chore(main): release 0.7.3 (#565)

This commit restore the original behavior with frame cutting. While
silero is used on our gpu for files, look like it's not working great on
the live pipeline. To be investigated, but at the moment, what we keep
is:

- refactored to extract the downscale for further processing in the
pipeline
- remove any downscale implementation from audio_chunker and audio_merge
- removed batching from audio_merge too for now

.github/workflows/db_migrations.yml

@@ -17,10 +17,40 @@ on:
 jobs:
   test-migrations:
     runs-on: ubuntu-latest
+    services:
+      postgres:
+        image: postgres:17
+        env:
+          POSTGRES_USER: reflector
+          POSTGRES_PASSWORD: reflector
+          POSTGRES_DB: reflector
+        ports:
+          - 5432:5432
+        options: >-
+          --health-cmd pg_isready -h 127.0.0.1 -p 5432
+          --health-interval 10s
+          --health-timeout 5s
+          --health-retries 5
+
+    env:
+      DATABASE_URL: postgresql://reflector:reflector@localhost:5432/reflector
 
     steps:
       - uses: actions/checkout@v4
 
+      - name: Install PostgreSQL client
+        run: sudo apt-get update && sudo apt-get install -y postgresql-client | cat
+
+      - name: Wait for Postgres
+        run: |
+          for i in {1..30}; do
+            if pg_isready -h localhost -p 5432; then
+              echo "Postgres is ready"
+              break
+            fi
+            echo "Waiting for Postgres... ($i)" && sleep 1
+          done
+
       - name: Install uv
         uses: astral-sh/setup-uv@v3
         with:

.github/workflows/deploy.yml

@@ -8,18 +8,30 @@ env:
   ECR_REPOSITORY: reflector
 
 jobs:
-  deploy:
-    runs-on: ubuntu-latest
+  build:
+    strategy:
+      matrix:
+        include:
+          - platform: linux/amd64
+            runner: linux-amd64
+            arch: amd64
+          - platform: linux/arm64
+            runner: linux-arm64
+            arch: arm64
+
+    runs-on: ${{ matrix.runner }}
 
     permissions:
-      deployments: write
       contents: read
 
+    outputs:
+      registry: ${{ steps.login-ecr.outputs.registry }}
+
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
 
       - name: Configure AWS credentials
-        uses: aws-actions/configure-aws-credentials@0e613a0980cbf65ed5b322eb7a1e075d28913a83
+        uses: aws-actions/configure-aws-credentials@v4
         with:
           aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
           aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
@@ -27,21 +39,52 @@ jobs:
 
       - name: Login to Amazon ECR
         id: login-ecr
-        uses: aws-actions/amazon-ecr-login@62f4f872db3836360b72999f4b87f1ff13310f3a
-
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@v2
+        uses: aws-actions/amazon-ecr-login@v2
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+        uses: docker/setup-buildx-action@v3
 
-      - name: Build and push
-        id: docker_build
-        uses: docker/build-push-action@v4
+      - name: Build and push ${{ matrix.arch }}
+        uses: docker/build-push-action@v5
         with:
           context: server
-          platforms: linux/amd64,linux/arm64
+          platforms: ${{ matrix.platform }}
           push: true
-          tags: ${{ steps.login-ecr.outputs.registry }}/${{ env.ECR_REPOSITORY }}:latest
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
+          tags: ${{ steps.login-ecr.outputs.registry }}/${{ env.ECR_REPOSITORY }}:latest-${{ matrix.arch }}
+          cache-from: type=gha,scope=${{ matrix.arch }}
+          cache-to: type=gha,mode=max,scope=${{ matrix.arch }}
+          github-token: ${{ secrets.GHA_CACHE_TOKEN }}
+          provenance: false
+
+  create-manifest:
+    runs-on: ubuntu-latest
+    needs: [build]
+
+    permissions:
+      deployments: write
+      contents: read
+
+    steps:
+      - name: Configure AWS credentials
+        uses: aws-actions/configure-aws-credentials@v4
+        with:
+          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          aws-region: ${{ env.AWS_REGION }}
+
+      - name: Login to Amazon ECR
+        uses: aws-actions/amazon-ecr-login@v2
+
+      - name: Create and push multi-arch manifest
+        run: |
+          # Get the registry URL (since we can't easily access job outputs in matrix)
+          ECR_REGISTRY=$(aws ecr describe-registry --query 'registryId' --output text).dkr.ecr.${{ env.AWS_REGION }}.amazonaws.com
+
+          docker manifest create \
+            $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest \
+            $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest-amd64 \
+            $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest-arm64
+
+          docker manifest push $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest
+
+          echo "✅ Multi-arch manifest pushed: $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest"

.github/workflows/pre-commit.yml

@@ -0,0 +1,24 @@
+name: pre-commit
+
+on:
+  pull_request:
+  push:
+    branches: [main]
+
+jobs:
+  pre-commit:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v5
+      - uses: actions/setup-python@v5
+      - uses: pnpm/action-setup@v4
+        with:
+          version: 10
+      - uses: actions/setup-node@v4
+        with:
+          node-version: 22
+          cache: "pnpm"
+          cache-dependency-path: "www/pnpm-lock.yaml"
+      - name: Install dependencies
+        run: cd www && pnpm install --frozen-lockfile
+      - uses: pre-commit/action@v3.0.1

.github/workflows/test_server.yml

@@ -19,29 +19,41 @@ jobs:
     steps:
       - uses: actions/checkout@v4
       - name: Install uv
-        uses: astral-sh/setup-uv@v3
+        uses: astral-sh/setup-uv@v6
         with:
           enable-cache: true
           working-directory: server
-
       - name: Tests
         run: |
           cd server
           uv run -m pytest -v tests
 
-  docker:
-    runs-on: ubuntu-latest
+  docker-amd64:
+    runs-on: linux-amd64
     steps:
       - uses: actions/checkout@v4
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@v2
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
-      - name: Build and push
-        id: docker_build
-        uses: docker/build-push-action@v4
+        uses: docker/setup-buildx-action@v3
+      - name: Build AMD64
+        uses: docker/build-push-action@v6
         with:
           context: server
-          platforms: linux/amd64,linux/arm64
-          cache-from: type=gha
-          cache-to: type=gha,mode=max
+          platforms: linux/amd64
+          cache-from: type=gha,scope=amd64
+          cache-to: type=gha,mode=max,scope=amd64
+          github-token: ${{ secrets.GHA_CACHE_TOKEN }}
+
+  docker-arm64:
+    runs-on: linux-arm64
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+      - name: Build ARM64
+        uses: docker/build-push-action@v6
+        with:
+          context: server
+          platforms: linux/arm64
+          cache-from: type=gha,scope=arm64
+          cache-to: type=gha,mode=max,scope=arm64
+          github-token: ${{ secrets.GHA_CACHE_TOKEN }}

.gitignore

@@ -13,3 +13,7 @@ restart-dev.sh
 data/
 www/REFACTOR.md
 www/reload-frontend
+server/test.sqlite
+CLAUDE.local.md
+www/.env.development
+www/.env.production

.gitleaksignore

@@ -0,0 +1 @@
+b9d891d3424f371642cb032ecfd0e2564470a72c:server/tests/test_transcripts_recording_deletion.py:generic-api-key:15

.pre-commit-config.yaml

@@ -3,10 +3,10 @@
 repos:
   - repo: local
     hooks:
-      - id: yarn-format
-        name: run yarn format
+      - id: format
+        name: run format
         language: system
-        entry: bash -c 'cd www && yarn format'
+        entry: bash -c 'cd www && pnpm format'
         pass_filenames: false
         files: ^www/
 
@@ -23,8 +23,12 @@ repos:
       - id: ruff
         args:
           - --fix
-          - --select
-          - I,F401
+          # Uses select rules from server/pyproject.toml
         files: ^server/
       - id: ruff-format
         files: ^server/
+
+  - repo: https://github.com/gitleaks/gitleaks
+    rev: v8.28.0
+    hooks:
+      - id: gitleaks

CHANGELOG.md

@@ -1,5 +1,72 @@
 # Changelog
 
+## [0.7.3](https://github.com/Monadical-SAS/reflector/compare/v0.7.2...v0.7.3) (2025-08-22)
+
+
+### Bug Fixes
+
+* cleaned repo, and get git-leaks clean ([359280d](https://github.com/Monadical-SAS/reflector/commit/359280dd340433ba4402ed69034094884c825e67))
+* restore previous behavior on live pipeline + audio downscaler ([#561](https://github.com/Monadical-SAS/reflector/issues/561)) ([9265d20](https://github.com/Monadical-SAS/reflector/commit/9265d201b590d23c628c5f19251b70f473859043))
+
+## [0.7.2](https://github.com/Monadical-SAS/reflector/compare/v0.7.1...v0.7.2) (2025-08-21)
+
+
+### Bug Fixes
+
+* docker image not loading libgomp.so.1 for torch ([#560](https://github.com/Monadical-SAS/reflector/issues/560)) ([773fccd](https://github.com/Monadical-SAS/reflector/commit/773fccd93e887c3493abc2e4a4864dddce610177))
+* include shared rooms to search ([#558](https://github.com/Monadical-SAS/reflector/issues/558)) ([499eced](https://github.com/Monadical-SAS/reflector/commit/499eced3360b84fb3a90e1c8a3b554290d21adc2))
+
+## [0.7.1](https://github.com/Monadical-SAS/reflector/compare/v0.7.0...v0.7.1) (2025-08-21)
+
+
+### Bug Fixes
+
+* webvtt db null expectation mismatch ([#556](https://github.com/Monadical-SAS/reflector/issues/556)) ([e67ad1a](https://github.com/Monadical-SAS/reflector/commit/e67ad1a4a2054467bfeb1e0258fbac5868aaaf21))
+
+## [0.7.0](https://github.com/Monadical-SAS/reflector/compare/v0.6.1...v0.7.0) (2025-08-21)
+
+
+### Features
+
+* delete recording with transcript ([#547](https://github.com/Monadical-SAS/reflector/issues/547)) ([99cc984](https://github.com/Monadical-SAS/reflector/commit/99cc9840b3f5de01e0adfbfae93234042d706d13))
+* pipeline improvement with file processing, parakeet, silero-vad ([#540](https://github.com/Monadical-SAS/reflector/issues/540)) ([bcc29c9](https://github.com/Monadical-SAS/reflector/commit/bcc29c9e0050ae215f89d460e9d645aaf6a5e486))
+* postgresql migration and removal of sqlite in pytest ([#546](https://github.com/Monadical-SAS/reflector/issues/546)) ([cd1990f](https://github.com/Monadical-SAS/reflector/commit/cd1990f8f0fe1503ef5069512f33777a73a93d7f))
+* search backend ([#537](https://github.com/Monadical-SAS/reflector/issues/537)) ([5f9b892](https://github.com/Monadical-SAS/reflector/commit/5f9b89260c9ef7f3c921319719467df22830453f))
+* search frontend  ([#551](https://github.com/Monadical-SAS/reflector/issues/551)) ([3657242](https://github.com/Monadical-SAS/reflector/commit/365724271ca6e615e3425125a69ae2b46ce39285))
+
+
+### Bug Fixes
+
+* evaluation cli event wrap ([#536](https://github.com/Monadical-SAS/reflector/issues/536)) ([941c3db](https://github.com/Monadical-SAS/reflector/commit/941c3db0bdacc7b61fea412f3746cc5a7cb67836))
+* use structlog not logging ([#550](https://github.com/Monadical-SAS/reflector/issues/550)) ([27e2f81](https://github.com/Monadical-SAS/reflector/commit/27e2f81fda5232e53edc729d3e99c5ef03adbfe9))
+
+## [0.6.1](https://github.com/Monadical-SAS/reflector/compare/v0.6.0...v0.6.1) (2025-08-06)
+
+
+### Bug Fixes
+
+* delayed waveform loading ([#538](https://github.com/Monadical-SAS/reflector/issues/538)) ([ef64146](https://github.com/Monadical-SAS/reflector/commit/ef64146325d03f64dd9a1fe40234fb3e7e957ae2))
+
+## [0.6.0](https://github.com/Monadical-SAS/reflector/compare/v0.5.0...v0.6.0) (2025-08-05)
+
+
+### ⚠ BREAKING CHANGES
+
+* Configuration keys have changed. Update your .env file:
+    - TRANSCRIPT_MODAL_API_KEY → TRANSCRIPT_API_KEY
+    - LLM_MODAL_API_KEY → (removed, use TRANSCRIPT_API_KEY)
+    - Add DIARIZATION_API_KEY and TRANSLATE_API_KEY if using those services
+
+### Features
+
+* implement service-specific Modal API keys with auto processor pattern ([#528](https://github.com/Monadical-SAS/reflector/issues/528)) ([650befb](https://github.com/Monadical-SAS/reflector/commit/650befb291c47a1f49e94a01ab37d8fdfcd2b65d))
+* use llamaindex everywhere ([#525](https://github.com/Monadical-SAS/reflector/issues/525)) ([3141d17](https://github.com/Monadical-SAS/reflector/commit/3141d172bc4d3b3d533370c8e6e351ea762169bf))
+
+
+### Miscellaneous Chores
+
+* **main:** release 0.6.0 ([ecdbf00](https://github.com/Monadical-SAS/reflector/commit/ecdbf003ea2476c3e95fd231adaeb852f2943df0))
+
 ## [0.5.0](https://github.com/Monadical-SAS/reflector/compare/v0.4.0...v0.5.0) (2025-07-31)
 
 

CLAUDE.md

@@ -62,7 +62,7 @@ uv run python -m reflector.tools.process path/to/audio.wav
 **Setup:**
 ```bash
 # Install dependencies
-yarn install
+pnpm install
 
 # Copy configuration templates
 cp .env_template .env
@@ -72,19 +72,19 @@ cp config-template.ts config.ts
 **Development:**
 ```bash
 # Start development server
-yarn dev
+pnpm dev
 
 # Generate TypeScript API client from OpenAPI spec
-yarn openapi
+pnpm openapi
 
 # Lint code
-yarn lint
+pnpm lint
 
 # Format code
-yarn format
+pnpm format
 
 # Build for production
-yarn build
+pnpm build
 ```
 
 ### Docker Compose (Full Stack)
@@ -144,7 +144,9 @@ All endpoints prefixed `/v1/`:
 **Backend** (`server/.env`):
 - `DATABASE_URL` - Database connection string
 - `REDIS_URL` - Redis broker for Celery
-- `MODAL_TOKEN_ID`, `MODAL_TOKEN_SECRET` - Modal.com GPU processing
+- `TRANSCRIPT_BACKEND=modal` + `TRANSCRIPT_MODAL_API_KEY` - Modal.com transcription
+- `DIARIZATION_BACKEND=modal` + `DIARIZATION_MODAL_API_KEY` - Modal.com diarization
+- `TRANSLATION_BACKEND=modal` + `TRANSLATION_MODAL_API_KEY` - Modal.com translation
 - `WHEREBY_API_KEY` - Video platform integration
 - `REFLECTOR_AUTH_BACKEND` - Authentication method (none, jwt)
 

IMPLEMENTATION_STATUS.md

@@ -1,264 +0,0 @@
-# Daily.co Migration Implementation Status
-
-## Completed Components
-
-### 1. Platform Abstraction Layer (`server/reflector/video_platforms/`)
-- **base.py**: Abstract interface defining all platform operations
-- **whereby.py**: Whereby implementation wrapping existing functionality
-- **daily.py**: Daily.co client implementation (ready for testing when credentials available)
-- **mock.py**: Mock implementation for unit testing
-- **registry.py**: Platform registration and discovery
-- **factory.py**: Factory methods for creating platform clients
-
-### 2. Database Updates
-- **Models**: Added `platform` field to Room and Meeting tables
-- **Migration**: Created migration `20250801180012_add_platform_support.py`
-- **Controllers**: Updated to handle platform field
-
-### 3. Configuration
-- **Settings**: Added Daily.co configuration variables
-- **Feature Flags**:
-  - `DAILY_MIGRATION_ENABLED`: Master switch for migration
-  - `DAILY_MIGRATION_ROOM_IDS`: List of specific rooms to migrate
-  - `DEFAULT_VIDEO_PLATFORM`: Default platform when migration enabled
-
-### 4. Backend API Updates
-- **Room Creation**: Now assigns platform based on feature flags
-- **Meeting Creation**: Uses platform abstraction instead of direct Whereby calls
-- **Response Models**: Include platform field
-- **Webhook Handler**: Added Daily.co webhook endpoint at `/v1/daily_webhook`
-
-### 5. Frontend Components (`www/app/[roomName]/components/`)
-- **RoomContainer.tsx**: Platform-agnostic container that routes to appropriate component
-- **WherebyRoom.tsx**: Extracted existing Whereby functionality with consent management
-- **DailyRoom.tsx**: Daily.co implementation using DailyIframe
-- **Dependencies**: Added `@daily-co/daily-js` and `@daily-co/daily-react`
-
-## How It Works
-
-1. **Platform Selection**:
-   - If `DAILY_MIGRATION_ENABLED=false` → Always use Whereby
-   - If enabled and room ID in `DAILY_MIGRATION_ROOM_IDS` → Use Daily
-   - Otherwise → Use `DEFAULT_VIDEO_PLATFORM`
-
-2. **Meeting Creation Flow**:
-   ```python
-   platform = get_platform_for_room(room.id)
-   client = create_platform_client(platform)
-   meeting_data = await client.create_meeting(...)
-   ```
-
-3. **Testing Without Credentials**:
-   - Use `platform="mock"` in tests
-   - Mock client simulates all operations
-   - No external API calls needed
-
-## Next Steps
-
-### When Daily.co Credentials Available:
-
-1. **Set Environment Variables**:
-   ```bash
-   DAILY_API_KEY=your-key
-   DAILY_WEBHOOK_SECRET=your-secret
-   DAILY_SUBDOMAIN=your-subdomain
-   AWS_DAILY_S3_BUCKET=your-bucket
-   AWS_DAILY_ROLE_ARN=your-role
-   ```
-
-2. **Run Database Migration**:
-   ```bash
-   cd server
-   uv run alembic upgrade head
-   ```
-
-3. **Test Platform Creation**:
-   ```python
-   from reflector.video_platforms.factory import create_platform_client
-   client = create_platform_client("daily")
-   # Test operations...
-   ```
-
-### 6. Testing & Validation (`server/tests/`)
-- **test_video_platforms.py**: Comprehensive unit tests for all platform clients
-- **test_daily_webhook.py**: Integration tests for Daily.co webhook handling
-- **utils/video_platform_test_utils.py**: Testing utilities and helpers
-- **Mock Testing**: Full test coverage using mock platform client
-- **Webhook Testing**: HMAC signature validation and event processing tests
-
-### All Core Implementation Complete ✅
-
-The Daily.co migration implementation is now complete and ready for testing with actual credentials:
-
-- ✅ Platform abstraction layer with factory pattern
-- ✅ Database schema migration
-- ✅ Feature flag system for gradual rollout
-- ✅ Backend API integration with webhook handling
-- ✅ Frontend platform-agnostic components
-- ✅ Comprehensive test suite with >95% coverage
-
-## Daily.co Webhook Integration
-
-### Webhook Configuration
-
-Daily.co webhooks are configured via API (no dashboard interface). Use the Daily.co REST API to set up webhook endpoints:
-
-```bash
-# Configure webhook endpoint
-curl -X POST https://api.daily.co/v1/webhook-endpoints \
-  -H "Authorization: Bearer ${DAILY_API_KEY}" \
-  -H "Content-Type: application/json" \
-  -d '{
-    "url": "https://yourdomain.com/v1/daily_webhook",
-    "events": [
-      "participant.joined",
-      "participant.left",
-      "recording.started",
-      "recording.ready-to-download",
-      "recording.error"
-    ]
-  }'
-```
-
-### Webhook Event Examples
-
-**Participant Joined:**
-```json
-{
-  "type": "participant.joined",
-  "id": "evt_participant_joined_1640995200",
-  "ts": 1640995200000,
-  "data": {
-    "room": {"name": "test-room-123-abc"},
-    "participant": {
-      "id": "participant-123",
-      "user_name": "John Doe",
-      "session_id": "session-456"
-    }
-  }
-}
-```
-
-**Recording Ready:**
-```json
-{
-  "type": "recording.ready-to-download",
-  "id": "evt_recording_ready_1640995200",
-  "ts": 1640995200000,
-  "data": {
-    "room": {"name": "test-room-123-abc"},
-    "recording": {
-      "id": "recording-789",
-      "status": "finished",
-      "download_url": "https://bucket.s3.amazonaws.com/recording.mp4",
-      "start_time": "2025-01-01T10:00:00Z",
-      "duration": 1800
-    }
-  }
-}
-```
-
-### Webhook Signature Verification
-
-Daily.co uses HMAC-SHA256 for webhook verification:
-
-```python
-import hmac
-import hashlib
-
-def verify_daily_webhook(body: bytes, signature: str, secret: str) -> bool:
-    expected = hmac.new(secret.encode(), body, hashlib.sha256).hexdigest()
-    return hmac.compare_digest(expected, signature)
-```
-
-Signature is sent in the `X-Daily-Signature` header.
-
-### Recording Processing Flow
-
-1. **Daily.co Meeting Ends** → Recording processed
-2. **Webhook Fired** → `recording.ready-to-download` event
-3. **Webhook Handler** → Extracts download URL and recording ID
-4. **Background Task** → `process_recording_from_url.delay()` queued
-5. **Download & Process** → Audio downloaded, validated, transcribed
-6. **ML Pipeline** → Same processing as Whereby recordings
-
-```python
-# New Celery task for Daily.co recordings
-@shared_task
-@asynctask
-async def process_recording_from_url(recording_url: str, meeting_id: str, recording_id: str):
-    # Downloads from Daily.co URL → Creates transcript → Triggers ML pipeline
-    # Identical processing to S3-based recordings after download
-```
-
-## Testing the Current Implementation
-
-### Running the Test Suite
-
-```bash
-# Run all video platform tests
-uv run pytest tests/test_video_platforms.py -v
-
-# Run webhook integration tests
-uv run pytest tests/test_daily_webhook.py -v
-
-# Run with coverage
-uv run pytest tests/test_video_platforms.py tests/test_daily_webhook.py --cov=reflector.video_platforms --cov=reflector.views.daily
-```
-
-### Manual Testing with Mock Platform
-
-```python
-from reflector.video_platforms.factory import create_platform_client
-
-# Create mock client (no credentials needed)
-client = create_platform_client("mock")
-
-# Test operations
-from reflector.db.rooms import Room
-from datetime import datetime, timedelta
-
-mock_room = Room(id="test-123", name="Test Room", recording_type="cloud")
-meeting = await client.create_meeting(
-    room_name_prefix="test",
-    end_date=datetime.utcnow() + timedelta(hours=1),
-    room=mock_room
-)
-print(f"Created meeting: {meeting.room_url}")
-```
-
-### Testing Daily.co Recording Processing
-
-```python
-# Test webhook payload processing
-from reflector.views.daily import daily_webhook
-from reflector.worker.process import process_recording_from_url
-
-# Simulate webhook event
-event_data = {
-    "type": "recording.ready-to-download",
-    "id": "evt_123",
-    "ts": 1640995200000,
-    "data": {
-        "room": {"name": "test-room-123"},
-        "recording": {
-            "id": "rec-456",
-            "download_url": "https://daily.co/recordings/test.mp4"
-        }
-    }
-}
-
-# Test processing task (when credentials available)
-await process_recording_from_url(
-    recording_url="https://daily.co/recordings/test.mp4",
-    meeting_id="meeting-123",
-    recording_id="rec-456"
-)
-```
-
-## Architecture Benefits
-
-1. **Testable**: Mock implementation allows testing without external dependencies
-2. **Extensible**: Easy to add new platforms (Zoom, Teams, etc.)
-3. **Gradual Migration**: Feature flags enable room-by-room migration
-4. **Rollback Ready**: Can disable Daily.co instantly via feature flag

PLAN.md

@@ -1,287 +0,0 @@
-# Daily.co Migration Plan - Feature Parity Approach
-
-## Overview
-
-This plan outlines a systematic migration from Whereby to Daily.co, focusing on **1:1 feature parity** without introducing new capabilities. The goal is to improve code quality, developer experience, and platform reliability while maintaining the exact same user experience and processing pipeline.
-
-## Migration Principles
-
-1. **No Breaking Changes**: Existing recordings and workflows must continue to work
-2. **Feature Parity First**: Match current functionality exactly before adding improvements
-3. **Gradual Rollout**: Use feature flags to control migration per room/user
-4. **Minimal Risk**: Keep changes isolated and reversible
-
-## Phase 1: Foundation
-
-### 1.1 Environment Setup
-**Owner**: Backend Developer
-
-- [ ] Create Daily.co account and obtain API credentials (PENDING - User to provide)
-- [x] Add environment variables to `.env` files:
-  ```bash
-  DAILY_API_KEY=your-api-key
-  DAILY_WEBHOOK_SECRET=your-webhook-secret
-  DAILY_SUBDOMAIN=your-subdomain
-  AWS_DAILY_ROLE_ARN=arn:aws:iam::xxx:role/daily-recording
-  ```
-- [ ] Set up Daily.co webhook endpoint in dashboard (PENDING - Credentials needed)
-- [ ] Configure S3 bucket permissions for Daily.co (PENDING - Credentials needed)
-
-### 1.2 Database Migration
-**Owner**: Backend Developer
-
-- [x] Create Alembic migration:
-  ```python
-  # server/migrations/versions/20250801180012_add_platform_support.py
-  def upgrade():
-      op.add_column('rooms', sa.Column('platform', sa.String(), server_default='whereby'))
-      op.add_column('meetings', sa.Column('platform', sa.String(), server_default='whereby'))
-  ```
-- [ ] Run migration on development database (USER TO RUN: `uv run alembic upgrade head`)
-- [x] Update models to include platform field
-
-### 1.3 Feature Flag System
-**Owner**: Full-stack Developer
-
-- [x] Implement feature flag in backend settings:
-  ```python
-  DAILY_MIGRATION_ENABLED = env.bool("DAILY_MIGRATION_ENABLED", False)
-  DAILY_MIGRATION_ROOM_IDS = env.list("DAILY_MIGRATION_ROOM_IDS", [])
-  ```
-- [x] Add platform selection logic to room creation
-- [ ] Create admin UI to toggle platform per room (FUTURE - Not in Phase 1)
-
-### 1.4 Daily.co API Client
-**Owner**: Backend Developer
-
-- [x] Create `server/reflector/video_platforms/` with core functionality:
-  - `create_meeting()` - Match Whereby's meeting creation
-  - `get_room_sessions()` - Room status checking
-  - `delete_room()` - Cleanup functionality
-- [x] Add comprehensive error handling
-- [ ] Write unit tests for API client (Phase 4)
-
-## Phase 2: Backend Integration
-
-### 2.1 Webhook Handler
-**Owner**: Backend Developer
-
-- [x] Create `server/reflector/views/daily.py` webhook endpoint
-- [x] Implement HMAC signature verification
-- [x] Handle events:
-  - `participant.joined`
-  - `participant.left`
-  - `recording.started`
-  - `recording.ready-to-download`
-- [x] Map Daily.co events to existing database updates
-- [x] Register webhook router in main app
-- [ ] Add webhook tests with mocked events (Phase 4)
-
-### 2.2 Room Management Updates
-**Owner**: Backend Developer
-
-- [x] Update `server/reflector/views/rooms.py`:
-  ```python
-  # Uses platform abstraction layer
-  platform = get_platform_for_room(room.id)
-  client = create_platform_client(platform)
-  meeting_data = await client.create_meeting(...)
-  ```
-- [x] Ensure room URLs are stored correctly
-- [x] Update meeting status checks to support both platforms
-- [ ] Test room creation/deletion for both platforms (Phase 4)
-
-## Phase 3: Frontend Migration
-
-### 3.1 Daily.co React Setup
-**Owner**: Frontend Developer
-
-- [x] Install Daily.co packages:
-  ```bash
-  yarn add @daily-co/daily-react @daily-co/daily-js
-  ```
-- [x] Create platform-agnostic components structure
-- [x] Set up TypeScript interfaces for meeting data
-
-### 3.2 Room Component Refactor
-**Owner**: Frontend Developer
-
-- [x] Create platform-agnostic room component:
-  ```tsx
-  // www/app/[roomName]/components/RoomContainer.tsx
-  export default function RoomContainer({ params }) {
-    const platform = meeting.response.platform || "whereby";
-    if (platform === 'daily') {
-      return <DailyRoom meeting={meeting.response} />
-    }
-    return <WherebyRoom meeting={meeting.response} />
-  }
-  ```
-- [x] Implement `DailyRoom` component with:
-  - Call initialization using DailyIframe
-  - Recording consent flow
-  - Leave meeting handling
-- [x] Extract `WherebyRoom` component maintaining existing functionality
-- [x] Simplified focus management (Daily.co handles this internally)
-
-### 3.3 Consent Dialog Integration
-**Owner**: Frontend Developer
-
-- [x] Adapt consent dialog for Daily.co (uses same API endpoints)
-- [x] Ensure recording status is properly tracked
-- [x] Maintain consistent consent UI across both platforms
-- [ ] Test consent flow with Daily.co recordings (Phase 4)
-
-## Phase 4: Testing & Validation
-
-### 4.1 Unit Testing ✅
-**Owner**: Backend Developer
-
-- [x] Create comprehensive unit tests for all platform clients
-- [x] Test mock platform client with full coverage
-- [x] Test platform factory and registry functionality
-- [x] Test webhook signature verification for all platforms
-- [x] Test meeting lifecycle operations (create, delete, sessions)
-
-### 4.2 Integration Testing ✅
-**Owner**: Backend Developer
-
-- [x] Create webhook integration tests with mocked HTTP client
-- [x] Test Daily.co webhook event processing
-- [x] Test participant join/leave event handling
-- [x] Test recording start/ready event processing
-- [x] Test webhook signature validation with HMAC
-- [x] Test error handling for malformed events
-
-### 4.3 Test Utilities ✅
-**Owner**: Backend Developer
-
-- [x] Create video platform test helper utilities
-- [x] Create webhook event generators for testing
-- [x] Create platform-agnostic test scenarios
-- [x] Implement mock data factories for consistent testing
-
-### 4.4 Ready for Live Testing
-**Owner**: QA + Development Team
-
-- [ ] Test complete flow with actual Daily.co credentials:
-  - Room creation
-  - Join meeting
-  - Recording consent
-  - Recording to S3
-  - Webhook processing
-  - Transcript generation
-- [ ] Verify S3 paths are compatible
-- [ ] Check recording format (MP4) matches
-- [ ] Ensure processing pipeline works unchanged
-
-## Phase 5: Gradual Rollout
-
-### 5.1 Internal Testing
-**Owner**: Development Team
-
-- [ ] Enable Daily.co for internal test rooms
-- [ ] Monitor logs and error rates
-- [ ] Fix any issues discovered
-- [ ] Verify recordings process correctly
-
-### 5.2 Beta Rollout
-**Owner**: DevOps + Product
-
-- [ ] Select beta users/rooms
-- [ ] Enable Daily.co via feature flag
-- [ ] Monitor metrics:
-  - Error rates
-  - Recording success
-  - User feedback
-- [ ] Create rollback plan
-
-### 5.3 Full Migration
-**Owner**: DevOps + Product
-
-- [ ] Gradually increase Daily.co usage
-- [ ] Monitor all metrics
-- [ ] Plan Whereby sunset timeline
-- [ ] Update documentation
-
-## Success Criteria
-
-### Technical Metrics
-- [x] Comprehensive test coverage (>95% for platform abstraction)
-- [x] Mock testing confirms API integration patterns work
-- [x] Webhook processing tested with realistic event payloads
-- [x] Error handling validated for all failure scenarios
-- [ ] Live API error rate < 0.1% (pending credentials)
-- [ ] Live webhook delivery rate > 99.9% (pending credentials)
-- [ ] Recording success rate matches Whereby (pending credentials)
-
-### User Experience
-- [x] Platform-agnostic components maintain existing UX
-- [x] Recording consent flow preserved across platforms
-- [x] Participant tracking architecture unchanged
-- [ ] Live call quality validation (pending credentials)
-- [ ] Live user acceptance testing (pending credentials)
-
-### Code Quality ✅
-- [x] Removed 70+ lines of focus management code in WherebyRoom extraction
-- [x] Improved TypeScript coverage with platform interfaces
-- [x] Better error handling with platform abstraction
-- [x] Cleaner React component structure with platform routing
-
-## Rollback Plan
-
-If issues arise during migration:
-
-1. **Immediate**: Disable Daily.co feature flag
-2. **Short-term**: Revert frontend components via git
-3. **Database**: Platform field defaults to 'whereby'
-4. **Full rollback**: Remove Daily.co code (isolated in separate files)
-
-## Post-Migration Opportunities
-
-Once feature parity is achieved and stable:
-
-1. **Raw-tracks recording** for better diarization
-2. **Real-time transcription** via Daily.co API
-3. **Advanced analytics** and participant insights
-4. **Custom UI** improvements
-5. **Performance optimizations**
-
-## Phase Dependencies
-
-- ✅ Backend Integration requires Foundation to be complete
-- ✅ Frontend Migration can start after Backend API client is ready
-- ✅ Testing requires both Backend and Frontend to be complete
-- ⏳ Rollout begins after successful testing (pending Daily.co credentials)
-
-## Risk Matrix
-
-| Risk | Probability | Impact | Mitigation |
-|------|-------------|---------|------------|
-| API differences | Low | Medium | Abstraction layer |
-| Recording format issues | Low | High | Extensive testing |
-| User confusion | Low | Low | Gradual rollout |
-| Performance degradation | Low | Medium | Monitoring |
-
-## Communication Plan
-
-1. **Week 1**: Announce migration plan to team
-2. **Week 2**: Update on development progress
-3. **Beta Launch**: Email to beta users
-4. **Full Launch**: User notification (if UI changes)
-5. **Post-Launch**: Success metrics report
-
----
-
-## Implementation Status: COMPLETE ✅
-
-All development phases are complete and ready for live testing:
-
-✅ **Phase 1**: Foundation (database, config, feature flags)
-✅ **Phase 2**: Backend Integration (API clients, webhooks)
-✅ **Phase 3**: Frontend Migration (platform components)
-✅ **Phase 4**: Testing & Validation (comprehensive test suite)
-
-**Next Steps**: Obtain Daily.co credentials and run live integration testing before gradual rollout.
-
-This implementation prioritizes stability and risk mitigation through a phased approach. The modular design allows for easy adjustments based on live testing findings.

README.md

@@ -1,43 +1,60 @@
 <div align="center">
+<img width="100" alt="image" src="https://github.com/user-attachments/assets/66fb367b-2c89-4516-9912-f47ac59c6a7f"/>
 
 # Reflector
 
-Reflector Audio Management and Analysis is a cutting-edge web application under development by Monadical. It utilizes AI to record meetings, providing a permanent record with transcripts, translations, and automated summaries.
+Reflector is an AI-powered audio transcription and meeting analysis platform that provides real-time transcription, speaker diarization, translation and summarization for audio content and live meetings. It works 100% with local models (whisper/parakeet, pyannote, seamless-m4t, and your local llm like phi-4).
 
-[![Tests](https://github.com/monadical-sas/reflector/actions/workflows/pytests.yml/badge.svg?branch=main&event=push)](https://github.com/monadical-sas/reflector/actions/workflows/pytests.yml)
+[![Tests](https://github.com/monadical-sas/reflector/actions/workflows/test_server.yml/badge.svg?branch=main&event=push)](https://github.com/monadical-sas/reflector/actions/workflows/test_server.yml)
 [![License: MIT](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/licenses/MIT)
 </div>
-
-## Screenshots
+</div>
 <table>
   <tr>
     <td>
-      <a href="https://github.com/user-attachments/assets/3a976930-56c1-47ef-8c76-55d3864309e3">
-        <img width="700" alt="image" src="https://github.com/user-attachments/assets/3a976930-56c1-47ef-8c76-55d3864309e3" />
+      <a href="https://github.com/user-attachments/assets/21f5597c-2930-4899-a154-f7bd61a59e97">
+        <img width="700" alt="image" src="https://github.com/user-attachments/assets/21f5597c-2930-4899-a154-f7bd61a59e97" />
       </a>
     </td>
     <td>
-      <a href="https://github.com/user-attachments/assets/bfe3bde3-08af-4426-a9a1-11ad5cd63b33">
-        <img width="700" alt="image" src="https://github.com/user-attachments/assets/bfe3bde3-08af-4426-a9a1-11ad5cd63b33" />
+      <a href="https://github.com/user-attachments/assets/f6b9399a-5e51-4bae-b807-59128d0a940c">
+        <img width="700" alt="image" src="https://github.com/user-attachments/assets/f6b9399a-5e51-4bae-b807-59128d0a940c" />
       </a>
     </td>
     <td>
-      <a href="https://github.com/user-attachments/assets/7b60c9d0-efe4-474f-a27b-ea13bd0fabdc">
-        <img width="700" alt="image" src="https://github.com/user-attachments/assets/7b60c9d0-efe4-474f-a27b-ea13bd0fabdc" />
+      <a href="https://github.com/user-attachments/assets/a42ce460-c1fd-4489-a995-270516193897">
+        <img width="700" alt="image" src="https://github.com/user-attachments/assets/a42ce460-c1fd-4489-a995-270516193897" />
+      </a>
+    </td>
+    <td>
+      <a href="https://github.com/user-attachments/assets/21929f6d-c309-42fe-9c11-f1299e50fbd4">
+        <img width="700" alt="image" src="https://github.com/user-attachments/assets/21929f6d-c309-42fe-9c11-f1299e50fbd4" />
       </a>
     </td>
   </tr>
 </table>
 
+## What is Reflector?
+
+Reflector is a web application that utilizes AI to process audio content, providing:
+
+- **Real-time Transcription**: Convert speech to text using [Whisper](https://github.com/openai/whisper) (multi-language) or [Parakeet](https://huggingface.co/nvidia/parakeet-tdt-0.6b-v2) (English) models
+- **Speaker Diarization**: Identify and label different speakers using [Pyannote](https://github.com/pyannote/pyannote-audio) 3.1
+- **Live Translation**: Translate audio content in real-time to many languages with [Facebook Seamless-M4T](https://github.com/facebookresearch/seamless_communication)
+- **Topic Detection & Summarization**: Extract key topics and generate concise summaries using LLMs
+- **Meeting Recording**: Create permanent records of meetings with searchable transcripts
+
+Currently we provide [modal.com](https://modal.com/) gpu template to deploy.
+
 ## Background
 
 The project architecture consists of three primary components:
 
-- **Front-End**: NextJS React project hosted on Vercel, located in `www/`.
 - **Back-End**: Python server that offers an API and data persistence, found in `server/`.
-- **GPU implementation**: Providing services such as speech-to-text transcription, topic generation, automated summaries, and translations. Most reliable option is Modal deployment
+- **Front-End**: NextJS React project hosted on Vercel, located in `www/`.
+- **GPU implementation**: Providing services such as speech-to-text transcription, topic generation, automated summaries, and translations.
 
-It also uses authentik for authentication if activated, and Vercel for deployment and configuration of the front-end.
+It also uses authentik for authentication if activated.
 
 ## Contribution Guidelines
 
@@ -79,7 +96,7 @@ Start with `cd www`.
 **Installation**
 
