chore(main): release 0.9.0 (#603)

* fix: kv use tls explicit

* fix: token refresh locking

* remove logs

* compile fix

* compile fix

---------

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

.github/workflows/db_migrations.yml

@@ -2,6 +2,8 @@ name: Test Database Migrations
 
 on:
   push:
+    branches:
+      - main
     paths:
       - "server/migrations/**"
       - "server/reflector/db/**"
@@ -17,10 +19,43 @@ on:
 jobs:
   test-migrations:
     runs-on: ubuntu-latest
+    concurrency:
+      group: db-ubuntu-latest-${{ github.ref }}
+      cancel-in-progress: true
+    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:
+  build:
-    runs-on: ubuntu-latest
+    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
+        uses: aws-actions/amazon-ecr-login@v2
-
-      - name: Set up QEMU
-        uses: docker/setup-qemu-action@v2
 
       - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v2
+        uses: docker/setup-buildx-action@v3
 
-      - name: Build and push
+      - name: Build and push ${{ matrix.arch }}
-        id: docker_build
+        uses: docker/build-push-action@v5
-        uses: docker/build-push-action@v4
         with:
           context: server
-          platforms: linux/amd64,linux/arm64
+          platforms: ${{ matrix.platform }}
           push: true
-          tags: ${{ steps.login-ecr.outputs.registry }}/${{ env.ECR_REPOSITORY }}:latest
+          tags: ${{ steps.login-ecr.outputs.registry }}/${{ env.ECR_REPOSITORY }}:latest-${{ matrix.arch }}
-          cache-from: type=gha
+          cache-from: type=gha,scope=${{ matrix.arch }}
-          cache-to: type=gha,mode=max
+          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_next_server.yml

@@ -0,0 +1,45 @@
+name: Test Next Server
+
+on:
+  pull_request:
+    paths:
+      - "www/**"
+  push:
+    branches:
+      - main
+    paths:
+      - "www/**"
+
+jobs:
+  test-next-server:
+    runs-on: ubuntu-latest
+
+    defaults:
+      run:
+        working-directory: ./www
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Setup Node.js
+      uses: actions/setup-node@v4
+      with:
+        node-version: '20'
+
+    - name: Install pnpm
+      uses: pnpm/action-setup@v4
+      with:
+        version: 8
+
+    - name: Setup Node.js cache
+      uses: actions/setup-node@v4
+      with:
+        node-version: '20'
+        cache: 'pnpm'
+        cache-dependency-path: './www/pnpm-lock.yaml'
+
+    - name: Install dependencies
+      run: pnpm install
+
+    - name: Run tests
+      run: pnpm test

.github/workflows/test_server.yml

@@ -5,12 +5,17 @@ on:
     paths:
       - "server/**"
   push:
+    branches:
+      - main
     paths:
       - "server/**"
 
 jobs:
   pytest:
     runs-on: ubuntu-latest
+    concurrency:
+      group: pytest-${{ github.ref }}
+      cancel-in-progress: true
     services:
       redis:
         image: redis:6
@@ -19,29 +24,47 @@ 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:
+  docker-amd64:
-    runs-on: ubuntu-latest
+    runs-on: linux-amd64
+    concurrency:
+      group: docker-amd64-${{ github.ref }}
+      cancel-in-progress: true
     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
+        uses: docker/setup-buildx-action@v3
-      - name: Build and push
+      - name: Build AMD64
-        id: docker_build
+        uses: docker/build-push-action@v6
-        uses: docker/build-push-action@v4
         with:
           context: server
-          platforms: linux/amd64,linux/arm64
+          platforms: linux/amd64
-          cache-from: type=gha
+          cache-from: type=gha,scope=amd64
-          cache-to: type=gha,mode=max
+          cache-to: type=gha,mode=max,scope=amd64
+          github-token: ${{ secrets.GHA_CACHE_TOKEN }}
+
+  docker-arm64:
+    runs-on: linux-arm64
+    concurrency:
+      group: docker-arm64-${{ github.ref }}
+      cancel-in-progress: true
+    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 +13,8 @@ restart-dev.sh
 data/
 www/REFACTOR.md
 www/reload-frontend
+server/test.sqlite
+CLAUDE.local.md
+www/.env.development
+www/.env.production
+.playwright-mcp

.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
+      - id: format
-        name: run yarn 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
+          # Uses select rules from server/pyproject.toml
-          - I,F401
         files: ^server/
       - id: ruff-format
         files: ^server/
+
+  - repo: https://github.com/gitleaks/gitleaks
+    rev: v8.28.0
+    hooks:
+      - id: gitleaks

CHANGELOG.md

@@ -1,5 +1,116 @@
 # Changelog
 
+## [0.9.0](https://github.com/Monadical-SAS/reflector/compare/v0.8.2...v0.9.0) (2025-09-06)
+
+
+### Features
+
+* frontend openapi react query ([#606](https://github.com/Monadical-SAS/reflector/issues/606)) ([c4d2825](https://github.com/Monadical-SAS/reflector/commit/c4d2825c81f81ad8835629fbf6ea8c7383f8c31b))
+
+
+### Bug Fixes
+
+* align whisper transcriber api with parakeet ([#602](https://github.com/Monadical-SAS/reflector/issues/602)) ([0663700](https://github.com/Monadical-SAS/reflector/commit/0663700a615a4af69a03c96c410f049e23ec9443))
+* kv use tls explicit ([#610](https://github.com/Monadical-SAS/reflector/issues/610)) ([08d88ec](https://github.com/Monadical-SAS/reflector/commit/08d88ec349f38b0d13e0fa4cb73486c8dfd31836))
+* source kind for file processing ([#601](https://github.com/Monadical-SAS/reflector/issues/601)) ([dc82f8b](https://github.com/Monadical-SAS/reflector/commit/dc82f8bb3bdf3ab3d4088e592a30fd63907319e1))
+* token refresh locking ([#613](https://github.com/Monadical-SAS/reflector/issues/613)) ([7f5a4c9](https://github.com/Monadical-SAS/reflector/commit/7f5a4c9ddc7fd098860c8bdda2ca3b57f63ded2f))
+
+## [0.8.2](https://github.com/Monadical-SAS/reflector/compare/v0.8.1...v0.8.2) (2025-08-29)
+
+
+### Bug Fixes
+
+* search-logspam ([#593](https://github.com/Monadical-SAS/reflector/issues/593)) ([695d1a9](https://github.com/Monadical-SAS/reflector/commit/695d1a957d4cd862753049f9beed88836cabd5ab))
+
+## [0.8.1](https://github.com/Monadical-SAS/reflector/compare/v0.8.0...v0.8.1) (2025-08-29)
+
+
+### Bug Fixes
+
+* make webhook secret/url allowing null ([#590](https://github.com/Monadical-SAS/reflector/issues/590)) ([84a3812](https://github.com/Monadical-SAS/reflector/commit/84a381220bc606231d08d6f71d4babc818fa3c75))
+
+## [0.8.0](https://github.com/Monadical-SAS/reflector/compare/v0.7.3...v0.8.0) (2025-08-29)
+
+
+### Features
+
+* **cleanup:** add automatic data retention for public instances ([#574](https://github.com/Monadical-SAS/reflector/issues/574)) ([6f0c7c1](https://github.com/Monadical-SAS/reflector/commit/6f0c7c1a5e751713366886c8e764c2009e12ba72))
+* **rooms:** add webhook for transcript completion ([#578](https://github.com/Monadical-SAS/reflector/issues/578)) ([88ed7cf](https://github.com/Monadical-SAS/reflector/commit/88ed7cfa7804794b9b54cad4c3facc8a98cf85fd))
+
+
+### Bug Fixes
+
+* file pipeline status reporting and websocket updates ([#589](https://github.com/Monadical-SAS/reflector/issues/589)) ([9dfd769](https://github.com/Monadical-SAS/reflector/commit/9dfd76996f851cc52be54feea078adbc0816dc57))
+* Igor/evaluation ([#575](https://github.com/Monadical-SAS/reflector/issues/575)) ([124ce03](https://github.com/Monadical-SAS/reflector/commit/124ce03bf86044c18313d27228a25da4bc20c9c5))
+* optimize parakeet transcription batching algorithm ([#577](https://github.com/Monadical-SAS/reflector/issues/577)) ([7030e0f](https://github.com/Monadical-SAS/reflector/commit/7030e0f23649a8cf6c1eb6d5889684a41ce849ec))
+
+## [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>
-
+</div>
-## Screenshots
 <table>
   <tr>
     <td>
-      <a href="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/3a976930-56c1-47ef-8c76-55d3864309e3" />
+        <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">
+      <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/bfe3bde3-08af-4426-a9a1-11ad5cd63b33" />
+        <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">
+      <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/7b60c9d0-efe4-474f-a27b-ea13bd0fabdc" />
+        <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 local models 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
 
