.split("/", 1)
-        self.boto_config = Config(retries={"max_attempts": 3, "mode": "adaptive"})
+
+        config_kwargs: dict = {"retries": {"max_attempts": 3, "mode": "adaptive"}}
+        if aws_endpoint_url:
+            config_kwargs["s3"] = {"addressing_style": "path"}
+        self.boto_config = Config(**config_kwargs)
+
         self.session = aioboto3.Session(
             aws_access_key_id=aws_access_key_id,
             aws_secret_access_key=aws_secret_access_key,
             region_name=aws_region,
         )
-        self.base_url = f"https://{self._bucket_name}.s3.amazonaws.com/"
+        if aws_endpoint_url:
+            self.base_url = f"{aws_endpoint_url}/{self._bucket_name}/"
+        else:
+            self.base_url = f"https://{self._bucket_name}.s3.amazonaws.com/"
 
     # Implement credential properties
     @property
@@ -139,7 +149,9 @@ class AwsStorage(Storage):
         s3filename = f"{folder}/{filename}" if folder else filename
         logger.info(f"Uploading {filename} to S3 {actual_bucket}/{folder}")
 
-        async with self.session.client("s3", config=self.boto_config) as client:
+        async with self.session.client(
+            "s3", config=self.boto_config, endpoint_url=self._endpoint_url
+        ) as client:
             if isinstance(data, bytes):
                 await client.put_object(Bucket=actual_bucket, Key=s3filename, Body=data)
             else:
@@ -162,7 +174,9 @@ class AwsStorage(Storage):
         actual_bucket = bucket or self._bucket_name
         folder = self.aws_folder
         s3filename = f"{folder}/{filename}" if folder else filename
-        async with self.session.client("s3", config=self.boto_config) as client:
+        async with self.session.client(
+            "s3", config=self.boto_config, endpoint_url=self._endpoint_url
+        ) as client:
             presigned_url = await client.generate_presigned_url(
                 operation,
                 Params={"Bucket": actual_bucket, "Key": s3filename},
@@ -177,7 +191,9 @@ class AwsStorage(Storage):
         folder = self.aws_folder
         logger.info(f"Deleting {filename} from S3 {actual_bucket}/{folder}")
         s3filename = f"{folder}/{filename}" if folder else filename
-        async with self.session.client("s3", config=self.boto_config) as client:
+        async with self.session.client(
+            "s3", config=self.boto_config, endpoint_url=self._endpoint_url
+        ) as client:
             await client.delete_object(Bucket=actual_bucket, Key=s3filename)
 
     @handle_s3_client_errors("download")
@@ -186,7 +202,9 @@ class AwsStorage(Storage):
         folder = self.aws_folder
         logger.info(f"Downloading {filename} from S3 {actual_bucket}/{folder}")
         s3filename = f"{folder}/{filename}" if folder else filename
-        async with self.session.client("s3", config=self.boto_config) as client:
+        async with self.session.client(
+            "s3", config=self.boto_config, endpoint_url=self._endpoint_url
+        ) as client:
             response = await client.get_object(Bucket=actual_bucket, Key=s3filename)
             return await response["Body"].read()
 
@@ -201,7 +219,9 @@ class AwsStorage(Storage):
         logger.info(f"Listing objects from S3 {actual_bucket} with prefix '{s3prefix}'")
 
         keys = []
-        async with self.session.client("s3", config=self.boto_config) as client:
+        async with self.session.client(
+            "s3", config=self.boto_config, endpoint_url=self._endpoint_url
+        ) as client:
             paginator = client.get_paginator("list_objects_v2")
             async for page in paginator.paginate(Bucket=actual_bucket, Prefix=s3prefix):
                 if "Contents" in page:
@@ -227,7 +247,9 @@ class AwsStorage(Storage):
         folder = self.aws_folder
         logger.info(f"Streaming {filename} from S3 {actual_bucket}/{folder}")
         s3filename = f"{folder}/{filename}" if folder else filename
-        async with self.session.client("s3", config=self.boto_config) as client:
+        async with self.session.client(
+            "s3", config=self.boto_config, endpoint_url=self._endpoint_url
+        ) as client:
             await client.download_fileobj(
                 Bucket=actual_bucket, Key=s3filename, Fileobj=fileobj
             )