 ```bash
-yarn install
+pnpm install
 cp .env_template .env
 cp config-template.ts config.ts
 ```
@@ -89,7 +106,7 @@ Then, fill in the environment variables in `.env` and the configuration in `conf
 **Run in development mode**
 
 ```bash
-yarn dev
+pnpm dev
 ```
 
 Then (after completing server setup and starting it) open [http://localhost:3000](http://localhost:3000) to view it in the browser.
@@ -99,7 +116,7 @@ Then (after completing server setup and starting it) open [http://localhost:3000
 To generate the TypeScript files from the openapi.json file, make sure the python server is running, then run:
 
 ```bash
-yarn openapi
+pnpm openapi
 ```
 
 ### Backend

REFACTOR_WHEREBY_FINDING.md

@@ -1,586 +0,0 @@
-# Whereby to Daily.co Migration Feasibility Analysis
-
-## Executive Summary
-
-After analysis of the current Whereby integration and Daily.co's capabilities, migrating to Daily.co is technically feasible. The migration can be done in phases:
-
-1. **Phase 1**: Feature parity with current implementation (standard cloud recording)
-2. **Phase 2**: Enhanced capabilities with raw-tracks recording for improved diarization
-
-### Current Implementation Analysis
-
-Based on code review:
-- **Webhook handling**: The current webhook handler (`server/reflector/views/whereby.py`) only tracks `num_clients`, not individual participants
-- **Focus management**: The frontend has 70+ lines managing focus between Whereby embed and consent dialog
-- **Participant tracking**: No participant names or IDs are captured in the current implementation
-- **Recording type**: Cloud recording to S3 in MP4 format with mixed audio
-
-### Migration Approach
-
-**Phase 1**: 1:1 feature replacement maintaining current functionality:
-- Standard cloud recording (same as current Whereby implementation)
-- Same recording workflow: Video platform → S3 → Reflector processing
-- No changes to existing diarization or transcription pipeline
-
-**Phase 2**: Enhanced capabilities (future implementation):
-- Raw-tracks recording for speaker-separated audio
-- Improved diarization with participant-to-audio mapping
-- Per-participant transcription accuracy
-
-## Current Whereby Integration Analysis
-
-### Backend Integration
-
-#### Core API Module (`server/reflector/whereby.py`)
-- **Meeting Creation**: Creates rooms with S3 recording configuration
-- **Session Monitoring**: Tracks meeting status via room sessions API
-- **Logo Upload**: Handles branding for meetings
-- **Key Functions**:
-  ```python
-  create_meeting(room_name, logo_s3_url) -> dict
-  monitor_room_session(meeting_link) -> dict
-  upload_logo(file_stream, content_type) -> str
-  ```
-
-#### Webhook Handler (`server/reflector/views/whereby.py`)
-- **Endpoint**: `/v1/whereby_webhook`
-- **Security**: HMAC signature validation
-- **Events Handled**:
-  - `room.participant.joined`
-  - `room.participant.left`
-- **Pain Point**: Delay between actual join/leave and webhook delivery
-
-#### Room Management (`server/reflector/views/rooms.py`)
-- Creates meetings via Whereby API
-- Stores meeting data in database
-- Manages recording lifecycle
-
-### Frontend Integration
-
-#### Main Room Component (`www/app/[roomName]/page.tsx`)
-- Uses `@whereby.com/browser-sdk` (v3.3.4)
-- Implements custom `<whereby-embed>` element
-- Handles recording consent
-- Focus management for accessibility
-
-#### Configuration
-- Environment Variables:
-  - `WHEREBY_API_URL`, `WHEREBY_API_KEY`, `WHEREBY_WEBHOOK_SECRET`
-  - AWS S3 credentials for recordings
-- Recording workflow: Whereby → S3 → Reflector processing pipeline
-
-## Daily.co Capabilities Analysis
-
-### REST API Features
-
-#### Room Management
-```
-POST /rooms - Create room with configuration
-GET /rooms/:name/presence - Real-time participant data
-POST /rooms/:name/recordings/start - Start recording
-```
-
-#### Recording Options
-```json
-{
-  "enable_recording": "raw-tracks"  // Key feature for diarization
-}
-```
-
-#### Webhook Events
-- `participant.joined` / `participant.left`
-- `waiting-participant.joined` / `waiting-participant.left`
-- `recording.started` / `recording.ready-to-download`
-- `recording.error`
-
-### React SDK (@daily-co/daily-react)
-
-#### Modern Hook-based Architecture
-```jsx
-// Participant tracking
-const participantIds = useParticipantIds({ filter: 'remote' });
-const [username, videoState] = useParticipantProperty(id, ['user_name', 'tracks.video.state']);
-
-// Recording management
-const { isRecording, startRecording, stopRecording } = useRecording();
-
-// Real-time participant data
-const participants = useParticipants();
-```
-
-## Feature Comparison
-
-| Feature | Whereby | Daily.co |
-|---------|---------|----------|
-| **Room Creation** | REST API | REST API |
-| **Recording Types** | Cloud (MP4) | Cloud (MP4), Local, Raw-tracks |
-| **S3 Integration** | Direct upload | Direct upload with IAM roles |
-| **Frontend Integration** | Custom element | React hooks or iframe |
-| **Webhooks** | HMAC verified | HMAC verified |
-| **Participant Data** | Via webhooks | Via webhooks + Presence API |
-| **Recording Trigger** | Automatic/manual | Automatic/manual |
-
-## Migration Plan
-
-### Phase 1: Backend API Client
-
-#### 1.1 Create Daily.co API Client (`server/reflector/daily.py`)
-
-```python
-from datetime import datetime
-import httpx
-from reflector.db.rooms import Room
-from reflector.settings import settings
-
-class DailyClient:
-    def __init__(self):
-        self.base_url = "https://api.daily.co/v1"
-        self.headers = {
-            "Authorization": f"Bearer {settings.DAILY_API_KEY}",
-            "Content-Type": "application/json"
-        }
-        self.timeout = 10
-
-    async def create_meeting(self, room_name_prefix: str, end_date: datetime, room: Room) -> dict:
-        """Create a Daily.co room matching current Whereby functionality."""
-        data = {
-            "name": f"{room_name_prefix}-{datetime.now().strftime('%Y%m%d%H%M%S')}",
-            "privacy": "private" if room.is_locked else "public",
-            "properties": {
-                "enable_recording": "raw-tracks", #"cloud",
-                "enable_chat": True,
-                "enable_screenshare": True,
-                "start_video_off": False,
-                "start_audio_off": False,
-                "exp": int(end_date.timestamp()),
-                "enable_recording_ui": False,  # We handle consent ourselves
-            }
-        }
-
-        # if room.recording_type == "cloud":
-        data["properties"]["recording_bucket"] = {
-            "bucket_name": settings.AWS_S3_BUCKET,
-            "bucket_region": settings.AWS_REGION,
-            "assume_role_arn": settings.AWS_DAILY_ROLE_ARN,
-            "path": f"recordings/{data['name']}"
-        }
-
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                f"{self.base_url}/rooms",
-                headers=self.headers,
-                json=data,
-                timeout=self.timeout
-            )
-            response.raise_for_status()
-            room_data = response.json()
-
-            # Return in Whereby-compatible format
-            return {
-                "roomUrl": room_data["url"],
-                "hostRoomUrl": room_data["url"] + "?t=" + room_data["config"]["token"],
-                "roomName": room_data["name"],
-                "meetingId": room_data["id"]
-            }
-
-    async def get_room_sessions(self, room_name: str) -> dict:
-        """Get room session data (similar to Whereby's insights)."""
-        async with httpx.AsyncClient() as client:
-            response = await client.get(
-                f"{self.base_url}/rooms/{room_name}",
-                headers=self.headers,
-                timeout=self.timeout
-            )
-            response.raise_for_status()
-            return response.json()
-```
-
-#### 1.2 Update Webhook Handler (`server/reflector/views/daily.py`)
-
-```python
-import hmac
-import json
-from datetime import datetime
-from hashlib import sha256
-from fastapi import APIRouter, HTTPException, Request
-from pydantic import BaseModel
-from reflector.db.meetings import meetings_controller
-from reflector.settings import settings
-
-router = APIRouter()
-
-class DailyWebhookEvent(BaseModel):
-    type: str
-    id: str
-    ts: int
-    data: dict
-
-def verify_daily_webhook(body: bytes, signature: str) -> bool:
-    """Verify Daily.co webhook signature."""
-    expected = hmac.new(
-        settings.DAILY_WEBHOOK_SECRET.encode(),
-        body,
-        sha256
-    ).hexdigest()
-    return hmac.compare_digest(expected, signature)
-
-@router.post("/daily")
-async def daily_webhook(event: DailyWebhookEvent, request: Request):
-    # Verify webhook signature
-    body = await request.body()
-    signature = request.headers.get("X-Daily-Signature", "")
-
-    if not verify_daily_webhook(body, signature):
-        raise HTTPException(status_code=401, detail="Invalid webhook signature")
-
-    # Handle participant events
-    if event.type == "participant.joined":
-        meeting = await meetings_controller.get_by_room_name(event.data["room_name"])
-        if meeting:
-            # Update participant info immediately
-            await meetings_controller.add_participant(
-                meeting.id,
-                participant_id=event.data["participant"]["user_id"],
-                name=event.data["participant"]["user_name"],
-                joined_at=datetime.fromtimestamp(event.ts / 1000)
-            )
-
-    elif event.type == "participant.left":
-        meeting = await meetings_controller.get_by_room_name(event.data["room_name"])
-        if meeting:
-            await meetings_controller.remove_participant(
-                meeting.id,
-                participant_id=event.data["participant"]["user_id"],
-                left_at=datetime.fromtimestamp(event.ts / 1000)
-            )
-
-    elif event.type == "recording.ready-to-download":
-        # Process cloud recording (same as Whereby)
-        meeting = await meetings_controller.get_by_room_name(event.data["room_name"])
-        if meeting:
-            # Queue standard processing task
-            from reflector.worker.tasks import process_recording
-            process_recording.delay(
-                meeting_id=meeting.id,
-                recording_url=event.data["download_link"],
-                recording_id=event.data["recording_id"]
-            )
-
-    return {"status": "ok"}
-```
-
-### Phase 2: Frontend Components
-
-#### 2.1 Replace Whereby SDK with Daily React
-
-First, update dependencies:
-```bash
-# Remove Whereby
-yarn remove @whereby.com/browser-sdk
-
-# Add Daily.co
-yarn add @daily-co/daily-react @daily-co/daily-js
-```
-
-#### 2.2 New Room Component (`www/app/[roomName]/page.tsx`)
-
-```tsx
-"use client";
-
-import { useCallback, useEffect, useRef, useState } from "react";
-import {
-  DailyProvider,
-  useDaily,
-  useParticipantIds,
-  useRecording,
-  useDailyEvent,
-  useLocalParticipant,
-} from "@daily-co/daily-react";
-import { Box, Button, Text, VStack, HStack, Spinner } from "@chakra-ui/react";
-import { toaster } from "../components/ui/toaster";
-import useRoomMeeting from "./useRoomMeeting";
-import { useRouter } from "next/navigation";
-import { notFound } from "next/navigation";
-import useSessionStatus from "../lib/useSessionStatus";
-import { useRecordingConsent } from "../recordingConsentContext";
-import DailyIframe from "@daily-co/daily-js";
-
-// Daily.co Call Interface Component
-function CallInterface() {
-  const daily = useDaily();
-  const { isRecording, startRecording, stopRecording } = useRecording();
-  const localParticipant = useLocalParticipant();
-  const participantIds = useParticipantIds({ filter: "remote" });
-
-  // Real-time participant tracking
-  useDailyEvent("participant-joined", useCallback((event) => {
-    console.log(`${event.participant.user_name} joined the call`);
-    // No need for webhooks - we have immediate access!
-  }, []));
-
-  useDailyEvent("participant-left", useCallback((event) => {
-    console.log(`${event.participant.user_name} left the call`);
-  }, []));
-
-  return (
-    <Box position="relative" width="100vw" height="100vh">
-      {/* Daily.co automatically handles the video/audio UI */}
-      <Box
-        as="iframe"
-        src={daily?.iframe()?.src}
-        width="100%"
-        height="100%"
-        allow="camera; microphone; fullscreen; speaker; display-capture"
-        style={{ border: "none" }}
-      />
-
-      {/* Recording status indicator */}
-      {isRecording && (
-        <Box
-          position="absolute"
-          top={4}
-          right={4}
-          bg="red.500"
-          color="white"
-          px={3}
-          py={1}
-          borderRadius="md"
-          fontSize="sm"
-        >
-          Recording
-        </Box>
-      )}
-
-      {/* Participant count with real-time data */}
-      <Box position="absolute" bottom={4} left={4} bg="gray.800" color="white" px={3} py={1} borderRadius="md">
-        Participants: {participantIds.length + 1}
-      </Box>
-    </Box>
-  );
-}
-
-// Main Room Component with Daily.co Integration
-export default function Room({ params }: { params: { roomName: string } }) {
-  const roomName = params.roomName;
-  const meeting = useRoomMeeting(roomName);
-  const router = useRouter();
-  const { isLoading, isAuthenticated } = useSessionStatus();
-  const [dailyUrl, setDailyUrl] = useState<string | null>(null);
-  const [callFrame, setCallFrame] = useState<DailyIframe | null>(null);
-
-  // Initialize Daily.co call
-  useEffect(() => {
-    if (!meeting?.response?.room_url) return;
-
-    const frame = DailyIframe.createCallObject({
-      showLeaveButton: true,
-      showFullscreenButton: true,
-    });
-
-    frame.on("left-meeting", () => {
-      router.push("/browse");
-    });
-
-    setCallFrame(frame);
-    setDailyUrl(meeting.response.room_url);
-
-    return () => {
-      frame.destroy();
-    };
-  }, [meeting?.response?.room_url, router]);
-
-  if (isLoading) {
-    return (
-      <Box display="flex" justifyContent="center" alignItems="center" height="100vh">
-        <Spinner color="blue.500" size="xl" />
-      </Box>
-    );
-  }
-
-  if (!dailyUrl || !callFrame) {
-    return null;
-  }
-
-  return (
-    <DailyProvider callObject={callFrame} url={dailyUrl}>
-      <CallInterface />
-      <ConsentDialog meetingId={meeting?.response?.id} />
-    </DailyProvider>
-  );
-}
-
-### Phase 3: Testing & Validation
-
-For Phase 1 (feature parity), the existing processing pipeline remains unchanged:
-
-1. Daily.co records meeting to S3 (same as Whereby)
-2. Webhook notifies when recording is ready
-3. Existing pipeline downloads and processes the MP4 file
-4. Current diarization and transcription tools continue to work
-
-Key validation points:
-- Recording format matches (MP4 with mixed audio)
-- S3 paths are compatible
-- Processing pipeline requires no changes
-- Transcript quality remains the same
-
-## Future Enhancement: Raw-Tracks Recording (Phase 2)
-
-### Raw-Tracks Processing for Enhanced Diarization
-
-Daily.co's raw-tracks recording provides individual audio streams per participant, enabling:
-
-```python
-@shared_task
-def process_daily_raw_tracks(meeting_id: str, recording_id: str, tracks: list):
-    """Process Daily.co raw-tracks with perfect speaker attribution."""
-
-    for track in tracks:
-        participant_id = track["participant_id"]
-        participant_name = track["participant_name"]
-        track_url = track["download_url"]
-
-        # Download individual participant audio
-        response = download_track(track_url)
-
-        # Process with known speaker identity
-        transcript = transcribe_audio(
-            audio_data=response.content,
-            speaker_id=participant_id,
-            speaker_name=participant_name
-        )
-
-        # Store with accurate speaker mapping
-        save_transcript_segment(
-            meeting_id=meeting_id,
-            speaker_id=participant_id,
-            text=transcript.text,
-            timestamps=transcript.timestamps
-        )
-```
-
-### Benefits of Raw-Tracks (Future)
-
-1. **Deterministic Speaker Attribution**: Each audio track is already speaker-separated
-2. **Improved Transcription Accuracy**: Clean audio without cross-talk
-3. **Parallel Processing**: Process multiple speakers simultaneously
-4. **Better Metrics**: Accurate talk-time per participant
-
-### Phase 4: Database & Configuration
-
-#### 4.1 Environment Variable Updates
-
-Update `.env` files:
-
-```bash
-# Remove Whereby variables
-# WHEREBY_API_URL=https://api.whereby.dev/v1
-# WHEREBY_API_KEY=your-whereby-key
-# WHEREBY_WEBHOOK_SECRET=your-whereby-secret
-# AWS_WHEREBY_S3_BUCKET=whereby-recordings
-# AWS_WHEREBY_ACCESS_KEY_ID=whereby-key
-# AWS_WHEREBY_ACCESS_KEY_SECRET=whereby-secret
-
-# Add Daily.co variables
-DAILY_API_KEY=your-daily-api-key
-DAILY_WEBHOOK_SECRET=your-daily-webhook-secret
-AWS_DAILY_S3_BUCKET=daily-recordings
-AWS_DAILY_ROLE_ARN=arn:aws:iam::123456789:role/daily-recording-role
-AWS_REGION=us-west-2
-```
-
-#### 4.2 Database Migration
-
-```sql
--- Alembic migration to support Daily.co
--- server/alembic/versions/xxx_migrate_to_daily.py
-
-def upgrade():
-    # Add platform field to support gradual migration
-    op.add_column('rooms', sa.Column('platform', sa.String(), server_default='whereby'))
-    op.add_column('meetings', sa.Column('platform', sa.String(), server_default='whereby'))
-
-    # No other schema changes needed for feature parity
-
-def downgrade():
-    op.drop_column('meetings', 'platform')
-    op.drop_column('rooms', 'platform')
-```
-
-#### 4.3 Settings Update (`server/reflector/settings.py`)
-
-```python
-class Settings(BaseSettings):
-    # Remove Whereby settings
-    # WHEREBY_API_URL: str = "https://api.whereby.dev/v1"
-    # WHEREBY_API_KEY: str
-    # WHEREBY_WEBHOOK_SECRET: str
-    # AWS_WHEREBY_S3_BUCKET: str
-    # AWS_WHEREBY_ACCESS_KEY_ID: str
-    # AWS_WHEREBY_ACCESS_KEY_SECRET: str
-
-    # Add Daily.co settings
-    DAILY_API_KEY: str
-    DAILY_WEBHOOK_SECRET: str
-    AWS_DAILY_S3_BUCKET: str
-    AWS_DAILY_ROLE_ARN: str
-    AWS_REGION: str = "us-west-2"
-
-    # Daily.co room URL pattern
-    DAILY_ROOM_URL_PATTERN: str = "https://{subdomain}.daily.co/{room_name}"
-    DAILY_SUBDOMAIN: str = "reflector"  # Your Daily.co subdomain
-```
-
-## Technical Differences
-
-### Phase 1 Implementation
-1. **Frontend**: Replace `<whereby-embed>` custom element with Daily.co React components or iframe
-2. **Backend**: Create Daily.co API client matching Whereby's functionality
-3. **Webhooks**: Map Daily.co events to existing database operations
-4. **Recording**: Maintain same MP4 format and S3 storage
-
-### Phase 2 Capabilities (Future)
-1. **Raw-tracks recording**: Individual audio streams per participant
-2. **Presence API**: Real-time participant data without webhook delays
-3. **Transcription API**: Built-in transcription services
-4. **Advanced recording options**: Multiple formats and layouts
-
-## Risks and Mitigation
-
-### Risk 1: API Differences
-- **Mitigation**: Create abstraction layer to minimize changes
-- Comprehensive testing of all endpoints
-
-### Risk 2: Recording Format Changes
-- **Mitigation**: Build adapter for raw-tracks processing
-- Maintain backward compatibility during transition
-
-### Risk 3: User Experience Changes
-- **Mitigation**: A/B testing with gradual rollout
-- Feature parity checklist before full migration
-
-## Recommendation
-
-Migration to Daily.co is technically feasible and can be implemented in phases:
-
-### Phase 1: Feature Parity
-- Replace Whereby with Daily.co maintaining exact same functionality
-- Use standard cloud recording (MP4 to S3)
-- No changes to processing pipeline
-
-### Phase 2: Enhanced Capabilities (Future)
-- Enable raw-tracks recording for improved diarization
-- Implement participant-level audio processing
-- Add real-time features using Presence API
-
-## Next Steps
-
-1. Set up Daily.co account and obtain API credentials
-2. Implement feature flag system for gradual migration
-3. Create Daily.co API client matching Whereby functionality
-4. Update frontend to support both platforms
-5. Test thoroughly before rollout
-
----
-
-*Analysis based on current codebase review and API documentation comparison.*

compose.yml

@@ -39,11 +39,12 @@ services:
     image: node:18
     ports:
       - "3000:3000"
-    command: sh -c "yarn install && yarn dev"
+    command: sh -c "corepack enable && pnpm install && pnpm dev"
     restart: unless-stopped
     working_dir: /app
     volumes:
       - ./www:/app/
+      - /app/node_modules
     env_file:
       - ./www/.env.local
 

server/.gitignore

@@ -176,7 +176,8 @@ artefacts/
 audio_*.wav
 
 # ignore local database
-reflector.sqlite3
+*.sqlite3
+*.db
 data/
 
 dump.rdb

server/Dockerfile

@@ -1,7 +1,8 @@
 FROM python:3.12-slim
 
 ENV PYTHONUNBUFFERED=1 \
-    UV_LINK_MODE=copy
+    UV_LINK_MODE=copy \
+    UV_NO_CACHE=1
 
 # builder install base dependencies
 WORKDIR /tmp
@@ -13,8 +14,8 @@ ENV PATH="/root/.local/bin/:$PATH"
 # install application dependencies
 RUN mkdir -p /app
 WORKDIR /app
-COPY pyproject.toml uv.lock /app/
-RUN touch README.md && env uv sync --compile-bytecode --locked
+COPY pyproject.toml uv.lock README.md /app/
+RUN uv sync --compile-bytecode --locked
 
 # pre-download nltk packages
 RUN uv run python -c "import nltk; nltk.download('punkt_tab'); nltk.download('averaged_perceptron_tagger_eng')"
@@ -26,4 +27,15 @@ COPY migrations /app/migrations
 COPY reflector /app/reflector
 WORKDIR /app
 
+# Create symlink for libgomp if it doesn't exist (for ARM64 compatibility)
+RUN if [ "$(uname -m)" = "aarch64" ] && [ ! -f /usr/lib/libgomp.so.1 ]; then \
+    LIBGOMP_PATH=$(find /app/.venv/lib -path "*/torch.libs/libgomp*.so.*" 2>/dev/null | head -n1); \
+    if [ -n "$LIBGOMP_PATH" ]; then \
+    ln -sf "$LIBGOMP_PATH" /usr/lib/libgomp.so.1; \
+    fi \
+    fi
+
+# Pre-check just to make sure the image will not fail
+RUN uv run python -c "import silero_vad.model"
+
 CMD ["./runserver.sh"]

server/README.md

@@ -40,3 +40,5 @@ uv run python -c "from reflector.pipelines.main_live_pipeline import task_pipeli
 ```bash
 uv run python -c "from reflector.pipelines.main_live_pipeline import pipeline_post; pipeline_post(transcript_id='TRANSCRIPT_ID')"
 ```
+
+.

server/env.example

@@ -24,7 +24,6 @@ AUTH_JWT_AUDIENCE=
 ## Using serverless modal.com (require reflector-gpu-modal deployed)
 #TRANSCRIPT_BACKEND=modal
 #TRANSCRIPT_URL=https://xxxxx--reflector-transcriber-web.modal.run
-#TRANSLATE_URL=https://xxxxx--reflector-translator-web.modal.run
 #TRANSCRIPT_MODAL_API_KEY=xxxxx
 
 TRANSCRIPT_BACKEND=modal
@@ -32,11 +31,13 @@ TRANSCRIPT_URL=https://monadical-sas--reflector-transcriber-web.modal.run
 TRANSCRIPT_MODAL_API_KEY=
 
 ## =======================================================
-## Transcription backend
+## Translation backend
 ##
 ## Only available in modal atm
 ## =======================================================
+TRANSLATION_BACKEND=modal
 TRANSLATE_URL=https://monadical-sas--reflector-translator-web.modal.run
+#TRANSLATION_MODAL_API_KEY=xxxxx
 
 ## =======================================================
 ## LLM backend
@@ -59,7 +60,9 @@ LLM_API_KEY=sk-
 ## To allow diarization, you need to expose expose the files to be dowloded by the pipeline
 ## =======================================================
 DIARIZATION_ENABLED=false
+DIARIZATION_BACKEND=modal
 DIARIZATION_URL=https://monadical-sas--reflector-diarizer-web.modal.run
+#DIARIZATION_MODAL_API_KEY=xxxxx
 
 
 ## =======================================================

server/gpu/modal_deployments/README.md

@@ -4,7 +4,8 @@ This repository hold an API for the GPU implementation of the Reflector API serv
 and use [Modal.com](https://modal.com)
 
 - `reflector_diarizer.py` - Diarization API
-- `reflector_transcriber.py` - Transcription API
+- `reflector_transcriber.py` - Transcription API (Whisper)
+- `reflector_transcriber_parakeet.py` - Transcription API (NVIDIA Parakeet)
 - `reflector_translator.py` - Translation API
 
 ## Modal.com deployment
@@ -19,21 +20,29 @@ $ modal deploy reflector_transcriber.py
 ...
 └── 🔨 Created web => https://xxxx--reflector-transcriber-web.modal.run
 
+$ modal deploy reflector_transcriber_parakeet.py
+...
+└── 🔨 Created web => https://xxxx--reflector-transcriber-parakeet-web.modal.run
+
 $ modal deploy reflector_llm.py
 ...
 └── 🔨 Created web => https://xxxx--reflector-llm-web.modal.run
 ```
 
-Then in your reflector api configuration `.env`, you can set theses keys:
+Then in your reflector api configuration `.env`, you can set these keys:
 
 ```
 TRANSCRIPT_BACKEND=modal
 TRANSCRIPT_URL=https://xxxx--reflector-transcriber-web.modal.run
 TRANSCRIPT_MODAL_API_KEY=REFLECTOR_APIKEY
 
-LLM_BACKEND=modal
-LLM_URL=https://xxxx--reflector-llm-web.modal.run
-LLM_MODAL_API_KEY=REFLECTOR_APIKEY
+DIARIZATION_BACKEND=modal
+DIARIZATION_URL=https://xxxx--reflector-diarizer-web.modal.run
+DIARIZATION_MODAL_API_KEY=REFLECTOR_APIKEY
+
+TRANSLATION_BACKEND=modal
+TRANSLATION_URL=https://xxxx--reflector-translator-web.modal.run
+TRANSLATION_MODAL_API_KEY=REFLECTOR_APIKEY
 ```
 
 ## API
@@ -64,6 +73,86 @@ Authorization: bearer <REFLECTOR_APIKEY>
 
 ### Transcription
 
+#### Parakeet Transcriber (`reflector_transcriber_parakeet.py`)
+
+NVIDIA Parakeet is a state-of-the-art ASR model optimized for real-time transcription with superior word-level timestamps.
+
+**GPU Configuration:**
+- **A10G GPU** - Used for `/v1/audio/transcriptions` endpoint (small files, live transcription)
+  - Higher concurrency (max_inputs=10)
+  - Optimized for multiple small audio files
+  - Supports batch processing for efficiency
+
+- **L40S GPU** - Used for `/v1/audio/transcriptions-from-url` endpoint (large files)
+  - Lower concurrency but more powerful processing
+  - Optimized for single large audio files
+  - VAD-based chunking for long-form audio
+
+##### `/v1/audio/transcriptions` - Small file transcription
+
+**request** (multipart/form-data)
+- `file` or `files[]` - audio file(s) to transcribe
+- `model` - model name (default: `nvidia/parakeet-tdt-0.6b-v2`)
+- `language` - language code (default: `en`)
+- `batch` - whether to use batch processing for multiple files (default: `true`)
+
+**response**
+```json
+{
+    "text": "transcribed text",
+    "words": [
+        {"word": "hello", "start": 0.0, "end": 0.5},
+        {"word": "world", "start": 0.5, "end": 1.0}
+    ],
+    "filename": "audio.mp3"
+}
+```
+
+For multiple files with batch=true:
+```json
+{
+    "results": [
+        {
+            "filename": "audio1.mp3",
+            "text": "transcribed text",
+            "words": [...]
+        },
+        {
+            "filename": "audio2.mp3",
+            "text": "transcribed text",
+            "words": [...]
+        }
+    ]
+}
+```
+
+##### `/v1/audio/transcriptions-from-url` - Large file transcription
+
+**request** (application/json)
+```json
+{
+    "audio_file_url": "https://example.com/audio.mp3",
+    "model": "nvidia/parakeet-tdt-0.6b-v2",
+    "language": "en",
+    "timestamp_offset": 0.0
+}
+```
+
+**response**
+```json
+{
+    "text": "transcribed text from large file",
+    "words": [
+        {"word": "hello", "start": 0.0, "end": 0.5},
+        {"word": "world", "start": 0.5, "end": 1.0}
+    ]
+}
+```
+
+**Supported file types:** mp3, mp4, mpeg, mpga, m4a, wav, webm
+
+#### Whisper Transcriber (`reflector_transcriber.py`)
+
 `POST /transcribe`
 
 **request** (multipart/form-data)

server/gpu/modal_deployments/reflector_diarizer.py

@@ -4,14 +4,80 @@ Reflector GPU backend - diarizer
 """
 
 import os
+import uuid
+from typing import Mapping, NewType
+from urllib.parse import urlparse
 
-import modal.gpu
-from modal import App, Image, Secret, asgi_app, enter, method
-from pydantic import BaseModel
+import modal
 
 PYANNOTE_MODEL_NAME: str = "pyannote/speaker-diarization-3.1"
 MODEL_DIR = "/root/diarization_models"
-app = App(name="reflector-diarizer")
+UPLOADS_PATH = "/uploads"
+SUPPORTED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
+
+DiarizerUniqFilename = NewType("DiarizerUniqFilename", str)
+AudioFileExtension = NewType("AudioFileExtension", str)
+
+app = modal.App(name="reflector-diarizer")
+
+# Volume for temporary file uploads
+upload_volume = modal.Volume.from_name("diarizer-uploads", create_if_missing=True)
+
+
+def detect_audio_format(url: str, headers: Mapping[str, str]) -> AudioFileExtension:
+    parsed_url = urlparse(url)
+    url_path = parsed_url.path
+
+    for ext in SUPPORTED_FILE_EXTENSIONS:
+        if url_path.lower().endswith(f".{ext}"):
+            return AudioFileExtension(ext)
+
+    content_type = headers.get("content-type", "").lower()
+    if "audio/mpeg" in content_type or "audio/mp3" in content_type:
+        return AudioFileExtension("mp3")
+    if "audio/wav" in content_type:
+        return AudioFileExtension("wav")
+    if "audio/mp4" in content_type:
+        return AudioFileExtension("mp4")
+
+    raise ValueError(
+        f"Unsupported audio format for URL: {url}. "
+        f"Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
+    )
+
+
+def download_audio_to_volume(
+    audio_file_url: str,
+) -> tuple[DiarizerUniqFilename, AudioFileExtension]:
+    import requests
+    from fastapi import HTTPException
+
+    print(f"Checking audio file at: {audio_file_url}")
+    response = requests.head(audio_file_url, allow_redirects=True)
+    if response.status_code == 404:
+        raise HTTPException(status_code=404, detail="Audio file not found")
+
+    print(f"Downloading audio file from: {audio_file_url}")
+    response = requests.get(audio_file_url, allow_redirects=True)
+
+    if response.status_code != 200:
+        print(f"Download failed with status {response.status_code}: {response.text}")
+        raise HTTPException(
+            status_code=response.status_code,
+            detail=f"Failed to download audio file: {response.status_code}",
+        )
+
+    audio_suffix = detect_audio_format(audio_file_url, response.headers)
+    unique_filename = DiarizerUniqFilename(f"{uuid.uuid4()}.{audio_suffix}")
+    file_path = f"{UPLOADS_PATH}/{unique_filename}"
+
+    print(f"Writing file to: {file_path} (size: {len(response.content)} bytes)")
+    with open(file_path, "wb") as f:
+        f.write(response.content)
+
+    upload_volume.commit()
+    print(f"File saved as: {unique_filename}")
+    return unique_filename, audio_suffix
 
 
 def migrate_cache_llm():
@@ -39,7 +105,7 @@ def download_pyannote_audio():
 
 
 diarizer_image = (
-    Image.debian_slim(python_version="3.10.8")
+    modal.Image.debian_slim(python_version="3.10.8")
     .pip_install(
         "pyannote.audio==3.1.0",
         "requests",
@@ -55,7 +121,8 @@ diarizer_image = (
         "hf-transfer",
     )
     .run_function(
-        download_pyannote_audio, secrets=[Secret.from_name("my-huggingface-secret")]
+        download_pyannote_audio,
+        secrets=[modal.Secret.from_name("hf_token")],
     )
     .run_function(migrate_cache_llm)
     .env(
@@ -70,53 +137,60 @@ diarizer_image = (
 
 
 @app.cls(
-    gpu=modal.gpu.A100(size="40GB"),
+    gpu="A100",
     timeout=60 * 30,
-    scaledown_window=60,
-    allow_concurrent_inputs=1,
     image=diarizer_image,
+    volumes={UPLOADS_PATH: upload_volume},
+    enable_memory_snapshot=True,
+    experimental_options={"enable_gpu_snapshot": True},
+    secrets=[
+        modal.Secret.from_name("hf_token"),
+    ],
 )
+@modal.concurrent(max_inputs=1)
 class Diarizer:
-    @enter()
+    @modal.enter(snap=True)
     def enter(self):
         import torch
         from pyannote.audio import Pipeline
 
         self.use_gpu = torch.cuda.is_available()
         self.device = "cuda" if self.use_gpu else "cpu"
+        print(f"Using device: {self.device}")
         self.diarization_pipeline = Pipeline.from_pretrained(
-            PYANNOTE_MODEL_NAME, cache_dir=MODEL_DIR
+            PYANNOTE_MODEL_NAME,
+            cache_dir=MODEL_DIR,
+            use_auth_token=os.environ["HF_TOKEN"],
         )
         self.diarization_pipeline.to(torch.device(self.device))
 
-    @method()
-    def diarize(self, audio_data: str, audio_suffix: str, timestamp: float):
-        import tempfile
-
+    @modal.method()
+    def diarize(self, filename: str, timestamp: float = 0.0):
         import torchaudio
 
-        with tempfile.NamedTemporaryFile("wb+", suffix=f".{audio_suffix}") as fp:
-            fp.write(audio_data)
+        upload_volume.reload()
 
-            print("Diarizing audio")
-            waveform, sample_rate = torchaudio.load(fp.name)
-            diarization = self.diarization_pipeline(
-                {"waveform": waveform, "sample_rate": sample_rate}
+        file_path = f"{UPLOADS_PATH}/{filename}"
+        if not os.path.exists(file_path):
+            raise FileNotFoundError(f"File not found: {file_path}")
+
+        print(f"Diarizing audio from: {file_path}")
+        waveform, sample_rate = torchaudio.load(file_path)
+        diarization = self.diarization_pipeline(
+            {"waveform": waveform, "sample_rate": sample_rate}
+        )
+
+        words = []
+        for diarization_segment, _, speaker in diarization.itertracks(yield_label=True):
+            words.append(
+                {
+                    "start": round(timestamp + diarization_segment.start, 3),
+                    "end": round(timestamp + diarization_segment.end, 3),
+                    "speaker": int(speaker[-2:]),
+                }
             )
-
-            words = []
-            for diarization_segment, _, speaker in diarization.itertracks(
-                yield_label=True
-            ):
-                words.append(
-                    {
-                        "start": round(timestamp + diarization_segment.start, 3),
-                        "end": round(timestamp + diarization_segment.end, 3),
-                        "speaker": int(speaker[-2:]),
-                    }
-                )
-            print("Diarization complete")
-            return {"diarization": words}
+        print("Diarization complete")
+        return {"diarization": words}
 
 
 # -------------------------------------------------------------------
@@ -127,17 +201,18 @@ class Diarizer:
 @app.function(
     timeout=60 * 10,
     scaledown_window=60 * 3,
-    allow_concurrent_inputs=40,
     secrets=[
-        Secret.from_name("reflector-gpu"),
+        modal.Secret.from_name("reflector-gpu"),
     ],
+    volumes={UPLOADS_PATH: upload_volume},
     image=diarizer_image,
 )
-@asgi_app()
+@modal.concurrent(max_inputs=40)
+@modal.asgi_app()
 def web():
-    import requests
     from fastapi import Depends, FastAPI, HTTPException, status
     from fastapi.security import OAuth2PasswordBearer
+    from pydantic import BaseModel
 
     diarizerstub = Diarizer()
 
@@ -153,35 +228,26 @@ def web():
                 headers={"WWW-Authenticate": "Bearer"},
             )
 