@@ -72,6 +89,8 @@ Note: We currently do not have instructions for Windows users.
 
 ## Installation
 
+*Note: we're working toward better installation, theses instructions are not accurate for now*
+
 ### Frontend
 
 Start with `cd www`.
@@ -79,7 +98,7 @@ Start with `cd www`.
 **Installation**
 
 ```bash
-yarn install
+pnpm install
 cp .env_template .env
 cp config-template.ts config.ts
 ```
@@ -89,7 +108,7 @@ 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 +118,7 @@ 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

@@ -6,6 +6,7 @@ services:
       - 1250:1250
     volumes:
       - ./server/:/app/
+      - /app/.venv
     env_file:
       - ./server/.env
     environment:
@@ -16,6 +17,7 @@ services:
       context: server
     volumes:
       - ./server/:/app/
+      - /app/.venv
     env_file:
       - ./server/.env
     environment:
@@ -26,6 +28,7 @@ services:
       context: server
     volumes:
       - ./server/:/app/
+      - /app/.venv
     env_file:
       - ./server/.env
     environment:
@@ -39,11 +42,12 @@ 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/
+COPY pyproject.toml uv.lock README.md /app/
-RUN touch README.md && env uv sync --compile-bytecode --locked
+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/docs/data_retention.md

@@ -0,0 +1,95 @@
+# Data Retention and Cleanup
+
+## Overview
+
+For public instances of Reflector, a data retention policy is automatically enforced to delete anonymous user data after a configurable period (default: 7 days). This ensures compliance with privacy expectations and prevents unbounded storage growth.
+
+## Configuration
+
+### Environment Variables
+
+- `PUBLIC_MODE` (bool): Must be set to `true` to enable automatic cleanup
+- `PUBLIC_DATA_RETENTION_DAYS` (int): Number of days to retain anonymous data (default: 7)
+
+### What Gets Deleted
+
+When data reaches the retention period, the following items are automatically removed:
+
+1. **Transcripts** from anonymous users (where `user_id` is NULL):
+   - Database records
+   - Local files (audio.wav, audio.mp3, audio.json waveform)
+   - Storage files (cloud storage if configured)
+
+## Automatic Cleanup
+
+### Celery Beat Schedule
+
+When `PUBLIC_MODE=true`, a Celery beat task runs daily at 3 AM to clean up old data:
+
+```python
+# Automatically scheduled when PUBLIC_MODE=true
+"cleanup_old_public_data": {
+    "task": "reflector.worker.cleanup.cleanup_old_public_data",
+    "schedule": crontab(hour=3, minute=0),  # Daily at 3 AM
+}
+```
+
+### Running the Worker
+
+Ensure both Celery worker and beat scheduler are running:
+
+```bash
+# Start Celery worker
+uv run celery -A reflector.worker.app worker --loglevel=info
+
+# Start Celery beat scheduler (in another terminal)
+uv run celery -A reflector.worker.app beat
+```
+
+## Manual Cleanup
+
+For testing or manual intervention, use the cleanup tool:
+
+```bash
+# Delete data older than 7 days (default)
+uv run python -m reflector.tools.cleanup_old_data
+
+# Delete data older than 30 days
+uv run python -m reflector.tools.cleanup_old_data --days 30
+```
+
+Note: The manual tool uses the same implementation as the Celery worker task to ensure consistency.
+
+## Important Notes
+
+1. **User Data Deletion**: Only anonymous data (where `user_id` is NULL) is deleted. Authenticated user data is preserved.
+
+2. **Storage Cleanup**: The system properly cleans up both local files and cloud storage when configured.
+
+3. **Error Handling**: If individual deletions fail, the cleanup continues and logs errors. Failed deletions are reported in the task output.
+
+4. **Public Instance Only**: The automatic cleanup task only runs when `PUBLIC_MODE=true` to prevent accidental data loss in private deployments.
+
+## Testing
+
+Run the cleanup tests:
+
+```bash
+uv run pytest tests/test_cleanup.py -v
+```
+
+## Monitoring
+
+Check Celery logs for cleanup task execution:
+
+```bash
+# Look for cleanup task logs
+grep "cleanup_old_public_data" celery.log
+grep "Starting cleanup of old public data" celery.log
+```
+
+Task statistics are logged after each run:
+- Number of transcripts deleted
+- Number of meetings deleted
+- Number of orphaned recordings deleted
+- Any errors encountered