server/reflector/views/daily.py

@@ -80,7 +80,14 @@ async def webhook(request: Request):
     try:
         event = event_adapter.validate_python(body_json)
     except Exception as e:
-        logger.error("Failed to parse webhook event", error=str(e), body=body.decode())
+        err_detail = str(e)
+        if hasattr(e, "errors"):
+            err_detail = f"{err_detail}; errors={e.errors()!r}"
+        logger.error(
+            "Failed to parse webhook event",
+            error=err_detail,
+            body=body.decode(),
+        )
         raise HTTPException(status_code=422, detail="Invalid event format")
 
     match event:

server/reflector/views/transcripts_upload.py

@@ -5,7 +5,7 @@ from fastapi import APIRouter, Depends, HTTPException, UploadFile
 from pydantic import BaseModel
 
 import reflector.auth as auth
-from reflector.db.transcripts import transcripts_controller
+from reflector.db.transcripts import SourceKind, transcripts_controller
 from reflector.pipelines.main_file_pipeline import task_pipeline_file_process
 
 router = APIRouter()
@@ -88,8 +88,10 @@ async def transcript_record_upload(
     finally:
         container.close()
 
-    # set the status to "uploaded"
-    await transcripts_controller.update(transcript, {"status": "uploaded"})
+    # set the status to "uploaded" and mark as file source
+    await transcripts_controller.update(
+        transcript, {"status": "uploaded", "source_kind": SourceKind.FILE}
+    )
 
     # launch a background task to process the file
     task_pipeline_file_process.delay(transcript_id=transcript_id)

server/reflector/views/user_websocket.py

@@ -1,6 +1,6 @@
 from typing import Optional
 
-from fastapi import APIRouter, WebSocket
+from fastapi import APIRouter, WebSocket, WebSocketDisconnect
 
 from reflector.auth.auth_jwt import JWTAuth  # type: ignore
 from reflector.db.users import user_controller
@@ -60,6 +60,8 @@ async def user_events_websocket(websocket: WebSocket):
     try:
         while True:
             await websocket.receive()
+    except (RuntimeError, WebSocketDisconnect):
+        pass
     finally:
         if room_id:
             await ws_manager.remove_user_from_room(room_id, websocket)

server/reflector/ws_manager.py

@@ -48,7 +48,15 @@ class RedisPubSubManager:
         if not self.redis_connection:
             await self.connect()
         message = json.dumps(message)
-        await self.redis_connection.publish(room_id, message)
+        try:
+            await self.redis_connection.publish(room_id, message)
+        except RuntimeError:
+            # Celery workers run each task in a new event loop (asyncio.run),
+            # which closes the previous loop. Cached Redis connection is dead.
+            # Reconnect on the current loop and retry.
+            self.redis_connection = None
+            await self.connect()
+            await self.redis_connection.publish(room_id, message)
 
     async def subscribe(self, room_id: str) -> redis.Redis:
         await self.pubsub.subscribe(room_id)

server/scripts/recreate_daily_webhook.py

@@ -15,8 +15,7 @@ from reflector.settings import settings
 
 async def setup_webhook(webhook_url: str):
     """
-    Create or update Daily.co webhook for this environment using dailyco_api module.
-    Uses DAILY_WEBHOOK_UUID to identify existing webhook.
+    Create Daily.co webhook. Deletes any existing webhooks first, then creates the new one.
     """
     if not settings.DAILY_API_KEY:
         print("Error: DAILY_API_KEY not set")
@@ -35,79 +34,37 @@ async def setup_webhook(webhook_url: str):
     ]
 
     async with DailyApiClient(api_key=settings.DAILY_API_KEY) as client:
-        webhook_uuid = settings.DAILY_WEBHOOK_UUID
+        webhooks = await client.list_webhooks()
+        for wh in webhooks:
+            await client.delete_webhook(wh.uuid)
+            print(f"Deleted webhook {wh.uuid}")
 