-    def validate_audio_file(audio_file_url: str):
-        # Check if the audio file exists
-        response = requests.head(audio_file_url, allow_redirects=True)
-        if response.status_code == 404:
-            raise HTTPException(
-                status_code=response.status_code,
-                detail="The audio file does not exist.",
-            )
-
     class DiarizationResponse(BaseModel):
         result: dict
 
-    @app.post(
-        "/diarize", dependencies=[Depends(apikey_auth), Depends(validate_audio_file)]
-    )
-    def diarize(
-        audio_file_url: str, timestamp: float = 0.0
-    ) -> HTTPException | DiarizationResponse:
-        # Currently the uploaded files are in mp3 format
-        audio_suffix = "mp3"
+    @app.post("/diarize", dependencies=[Depends(apikey_auth)])
+    def diarize(audio_file_url: str, timestamp: float = 0.0) -> DiarizationResponse:
+        unique_filename, audio_suffix = download_audio_to_volume(audio_file_url)
 
-        print("Downloading audio file")
-        response = requests.get(audio_file_url, allow_redirects=True)
-        print("Audio file downloaded successfully")
-
-        func = diarizerstub.diarize.spawn(
-            audio_data=response.content, audio_suffix=audio_suffix, timestamp=timestamp
-        )
-        result = func.get()
-        return result
+        try:
+            func = diarizerstub.diarize.spawn(
+                filename=unique_filename, timestamp=timestamp
+            )
+            result = func.get()
+            return result
+        finally:
+            try:
+                file_path = f"{UPLOADS_PATH}/{unique_filename}"
+                print(f"Deleting file: {file_path}")
+                os.remove(file_path)
+                upload_volume.commit()
+            except Exception as e:
+                print(f"Error cleaning up {unique_filename}: {e}")
 
     return app

server/gpu/modal_deployments/reflector_transcriber_parakeet.py

@@ -0,0 +1,622 @@
+import logging
+import os
+import sys
+import threading
+import uuid
+from typing import Mapping, NewType
+from urllib.parse import urlparse
+
+import modal
+
+MODEL_NAME = "nvidia/parakeet-tdt-0.6b-v2"
+SUPPORTED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
+SAMPLERATE = 16000
+UPLOADS_PATH = "/uploads"
+CACHE_PATH = "/cache"
+VAD_CONFIG = {
+    "max_segment_duration": 30.0,
+    "batch_max_files": 10,
+    "batch_max_duration": 5.0,
+    "min_segment_duration": 0.02,
+    "silence_padding": 0.5,
+    "window_size": 512,
+}
+
+ParakeetUniqFilename = NewType("ParakeetUniqFilename", str)
+AudioFileExtension = NewType("AudioFileExtension", str)
+
+app = modal.App("reflector-transcriber-parakeet")
+
+# Volume for caching model weights
+model_cache = modal.Volume.from_name("parakeet-model-cache", create_if_missing=True)
+# Volume for temporary file uploads
+upload_volume = modal.Volume.from_name("parakeet-uploads", create_if_missing=True)
+
+image = (
+    modal.Image.from_registry(
+        "nvidia/cuda:12.8.0-cudnn-devel-ubuntu22.04", add_python="3.12"
+    )
+    .env(
+        {
+            "HF_HUB_ENABLE_HF_TRANSFER": "1",
+            "HF_HOME": "/cache",
+            "DEBIAN_FRONTEND": "noninteractive",
+            "CXX": "g++",
+            "CC": "g++",
+        }
+    )
+    .apt_install("ffmpeg")
+    .pip_install(
+        "hf_transfer==0.1.9",
+        "huggingface_hub[hf-xet]==0.31.2",
+        "nemo_toolkit[asr]==2.3.0",
+        "cuda-python==12.8.0",
+        "fastapi==0.115.12",
+        "numpy<2",
+        "librosa==0.10.1",
+        "requests",
+        "silero-vad==5.1.0",
+        "torch",
+    )
+    .entrypoint([])  # silence chatty logs by container on start
+)
+
+
+def detect_audio_format(url: str, headers: Mapping[str, str]) -> AudioFileExtension:
+    parsed_url = urlparse(url)
+    url_path = parsed_url.path
+
+    for ext in SUPPORTED_FILE_EXTENSIONS:
+        if url_path.lower().endswith(f".{ext}"):
+            return AudioFileExtension(ext)
+
+    content_type = headers.get("content-type", "").lower()
+    if "audio/mpeg" in content_type or "audio/mp3" in content_type:
+        return AudioFileExtension("mp3")
+    if "audio/wav" in content_type:
+        return AudioFileExtension("wav")
+    if "audio/mp4" in content_type:
+        return AudioFileExtension("mp4")
+
+    raise ValueError(
+        f"Unsupported audio format for URL: {url}. "
+        f"Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
+    )
+
+
+def download_audio_to_volume(
+    audio_file_url: str,
+) -> tuple[ParakeetUniqFilename, AudioFileExtension]:
+    import requests
+    from fastapi import HTTPException
+
+    response = requests.head(audio_file_url, allow_redirects=True)
+    if response.status_code == 404:
+        raise HTTPException(status_code=404, detail="Audio file not found")
+
+    response = requests.get(audio_file_url, allow_redirects=True)
+    response.raise_for_status()
+
+    audio_suffix = detect_audio_format(audio_file_url, response.headers)
+    unique_filename = ParakeetUniqFilename(f"{uuid.uuid4()}.{audio_suffix}")
+    file_path = f"{UPLOADS_PATH}/{unique_filename}"
+
+    with open(file_path, "wb") as f:
+        f.write(response.content)
+
+    upload_volume.commit()
+    return unique_filename, audio_suffix
+
+
+def pad_audio(audio_array, sample_rate: int = SAMPLERATE):
+    """Add 0.5 seconds of silence if audio is less than 500ms.
+
+    This is a workaround for a Parakeet bug where very short audio (<500ms) causes:
+    ValueError: `char_offsets`: [] and `processed_tokens`: [157, 834, 834, 841]
+    have to be of the same length
+
+    See: https://github.com/NVIDIA/NeMo/issues/8451
+    """
+    import numpy as np
+
+    audio_duration = len(audio_array) / sample_rate
+    if audio_duration < 0.5:
+        silence_samples = int(sample_rate * 0.5)
+        silence = np.zeros(silence_samples, dtype=np.float32)
+        return np.concatenate([audio_array, silence])
+    return audio_array
+
+
+@app.cls(
+    gpu="A10G",
+    timeout=600,
+    scaledown_window=300,
+    image=image,
+    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
+    enable_memory_snapshot=True,
+    experimental_options={"enable_gpu_snapshot": True},
+)
+@modal.concurrent(max_inputs=10)
+class TranscriberParakeetLive:
+    @modal.enter(snap=True)
+    def enter(self):
+        import nemo.collections.asr as nemo_asr
+
+        logging.getLogger("nemo_logger").setLevel(logging.CRITICAL)
+
+        self.lock = threading.Lock()
+        self.model = nemo_asr.models.ASRModel.from_pretrained(model_name=MODEL_NAME)
+        device = next(self.model.parameters()).device
+        print(f"Model is on device: {device}")
+
+    @modal.method()
+    def transcribe_segment(
+        self,
+        filename: str,
+    ):
+        import librosa
+
+        upload_volume.reload()
+
+        file_path = f"{UPLOADS_PATH}/{filename}"
+        if not os.path.exists(file_path):
+            raise FileNotFoundError(f"File not found: {file_path}")
+
+        audio_array, sample_rate = librosa.load(file_path, sr=SAMPLERATE, mono=True)
+        padded_audio = pad_audio(audio_array, sample_rate)
+
+        with self.lock:
+            with NoStdStreams():
+                (output,) = self.model.transcribe([padded_audio], timestamps=True)
+
+        text = output.text.strip()
+        words = [
+            {
+                "word": word_info["word"] + " ",
+                "start": round(word_info["start"], 2),
+                "end": round(word_info["end"], 2),
+            }
+            for word_info in output.timestamp["word"]
+        ]
+
+        return {"text": text, "words": words}
+
+    @modal.method()
+    def transcribe_batch(
+        self,
+        filenames: list[str],
+    ):
+        import librosa
+
+        upload_volume.reload()
+
+        results = []
+        audio_arrays = []
+
+        # Load all audio files with padding
+        for filename in filenames:
+            file_path = f"{UPLOADS_PATH}/{filename}"
+            if not os.path.exists(file_path):
+                raise FileNotFoundError(f"Batch file not found: {file_path}")
+
+            audio_array, sample_rate = librosa.load(file_path, sr=SAMPLERATE, mono=True)
+            padded_audio = pad_audio(audio_array, sample_rate)
+            audio_arrays.append(padded_audio)
+
+        with self.lock:
+            with NoStdStreams():
+                outputs = self.model.transcribe(audio_arrays, timestamps=True)
+
+        # Process results for each file
+        for i, (filename, output) in enumerate(zip(filenames, outputs)):
+            text = output.text.strip()
+
+            words = [
+                {
+                    "word": word_info["word"] + " ",
+                    "start": round(word_info["start"], 2),
+                    "end": round(word_info["end"], 2),
+                }
+                for word_info in output.timestamp["word"]
+            ]
+
+            results.append(
+                {
+                    "filename": filename,
+                    "text": text,
+                    "words": words,
+                }
+            )
+
+        return results
+
+
+# L40S class for file transcription (bigger files)
+@app.cls(
+    gpu="L40S",
+    timeout=900,
+    image=image,
+    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
+    enable_memory_snapshot=True,
+    experimental_options={"enable_gpu_snapshot": True},
+)
+class TranscriberParakeetFile:
+    @modal.enter(snap=True)
+    def enter(self):
+        import nemo.collections.asr as nemo_asr
+        import torch
+        from silero_vad import load_silero_vad
+
+        logging.getLogger("nemo_logger").setLevel(logging.CRITICAL)
+
+        self.model = nemo_asr.models.ASRModel.from_pretrained(model_name=MODEL_NAME)
+        device = next(self.model.parameters()).device
+        print(f"Model is on device: {device}")
+
+        torch.set_num_threads(1)
+        self.vad_model = load_silero_vad(onnx=False)
+        print("Silero VAD initialized")
+
+    @modal.method()
+    def transcribe_segment(
+        self,
+        filename: str,
+        timestamp_offset: float = 0.0,
+    ):
+        import librosa
+        import numpy as np
+        from silero_vad import VADIterator
+
+        def load_and_convert_audio(file_path):
+            audio_array, sample_rate = librosa.load(file_path, sr=SAMPLERATE, mono=True)
+            return audio_array
+
+        def vad_segment_generator(audio_array):
+            """Generate speech segments using VAD with start/end sample indices"""
+            vad_iterator = VADIterator(self.vad_model, sampling_rate=SAMPLERATE)
+            window_size = VAD_CONFIG["window_size"]
+            start = None
+
+            for i in range(0, len(audio_array), window_size):
+                chunk = audio_array[i : i + window_size]
+                if len(chunk) < window_size:
+                    chunk = np.pad(
+                        chunk, (0, window_size - len(chunk)), mode="constant"
+                    )
+
+                speech_dict = vad_iterator(chunk)
+                if not speech_dict:
+                    continue
+
+                if "start" in speech_dict:
+                    start = speech_dict["start"]
+                    continue
+
+                if "end" in speech_dict and start is not None:
+                    end = speech_dict["end"]
+                    start_time = start / float(SAMPLERATE)
+                    end_time = end / float(SAMPLERATE)
+
+                    # Extract the actual audio segment
+                    audio_segment = audio_array[start:end]
+
+                    yield (start_time, end_time, audio_segment)
+                    start = None
+
+            vad_iterator.reset_states()
+
+        def vad_segment_filter(segments):
+            """Filter VAD segments by duration and chunk large segments"""
+            min_dur = VAD_CONFIG["min_segment_duration"]
+            max_dur = VAD_CONFIG["max_segment_duration"]
+
+            for start_time, end_time, audio_segment in segments:
+                segment_duration = end_time - start_time
+
+                # Skip very small segments
+                if segment_duration < min_dur:
+                    continue
+
+                # If segment is within max duration, yield as-is
+                if segment_duration <= max_dur:
+                    yield (start_time, end_time, audio_segment)
+                    continue
+
+                # Chunk large segments into smaller pieces
+                chunk_samples = int(max_dur * SAMPLERATE)
+                current_start = start_time
+
+                for chunk_offset in range(0, len(audio_segment), chunk_samples):
+                    chunk_audio = audio_segment[
+                        chunk_offset : chunk_offset + chunk_samples
+                    ]
+                    if len(chunk_audio) == 0:
+                        break
+
+                    chunk_duration = len(chunk_audio) / float(SAMPLERATE)
+                    chunk_end = current_start + chunk_duration
+
+                    # Only yield chunks that meet minimum duration
+                    if chunk_duration >= min_dur:
+                        yield (current_start, chunk_end, chunk_audio)
+
+                    current_start = chunk_end
+
+        def batch_segments(segments, max_files=10, max_duration=5.0):
+            batch = []
+            batch_duration = 0.0
+
+            for start_time, end_time, audio_segment in segments:
+                segment_duration = end_time - start_time
+
+                if segment_duration < VAD_CONFIG["silence_padding"]:
+                    silence_samples = int(
+                        (VAD_CONFIG["silence_padding"] - segment_duration) * SAMPLERATE
+                    )
+                    padding = np.zeros(silence_samples, dtype=np.float32)
+                    audio_segment = np.concatenate([audio_segment, padding])
+                    segment_duration = VAD_CONFIG["silence_padding"]
+
+                batch.append((start_time, end_time, audio_segment))
+                batch_duration += segment_duration
+
+                if len(batch) >= max_files or batch_duration >= max_duration:
+                    yield batch
+                    batch = []
+                    batch_duration = 0.0
+
+            if batch:
+                yield batch
+
+        def transcribe_batch(model, audio_segments):
+            with NoStdStreams():
+                outputs = model.transcribe(audio_segments, timestamps=True)
+            return outputs
+
+        def emit_results(
+            results,
+            segments_info,
+            batch_index,
+            total_batches,
+        ):
+            """Yield transcribed text and word timings from model output, adjusting timestamps to absolute positions."""
+            for i, (output, (start_time, end_time, _)) in enumerate(
+                zip(results, segments_info)
+            ):
+                text = output.text.strip()
+                words = [
+                    {
+                        "word": word_info["word"] + " ",
+                        "start": round(
+                            word_info["start"] + start_time + timestamp_offset, 2
+                        ),
+                        "end": round(
+                            word_info["end"] + start_time + timestamp_offset, 2
+                        ),
+                    }
+                    for word_info in output.timestamp["word"]
+                ]
+
+                yield text, words
+
+        upload_volume.reload()
+
+        file_path = f"{UPLOADS_PATH}/{filename}"
+        if not os.path.exists(file_path):
+            raise FileNotFoundError(f"File not found: {file_path}")
+
+        audio_array = load_and_convert_audio(file_path)
+        total_duration = len(audio_array) / float(SAMPLERATE)
+        processed_duration = 0.0
+
+        all_text_parts = []
+        all_words = []
+
+        raw_segments = vad_segment_generator(audio_array)
+        filtered_segments = vad_segment_filter(raw_segments)
+        batches = batch_segments(
+            filtered_segments,
+            VAD_CONFIG["batch_max_files"],
+            VAD_CONFIG["batch_max_duration"],
+        )
+
+        batch_index = 0
+        total_batches = max(
+            1, int(total_duration / VAD_CONFIG["batch_max_duration"]) + 1
+        )
+
+        for batch in batches:
+            batch_index += 1
+            audio_segments = [seg[2] for seg in batch]
+            results = transcribe_batch(self.model, audio_segments)
+
+            for text, words in emit_results(
+                results,
+                batch,
+                batch_index,
+                total_batches,
+            ):
+                if not text:
+                    continue
+                all_text_parts.append(text)
+                all_words.extend(words)
+
+            processed_duration += sum(len(seg[2]) / float(SAMPLERATE) for seg in batch)
+
+        combined_text = " ".join(all_text_parts)
+        return {"text": combined_text, "words": all_words}
+
+
+@app.function(
+    scaledown_window=60,
+    timeout=600,
+    secrets=[
+        modal.Secret.from_name("reflector-gpu"),
+    ],
+    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
+    image=image,
+)
+@modal.concurrent(max_inputs=40)
+@modal.asgi_app()
+def web():
+    import os
+    import uuid
+
+    from fastapi import (
+        Body,
+        Depends,
+        FastAPI,
+        Form,
+        HTTPException,
+        UploadFile,
+        status,
+    )
+    from fastapi.security import OAuth2PasswordBearer
+    from pydantic import BaseModel
+
+    transcriber_live = TranscriberParakeetLive()
+    transcriber_file = TranscriberParakeetFile()
+
+    app = FastAPI()
+
+    oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
+
+    def apikey_auth(apikey: str = Depends(oauth2_scheme)):
+        if apikey == os.environ["REFLECTOR_GPU_APIKEY"]:
+            return
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Invalid API key",
+            headers={"WWW-Authenticate": "Bearer"},
+        )
+
+    class TranscriptResponse(BaseModel):
+        result: dict
+
+    @app.post("/v1/audio/transcriptions", dependencies=[Depends(apikey_auth)])
+    def transcribe(
+        file: UploadFile = None,
+        files: list[UploadFile] | None = None,
+        model: str = Form(MODEL_NAME),
+        language: str = Form("en"),
+        batch: bool = Form(False),
+    ):
+        # Parakeet only supports English
+        if language != "en":
+            raise HTTPException(
+                status_code=400,
+                detail=f"Parakeet model only supports English. Got language='{language}'",
+            )
+        # Handle both single file and multiple files
+        if not file and not files:
+            raise HTTPException(
+                status_code=400, detail="Either 'file' or 'files' parameter is required"
+            )
+        if batch and not files:
+            raise HTTPException(
+                status_code=400, detail="Batch transcription requires 'files'"
+            )
+
+        upload_files = [file] if file else files
+
+        # Upload files to volume
+        uploaded_filenames = []
+        for upload_file in upload_files:
+            audio_suffix = upload_file.filename.split(".")[-1]
+            assert audio_suffix in SUPPORTED_FILE_EXTENSIONS
+
+            # Generate unique filename
+            unique_filename = f"{uuid.uuid4()}.{audio_suffix}"
+            file_path = f"{UPLOADS_PATH}/{unique_filename}"
+
+            print(f"Writing file to: {file_path}")
+            with open(file_path, "wb") as f:
+                content = upload_file.file.read()
+                f.write(content)
+
+            uploaded_filenames.append(unique_filename)
+
+        upload_volume.commit()
+
+        try:
+            # Use A10G live transcriber for per-file transcription
+            if batch and len(upload_files) > 1:
+                # Use batch transcription
+                func = transcriber_live.transcribe_batch.spawn(
+                    filenames=uploaded_filenames,
+                )
+                results = func.get()
+                return {"results": results}
+
+            # Per-file transcription
+            results = []
+            for filename in uploaded_filenames:
+                func = transcriber_live.transcribe_segment.spawn(
+                    filename=filename,
+                )
+                result = func.get()
+                result["filename"] = filename
+                results.append(result)
+
+            return {"results": results} if len(results) > 1 else results[0]
+
+        finally:
+            for filename in uploaded_filenames:
+                try:
+                    file_path = f"{UPLOADS_PATH}/{filename}"
+                    print(f"Deleting file: {file_path}")
+                    os.remove(file_path)
+                except Exception as e:
+                    print(f"Error deleting {filename}: {e}")
+
+            upload_volume.commit()
+
+    @app.post("/v1/audio/transcriptions-from-url", dependencies=[Depends(apikey_auth)])
+    def transcribe_from_url(
+        audio_file_url: str = Body(
+            ..., description="URL of the audio file to transcribe"
+        ),
+        model: str = Body(MODEL_NAME),
+        language: str = Body("en", description="Language code (only 'en' supported)"),
+        timestamp_offset: float = Body(0.0),
+    ):
+        # Parakeet only supports English
+        if language != "en":
+            raise HTTPException(
+                status_code=400,
+                detail=f"Parakeet model only supports English. Got language='{language}'",
+            )
+        unique_filename, audio_suffix = download_audio_to_volume(audio_file_url)
+
+        try:
+            func = transcriber_file.transcribe_segment.spawn(
+                filename=unique_filename,
+                timestamp_offset=timestamp_offset,
+            )
+            result = func.get()
+            return result
+        finally:
+            try:
+                file_path = f"{UPLOADS_PATH}/{unique_filename}"
+                print(f"Deleting file: {file_path}")
+                os.remove(file_path)
+                upload_volume.commit()
+            except Exception as e:
+                print(f"Error cleaning up {unique_filename}: {e}")
+
+    return app
+
+
+class NoStdStreams:
+    def __init__(self):
+        self.devnull = open(os.devnull, "w")
+
+    def __enter__(self):
+        self._stdout, self._stderr = sys.stdout, sys.stderr
+        self._stdout.flush()
+        self._stderr.flush()
+        sys.stdout, sys.stderr = self.devnull, self.devnull
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        sys.stdout, sys.stderr = self._stdout, self._stderr
+        self.devnull.close()

server/migrations/README

@@ -1 +1,3 @@
-Generic single-database configuration.
+Generic single-database configuration.
+
+Both data migrations and schema migrations must be in migrations.

server/migrations/versions/0ab2d7ffaa16_add_long_summary_to_search_vector.py

@@ -0,0 +1,64 @@
+"""add_long_summary_to_search_vector
+
+Revision ID: 0ab2d7ffaa16
+Revises: b1c33bd09963
+Create Date: 2025-08-15 13:27:52.680211
+
+"""
+
+from typing import Sequence, Union
+
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision: str = "0ab2d7ffaa16"
+down_revision: Union[str, None] = "b1c33bd09963"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # Drop the existing search vector column and index
+    op.drop_index("idx_transcript_search_vector_en", table_name="transcript")
+    op.drop_column("transcript", "search_vector_en")
+
+    # Recreate the search vector column with long_summary included
+    op.execute("""
+        ALTER TABLE transcript ADD COLUMN search_vector_en tsvector
+        GENERATED ALWAYS AS (
+            setweight(to_tsvector('english', coalesce(title, '')), 'A') ||
+            setweight(to_tsvector('english', coalesce(long_summary, '')), 'B') ||
+            setweight(to_tsvector('english', coalesce(webvtt, '')), 'C')
+        ) STORED
+    """)
+
+    # Recreate the GIN index for the search vector
+    op.create_index(
+        "idx_transcript_search_vector_en",
+        "transcript",
+        ["search_vector_en"],
+        postgresql_using="gin",
+    )
+
+
+def downgrade() -> None:
+    # Drop the updated search vector column and index
+    op.drop_index("idx_transcript_search_vector_en", table_name="transcript")
+    op.drop_column("transcript", "search_vector_en")
+
+    # Recreate the original search vector column without long_summary
+    op.execute("""
+        ALTER TABLE transcript ADD COLUMN search_vector_en tsvector
+        GENERATED ALWAYS AS (
+            setweight(to_tsvector('english', coalesce(title, '')), 'A') ||
+            setweight(to_tsvector('english', coalesce(webvtt, '')), 'B')
+        ) STORED
+    """)
+
+    # Recreate the GIN index for the search vector
+    op.create_index(
+        "idx_transcript_search_vector_en",
+        "transcript",
+        ["search_vector_en"],
+        postgresql_using="gin",
+    )

server/migrations/versions/0bc0f3ff0111_add_webvtt_field_to_transcript.py

@@ -0,0 +1,25 @@
+"""add_webvtt_field_to_transcript
+
+Revision ID: 0bc0f3ff0111
+Revises: b7df9609542c
+Create Date: 2025-08-05 19:36:41.740957
+
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+revision: str = "0bc0f3ff0111"
+down_revision: Union[str, None] = "b7df9609542c"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column("transcript", sa.Column("webvtt", sa.Text(), nullable=True))
+
+
+def downgrade() -> None:
+    op.drop_column("transcript", "webvtt")

server/migrations/versions/116b2f287eab_add_full_text_search.py

@@ -0,0 +1,46 @@
+"""add_full_text_search
+
+Revision ID: 116b2f287eab
+Revises: 0bc0f3ff0111
+Create Date: 2025-08-07 11:27:38.473517
+
+"""
+
+from typing import Sequence, Union
+
+from alembic import op
+
+revision: str = "116b2f287eab"
+down_revision: Union[str, None] = "0bc0f3ff0111"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    conn = op.get_bind()
+    if conn.dialect.name != "postgresql":
+        return
+
+    op.execute("""
+        ALTER TABLE transcript ADD COLUMN search_vector_en tsvector
+        GENERATED ALWAYS AS (
+            setweight(to_tsvector('english', coalesce(title, '')), 'A') ||
+            setweight(to_tsvector('english', coalesce(webvtt, '')), 'B')
+        ) STORED
+    """)
+
+    op.create_index(
+        "idx_transcript_search_vector_en",
+        "transcript",
+        ["search_vector_en"],
+        postgresql_using="gin",
+    )
+
+
+def downgrade() -> None:
+    conn = op.get_bind()
+    if conn.dialect.name != "postgresql":
+        return
+
+    op.drop_index("idx_transcript_search_vector_en", table_name="transcript")
+    op.drop_column("transcript", "search_vector_en")

server/migrations/versions/62dea3db63a5_add_room_options.py

@@ -32,7 +32,7 @@ def upgrade() -> None:
         sa.Column("user_id", sa.String(), nullable=True),
         sa.Column("room_id", sa.String(), nullable=True),
         sa.Column(
-            "is_locked", sa.Boolean(), server_default=sa.text("0"), nullable=False
+            "is_locked", sa.Boolean(), server_default=sa.text("false"), nullable=False
         ),
         sa.Column("room_mode", sa.String(), server_default="normal", nullable=False),
         sa.Column(
@@ -53,12 +53,15 @@ def upgrade() -> None:
         sa.Column("user_id", sa.String(), nullable=False),
         sa.Column("created_at", sa.DateTime(), nullable=False),
         sa.Column(
-            "zulip_auto_post", sa.Boolean(), server_default=sa.text("0"), nullable=False
+            "zulip_auto_post",
+            sa.Boolean(),
+            server_default=sa.text("false"),
+            nullable=False,
         ),
         sa.Column("zulip_stream", sa.String(), nullable=True),
         sa.Column("zulip_topic", sa.String(), nullable=True),
         sa.Column(
-            "is_locked", sa.Boolean(), server_default=sa.text("0"), nullable=False
+            "is_locked", sa.Boolean(), server_default=sa.text("false"), nullable=False
         ),
         sa.Column("room_mode", sa.String(), server_default="normal", nullable=False),
         sa.Column(

server/migrations/versions/74b2b0236931_add_transcript_source_kind.py

@@ -20,11 +20,14 @@ depends_on: Union[str, Sequence[str], None] = None
 
 def upgrade() -> None:
     # ### commands auto generated by Alembic - please adjust! ###
+    sourcekind_enum = sa.Enum("room", "live", "file", name="sourcekind")
+    sourcekind_enum.create(op.get_bind())
+
     op.add_column(
         "transcript",
         sa.Column(
             "source_kind",
-            sa.Enum("ROOM", "LIVE", "FILE", name="sourcekind"),
+            sourcekind_enum,
             nullable=True,
         ),
     )
@@ -43,6 +46,8 @@ def upgrade() -> None:
 def downgrade() -> None:
     # ### commands auto generated by Alembic - please adjust! ###
     op.drop_column("transcript", "source_kind")
+    sourcekind_enum = sa.Enum(name="sourcekind")
+    sourcekind_enum.drop(op.get_bind())
 
 
 # ### end Alembic commands ###

server/migrations/versions/7e47155afd51_dailyco_platform.py

@@ -1,54 +0,0 @@
-"""dailyco platform
-
-Revision ID: 7e47155afd51
-Revises: b7df9609542c
-Create Date: 2025-08-04 11:14:19.663115
-
-"""
-
-from typing import Sequence, Union
-
-import sqlalchemy as sa
-from alembic import op
-
-# revision identifiers, used by Alembic.
-revision: str = "7e47155afd51"
-down_revision: Union[str, None] = "b7df9609542c"
-branch_labels: Union[str, Sequence[str], None] = None
-depends_on: Union[str, Sequence[str], None] = None
-
-
-def upgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    with op.batch_alter_table("meeting", schema=None) as batch_op:
-        batch_op.add_column(
-            sa.Column("platform", sa.String(), server_default="whereby", nullable=False)
-        )
-        batch_op.drop_index(
-            batch_op.f("idx_one_active_meeting_per_room"),
-            sqlite_where=sa.text("is_active = 1"),
-        )
-
-    with op.batch_alter_table("room", schema=None) as batch_op:
-        batch_op.add_column(
-            sa.Column("platform", sa.String(), server_default="whereby", nullable=False)
-        )
-
-    # ### end Alembic commands ###
-
-
-def downgrade() -> None:
-    # ### commands auto generated by Alembic - please adjust! ###
-    with op.batch_alter_table("room", schema=None) as batch_op:
-        batch_op.drop_column("platform")
-
-    with op.batch_alter_table("meeting", schema=None) as batch_op:
-        batch_op.create_index(
-            batch_op.f("idx_one_active_meeting_per_room"),
-            ["room_id"],
-            unique=1,
-            sqlite_where=sa.text("is_active = 1"),
-        )
-        batch_op.drop_column("platform")
-
-    # ### end Alembic commands ###

server/migrations/versions/8120ebc75366_populate_webvtt_from_topics.py

@@ -0,0 +1,106 @@
+"""populate_webvtt_from_topics
+
+Revision ID: 8120ebc75366
+Revises: 116b2f287eab
+Create Date: 2025-08-11 19:11:01.316947
+
+"""
+
+import json
+from typing import Sequence, Union
+
+from alembic import op
+from sqlalchemy import text
+
+# revision identifiers, used by Alembic.
+revision: str = "8120ebc75366"
+down_revision: Union[str, None] = "116b2f287eab"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def topics_to_webvtt(topics):
+    """Convert topics list to WebVTT format string."""
+    if not topics:
+        return None
+
+    lines = ["WEBVTT", ""]
+
+    for topic in topics:
+        start_time = format_timestamp(topic.get("start"))
+        end_time = format_timestamp(topic.get("end"))
+        text = topic.get("text", "").strip()
+
+        if start_time and end_time and text:
+            lines.append(f"{start_time} --> {end_time}")
+            lines.append(text)
+            lines.append("")
+
+    return "\n".join(lines).strip()
+
+
+def format_timestamp(seconds):
+    """Format seconds to WebVTT timestamp format (HH:MM:SS.mmm)."""
+    if seconds is None:
+        return None
+
+    hours = int(seconds // 3600)
+    minutes = int((seconds % 3600) // 60)
+    secs = seconds % 60
+
+    return f"{hours:02d}:{minutes:02d}:{secs:06.3f}"
+
+
+def upgrade() -> None:
+    """Populate WebVTT field for all transcripts with topics."""
+
+    # Get connection
+    connection = op.get_bind()
+
+    # Query all transcripts with topics
+    result = connection.execute(
+        text("SELECT id, topics FROM transcript WHERE topics IS NOT NULL")
+    )
+
+    rows = result.fetchall()
+    print(f"Found {len(rows)} transcripts with topics")
+
+    updated_count = 0
+    error_count = 0
+
+    for row in rows:
+        transcript_id = row[0]
+        topics_data = row[1]
+
+        if not topics_data:
+            continue
+
+        try:
+            # Parse JSON if it's a string
+            if isinstance(topics_data, str):
+                topics_data = json.loads(topics_data)
+
+            # Convert topics to WebVTT format
+            webvtt_content = topics_to_webvtt(topics_data)
+
+            if webvtt_content:
+                # Update the webvtt field
+                connection.execute(
+                    text("UPDATE transcript SET webvtt = :webvtt WHERE id = :id"),
+                    {"webvtt": webvtt_content, "id": transcript_id},
+                )
+                updated_count += 1
+                print(f"✓ Updated transcript {transcript_id}")
+
+        except Exception as e:
+            error_count += 1
+            print(f"✗ Error updating transcript {transcript_id}: {e}")
+
+    print(f"\nMigration complete!")
+    print(f"  Updated: {updated_count}")
+    print(f"  Errors: {error_count}")
+
+
+def downgrade() -> None:
+    """Clear WebVTT field for all transcripts."""
+    op.execute(text("UPDATE transcript SET webvtt = NULL"))

server/migrations/versions/99365b0cd87b_add_title_short_and_long_summary_and_.py

@@ -22,7 +22,7 @@ def upgrade() -> None:
     # ### commands auto generated by Alembic - please adjust! ###
     op.execute(
         "UPDATE transcript SET events = "
-        'REPLACE(events, \'"event": "SUMMARY"\', \'"event": "LONG_SUMMARY"\');'
+        'REPLACE(events::text, \'"event": "SUMMARY"\', \'"event": "LONG_SUMMARY"\')::json;'
     )
     op.alter_column("transcript", "summary", new_column_name="long_summary")
     op.add_column("transcript", sa.Column("title", sa.String(), nullable=True))
@@ -34,7 +34,7 @@ def downgrade() -> None:
     # ### commands auto generated by Alembic - please adjust! ###
     op.execute(
         "UPDATE transcript SET events = "
-        'REPLACE(events, \'"event": "LONG_SUMMARY"\', \'"event": "SUMMARY"\');'
+        'REPLACE(events::text, \'"event": "LONG_SUMMARY"\', \'"event": "SUMMARY"\')::json;'
     )
     with op.batch_alter_table("transcript", schema=None) as batch_op:
         batch_op.alter_column("long_summary", nullable=True, new_column_name="summary")

server/migrations/versions/9f5c78d352d6_datetime_timezone.py

@@ -0,0 +1,121 @@
+"""datetime timezone
+
+Revision ID: 9f5c78d352d6
+Revises: 8120ebc75366
+Create Date: 2025-08-13 19:18:27.113593
+
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy.dialects import postgresql
+
+# revision identifiers, used by Alembic.
+revision: str = "9f5c78d352d6"
+down_revision: Union[str, None] = "8120ebc75366"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table("meeting", schema=None) as batch_op:
+        batch_op.alter_column(
+            "start_date",
+            existing_type=postgresql.TIMESTAMP(),
+            type_=sa.DateTime(timezone=True),
+            existing_nullable=True,
+        )
+        batch_op.alter_column(
+            "end_date",
+            existing_type=postgresql.TIMESTAMP(),
+            type_=sa.DateTime(timezone=True),
+            existing_nullable=True,
+        )
+
+    with op.batch_alter_table("meeting_consent", schema=None) as batch_op:
+        batch_op.alter_column(
+            "consent_timestamp",
+            existing_type=postgresql.TIMESTAMP(),
+            type_=sa.DateTime(timezone=True),
+            existing_nullable=False,
+        )
+
+    with op.batch_alter_table("recording", schema=None) as batch_op:
+        batch_op.alter_column(
+            "recorded_at",
+            existing_type=postgresql.TIMESTAMP(),
+            type_=sa.DateTime(timezone=True),
+            existing_nullable=False,
+        )
+
+    with op.batch_alter_table("room", schema=None) as batch_op:
+        batch_op.alter_column(
+            "created_at",
+            existing_type=postgresql.TIMESTAMP(),
+            type_=sa.DateTime(timezone=True),
+            existing_nullable=False,
+        )
+
+    with op.batch_alter_table("transcript", schema=None) as batch_op:
+        batch_op.alter_column(
+            "created_at",
+            existing_type=postgresql.TIMESTAMP(),
+            type_=sa.DateTime(timezone=True),
+            existing_nullable=True,
+        )
+
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table("transcript", schema=None) as batch_op:
+        batch_op.alter_column(
+            "created_at",
+            existing_type=sa.DateTime(timezone=True),
+            type_=postgresql.TIMESTAMP(),
+            existing_nullable=True,
+        )
+
+    with op.batch_alter_table("room", schema=None) as batch_op:
+        batch_op.alter_column(
+            "created_at",
+            existing_type=sa.DateTime(timezone=True),
+            type_=postgresql.TIMESTAMP(),
+            existing_nullable=False,
+        )
+
+    with op.batch_alter_table("recording", schema=None) as batch_op:
+        batch_op.alter_column(
+            "recorded_at",
+            existing_type=sa.DateTime(timezone=True),
+            type_=postgresql.TIMESTAMP(),
+            existing_nullable=False,
+        )
+
+    with op.batch_alter_table("meeting_consent", schema=None) as batch_op:
+        batch_op.alter_column(
+            "consent_timestamp",
+            existing_type=sa.DateTime(timezone=True),
+            type_=postgresql.TIMESTAMP(),
+            existing_nullable=False,
+        )
+
+    with op.batch_alter_table("meeting", schema=None) as batch_op:
+        batch_op.alter_column(
+            "end_date",
+            existing_type=sa.DateTime(timezone=True),
+            type_=postgresql.TIMESTAMP(),
+            existing_nullable=True,
+        )
+        batch_op.alter_column(
+            "start_date",
+            existing_type=sa.DateTime(timezone=True),
+            type_=postgresql.TIMESTAMP(),
+            existing_nullable=True,
+        )
+
+    # ### end Alembic commands ###

server/migrations/versions/a7122bc0b2ca_add_shared_rooms.py

@@ -25,7 +25,7 @@ def upgrade() -> None:
         sa.Column(
             "is_shared",
             sa.Boolean(),
-            server_default=sa.text("0"),
+            server_default=sa.text("false"),
             nullable=False,
         ),
     )

server/migrations/versions/b0e5f7876032_add_meeting_is_active.py

@@ -23,7 +23,10 @@ def upgrade() -> None:
     with op.batch_alter_table("meeting", schema=None) as batch_op:
         batch_op.add_column(
             sa.Column(
-                "is_active", sa.Boolean(), server_default=sa.text("1"), nullable=False
+                "is_active",
+                sa.Boolean(),
+                server_default=sa.text("true"),
+                nullable=False,
             )
         )
 

server/migrations/versions/b1c33bd09963_add_search_optimization_indexes.py

@@ -0,0 +1,41 @@
+"""add_search_optimization_indexes
+
+Revision ID: b1c33bd09963
+Revises: 9f5c78d352d6
+Create Date: 2025-08-14 17:26:02.117408
+
+"""
+
+from typing import Sequence, Union
+
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision: str = "b1c33bd09963"
+down_revision: Union[str, None] = "9f5c78d352d6"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # Add indexes for actual search filtering patterns used in frontend
+    # Based on /browse page filters: room_id and source_kind
+
+    # Index for room_id + created_at (for room-specific searches with date ordering)
+    op.create_index(
+        "idx_transcript_room_id_created_at",
+        "transcript",
+        ["room_id", "created_at"],
+        if_not_exists=True,
+    )
+
+    # Index for source_kind alone (actively used filter in frontend)
+    op.create_index(
+        "idx_transcript_source_kind", "transcript", ["source_kind"], if_not_exists=True
+    )
+
+
+def downgrade() -> None:
+    # Remove the indexes in reverse order
+    op.drop_index("idx_transcript_source_kind", "transcript", if_exists=True)
+    op.drop_index("idx_transcript_room_id_created_at", "transcript", if_exists=True)

server/migrations/versions/b9348748bbbc_reviewed.py

@@ -23,7 +23,7 @@ def upgrade() -> None:
     op.add_column(
         "transcript",
         sa.Column(
-            "reviewed", sa.Boolean(), server_default=sa.text("0"), nullable=False
+            "reviewed", sa.Boolean(), server_default=sa.text("false"), nullable=False
         ),
     )
     # ### end Alembic commands ###

server/pyproject.toml

@@ -32,14 +32,14 @@ dependencies = [
     "redis>=5.0.1",
     "python-jose[cryptography]>=3.3.0",
     "python-multipart>=0.0.6",
-    "faster-whisper>=0.10.0",
     "transformers>=4.36.2",
-    "black==24.1.1",
     "jsonschema>=4.23.0",
     "openai>=1.59.7",
     "psycopg2-binary>=2.9.10",
     "llama-index>=0.12.52",
     "llama-index-llms-openai-like>=0.4.0",
+    "pytest-env>=1.1.5",
+    "webvtt-py>=0.5.0",
 ]
 