server/docs/gpu/api-transcription.md

@@ -0,0 +1,194 @@
+## Reflector GPU Transcription API (Specification)
+
+This document defines the Reflector GPU transcription API that all implementations must adhere to. Current implementations include NVIDIA Parakeet (NeMo) and Whisper (faster-whisper), both deployed on Modal.com. The API surface and response shapes are OpenAI/Whisper-compatible, so clients can switch implementations by changing only the base URL.
+
+### Base URL and Authentication
+
+- Example base URLs (Modal web endpoints):
+
+  - Parakeet: `https://<account>--reflector-transcriber-parakeet-web.modal.run`
+  - Whisper: `https://<account>--reflector-transcriber-web.modal.run`
+
+- All endpoints are served under `/v1` and require a Bearer token:
+
+```
+Authorization: Bearer <REFLECTOR_GPU_APIKEY>
+```
+
+Note: To switch implementations, deploy the desired variant and point `TRANSCRIPT_URL` to its base URL. The API is identical.
+
+### Supported file types
+
+`mp3, mp4, mpeg, mpga, m4a, wav, webm`
+
+### Models and languages
+
+- Parakeet (NVIDIA NeMo): default `nvidia/parakeet-tdt-0.6b-v2`
+  - Language support: only `en`. Other languages return HTTP 400.
+- Whisper (faster-whisper): default `large-v2` (or deployment-specific)
+  - Language support: multilingual (per Whisper model capabilities).
+
+Note: The `model` parameter is accepted by all implementations for interface parity. Some backends may treat it as informational.
+
+### Endpoints
+
+#### POST /v1/audio/transcriptions
+
+Transcribe one or more uploaded audio files.
+
+Request: multipart/form-data
+
+- `file` (File) — optional. Single file to transcribe.
+- `files` (File[]) — optional. One or more files to transcribe.
+- `model` (string) — optional. Defaults to the implementation-specific model (see above).
+- `language` (string) — optional, defaults to `en`.
+  - Parakeet: only `en` is accepted; other values return HTTP 400
+  - Whisper: model-dependent; typically multilingual
+- `batch` (boolean) — optional, defaults to `false`.
+
+Notes:
+
+- Provide either `file` or `files`, not both. If neither is provided, HTTP 400.
+- `batch` requires `files`; using `batch=true` without `files` returns HTTP 400.
+- Response shape for multiple files is the same regardless of `batch`.
+- Files sent to this endpoint are processed in a single pass (no VAD/chunking). This is intended for short clips (roughly ≤ 30s; depends on GPU memory/model). For longer audio, prefer `/v1/audio/transcriptions-from-url` which supports VAD-based chunking.
+
+Responses
+
+Single file 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"
+}
+```
+
+Multiple files response:
+
+```json
+{
+  "results": [
+    {"filename": "a1.mp3", "text": "...", "words": [...]},
+    {"filename": "a2.mp3", "text": "...", "words": [...]}]
+}
+```
+
+Notes:
+
+- Word objects always include keys: `word`, `start`, `end`.
+- Some implementations may include a trailing space in `word` to match Whisper tokenization behavior; clients should trim if needed.
+
+Example curl (single file):
+
+```bash
+curl -X POST \
+  -H "Authorization: Bearer $REFLECTOR_GPU_APIKEY" \
+  -F "file=@/path/to/audio.mp3" \
+  -F "language=en" \
+  "$BASE_URL/v1/audio/transcriptions"
+```
+
+Example curl (multiple files, batch):
+
+```bash
+curl -X POST \
+  -H "Authorization: Bearer $REFLECTOR_GPU_APIKEY" \
+  -F "files=@/path/a1.mp3" -F "files=@/path/a2.mp3" \
+  -F "batch=true" -F "language=en" \
+  "$BASE_URL/v1/audio/transcriptions"
+```
+
+#### POST /v1/audio/transcriptions-from-url
+
+Transcribe a single remote audio file by URL.
+
+Request: application/json
+
+Body parameters:
+
+- `audio_file_url` (string) — required. URL of the audio file to transcribe.
+- `model` (string) — optional. Defaults to the implementation-specific model (see above).
+- `language` (string) — optional, defaults to `en`. Parakeet only accepts `en`.
+- `timestamp_offset` (number) — optional, defaults to `0.0`. Added to each word's `start`/`end` in the response.
+
+```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",
+  "words": [
+    { "word": "hello", "start": 10.0, "end": 10.5 },
+    { "word": "world", "start": 10.5, "end": 11.0 }
+  ]
+}
+```
+
+Notes:
+
+- `timestamp_offset` is added to each word’s `start`/`end` in the response.
+- Implementations may perform VAD-based chunking and batching for long-form audio; word timings are adjusted accordingly.
+
+Example curl:
+
+```bash
+curl -X POST \
+  -H "Authorization: Bearer $REFLECTOR_GPU_APIKEY" \
+  -H "Content-Type: application/json" \
+  -d '{
+        "audio_file_url": "https://example.com/audio.mp3",
+        "language": "en",
+        "timestamp_offset": 0
+      }' \
+  "$BASE_URL/v1/audio/transcriptions-from-url"
+```
+
+### Error handling
+
+- 400 Bad Request
+  - Parakeet: `language` other than `en`
+  - Missing required parameters (`file`/`files` for upload; `audio_file_url` for URL endpoint)
+  - Unsupported file extension
+- 401 Unauthorized
+  - Missing or invalid Bearer token
+- 404 Not Found
+  - `audio_file_url` does not exist
+
+### Implementation details
+
+- GPUs: A10G for small-file/live, L40S for large-file URL transcription (subject to deployment)
+- VAD chunking and segment batching; word timings adjusted and overlapping ends constrained
+- Pads very short segments (< 0.5s) to avoid model crashes on some backends
+
+### Server configuration (Reflector API)
+
+Set the Reflector server to use the Modal backend and point `TRANSCRIPT_URL` to your chosen deployment:
+
+```
+TRANSCRIPT_BACKEND=modal
+TRANSCRIPT_URL=https://<account>--reflector-transcriber-parakeet-web.modal.run
+TRANSCRIPT_MODAL_API_KEY=<REFLECTOR_GPU_APIKEY>
+```
+
+### Conformance tests
+
+Use the pytest-based conformance tests to validate any new implementation (including self-hosted) against this spec:
+
+```
+TRANSCRIPT_URL=https://<your-deployment-base> \
+TRANSCRIPT_MODAL_API_KEY=your-api-key \
+uv run -m pytest -m gpu_modal --no-cov server/tests/test_gpu_modal_transcript.py
+```