-        if webhook_uuid:
-            print(f"Updating existing webhook {webhook_uuid}...")
-            try:
-                # Note: Daily.co doesn't support PATCH well, so we delete + recreate
-                await client.delete_webhook(webhook_uuid)
-                print(f"Deleted old webhook {webhook_uuid}")
+        request = CreateWebhookRequest(
+            url=webhook_url,
+            eventTypes=event_types,
+            hmac=settings.DAILY_WEBHOOK_SECRET,
+        )
+        result = await client.create_webhook(request)
+        webhook_uuid = result.uuid
 
-                request = CreateWebhookRequest(
-                    url=webhook_url,
-                    eventTypes=event_types,
-                    hmac=settings.DAILY_WEBHOOK_SECRET,
-                )
-                result = await client.create_webhook(request)
+        print(f"✓ Created webhook {webhook_uuid} (state: {result.state})")
+        print(f"  URL: {result.url}")
 
-                print(
-                    f"✓ Created replacement webhook {result.uuid} (state: {result.state})"
-                )
-                print(f"  URL: {result.url}")
+        env_file = Path(__file__).parent.parent / ".env"
+        if env_file.exists():
+            lines = env_file.read_text().splitlines()
+            updated = False
+            for i, line in enumerate(lines):
+                if line.startswith("DAILY_WEBHOOK_UUID="):
+                    lines[i] = f"DAILY_WEBHOOK_UUID={webhook_uuid}"
+                    updated = True
+                    break
+            if not updated:
+                lines.append(f"DAILY_WEBHOOK_UUID={webhook_uuid}")
+            env_file.write_text("\n".join(lines) + "\n")
+            print("✓ Saved DAILY_WEBHOOK_UUID to .env")
 
-                webhook_uuid = result.uuid
-
-            except Exception as e:
-                if hasattr(e, "response") and e.response.status_code == 404:
-                    print(f"Webhook {webhook_uuid} not found, creating new one...")
-                    webhook_uuid = None  # Fall through to creation
-                else:
-                    print(f"Error updating webhook: {e}")
-                    return 1
-
-        if not webhook_uuid:
-            print("Creating new webhook...")
-            request = CreateWebhookRequest(
-                url=webhook_url,
-                eventTypes=event_types,
-                hmac=settings.DAILY_WEBHOOK_SECRET,
-            )
-            result = await client.create_webhook(request)
-            webhook_uuid = result.uuid
-
-            print(f"✓ Created webhook {webhook_uuid} (state: {result.state})")
-            print(f"  URL: {result.url}")
-            print()
-            print("=" * 60)
-            print("IMPORTANT: Add this to your environment variables:")
-            print("=" * 60)
-            print(f"DAILY_WEBHOOK_UUID: {webhook_uuid}")
-            print("=" * 60)
-            print()
-
-            # Try to write UUID to .env file
-            env_file = Path(__file__).parent.parent / ".env"
-            if env_file.exists():
-                lines = env_file.read_text().splitlines()
-                updated = False
-
-                # Update existing DAILY_WEBHOOK_UUID line or add it
-                for i, line in enumerate(lines):
-                    if line.startswith("DAILY_WEBHOOK_UUID="):
-                        lines[i] = f"DAILY_WEBHOOK_UUID={webhook_uuid}"
-                        updated = True
-                        break
-
-                if not updated:
-                    lines.append(f"DAILY_WEBHOOK_UUID={webhook_uuid}")
-
-                env_file.write_text("\n".join(lines) + "\n")
-                print(f"✓ Also saved to local .env file")
-            else:
-                print(f"⚠ Local .env file not found - please add manually")
-
-            return 0
+        return 0
 