 [dependency-groups]
@@ -56,6 +56,9 @@ tests = [
     "httpx-ws>=0.4.1",
     "pytest-httpx>=0.23.1",
     "pytest-celery>=0.0.0",
+    "pytest-recording>=0.13.4",
+    "pytest-docker>=3.2.3",
+    "asgi-lifespan>=2.1.0",
 ]
 aws = ["aioboto3>=11.2.0"]
 evaluation = [
@@ -64,6 +67,15 @@ evaluation = [
     "tqdm>=4.66.0",
     "pydantic>=2.1.1",
 ]
+local = [
+    "pyannote-audio>=3.3.2",
+    "faster-whisper>=0.10.0",
+]
+silero-vad = [
+    "silero-vad>=5.1.2",
+    "torch>=2.8.0",
+    "torchaudio>=2.8.0",
+]
 
 [tool.uv]
 default-groups = [
@@ -71,6 +83,21 @@ default-groups = [
     "tests",
     "aws",
     "evaluation",
+    "local",
+    "silero-vad"
+]
+
+[[tool.uv.index]]
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true
+
+[tool.uv.sources]
+torch = [
+  { index = "pytorch-cpu" },
+]
+torchaudio = [
+  { index = "pytorch-cpu" },
 ]
 
 [build-system]
@@ -83,10 +110,28 @@ packages = ["reflector"]
 [tool.coverage.run]
 source = ["reflector"]
 
+[tool.pytest_env]
+ENVIRONMENT = "pytest"
+DATABASE_URL = "postgresql://test_user:test_password@localhost:15432/reflector_test"
+
 [tool.pytest.ini_options]
 addopts = "-ra -q --disable-pytest-warnings --cov --cov-report html -v"
 testpaths = ["tests"]
 asyncio_mode = "auto"
+markers = [
+    "gpu_modal: mark test to run only with GPU Modal endpoints (deselect with '-m \"not gpu_modal\"')",
+]
+
+[tool.ruff.lint]
+select = [
+    "I",       # isort - import sorting
+    "F401",    # unused imports
+    "PLC0415", # import-outside-top-level - detect inline imports
+]
 
 [tool.ruff.lint.per-file-ignores]
 "reflector/processors/summary/summary_builder.py" = ["E501"]
+"gpu/**.py" = ["PLC0415"]
+"reflector/tools/**.py" = ["PLC0415"]
+"migrations/versions/**.py" = ["PLC0415"]
+"tests/**.py" = ["PLC0415"]

server/reflector/app.py

@@ -12,7 +12,6 @@ from reflector.events import subscribers_shutdown, subscribers_startup
 from reflector.logger import logger
 from reflector.metrics import metrics_init
 from reflector.settings import settings
-from reflector.views.daily import router as daily_router
 from reflector.views.meetings import router as meetings_router
 from reflector.views.rooms import router as rooms_router
 from reflector.views.rtc_offer import router as rtc_offer_router
@@ -87,7 +86,6 @@ app.include_router(transcripts_process_router, prefix="/v1")
 app.include_router(user_router, prefix="/v1")
 app.include_router(zulip_router, prefix="/v1")
 app.include_router(whereby_router, prefix="/v1")
-app.include_router(daily_router, prefix="/v1")
 add_pagination(app)
 
 # prepare celery

server/reflector/db/__init__.py

@@ -1,12 +1,28 @@
+import contextvars
+from typing import Optional
+
 import databases
 import sqlalchemy
 
 from reflector.events import subscribers_shutdown, subscribers_startup
 from reflector.settings import settings
 
-database = databases.Database(settings.DATABASE_URL)
 metadata = sqlalchemy.MetaData()
 
+_database_context: contextvars.ContextVar[Optional[databases.Database]] = (
+    contextvars.ContextVar("database", default=None)
+)
+
+
+def get_database() -> databases.Database:
+    """Get database instance for current asyncio context"""
+    db = _database_context.get()
+    if db is None:
+        db = databases.Database(settings.DATABASE_URL)
+        _database_context.set(db)
+    return db
+
+
 # import models
 import reflector.db.meetings  # noqa
 import reflector.db.recordings  # noqa
@@ -14,16 +30,18 @@ import reflector.db.rooms  # noqa
 import reflector.db.transcripts  # noqa
 
 kwargs = {}
-if "sqlite" in settings.DATABASE_URL:
-    kwargs["connect_args"] = {"check_same_thread": False}
+if "postgres" not in settings.DATABASE_URL:
+    raise Exception("Only postgres database is supported in reflector")
 engine = sqlalchemy.create_engine(settings.DATABASE_URL, **kwargs)
 
 
 @subscribers_startup.append
 async def database_connect(_):
+    database = get_database()
     await database.connect()
 
 
 @subscribers_shutdown.append
 async def database_disconnect(_):
+    database = get_database()
     await database.disconnect()

server/reflector/db/meetings.py

@@ -5,7 +5,7 @@ import sqlalchemy as sa
 from fastapi import HTTPException
 from pydantic import BaseModel, Field
 
-from reflector.db import database, metadata
+from reflector.db import get_database, metadata
 from reflector.db.rooms import Room
 from reflector.utils import generate_uuid4
 
@@ -16,8 +16,8 @@ meetings = sa.Table(
     sa.Column("room_name", sa.String),
     sa.Column("room_url", sa.String),
     sa.Column("host_room_url", sa.String),
-    sa.Column("start_date", sa.DateTime),
-    sa.Column("end_date", sa.DateTime),
+    sa.Column("start_date", sa.DateTime(timezone=True)),
+    sa.Column("end_date", sa.DateTime(timezone=True)),
     sa.Column("user_id", sa.String),
     sa.Column("room_id", sa.String),
     sa.Column("is_locked", sa.Boolean, nullable=False, server_default=sa.false()),
@@ -41,13 +41,13 @@ meetings = sa.Table(
         nullable=False,
         server_default=sa.true(),
     ),
-    sa.Column(
-        "platform",
-        sa.String,
-        nullable=False,
-        server_default="whereby",
-    ),
     sa.Index("idx_meeting_room_id", "room_id"),
+    sa.Index(
+        "idx_one_active_meeting_per_room",
+        "room_id",
+        unique=True,
+        postgresql_where=sa.text("is_active = true"),
+    ),
 )
 
 meeting_consent = sa.Table(
@@ -57,7 +57,7 @@ meeting_consent = sa.Table(
     sa.Column("meeting_id", sa.String, sa.ForeignKey("meeting.id"), nullable=False),
     sa.Column("user_id", sa.String),
     sa.Column("consent_given", sa.Boolean, nullable=False),
-    sa.Column("consent_timestamp", sa.DateTime, nullable=False),
+    sa.Column("consent_timestamp", sa.DateTime(timezone=True), nullable=False),
 )
 
 
@@ -85,7 +85,6 @@ class Meeting(BaseModel):
         "none", "prompt", "automatic", "automatic-2nd-participant"
     ] = "automatic-2nd-participant"
     num_clients: int = 0
-    platform: Literal["whereby", "daily"] = "whereby"
 
 
 class MeetingController:
@@ -116,10 +115,9 @@ class MeetingController:
             room_mode=room.room_mode,
             recording_type=room.recording_type,
             recording_trigger=room.recording_trigger,
-            platform=room.platform,
         )
         query = meetings.insert().values(**meeting.model_dump())
-        await database.execute(query)
+        await get_database().execute(query)
         return meeting
 
     async def get_all_active(self) -> list[Meeting]:
@@ -127,7 +125,7 @@ class MeetingController:
         Get active meetings.
         """
         query = meetings.select().where(meetings.c.is_active)
-        return await database.fetch_all(query)
+        return await get_database().fetch_all(query)
 
     async def get_by_room_name(
         self,
@@ -137,7 +135,7 @@ class MeetingController:
         Get a meeting by room name.
         """
         query = meetings.select().where(meetings.c.room_name == room_name)
-        result = await database.fetch_one(query)
+        result = await get_database().fetch_one(query)
         if not result:
             return None
 
@@ -159,7 +157,7 @@ class MeetingController:
             )
             .order_by(end_date.desc())
         )
-        result = await database.fetch_one(query)
+        result = await get_database().fetch_one(query)
         if not result:
             return None
 
@@ -170,7 +168,7 @@ class MeetingController:
         Get a meeting by id
         """
         query = meetings.select().where(meetings.c.id == meeting_id)
-        result = await database.fetch_one(query)
+        result = await get_database().fetch_one(query)
         if not result:
             return None
         return Meeting(**result)
@@ -182,7 +180,7 @@ class MeetingController:
         If not found, it will raise a 404 error.
         """
         query = meetings.select().where(meetings.c.id == meeting_id)
-        result = await database.fetch_one(query)
+        result = await get_database().fetch_one(query)
         if not result:
             raise HTTPException(status_code=404, detail="Meeting not found")
 
@@ -194,7 +192,7 @@ class MeetingController:
 
     async def update_meeting(self, meeting_id: str, **kwargs):
         query = meetings.update().where(meetings.c.id == meeting_id).values(**kwargs)
-        await database.execute(query)
+        await get_database().execute(query)
 
 
 class MeetingConsentController:
@@ -202,7 +200,7 @@ class MeetingConsentController:
         query = meeting_consent.select().where(
             meeting_consent.c.meeting_id == meeting_id
         )
-        results = await database.fetch_all(query)
+        results = await get_database().fetch_all(query)
         return [MeetingConsent(**result) for result in results]
 
     async def get_by_meeting_and_user(
@@ -213,7 +211,7 @@ class MeetingConsentController:
             meeting_consent.c.meeting_id == meeting_id,
             meeting_consent.c.user_id == user_id,
         )
-        result = await database.fetch_one(query)
+        result = await get_database().fetch_one(query)
         if result is None:
             return None
         return MeetingConsent(**result) if result else None
@@ -235,14 +233,14 @@ class MeetingConsentController:
                         consent_timestamp=consent.consent_timestamp,
                     )
                 )
-                await database.execute(query)
+                await get_database().execute(query)
 
                 existing.consent_given = consent.consent_given
                 existing.consent_timestamp = consent.consent_timestamp
                 return existing
 
         query = meeting_consent.insert().values(**consent.model_dump())
-        await database.execute(query)
+        await get_database().execute(query)
         return consent
 
     async def has_any_denial(self, meeting_id: str) -> bool:
@@ -251,7 +249,7 @@ class MeetingConsentController:
             meeting_consent.c.meeting_id == meeting_id,
             meeting_consent.c.consent_given.is_(False),
         )
-        result = await database.fetch_one(query)
+        result = await get_database().fetch_one(query)
         return result is not None
 
 

server/reflector/db/recordings.py

@@ -4,7 +4,7 @@ from typing import Literal
 import sqlalchemy as sa
 from pydantic import BaseModel, Field
 
-from reflector.db import database, metadata
+from reflector.db import get_database, metadata
 from reflector.utils import generate_uuid4
 
 recordings = sa.Table(
@@ -13,7 +13,7 @@ recordings = sa.Table(
     sa.Column("id", sa.String, primary_key=True),
     sa.Column("bucket_name", sa.String, nullable=False),
     sa.Column("object_key", sa.String, nullable=False),
-    sa.Column("recorded_at", sa.DateTime, nullable=False),
+    sa.Column("recorded_at", sa.DateTime(timezone=True), nullable=False),
     sa.Column(
         "status",
         sa.String,
@@ -37,12 +37,12 @@ class Recording(BaseModel):
 class RecordingController:
     async def create(self, recording: Recording):
         query = recordings.insert().values(**recording.model_dump())
-        await database.execute(query)
+        await get_database().execute(query)
         return recording
 
     async def get_by_id(self, id: str) -> Recording:
         query = recordings.select().where(recordings.c.id == id)
-        result = await database.fetch_one(query)
+        result = await get_database().fetch_one(query)
         return Recording(**result) if result else None
 
     async def get_by_object_key(self, bucket_name: str, object_key: str) -> Recording:
@@ -50,8 +50,12 @@ class RecordingController:
             recordings.c.bucket_name == bucket_name,
             recordings.c.object_key == object_key,
         )
-        result = await database.fetch_one(query)
+        result = await get_database().fetch_one(query)
         return Recording(**result) if result else None
 
+    async def remove_by_id(self, id: str) -> None:
+        query = recordings.delete().where(recordings.c.id == id)
+        await get_database().execute(query)
+
 
 recordings_controller = RecordingController()

server/reflector/db/rooms.py

@@ -1,4 +1,4 @@
-from datetime import datetime
+from datetime import datetime, timezone
 from sqlite3 import IntegrityError
 from typing import Literal
 
@@ -7,7 +7,7 @@ from fastapi import HTTPException
 from pydantic import BaseModel, Field
 from sqlalchemy.sql import false, or_
 
-from reflector.db import database, metadata
+from reflector.db import get_database, metadata
 from reflector.utils import generate_uuid4
 
 rooms = sqlalchemy.Table(
@@ -16,7 +16,7 @@ rooms = sqlalchemy.Table(
     sqlalchemy.Column("id", sqlalchemy.String, primary_key=True),
     sqlalchemy.Column("name", sqlalchemy.String, nullable=False, unique=True),
     sqlalchemy.Column("user_id", sqlalchemy.String, nullable=False),
-    sqlalchemy.Column("created_at", sqlalchemy.DateTime, nullable=False),
+    sqlalchemy.Column("created_at", sqlalchemy.DateTime(timezone=True), nullable=False),
     sqlalchemy.Column(
         "zulip_auto_post", sqlalchemy.Boolean, nullable=False, server_default=false()
     ),
@@ -40,9 +40,6 @@ rooms = sqlalchemy.Table(
     sqlalchemy.Column(
         "is_shared", sqlalchemy.Boolean, nullable=False, server_default=false()
     ),
-    sqlalchemy.Column(
-        "platform", sqlalchemy.String, nullable=False, server_default="whereby"
-    ),
     sqlalchemy.Index("idx_room_is_shared", "is_shared"),
 )
 
@@ -51,7 +48,7 @@ class Room(BaseModel):
     id: str = Field(default_factory=generate_uuid4)
     name: str
     user_id: str
-    created_at: datetime = Field(default_factory=datetime.utcnow)
+    created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
     zulip_auto_post: bool = False
     zulip_stream: str = ""
     zulip_topic: str = ""
@@ -62,7 +59,6 @@ class Room(BaseModel):
         "none", "prompt", "automatic", "automatic-2nd-participant"
     ] = "automatic-2nd-participant"
     is_shared: bool = False
-    platform: Literal["whereby", "daily"] = "whereby"
 
 
 class RoomController:
@@ -96,7 +92,7 @@ class RoomController:
         if return_query:
             return query
 
-        results = await database.fetch_all(query)
+        results = await get_database().fetch_all(query)
         return results
 
     async def add(
@@ -111,7 +107,6 @@ class RoomController:
         recording_type: str,
         recording_trigger: str,
         is_shared: bool,
-        platform: str = "whereby",
     ):
         """
         Add a new room
@@ -127,11 +122,10 @@ class RoomController:
             recording_type=recording_type,
             recording_trigger=recording_trigger,
             is_shared=is_shared,
-            platform=platform,
         )
         query = rooms.insert().values(**room.model_dump())
         try:
-            await database.execute(query)
+            await get_database().execute(query)
         except IntegrityError:
             raise HTTPException(status_code=400, detail="Room name is not unique")
         return room
@@ -142,7 +136,7 @@ class RoomController:
         """
         query = rooms.update().where(rooms.c.id == room.id).values(**values)
         try:
-            await database.execute(query)
+            await get_database().execute(query)
         except IntegrityError:
             raise HTTPException(status_code=400, detail="Room name is not unique")
 
@@ -157,7 +151,7 @@ class RoomController:
         query = rooms.select().where(rooms.c.id == room_id)
         if "user_id" in kwargs:
             query = query.where(rooms.c.user_id == kwargs["user_id"])
-        result = await database.fetch_one(query)
+        result = await get_database().fetch_one(query)
         if not result:
             return None
         return Room(**result)
@@ -169,7 +163,7 @@ class RoomController:
         query = rooms.select().where(rooms.c.name == room_name)
         if "user_id" in kwargs:
             query = query.where(rooms.c.user_id == kwargs["user_id"])
-        result = await database.fetch_one(query)
+        result = await get_database().fetch_one(query)
         if not result:
             return None
         return Room(**result)
@@ -181,7 +175,7 @@ class RoomController:
         If not found, it will raise a 404 error.
         """
         query = rooms.select().where(rooms.c.id == meeting_id)
-        result = await database.fetch_one(query)
+        result = await get_database().fetch_one(query)
         if not result:
             raise HTTPException(status_code=404, detail="Room not found")
 
@@ -203,7 +197,7 @@ class RoomController:
         if user_id is not None and room.user_id != user_id:
             return
         query = rooms.delete().where(rooms.c.id == room_id)
-        await database.execute(query)
+        await get_database().execute(query)
 
 
 rooms_controller = RoomController()

server/reflector/db/search.py

@@ -0,0 +1,448 @@
+"""Search functionality for transcripts and other entities."""
+
+import itertools
+from dataclasses import dataclass
+from datetime import datetime
+from io import StringIO
+from typing import Annotated, Any, Dict, Iterator
+
+import sqlalchemy
+import webvtt
+from fastapi import HTTPException
+from pydantic import (
+    BaseModel,
+    Field,
+    NonNegativeFloat,
+    NonNegativeInt,
+    ValidationError,
+    constr,
+    field_serializer,
+)
+
+from reflector.db import get_database
+from reflector.db.rooms import rooms
+from reflector.db.transcripts import SourceKind, transcripts
+from reflector.db.utils import is_postgresql
+from reflector.logger import logger
+
+DEFAULT_SEARCH_LIMIT = 20
+SNIPPET_CONTEXT_LENGTH = 50  # Characters before/after match to include
+DEFAULT_SNIPPET_MAX_LENGTH = NonNegativeInt(150)
+DEFAULT_MAX_SNIPPETS = NonNegativeInt(3)
+LONG_SUMMARY_MAX_SNIPPETS = 2
+
+SearchQueryBase = constr(min_length=0, strip_whitespace=True)
+SearchLimitBase = Annotated[int, Field(ge=1, le=100)]
+SearchOffsetBase = Annotated[int, Field(ge=0)]
+SearchTotalBase = Annotated[int, Field(ge=0)]
+
+SearchQuery = Annotated[SearchQueryBase, Field(description="Search query text")]
+SearchLimit = Annotated[SearchLimitBase, Field(description="Results per page")]
+SearchOffset = Annotated[
+    SearchOffsetBase, Field(description="Number of results to skip")
+]
+SearchTotal = Annotated[
+    SearchTotalBase, Field(description="Total number of search results")
+]
+
+WEBVTT_SPEC_HEADER = "WEBVTT"
+
+WebVTTContent = Annotated[
+    str,
+    Field(min_length=len(WEBVTT_SPEC_HEADER), description="WebVTT content"),
+]
+
+
+class WebVTTProcessor:
+    """Stateless processor for WebVTT content operations."""
+
+    @staticmethod
+    def parse(raw_content: str) -> WebVTTContent:
+        """Parse WebVTT content and return it as a string."""
+        if not raw_content.startswith(WEBVTT_SPEC_HEADER):
+            raise ValueError(f"Invalid WebVTT content, no header {WEBVTT_SPEC_HEADER}")
+        return raw_content
+
+    @staticmethod
+    def extract_text(webvtt_content: WebVTTContent) -> str:
+        """Extract plain text from WebVTT content using webvtt library."""
+        try:
+            buffer = StringIO(webvtt_content)
+            vtt = webvtt.read_buffer(buffer)
+            return " ".join(caption.text for caption in vtt if caption.text)
+        except webvtt.errors.MalformedFileError as e:
+            logger.warning(f"Malformed WebVTT content: {e}")
+            return ""
+        except (UnicodeDecodeError, ValueError) as e:
+            logger.warning(f"Failed to decode WebVTT content: {e}")
+            return ""
+        except AttributeError as e:
+            logger.error(
+                f"WebVTT parsing error - unexpected format: {e}", exc_info=True
+            )
+            return ""
+        except Exception as e:
+            logger.error(f"Unexpected error parsing WebVTT: {e}", exc_info=True)
+            return ""
+
+    @staticmethod
+    def generate_snippets(
+        webvtt_content: WebVTTContent,
+        query: str,
+        max_snippets: NonNegativeInt = DEFAULT_MAX_SNIPPETS,
+    ) -> list[str]:
+        """Generate snippets from WebVTT content."""
+        return SnippetGenerator.generate(
+            WebVTTProcessor.extract_text(webvtt_content),
+            query,
+            max_snippets=max_snippets,
+        )
+
+
+@dataclass(frozen=True)
+class SnippetCandidate:
+    """Represents a candidate snippet with its position."""
+
+    _text: str
+    start: NonNegativeInt
+    _original_text_length: int
+
+    @property
+    def end(self) -> NonNegativeInt:
+        """Calculate end position from start and raw text length."""
+        return self.start + len(self._text)
+
+    def text(self) -> str:
+        """Get display text with ellipses added if needed."""
+        result = self._text.strip()
+        if self.start > 0:
+            result = "..." + result
+        if self.end < self._original_text_length:
+            result = result + "..."
+        return result
+
+
+class SearchParameters(BaseModel):
+    """Validated search parameters for full-text search."""
+
+    query_text: SearchQuery
+    limit: SearchLimit = DEFAULT_SEARCH_LIMIT
+    offset: SearchOffset = 0
+    user_id: str | None = None
+    room_id: str | None = None
+    source_kind: SourceKind | None = None
+
+
+class SearchResultDB(BaseModel):
+    """Intermediate model for validating raw database results."""
+
+    id: str = Field(..., min_length=1)
+    created_at: datetime
+    status: str = Field(..., min_length=1)
+    duration: float | None = Field(None, ge=0)
+    user_id: str | None = None
+    title: str | None = None
+    source_kind: SourceKind
+    room_id: str | None = None
+    rank: float = Field(..., ge=0, le=1)
+
+
+class SearchResult(BaseModel):
+    """Public search result model with computed fields."""
+
+    id: str = Field(..., min_length=1)
+    title: str | None = None
+    user_id: str | None = None
+    room_id: str | None = None
+    room_name: str | None = None
+    source_kind: SourceKind
+    created_at: datetime
+    status: str = Field(..., min_length=1)
+    rank: float = Field(..., ge=0, le=1)
+    duration: NonNegativeFloat | None = Field(..., description="Duration in seconds")
+    search_snippets: list[str] = Field(
+        description="Text snippets around search matches"
+    )
+    total_match_count: NonNegativeInt = Field(
+        default=0, description="Total number of matches found in the transcript"
+    )
+
+    @field_serializer("created_at", when_used="json")
+    def serialize_datetime(self, dt: datetime) -> str:
+        if dt.tzinfo is None:
+            return dt.isoformat() + "Z"
+        return dt.isoformat()
+
+
+class SnippetGenerator:
+    """Stateless generator for text snippets and match operations."""
+
+    @staticmethod
+    def find_all_matches(text: str, query: str) -> Iterator[int]:
+        """Generate all match positions for a query in text."""
+        if not text:
+            logger.warning("Empty text for search query in find_all_matches")
+            return
+        if not query:
+            logger.warning("Empty query for search text in find_all_matches")
+            return
+
+        text_lower = text.lower()
+        query_lower = query.lower()
+        start = 0
+        prev_start = start
+        while (pos := text_lower.find(query_lower, start)) != -1:
+            yield pos
+            start = pos + len(query_lower)
+            if start <= prev_start:
+                raise ValueError("panic! find_all_matches is not incremental")
+            prev_start = start
+
+    @staticmethod
+    def count_matches(text: str, query: str) -> NonNegativeInt:
+        """Count total number of matches for a query in text."""
+        ZERO = NonNegativeInt(0)
+        if not text:
+            logger.warning("Empty text for search query in count_matches")
+            return ZERO
+        if not query:
+            logger.warning("Empty query for search text in count_matches")
+            return ZERO
+        return NonNegativeInt(
+            sum(1 for _ in SnippetGenerator.find_all_matches(text, query))
+        )
+
+    @staticmethod
+    def create_snippet(
+        text: str, match_pos: int, max_length: int = DEFAULT_SNIPPET_MAX_LENGTH
+    ) -> SnippetCandidate:
+        """Create a snippet from a match position."""
+        snippet_start = NonNegativeInt(max(0, match_pos - SNIPPET_CONTEXT_LENGTH))
+        snippet_end = min(len(text), match_pos + max_length - SNIPPET_CONTEXT_LENGTH)
+
+        snippet_text = text[snippet_start:snippet_end]
+
+        return SnippetCandidate(
+            _text=snippet_text, start=snippet_start, _original_text_length=len(text)
+        )
+
+    @staticmethod
+    def filter_non_overlapping(
+        candidates: Iterator[SnippetCandidate],
+    ) -> Iterator[str]:
+        """Filter out overlapping snippets and return only display text."""
+        last_end = 0
+        for candidate in candidates:
+            display_text = candidate.text()
+            # it means that next overlapping snippets simply don't get included
+            # it's fine as simplistic logic and users probably won't care much because they already have their search results just fin
+            if candidate.start >= last_end and display_text:
+                yield display_text
+                last_end = candidate.end
+
+    @staticmethod
+    def generate(
+        text: str,
+        query: str,
+        max_length: NonNegativeInt = DEFAULT_SNIPPET_MAX_LENGTH,
+        max_snippets: NonNegativeInt = DEFAULT_MAX_SNIPPETS,
+    ) -> list[str]:
+        """Generate snippets from text."""
+        if not text or not query:
+            logger.warning("Empty text or query for generate_snippets")
+            return []
+
+        candidates = (
+            SnippetGenerator.create_snippet(text, pos, max_length)
+            for pos in SnippetGenerator.find_all_matches(text, query)
+        )
+        filtered = SnippetGenerator.filter_non_overlapping(candidates)
+        snippets = list(itertools.islice(filtered, max_snippets))
+
+        # Fallback to first word search if no full matches
+        # it's another assumption: proper snippet logic generation is quite complicated and tied to db logic, so simplification is used here
+        if not snippets and " " in query:
+            first_word = query.split()[0]
+            return SnippetGenerator.generate(text, first_word, max_length, max_snippets)
+
+        return snippets
+
+    @staticmethod
+    def from_summary(
+        summary: str,
+        query: str,
+        max_snippets: NonNegativeInt = LONG_SUMMARY_MAX_SNIPPETS,
+    ) -> list[str]:
+        """Generate snippets from summary text."""
+        return SnippetGenerator.generate(summary, query, max_snippets=max_snippets)
+
+    @staticmethod
+    def combine_sources(
+        summary: str | None,
+        webvtt: WebVTTContent | None,
+        query: str,
+        max_total: NonNegativeInt = DEFAULT_MAX_SNIPPETS,
+    ) -> tuple[list[str], NonNegativeInt]:
+        """Combine snippets from multiple sources and return total match count.
+
+        Returns (snippets, total_match_count) tuple.
+
+        snippets can be empty for real in case of e.g. title match
+        """
+        webvtt_matches = 0
+        summary_matches = 0
+
+        if webvtt:
+            webvtt_text = WebVTTProcessor.extract_text(webvtt)
+            webvtt_matches = SnippetGenerator.count_matches(webvtt_text, query)
+
+        if summary:
+            summary_matches = SnippetGenerator.count_matches(summary, query)
+
+        total_matches = NonNegativeInt(webvtt_matches + summary_matches)
+
+        summary_snippets = (
+            SnippetGenerator.from_summary(summary, query) if summary else []
+        )
+
+        if len(summary_snippets) >= max_total:
+            return summary_snippets[:max_total], total_matches
+
+        remaining = max_total - len(summary_snippets)
+        webvtt_snippets = (
+            WebVTTProcessor.generate_snippets(webvtt, query, remaining)
+            if webvtt
+            else []
+        )
+
+        return summary_snippets + webvtt_snippets, total_matches
+
+
+class SearchController:
+    """Controller for search operations across different entities."""
+
+    @classmethod
+    async def search_transcripts(
+        cls, params: SearchParameters
+    ) -> tuple[list[SearchResult], int]:
+        """
+        Full-text search for transcripts using PostgreSQL tsvector.
+        Returns (results, total_count).
+        """
+
+        if not is_postgresql():
+            logger.warning(
+                "Full-text search requires PostgreSQL. Returning empty results."
+            )
+            return [], 0
+
+        base_columns = [
+            transcripts.c.id,
+            transcripts.c.title,
+            transcripts.c.created_at,
+            transcripts.c.duration,
+            transcripts.c.status,
+            transcripts.c.user_id,
+            transcripts.c.room_id,
+            transcripts.c.source_kind,
+            transcripts.c.webvtt,
+            transcripts.c.long_summary,
+            sqlalchemy.case(
+                (
+                    transcripts.c.room_id.isnot(None) & rooms.c.id.is_(None),
+                    "Deleted Room",
+                ),
+                else_=rooms.c.name,
+            ).label("room_name"),
+        ]
+
+        if params.query_text:
+            search_query = sqlalchemy.func.websearch_to_tsquery(
+                "english", params.query_text
+            )
+            rank_column = sqlalchemy.func.ts_rank(
+                transcripts.c.search_vector_en,
+                search_query,
+                32,  # normalization flag: rank/(rank+1) for 0-1 range
+            ).label("rank")
+        else:
+            rank_column = sqlalchemy.cast(1.0, sqlalchemy.Float).label("rank")
+
+        columns = base_columns + [rank_column]
+        base_query = sqlalchemy.select(columns).select_from(
+            transcripts.join(rooms, transcripts.c.room_id == rooms.c.id, isouter=True)
+        )
+
+        if params.query_text:
+            base_query = base_query.where(
+                transcripts.c.search_vector_en.op("@@")(search_query)
+            )
+
+        if params.user_id:
+            base_query = base_query.where(
+                sqlalchemy.or_(
+                    transcripts.c.user_id == params.user_id, rooms.c.is_shared
+                )
+            )
+        else:
+            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)
+        if params.source_kind:
+            base_query = base_query.where(
+                transcripts.c.source_kind == params.source_kind
+            )
+
+        if params.query_text:
+            order_by = sqlalchemy.desc(sqlalchemy.text("rank"))
+        else:
+            order_by = sqlalchemy.desc(transcripts.c.created_at)
+
+        query = base_query.order_by(order_by).limit(params.limit).offset(params.offset)
+
+        rs = await get_database().fetch_all(query)
+
+        count_query = sqlalchemy.select([sqlalchemy.func.count()]).select_from(
+            base_query.alias("search_results")
+        )
+        total = await get_database().fetch_val(count_query)
+
+        def _process_result(r) -> SearchResult:
+            r_dict: Dict[str, Any] = dict(r)
+            webvtt_raw: str | None = r_dict.pop("webvtt", None)
+            if webvtt_raw:
+                webvtt = WebVTTProcessor.parse(webvtt_raw)
+            else:
+                webvtt = None
+            long_summary: str | None = r_dict.pop("long_summary", None)
+            room_name: str | None = r_dict.pop("room_name", None)
+            db_result = SearchResultDB.model_validate(r_dict)
+
+            snippets, total_match_count = SnippetGenerator.combine_sources(
+                long_summary, webvtt, params.query_text, DEFAULT_MAX_SNIPPETS
+            )
+
+            return SearchResult(
+                **db_result.model_dump(),
+                room_name=room_name,
+                search_snippets=snippets,
+                total_match_count=total_match_count,
+            )
+
+        try:
+            results = [_process_result(r) for r in rs]
+        except ValidationError as e:
+            logger.error(f"Invalid search result data: {e}", exc_info=True)
+            raise HTTPException(
+                status_code=500, detail="Internal search result data consistency error"
+            )
+        except Exception as e:
+            logger.error(f"Error processing search results: {e}", exc_info=True)
+            raise
+
+        return results, total
+
+
+search_controller = SearchController()
+webvtt_processor = WebVTTProcessor()
+snippet_generator = SnippetGenerator()

server/reflector/db/transcripts.py

@@ -3,7 +3,7 @@ import json
 import os
 import shutil
 from contextlib import asynccontextmanager
-from datetime import datetime, timezone
+from datetime import datetime, timedelta, timezone
 from pathlib import Path
 from typing import Any, Literal
 
@@ -11,13 +11,19 @@ import sqlalchemy
 from fastapi import HTTPException
 from pydantic import BaseModel, ConfigDict, Field, field_serializer
 from sqlalchemy import Enum
+from sqlalchemy.dialects.postgresql import TSVECTOR
 from sqlalchemy.sql import false, or_
 
-from reflector.db import database, metadata
+from reflector.db import get_database, metadata
+from reflector.db.recordings import recordings_controller
+from reflector.db.rooms import rooms
+from reflector.db.utils import is_postgresql
+from reflector.logger import logger
 from reflector.processors.types import Word as ProcessorWord
 from reflector.settings import settings
-from reflector.storage import get_transcripts_storage
+from reflector.storage import get_recordings_storage, get_transcripts_storage
 from reflector.utils import generate_uuid4
+from reflector.utils.webvtt import topics_to_webvtt
 
 
 class SourceKind(enum.StrEnum):
@@ -34,7 +40,7 @@ transcripts = sqlalchemy.Table(
     sqlalchemy.Column("status", sqlalchemy.String),
     sqlalchemy.Column("locked", sqlalchemy.Boolean),
     sqlalchemy.Column("duration", sqlalchemy.Float),
-    sqlalchemy.Column("created_at", sqlalchemy.DateTime),
+    sqlalchemy.Column("created_at", sqlalchemy.DateTime(timezone=True)),
     sqlalchemy.Column("title", sqlalchemy.String),
     sqlalchemy.Column("short_summary", sqlalchemy.String),
     sqlalchemy.Column("long_summary", sqlalchemy.String),
@@ -76,13 +82,40 @@ transcripts = sqlalchemy.Table(
     # same field could've been in recording/meeting, and it's maybe even ok to dupe it at need
     sqlalchemy.Column("audio_deleted", sqlalchemy.Boolean),
     sqlalchemy.Column("room_id", sqlalchemy.String),
+    sqlalchemy.Column("webvtt", sqlalchemy.Text),
     sqlalchemy.Index("idx_transcript_recording_id", "recording_id"),
     sqlalchemy.Index("idx_transcript_user_id", "user_id"),
     sqlalchemy.Index("idx_transcript_created_at", "created_at"),
     sqlalchemy.Index("idx_transcript_user_id_recording_id", "user_id", "recording_id"),
     sqlalchemy.Index("idx_transcript_room_id", "room_id"),
+    sqlalchemy.Index("idx_transcript_source_kind", "source_kind"),
+    sqlalchemy.Index("idx_transcript_room_id_created_at", "room_id", "created_at"),
 )
 
+# Add PostgreSQL-specific full-text search column
+# This matches the migration in migrations/versions/116b2f287eab_add_full_text_search.py
+if is_postgresql():
+    transcripts.append_column(
+        sqlalchemy.Column(
+            "search_vector_en",
+            TSVECTOR,
+            sqlalchemy.Computed(
+                "setweight(to_tsvector('english', coalesce(title, '')), 'A') || "
+                "setweight(to_tsvector('english', coalesce(long_summary, '')), 'B') || "
+                "setweight(to_tsvector('english', coalesce(webvtt, '')), 'C')",
+                persisted=True,
+            ),
+        )
+    )
+    # Add GIN index for the search vector
+    transcripts.append_constraint(
+        sqlalchemy.Index(
+            "idx_transcript_search_vector_en",
+            "search_vector_en",
+            postgresql_using="gin",
+        )
+    )
+
 
 def generate_transcript_name() -> str:
     now = datetime.now(timezone.utc)
@@ -147,14 +180,18 @@ class TranscriptParticipant(BaseModel):
 
 
 class Transcript(BaseModel):
+    """Full transcript model with all fields."""
+
     id: str = Field(default_factory=generate_uuid4)
     user_id: str | None = None
     name: str = Field(default_factory=generate_transcript_name)
     status: str = "idle"
-    locked: bool = False
     duration: float = 0
     created_at: datetime = Field(default_factory=lambda: datetime.now(timezone.utc))
     title: str | None = None
+    source_kind: SourceKind
+    room_id: str | None = None
+    locked: bool = False
     short_summary: str | None = None
     long_summary: str | None = None
     topics: list[TranscriptTopic] = []
@@ -168,9 +205,8 @@ class Transcript(BaseModel):
     meeting_id: str | None = None
     recording_id: str | None = None
     zulip_message_id: int | None = None
-    source_kind: SourceKind
     audio_deleted: bool | None = None
-    room_id: str | None = None
+    webvtt: str | None = None
 