server/docs/webhook.md

@@ -0,0 +1,212 @@
+# Reflector Webhook Documentation
+
+## Overview
+
+Reflector supports webhook notifications to notify external systems when transcript processing is completed. Webhooks can be configured per room and are triggered automatically after a transcript is successfully processed.
+
+## Configuration
+
+Webhooks are configured at the room level with two fields:
+- `webhook_url`: The HTTPS endpoint to receive webhook notifications
+- `webhook_secret`: Optional secret key for HMAC signature verification (auto-generated if not provided)
+
+## Events
+
+### `transcript.completed`
+
+Triggered when a transcript has been fully processed, including transcription, diarization, summarization, and topic detection.
+
+### `test`
+
+A test event that can be triggered manually to verify webhook configuration.
+
+## Webhook Request Format
+
+### Headers
+
+All webhook requests include the following headers:
+
+| Header | Description | Example |
+|--------|-------------|---------|
+| `Content-Type` | Always `application/json` | `application/json` |
+| `User-Agent` | Identifies Reflector as the source | `Reflector-Webhook/1.0` |
+| `X-Webhook-Event` | The event type | `transcript.completed` or `test` |
+| `X-Webhook-Retry` | Current retry attempt number | `0`, `1`, `2`... |
+| `X-Webhook-Signature` | HMAC signature (if secret configured) | `t=1735306800,v1=abc123...` |
+
+### Signature Verification
+
+If a webhook secret is configured, Reflector includes an HMAC-SHA256 signature in the `X-Webhook-Signature` header to verify the webhook authenticity.
+
+The signature format is: `t={timestamp},v1={signature}`
+
+To verify the signature:
+1. Extract the timestamp and signature from the header
+2. Create the signed payload: `{timestamp}.{request_body}`
+3. Compute HMAC-SHA256 of the signed payload using your webhook secret
+4. Compare the computed signature with the received signature
+
+Example verification (Python):
+```python
+import hmac
+import hashlib
+
+def verify_webhook_signature(payload: bytes, signature_header: str, secret: str) -> bool:
+    # Parse header: "t=1735306800,v1=abc123..."
+    parts = dict(part.split("=") for part in signature_header.split(","))
+    timestamp = parts["t"]
+    received_signature = parts["v1"]
+
+    # Create signed payload
+    signed_payload = f"{timestamp}.{payload.decode('utf-8')}"
+
+    # Compute expected signature
+    expected_signature = hmac.new(
+        secret.encode("utf-8"),
+        signed_payload.encode("utf-8"),
+        hashlib.sha256
+    ).hexdigest()
+
+    # Compare signatures
+    return hmac.compare_digest(expected_signature, received_signature)
+```
+
+## Event Payloads
+
+### `transcript.completed` Event
+
+This event includes a convenient URL for accessing the transcript:
+- `frontend_url`: Direct link to view the transcript in the web interface
+
+```json
+{
+  "event": "transcript.completed",
+  "event_id": "transcript.completed-abc-123-def-456",
+  "timestamp": "2025-08-27T12:34:56.789012Z",
+  "transcript": {
+    "id": "abc-123-def-456",
+    "room_id": "room-789",
+    "created_at": "2025-08-27T12:00:00Z",
+    "duration": 1800.5,
+    "title": "Q3 Product Planning Meeting",
+    "short_summary": "Team discussed Q3 product roadmap, prioritizing mobile app features and API improvements.",
+    "long_summary": "The product team met to finalize the Q3 roadmap. Key decisions included...",
+    "webvtt": "WEBVTT\n\n00:00:00.000 --> 00:00:05.000\n<v Speaker 1>Welcome everyone to today's meeting...",
+    "topics": [
+      {
+        "title": "Introduction and Agenda",
+        "summary": "Meeting kickoff with agenda review",
+        "timestamp": 0.0,
+        "duration": 120.0,
+        "webvtt": "WEBVTT\n\n00:00:00.000 --> 00:00:05.000\n<v Speaker 1>Welcome everyone..."
+      },
+      {
+        "title": "Mobile App Features Discussion",
+        "summary": "Team reviewed proposed mobile app features for Q3",
+        "timestamp": 120.0,
+        "duration": 600.0,
+        "webvtt": "WEBVTT\n\n00:02:00.000 --> 00:02:10.000\n<v Speaker 2>Let's talk about the mobile app..."
+      }
+    ],
+    "participants": [
+      {
+        "id": "participant-1",
+        "name": "John Doe",
+        "speaker": "Speaker 1"
+      },
+      {
+        "id": "participant-2",
+        "name": "Jane Smith",
+        "speaker": "Speaker 2"
+      }
+    ],
+    "source_language": "en",
+    "target_language": "en",
+    "status": "completed",
+    "frontend_url": "https://app.reflector.com/transcripts/abc-123-def-456"
+  },
+  "room": {
+    "id": "room-789",
+    "name": "Product Team Room"
+  }
+}
+```
+
+### `test` Event
+
+```json
+{
+  "event": "test",
+  "event_id": "test.2025-08-27T12:34:56.789012Z",
+  "timestamp": "2025-08-27T12:34:56.789012Z",
+  "message": "This is a test webhook from Reflector",
+  "room": {
+    "id": "room-789",
+    "name": "Product Team Room"
+  }
+}
+```
+
+## Retry Policy
+
+Webhooks are delivered with automatic retry logic to handle transient failures. When a webhook delivery fails due to server errors or network issues, Reflector will automatically retry the delivery multiple times over an extended period.
+
+### Retry Mechanism
+
+Reflector implements an exponential backoff strategy for webhook retries:
+
+- **Initial retry delay**: 60 seconds after the first failure
+- **Exponential backoff**: Each subsequent retry waits approximately twice as long as the previous one
+- **Maximum retry interval**: 1 hour (backoff is capped at this duration)
+- **Maximum retry attempts**: 30 attempts total
+- **Total retry duration**: Retries continue for approximately 24 hours
+
+### How Retries Work
+
+When a webhook fails, Reflector will:
+1. Wait 60 seconds, then retry (attempt #1)
+2. If it fails again, wait ~2 minutes, then retry (attempt #2)
+3. Continue doubling the wait time up to a maximum of 1 hour between attempts
+4. Keep retrying at 1-hour intervals until successful or 30 attempts are exhausted
+
+The `X-Webhook-Retry` header indicates the current retry attempt number (0 for the initial attempt, 1 for first retry, etc.), allowing your endpoint to track retry attempts.
+
+### Retry Behavior by HTTP Status Code
+
+| Status Code | Behavior |
+|-------------|----------|
+| 2xx (Success) | No retry, webhook marked as delivered |
+| 4xx (Client Error) | No retry, request is considered permanently failed |
+| 5xx (Server Error) | Automatic retry with exponential backoff |
+| Network/Timeout Error | Automatic retry with exponential backoff |
+
+**Important Notes:**
+- Webhooks timeout after 30 seconds. If your endpoint takes longer to respond, it will be considered a timeout error and retried.
+- During the retry period (~24 hours), you may receive the same webhook multiple times if your endpoint experiences intermittent failures.
+- There is no mechanism to manually retry failed webhooks after the retry period expires.
+
+## Testing Webhooks
+
+You can test your webhook configuration before processing transcripts:
+
+```http
+POST /v1/rooms/{room_id}/webhook/test
+```
+
+Response:
+```json
+{
+  "success": true,
+  "status_code": 200,
+  "message": "Webhook test successful",
+  "response_preview": "OK"
+}
+```
+
+Or in case of failure:
+```json
+{
+  "success": false,
+  "error": "Webhook request timed out (10 seconds)"
+}
+```

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
+DIARIZATION_BACKEND=modal
-LLM_URL=https://xxxx--reflector-llm-web.modal.run
+DIARIZATION_URL=https://xxxx--reflector-diarizer-web.modal.run
-LLM_MODAL_API_KEY=REFLECTOR_APIKEY
+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
+import modal
-from modal import App, Image, Secret, asgi_app, enter, method
-from pydantic import BaseModel
 
 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(
@@ -70,44 +137,51 @@ 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()
+    @modal.method()
-    def diarize(self, audio_data: str, audio_suffix: str, timestamp: float):
+    def diarize(self, filename: str, timestamp: float = 0.0):
-        import tempfile
-
         import torchaudio
 
-        with tempfile.NamedTemporaryFile("wb+", suffix=f".{audio_suffix}") as fp:
+        upload_volume.reload()
-            fp.write(audio_data)
 
-            print("Diarizing audio")
+        file_path = f"{UPLOADS_PATH}/{filename}"
-            waveform, sample_rate = torchaudio.load(fp.name)
+        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(
+        for diarization_segment, _, speaker in diarization.itertracks(yield_label=True):
-                yield_label=True
-            ):
             words.append(
                 {
                     "start": round(timestamp + diarization_segment.start, 3),
@@ -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(
+    @app.post("/diarize", dependencies=[Depends(apikey_auth)])
-        "/diarize", dependencies=[Depends(apikey_auth), Depends(validate_audio_file)]
+    def diarize(audio_file_url: str, timestamp: float = 0.0) -> DiarizationResponse:
-    )
+        unique_filename, audio_suffix = download_audio_to_volume(audio_file_url)
-    def diarize(
-        audio_file_url: str, timestamp: float = 0.0
-    ) -> HTTPException | DiarizationResponse:
-        # Currently the uploaded files are in mp3 format
-        audio_suffix = "mp3"
-
-        print("Downloading audio file")
-        response = requests.get(audio_file_url, allow_redirects=True)
-        print("Audio file downloaded successfully")
 
+        try:
             func = diarizerstub.diarize.spawn(
-            audio_data=response.content, audio_suffix=audio_suffix, timestamp=timestamp
+                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.py

@@ -1,41 +1,78 @@
 import os
-import tempfile
+import sys
 import threading
+import uuid
+from typing import Generator, Mapping, NamedTuple, NewType, TypedDict
+from urllib.parse import urlparse
 
 import modal
-from pydantic import BaseModel
-
-MODELS_DIR = "/models"
 
 MODEL_NAME = "large-v2"
 MODEL_COMPUTE_TYPE: str = "float16"
 MODEL_NUM_WORKERS: int = 1
-
 MINUTES = 60  # seconds
+SAMPLERATE = 16000
+UPLOADS_PATH = "/uploads"
+CACHE_PATH = "/models"
+SUPPORTED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
+VAD_CONFIG = {
+    "batch_max_duration": 30.0,
+    "silence_padding": 0.5,
+    "window_size": 512,
+}
 