 
 if __name__ == "__main__":
@@ -117,11 +74,7 @@ if __name__ == "__main__":
             "Example: python recreate_daily_webhook.py https://example.com/v1/daily/webhook"
         )
         print()
-        print("Behavior:")
-        print("  - If DAILY_WEBHOOK_UUID set: Deletes old webhook, creates new one")
-        print(
-            "  - If DAILY_WEBHOOK_UUID empty: Creates new webhook, saves UUID to .env"
-        )
+        print("Deletes all existing webhooks, then creates a new one.")
         sys.exit(1)
 
     sys.exit(asyncio.run(setup_webhook(sys.argv[1])))

server/tests/test_hatchet_dispatch.py

@@ -255,7 +255,7 @@ async def test_validation_locked_transcript():
 @pytest.mark.usefixtures("setup_database")
 @pytest.mark.asyncio
 async def test_validation_idle_transcript():
-    """Test that validation rejects idle transcripts (not ready)."""
+    """Test that validation rejects idle transcripts without recording (file upload not ready)."""
     from reflector.services.transcript_process import (
         ValidationNotReady,
         validate_transcript_for_processing,
@@ -274,6 +274,34 @@ async def test_validation_idle_transcript():
     assert "not ready" in result.detail.lower()
 
 
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_validation_idle_transcript_with_recording_allowed():
+    """Test that validation allows idle transcripts with recording_id (multitrack ready/retry)."""
+    from reflector.services.transcript_process import (
+        ValidationOk,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="idle",
+        source_kind="room",
+        recording_id="test-recording-id",
+    )
+
+    with patch(
+        "reflector.services.transcript_process.task_is_scheduled_or_active"
+    ) as mock_celery_check:
+        mock_celery_check.return_value = False
+
+        result = await validate_transcript_for_processing(mock_transcript)
+
+    assert isinstance(result, ValidationOk)
+    assert result.recording_id == "test-recording-id"
+
+
 @pytest.mark.usefixtures("setup_database")
 @pytest.mark.asyncio
 async def test_prepare_multitrack_config():

server/tests/test_llm_retry.py

@@ -8,7 +8,6 @@ from pydantic import BaseModel, Field
 from workflows.errors import WorkflowRuntimeError, WorkflowTimeoutError
 
 from reflector.llm import LLM, LLMParseError, StructuredOutputWorkflow
-from reflector.settings import Settings
 from reflector.utils.retry import RetryException
 
 
@@ -27,57 +26,6 @@ def make_completion_response(text: str):
     return response
 
 
-class TestLLMEnableThinking:
-    """Test that LLM_ENABLE_THINKING setting is passed through to OpenAILike"""
-
-    def test_enable_thinking_false_passed_in_extra_body(self):
-        """enable_thinking=False should be in extra_body when LLM_ENABLE_THINKING=False"""
-        settings = Settings(
-            LLM_ENABLE_THINKING=False,
-            LLM_URL="http://fake",
-            LLM_API_KEY="fake",
-        )
-
-        with (
-            patch("reflector.llm.OpenAILike") as mock_openai,
-            patch("reflector.llm.Settings"),
-        ):
-            LLM(settings=settings)
-            extra_body = mock_openai.call_args.kwargs["additional_kwargs"]["extra_body"]
-            assert extra_body["enable_thinking"] is False
-
-    def test_enable_thinking_true_passed_in_extra_body(self):
-        """enable_thinking=True should be in extra_body when LLM_ENABLE_THINKING=True"""
-        settings = Settings(
-            LLM_ENABLE_THINKING=True,
-            LLM_URL="http://fake",
-            LLM_API_KEY="fake",
-        )
-
-        with (
-            patch("reflector.llm.OpenAILike") as mock_openai,
-            patch("reflector.llm.Settings"),
-        ):
-            LLM(settings=settings)
-            extra_body = mock_openai.call_args.kwargs["additional_kwargs"]["extra_body"]
-            assert extra_body["enable_thinking"] is True
-
-    def test_enable_thinking_none_not_in_extra_body(self):
-        """enable_thinking should not be in extra_body when LLM_ENABLE_THINKING is None (default)"""
-        settings = Settings(
-            LLM_URL="http://fake",
-            LLM_API_KEY="fake",
-        )
-
-        with (
-            patch("reflector.llm.OpenAILike") as mock_openai,
-            patch("reflector.llm.Settings"),
-        ):
-            LLM(settings=settings)
-            extra_body = mock_openai.call_args.kwargs["additional_kwargs"]["extra_body"]
-            assert "enable_thinking" not in extra_body
-
-
 class TestLLMParseErrorRecovery:
     """Test parse error recovery with Workflow feedback loop"""
 