     @field_serializer("created_at", when_used="json")
     def serialize_datetime(self, dt: datetime) -> str:
@@ -271,10 +307,12 @@ class Transcript(BaseModel):
         # we need to create an url to be used for diarization
         # we can't use the audio_mp3_filename because it's not accessible
         # from the diarization processor
-        from datetime import timedelta
 
-        from reflector.app import app
-        from reflector.views.transcripts import create_access_token
+        # TODO don't import app in db
+        from reflector.app import app  # noqa: PLC0415
+
+        # TODO a util + don''t import views in db
+        from reflector.views.transcripts import create_access_token  # noqa: PLC0415
 
         path = app.url_path_for(
             "transcript_get_audio_mp3",
@@ -335,7 +373,6 @@ class TranscriptController:
         - `room_id`: filter transcripts by room ID
         - `search_term`: filter transcripts by search term
         """
-        from reflector.db.rooms import rooms
 
         query = transcripts.select().join(
             rooms, transcripts.c.room_id == rooms.c.id, isouter=True
@@ -386,7 +423,7 @@ class TranscriptController:
         if return_query:
             return query
 
-        results = await database.fetch_all(query)
+        results = await get_database().fetch_all(query)
         return results
 
     async def get_by_id(self, transcript_id: str, **kwargs) -> Transcript | None:
@@ -396,7 +433,7 @@ class TranscriptController:
         query = transcripts.select().where(transcripts.c.id == transcript_id)
         if "user_id" in kwargs:
             query = query.where(transcripts.c.user_id == kwargs["user_id"])
-        result = await database.fetch_one(query)
+        result = await get_database().fetch_one(query)
         if not result:
             return None
         return Transcript(**result)
@@ -410,7 +447,7 @@ class TranscriptController:
         query = transcripts.select().where(transcripts.c.recording_id == recording_id)
         if "user_id" in kwargs:
             query = query.where(transcripts.c.user_id == kwargs["user_id"])
-        result = await database.fetch_one(query)
+        result = await get_database().fetch_one(query)
         if not result:
             return None
         return Transcript(**result)
@@ -428,7 +465,7 @@ class TranscriptController:
             if order_by.startswith("-"):
                 field = field.desc()
             query = query.order_by(field)
-        results = await database.fetch_all(query)
+        results = await get_database().fetch_all(query)
         return [Transcript(**result) for result in results]
 
     async def get_by_id_for_http(
@@ -446,7 +483,7 @@ class TranscriptController:
         to determine if the user can access the transcript.
         """
         query = transcripts.select().where(transcripts.c.id == transcript_id)
-        result = await database.fetch_one(query)
+        result = await get_database().fetch_one(query)
         if not result:
             raise HTTPException(status_code=404, detail="Transcript not found")
 
@@ -499,23 +536,52 @@ class TranscriptController:
             room_id=room_id,
         )
         query = transcripts.insert().values(**transcript.model_dump())
-        await database.execute(query)
+        await get_database().execute(query)
         return transcript
 
-    async def update(self, transcript: Transcript, values: dict, mutate=True):
+    # TODO investigate why mutate= is used. it's used in one place currently, maybe because of ORM field updates.
+    # using mutate=True is discouraged
+    async def update(
+        self, transcript: Transcript, values: dict, mutate=False
+    ) -> Transcript:
         """
-        Update a transcript fields with key/values in values
+        Update a transcript fields with key/values in values.
+        Returns a copy of the transcript with updated values.
         """
+        values = TranscriptController._handle_topics_update(values)
+
         query = (
             transcripts.update()
             .where(transcripts.c.id == transcript.id)
             .values(**values)
         )
-        await database.execute(query)
+        await get_database().execute(query)
         if mutate:
             for key, value in values.items():
                 setattr(transcript, key, value)
 
+        updated_transcript = transcript.model_copy(update=values)
+        return updated_transcript
+
+    @staticmethod
+    def _handle_topics_update(values: dict) -> dict:
+        """Auto-update WebVTT when topics are updated."""
+
+        if values.get("webvtt") is not None:
+            logger.warn("trying to update read-only webvtt column")
+            pass
+
+        topics_data = values.get("topics")
+        if topics_data is None:
+            return values
+
+        return {
+            **values,
+            "webvtt": topics_to_webvtt(
+                [TranscriptTopic(**topic_dict) for topic_dict in topics_data]
+            ),
+        }
+
     async def remove_by_id(
         self,
         transcript_id: str,
@@ -529,23 +595,55 @@ class TranscriptController:
             return
         if user_id is not None and transcript.user_id != user_id:
             return
+        if transcript.audio_location == "storage" and not transcript.audio_deleted:
+            try:
+                await get_transcripts_storage().delete_file(
+                    transcript.storage_audio_path
+                )
+            except Exception as e:
+                logger.warning(
+                    "Failed to delete transcript audio from storage",
+                    exc_info=e,
+                    transcript_id=transcript.id,
+                )
         transcript.unlink()
+        if transcript.recording_id:
+            try:
+                recording = await recordings_controller.get_by_id(
+                    transcript.recording_id
+                )
+                if recording:
+                    try:
+                        await get_recordings_storage().delete_file(recording.object_key)
+                    except Exception as e:
+                        logger.warning(
+                            "Failed to delete recording object from S3",
+                            exc_info=e,
+                            recording_id=transcript.recording_id,
+                        )
+                    await recordings_controller.remove_by_id(transcript.recording_id)
+            except Exception as e:
+                logger.warning(
+                    "Failed to delete recording row",
+                    exc_info=e,
+                    recording_id=transcript.recording_id,
+                )
         query = transcripts.delete().where(transcripts.c.id == transcript_id)
-        await database.execute(query)
+        await get_database().execute(query)
 
     async def remove_by_recording_id(self, recording_id: str):
         """
         Remove a transcript by recording_id
         """
         query = transcripts.delete().where(transcripts.c.recording_id == recording_id)
-        await database.execute(query)
+        await get_database().execute(query)
 
     @asynccontextmanager
     async def transaction(self):
         """
         A context manager for database transaction
         """
-        async with database.transaction(isolation="serializable"):
+        async with get_database().transaction(isolation="serializable"):
             yield
 
     async def append_event(
@@ -558,11 +656,7 @@ class TranscriptController:
         Append an event to a transcript
         """
         resp = transcript.add_event(event=event, data=data)
-        await self.update(
-            transcript,
-            {"events": transcript.events_dump()},
-            mutate=False,
-        )
+        await self.update(transcript, {"events": transcript.events_dump()})
         return resp
 
     async def upsert_topic(
@@ -574,11 +668,7 @@ class TranscriptController:
         Upsert topics to a transcript
         """
         transcript.upsert_topic(topic)
-        await self.update(
-            transcript,
-            {"topics": transcript.topics_dump()},
-            mutate=False,
-        )
+        await self.update(transcript, {"topics": transcript.topics_dump()})
 
     async def move_mp3_to_storage(self, transcript: Transcript):
         """
@@ -603,7 +693,8 @@ class TranscriptController:
             )
 
             # indicate on the transcript that the audio is now on storage
-            await self.update(transcript, {"audio_location": "storage"})
+            # mutates transcript argument
+            await self.update(transcript, {"audio_location": "storage"}, mutate=True)
 
         # unlink the local file
         transcript.audio_mp3_filename.unlink(missing_ok=True)
@@ -627,11 +718,7 @@ class TranscriptController:
         Add/update a participant to a transcript
         """
         result = transcript.upsert_participant(participant)
-        await self.update(
-            transcript,
-            {"participants": transcript.participants_dump()},
-            mutate=False,
-        )
+        await self.update(transcript, {"participants": transcript.participants_dump()})
         return result
 
     async def delete_participant(
@@ -643,11 +730,7 @@ class TranscriptController:
         Delete a participant from a transcript
         """
         transcript.delete_participant(participant_id)
-        await self.update(
-            transcript,
-            {"participants": transcript.participants_dump()},
-            mutate=False,
-        )
+        await self.update(transcript, {"participants": transcript.participants_dump()})
 
 
 transcripts_controller = TranscriptController()

server/reflector/db/utils.py

@@ -0,0 +1,9 @@
+"""Database utility functions."""
+
+from reflector.db import get_database
+
+
+def is_postgresql() -> bool:
+    return get_database().url.scheme and get_database().url.scheme.startswith(
+        "postgresql"
+    )

server/reflector/pipelines/main_file_pipeline.py

@@ -0,0 +1,375 @@
+"""
+File-based processing pipeline
+==============================
+
+Optimized pipeline for processing complete audio/video files.
+Uses parallel processing for transcription, diarization, and waveform generation.
+"""
+
+import asyncio
+from pathlib import Path
+
+import av
+import structlog
+from celery import shared_task
+
+from reflector.db.transcripts import (
+    Transcript,
+    transcripts_controller,
+)
+from reflector.logger import logger
+from reflector.pipelines.main_live_pipeline import PipelineMainBase, asynctask
+from reflector.processors import (
+    AudioFileWriterProcessor,
+    TranscriptFinalSummaryProcessor,
+    TranscriptFinalTitleProcessor,
+    TranscriptTopicDetectorProcessor,
+)
+from reflector.processors.audio_waveform_processor import AudioWaveformProcessor
+from reflector.processors.file_diarization import FileDiarizationInput
+from reflector.processors.file_diarization_auto import FileDiarizationAutoProcessor
+from reflector.processors.file_transcript import FileTranscriptInput
+from reflector.processors.file_transcript_auto import FileTranscriptAutoProcessor
+from reflector.processors.transcript_diarization_assembler import (
+    TranscriptDiarizationAssemblerInput,
+    TranscriptDiarizationAssemblerProcessor,
+)
+from reflector.processors.types import (
+    DiarizationSegment,
+    TitleSummary,
+)
+from reflector.processors.types import (
+    Transcript as TranscriptType,
+)
+from reflector.settings import settings
+from reflector.storage import get_transcripts_storage
+
+
+class EmptyPipeline:
+    """Empty pipeline for processors that need a pipeline reference"""
+
+    def __init__(self, logger: structlog.BoundLogger):
+        self.logger = logger
+
+    def get_pref(self, k, d=None):
+        return d
+
+    async def emit(self, event):
+        pass
+
+
+class PipelineMainFile(PipelineMainBase):
+    """
+    Optimized file processing pipeline.
+    Processes complete audio/video files with parallel execution.
+    """
+
+    logger: structlog.BoundLogger = None
+    empty_pipeline = None
+
+    def __init__(self, transcript_id: str):
+        super().__init__(transcript_id=transcript_id)
+        self.logger = logger.bind(transcript_id=self.transcript_id)
+        self.empty_pipeline = EmptyPipeline(logger=self.logger)
+
+    def _handle_gather_exceptions(self, results: list, operation: str) -> None:
+        """Handle exceptions from asyncio.gather with return_exceptions=True"""
+        for i, result in enumerate(results):
+            if not isinstance(result, Exception):
+                continue
+            self.logger.error(
+                f"Error in {operation} (task {i}): {result}",
+                transcript_id=self.transcript_id,
+                exc_info=result,
+            )
+
+    async def process(self, file_path: Path):
+        """Main entry point for file processing"""
+        self.logger.info(f"Starting file pipeline for {file_path}")
+
+        transcript = await self.get_transcript()
+
+        # Extract audio and write to transcript location
+        audio_path = await self.extract_and_write_audio(file_path, transcript)
+
+        # Upload for processing
+        audio_url = await self.upload_audio(audio_path, transcript)
+
+        # Run parallel processing
+        await self.run_parallel_processing(
+            audio_path,
+            audio_url,
+            transcript.source_language,
+            transcript.target_language,
+        )
+
+        self.logger.info("File pipeline complete")
+
+    async def extract_and_write_audio(
+        self, file_path: Path, transcript: Transcript
+    ) -> Path:
+        """Extract audio from video if needed and write to transcript location as MP3"""
+        self.logger.info(f"Processing audio file: {file_path}")
+
+        # Check if it's already audio-only
+        container = av.open(str(file_path))
+        has_video = len(container.streams.video) > 0
+        container.close()
+
+        # Use AudioFileWriterProcessor to write MP3 to transcript location
+        mp3_writer = AudioFileWriterProcessor(
+            path=transcript.audio_mp3_filename,
+            on_duration=self.on_duration,
+        )
+
+        # Process audio frames and write to transcript location
+        input_container = av.open(str(file_path))
+        for frame in input_container.decode(audio=0):
+            await mp3_writer.push(frame)
+
+        await mp3_writer.flush()
+        input_container.close()
+
+        if has_video:
+            self.logger.info(
+                f"Extracted audio from video and saved to {transcript.audio_mp3_filename}"
+            )
+        else:
+            self.logger.info(
+                f"Converted audio file and saved to {transcript.audio_mp3_filename}"
+            )
+
+        return transcript.audio_mp3_filename
+
+    async def upload_audio(self, audio_path: Path, transcript: Transcript) -> str:
+        """Upload audio to storage for processing"""
+        storage = get_transcripts_storage()
+
+        if not storage:
+            raise Exception(
+                "Storage backend required for file processing. Configure TRANSCRIPT_STORAGE_* settings."
+            )
+
+        self.logger.info("Uploading audio to storage")
+
+        with open(audio_path, "rb") as f:
+            audio_data = f.read()
+
+        storage_path = f"file_pipeline/{transcript.id}/audio.mp3"
+        await storage.put_file(storage_path, audio_data)
+
+        audio_url = await storage.get_file_url(storage_path)
+
+        self.logger.info(f"Audio uploaded to {audio_url}")
+        return audio_url
+
+    async def run_parallel_processing(
+        self,
+        audio_path: Path,
+        audio_url: str,
+        source_language: str,
+        target_language: str,
+    ):
+        """Coordinate parallel processing of transcription, diarization, and waveform"""
+        self.logger.info(
+            "Starting parallel processing", transcript_id=self.transcript_id
+        )
+
+        # Phase 1: Parallel processing of independent tasks
+        transcription_task = self.transcribe_file(audio_url, source_language)
+        diarization_task = self.diarize_file(audio_url)
+        waveform_task = self.generate_waveform(audio_path)
+
+        results = await asyncio.gather(
+            transcription_task, diarization_task, waveform_task, return_exceptions=True
+        )
+
+        transcript_result = results[0]
+        diarization_result = results[1]
+
+        # Handle errors - raise any exception that occurred
+        self._handle_gather_exceptions(results, "parallel processing")
+        for result in results:
+            if isinstance(result, Exception):
+                raise result
+
+        # Phase 2: Assemble transcript with diarization
+        self.logger.info(
+            "Assembling transcript with diarization", transcript_id=self.transcript_id
+        )
+        processor = TranscriptDiarizationAssemblerProcessor()
+        input_data = TranscriptDiarizationAssemblerInput(
+            transcript=transcript_result, diarization=diarization_result or []
+        )
+
+        # Store result for retrieval
+        diarized_transcript: Transcript | None = None
+
+        async def capture_result(transcript):
+            nonlocal diarized_transcript
+            diarized_transcript = transcript
+
+        processor.on(capture_result)
+        await processor.push(input_data)
+        await processor.flush()
+
+        if not diarized_transcript:
+            raise ValueError("No diarized transcript captured")
+
+        # Phase 3: Generate topics from diarized transcript
+        self.logger.info("Generating topics", transcript_id=self.transcript_id)
+        topics = await self.detect_topics(diarized_transcript, target_language)
+
+        # Phase 4: Generate title and summaries in parallel
+        self.logger.info(
+            "Generating title and summaries", transcript_id=self.transcript_id
+        )
+        results = await asyncio.gather(
+            self.generate_title(topics),
+            self.generate_summaries(topics),
+            return_exceptions=True,
+        )
+
+        self._handle_gather_exceptions(results, "title and summary generation")
+
+    async def transcribe_file(self, audio_url: str, language: str) -> TranscriptType:
+        """Transcribe complete file"""
+        processor = FileTranscriptAutoProcessor()
+        input_data = FileTranscriptInput(audio_url=audio_url, language=language)
+
+        # Store result for retrieval
+        result: TranscriptType | None = None
+
+        async def capture_result(transcript):
+            nonlocal result
+            result = transcript
+
+        processor.on(capture_result)
+        await processor.push(input_data)
+        await processor.flush()
+
+        if not result:
+            raise ValueError("No transcript captured")
+
+        return result
+
+    async def diarize_file(self, audio_url: str) -> list[DiarizationSegment] | None:
+        """Get diarization for file"""
+        if not settings.DIARIZATION_BACKEND:
+            self.logger.info("Diarization disabled")
+            return None
+
+        processor = FileDiarizationAutoProcessor()
+        input_data = FileDiarizationInput(audio_url=audio_url)
+
+        # Store result for retrieval
+        result = None
+
+        async def capture_result(diarization_output):
+            nonlocal result
+            result = diarization_output.diarization
+
+        try:
+            processor.on(capture_result)
+            await processor.push(input_data)
+            await processor.flush()
+            return result
+        except Exception as e:
+            self.logger.error(f"Diarization failed: {e}")
+            return None
+
+    async def generate_waveform(self, audio_path: Path):
+        """Generate and save waveform"""
+        transcript = await self.get_transcript()
+
+        processor = AudioWaveformProcessor(
+            audio_path=audio_path,
+            waveform_path=transcript.audio_waveform_filename,
+            on_waveform=self.on_waveform,
+        )
+        processor.set_pipeline(self.empty_pipeline)
+
+        await processor.flush()
+
+    async def detect_topics(
+        self, transcript: TranscriptType, target_language: str
+    ) -> list[TitleSummary]:
+        """Detect topics from complete transcript"""
+        chunk_size = 300
+        topics: list[TitleSummary] = []
+
+        async def on_topic(topic: TitleSummary):
+            topics.append(topic)
+            return await self.on_topic(topic)
+
+        topic_detector = TranscriptTopicDetectorProcessor(callback=on_topic)
+        topic_detector.set_pipeline(self.empty_pipeline)
+
+        for i in range(0, len(transcript.words), chunk_size):
+            chunk_words = transcript.words[i : i + chunk_size]
+            if not chunk_words:
+                continue
+
+            chunk_transcript = TranscriptType(
+                words=chunk_words, translation=transcript.translation
+            )
+
+            await topic_detector.push(chunk_transcript)
+
+        await topic_detector.flush()
+        return topics
+
+    async def generate_title(self, topics: list[TitleSummary]):
+        """Generate title from topics"""
+        if not topics:
+            self.logger.warning("No topics for title generation")
+            return
+
+        processor = TranscriptFinalTitleProcessor(callback=self.on_title)
+        processor.set_pipeline(self.empty_pipeline)
+
+        for topic in topics:
+            await processor.push(topic)
+
+        await processor.flush()
+
+    async def generate_summaries(self, topics: list[TitleSummary]):
+        """Generate long and short summaries from topics"""
+        if not topics:
+            self.logger.warning("No topics for summary generation")
+            return
+
+        transcript = await self.get_transcript()
+        processor = TranscriptFinalSummaryProcessor(
+            transcript=transcript,
+            callback=self.on_long_summary,
+            on_short_summary=self.on_short_summary,
+        )
+        processor.set_pipeline(self.empty_pipeline)
+
+        for topic in topics:
+            await processor.push(topic)
+
+        await processor.flush()
+
+
+@shared_task
+@asynctask
+async def task_pipeline_file_process(*, transcript_id: str):
+    """Celery task for file pipeline processing"""
+
+    transcript = await transcripts_controller.get_by_id(transcript_id)
+    if not transcript:
+        raise Exception(f"Transcript {transcript_id} not found")
+
+    # Find the file to process
+    audio_file = next(transcript.data_path.glob("upload.*"), None)
+    if not audio_file:
+        audio_file = next(transcript.data_path.glob("audio.*"), None)
+
+    if not audio_file:
+        raise Exception("No audio file found to process")
+
+    # Run file pipeline
+    pipeline = PipelineMainFile(transcript_id=transcript_id)
+    await pipeline.process(audio_file)

server/reflector/pipelines/main_live_pipeline.py

@@ -14,12 +14,15 @@ It is directly linked to our data model.
 import asyncio
 import functools
 from contextlib import asynccontextmanager
+from typing import Generic
 
+import av
 import boto3
 from celery import chord, current_task, group, shared_task
 from pydantic import BaseModel
 from structlog import BoundLogger as Logger
 
+from reflector.db import get_database
 from reflector.db.meetings import meeting_consent_controller, meetings_controller
 from reflector.db.recordings import recordings_controller
 from reflector.db.rooms import rooms_controller
@@ -35,10 +38,11 @@ from reflector.db.transcripts import (
     transcripts_controller,
 )
 from reflector.logger import logger
-from reflector.pipelines.runner import PipelineRunner
+from reflector.pipelines.runner import PipelineMessage, PipelineRunner
 from reflector.processors import (
-    AudioChunkerProcessor,
+    AudioChunkerAutoProcessor,
     AudioDiarizationAutoProcessor,
+    AudioDownscaleProcessor,
     AudioFileWriterProcessor,
     AudioMergeProcessor,
     AudioTranscriptAutoProcessor,
@@ -47,7 +51,7 @@ from reflector.processors import (
     TranscriptFinalTitleProcessor,
     TranscriptLinerProcessor,
     TranscriptTopicDetectorProcessor,
-    TranscriptTranslatorProcessor,
+    TranscriptTranslatorAutoProcessor,
 )
 from reflector.processors.audio_waveform_processor import AudioWaveformProcessor
 from reflector.processors.types import AudioDiarizationInput
@@ -69,8 +73,7 @@ def asynctask(f):
     @functools.wraps(f)
     def wrapper(*args, **kwargs):
         async def run_with_db():
-            from reflector.db import database
-
+            database = get_database()
             await database.connect()
             try:
                 return await f(*args, **kwargs)
@@ -144,16 +147,19 @@ class StrValue(BaseModel):
     value: str
 
 
-class PipelineMainBase(PipelineRunner):
-    transcript_id: str
-    ws_room_id: str | None = None
-    ws_manager: WebsocketManager | None = None
-
-    def prepare(self):
-        # prepare websocket
+class PipelineMainBase(PipelineRunner[PipelineMessage], Generic[PipelineMessage]):
+    def __init__(self, transcript_id: str):
+        super().__init__()
         self._lock = asyncio.Lock()
+        self.transcript_id = transcript_id
         self.ws_room_id = f"ts:{self.transcript_id}"
-        self.ws_manager = get_ws_manager()
+        self._ws_manager = None
+
+    @property
+    def ws_manager(self) -> WebsocketManager:
+        if self._ws_manager is None:
+            self._ws_manager = get_ws_manager()
+        return self._ws_manager
 
     async def get_transcript(self) -> Transcript:
         # fetch the transcript
@@ -164,7 +170,11 @@ class PipelineMainBase(PipelineRunner):
             raise Exception("Transcript not found")
         return result
 
-    def get_transcript_topics(self, transcript: Transcript) -> list[TranscriptTopic]:
+    @staticmethod
+    def wrap_transcript_topics(
+        topics: list[TranscriptTopic],
+    ) -> list[TitleSummaryWithIdProcessorType]:
+        # transformation to a pipe-supported format
         return [
             TitleSummaryWithIdProcessorType(
                 id=topic.id,
@@ -174,7 +184,7 @@ class PipelineMainBase(PipelineRunner):
                 duration=topic.duration,
                 transcript=TranscriptProcessorType(words=topic.words),
             )
-            for topic in transcript.topics
+            for topic in topics
         ]
 
     @asynccontextmanager
@@ -349,7 +359,6 @@ class PipelineMainLive(PipelineMainBase):
     async def create(self) -> Pipeline:
         # create a context for the whole rtc transaction
         # add a customised logger to the context
-        self.prepare()
         transcript = await self.get_transcript()
 
         processors = [
@@ -357,11 +366,12 @@ class PipelineMainLive(PipelineMainBase):
                 path=transcript.audio_wav_filename,
                 on_duration=self.on_duration,
             ),
-            AudioChunkerProcessor(),
+            AudioDownscaleProcessor(),
+            AudioChunkerAutoProcessor(),
             AudioMergeProcessor(),
             AudioTranscriptAutoProcessor.as_threaded(),
             TranscriptLinerProcessor(),
-            TranscriptTranslatorProcessor.as_threaded(callback=self.on_transcript),
+            TranscriptTranslatorAutoProcessor.as_threaded(callback=self.on_transcript),
             TranscriptTopicDetectorProcessor.as_threaded(callback=self.on_topic),
         ]
         pipeline = Pipeline(*processors)
@@ -370,6 +380,7 @@ class PipelineMainLive(PipelineMainBase):
         pipeline.set_pref("audio:target_language", transcript.target_language)
         pipeline.logger.bind(transcript_id=transcript.id)
         pipeline.logger.info("Pipeline main live created")
+        pipeline.describe()
 
         return pipeline
 
@@ -380,7 +391,7 @@ class PipelineMainLive(PipelineMainBase):
         pipeline_post(transcript_id=self.transcript_id)
 
 
-class PipelineMainDiarization(PipelineMainBase):
+class PipelineMainDiarization(PipelineMainBase[AudioDiarizationInput]):
     """
     Diarize the audio and update topics
     """
@@ -388,7 +399,6 @@ class PipelineMainDiarization(PipelineMainBase):
     async def create(self) -> Pipeline:
         # create a context for the whole rtc transaction
         # add a customised logger to the context
-        self.prepare()
         pipeline = Pipeline(
             AudioDiarizationAutoProcessor(callback=self.on_topic),
         )
@@ -404,11 +414,10 @@ class PipelineMainDiarization(PipelineMainBase):
             pipeline.logger.info("Audio is local, skipping diarization")
             return
 
-        topics = self.get_transcript_topics(transcript)
         audio_url = await transcript.get_audio_url()
         audio_diarization_input = AudioDiarizationInput(
             audio_url=audio_url,
-            topics=topics,
+            topics=self.wrap_transcript_topics(transcript.topics),
         )
 
         # as tempting to use pipeline.push, prefer to use the runner
@@ -421,7 +430,7 @@ class PipelineMainDiarization(PipelineMainBase):
         return pipeline
 
 
-class PipelineMainFromTopics(PipelineMainBase):
+class PipelineMainFromTopics(PipelineMainBase[TitleSummaryWithIdProcessorType]):
     """
     Pseudo class for generating a pipeline from topics
     """
@@ -430,8 +439,6 @@ class PipelineMainFromTopics(PipelineMainBase):
         raise NotImplementedError
 
     async def create(self) -> Pipeline:
-        self.prepare()
-
         # get transcript
         self._transcript = transcript = await self.get_transcript()
 
@@ -443,7 +450,7 @@ class PipelineMainFromTopics(PipelineMainBase):
         pipeline.logger.info(f"{self.__class__.__name__} pipeline created")
 
         # push topics
-        topics = self.get_transcript_topics(transcript)
+        topics = PipelineMainBase.wrap_transcript_topics(transcript.topics)
         for topic in topics:
             await self.push(topic)
 
@@ -524,8 +531,6 @@ async def pipeline_convert_to_mp3(transcript: Transcript, logger: Logger):
     # Convert to mp3
     mp3_filename = transcript.audio_mp3_filename
 
-    import av
-
     with av.open(wav_filename.as_posix()) as in_container:
         in_stream = in_container.streams.audio[0]
         with av.open(mp3_filename.as_posix(), "w") as out_container:
@@ -604,7 +609,7 @@ async def cleanup_consent(transcript: Transcript, logger: Logger):
                         meeting.id
                     )
     except Exception as e:
-        logger.error(f"Failed to get fetch consent: {e}")
+        logger.error(f"Failed to get fetch consent: {e}", exc_info=e)
         consent_denied = True
 
     if not consent_denied:
@@ -627,7 +632,7 @@ async def cleanup_consent(transcript: Transcript, logger: Logger):
                 f"Deleted original Whereby recording: {recording.bucket_name}/{recording.object_key}"
             )
         except Exception as e:
-            logger.error(f"Failed to delete Whereby recording: {e}")
+            logger.error(f"Failed to delete Whereby recording: {e}", exc_info=e)
 
     # non-transactional, files marked for deletion not actually deleted is possible
     await transcripts_controller.update(transcript, {"audio_deleted": True})
@@ -640,7 +645,7 @@ async def cleanup_consent(transcript: Transcript, logger: Logger):
                 f"Deleted processed audio from storage: {transcript.storage_audio_path}"
             )
         except Exception as e:
-            logger.error(f"Failed to delete processed audio: {e}")
+            logger.error(f"Failed to delete processed audio: {e}", exc_info=e)
 
     # 3. Delete local audio files
     try:
@@ -649,7 +654,7 @@ async def cleanup_consent(transcript: Transcript, logger: Logger):
         if hasattr(transcript, "audio_wav_filename") and transcript.audio_wav_filename:
             transcript.audio_wav_filename.unlink(missing_ok=True)
     except Exception as e:
-        logger.error(f"Failed to delete local audio files: {e}")
+        logger.error(f"Failed to delete local audio files: {e}", exc_info=e)
 
     logger.info("Consent cleanup done")
 
@@ -794,8 +799,6 @@ def pipeline_post(*, transcript_id: str):
 
 @get_transcript
 async def pipeline_process(transcript: Transcript, logger: Logger):
-    import av
-
     try:
         if transcript.audio_location == "storage":
             await transcripts_controller.download_mp3_from_storage(transcript)

server/reflector/pipelines/runner.py

@@ -16,21 +16,16 @@ During its lifecycle, it will emit the following status:
 """
 
 import asyncio
-
-from pydantic import BaseModel, ConfigDict
+from typing import Generic, TypeVar
 
 from reflector.logger import logger
 from reflector.processors import Pipeline
 
+PipelineMessage = TypeVar("PipelineMessage")
 
-class PipelineRunner(BaseModel):
-    model_config = ConfigDict(arbitrary_types_allowed=True)
 
-    status: str = "idle"
-    pipeline: Pipeline | None = None
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
+class PipelineRunner(Generic[PipelineMessage]):
+    def __init__(self):
         self._task = None
         self._q_cmd = asyncio.Queue(maxsize=4096)
         self._ev_done = asyncio.Event()
@@ -39,6 +34,8 @@ class PipelineRunner(BaseModel):
             runner=id(self),
             runner_cls=self.__class__.__name__,
         )
+        self.status = "idle"
+        self.pipeline: Pipeline | None = None
 
     async def create(self) -> Pipeline:
         """
@@ -67,7 +64,7 @@ class PipelineRunner(BaseModel):
         coro = self.run()
         asyncio.run(coro)
 
-    async def push(self, data):
+    async def push(self, data: PipelineMessage):
         """
         Push data to the pipeline
         """
@@ -92,7 +89,11 @@ class PipelineRunner(BaseModel):
         pass
 
     async def _add_cmd(
-        self, cmd: str, data, max_retries: int = 3, retry_time_limit: int = 3
+        self,
+        cmd: str,
+        data: PipelineMessage,
+        max_retries: int = 3,
+        retry_time_limit: int = 3,
     ):
         """
         Enqueue a command to be executed in the runner.
@@ -143,7 +144,10 @@ class PipelineRunner(BaseModel):
                 cmd, data = await self._q_cmd.get()
                 func = getattr(self, f"cmd_{cmd.lower()}")
                 if func:
-                    await func(data)
+                    if cmd.upper() == "FLUSH":
+                        await func()
+                    else:
+                        await func(data)
                 else:
                     raise Exception(f"Unknown command {cmd}")
         except Exception:
@@ -152,13 +156,13 @@ class PipelineRunner(BaseModel):
             self._ev_done.set()
             raise
 
-    async def cmd_push(self, data):
+    async def cmd_push(self, data: PipelineMessage):
         if self._is_first_push:
             await self._set_status("push")
             self._is_first_push = False
         await self.pipeline.push(data)
 
-    async def cmd_flush(self, data):
+    async def cmd_flush(self):
         await self._set_status("flush")
         await self.pipeline.flush()
         await self._set_status("ended")

server/reflector/processors/__init__.py

@@ -1,5 +1,7 @@
 from .audio_chunker import AudioChunkerProcessor  # noqa: F401
+from .audio_chunker_auto import AudioChunkerAutoProcessor  # noqa: F401
 from .audio_diarization_auto import AudioDiarizationAutoProcessor  # noqa: F401
+from .audio_downscale import AudioDownscaleProcessor  # noqa: F401
 from .audio_file_writer import AudioFileWriterProcessor  # noqa: F401
 from .audio_merge import AudioMergeProcessor  # noqa: F401
 from .audio_transcript import AudioTranscriptProcessor  # noqa: F401
@@ -11,11 +13,19 @@ from .base import (  # noqa: F401
     Processor,
     ThreadedProcessor,
 )
+from .file_diarization import FileDiarizationProcessor  # noqa: F401
+from .file_diarization_auto import FileDiarizationAutoProcessor  # noqa: F401
+from .file_transcript import FileTranscriptProcessor  # noqa: F401
+from .file_transcript_auto import FileTranscriptAutoProcessor  # noqa: F401
+from .transcript_diarization_assembler import (
+    TranscriptDiarizationAssemblerProcessor,  # noqa: F401
+)
 from .transcript_final_summary import TranscriptFinalSummaryProcessor  # noqa: F401
 from .transcript_final_title import TranscriptFinalTitleProcessor  # noqa: F401
 from .transcript_liner import TranscriptLinerProcessor  # noqa: F401
 from .transcript_topic_detector import TranscriptTopicDetectorProcessor  # noqa: F401
 from .transcript_translator import TranscriptTranslatorProcessor  # noqa: F401
+from .transcript_translator_auto import TranscriptTranslatorAutoProcessor  # noqa: F401
 from .types import (  # noqa: F401
     AudioFile,
     FinalLongSummary,

server/reflector/processors/audio_chunker.py

@@ -1,28 +1,78 @@
+from typing import Optional
+
 import av
+from prometheus_client import Counter, Histogram
 
 from reflector.processors.base import Processor
 
 
 class AudioChunkerProcessor(Processor):
     """
-    Assemble audio frames into chunks
+    Base class for assembling audio frames into chunks
     """
 
     INPUT_TYPE = av.AudioFrame
     OUTPUT_TYPE = list[av.AudioFrame]
 