-volume = modal.Volume.from_name("models", create_if_missing=True)
+
+WhisperUniqFilename = NewType("WhisperUniqFilename", str)
+AudioFileExtension = NewType("AudioFileExtension", str)
 
 app = modal.App("reflector-transcriber")
 
+model_cache = modal.Volume.from_name("models", create_if_missing=True)
+upload_volume = modal.Volume.from_name("whisper-uploads", create_if_missing=True)
+
+
+class TimeSegment(NamedTuple):
+    """Represents a time segment with start and end times."""
+
+    start: float
+    end: float
+
+
+class AudioSegment(NamedTuple):
+    """Represents an audio segment with timing and audio data."""
+
+    start: float
+    end: float
+    audio: any
+
+
+class TranscriptResult(NamedTuple):
+    """Represents a transcription result with text and word timings."""
+
+    text: str
+    words: list["WordTiming"]
+
+
+class WordTiming(TypedDict):
+    """Represents a word with its timing information."""
+
+    word: str
+    start: float
+    end: float
+
 
 def download_model():
     from faster_whisper import download_model
 
-    volume.reload()
+    model_cache.reload()
 
-    download_model(MODEL_NAME, cache_dir=MODELS_DIR)
+    download_model(MODEL_NAME, cache_dir=CACHE_PATH)
 
-    volume.commit()
+    model_cache.commit()
 
 
 image = (
     modal.Image.debian_slim(python_version="3.12")
-    .pip_install(
-        "huggingface_hub==0.27.1",
-        "hf-transfer==0.1.9",
-        "torch==2.5.1",
-        "faster-whisper==1.1.1",
-    )
     .env(
         {
             "HF_HUB_ENABLE_HF_TRANSFER": "1",
@@ -45,19 +82,98 @@ image = (
             ),
         }
     )
-    .run_function(download_model, volumes={MODELS_DIR: volume})
+    .apt_install("ffmpeg")
+    .pip_install(
+        "huggingface_hub==0.27.1",
+        "hf-transfer==0.1.9",
+        "torch==2.5.1",
+        "faster-whisper==1.1.1",
+        "fastapi==0.115.12",
+        "requests",
+        "librosa==0.10.1",
+        "numpy<2",
+        "silero-vad==5.1.0",
     )
+    .run_function(download_model, volumes={CACHE_PATH: model_cache})
+)
+
+
+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[WhisperUniqFilename, 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 = WhisperUniqFilename(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 0.5s of silence if audio is shorter than the silence_padding window.
+
+    Whisper does not require this strictly, but aligning behavior with Parakeet
+    avoids edge-case crashes on extremely short inputs and makes comparisons easier.
+    """
+    import numpy as np
+
+    audio_duration = len(audio_array) / sample_rate
+    if audio_duration < VAD_CONFIG["silence_padding"]:
+        silence_samples = int(sample_rate * VAD_CONFIG["silence_padding"])
+        silence = np.zeros(silence_samples, dtype=np.float32)
+        return np.concatenate([audio_array, silence])
+    return audio_array
 
 
 @app.cls(
     gpu="A10G",
     timeout=5 * MINUTES,
     scaledown_window=5 * MINUTES,
-    allow_concurrent_inputs=6,
     image=image,
-    volumes={MODELS_DIR: volume},
+    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
 )
-class Transcriber:
+@modal.concurrent(max_inputs=10)
+class TranscriberWhisperLive:
+    """Live transcriber class for small audio segments (A10G).
+
+    Mirrors the Parakeet live class API but uses Faster-Whisper under the hood.
+    """
+
     @modal.enter()
     def enter(self):
         import faster_whisper
@@ -71,23 +187,28 @@ class Transcriber:
             device=self.device,
             compute_type=MODEL_COMPUTE_TYPE,
             num_workers=MODEL_NUM_WORKERS,
-            download_root=MODELS_DIR,
+            download_root=CACHE_PATH,
             local_files_only=True,
         )
+        print(f"Model is on device: {self.device}")
 
     @modal.method()
     def transcribe_segment(
         self,
-        audio_data: str,
+        filename: str,
-        audio_suffix: str,
+        language: str = "en",
-        language: str,
     ):
-        with tempfile.NamedTemporaryFile("wb+", suffix=f".{audio_suffix}") as fp:
+        """Transcribe a single uploaded audio file by filename."""
-            fp.write(audio_data)
+        upload_volume.reload()
+
+        file_path = f"{UPLOADS_PATH}/{filename}"
+        if not os.path.exists(file_path):
+            raise FileNotFoundError(f"File not found: {file_path}")
 
         with self.lock:
+            with NoStdStreams():
                 segments, _ = self.model.transcribe(
-                    fp.name,
+                    file_path,
                     language=language,
                     beam_size=5,
                     word_timestamps=True,
@@ -96,66 +217,392 @@ class Transcriber:
                 )
 
         segments = list(segments)
-            text = "".join(segment.text for segment in segments)
+        text = "".join(segment.text for segment in segments).strip()
         words = [
-                {"word": word.word, "start": word.start, "end": word.end}
+            {
+                "word": word.word,
+                "start": round(float(word.start), 2),
+                "end": round(float(word.end), 2),
+            }
             for segment in segments
             for word in segment.words
         ]
 