server/tests/test_storage.py

@@ -319,3 +319,51 @@ def test_aws_storage_constructor_rejects_mixed_auth():
             aws_secret_access_key="test-secret",
             aws_role_arn="arn:aws:iam::123456789012:role/test-role",
         )
+
+
+@pytest.mark.asyncio
+async def test_aws_storage_custom_endpoint_url():
+    """Test that custom endpoint_url configures path-style addressing and passes endpoint to client."""
+    storage = AwsStorage(
+        aws_bucket_name="reflector-media",
+        aws_region="garage",
+        aws_access_key_id="GKtest",
+        aws_secret_access_key="secret",
+        aws_endpoint_url="http://garage:3900",
+    )
+    assert storage._endpoint_url == "http://garage:3900"
+    assert storage.boto_config.s3["addressing_style"] == "path"
+    assert storage.base_url == "http://garage:3900/reflector-media/"
+    # retries config preserved (merge, not replace)
+    assert storage.boto_config.retries["max_attempts"] == 3
+
+    mock_client = AsyncMock()
+    mock_client.put_object = AsyncMock()
+    mock_client.__aenter__ = AsyncMock(return_value=mock_client)
+    mock_client.__aexit__ = AsyncMock(return_value=None)
+    mock_client.generate_presigned_url = AsyncMock(
+        return_value="http://garage:3900/reflector-media/test.txt"
+    )
+
+    with patch.object(
+        storage.session, "client", return_value=mock_client
+    ) as mock_session_client:
+        await storage.put_file("test.txt", b"data")
+        mock_session_client.assert_called_with(
+            "s3", config=storage.boto_config, endpoint_url="http://garage:3900"
+        )
+
+
+@pytest.mark.asyncio
+async def test_aws_storage_none_endpoint_url():
+    """Test that None endpoint preserves current AWS behavior."""
+    storage = AwsStorage(
+        aws_bucket_name="reflector-bucket",
+        aws_region="us-east-1",
+        aws_access_key_id="AKIAtest",
+        aws_secret_access_key="secret",
+    )
+    assert storage._endpoint_url is None
+    assert storage.base_url == "https://reflector-bucket.s3.amazonaws.com/"
+    # No s3 addressing_style override — boto_config should only have retries
+    assert not hasattr(storage.boto_config, "s3") or storage.boto_config.s3 is None

server/uv.lock

@@ -235,12 +235,6 @@ wheels = [
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 ]
 
-[[package]]
-name = "antlr4-python3-runtime"
-version = "4.9.3"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/3e/38/7859ff46355f76f8d19459005ca000b6e7012f2f1ca597746cbcd1fbfe5e/antlr4-python3-runtime-4.9.3.tar.gz", hash = "sha256:f224469b4168294902bb1efa80a8bf7855f24c99aef99cbefc1bcd3cce77881b", size = 117034 }
-
 [[package]]
 name = "anyio"
 version = "4.9.0"
@@ -267,21 +261,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/2f/f5/c36551e93acba41a59939ae6a0fb77ddb3f2e8e8caa716410c65f7341f72/asgi_lifespan-2.1.0-py3-none-any.whl", hash = "sha256:ed840706680e28428c01e14afb3875d7d76d3206f3d5b2f2294e059b5c23804f", size = 10895 },
 ]
 