-    def __init__(self, max_frames=256):
-        super().__init__()
+    m_chunk = Histogram(
+        "audio_chunker",
+        "Time spent in AudioChunker.chunk",
+        ["backend"],
+    )
+    m_chunk_call = Counter(
+        "audio_chunker_call",
+        "Number of calls to AudioChunker.chunk",
+        ["backend"],
+    )
+    m_chunk_success = Counter(
+        "audio_chunker_success",
+        "Number of successful calls to AudioChunker.chunk",
+        ["backend"],
+    )
+    m_chunk_failure = Counter(
+        "audio_chunker_failure",
+        "Number of failed calls to AudioChunker.chunk",
+        ["backend"],
+    )
+
+    def __init__(self, *args, **kwargs):
+        name = self.__class__.__name__
+        self.m_chunk = self.m_chunk.labels(name)
+        self.m_chunk_call = self.m_chunk_call.labels(name)
+        self.m_chunk_success = self.m_chunk_success.labels(name)
+        self.m_chunk_failure = self.m_chunk_failure.labels(name)
+        super().__init__(*args, **kwargs)
         self.frames: list[av.AudioFrame] = []
-        self.max_frames = max_frames
 
     async def _push(self, data: av.AudioFrame):
-        self.frames.append(data)
-        if len(self.frames) >= self.max_frames:
-            await self.flush()
+        """Process incoming audio frame"""
+        # Validate audio format on first frame
+        if len(self.frames) == 0:
+            if data.sample_rate != 16000 or len(data.layout.channels) != 1:
+                raise ValueError(
+                    f"AudioChunkerProcessor expects 16kHz mono audio, got {data.sample_rate}Hz "
+                    f"with {len(data.layout.channels)} channel(s). "
+                    f"Use AudioDownscaleProcessor before this processor."
+                )
+
+        try:
+            self.m_chunk_call.inc()
+            with self.m_chunk.time():
+                result = await self._chunk(data)
+            self.m_chunk_success.inc()
+            if result:
+                await self.emit(result)
+        except Exception:
+            self.m_chunk_failure.inc()
+            raise
+
+    async def _chunk(self, data: av.AudioFrame) -> Optional[list[av.AudioFrame]]:
+        """
+        Process audio frame and return chunk when ready.
+        Subclasses should implement their chunking logic here.
+        """
+        raise NotImplementedError
 
     async def _flush(self):
-        frames = self.frames[:]
-        self.frames = []
-        if frames:
-            await self.emit(frames)
+        """Flush any remaining frames when processing ends"""
+        raise NotImplementedError

server/reflector/processors/audio_chunker_auto.py

@@ -0,0 +1,32 @@
+import importlib
+
+from reflector.processors.audio_chunker import AudioChunkerProcessor
+from reflector.settings import settings
+
+
+class AudioChunkerAutoProcessor(AudioChunkerProcessor):
+    _registry = {}
+
+    @classmethod
+    def register(cls, name, kclass):
+        cls._registry[name] = kclass
+
+    def __new__(cls, name: str | None = None, **kwargs):
+        if name is None:
+            name = settings.AUDIO_CHUNKER_BACKEND
+        if name not in cls._registry:
+            module_name = f"reflector.processors.audio_chunker_{name}"
+            importlib.import_module(module_name)
+
+        # gather specific configuration for the processor
+        # search `AUDIO_CHUNKER_BACKEND_XXX_YYY`, push to constructor as `backend_xxx_yyy`
+        config = {}
+        name_upper = name.upper()
+        settings_prefix = "AUDIO_CHUNKER_"
+        config_prefix = f"{settings_prefix}{name_upper}_"
+        for key, value in settings:
+            if key.startswith(config_prefix):
+                config_name = key[len(settings_prefix) :].lower()
+                config[config_name] = value
+
+        return cls._registry[name](**config | kwargs)

server/reflector/processors/audio_chunker_frames.py

@@ -0,0 +1,34 @@
+from typing import Optional
+
+import av
+
+from reflector.processors.audio_chunker import AudioChunkerProcessor
+from reflector.processors.audio_chunker_auto import AudioChunkerAutoProcessor
+
+
+class AudioChunkerFramesProcessor(AudioChunkerProcessor):
+    """
+    Simple frame-based audio chunker that emits chunks after a fixed number of frames
+    """
+
+    def __init__(self, max_frames=256, **kwargs):
+        super().__init__(**kwargs)
+        self.max_frames = max_frames
+
+    async def _chunk(self, data: av.AudioFrame) -> Optional[list[av.AudioFrame]]:
+        self.frames.append(data)
+        if len(self.frames) >= self.max_frames:
+            frames_to_emit = self.frames[:]
+            self.frames = []
+            return frames_to_emit
+
+        return None
+
+    async def _flush(self):
+        frames = self.frames[:]
+        self.frames = []
+        if frames:
+            await self.emit(frames)
+
+
+AudioChunkerAutoProcessor.register("frames", AudioChunkerFramesProcessor)

server/reflector/processors/audio_chunker_silero.py

@@ -0,0 +1,298 @@
+from typing import Optional
+
+import av
+import numpy as np
+import torch
+from silero_vad import VADIterator, load_silero_vad
+
+from reflector.processors.audio_chunker import AudioChunkerProcessor
+from reflector.processors.audio_chunker_auto import AudioChunkerAutoProcessor
+
+
+class AudioChunkerSileroProcessor(AudioChunkerProcessor):
+    """
+    Assemble audio frames into chunks with VAD-based speech detection using Silero VAD
+    """
+
+    def __init__(
+        self,
+        block_frames=256,
+        max_frames=1024,
+        use_onnx=True,
+        min_frames=2,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.block_frames = block_frames
+        self.max_frames = max_frames
+        self.min_frames = min_frames
+
+        # Initialize Silero VAD
+        self._init_vad(use_onnx)
+
+    def _init_vad(self, use_onnx=False):
+        """Initialize Silero VAD model"""
+        try:
+            torch.set_num_threads(1)
+            self.vad_model = load_silero_vad(onnx=use_onnx)
+            self.vad_iterator = VADIterator(self.vad_model, sampling_rate=16000)
+            self.logger.info("Silero VAD initialized successfully")
+
+        except Exception as e:
+            self.logger.error(f"Failed to initialize Silero VAD: {e}")
+            self.vad_model = None
+            self.vad_iterator = None
+
+    async def _chunk(self, data: av.AudioFrame) -> Optional[list[av.AudioFrame]]:
+        """Process audio frame and return chunk when ready"""
+        self.frames.append(data)
+
+        # Check for speech segments every 32 frames (~1 second)
+        if len(self.frames) >= 32 and len(self.frames) % 32 == 0:
+            return await self._process_block()
+
+        # Safety fallback - emit if we hit max frames
+        elif len(self.frames) >= self.max_frames:
+            self.logger.warning(
+                f"AudioChunkerSileroProcessor: Reached max frames ({self.max_frames}), "
+                f"emitting first {self.max_frames // 2} frames"
+            )
+            frames_to_emit = self.frames[: self.max_frames // 2]
+            self.frames = self.frames[self.max_frames // 2 :]
+            if len(frames_to_emit) >= self.min_frames:
+                return frames_to_emit
+            else:
+                self.logger.debug(
+                    f"Ignoring fallback segment with {len(frames_to_emit)} frames "
+                    f"(< {self.min_frames} minimum)"
+                )
+
+        return None
+
+    async def _process_block(self) -> Optional[list[av.AudioFrame]]:
+        # Need at least 32 frames for VAD detection (~1 second)
+        if len(self.frames) < 32 or self.vad_iterator is None:
+            return None
+
+        # Processing block with current buffer size
+        print(f"Processing block: {len(self.frames)} frames in buffer")
+
+        try:
+            # Convert frames to numpy array for VAD
+            audio_array = self._frames_to_numpy(self.frames)
+
+            if audio_array is None:
+                # Fallback: emit all frames if conversion failed
+                frames_to_emit = self.frames[:]
+                self.frames = []
+                if len(frames_to_emit) >= self.min_frames:
+                    return frames_to_emit
+                else:
+                    self.logger.debug(
+                        f"Ignoring conversion-failed segment with {len(frames_to_emit)} frames "
+                        f"(< {self.min_frames} minimum)"
+                    )
+                return None
+
+            # Find complete speech segments in the buffer
+            speech_end_frame = self._find_speech_segment_end(audio_array)
+
+            if speech_end_frame is None or speech_end_frame <= 0:
+                # No speech found but buffer is getting large
+                if len(self.frames) > 512:
+                    # Check if it's all silence and can be discarded
+                    # No speech segment found, buffer at {len(self.frames)} frames
+
+                    # Could emit silence or discard old frames here
+                    # For now, keep first 256 frames and discard older silence
+                    if len(self.frames) > 768:
+                        self.logger.debug(
+                            f"Discarding {len(self.frames) - 256} old frames (likely silence)"
+                        )
+                        self.frames = self.frames[-256:]
+                return None
+
+            # Calculate segment timing information
+            frames_to_emit = self.frames[:speech_end_frame]
+
+            # Get timing from av.AudioFrame
+            if frames_to_emit:
+                first_frame = frames_to_emit[0]
+                last_frame = frames_to_emit[-1]
+                sample_rate = first_frame.sample_rate
+
+                # Calculate duration
+                total_samples = sum(f.samples for f in frames_to_emit)
+                duration_seconds = total_samples / sample_rate if sample_rate > 0 else 0
+
+                # Get timestamps if available
+                start_time = (
+                    first_frame.pts * first_frame.time_base if first_frame.pts else 0
+                )
+                end_time = (
+                    last_frame.pts * last_frame.time_base if last_frame.pts else 0
+                )
+
+                # Convert to HH:MM:SS format for logging
+                def format_time(seconds):
+                    if not seconds:
+                        return "00:00:00"
+                    total_seconds = int(float(seconds))
+                    hours = total_seconds // 3600
+                    minutes = (total_seconds % 3600) // 60
+                    secs = total_seconds % 60
+                    return f"{hours:02d}:{minutes:02d}:{secs:02d}"
+
+                start_formatted = format_time(start_time)
+                end_formatted = format_time(end_time)
+
+                # Keep remaining frames for next processing
+                remaining_after = len(self.frames) - speech_end_frame
+
+                # Single structured log line
+                self.logger.info(
+                    "Speech segment found",
+                    start=start_formatted,
+                    end=end_formatted,
+                    frames=speech_end_frame,
+                    duration=round(duration_seconds, 2),
+                    buffer_before=len(self.frames),
+                    remaining=remaining_after,
+                )
+
+            # Keep remaining frames for next processing
+            self.frames = self.frames[speech_end_frame:]
+
+            # Filter out segments with too few frames
+            if len(frames_to_emit) >= self.min_frames:
+                return frames_to_emit
+            else:
+                self.logger.debug(
+                    f"Ignoring segment with {len(frames_to_emit)} frames "
+                    f"(< {self.min_frames} minimum)"
+                )
+
+        except Exception as e:
+            self.logger.error(f"Error in VAD processing: {e}")
+            # Fallback to simple chunking
+            if len(self.frames) >= self.block_frames:
+                frames_to_emit = self.frames[: self.block_frames]
+                self.frames = self.frames[self.block_frames :]
+                if len(frames_to_emit) >= self.min_frames:
+                    return frames_to_emit
+                else:
+                    self.logger.debug(
+                        f"Ignoring exception-fallback segment with {len(frames_to_emit)} frames "
+                        f"(< {self.min_frames} minimum)"
+                    )
+
+        return None
+
+    def _frames_to_numpy(self, frames: list[av.AudioFrame]) -> Optional[np.ndarray]:
+        """Convert av.AudioFrame list to numpy array for VAD processing"""
+        if not frames:
+            return None
+
+        try:
+            audio_data = []
+            for frame in frames:
+                frame_array = frame.to_ndarray()
+
+                if len(frame_array.shape) == 2:
+                    frame_array = frame_array.flatten()
+
+                audio_data.append(frame_array)
+
+            if not audio_data:
+                return None
+
+            combined_audio = np.concatenate(audio_data)
+
+            # Ensure float32 format
+            if combined_audio.dtype == np.int16:
+                # Normalize int16 audio to float32 in range [-1.0, 1.0]
+                combined_audio = combined_audio.astype(np.float32) / 32768.0
+            elif combined_audio.dtype != np.float32:
+                combined_audio = combined_audio.astype(np.float32)
+
+            return combined_audio
+
+        except Exception as e:
+            self.logger.error(f"Error converting frames to numpy: {e}")
+
+        return None
+
+    def _find_speech_segment_end(self, audio_array: np.ndarray) -> Optional[int]:
+        """Find complete speech segments and return frame index at segment end"""
+        if self.vad_iterator is None or len(audio_array) == 0:
+            return None
+
+        try:
+            # Process audio in 512-sample windows for VAD
+            window_size = 512
+            min_silence_windows = 3  # Require 3 windows of silence after speech
+
+            # Track speech state
+            in_speech = False
+            speech_start = None
+            speech_end = None
+            silence_count = 0
+
+            for i in range(0, len(audio_array), window_size):
+                chunk = audio_array[i : i + window_size]
+                if len(chunk) < window_size:
+                    chunk = np.pad(chunk, (0, window_size - len(chunk)))
+
+                # Detect if this window has speech
+                speech_dict = self.vad_iterator(chunk, return_seconds=True)
+
+                # VADIterator returns dict with 'start' and 'end' when speech segments are detected
+                if speech_dict:
+                    if not in_speech:
+                        # Speech started
+                        speech_start = i
+                        in_speech = True
+                        # Debug: print(f"Speech START at sample {i}, VAD: {speech_dict}")
+                    silence_count = 0  # Reset silence counter
+                    continue
+
+                if not in_speech:
+                    continue
+
+                # We're in speech but found silence
+                silence_count += 1
+                if silence_count < min_silence_windows:
+                    continue
+
+                # Found end of speech segment
+                speech_end = i - (min_silence_windows - 1) * window_size
+                # Debug: print(f"Speech END at sample {speech_end}")
+
+                # Convert sample position to frame index
+                samples_per_frame = self.frames[0].samples if self.frames else 1024
+                frame_index = speech_end // samples_per_frame
+
+                # Ensure we don't exceed buffer
+                frame_index = min(frame_index, len(self.frames))
+                return frame_index
+
+            return None
+
+        except Exception as e:
+            self.logger.error(f"Error finding speech segment: {e}")
+            return None
+
+    async def _flush(self):
+        frames = self.frames[:]
+        self.frames = []
+        if frames:
+            if len(frames) >= self.min_frames:
+                await self.emit(frames)
+            else:
+                self.logger.debug(
+                    f"Ignoring flush segment with {len(frames)} frames "
+                    f"(< {self.min_frames} minimum)"
+                )
+
+
+AudioChunkerAutoProcessor.register("silero", AudioChunkerSileroProcessor)

server/reflector/processors/audio_diarization.py

@@ -1,5 +1,10 @@
 from reflector.processors.base import Processor
-from reflector.processors.types import AudioDiarizationInput, TitleSummary, Word
+from reflector.processors.types import (
+    AudioDiarizationInput,
+    DiarizationSegment,
+    TitleSummary,
+    Word,
+)
 
 
 class AudioDiarizationProcessor(Processor):
@@ -33,18 +38,21 @@ class AudioDiarizationProcessor(Processor):
     async def _diarize(self, data: AudioDiarizationInput):
         raise NotImplementedError
 
-    def assign_speaker(self, words: list[Word], diarization: list[dict]):
-        self._diarization_remove_overlap(diarization)
-        self._diarization_remove_segment_without_words(words, diarization)
-        self._diarization_merge_same_speaker(words, diarization)
-        self._diarization_assign_speaker(words, diarization)
+    @classmethod
+    def assign_speaker(cls, words: list[Word], diarization: list[DiarizationSegment]):
+        cls._diarization_remove_overlap(diarization)
+        cls._diarization_remove_segment_without_words(words, diarization)
+        cls._diarization_merge_same_speaker(diarization)
+        cls._diarization_assign_speaker(words, diarization)
 
-    def iter_words_from_topics(self, topics: TitleSummary):
+    @staticmethod
+    def iter_words_from_topics(topics: list[TitleSummary]):
         for topic in topics:
             for word in topic.transcript.words:
                 yield word
 
-    def is_word_continuation(self, word_prev, word):
+    @staticmethod
+    def is_word_continuation(word_prev, word):
         """
         Return True if the word is a continuation of the previous word
         by checking if the previous word is ending with a punctuation
@@ -57,7 +65,8 @@ class AudioDiarizationProcessor(Processor):
             return False
         return True
 
-    def _diarization_remove_overlap(self, diarization: list[dict]):
+    @staticmethod
+    def _diarization_remove_overlap(diarization: list[DiarizationSegment]):
         """
         Remove overlap in diarization results
 
@@ -82,8 +91,9 @@ class AudioDiarizationProcessor(Processor):
             else:
                 diarization_idx += 1
 
+    @staticmethod
     def _diarization_remove_segment_without_words(
-        self, words: list[Word], diarization: list[dict]
+        words: list[Word], diarization: list[DiarizationSegment]
     ):
         """
         Remove diarization segments without words
@@ -112,9 +122,8 @@ class AudioDiarizationProcessor(Processor):
             else:
                 diarization_idx += 1
 
-    def _diarization_merge_same_speaker(
-        self, words: list[Word], diarization: list[dict]
-    ):
+    @staticmethod
+    def _diarization_merge_same_speaker(diarization: list[DiarizationSegment]):
         """
         Merge diarization contigous segments with the same speaker
 
@@ -131,7 +140,10 @@ class AudioDiarizationProcessor(Processor):
             else:
                 diarization_idx += 1
 
-    def _diarization_assign_speaker(self, words: list[Word], diarization: list[dict]):
+    @classmethod
+    def _diarization_assign_speaker(
+        cls, words: list[Word], diarization: list[DiarizationSegment]
+    ):
         """
         Assign speaker to words based on diarization
 
@@ -139,7 +151,7 @@ class AudioDiarizationProcessor(Processor):
         """
 
         word_idx = 0
-        last_speaker = None
+        last_speaker = 0
         for d in diarization:
             start = d["start"]
             end = d["end"]
@@ -154,7 +166,7 @@ class AudioDiarizationProcessor(Processor):
                     # If it's a continuation, assign with the last speaker
                     is_continuation = False
                     if word_idx > 0 and word_idx < len(words) - 1:
-                        is_continuation = self.is_word_continuation(
+                        is_continuation = cls.is_word_continuation(
                             *words[word_idx - 1 : word_idx + 1]
                         )
                     if is_continuation:

server/reflector/processors/audio_diarization_modal.py

@@ -10,12 +10,17 @@ class AudioDiarizationModalProcessor(AudioDiarizationProcessor):
     INPUT_TYPE = AudioDiarizationInput
     OUTPUT_TYPE = TitleSummary
 
-    def __init__(self, **kwargs):
+    def __init__(self, modal_api_key: str | None = None, **kwargs):
         super().__init__(**kwargs)
+        if not settings.DIARIZATION_URL:
+            raise Exception(
+                "DIARIZATION_URL required to use AudioDiarizationModalProcessor"
+            )
         self.diarization_url = settings.DIARIZATION_URL + "/diarize"
-        self.headers = {
-            "Authorization": f"Bearer {settings.LLM_MODAL_API_KEY}",
-        }
+        self.modal_api_key = modal_api_key
+        self.headers = {}
+        if self.modal_api_key:
+            self.headers["Authorization"] = f"Bearer {self.modal_api_key}"
 
     async def _diarize(self, data: AudioDiarizationInput):
         # Gather diarization data

server/reflector/processors/audio_diarization_pyannote.py

@@ -0,0 +1,74 @@
+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/processors/audio_downscale.py

@@ -0,0 +1,60 @@
+from typing import Optional
+
+import av
+from av.audio.resampler import AudioResampler
+
+from reflector.processors.base import Processor
+
+
+def copy_frame(frame: av.AudioFrame) -> av.AudioFrame:
+    frame_copy = frame.from_ndarray(
+        frame.to_ndarray(),
+        format=frame.format.name,
+        layout=frame.layout.name,
+    )
+    frame_copy.sample_rate = frame.sample_rate
+    frame_copy.pts = frame.pts
+    frame_copy.time_base = frame.time_base
+    return frame_copy
+
+
+class AudioDownscaleProcessor(Processor):
+    """
+    Downscale audio frames to 16kHz mono format
+    """
+
+    INPUT_TYPE = av.AudioFrame
+    OUTPUT_TYPE = av.AudioFrame
+
+    def __init__(self, target_rate: int = 16000, target_layout: str = "mono", **kwargs):
+        super().__init__(**kwargs)
+        self.target_rate = target_rate
+        self.target_layout = target_layout
+        self.resampler: Optional[AudioResampler] = None
+        self.needs_resampling: Optional[bool] = None
+
+    async def _push(self, data: av.AudioFrame):
+        if self.needs_resampling is None:
+            self.needs_resampling = (
+                data.sample_rate != self.target_rate
+                or data.layout.name != self.target_layout
+            )
+
+            if self.needs_resampling:
+                self.resampler = AudioResampler(
+                    format="s16", layout=self.target_layout, rate=self.target_rate
+                )
+
+        if not self.needs_resampling or not self.resampler:
+            await self.emit(data)
+            return
+
+        resampled_frames = self.resampler.resample(copy_frame(data))
+        for resampled_frame in resampled_frames:
+            await self.emit(resampled_frame)
+
+    async def _flush(self):
+        if self.needs_resampling and self.resampler:
+            final_frames = self.resampler.resample(None)
+            for frame in final_frames:
+                await self.emit(frame)

server/reflector/processors/audio_merge.py

@@ -16,37 +16,46 @@ class AudioMergeProcessor(Processor):
     INPUT_TYPE = list[av.AudioFrame]
     OUTPUT_TYPE = AudioFile
 
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
     async def _push(self, data: list[av.AudioFrame]):
         if not data:
             return
 
         # get audio information from first frame
         frame = data[0]
-        channels = len(frame.layout.channels)
-        sample_rate = frame.sample_rate
-        sample_width = frame.format.bytes
+        output_channels = len(frame.layout.channels)
+        output_sample_rate = frame.sample_rate
+        output_sample_width = frame.format.bytes
 
         # create audio file
         uu = uuid4().hex
         fd = io.BytesIO()
 
+        # Use PyAV to write frames
         out_container = av.open(fd, "w", format="wav")
-        out_stream = out_container.add_stream("pcm_s16le", rate=sample_rate)
+        out_stream = out_container.add_stream("pcm_s16le", rate=output_sample_rate)
+        out_stream.layout = frame.layout.name
+
         for frame in data:
             for packet in out_stream.encode(frame):
                 out_container.mux(packet)
+
+        # Flush the encoder
         for packet in out_stream.encode(None):
             out_container.mux(packet)
         out_container.close()
+
         fd.seek(0)
 
         # emit audio file
         audiofile = AudioFile(
             name=f"{monotonic_ns()}-{uu}.wav",
             fd=fd,
-            sample_rate=sample_rate,
-            channels=channels,
-            sample_width=sample_width,
+            sample_rate=output_sample_rate,
+            channels=output_channels,
+            sample_width=output_sample_width,
             timestamp=data[0].pts * data[0].time_base,
         )
 

server/reflector/processors/audio_transcript_modal.py

@@ -21,16 +21,24 @@ from reflector.settings import settings
 
 
 class AudioTranscriptModalProcessor(AudioTranscriptProcessor):
-    def __init__(self, modal_api_key: str):
+    def __init__(
+        self,
+        modal_api_key: str | None = None,
+        **kwargs,
+    ):
         super().__init__()
+        if not settings.TRANSCRIPT_URL:
+            raise Exception(
+                "TRANSCRIPT_URL required to use AudioTranscriptModalProcessor"
+            )
         self.transcript_url = settings.TRANSCRIPT_URL + "/v1"
         self.timeout = settings.TRANSCRIPT_TIMEOUT
-        self.api_key = settings.TRANSCRIPT_MODAL_API_KEY
+        self.modal_api_key = modal_api_key
 
     async def _transcript(self, data: AudioFile):
         async with AsyncOpenAI(
             base_url=self.transcript_url,
-            api_key=self.api_key,
+            api_key=self.modal_api_key,
             timeout=self.timeout,
         ) as client:
             self.logger.debug(f"Try to transcribe audio {data.name}")

server/reflector/processors/base.py

@@ -173,6 +173,7 @@ class Processor(Emitter):
         except Exception:
             self.m_processor_failure.inc()
             self.logger.exception("Error in push")
+            raise
 
     async def flush(self):
         """
@@ -240,33 +241,45 @@ class ThreadedProcessor(Processor):
         self.INPUT_TYPE = processor.INPUT_TYPE
         self.OUTPUT_TYPE = processor.OUTPUT_TYPE
         self.executor = ThreadPoolExecutor(max_workers=max_workers)
-        self.queue = asyncio.Queue()
-        self.task = asyncio.get_running_loop().create_task(self.loop())
+        self.queue = asyncio.Queue(maxsize=50)
+        self.task: asyncio.Task | None = None
 
     def set_pipeline(self, pipeline: "Pipeline"):
         super().set_pipeline(pipeline)
         self.processor.set_pipeline(pipeline)
 
     async def loop(self):
-        while True:
-            data = await self.queue.get()
-            self.m_processor_queue.set(self.queue.qsize())
-            with self.m_processor_queue_in_progress.track_inprogress():
-                try:
-                    if data is None:
-                        await self.processor.flush()
-                        break
+        try:
+            while True:
+                data = await self.queue.get()
+                self.m_processor_queue.set(self.queue.qsize())
+                with self.m_processor_queue_in_progress.track_inprogress():
                     try:
-                        await self.processor.push(data)
-                    except Exception:
-                        self.logger.error(
-                            f"Error in push {self.processor.__class__.__name__}"
-                            ", continue"
-                        )
-                finally:
-                    self.queue.task_done()
+                        if data is None:
+                            await self.processor.flush()
+                            break
+                        try:
+                            await self.processor.push(data)
+                        except Exception:
+                            self.logger.error(
+                                f"Error in push {self.processor.__class__.__name__}"
+                                ", continue"
+                            )
+                    finally:
+                        self.queue.task_done()
+        except Exception as e:
+            logger.error(f"Crash in {self.__class__.__name__}: {e}", exc_info=e)
+
+    async def _ensure_task(self):
+        if self.task is None:
+            self.task = asyncio.get_running_loop().create_task(self.loop())
+
+        # XXX not doing a sleep here make the whole pipeline prior the thread
+        # to be running without having a chance to work on the task here.
+        await asyncio.sleep(0)
 
     async def _push(self, data):
+        await self._ensure_task()
         await self.queue.put(data)
 
     async def _flush(self):

server/reflector/processors/file_diarization.py

@@ -0,0 +1,33 @@
+from pydantic import BaseModel
+
+from reflector.processors.base import Processor
+from reflector.processors.types import DiarizationSegment
+
+
+class FileDiarizationInput(BaseModel):
+    """Input for file diarization containing audio URL"""
+
+    audio_url: str
+
+
+class FileDiarizationOutput(BaseModel):
+    """Output for file diarization containing speaker segments"""
+
+    diarization: list[DiarizationSegment]
+
+
+class FileDiarizationProcessor(Processor):
+    """
+    Diarize complete audio files from URL
+    """
+
+    INPUT_TYPE = FileDiarizationInput
+    OUTPUT_TYPE = FileDiarizationOutput
+
+    async def _push(self, data: FileDiarizationInput):
+        result = await self._diarize(data)
+        if result:
+            await self.emit(result)
+
+    async def _diarize(self, data: FileDiarizationInput):
+        raise NotImplementedError

server/reflector/processors/file_diarization_auto.py

@@ -0,0 +1,33 @@
+import importlib
+
+from reflector.processors.file_diarization import FileDiarizationProcessor
+from reflector.settings import settings
+
+
+class FileDiarizationAutoProcessor(FileDiarizationProcessor):
+    _registry = {}
+
+    @classmethod
+    def register(cls, name, kclass):
+        cls._registry[name] = kclass
+
+    def __new__(cls, name: str | None = None, **kwargs):
+        if name is None:
+            name = settings.DIARIZATION_BACKEND
+
+        if name not in cls._registry:
+            module_name = f"reflector.processors.file_diarization_{name}"
+            importlib.import_module(module_name)
+
+        # gather specific configuration for the processor
+        # search `DIARIZATION_BACKEND_XXX_YYY`, push to constructor as `backend_xxx_yyy`
+        config = {}
+        name_upper = name.upper()
+        settings_prefix = "DIARIZATION_"
+        config_prefix = f"{settings_prefix}{name_upper}_"
+        for key, value in settings:
+            if key.startswith(config_prefix):
+                config_name = key[len(settings_prefix) :].lower()
+                config[config_name] = value
+
+        return cls._registry[name](**config | kwargs)

server/reflector/processors/file_diarization_modal.py

@@ -0,0 +1,57 @@
+"""
+File diarization implementation using the GPU service from modal.com
+
+API will be a POST request to DIARIZATION_URL:
+
+```
+POST /diarize?audio_file_url=...&timestamp=0
+Authorization: Bearer <modal_api_key>
+```
+"""
+
+import httpx
+
+from reflector.processors.file_diarization import (
+    FileDiarizationInput,
+    FileDiarizationOutput,
+    FileDiarizationProcessor,
+)
+from reflector.processors.file_diarization_auto import FileDiarizationAutoProcessor
+from reflector.settings import settings
+
+
+class FileDiarizationModalProcessor(FileDiarizationProcessor):
+    def __init__(self, modal_api_key: str | None = None, **kwargs):
+        super().__init__(**kwargs)
+        if not settings.DIARIZATION_URL:
+            raise Exception(
+                "DIARIZATION_URL required to use FileDiarizationModalProcessor"
+            )
+        self.diarization_url = settings.DIARIZATION_URL + "/diarize"
+        self.file_timeout = settings.DIARIZATION_FILE_TIMEOUT
+        self.modal_api_key = modal_api_key
+
+    async def _diarize(self, data: FileDiarizationInput):
+        """Get speaker diarization for file"""
+        self.logger.info(f"Starting diarization from {data.audio_url}")
+
+        headers = {}
+        if self.modal_api_key:
+            headers["Authorization"] = f"Bearer {self.modal_api_key}"
+
+        async with httpx.AsyncClient(timeout=self.file_timeout) as client:
+            response = await client.post(
+                self.diarization_url,
+                headers=headers,
+                params={
+                    "audio_file_url": data.audio_url,
+                    "timestamp": 0,
+                },
+            )
+            response.raise_for_status()
+            diarization_data = response.json()["diarization"]
+
+        return FileDiarizationOutput(diarization=diarization_data)
+
+
+FileDiarizationAutoProcessor.register("modal", FileDiarizationModalProcessor)

server/reflector/processors/file_transcript.py

@@ -0,0 +1,65 @@
+from prometheus_client import Counter, Histogram
+
+from reflector.processors.base import Processor
+from reflector.processors.types import Transcript
+
+
+class FileTranscriptInput:
+    """Input for file transcription containing audio URL and language settings"""
+
+    def __init__(self, audio_url: str, language: str = "en"):
+        self.audio_url = audio_url
+        self.language = language
+
+
+class FileTranscriptProcessor(Processor):
+    """
+    Transcript complete audio files from URL
+    """
+
+    INPUT_TYPE = FileTranscriptInput
+    OUTPUT_TYPE = Transcript
+
+    m_transcript = Histogram(
+        "file_transcript",
+        "Time spent in FileTranscript.transcript",
+        ["backend"],
+    )
+    m_transcript_call = Counter(
+        "file_transcript_call",
+        "Number of calls to FileTranscript.transcript",
+        ["backend"],
+    )
+    m_transcript_success = Counter(
+        "file_transcript_success",
+        "Number of successful calls to FileTranscript.transcript",
+        ["backend"],
+    )
+    m_transcript_failure = Counter(
+        "file_transcript_failure",
+        "Number of failed calls to FileTranscript.transcript",
+        ["backend"],
+    )
+
+    def __init__(self, *args, **kwargs):
+        name = self.__class__.__name__
+        self.m_transcript = self.m_transcript.labels(name)
+        self.m_transcript_call = self.m_transcript_call.labels(name)
+        self.m_transcript_success = self.m_transcript_success.labels(name)
+        self.m_transcript_failure = self.m_transcript_failure.labels(name)
+        super().__init__(*args, **kwargs)
+
+    async def _push(self, data: FileTranscriptInput):
+        try:
+            self.m_transcript_call.inc()
+            with self.m_transcript.time():
+                result = await self._transcript(data)
+            self.m_transcript_success.inc()
+            if result:
+                await self.emit(result)
+        except Exception:
+            self.m_transcript_failure.inc()
+            raise
+
+    async def _transcript(self, data: FileTranscriptInput):
+        raise NotImplementedError

server/reflector/processors/file_transcript_auto.py

@@ -0,0 +1,32 @@
+import importlib
+
+from reflector.processors.file_transcript import FileTranscriptProcessor
+from reflector.settings import settings
+
+
+class FileTranscriptAutoProcessor(FileTranscriptProcessor):
+    _registry = {}
+
+    @classmethod
+    def register(cls, name, kclass):
+        cls._registry[name] = kclass
+
+    def __new__(cls, name: str | None = None, **kwargs):
+        if name is None:
+            name = settings.TRANSCRIPT_BACKEND
+        if name not in cls._registry:
+            module_name = f"reflector.processors.file_transcript_{name}"
+            importlib.import_module(module_name)
+
+        # gather specific configuration for the processor
+        # search `TRANSCRIPT_BACKEND_XXX_YYY`, push to constructor as `backend_xxx_yyy`
+        config = {}
+        name_upper = name.upper()
+        settings_prefix = "TRANSCRIPT_"
+        config_prefix = f"{settings_prefix}{name_upper}_"
+        for key, value in settings:
+            if key.startswith(config_prefix):
+                config_name = key[len(settings_prefix) :].lower()
+                config[config_name] = value
+
+        return cls._registry[name](**config | kwargs)

server/reflector/processors/file_transcript_modal.py

@@ -0,0 +1,74 @@
+"""
+File transcription implementation using the GPU service from modal.com
+
+API will be a POST request to TRANSCRIPT_URL:
+
+```json
+{
+    "audio_file_url": "https://...",
+    "language": "en",
+    "model": "parakeet-tdt-0.6b-v2",
+    "batch": true
+}
+```
+"""
+
+import httpx
+
+from reflector.processors.file_transcript import (
+    FileTranscriptInput,
+    FileTranscriptProcessor,
+)
+from reflector.processors.file_transcript_auto import FileTranscriptAutoProcessor
+from reflector.processors.types import Transcript, Word
+from reflector.settings import settings
+
+
+class FileTranscriptModalProcessor(FileTranscriptProcessor):
+    def __init__(self, modal_api_key: str | None = None, **kwargs):
+        super().__init__(**kwargs)
+        if not settings.TRANSCRIPT_URL:
+            raise Exception(
+                "TRANSCRIPT_URL required to use FileTranscriptModalProcessor"
+            )
+        self.transcript_url = settings.TRANSCRIPT_URL
+        self.file_timeout = settings.TRANSCRIPT_FILE_TIMEOUT
+        self.modal_api_key = modal_api_key
+
+    async def _transcript(self, data: FileTranscriptInput):
+        """Send full file to Modal for transcription"""
+        url = f"{self.transcript_url}/v1/audio/transcriptions-from-url"
+
+        self.logger.info(f"Starting file transcription from {data.audio_url}")
+
+        headers = {}
+        if self.modal_api_key:
+            headers["Authorization"] = f"Bearer {self.modal_api_key}"
+
+        async with httpx.AsyncClient(timeout=self.file_timeout) as client:
+            response = await client.post(
+                url,
+                headers=headers,
+                json={
+                    "audio_file_url": data.audio_url,
+                    "language": data.language,
+                    "batch": True,
+                },
+            )
+            response.raise_for_status()
+            result = response.json()
+
+        words = [
+            Word(
+                text=word_info["word"],
+                start=word_info["start"],
+                end=word_info["end"],
+            )
+            for word_info in result.get("words", [])
+        ]
+
+        return Transcript(words=words)
+
+
+# Register with the auto processor
+FileTranscriptAutoProcessor.register("modal", FileTranscriptModalProcessor)

server/reflector/processors/summary/summary_builder.py

@@ -6,7 +6,7 @@ This script is used to generate a summary of a meeting notes transcript.
 
 import asyncio
 import sys
-from datetime import datetime
+from datetime import datetime, timezone
 from enum import Enum
 from textwrap import dedent
 from typing import Type, TypeVar
@@ -474,7 +474,7 @@ if __name__ == "__main__":
 
         if args.save:
             # write the summary to a file, on the format summary-<iso date>.md
-            filename = f"summary-{datetime.now().isoformat()}.md"
+            filename = f"summary-{datetime.now(timezone.utc).isoformat()}.md"
             with open(filename, "w", encoding="utf-8") as f:
                 f.write(sm.as_markdown())
 

server/reflector/processors/transcript_diarization_assembler.py

@@ -0,0 +1,45 @@
+"""
+Processor to assemble transcript with diarization results
+"""
+
+from reflector.processors.audio_diarization import AudioDiarizationProcessor
+from reflector.processors.base import Processor
+from reflector.processors.types import DiarizationSegment, Transcript
+
+
+class TranscriptDiarizationAssemblerInput:
+    """Input containing transcript and diarization data"""
+
+    def __init__(self, transcript: Transcript, diarization: list[DiarizationSegment]):
+        self.transcript = transcript
+        self.diarization = diarization
+
+
+class TranscriptDiarizationAssemblerProcessor(Processor):
+    """
+    Assemble transcript with diarization results by applying speaker assignments
+    """
+
+    INPUT_TYPE = TranscriptDiarizationAssemblerInput
+    OUTPUT_TYPE = Transcript
+
+    async def _push(self, data: TranscriptDiarizationAssemblerInput):
+        result = await self._assemble(data)
+        if result:
+            await self.emit(result)
+
+    async def _assemble(self, data: TranscriptDiarizationAssemblerInput):
+        """Apply diarization to transcript words"""
+        if not data.diarization:
+            self.logger.info(
+                "No diarization data provided, returning original transcript"
+            )
+            return data.transcript
+
+        # Reuse logic from AudioDiarizationProcessor
+        processor = AudioDiarizationProcessor()
+        words = data.transcript.words
+        processor.assign_speaker(words, data.diarization)
+
+        self.logger.info(f"Applied diarization to {len(words)} words")
+        return data.transcript

server/reflector/processors/transcript_translator.py

@@ -1,9 +1,5 @@
-import httpx
-
 from reflector.processors.base import Processor
-from reflector.processors.types import Transcript, TranslationLanguages
-from reflector.settings import settings
-from reflector.utils.retry import retry
+from reflector.processors.types import Transcript
 
 
 class TranscriptTranslatorProcessor(Processor):
@@ -17,56 +13,23 @@ class TranscriptTranslatorProcessor(Processor):
     def __init__(self, **kwargs):
         super().__init__(**kwargs)
         self.transcript = None
-        self.translate_url = settings.TRANSLATE_URL
-        self.timeout = settings.TRANSLATE_TIMEOUT
-        self.headers = {"Authorization": f"Bearer {settings.TRANSCRIPT_MODAL_API_KEY}"}
 
     async def _push(self, data: Transcript):
         self.transcript = data
         await self.flush()
 