         return {"text": text, "words": words}
 
+    @modal.method()
+    def transcribe_batch(
+        self,
+        filenames: list[str],
+        language: str = "en",
+    ):
+        """Transcribe multiple uploaded audio files and return per-file results."""
+        upload_volume.reload()
+
+        results = []
+        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}")
+
+            with self.lock:
+                with NoStdStreams():
+                    segments, _ = self.model.transcribe(
+                        file_path,
+                        language=language,
+                        beam_size=5,
+                        word_timestamps=True,
+                        vad_filter=True,
+                        vad_parameters={"min_silence_duration_ms": 500},
+                    )
+
+            segments = list(segments)
+            text = "".join(seg.text for seg in segments).strip()
+            words = [
+                {
+                    "word": w.word,
+                    "start": round(float(w.start), 2),
+                    "end": round(float(w.end), 2),
+                }
+                for seg in segments
+                for w in seg.words
+            ]
+
+            results.append(
+                {
+                    "filename": filename,
+                    "text": text,
+                    "words": words,
+                }
+            )
+
+        return results
+
+
+@app.cls(
+    gpu="L40S",
+    timeout=15 * MINUTES,
+    image=image,
+    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
+)
+class TranscriberWhisperFile:
+    """File transcriber for larger/longer audio, using VAD-driven batching (L40S)."""
+
+    @modal.enter()
+    def enter(self):
+        import faster_whisper
+        import torch
+        from silero_vad import load_silero_vad
+
+        self.lock = threading.Lock()
+        self.use_gpu = torch.cuda.is_available()
+        self.device = "cuda" if self.use_gpu else "cpu"
+        self.model = faster_whisper.WhisperModel(
+            MODEL_NAME,
+            device=self.device,
+            compute_type=MODEL_COMPUTE_TYPE,
+            num_workers=MODEL_NUM_WORKERS,
+            download_root=CACHE_PATH,
+            local_files_only=True,
+        )
+        self.vad_model = load_silero_vad(onnx=False)
+
+    @modal.method()
+    def transcribe_segment(
+        self, filename: str, timestamp_offset: float = 0.0, language: str = "en"
+    ):
+        import librosa
+        import numpy as np
+        from silero_vad import VADIterator
+
+        def vad_segments(
+            audio_array,
+            sample_rate: int = SAMPLERATE,
+            window_size: int = VAD_CONFIG["window_size"],
+        ) -> Generator[TimeSegment, None, None]:
+            """Generate speech segments as TimeSegment using Silero VAD."""
+            iterator = VADIterator(self.vad_model, sampling_rate=sample_rate)
+            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 = iterator(chunk)
+                if not speech:
+                    continue
+                if "start" in speech:
+                    start = speech["start"]
+                    continue
+                if "end" in speech and start is not None:
+                    end = speech["end"]
+                    yield TimeSegment(
+                        start / float(SAMPLERATE), end / float(SAMPLERATE)
+                    )
+                    start = None
+            iterator.reset_states()
+
+        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, _sr = librosa.load(file_path, sr=SAMPLERATE, mono=True)
+
+        # Batch segments up to ~30s windows by merging contiguous VAD segments
+        merged_batches: list[TimeSegment] = []
+        batch_start = None
+        batch_end = None
+        max_duration = VAD_CONFIG["batch_max_duration"]
+        for segment in vad_segments(audio_array):
+            seg_start, seg_end = segment.start, segment.end
+            if batch_start is None:
+                batch_start, batch_end = seg_start, seg_end
+                continue
+            if seg_end - batch_start <= max_duration:
+                batch_end = seg_end
+            else:
+                merged_batches.append(TimeSegment(batch_start, batch_end))
+                batch_start, batch_end = seg_start, seg_end
+        if batch_start is not None and batch_end is not None:
+            merged_batches.append(TimeSegment(batch_start, batch_end))
+
+        all_text = []
+        all_words = []
+
+        for segment in merged_batches:
+            start_time, end_time = segment.start, segment.end
+            s_idx = int(start_time * SAMPLERATE)
+            e_idx = int(end_time * SAMPLERATE)
+            segment = audio_array[s_idx:e_idx]
+            segment = pad_audio(segment, SAMPLERATE)
+
+            with self.lock:
+                segments, _ = self.model.transcribe(
+                    segment,
+                    language=language,
+                    beam_size=5,
+                    word_timestamps=True,
+                    vad_filter=True,
+                    vad_parameters={"min_silence_duration_ms": 500},
+                )
+
+            segments = list(segments)
+            text = "".join(seg.text for seg in segments).strip()
+            words = [
+                {
+                    "word": w.word,
+                    "start": round(float(w.start) + start_time + timestamp_offset, 2),
+                    "end": round(float(w.end) + start_time + timestamp_offset, 2),
+                }
+                for seg in segments
+                for w in seg.words
+            ]
+            if text:
+                all_text.append(text)
+            all_words.extend(words)
+
+        return {"text": " ".join(all_text), "words": all_words}
+
+
+def detect_audio_format(url: str, headers: dict) -> str:
+    from urllib.parse import urlparse
+
+    from fastapi import HTTPException
+
+    url_path = urlparse(url).path
+    for ext in SUPPORTED_FILE_EXTENSIONS:
+        if url_path.lower().endswith(f".{ext}"):
+            return ext
+
+    content_type = headers.get("content-type", "").lower()
+    if "audio/mpeg" in content_type or "audio/mp3" in content_type:
+        return "mp3"
+    if "audio/wav" in content_type:
+        return "wav"
+    if "audio/mp4" in content_type:
+        return "mp4"
+
+    raise HTTPException(
+        status_code=400,
+        detail=(
+            f"Unsupported audio format for URL. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
+        ),
+    )
+
+
+def download_audio_to_volume(audio_file_url: str) -> tuple[str, str]:
+    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 = 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
+
 
 @app.function(
     scaledown_window=60,
-    timeout=60,
+    timeout=600,
-    allow_concurrent_inputs=40,
     secrets=[
         modal.Secret.from_name("reflector-gpu"),
     ],
-    volumes={MODELS_DIR: volume},
+    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
+    image=image,
 )
+@modal.concurrent(max_inputs=40)
 @modal.asgi_app()
 def web():
-    from fastapi import Body, Depends, FastAPI, HTTPException, UploadFile, status
+    from fastapi import (
+        Body,
+        Depends,
+        FastAPI,
+        Form,
+        HTTPException,
+        UploadFile,
+        status,
+    )
     from fastapi.security import OAuth2PasswordBearer
-    from typing_extensions import Annotated
 
-    transcriber = Transcriber()
+    transcriber_live = TranscriberWhisperLive()
+    transcriber_file = TranscriberWhisperFile()
 
     app = FastAPI()
 
     oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
 
-    supported_file_types = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
-
     def apikey_auth(apikey: str = Depends(oauth2_scheme)):
-        if apikey != os.environ["REFLECTOR_GPU_APIKEY"]:
+        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):
+    class TranscriptResponse(dict):
-        result: dict
+        pass
 
     @app.post("/v1/audio/transcriptions", dependencies=[Depends(apikey_auth)])
     def transcribe(
-        file: UploadFile,
+        file: UploadFile = None,
-        model: str = "whisper-1",
+        files: list[UploadFile] | None = None,
-        language: Annotated[str, Body(...)] = "en",
+        model: str = Form(MODEL_NAME),
-    ) -> TranscriptResponse:
+        language: str = Form("en"),
-        audio_data = file.file.read()
+        batch: bool = Form(False),
-        audio_suffix = file.filename.split(".")[-1]
+    ):
-        assert audio_suffix in supported_file_types
+        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'"
+            )
 
-        func = transcriber.transcribe_segment.spawn(
+        upload_files = [file] if file else files
-            audio_data=audio_data,
+
-            audio_suffix=audio_suffix,
+        uploaded_filenames: list[str] = []
+        for upload_file in upload_files:
+            audio_suffix = upload_file.filename.split(".")[-1]
+            if audio_suffix not in SUPPORTED_FILE_EXTENSIONS:
+                raise HTTPException(
+                    status_code=400,
+                    detail=(
+                        f"Unsupported audio format. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
+                    ),
+                )
+
+            unique_filename = f"{uuid.uuid4()}.{audio_suffix}"
+            file_path = f"{UPLOADS_PATH}/{unique_filename}"
+            with open(file_path, "wb") as f:
+                content = upload_file.file.read()
+                f.write(content)
+            uploaded_filenames.append(unique_filename)
+
+        upload_volume.commit()
+
+        try:
+            if batch and len(upload_files) > 1:
+                func = transcriber_live.transcribe_batch.spawn(
+                    filenames=uploaded_filenames,
+                    language=language,
+                )
+                results = func.get()
+                return {"results": results}
+
+            results = []
+            for filename in uploaded_filenames:
+                func = transcriber_live.transcribe_segment.spawn(
+                    filename=filename,
+                    language=language,
+                )
+                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}"
+                    os.remove(file_path)
+                except Exception:
+                    pass
+            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"),
+        timestamp_offset: float = Body(0.0),
+    ):
+        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,
                 language=language,
             )
             result = func.get()
             return result
+        finally:
+            try:
+                file_path = f"{UPLOADS_PATH}/{unique_filename}"
+                os.remove(file_path)
+                upload_volume.commit()
+            except Exception:
+                pass
 
     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/gpu/modal_deployments/reflector_transcriber_parakeet.py