-[[package]]
-name = "asteroid-filterbanks"
-version = "0.4.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "numpy" },
-    { name = "torch", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
-    { name = "typing-extensions" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/90/fa/5c2be1f96dc179f83cdd3bb267edbd1f47d08f756785c016d5c2163901a7/asteroid-filterbanks-0.4.0.tar.gz", hash = "sha256:415f89d1dcf2b13b35f03f7a9370968ac4e6fa6800633c522dac992b283409b9", size = 24599 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/c5/7c/83ff6046176a675e6a1e8aeefed8892cd97fe7c46af93cc540d1b24b8323/asteroid_filterbanks-0.4.0-py3-none-any.whl", hash = "sha256:4932ac8b6acc6e08fb87cbe8ece84215b5a74eee284fe83acf3540a72a02eaf5", size = 29912 },
-]
-
 [[package]]
 name = "async-timeout"
 version = "5.0.1"
@@ -603,56 +582,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a7/06/3d6badcf13db419e25b07041d9c7b4a2c331d3f4e7134445ec5df57714cd/coloredlogs-15.0.1-py2.py3-none-any.whl", hash = "sha256:612ee75c546f53e92e70049c9dbfcc18c935a2b9a53b66085ce9ef6a6e5c0934", size = 46018 },
 ]
 
-[[package]]
-name = "colorlog"
-version = "6.9.0"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "colorama", marker = "sys_platform == 'win32'" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/d3/7a/359f4d5df2353f26172b3cc39ea32daa39af8de522205f512f458923e677/colorlog-6.9.0.tar.gz", hash = "sha256:bfba54a1b93b94f54e1f4fe48395725a3d92fd2a4af702f6bd70946bdc0c6ac2", size = 16624 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/e3/51/9b208e85196941db2f0654ad0357ca6388ab3ed67efdbfc799f35d1f83aa/colorlog-6.9.0-py3-none-any.whl", hash = "sha256:5906e71acd67cb07a71e779c47c4bcb45fb8c2993eebe9e5adcd6a6f1b283eff", size = 11424 },
-]
-
-[[package]]
-name = "contourpy"
-version = "1.3.3"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "numpy" },
-]
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 [[package]]
 name = "soupsieve"
 version = "2.7"
@@ -3779,29 +3210,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e7/9c/0e6afc12c269578be5c0c1c9f4b49a8d32770a080260c333ac04cc1c832d/soupsieve-2.7-py3-none-any.whl", hash = "sha256:6e60cc5c1ffaf1cebcc12e8188320b72071e922c2e897f737cadce79ad5d30c4", size = 36677 },
 ]
 
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-version = "1.0.3"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "huggingface-hub" },
-    { name = "hyperpyyaml" },
-    { name = "joblib" },
-    { name = "numpy" },
-    { name = "packaging" },
-    { name = "scipy" },
-    { name = "sentencepiece" },
-    { name = "torch", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform == 'darwin'" },
-    { name = "torch", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "sys_platform != 'darwin'" },
-    { name = "torchaudio", version = "2.8.0", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or sys_platform == 'darwin'" },
-    { name = "torchaudio", version = "2.8.0+cpu", source = { registry = "https://download.pytorch.org/whl/cpu" }, marker = "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')" },
-    { name = "tqdm" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/ab/10/87e666544a4e0cec7cbdc09f26948994831ae0f8bbc58de3bf53b68285ff/speechbrain-1.0.3.tar.gz", hash = "sha256:fcab3c6e90012cecb1eed40ea235733b550137e73da6bfa2340ba191ec714052", size = 747735 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/58/13/e61f1085aebee17d5fc2df19fcc5177c10379be52578afbecdd615a831c9/speechbrain-1.0.3-py3-none-any.whl", hash = "sha256:9859d4c1b1fb3af3b85523c0c89f52e45a04f305622ed55f31aa32dd2fba19e9", size = 864091 },
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-
 [[package]]
 name = "sqlalchemy"
 version = "1.4.54"
@@ -3883,15 +3291,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/a2/09/77d55d46fd61b4a135c444fc97158ef34a095e5681d0a6c10b75bf356191/sympy-1.14.0-py3-none-any.whl", hash = "sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5", size = 6299353 },
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-[[package]]
-name = "tabulate"
-version = "0.9.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/ec/fe/802052aecb21e3797b8f7902564ab6ea0d60ff8ca23952079064155d1ae1/tabulate-0.9.0.tar.gz", hash = "sha256:0095b12bf5966de529c0feb1fa08671671b3368eec77d7ef7ab114be2c068b3c", size = 81090 }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/40/44/4a5f08c96eb108af5cb50b41f76142f0afa346dfa99d5296fe7202a11854/tabulate-0.9.0-py3-none-any.whl", hash = "sha256:024ca478df22e9340661486f85298cff5f6dcdba14f3813e8830015b9ed1948f", size = 35252 },
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-
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 name = "tenacity"
 version = "9.1.2"
@@ -3901,29 +3300,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/e5/30/643397144bfbfec6f6ef821f36f33e57d35946c44a2352d3c9f0ae847619/tenacity-9.1.2-py3-none-any.whl", hash = "sha256:f77bf36710d8b73a50b2dd155c97b870017ad21afe6ab300326b0371b3b05138", size = 28248 },
 ]
 