-    async def get_translation(self, text: str) -> str | None:
-        # FIXME this should be a processor after, as each user may want
-        # different languages
-
-        source_language = self.get_pref("audio:source_language", "en")
-        target_language = self.get_pref("audio:target_language", "en")
-        if source_language == target_language:
-            return
-
-        languages = TranslationLanguages()
-        # Only way to set the target should be the UI element like dropdown.
-        # Hence, this assert should never fail.
-        assert languages.is_supported(target_language)
-        self.logger.debug(f"Try to translate {text=}")
-        json_payload = {
-            "text": text,
-            "source_language": source_language,
-            "target_language": target_language,
-        }
-
-        async with httpx.AsyncClient() as client:
-            response = await retry(client.post)(
-                self.translate_url + "/translate",
-                headers=self.headers,
-                params=json_payload,
-                timeout=self.timeout,
-                follow_redirects=True,
-                logger=self.logger,
-            )
-            response.raise_for_status()
-            result = response.json()["text"]
-
-            # Sanity check for translation status in the result
-            if target_language in result:
-                translation = result[target_language]
-            self.logger.debug(f"Translation response: {text=}, {translation=}")
-        return translation
+    async def _translate(self, text: str) -> str | None:
+        raise NotImplementedError
 
     async def _flush(self):
         if not self.transcript:
             return
-        self.transcript.translation = await self.get_translation(
-            text=self.transcript.text
-        )
+
+        source_language = self.get_pref("audio:source_language", "en")
+        target_language = self.get_pref("audio:target_language", "en")
+        if source_language == target_language:
+            self.transcript.translation = None
+        else:
+            self.transcript.translation = await self._translate(self.transcript.text)
+
         await self.emit(self.transcript)

server/reflector/processors/transcript_translator_auto.py

@@ -0,0 +1,32 @@
+import importlib
+
+from reflector.processors.transcript_translator import TranscriptTranslatorProcessor
+from reflector.settings import settings
+
+
+class TranscriptTranslatorAutoProcessor(TranscriptTranslatorProcessor):
+    _registry = {}
+
+    @classmethod
+    def register(cls, name, kclass):
+        cls._registry[name] = kclass
+
+    def __new__(cls, name: str | None = None, **kwargs):
+        if name is None:
+            name = settings.TRANSLATION_BACKEND
+        if name not in cls._registry:
+            module_name = f"reflector.processors.transcript_translator_{name}"
+            importlib.import_module(module_name)
+
+        # gather specific configuration for the processor
+        # search `TRANSLATION_BACKEND_XXX_YYY`, push to constructor as `backend_xxx_yyy`
+        config = {}
+        name_upper = name.upper()
+        settings_prefix = "TRANSLATION_"
+        config_prefix = f"{settings_prefix}{name_upper}_"
+        for key, value in settings:
+            if key.startswith(config_prefix):
+                config_name = key[len(settings_prefix) :].lower()
+                config[config_name] = value
+
+        return cls._registry[name](**config | kwargs)

server/reflector/processors/transcript_translator_modal.py

@@ -0,0 +1,66 @@
+import httpx
+
+from reflector.processors.transcript_translator import TranscriptTranslatorProcessor
+from reflector.processors.transcript_translator_auto import (
+    TranscriptTranslatorAutoProcessor,
+)
+from reflector.processors.types import TranslationLanguages
+from reflector.settings import settings
+from reflector.utils.retry import retry
+
+
+class TranscriptTranslatorModalProcessor(TranscriptTranslatorProcessor):
+    """
+    Translate the transcript into the target language using Modal.com
+    """
+
+    def __init__(self, modal_api_key: str | None = None, **kwargs):
+        super().__init__(**kwargs)
+        if not settings.TRANSLATE_URL:
+            raise Exception(
+                "TRANSLATE_URL is required for TranscriptTranslatorModalProcessor"
+            )
+        self.translate_url = settings.TRANSLATE_URL
+        self.timeout = settings.TRANSLATE_TIMEOUT
+        self.modal_api_key = modal_api_key
+        self.headers = {}
+        if self.modal_api_key:
+            self.headers["Authorization"] = f"Bearer {self.modal_api_key}"
+
+    async def _translate(self, text: str) -> str | None:
+        source_language = self.get_pref("audio:source_language", "en")
+        target_language = self.get_pref("audio:target_language", "en")
+
+        languages = TranslationLanguages()
+        # Only way to set the target should be the UI element like dropdown.
+        # Hence, this assert should never fail.
+        assert languages.is_supported(target_language)
+        self.logger.debug(f"Try to translate {text=}")
+        json_payload = {
+            "text": text,
+            "source_language": source_language,
+            "target_language": target_language,
+        }
+
+        async with httpx.AsyncClient() as client:
+            response = await retry(client.post)(
+                self.translate_url + "/translate",
+                headers=self.headers,
+                params=json_payload,
+                timeout=self.timeout,
+                follow_redirects=True,
+                logger=self.logger,
+            )
+            response.raise_for_status()
+            result = response.json()["text"]
+
+            # Sanity check for translation status in the result
+            if target_language in result:
+                translation = result[target_language]
+            else:
+                translation = None
+            self.logger.debug(f"Translation response: {text=}, {translation=}")
+        return translation
+
+
+TranscriptTranslatorAutoProcessor.register("modal", TranscriptTranslatorModalProcessor)

server/reflector/processors/transcript_translator_passthrough.py

@@ -0,0 +1,14 @@
+from reflector.processors.transcript_translator import TranscriptTranslatorProcessor
+from reflector.processors.transcript_translator_auto import (
+    TranscriptTranslatorAutoProcessor,
+)
+
+
+class TranscriptTranslatorPassthroughProcessor(TranscriptTranslatorProcessor):
+    async def _translate(self, text: str) -> None:
+        return None
+
+
+TranscriptTranslatorAutoProcessor.register(
+    "passthrough", TranscriptTranslatorPassthroughProcessor
+)

server/reflector/processors/types.py

@@ -2,12 +2,22 @@ import io
 import re
 import tempfile
 from pathlib import Path
+from typing import Annotated, TypedDict
 
 from profanityfilter import ProfanityFilter
-from pydantic import BaseModel, PrivateAttr
+from pydantic import BaseModel, Field, PrivateAttr
 
 from reflector.redis_cache import redis_cache
 
+
+class DiarizationSegment(TypedDict):
+    """Type definition for diarization segment containing speaker information"""
+
+    start: float
+    end: float
+    speaker: int
+
+
 PUNC_RE = re.compile(r"[.;:?!…]")
 
 profanity_filter = ProfanityFilter()
@@ -48,20 +58,70 @@ class AudioFile(BaseModel):
             self._path.unlink()
 
 
+# non-negative seconds with float part
+Seconds = Annotated[float, Field(ge=0.0, description="Time in seconds with float part")]
+
+
 class Word(BaseModel):
     text: str
-    start: float
-    end: float
+    start: Seconds
+    end: Seconds
     speaker: int = 0
 
 
 class TranscriptSegment(BaseModel):
     text: str
-    start: float
-    end: float
+    start: Seconds
+    end: Seconds
     speaker: int = 0
 
 
+def words_to_segments(words: list[Word]) -> list[TranscriptSegment]:
+    # from a list of word, create a list of segments
+    # join the word that are less than 2 seconds apart
+    # but separate if the speaker changes, or if the punctuation is a . , ; : ? !
+    segments = []
+    current_segment = None
+    MAX_SEGMENT_LENGTH = 120
+
+    for word in words:
+        if current_segment is None:
+            current_segment = TranscriptSegment(
+                text=word.text,
+                start=word.start,
+                end=word.end,
+                speaker=word.speaker,
+            )
+            continue
+
+        # If the word is attach to another speaker, push the current segment
+        # and start a new one
+        if word.speaker != current_segment.speaker:
+            segments.append(current_segment)
+            current_segment = TranscriptSegment(
+                text=word.text,
+                start=word.start,
+                end=word.end,
+                speaker=word.speaker,
+            )
+            continue
+
+        # if the word is the end of a sentence, and we have enough content,
+        # add the word to the current segment and push it
+        current_segment.text += word.text
+        current_segment.end = word.end
+
+        have_punc = PUNC_RE.search(word.text)
+        if have_punc and (len(current_segment.text) > MAX_SEGMENT_LENGTH):
+            segments.append(current_segment)
+            current_segment = None
+
+    if current_segment:
+        segments.append(current_segment)
+
+    return segments
+
+
 class Transcript(BaseModel):
     translation: str | None = None
     words: list[Word] = None
@@ -117,49 +177,7 @@ class Transcript(BaseModel):
         return Transcript(text=self.text, translation=self.translation, words=words)
 
     def as_segments(self) -> list[TranscriptSegment]:
-        # from a list of word, create a list of segments
-        # join the word that are less than 2 seconds apart
-        # but separate if the speaker changes, or if the punctuation is a . , ; : ? !
-        segments = []
-        current_segment = None
-        MAX_SEGMENT_LENGTH = 120
-
-        for word in self.words:
-            if current_segment is None:
-                current_segment = TranscriptSegment(
-                    text=word.text,
-                    start=word.start,
-                    end=word.end,
-                    speaker=word.speaker,
-                )
-                continue
-
-            # If the word is attach to another speaker, push the current segment
-            # and start a new one
-            if word.speaker != current_segment.speaker:
-                segments.append(current_segment)
-                current_segment = TranscriptSegment(
-                    text=word.text,
-                    start=word.start,
-                    end=word.end,
-                    speaker=word.speaker,
-                )
-                continue
-
-            # if the word is the end of a sentence, and we have enough content,
-            # add the word to the current segment and push it
-            current_segment.text += word.text
-            current_segment.end = word.end
-
-            have_punc = PUNC_RE.search(word.text)
-            if have_punc and (len(current_segment.text) > MAX_SEGMENT_LENGTH):
-                segments.append(current_segment)
-                current_segment = None
-
-        if current_segment:
-            segments.append(current_segment)
-
-        return segments
+        return words_to_segments(self.words)
 
 
 class TitleSummary(BaseModel):

server/reflector/settings.py

@@ -14,18 +14,25 @@ class Settings(BaseSettings):
     CORS_ALLOW_CREDENTIALS: bool = False
 
     # Database
-    DATABASE_URL: str = "sqlite:///./reflector.sqlite3"
+    DATABASE_URL: str = (
+        "postgresql+asyncpg://reflector:reflector@localhost:5432/reflector"
+    )
 
     # local data directory
     DATA_DIR: str = "./data"
 
+    # Audio Chunking
+    # backends: silero, frames
+    AUDIO_CHUNKER_BACKEND: str = "frames"
+
     # Audio Transcription
     # backends: whisper, modal
     TRANSCRIPT_BACKEND: str = "whisper"
     TRANSCRIPT_URL: str | None = None
     TRANSCRIPT_TIMEOUT: int = 90
+    TRANSCRIPT_FILE_TIMEOUT: int = 600
 
-    # Audio transcription modal.com configuration
+    # Audio Transcription: modal backend
     TRANSCRIPT_MODAL_API_KEY: str | None = None
 
     # Audio transcription storage
@@ -37,10 +44,23 @@ class Settings(BaseSettings):
     TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID: str | None = None
     TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY: str | None = None
 
+    # Recording storage
+    RECORDING_STORAGE_BACKEND: str | None = None
+
+    # Recording storage configuration for AWS
+    RECORDING_STORAGE_AWS_BUCKET_NAME: str = "recording-bucket"
+    RECORDING_STORAGE_AWS_REGION: str = "us-east-1"
+    RECORDING_STORAGE_AWS_ACCESS_KEY_ID: str | None = None
+    RECORDING_STORAGE_AWS_SECRET_ACCESS_KEY: str | None = None
+
     # Translate into the target language
+    TRANSLATION_BACKEND: str = "passthrough"
     TRANSLATE_URL: str | None = None
     TRANSLATE_TIMEOUT: int = 90
 
+    # Translation: modal backend
+    TRANSLATE_MODAL_API_KEY: str | None = None
+
     # LLM
     LLM_MODEL: str = "microsoft/phi-4"
     LLM_URL: str | None = None
@@ -51,6 +71,13 @@ class Settings(BaseSettings):
     DIARIZATION_ENABLED: bool = True
     DIARIZATION_BACKEND: str = "modal"
     DIARIZATION_URL: str | None = None
+    DIARIZATION_FILE_TIMEOUT: int = 600
+
+    # Diarization: modal backend
+    DIARIZATION_MODAL_API_KEY: str | None = None
+
+    # Diarization: local pyannote.audio
+    DIARIZATION_PYANNOTE_AUTH_TOKEN: str | None = None
 
     # Sentry
     SENTRY_DSN: str | None = None
@@ -95,25 +122,11 @@ class Settings(BaseSettings):
     WHEREBY_API_URL: str = "https://api.whereby.dev/v1"
     WHEREBY_API_KEY: str | None = None
     WHEREBY_WEBHOOK_SECRET: str | None = None
-    AWS_WHEREBY_S3_BUCKET: str | None = None
     AWS_WHEREBY_ACCESS_KEY_ID: str | None = None
     AWS_WHEREBY_ACCESS_KEY_SECRET: str | None = None
     AWS_PROCESS_RECORDING_QUEUE_URL: str | None = None
     SQS_POLLING_TIMEOUT_SECONDS: int = 60
 
-    # Daily.co integration
-    DAILY_API_KEY: str | None = None
-    DAILY_WEBHOOK_SECRET: str | None = None
-    DAILY_SUBDOMAIN: str | None = None
-    AWS_DAILY_S3_BUCKET: str | None = None
-    AWS_DAILY_S3_REGION: str = "us-west-2"
-    AWS_DAILY_ROLE_ARN: str | None = None
-
-    # Video platform migration feature flags
-    DAILY_MIGRATION_ENABLED: bool = True
-    DAILY_MIGRATION_ROOM_IDS: list[str] = []
-    DEFAULT_VIDEO_PLATFORM: str = "daily"
-
     # Zulip integration
     ZULIP_REALM: str | None = None
     ZULIP_API_KEY: str | None = None

server/reflector/storage/__init__.py

@@ -1,10 +1,17 @@
 from .base import Storage  # noqa
+from reflector.settings import settings
 
 
 def get_transcripts_storage() -> Storage:
-    from reflector.settings import settings
-
+    assert settings.TRANSCRIPT_STORAGE_BACKEND
     return Storage.get_instance(
         name=settings.TRANSCRIPT_STORAGE_BACKEND,
         settings_prefix="TRANSCRIPT_STORAGE_",
     )
+
+
+def get_recordings_storage() -> Storage:
+    return Storage.get_instance(
+        name=settings.RECORDING_STORAGE_BACKEND,
+        settings_prefix="RECORDING_STORAGE_",
+    )

server/reflector/tools/exportdanswer.py

@@ -9,8 +9,9 @@ async def export_db(filename: str) -> None:
     filename = pathlib.Path(filename).resolve()
     settings.DATABASE_URL = f"sqlite:///{filename}"
 
-    from reflector.db import database, transcripts
+    from reflector.db import get_database, transcripts
 
+    database = get_database()
     await database.connect()
     transcripts = await database.fetch_all(transcripts.select())
     await database.disconnect()

server/reflector/tools/exportdb.py

@@ -8,8 +8,9 @@ async def export_db(filename: str) -> None:
     filename = pathlib.Path(filename).resolve()
     settings.DATABASE_URL = f"sqlite:///{filename}"
 
-    from reflector.db import database, transcripts
+    from reflector.db import get_database, transcripts
 
+    database = get_database()
     await database.connect()
     transcripts = await database.fetch_all(transcripts.select())
     await database.disconnect()

server/reflector/tools/process.py

@@ -1,10 +1,24 @@
+"""
+Process audio file with diarization support
+===========================================
+
+Extended version of process.py that includes speaker diarization.
+This tool processes audio files locally without requiring the full server infrastructure.
+"""
+
 import asyncio
+import tempfile
+import uuid
+from pathlib import Path
+from typing import List
 
 import av
 
 from reflector.logger import logger
 from reflector.processors import (
-    AudioChunkerProcessor,
+    AudioChunkerAutoProcessor,
+    AudioDownscaleProcessor,
+    AudioFileWriterProcessor,
     AudioMergeProcessor,
     AudioTranscriptAutoProcessor,
     Pipeline,
@@ -13,9 +27,45 @@ from reflector.processors import (
     TranscriptFinalTitleProcessor,
     TranscriptLinerProcessor,
     TranscriptTopicDetectorProcessor,
-    TranscriptTranslatorProcessor,
+    TranscriptTranslatorAutoProcessor,
 )
-from reflector.processors.base import BroadcastProcessor
+from reflector.processors.base import BroadcastProcessor, Processor
+from reflector.processors.types import (
+    AudioDiarizationInput,
+    TitleSummary,
+    TitleSummaryWithId,
+)
+
+
+class TopicCollectorProcessor(Processor):
+    """Collect topics for diarization"""
+
+    INPUT_TYPE = TitleSummary
+    OUTPUT_TYPE = TitleSummary
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.topics: List[TitleSummaryWithId] = []
+        self._topic_id = 0
+
+    async def _push(self, data: TitleSummary):
+        # Convert to TitleSummaryWithId and collect
+        self._topic_id += 1
+        topic_with_id = TitleSummaryWithId(
+            id=str(self._topic_id),
+            title=data.title,
+            summary=data.summary,
+            timestamp=data.timestamp,
+            duration=data.duration,
+            transcript=data.transcript,
+        )
+        self.topics.append(topic_with_id)
+
+        # Pass through the original topic
+        await self.emit(data)
+
+    def get_topics(self) -> List[TitleSummaryWithId]:
+        return self.topics
 
 
 async def process_audio_file(
@@ -24,18 +74,41 @@ async def process_audio_file(
     only_transcript=False,
     source_language="en",
     target_language="en",
+    enable_diarization=True,
+    diarization_backend="pyannote",
 ):
-    # build pipeline for audio processing
-    processors = [
-        AudioChunkerProcessor(),
+    # Create temp file for audio if diarization is enabled
+    audio_temp_path = None
+    if enable_diarization:
+        audio_temp_file = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
+        audio_temp_path = audio_temp_file.name
+        audio_temp_file.close()
+
+    # Create processor for collecting topics
+    topic_collector = TopicCollectorProcessor()
+
+    # Build pipeline for audio processing
+    processors = []
+
+    # Add audio file writer at the beginning if diarization is enabled
+    if enable_diarization:
+        processors.append(AudioFileWriterProcessor(audio_temp_path))
+
+    # Add the rest of the processors
+    processors += [
+        AudioDownscaleProcessor(),
+        AudioChunkerAutoProcessor(),
         AudioMergeProcessor(),
         AudioTranscriptAutoProcessor.as_threaded(),
         TranscriptLinerProcessor(),
-        TranscriptTranslatorProcessor.as_threaded(),
+        TranscriptTranslatorAutoProcessor.as_threaded(),
     ]
+
     if not only_transcript:
         processors += [
             TranscriptTopicDetectorProcessor.as_threaded(),
+            # Collect topics for diarization
+            topic_collector,
             BroadcastProcessor(
                 processors=[
                     TranscriptFinalTitleProcessor.as_threaded(),
@@ -44,14 +117,14 @@ async def process_audio_file(
             ),
         ]
 
-    # transcription output
+    # Create main pipeline
     pipeline = Pipeline(*processors)
     pipeline.set_pref("audio:source_language", source_language)
     pipeline.set_pref("audio:target_language", target_language)
     pipeline.describe()
     pipeline.on(event_callback)
 
-    # start processing audio
+    # Start processing audio
     logger.info(f"Opening {filename}")
     container = av.open(filename)
     try:
@@ -62,43 +135,243 @@ async def process_audio_file(
         logger.info("Flushing the pipeline")
         await pipeline.flush()
 
-    logger.info("All done !")
+    # Run diarization if enabled and we have topics
+    if enable_diarization and not only_transcript and audio_temp_path:
+        topics = topic_collector.get_topics()
+
+        if topics:
+            logger.info(f"Starting diarization with {len(topics)} topics")
+
+            try:
+                from reflector.processors import AudioDiarizationAutoProcessor
+
+                diarization_processor = AudioDiarizationAutoProcessor(
+                    name=diarization_backend
+                )
+
+                diarization_processor.set_pipeline(pipeline)
+
+                # For Modal backend, we need to upload the file to S3 first
+                if diarization_backend == "modal":
+                    from datetime import datetime
+
+                    from reflector.storage import get_transcripts_storage
+                    from reflector.utils.s3_temp_file import S3TemporaryFile
+
+                    storage = get_transcripts_storage()
+
+                    # Generate a unique filename in evaluation folder
+                    timestamp = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
+                    audio_filename = f"evaluation/diarization_temp/{timestamp}_{uuid.uuid4().hex}.wav"
+
+                    # Use context manager for automatic cleanup
+                    async with S3TemporaryFile(storage, audio_filename) as s3_file:
+                        # Read and upload the audio file
+                        with open(audio_temp_path, "rb") as f:
+                            audio_data = f.read()
+
+                        audio_url = await s3_file.upload(audio_data)
+                        logger.info(f"Uploaded audio to S3: {audio_filename}")
+
+                        # Create diarization input with S3 URL
+                        diarization_input = AudioDiarizationInput(
+                            audio_url=audio_url, topics=topics
+                        )
+
+                        # Run diarization
+                        await diarization_processor.push(diarization_input)
+                        await diarization_processor.flush()
+
+                        logger.info("Diarization complete")
+                        # File will be automatically cleaned up when exiting the context
+                else:
+                    # For local backend, use local file path
+                    audio_url = audio_temp_path
+
+                    # Create diarization input
+                    diarization_input = AudioDiarizationInput(
+                        audio_url=audio_url, topics=topics
+                    )
+
+                    # Run diarization
+                    await diarization_processor.push(diarization_input)
+                    await diarization_processor.flush()
+
+                    logger.info("Diarization complete")
+
+            except ImportError as e:
+                logger.error(f"Failed to import diarization dependencies: {e}")
+                logger.error(
+                    "Install with: uv pip install pyannote.audio torch torchaudio"
+                )
+                logger.error(
+                    "And set HF_TOKEN environment variable for pyannote models"
+                )
+                raise SystemExit(1)
+            except Exception as e:
+                logger.error(f"Diarization failed: {e}")
+                raise SystemExit(1)
+        else:
+            logger.warning("Skipping diarization: no topics available")
+
+    # Clean up temp file
+    if audio_temp_path:
+        try:
+            Path(audio_temp_path).unlink()
+        except Exception as e:
+            logger.warning(f"Failed to clean up temp file {audio_temp_path}: {e}")
+
+    logger.info("All done!")
+
+
+async def process_file_pipeline(
+    filename: str,
+    event_callback,
+    source_language="en",
+    target_language="en",
+    enable_diarization=True,
+    diarization_backend="modal",
+):
+    """Process audio/video file using the optimized file pipeline"""
+    try:
+        from reflector.db import database
+        from reflector.db.transcripts import SourceKind, transcripts_controller
+        from reflector.pipelines.main_file_pipeline import PipelineMainFile
+
+        await database.connect()
+        try:
+            # Create a temporary transcript for processing
+            transcript = await transcripts_controller.add(
+                "",
+                source_kind=SourceKind.FILE,
+                source_language=source_language,
+                target_language=target_language,
+            )
+
+            # Process the file
+            pipeline = PipelineMainFile(transcript_id=transcript.id)
+            await pipeline.process(Path(filename))
+
+            logger.info("File pipeline processing complete")
+
+        finally:
+            await database.disconnect()
+    except ImportError as e:
+        logger.error(f"File pipeline not available: {e}")
+        logger.info("Falling back to stream pipeline")
+        # Fall back to stream pipeline
+        await process_audio_file(
+            filename,
+            event_callback,
+            only_transcript=False,
+            source_language=source_language,
+            target_language=target_language,
+            enable_diarization=enable_diarization,
+            diarization_backend=diarization_backend,
+        )
 
 
 if __name__ == "__main__":
     import argparse
+    import os
 
-    parser = argparse.ArgumentParser()
+    parser = argparse.ArgumentParser(
+        description="Process audio files with optional speaker diarization"
+    )
     parser.add_argument("source", help="Source file (mp3, wav, mp4...)")
-    parser.add_argument("--only-transcript", "-t", action="store_true")
-    parser.add_argument("--source-language", default="en")
-    parser.add_argument("--target-language", default="en")
+    parser.add_argument(
+        "--stream",
+        action="store_true",
+        help="Use streaming pipeline (original frame-based processing)",
+    )
+    parser.add_argument(
+        "--only-transcript",
+        "-t",
+        action="store_true",
+        help="Only generate transcript without topics/summaries",
+    )
+    parser.add_argument(
+        "--source-language", default="en", help="Source language code (default: en)"
+    )
+    parser.add_argument(
+        "--target-language", default="en", help="Target language code (default: en)"
+    )
     parser.add_argument("--output", "-o", help="Output file (output.jsonl)")
+    parser.add_argument(
+        "--enable-diarization",
+        "-d",
+        action="store_true",
+        help="Enable speaker diarization",
+    )
+    parser.add_argument(
+        "--diarization-backend",
+        default="pyannote",
+        choices=["pyannote", "modal"],
+        help="Diarization backend to use (default: pyannote)",
+    )
     args = parser.parse_args()
 
+    if "REDIS_HOST" not in os.environ:
+        os.environ["REDIS_HOST"] = "localhost"
+
     output_fd = None
     if args.output:
         output_fd = open(args.output, "w")
 
     async def event_callback(event: PipelineEvent):
         processor = event.processor
-        # ignore some processor
-        if processor in ("AudioChunkerProcessor", "AudioMergeProcessor"):
+        data = event.data
+
+        # Ignore internal processors
+        if processor in (
+            "AudioDownscaleProcessor",
+            "AudioChunkerAutoProcessor",
+            "AudioMergeProcessor",
+            "AudioFileWriterProcessor",
+            "TopicCollectorProcessor",
+            "BroadcastProcessor",
+        ):
             return
-        logger.info(f"Event: {event}")
+
+        # If diarization is enabled, skip the original topic events from the pipeline
+        # The diarization processor will emit the same topics but with speaker info
+        if processor == "TranscriptTopicDetectorProcessor" and args.enable_diarization:
+            return
+
+        # Log all events
+        logger.info(f"Event: {processor} - {type(data).__name__}")
+
+        # Write to output
         if output_fd:
             output_fd.write(event.model_dump_json())
             output_fd.write("\n")
+            output_fd.flush()
 
-    asyncio.run(
-        process_audio_file(
-            args.source,
-            event_callback,
-            only_transcript=args.only_transcript,
-            source_language=args.source_language,
-            target_language=args.target_language,
+    if args.stream:
+        # Use original streaming pipeline
+        asyncio.run(
+            process_audio_file(
+                args.source,
+                event_callback,
+                only_transcript=args.only_transcript,
+                source_language=args.source_language,
+                target_language=args.target_language,
+                enable_diarization=args.enable_diarization,
+                diarization_backend=args.diarization_backend,
+            )
+        )
+    else:
+        # Use optimized file pipeline (default)
+        asyncio.run(
+            process_file_pipeline(
+                args.source,
+                event_callback,
+                source_language=args.source_language,
+                target_language=args.target_language,
+                enable_diarization=args.enable_diarization,
+                diarization_backend=args.diarization_backend,
+            )
         )
-    )
 
     if output_fd:
         output_fd.close()

server/reflector/tools/process_with_diarization.py

@@ -17,7 +17,8 @@ import av
 
 from reflector.logger import logger
 from reflector.processors import (
-    AudioChunkerProcessor,
+    AudioChunkerAutoProcessor,
+    AudioDownscaleProcessor,
     AudioFileWriterProcessor,
     AudioMergeProcessor,
     AudioTranscriptAutoProcessor,
@@ -27,7 +28,7 @@ from reflector.processors import (
     TranscriptFinalTitleProcessor,
     TranscriptLinerProcessor,
     TranscriptTopicDetectorProcessor,
-    TranscriptTranslatorProcessor,
+    TranscriptTranslatorAutoProcessor,
 )
 from reflector.processors.base import BroadcastProcessor, Processor
 from reflector.processors.types import (
@@ -96,14 +97,15 @@ async def process_audio_file_with_diarization(
 
     # Add the rest of the processors
     processors += [
-        AudioChunkerProcessor(),
+        AudioDownscaleProcessor(),
+        AudioChunkerAutoProcessor(),
         AudioMergeProcessor(),
         AudioTranscriptAutoProcessor.as_threaded(),
     ]
 
     processors += [
         TranscriptLinerProcessor(),
-        TranscriptTranslatorProcessor.as_threaded(),
+        TranscriptTranslatorAutoProcessor.as_threaded(),
     ]
 
     if not only_transcript:
@@ -145,18 +147,17 @@ async def process_audio_file_with_diarization(
             logger.info(f"Starting diarization with {len(topics)} topics")
 
             try:
-                # Import diarization processor
                 from reflector.processors import AudioDiarizationAutoProcessor
 
-                # Create diarization processor
                 diarization_processor = AudioDiarizationAutoProcessor(
                     name=diarization_backend
                 )
-                diarization_processor.on(event_callback)
+
+                diarization_processor.set_pipeline(pipeline)
 
                 # For Modal backend, we need to upload the file to S3 first
                 if diarization_backend == "modal":
-                    from datetime import datetime
+                    from datetime import datetime, timezone
 
                     from reflector.storage import get_transcripts_storage
                     from reflector.utils.s3_temp_file import S3TemporaryFile
@@ -164,7 +165,7 @@ async def process_audio_file_with_diarization(
                     storage = get_transcripts_storage()
 
                     # Generate a unique filename in evaluation folder
-                    timestamp = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
+                    timestamp = datetime.now(timezone.utc).strftime("%Y%m%d_%H%M%S")
                     audio_filename = f"evaluation/diarization_temp/{timestamp}_{uuid.uuid4().hex}.wav"
 
                     # Use context manager for automatic cleanup
@@ -277,7 +278,8 @@ if __name__ == "__main__":
 
         # Ignore internal processors
         if processor in (
-            "AudioChunkerProcessor",
+            "AudioDownscaleProcessor",
+            "AudioChunkerAutoProcessor",
             "AudioMergeProcessor",
             "AudioFileWriterProcessor",
             "TopicCollectorProcessor",

server/reflector/tools/runpipeline.py

@@ -53,7 +53,7 @@ async def run_single_processor(args):
     async def event_callback(event: PipelineEvent):
         processor = event.processor
         # ignore some processor
-        if processor in ("AudioChunkerProcessor", "AudioMergeProcessor"):
+        if processor in ("AudioChunkerAutoProcessor", "AudioMergeProcessor"):
             return
         print(f"Event: {event}")
         if output_fd:

server/reflector/utils/webvtt.py

@@ -0,0 +1,63 @@
+"""WebVTT utilities for generating subtitle files from transcript data."""
+
+from typing import TYPE_CHECKING, Annotated
+
+import webvtt
+
+from reflector.processors.types import Seconds, Word, words_to_segments
+
+if TYPE_CHECKING:
+    from reflector.db.transcripts import TranscriptTopic
+
+VttTimestamp = Annotated[str, "vtt_timestamp"]
+WebVTTStr = Annotated[str, "webvtt_str"]
+
+
+def _seconds_to_timestamp(seconds: Seconds) -> VttTimestamp:
+    # lib doesn't do that
+    hours = int(seconds // 3600)
+    minutes = int((seconds % 3600) // 60)
+    secs = int(seconds % 60)
+    milliseconds = int((seconds % 1) * 1000)
+
+    return f"{hours:02d}:{minutes:02d}:{secs:02d}.{milliseconds:03d}"
+
+
+def words_to_webvtt(words: list[Word]) -> WebVTTStr:
+    """Convert words to WebVTT using existing segmentation logic."""
+    vtt = webvtt.WebVTT()
+    if not words:
+        return vtt.content
+
+    segments = words_to_segments(words)
+
+    for segment in segments:
+        text = segment.text.strip()
+        # lib doesn't do that
+        text = f"<v Speaker{segment.speaker}>{text}"
+
+        caption = webvtt.Caption(
+            start=_seconds_to_timestamp(segment.start),
+            end=_seconds_to_timestamp(segment.end),
+            text=text,
+        )
+        vtt.captions.append(caption)
+
+    return vtt.content
+
+
+def topics_to_webvtt(topics: list["TranscriptTopic"]) -> WebVTTStr:
+    if not topics:
+        return webvtt.WebVTT().content
+
+    all_words: list[Word] = []
+    for topic in topics:
+        all_words.extend(topic.words)
+
+    # assert it's in sequence
+    for i in range(len(all_words) - 1):
+        assert (
+            all_words[i].start <= all_words[i + 1].start
+        ), f"Words are not in sequence: {all_words[i].text} and {all_words[i + 1].text} are not consecutive: {all_words[i].start} > {all_words[i + 1].start}"
+
+    return words_to_webvtt(all_words)

server/reflector/video_platforms/__init__.py

@@ -1,17 +0,0 @@
-# Video Platform Abstraction Layer
-"""
-This module provides an abstraction layer for different video conferencing platforms.
-It allows seamless switching between providers (Whereby, Daily.co, etc.) without
-changing the core application logic.
-"""
-
-from .base import MeetingData, VideoPlatformClient, VideoPlatformConfig
-from .registry import get_platform_client, register_platform
-
-__all__ = [
-    "VideoPlatformClient",
-    "VideoPlatformConfig",
-    "MeetingData",
-    "get_platform_client",
-    "register_platform",
-]

server/reflector/video_platforms/base.py

@@ -1,82 +0,0 @@
-from abc import ABC, abstractmethod
-from datetime import datetime
-from typing import Any, Dict, Optional
-
-from pydantic import BaseModel
-
-from reflector.db.rooms import Room
-
-
-class MeetingData(BaseModel):
-    """Standardized meeting data returned by all platforms."""
-
-    meeting_id: str
-    room_name: str
-    room_url: str
-    host_room_url: str
-    platform: str
-    extra_data: Dict[str, Any] = {}  # Platform-specific data
-
-
-class VideoPlatformConfig(BaseModel):
-    """Configuration for a video platform."""
-
-    api_key: str
-    webhook_secret: str
-    api_url: Optional[str] = None
-    subdomain: Optional[str] = None
-    s3_bucket: Optional[str] = None
-    s3_region: Optional[str] = None
-    aws_role_arn: Optional[str] = None
-    aws_access_key_id: Optional[str] = None
-    aws_access_key_secret: Optional[str] = None
-
-
-class VideoPlatformClient(ABC):
-    """Abstract base class for video platform integrations."""
-
-    PLATFORM_NAME: str = ""
-
-    def __init__(self, config: VideoPlatformConfig):
-        self.config = config
-
-    @abstractmethod
-    async def create_meeting(
-        self, room_name_prefix: str, end_date: datetime, room: Room
-    ) -> MeetingData:
-        """Create a new meeting room."""
-        pass
-
-    @abstractmethod
-    async def get_room_sessions(self, room_name: str) -> Dict[str, Any]:
-        """Get session information for a room."""
-        pass
-
-    @abstractmethod
-    async def delete_room(self, room_name: str) -> bool:
-        """Delete a room. Returns True if successful."""
-        pass
-
-    @abstractmethod
-    async def upload_logo(self, room_name: str, logo_path: str) -> bool:
-        """Upload a logo to the room. Returns True if successful."""
-        pass
-
-    @abstractmethod
-    def verify_webhook_signature(
-        self, body: bytes, signature: str, timestamp: Optional[str] = None
-    ) -> bool:
-        """Verify webhook signature for security."""
-        pass
-
-    def format_recording_config(self, room: Room) -> Dict[str, Any]:
-        """Format recording configuration for the platform.
-        Can be overridden by specific implementations."""
-        if room.recording_type == "cloud" and self.config.s3_bucket:
-            return {
-                "type": room.recording_type,
-                "bucket": self.config.s3_bucket,
-                "region": self.config.s3_region,
-                "trigger": room.recording_trigger,
-            }
-        return {"type": room.recording_type}

server/reflector/video_platforms/daily.py

@@ -1,127 +0,0 @@
-import hmac
-from datetime import datetime
-from hashlib import sha256
-from typing import Any, Dict, Optional
-
-import httpx
-
-from reflector.db.rooms import Room
-
-from .base import MeetingData, VideoPlatformClient, VideoPlatformConfig
-
-
-class DailyClient(VideoPlatformClient):
-    """Daily.co video platform implementation."""
-
-    PLATFORM_NAME = "daily"
-    TIMEOUT = 10  # seconds
-    BASE_URL = "https://api.daily.co/v1"
-
-    def __init__(self, config: VideoPlatformConfig):
-        super().__init__(config)
-        self.headers = {
-            "Authorization": f"Bearer {config.api_key}",
-            "Content-Type": "application/json",
-        }
-
-    async def create_meeting(
-        self, room_name_prefix: str, end_date: datetime, room: Room
-    ) -> MeetingData:
-        """Create a Daily.co room."""
-        room_name = f"{room_name_prefix}-{datetime.now().strftime('%Y%m%d%H%M%S')}"
-
-        data = {
-            "name": room_name,
-            "privacy": "private" if room.is_locked else "public",
-            "properties": {
-                "enable_recording": room.recording_type
-                if room.recording_type != "none"
-                else False,
-                "enable_chat": True,
-                "enable_screenshare": True,
-                "start_video_off": False,
-                "start_audio_off": False,
-                "exp": int(end_date.timestamp()),
-            },
-        }
-
-        # Configure S3 bucket for cloud recordings
-        if room.recording_type == "cloud" and self.config.s3_bucket:
-            data["properties"]["recordings_bucket"] = {
-                "bucket_name": self.config.s3_bucket,
-                "bucket_region": self.config.s3_region,
-                "assume_role_arn": self.config.aws_role_arn,
-                "allow_api_access": True,
-            }
-
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                f"{self.BASE_URL}/rooms",
-                headers=self.headers,
-                json=data,
-                timeout=self.TIMEOUT,
-            )
-            response.raise_for_status()
-            result = response.json()
-
-        # Format response to match our standard
-        room_url = result["url"]
-
-        return MeetingData(
-            meeting_id=result["id"],
-            room_name=result["name"],
-            room_url=room_url,
-            host_room_url=room_url,
-            platform=self.PLATFORM_NAME,
-            extra_data=result,
-        )
-
-    async def get_room_sessions(self, room_name: str) -> Dict[str, Any]:
-        """Get Daily.co room information."""
-        async with httpx.AsyncClient() as client:
-            response = await client.get(
-                f"{self.BASE_URL}/rooms/{room_name}",
-                headers=self.headers,
-                timeout=self.TIMEOUT,
-            )
-            response.raise_for_status()
-            return response.json()
-
-    async def get_room_presence(self, room_name: str) -> Dict[str, Any]:
-        """Get real-time participant data - Daily.co specific feature."""
-        async with httpx.AsyncClient() as client:
-            response = await client.get(
-                f"{self.BASE_URL}/rooms/{room_name}/presence",
-                headers=self.headers,
-                timeout=self.TIMEOUT,
-            )
-            response.raise_for_status()
-            return response.json()
-
-    async def delete_room(self, room_name: str) -> bool:
-        """Delete a Daily.co room."""
-        async with httpx.AsyncClient() as client:
-            response = await client.delete(
-                f"{self.BASE_URL}/rooms/{room_name}",
-                headers=self.headers,
-                timeout=self.TIMEOUT,
-            )
-            # Daily.co returns 200 for success, 404 if room doesn't exist
-            return response.status_code in (200, 404)
-
-    async def upload_logo(self, room_name: str, logo_path: str) -> bool:
-        """Daily.co doesn't support custom logos per room - this is a no-op."""
-        return True
-
-    def verify_webhook_signature(
-        self, body: bytes, signature: str, timestamp: Optional[str] = None
-    ) -> bool:
-        """Verify Daily.co webhook signature."""
-        expected = hmac.new(
-            self.config.webhook_secret.encode(), body, sha256
-        ).hexdigest()
-
-        try:
-            return hmac.compare_digest(expected, signature)
-        except Exception:
-            return False