@@ -0,0 +1,658 @@
+import logging
+import os
+import sys
+import threading
+import uuid
+from typing import Generator, Mapping, NamedTuple, NewType, TypedDict
+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 = {
+    "batch_max_duration": 30.0,
+    "silence_padding": 0.5,
+    "window_size": 512,
+}
+
+ParakeetUniqFilename = NewType("ParakeetUniqFilename", str)
+AudioFileExtension = NewType("AudioFileExtension", str)
+
+
+class TimeSegment(NamedTuple):
+    """Represents a time segment with start and end times."""
+
+    start: float
+    end: float
+
+
+class AudioSegment(NamedTuple):
+    """Represents an audio segment with timing and audio data."""
+
+    start: float
+    end: float
+    audio: any
+
+
+class TranscriptResult(NamedTuple):
+    """Represents a transcription result with text and word timings."""
+
+    text: str
+    words: list["WordTiming"]
+
+
+class WordTiming(TypedDict):
+    """Represents a word with its timing information."""
+
+    word: str
+    start: float
+    end: float
+
+
+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: list[WordTiming] = [
+            WordTiming(
+                # XXX the space added here is to match the output of whisper
+                # whisper add space to each words, while parakeet don't
+                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: list[WordTiming] = [
+                WordTiming(
+                    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,
+        ) -> Generator[TimeSegment, None, None]:
+            """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)
+
+                    yield TimeSegment(start_time, end_time)
+                    start = None
+
+            vad_iterator.reset_states()
+
+        def batch_speech_segments(
+            segments: Generator[TimeSegment, None, None], max_duration: int
+        ) -> Generator[TimeSegment, None, None]:
+            """
+            Input segments:
+              [0-2] [3-5] [6-8] [10-11] [12-15] [17-19] [20-22]
+
+                                  ↓ (max_duration=10)
+
+              Output batches:
+              [0-8]           [10-19]          [20-22]
+
+            Note: silences are kept for better transcription, previous implementation was
+            passing segments separatly, but the output was less accurate.
+            """
+            batch_start_time = None
+            batch_end_time = None
+
+            for segment in segments:
+                start_time, end_time = segment.start, segment.end
+                if batch_start_time is None or batch_end_time is None:
+                    batch_start_time = start_time
+                    batch_end_time = end_time
+                    continue
+
+                total_duration = end_time - batch_start_time
+
+                if total_duration <= max_duration:
+                    batch_end_time = end_time
+                    continue
+
+                yield TimeSegment(batch_start_time, batch_end_time)
+                batch_start_time = start_time
+                batch_end_time = end_time
+
+            if batch_start_time is None or batch_end_time is None:
+                return
+
+            yield TimeSegment(batch_start_time, batch_end_time)
+
+        def batch_segment_to_audio_segment(
+            segments: Generator[TimeSegment, None, None],
+            audio_array,
+        ) -> Generator[AudioSegment, None, None]:
+            """Extract audio segments and apply padding for Parakeet compatibility.
+
+            Uses pad_audio to ensure segments are at least 0.5s long, preventing
+            Parakeet crashes. This padding may cause slight timing overlaps between
+            segments, which are corrected by enforce_word_timing_constraints.
+            """
+            for segment in segments:
+                start_time, end_time = segment.start, segment.end
+                start_sample = int(start_time * SAMPLERATE)
+                end_sample = int(end_time * SAMPLERATE)
+                audio_segment = audio_array[start_sample:end_sample]
+
+                padded_segment = pad_audio(audio_segment, SAMPLERATE)
+
+                yield AudioSegment(start_time, end_time, padded_segment)
+
+        def transcribe_batch(model, audio_segments: list) -> list:
+            with NoStdStreams():
+                outputs = model.transcribe(audio_segments, timestamps=True)
+            return outputs
+
+        def enforce_word_timing_constraints(
+            words: list[WordTiming],
+        ) -> list[WordTiming]:
+            """Enforce that word end times don't exceed the start time of the next word.
+
+            Due to silence padding added in batch_segment_to_audio_segment for better
+            transcription accuracy, word timings from different segments may overlap.
+            This function ensures there are no overlaps by adjusting end times.
+            """
+            if len(words) <= 1:
+                return words
+
+            enforced_words = []
+            for i, word in enumerate(words):
+                enforced_word = word.copy()
+
+                if i < len(words) - 1:
+                    next_start = words[i + 1]["start"]
+                    if enforced_word["end"] > next_start:
+                        enforced_word["end"] = next_start
+
+                enforced_words.append(enforced_word)
+
+            return enforced_words
+
+        def emit_results(
+            results: list,
+            segments_info: list[AudioSegment],
+        ) -> Generator[TranscriptResult, None, None]:
+            """Yield transcribed text and word timings from model output, adjusting timestamps to absolute positions."""
+            for i, (output, segment) in enumerate(zip(results, segments_info)):
+                start_time, end_time = segment.start, segment.end
+                text = output.text.strip()
+                words: list[WordTiming] = [
+                    WordTiming(
+                        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 TranscriptResult(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)
+
+        all_text_parts: list[str] = []
+        all_words: list[WordTiming] = []
+
+        raw_segments = vad_segment_generator(audio_array)
+        speech_segments = batch_speech_segments(
+            raw_segments,
+            VAD_CONFIG["batch_max_duration"],
+        )
+        audio_segments = batch_segment_to_audio_segment(speech_segments, audio_array)
+
+        for batch in audio_segments:
+            audio_segment = batch.audio
+            results = transcribe_batch(self.model, [audio_segment])
+
+            for result in emit_results(
+                results,
+                [batch],
+            ):
+                if not result.text:
+                    continue
+                all_text_parts.append(result.text)
+                all_words.extend(result.words)
+
+        all_words = enforce_word_timing_constraints(all_words)
+
+        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.
+
+Both data migrations and schema migrations must be in migrations.

server/migrations/versions/0194f65cd6d3_add_webhook_fields_to_rooms.py

@@ -0,0 +1,36 @@
+"""Add webhook fields to rooms
+
+Revision ID: 0194f65cd6d3
+Revises: 5a8907fd1d78
+Create Date: 2025-08-27 09:03:19.610995
+
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision: str = "0194f65cd6d3"
+down_revision: Union[str, None] = "5a8907fd1d78"
+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("room", schema=None) as batch_op:
+        batch_op.add_column(sa.Column("webhook_url", sa.String(), nullable=True))
+        batch_op.add_column(sa.Column("webhook_secret", sa.String(), nullable=True))
+
+    # ### 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("webhook_secret")
+        batch_op.drop_column("webhook_url")
+
+    # ### end Alembic commands ###

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/5a8907fd1d78_add_cascade_delete_to_meeting_consent_.py

@@ -0,0 +1,50 @@
+"""add cascade delete to meeting consent foreign key
+
+Revision ID: 5a8907fd1d78
+Revises: 0ab2d7ffaa16
+Create Date: 2025-08-26 17:26:50.945491
+
+"""
+
+from typing import Sequence, Union
+
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision: str = "5a8907fd1d78"
+down_revision: Union[str, None] = "0ab2d7ffaa16"
+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_consent", schema=None) as batch_op:
+        batch_op.drop_constraint(
+            batch_op.f("meeting_consent_meeting_id_fkey"), type_="foreignkey"
+        )
+        batch_op.create_foreign_key(
+            batch_op.f("meeting_consent_meeting_id_fkey"),
+            "meeting",
+            ["meeting_id"],
+            ["id"],
+            ondelete="CASCADE",
+        )
+
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    with op.batch_alter_table("meeting_consent", schema=None) as batch_op:
+        batch_op.drop_constraint(
+            batch_op.f("meeting_consent_meeting_id_fkey"), type_="foreignkey"
+        )
+        batch_op.create_foreign_key(
+            batch_op.f("meeting_consent_meeting_id_fkey"),
+            "meeting",
+            ["meeting_id"],
+            ["id"],
+        )
+
+    # ### end Alembic commands ###

server/migrations/versions/61882a919591_webhook_url_and_secret_null_by_default.py