-[[package]]
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-version = "2.6.4"
-source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "numpy" },
-    { name = "packaging" },
-    { name = "protobuf" },
-]
-sdist = { url = "https://files.pythonhosted.org/packages/2b/c5/d4cc6e293fb837aaf9f76dd7745476aeba8ef7ef5146c3b3f9ee375fe7a5/tensorboardx-2.6.4.tar.gz", hash = "sha256:b163ccb7798b31100b9f5fa4d6bc22dad362d7065c2f24b51e50731adde86828", size = 4769801 }
-wheels = [
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-
-[[package]]
-name = "threadpoolctl"
-version = "3.6.0"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/b7/4d/08c89e34946fce2aec4fbb45c9016efd5f4d7f24af8e5d93296e935631d8/threadpoolctl-3.6.0.tar.gz", hash = "sha256:8ab8b4aa3491d812b623328249fab5302a68d2d71745c8a4c719a2fcaba9f44e", size = 21274 }
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 name = "tiktoken"
 version = "0.9.0"
@@ -4064,40 +3440,6 @@ wheels = [
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-    { name = "torch-pitch-shift" },
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@@ -4145,22 +3487,6 @@ wheels = [
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www/app/(app)/transcripts/[transcriptId]/processing/page.tsx

@@ -11,6 +11,7 @@ import {
 import { useRouter } from "next/navigation";
 import { useTranscriptGet } from "../../../../lib/apiHooks";
 import { parseNonEmptyString } from "../../../../lib/utils";
+import { useWebSockets } from "../../useWebSockets";
 
 type TranscriptProcessing = {
   params: Promise<{
@@ -24,6 +25,7 @@ export default function TranscriptProcessing(details: TranscriptProcessing) {
   const router = useRouter();
 
   const transcript = useTranscriptGet(transcriptId);
+  useWebSockets(transcriptId);
 
   useEffect(() => {
     const status = transcript.data?.status;

www/app/(app)/transcripts/useWebRTC.ts

@@ -23,7 +23,16 @@ const useWebRTC = (
     let p: Peer;
 
     try {
-      p = new Peer({ initiator: true, stream: stream });
+      p = new Peer({
+        initiator: true,
+        stream: stream,
+        // Disable trickle ICE: single SDP exchange (offer + answer) with all candidates.
+        // Required for HTTP-based signaling; trickle needs WebSocket for candidate exchange.
+        trickle: false,
+        config: {
+          iceServers: [{ urls: "stun:stun.l.google.com:19302" }],
+        },
+      });
     } catch (error) {
       setError(error as Error, "Error creating WebRTC");
       return;

www/app/(app)/transcripts/useWebSockets.ts

@@ -431,6 +431,7 @@ export const useWebSockets = (transcriptId: string | null): UseWebSockets => {
               );
             }
             setStatus(message.data);
+            invalidateTranscript(queryClient, transcriptId as NonEmptyString);
             if (message.data.value === "ended") {
               ws.close();
             }