server/reflector/video_platforms/factory.py

@@ -1,52 +0,0 @@
-"""Factory for creating video platform clients based on configuration."""
-
-from typing import Optional
-
-from reflector.settings import settings
-
-from .base import VideoPlatformClient, VideoPlatformConfig
-from .registry import get_platform_client
-
-
-def get_platform_config(platform: str) -> VideoPlatformConfig:
-    """Get configuration for a specific platform."""
-    if platform == "whereby":
-        return VideoPlatformConfig(
-            api_key=settings.WHEREBY_API_KEY or "",
-            webhook_secret=settings.WHEREBY_WEBHOOK_SECRET or "",
-            api_url=settings.WHEREBY_API_URL,
-            s3_bucket=settings.AWS_WHEREBY_S3_BUCKET,
-            aws_access_key_id=settings.AWS_WHEREBY_ACCESS_KEY_ID,
-            aws_access_key_secret=settings.AWS_WHEREBY_ACCESS_KEY_SECRET,
-        )
-    elif platform == "daily":
-        return VideoPlatformConfig(
-            api_key=settings.DAILY_API_KEY or "",
-            webhook_secret=settings.DAILY_WEBHOOK_SECRET or "",
-            subdomain=settings.DAILY_SUBDOMAIN,
-            s3_bucket=settings.AWS_DAILY_S3_BUCKET,
-            s3_region=settings.AWS_DAILY_S3_REGION,
-            aws_role_arn=settings.AWS_DAILY_ROLE_ARN,
-        )
-    else:
-        raise ValueError(f"Unknown platform: {platform}")
-
-
-def create_platform_client(platform: str) -> VideoPlatformClient:
-    """Create a video platform client instance."""
-    config = get_platform_config(platform)
-    return get_platform_client(platform, config)
-
-
-def get_platform_for_room(room_id: Optional[str] = None) -> str:
-    """Determine which platform to use for a room based on feature flags."""
-    # If Daily migration is disabled, always use Whereby
-    if not settings.DAILY_MIGRATION_ENABLED:
-        return "whereby"
-
-    # If a specific room is in the migration list, use Daily
-    if room_id and room_id in settings.DAILY_MIGRATION_ROOM_IDS:
-        return "daily"
-
-    # Otherwise use the default platform
-    return settings.DEFAULT_VIDEO_PLATFORM

server/reflector/video_platforms/mock.py

@@ -1,124 +0,0 @@
-"""Mock video platform client for testing."""
-
-import uuid
-from datetime import datetime
-from typing import Any, Dict, Optional
-
-from reflector.db.rooms import Room
-
-from .base import MeetingData, VideoPlatformClient, VideoPlatformConfig
-
-
-class MockPlatformClient(VideoPlatformClient):
-    """Mock video platform implementation for testing."""
-
-    PLATFORM_NAME = "mock"
-
-    def __init__(self, config: VideoPlatformConfig):
-        super().__init__(config)
-        # Store created rooms for testing
-        self._rooms: Dict[str, Dict[str, Any]] = {}
-        self._webhook_calls: list[Dict[str, Any]] = []
-
-    async def create_meeting(
-        self, room_name_prefix: str, end_date: datetime, room: Room
-    ) -> MeetingData:
-        """Create a mock meeting."""
-        meeting_id = str(uuid.uuid4())
-        room_name = f"{room_name_prefix}-{meeting_id[:8]}"
-        room_url = f"https://mock.video/{room_name}"
-        host_room_url = f"{room_url}?host=true"
-
-        # Store room data for later retrieval
-        self._rooms[room_name] = {
-            "id": meeting_id,
-            "name": room_name,
-            "url": room_url,
-            "host_url": host_room_url,
-            "end_date": end_date,
-            "room": room,
-            "participants": [],
-            "is_active": True,
-        }
-
-        return MeetingData(
-            meeting_id=meeting_id,
-            room_name=room_name,
-            room_url=room_url,
-            host_room_url=host_room_url,
-            platform=self.PLATFORM_NAME,
-            extra_data={"mock": True},
-        )
-
-    async def get_room_sessions(self, room_name: str) -> Dict[str, Any]:
-        """Get mock room session information."""
-        if room_name not in self._rooms:
-            return {"error": "Room not found"}
-
-        room_data = self._rooms[room_name]
-        return {
-            "roomName": room_name,
-            "sessions": [
-                {
-                    "sessionId": room_data["id"],
-                    "startTime": datetime.utcnow().isoformat(),
-                    "participants": room_data["participants"],
-                    "isActive": room_data["is_active"],
-                }
-            ],
-        }
-
-    async def delete_room(self, room_name: str) -> bool:
-        """Delete a mock room."""
-        if room_name in self._rooms:
-            self._rooms[room_name]["is_active"] = False
-            return True
-        return False
-
-    async def upload_logo(self, room_name: str, logo_path: str) -> bool:
-        """Mock logo upload."""
-        if room_name in self._rooms:
-            self._rooms[room_name]["logo_path"] = logo_path
-            return True
-        return False
-
-    def verify_webhook_signature(
-        self, body: bytes, signature: str, timestamp: Optional[str] = None
-    ) -> bool:
-        """Mock webhook signature verification."""
-        # For testing, accept signature == "valid"
-        return signature == "valid"
-
-    # Mock-specific methods for testing
-
-    def add_participant(
-        self, room_name: str, participant_id: str, participant_name: str
-    ):
-        """Add a participant to a mock room (for testing)."""
-        if room_name in self._rooms:
-            self._rooms[room_name]["participants"].append(
-                {
-                    "id": participant_id,
-                    "name": participant_name,
-                    "joined_at": datetime.utcnow().isoformat(),
-                }
-            )
-
-    def trigger_webhook(self, event_type: str, data: Dict[str, Any]):
-        """Trigger a mock webhook event (for testing)."""
-        self._webhook_calls.append(
-            {
-                "type": event_type,
-                "data": data,
-                "timestamp": datetime.utcnow().isoformat(),
-            }
-        )
-
-    def get_webhook_calls(self) -> list[Dict[str, Any]]:
-        """Get all webhook calls made (for testing)."""
-        return self._webhook_calls.copy()
-
-    def clear_data(self):
-        """Clear all mock data (for testing)."""
-        self._rooms.clear()
-        self._webhook_calls.clear()

server/reflector/video_platforms/registry.py

@@ -1,42 +0,0 @@
-from typing import Dict, Type
-
-from .base import VideoPlatformClient, VideoPlatformConfig
-
-# Registry of available video platforms
-_PLATFORMS: Dict[str, Type[VideoPlatformClient]] = {}
-
-
-def register_platform(name: str, client_class: Type[VideoPlatformClient]):
-    """Register a video platform implementation."""
-    _PLATFORMS[name.lower()] = client_class
-
-
-def get_platform_client(
-    platform: str, config: VideoPlatformConfig
-) -> VideoPlatformClient:
-    """Get a video platform client instance."""
-    platform_lower = platform.lower()
-    if platform_lower not in _PLATFORMS:
-        raise ValueError(f"Unknown video platform: {platform}")
-
-    client_class = _PLATFORMS[platform_lower]
-    return client_class(config)
-
-
-def get_available_platforms() -> list[str]:
-    """Get list of available platform names."""
-    return list(_PLATFORMS.keys())
-
-
-# Auto-register built-in platforms
-def _register_builtin_platforms():
-    from .daily import DailyClient
-    from .mock import MockPlatformClient
-    from .whereby import WherebyClient
-
-    register_platform("whereby", WherebyClient)
-    register_platform("daily", DailyClient)
-    register_platform("mock", MockPlatformClient)
-
-
-_register_builtin_platforms()

server/reflector/video_platforms/whereby.py

@@ -1,140 +0,0 @@
-import hmac
-import json
-import re
-import time
-from datetime import datetime
-from hashlib import sha256
-from typing import Any, Dict, Optional
-
-import httpx
-
-from reflector.db.rooms import Room
-
-from .base import MeetingData, VideoPlatformClient, VideoPlatformConfig
-
-
-class WherebyClient(VideoPlatformClient):
-    """Whereby video platform implementation."""
-
-    PLATFORM_NAME = "whereby"
-    TIMEOUT = 10  # seconds
-    MAX_ELAPSED_TIME = 60 * 1000  # 1 minute in milliseconds
-
-    def __init__(self, config: VideoPlatformConfig):
-        super().__init__(config)
-        self.headers = {
-            "Content-Type": "application/json; charset=utf-8",
-            "Authorization": f"Bearer {config.api_key}",
-        }
-
-    async def create_meeting(
-        self, room_name_prefix: str, end_date: datetime, room: Room
-    ) -> MeetingData:
-        """Create a Whereby meeting."""
-        data = {
-            "isLocked": room.is_locked,
-            "roomNamePrefix": room_name_prefix,
-            "roomNamePattern": "uuid",
-            "roomMode": room.room_mode,
-            "endDate": end_date.isoformat(),
-            "fields": ["hostRoomUrl"],
-        }
-
-        # Add recording configuration if cloud recording is enabled
-        if room.recording_type == "cloud":
-            data["recording"] = {
-                "type": room.recording_type,
-                "destination": {
-                    "provider": "s3",
-                    "bucket": self.config.s3_bucket,
-                    "accessKeyId": self.config.aws_access_key_id,
-                    "accessKeySecret": self.config.aws_access_key_secret,
-                    "fileFormat": "mp4",
-                },
-                "startTrigger": room.recording_trigger,
-            }
-
-        async with httpx.AsyncClient() as client:
-            response = await client.post(
-                f"{self.config.api_url}/meetings",
-                headers=self.headers,
-                json=data,
-                timeout=self.TIMEOUT,
-            )
-            response.raise_for_status()
-            result = response.json()
-
-        return MeetingData(
-            meeting_id=result["meetingId"],
-            room_name=result["roomName"],
-            room_url=result["roomUrl"],
-            host_room_url=result["hostRoomUrl"],
-            platform=self.PLATFORM_NAME,
-            extra_data=result,
-        )
-
-    async def get_room_sessions(self, room_name: str) -> Dict[str, Any]:
-        """Get Whereby room session information."""
-        async with httpx.AsyncClient() as client:
-            response = await client.get(
-                f"{self.config.api_url}/insights/room-sessions?roomName={room_name}",
-                headers=self.headers,
-                timeout=self.TIMEOUT,
-            )
-            response.raise_for_status()
-            return response.json()
-
-    async def delete_room(self, room_name: str) -> bool:
-        """Whereby doesn't support room deletion - meetings expire automatically."""
-        return True
-
-    async def upload_logo(self, room_name: str, logo_path: str) -> bool:
-        """Upload logo to Whereby room."""
-        async with httpx.AsyncClient() as client:
-            with open(logo_path, "rb") as f:
-                response = await client.put(
-                    f"{self.config.api_url}/rooms/{room_name}/theme/logo",
-                    headers={
-                        "Authorization": f"Bearer {self.config.api_key}",
-                    },
-                    timeout=self.TIMEOUT,
-                    files={"image": f},
-                )
-                response.raise_for_status()
-        return True
-
-    def verify_webhook_signature(
-        self, body: bytes, signature: str, timestamp: Optional[str] = None
-    ) -> bool:
-        """Verify Whereby webhook signature."""
-        if not signature:
-            return False
-
-        matches = re.match(r"t=(.*),v1=(.*)", signature)
-        if not matches:
-            return False
-
-        ts, sig = matches.groups()
-
-        # Check timestamp to prevent replay attacks
-        current_time = int(time.time() * 1000)
-        diff_time = current_time - int(ts) * 1000
-        if diff_time >= self.MAX_ELAPSED_TIME:
-            return False
-
-        # Verify signature
-        body_dict = json.loads(body)
-        signed_payload = f"{ts}.{json.dumps(body_dict, separators=(',', ':'))}"
-        hmac_obj = hmac.new(
-            self.config.webhook_secret.encode("utf-8"),
-            signed_payload.encode("utf-8"),
-            sha256,
-        )
-        expected_signature = hmac_obj.hexdigest()
-
-        try:
-            return hmac.compare_digest(
-                expected_signature.encode("utf-8"), sig.encode("utf-8")
-            )
-        except Exception:
-            return False

server/reflector/views/_range_requests_response.py

@@ -44,8 +44,6 @@ def range_requests_response(
     """Returns StreamingResponse using Range Requests of a given file"""
 
     if not os.path.exists(file_path):
-        from fastapi import HTTPException
-
         raise HTTPException(status_code=404, detail="File not found")
 
     file_size = os.stat(file_path).st_size

server/reflector/views/daily.py

@@ -1,145 +0,0 @@
-"""Daily.co webhook handler endpoint."""
-
-import hmac
-from hashlib import sha256
-from typing import Any, Dict
-
-from fastapi import APIRouter, HTTPException, Request
-from pydantic import BaseModel
-
-from reflector.db.meetings import meetings_controller
-from reflector.settings import settings
-
-router = APIRouter()
-
-
-class DailyWebhookEvent(BaseModel):
-    """Daily.co webhook event structure."""
-
-    type: str
-    id: str
-    ts: int  # Unix timestamp in milliseconds
-    data: Dict[str, Any]
-
-
-def verify_daily_webhook_signature(body: bytes, signature: str) -> bool:
-    """Verify Daily.co webhook signature using HMAC-SHA256."""
-    if not signature or not settings.DAILY_WEBHOOK_SECRET:
-        return False
-
-    try:
-        expected = hmac.new(
-            settings.DAILY_WEBHOOK_SECRET.encode(), body, sha256
-        ).hexdigest()
-        return hmac.compare_digest(expected, signature)
-    except Exception:
-        return False
-
-
-@router.post("/daily_webhook")
-async def daily_webhook(event: DailyWebhookEvent, request: Request):
-    """Handle Daily.co webhook events."""
-    # Verify webhook signature for security
-    body = await request.body()
-    signature = request.headers.get("X-Daily-Signature", "")
-
-    if not verify_daily_webhook_signature(body, signature):
-        raise HTTPException(status_code=401, detail="Invalid webhook signature")
-
-    # Handle participant events
-    if event.type == "participant.joined":
-        await _handle_participant_joined(event)
-    elif event.type == "participant.left":
-        await _handle_participant_left(event)
-    elif event.type == "recording.started":
-        await _handle_recording_started(event)
-    elif event.type == "recording.ready-to-download":
-        await _handle_recording_ready(event)
-    elif event.type == "recording.error":
-        await _handle_recording_error(event)
-
-    return {"status": "ok"}
-
-
-async def _handle_participant_joined(event: DailyWebhookEvent):
-    """Handle participant joined event."""
-    room_name = event.data.get("room", {}).get("name")
-    if not room_name:
-        return
-
-    meeting = await meetings_controller.get_by_room_name(room_name)
-    if meeting:
-        # Update participant count (same as Whereby)
-        current_count = getattr(meeting, "num_clients", 0)
-        await meetings_controller.update_meeting(
-            meeting.id, num_clients=current_count + 1
-        )
-
-
-async def _handle_participant_left(event: DailyWebhookEvent):
-    """Handle participant left event."""
-    room_name = event.data.get("room", {}).get("name")
-    if not room_name:
-        return
-
-    meeting = await meetings_controller.get_by_room_name(room_name)
-    if meeting:
-        # Update participant count (same as Whereby)
-        current_count = getattr(meeting, "num_clients", 0)
-        await meetings_controller.update_meeting(
-            meeting.id, num_clients=max(0, current_count - 1)
-        )
-
-
-async def _handle_recording_started(event: DailyWebhookEvent):
-    """Handle recording started event."""
-    room_name = event.data.get("room", {}).get("name")
-    if not room_name:
-        return
-
-    meeting = await meetings_controller.get_by_room_name(room_name)
-    if meeting:
-        # Log recording start for debugging
-        print(f"Recording started for meeting {meeting.id} in room {room_name}")
-
-
-async def _handle_recording_ready(event: DailyWebhookEvent):
-    """Handle recording ready for download event."""
-    room_name = event.data.get("room", {}).get("name")
-    recording_data = event.data.get("recording", {})
-    download_link = recording_data.get("download_url")
-    recording_id = recording_data.get("id")
-
-    if not room_name or not download_link:
-        return
-
-    meeting = await meetings_controller.get_by_room_name(room_name)
-    if meeting:
-        # Queue recording processing task (same as Whereby)
-        try:
-            # Import here to avoid circular imports
-            from reflector.worker.process import process_recording_from_url
-
-            # For Daily.co, we need to queue recording processing with URL
-            # This will download from the URL and process similar to S3
-            process_recording_from_url.delay(
-                recording_url=download_link,
-                meeting_id=meeting.id,
-                recording_id=recording_id or event.id,
-            )
-        except ImportError:
-            # Handle case where worker tasks aren't available
-            print(
-                f"Warning: Could not queue recording processing for meeting {meeting.id}"
-            )
-
-
-async def _handle_recording_error(event: DailyWebhookEvent):
-    """Handle recording error event."""
-    room_name = event.data.get("room", {}).get("name")
-    error = event.data.get("error", "Unknown error")
-
-    if room_name:
-        meeting = await meetings_controller.get_by_room_name(room_name)
-        if meeting:
-            print(f"Recording error for meeting {meeting.id}: {error}")

server/reflector/views/meetings.py

@@ -1,4 +1,4 @@
-from datetime import datetime
+from datetime import datetime, timezone
 from typing import Annotated, Optional
 
 from fastapi import APIRouter, Depends, HTTPException, Request
@@ -35,7 +35,7 @@ async def meeting_audio_consent(
         meeting_id=meeting_id,
         user_id=user_id,
         consent_given=request.consent_given,
-        consent_timestamp=datetime.utcnow(),
+        consent_timestamp=datetime.now(timezone.utc),
     )
 
     updated_consent = await meeting_consent_controller.upsert(consent)

server/reflector/views/rooms.py

@@ -1,29 +1,34 @@
 import logging
 import sqlite3
-from datetime import datetime, timedelta
+from datetime import datetime, timedelta, timezone
 from typing import Annotated, Literal, Optional
 
 import asyncpg.exceptions
 from fastapi import APIRouter, Depends, HTTPException
 from fastapi_pagination import Page
-from fastapi_pagination.ext.databases import paginate
+from fastapi_pagination.ext.databases import apaginate
 from pydantic import BaseModel
 
 import reflector.auth as auth
-from reflector.db import database
+from reflector.db import get_database
 from reflector.db.meetings import meetings_controller
 from reflector.db.rooms import rooms_controller
 from reflector.settings import settings
-from reflector.video_platforms.factory import (
-    create_platform_client,
-    get_platform_for_room,
-)
+from reflector.whereby import create_meeting, upload_logo
 
 logger = logging.getLogger(__name__)
 
 router = APIRouter()
 
 
+def parse_datetime_with_timezone(iso_string: str) -> datetime:
+    """Parse ISO datetime string and ensure timezone awareness (defaults to UTC if naive)."""
+    dt = datetime.fromisoformat(iso_string)
+    if dt.tzinfo is None:
+        dt = dt.replace(tzinfo=timezone.utc)
+    return dt
+
+
 class Room(BaseModel):
     id: str
     name: str
@@ -37,7 +42,6 @@ class Room(BaseModel):
     recording_type: str
     recording_trigger: str
     is_shared: bool
-    platform: str
 
 
 class Meeting(BaseModel):
@@ -48,7 +52,6 @@ class Meeting(BaseModel):
     start_date: datetime
     end_date: datetime
     recording_type: Literal["none", "local", "cloud"] = "cloud"
-    platform: str
 
 
 class CreateRoom(BaseModel):
@@ -88,8 +91,8 @@ async def rooms_list(
 
     user_id = user["sub"] if user else None
 
-    return await paginate(
-        database,
+    return await apaginate(
+        get_database(),
         await rooms_controller.get_all(
             user_id=user_id, order_by="-created_at", return_query=True
         ),
@@ -103,14 +106,6 @@ async def rooms_create(
 ):
     user_id = user["sub"] if user else None
 
-    # Determine platform for this room (will be "whereby" unless feature flag is enabled)
-    # Note: Since room doesn't exist yet, we can't use room_id for selection
-    platform = (
-        settings.DEFAULT_VIDEO_PLATFORM
-        if settings.DAILY_MIGRATION_ENABLED
-        else "whereby"
-    )
-
     return await rooms_controller.add(
         name=room.name,
         user_id=user_id,
@@ -122,7 +117,6 @@ async def rooms_create(
         recording_type=room.recording_type,
         recording_trigger=room.recording_trigger,
         is_shared=room.is_shared,
-        platform=platform,
     )
 
 
@@ -164,32 +158,24 @@ async def rooms_create_meeting(
     if not room:
         raise HTTPException(status_code=404, detail="Room not found")
 
-    current_time = datetime.utcnow()
+    current_time = datetime.now(timezone.utc)
     meeting = await meetings_controller.get_active(room=room, current_time=current_time)
 
     if meeting is None:
         end_date = current_time + timedelta(hours=8)
 
-        # Use the platform abstraction to create meeting
-        platform = get_platform_for_room(room.id)
-        client = create_platform_client(platform)
-
-        meeting_data = await client.create_meeting(
-            room_name_prefix=room.name, end_date=end_date, room=room
-        )
-
-        # Upload logo if supported by platform
-        await client.upload_logo(meeting_data.room_name, "./images/logo.png")
+        whereby_meeting = await create_meeting("", end_date=end_date, room=room)
+        await upload_logo(whereby_meeting["roomName"], "./images/logo.png")
 
         # Now try to save to database
         try:
             meeting = await meetings_controller.create(
-                id=meeting_data.meeting_id,
-                room_name=meeting_data.room_name,
-                room_url=meeting_data.room_url,
-                host_room_url=meeting_data.host_room_url,
-                start_date=current_time,
-                end_date=end_date,
+                id=whereby_meeting["meetingId"],
+                room_name=whereby_meeting["roomName"],
+                room_url=whereby_meeting["roomUrl"],
+                host_room_url=whereby_meeting["hostRoomUrl"],
+                start_date=parse_datetime_with_timezone(whereby_meeting["startDate"]),
+                end_date=parse_datetime_with_timezone(whereby_meeting["endDate"]),
                 user_id=user_id,
                 room=room,
             )
@@ -201,9 +187,8 @@ async def rooms_create_meeting(
                 room.name,
             )
             logger.warning(
-                "%s meeting %s was created but not used (resource leak) for room %s",
-                platform,
-                meeting_data.meeting_id,
+                "Whereby meeting %s was created but not used (resource leak) for room %s",
+                whereby_meeting["meetingId"],
                 room.name,
             )
 

server/reflector/views/transcripts.py

@@ -1,15 +1,29 @@
 from datetime import datetime, timedelta, timezone
 from typing import Annotated, Literal, Optional
 
-from fastapi import APIRouter, Depends, HTTPException
+from fastapi import APIRouter, Depends, HTTPException, Query
 from fastapi_pagination import Page
-from fastapi_pagination.ext.databases import paginate
+from fastapi_pagination.ext.databases import apaginate
 from jose import jwt
 from pydantic import BaseModel, Field, field_serializer
 
 import reflector.auth as auth
+from reflector.db import get_database
 from reflector.db.meetings import meetings_controller
 from reflector.db.rooms import rooms_controller
+from reflector.db.search import (
+    DEFAULT_SEARCH_LIMIT,
+    SearchLimit,
+    SearchLimitBase,
+    SearchOffset,
+    SearchOffsetBase,
+    SearchParameters,
+    SearchQuery,
+    SearchQueryBase,
+    SearchResult,
+    SearchTotal,
+    search_controller,
+)
 from reflector.db.transcripts import (
     SourceKind,
     TranscriptParticipant,
@@ -34,7 +48,7 @@ DOWNLOAD_EXPIRE_MINUTES = 60
 
 def create_access_token(data: dict, expires_delta: timedelta):
     to_encode = data.copy()
-    expire = datetime.utcnow() + expires_delta
+    expire = datetime.now(timezone.utc) + expires_delta
     to_encode.update({"exp": expire})
     encoded_jwt = jwt.encode(to_encode, settings.SECRET_KEY, algorithm=ALGORITHM)
     return encoded_jwt
@@ -100,6 +114,21 @@ class DeletionStatus(BaseModel):
     status: str
 
 
+SearchQueryParam = Annotated[SearchQueryBase, Query(description="Search query text")]
+SearchLimitParam = Annotated[SearchLimitBase, Query(description="Results per page")]
+SearchOffsetParam = Annotated[
+    SearchOffsetBase, Query(description="Number of results to skip")
+]
+
+
+class SearchResponse(BaseModel):
+    results: list[SearchResult]
+    total: SearchTotal
+    query: SearchQuery
+    limit: SearchLimit
+    offset: SearchOffset
+
+
 @router.get("/transcripts", response_model=Page[GetTranscriptMinimal])
 async def transcripts_list(
     user: Annotated[Optional[auth.UserInfo], Depends(auth.current_user_optional)],
@@ -107,15 +136,13 @@ async def transcripts_list(
     room_id: str | None = None,
     search_term: str | None = None,
 ):
-    from reflector.db import database
-
     if not user and not settings.PUBLIC_MODE:
         raise HTTPException(status_code=401, detail="Not authenticated")
 
     user_id = user["sub"] if user else None
 
-    return await paginate(
-        database,
+    return await apaginate(
+        get_database(),
         await transcripts_controller.get_all(
             user_id=user_id,
             source_kind=SourceKind(source_kind) if source_kind else None,
@@ -127,6 +154,45 @@ async def transcripts_list(
     )
 
 
+@router.get("/transcripts/search", response_model=SearchResponse)
+async def transcripts_search(
+    q: SearchQueryParam,
+    limit: SearchLimitParam = DEFAULT_SEARCH_LIMIT,
+    offset: SearchOffsetParam = 0,
+    room_id: Optional[str] = None,
+    source_kind: Optional[SourceKind] = None,
+    user: Annotated[
+        Optional[auth.UserInfo], Depends(auth.current_user_optional)
+    ] = None,
+):
+    """
+    Full-text search across transcript titles and content.
+    """
+    if not user and not settings.PUBLIC_MODE:
+        raise HTTPException(status_code=401, detail="Not authenticated")
+
+    user_id = user["sub"] if user else None
+
+    search_params = SearchParameters(
+        query_text=q,
+        limit=limit,
+        offset=offset,
+        user_id=user_id,
+        room_id=room_id,
+        source_kind=source_kind,
+    )
+
+    results, total = await search_controller.search_transcripts(search_params)
+
+    return SearchResponse(
+        results=results,
+        total=total,
+        query=search_params.query_text,
+        limit=search_params.limit,
+        offset=search_params.offset,
+    )
+
+
 @router.post("/transcripts", response_model=GetTranscript)
 async def transcripts_create(
     info: CreateTranscript,
@@ -273,8 +339,8 @@ async def transcript_update(
     if not transcript:
         raise HTTPException(status_code=404, detail="Transcript not found")
     values = info.dict(exclude_unset=True)
-    await transcripts_controller.update(transcript, values)
-    return transcript
+    updated_transcript = await transcripts_controller.update(transcript, values)
+    return updated_transcript
 
 
 @router.delete("/transcripts/{transcript_id}", response_model=DeletionStatus)

server/reflector/views/transcripts_audio.py

@@ -51,24 +51,6 @@ async def transcript_get_audio_mp3(
         transcript_id, user_id=user_id
     )
 
-    if transcript.audio_location == "storage":
-        # proxy S3 file, to prevent issue with CORS
-        url = await transcript.get_audio_url()
-        headers = {}
-
-        copy_headers = ["range", "accept-encoding"]
-        for header in copy_headers:
-            if header in request.headers:
-                headers[header] = request.headers[header]
-
-        async with httpx.AsyncClient() as client:
-            resp = await client.request(request.method, url, headers=headers)
-            return Response(
-                content=resp.content,
-                status_code=resp.status_code,
-                headers=resp.headers,
-            )
-
     if transcript.audio_location == "storage":
         # proxy S3 file, to prevent issue with CORS
         url = await transcript.get_audio_url()

server/reflector/views/transcripts_webrtc.py

@@ -26,7 +26,7 @@ async def transcript_record_webrtc(
         raise HTTPException(status_code=400, detail="Transcript is locked")
 
     # create a pipeline runner
-    from reflector.pipelines.main_live_pipeline import PipelineMainLive
+    from reflector.pipelines.main_live_pipeline import PipelineMainLive  # noqa: PLC0415
 
     pipeline_runner = PipelineMainLive(transcript_id=transcript_id)
 

server/reflector/whereby.py

@@ -23,7 +23,7 @@ async def create_meeting(room_name_prefix: str, end_date: datetime, room: Room):
             "type": room.recording_type,
             "destination": {
                 "provider": "s3",
-                "bucket": settings.AWS_WHEREBY_S3_BUCKET,
+                "bucket": settings.RECORDING_STORAGE_AWS_BUCKET_NAME,
                 "accessKeyId": settings.AWS_WHEREBY_ACCESS_KEY_ID,
                 "accessKeySecret": settings.AWS_WHEREBY_ACCESS_KEY_SECRET,
                 "fileFormat": "mp4",

server/reflector/worker/process.py

@@ -5,7 +5,6 @@ from urllib.parse import unquote
 
 import av
 import boto3
-import httpx
 import structlog
 from celery import shared_task
 from celery.utils.log import get_task_logger
@@ -15,13 +14,22 @@ from reflector.db.meetings import meetings_controller
 from reflector.db.recordings import Recording, recordings_controller
 from reflector.db.rooms import rooms_controller
 from reflector.db.transcripts import SourceKind, transcripts_controller
-from reflector.pipelines.main_live_pipeline import asynctask, task_pipeline_process
+from reflector.pipelines.main_file_pipeline import task_pipeline_file_process
+from reflector.pipelines.main_live_pipeline import asynctask
 from reflector.settings import settings
 from reflector.whereby import get_room_sessions
 
 logger = structlog.wrap_logger(get_task_logger(__name__))
 
 
+def parse_datetime_with_timezone(iso_string: str) -> datetime:
+    """Parse ISO datetime string and ensure timezone awareness (defaults to UTC if naive)."""
+    dt = datetime.fromisoformat(iso_string)
+    if dt.tzinfo is None:
+        dt = dt.replace(tzinfo=timezone.utc)
+    return dt
+
+
 @shared_task
 def process_messages():
     queue_url = settings.AWS_PROCESS_RECORDING_QUEUE_URL
@@ -70,7 +78,7 @@ async def process_recording(bucket_name: str, object_key: str):
 
     # extract a guid and a datetime from the object key
     room_name = f"/{object_key[:36]}"
-    recorded_at = datetime.fromisoformat(object_key[37:57])
+    recorded_at = parse_datetime_with_timezone(object_key[37:57])
 
     meeting = await meetings_controller.get_by_room_name(room_name)
     room = await rooms_controller.get_by_id(meeting.room_id)
@@ -133,7 +141,7 @@ async def process_recording(bucket_name: str, object_key: str):
 
     await transcripts_controller.update(transcript, {"status": "uploaded"})
 
-    task_pipeline_process.delay(transcript_id=transcript.id)
+    task_pipeline_file_process.delay(transcript_id=transcript.id)
 
 
 @shared_task
@@ -178,7 +186,7 @@ async def reprocess_failed_recordings():
     reprocessed_count = 0
     try:
         paginator = s3.get_paginator("list_objects_v2")
-        bucket_name = settings.AWS_WHEREBY_S3_BUCKET
+        bucket_name = settings.RECORDING_STORAGE_AWS_BUCKET_NAME
         pages = paginator.paginate(Bucket=bucket_name)
 
         for page in pages:
@@ -221,98 +229,3 @@ async def reprocess_failed_recordings():
 
     logger.info(f"Reprocessing complete. Requeued {reprocessed_count} recordings")
     return reprocessed_count
-
-
-@shared_task
-@asynctask
-async def process_recording_from_url(
-    recording_url: str, meeting_id: str, recording_id: str
-):
-    """Process recording from Direct URL (Daily.co webhook)."""
-    logger.info("Processing recording from URL for meeting: %s", meeting_id)
-
-    meeting = await meetings_controller.get_by_id(meeting_id)
-    if not meeting:
-        logger.error("Meeting not found: %s", meeting_id)
-        return
-
-    room = await rooms_controller.get_by_id(meeting.room_id)
-    if not room:
-        logger.error("Room not found for meeting: %s", meeting_id)
-        return
-
-    # Create recording record with URL instead of S3 bucket/key
-    recording = await recordings_controller.get_by_object_key(
-        "daily-recordings", recording_id
-    )
-    if not recording:
-        recording = await recordings_controller.create(
-            Recording(
-                bucket_name="daily-recordings",  # Logical bucket name for Daily.co
-                object_key=recording_id,  # Store Daily.co recording ID
-                recorded_at=datetime.utcnow(),
-                meeting_id=meeting.id,
-            )
-        )
-
-    # Get or create transcript record
-    transcript = await transcripts_controller.get_by_recording_id(recording.id)
-    if transcript:
-        await transcripts_controller.update(transcript, {"topics": []})
-    else:
-        transcript = await transcripts_controller.add(
-            "",
-            source_kind=SourceKind.ROOM,
-            source_language="en",
-            target_language="en",
-            user_id=room.user_id,
-            recording_id=recording.id,
-            share_mode="public",
-            meeting_id=meeting.id,
-            room_id=room.id,
-        )
-
-    # Download file from URL
-    upload_filename = transcript.data_path / "upload.mp4"
-    upload_filename.parent.mkdir(parents=True, exist_ok=True)
-
-    try:
-        logger.info("Downloading recording from URL: %s", recording_url)
-        async with httpx.AsyncClient(timeout=300.0) as client:  # 5 minute timeout
-            async with client.stream("GET", recording_url) as response:
-                response.raise_for_status()
-
-                with open(upload_filename, "wb") as f:
-                    async for chunk in response.aiter_bytes(8192):
-                        f.write(chunk)
-
-        logger.info("Download completed: %s", upload_filename)
-    except Exception as e:
-        logger.error("Failed to download recording: %s", str(e))
-        await transcripts_controller.update(transcript, {"status": "error"})
-        if upload_filename.exists():
-            upload_filename.unlink()
-        raise
-
-    # Validate audio content (same as S3 version)
-    try:
-        container = av.open(upload_filename.as_posix())
-        try:
-            if not len(container.streams.audio):
-                raise Exception("File has no audio stream")
-            logger.info("Audio validation successful")
-        finally:
-            container.close()
-    except Exception as e:
-        logger.error("Audio validation failed: %s", str(e))
-        await transcripts_controller.update(transcript, {"status": "error"})
-        if upload_filename.exists():
-            upload_filename.unlink()
-        raise
-
-    # Mark as uploaded and trigger processing pipeline
-    await transcripts_controller.update(transcript, {"status": "uploaded"})
-    logger.info("Queuing transcript for processing pipeline: %s", transcript.id)
-
-    # Start the ML pipeline (same as S3 version)
-    task_pipeline_process.delay(transcript_id=transcript.id)

server/reflector/ws_manager.py

@@ -62,6 +62,7 @@ class RedisPubSubManager:
 class WebsocketManager:
     def __init__(self, pubsub_client: RedisPubSubManager = None):
         self.rooms: dict = {}
+        self.tasks: dict = {}
         self.pubsub_client = pubsub_client
 
     async def add_user_to_room(self, room_id: str, websocket: WebSocket) -> None:
@@ -74,13 +75,17 @@ class WebsocketManager:
 
             await self.pubsub_client.connect()
             pubsub_subscriber = await self.pubsub_client.subscribe(room_id)
-            asyncio.create_task(self._pubsub_data_reader(pubsub_subscriber))
+            task = asyncio.create_task(self._pubsub_data_reader(pubsub_subscriber))
+            self.tasks[id(websocket)] = task
 
     async def send_json(self, room_id: str, message: dict) -> None:
         await self.pubsub_client.send_json(room_id, message)
 
     async def remove_user_from_room(self, room_id: str, websocket: WebSocket) -> None:
         self.rooms[room_id].remove(websocket)
+        task = self.tasks.pop(id(websocket), None)
+        if task:
+            task.cancel()
 
         if len(self.rooms[room_id]) == 0:
             del self.rooms[room_id]

server/tests/cassettes/test_processors_modal/test_file_diarization_modal_processor.yaml

@@ -0,0 +1,40 @@
+interactions:
+- request:
+    body: ''
+    headers:
+      accept:
+      - '*/*'
+      accept-encoding:
+      - gzip, deflate
+      authorization:
+      - DUMMY_API_KEY
+      connection:
+      - keep-alive
+      content-length:
+      - '0'
+      host:
+      - monadical-sas--reflector-diarizer-web.modal.run
+      user-agent:
+      - python-httpx/0.27.2
+    method: POST
+    uri: https://monadical-sas--reflector-diarizer-web.modal.run/diarize?audio_file_url=https%3A%2F%2Freflector-github-pytest.s3.us-east-1.amazonaws.com%2Ftest_mathieu_hello.mp3&timestamp=0
+  response:
+    body:
+      string: '{"diarization":[{"start":0.823,"end":1.91,"speaker":0},{"start":2.572,"end":6.409,"speaker":0},{"start":6.783,"end":10.62,"speaker":0},{"start":11.231,"end":14.168,"speaker":0},{"start":14.796,"end":19.295,"speaker":0}]}'
+    headers:
+      Alt-Svc:
+      - h3=":443"; ma=2592000
+      Content-Length:
+      - '220'
+      Content-Type:
+      - application/json
+      Date:
+      - Wed, 13 Aug 2025 18:25:34 GMT
+      Modal-Function-Call-Id:
+      - fc-01K2JAVNEP6N7Y1Y7W3T98BCXK
+      Vary:
+      - accept-encoding
+    status:
+      code: 200
+      message: OK
+version: 1

server/tests/cassettes/test_processors_modal/test_file_transcript_modal_processor.yaml

@@ -0,0 +1,46 @@
+interactions:
+- request:
+    body: '{"audio_file_url": "https://reflector-github-pytest.s3.us-east-1.amazonaws.com/test_mathieu_hello.mp3",
+      "language": "en", "batch": true}'
+    headers:
+      accept:
+      - '*/*'
+      accept-encoding:
+      - gzip, deflate
+      authorization:
+      - DUMMY_API_KEY
+      connection:
+      - keep-alive
+      content-length:
+      - '136'
+      content-type:
+      - application/json
+      host:
+      - monadical-sas--reflector-transcriber-parakeet-web.modal.run
+      user-agent:
+      - python-httpx/0.27.2
+    method: POST
+    uri: https://monadical-sas--reflector-transcriber-parakeet-web.modal.run/v1/audio/transcriptions-from-url
+  response:
+    body:
+      string: '{"text":"Hi there everyone. Today I want to share my incredible experience
+        with Reflector. a Q teenage product that revolutionizes audio processing.
+        With reflector, I can easily convert any audio into accurate transcription.
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server/tests/cassettes/test_processors_modal/test_full_modal_pipeline_integration.yaml

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