@@ -0,0 +1,28 @@
+"""webhook url and secret null by default
+
+
+Revision ID: 61882a919591
+Revises: 0194f65cd6d3
+Create Date: 2025-08-29 11:46:36.738091
+
+"""
+
+from typing import Sequence, Union
+
+# revision identifiers, used by Alembic.
+revision: str = "61882a919591"
+down_revision: Union[str, None] = "0194f65cd6d3"
+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! ###
+    pass
+    # ### end Alembic commands ###
+
+
+def downgrade() -> None:
+    # ### commands auto generated by Alembic - please adjust! ###
+    pass
+    # ### end Alembic commands ###

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/asynctask.py

@@ -0,0 +1,27 @@
+import asyncio
+import functools
+
+from reflector.db import get_database
+
+
+def asynctask(f):
+    @functools.wraps(f)
+    def wrapper(*args, **kwargs):
+        async def run_with_db():
+            database = get_database()
+            await database.connect()
+            try:
+                return await f(*args, **kwargs)
+            finally:
+                await database.disconnect()
+
+        coro = run_with_db()
+        try:
+            loop = asyncio.get_running_loop()
+        except RuntimeError:
+            loop = None
+        if loop and loop.is_running():
+            return loop.run_until_complete(coro)
+        return asyncio.run(coro)
+
+    return wrapper

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:
+if "postgres" not in settings.DATABASE_URL:
-    kwargs["connect_args"] = {"check_same_thread": False}
+    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("start_date", sa.DateTime(timezone=True)),
-    sa.Column("end_date", sa.DateTime),
+    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()),
@@ -41,23 +41,28 @@ 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(
     "meeting_consent",
     metadata,
     sa.Column("id", sa.String, primary_key=True),
-    sa.Column("meeting_id", sa.String, sa.ForeignKey("meeting.id"), nullable=False),
+    sa.Column(
+        "meeting_id",
+        sa.String,
+        sa.ForeignKey("meeting.id", ondelete="CASCADE"),
+        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 +90,6 @@ 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 +120,9 @@ 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 +130,7 @@ 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 +140,7 @@ 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 +162,7 @@ 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 +173,7 @@ 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 +185,7 @@ 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 +197,7 @@ 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 +205,7 @@ 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 +216,7 @@ 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 +238,14 @@ 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 +254,7 @@ 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 +1,5 @@
-from datetime import datetime
+import secrets
+from datetime import datetime, timezone
 from sqlite3 import IntegrityError
 from typing import Literal
 
@@ -7,7 +8,7 @@ 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 +17,7 @@ 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 +41,8 @@ rooms = sqlalchemy.Table(
     sqlalchemy.Column(
         "is_shared", sqlalchemy.Boolean, nullable=False, server_default=false()
     ),
-    sqlalchemy.Column(
+    sqlalchemy.Column("webhook_url", sqlalchemy.String, nullable=True),
-        "platform", sqlalchemy.String, nullable=False, server_default="whereby"
+    sqlalchemy.Column("webhook_secret", sqlalchemy.String, nullable=True),
-    ),
     sqlalchemy.Index("idx_room_is_shared", "is_shared"),
 )
 
@@ -51,7 +51,7 @@ 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 +62,8 @@ class Room(BaseModel):
         "none", "prompt", "automatic", "automatic-2nd-participant"
     ] = "automatic-2nd-participant"
     is_shared: bool = False
-    platform: Literal["whereby", "daily"] = "whereby"
+    webhook_url: str | None = None
+    webhook_secret: str | None = None
 
 
 class RoomController:
@@ -96,7 +97,7 @@ 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(
@@ -111,11 +112,15 @@ class RoomController:
         recording_type: str,
         recording_trigger: str,
         is_shared: bool,
-        platform: str = "whereby",
+        webhook_url: str = "",
+        webhook_secret: str = "",
     ):
         """
         Add a new room
         """
+        if webhook_url and not webhook_secret:
+            webhook_secret = secrets.token_urlsafe(32)
+
         room = Room(
             name=name,
             user_id=user_id,
@@ -127,11 +132,12 @@ class RoomController:
             recording_type=recording_type,
             recording_trigger=recording_trigger,
             is_shared=is_shared,
-            platform=platform,
+            webhook_url=webhook_url,
+            webhook_secret=webhook_secret,
         )
         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
@@ -140,9 +146,12 @@ class RoomController:
         """
         Update a room fields with key/values in values
         """
+        if values.get("webhook_url") and not values.get("webhook_secret"):
+            values["webhook_secret"] = secrets.token_urlsafe(32)
+
         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 +166,7 @@ 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 +178,7 @@ 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 +190,7 @@ 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 +212,7 @@ 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 +1,468 @@
+"""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 databases.interfaces import Record as DbRecord
+from fastapi import HTTPException
+from pydantic import (
+    BaseModel,
+    Field,
+    NonNegativeFloat,
+    NonNegativeInt,
+    TypeAdapter,
+    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
+from reflector.utils.string import NonEmptyString, try_parse_non_empty_string
+
+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=1, 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")]
+search_query_adapter = TypeAdapter(SearchQuery)
+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: SearchQuery,
+        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 | None = None
+    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: SearchQuery) -> 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
+        assert query is not None
+        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: SearchQuery,
+        max_length: NonNegativeInt = DEFAULT_SNIPPET_MAX_LENGTH,
+        max_snippets: NonNegativeInt = DEFAULT_MAX_SNIPPETS,
+    ) -> list[str]:
+        """Generate snippets from text."""
+        assert query is not None
+        if not text:
+            logger.warning("Empty text 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: SearchQuery,
+        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: NonEmptyString | None,
+        webvtt: WebVTTContent | None,
+        query: SearchQuery,
+        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
+        """
+
+        assert (
+            summary is not None or webvtt is not None
+        ), "At least one source must be present"
+
+        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"),
+        ]
+        search_query = None
+        if params.query_text is not None:
+            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 is not None:
+            # because already initialized based on params.query_text presence above
+            assert search_query is not None
+            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 is not None:
+            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: DbRecord) -> SearchResult:
+            r_dict: Dict[str, Any] = dict(r)
+
+            webvtt_raw: str | None = r_dict.pop("webvtt", None)
+            webvtt: WebVTTContent | None
+            if webvtt_raw:
+                webvtt = WebVTTProcessor.parse(webvtt_raw)
+            else:
+                webvtt = None
+
+            long_summary_r: str | None = r_dict.pop("long_summary", None)
+            long_summary: NonEmptyString = try_parse_non_empty_string(long_summary_r)
+            room_name: str | None = r_dict.pop("room_name", None)
+            db_result = SearchResultDB.model_validate(r_dict)
+
+            at_least_one_source = webvtt is not None or long_summary is not None
+            has_query = params.query_text is not None
+            snippets, total_match_count = (
+                SnippetGenerator.combine_sources(
+                    long_summary, webvtt, params.query_text, DEFAULT_MAX_SNIPPETS
+                )
+                if has_query and at_least_one_source
+                else ([], 0)
+            )
+
+            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),
@@ -76,11 +82,38 @@ 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",
+        )
     )
 
 
@@ -89,6 +122,15 @@ def generate_transcript_name() -> str:
     return f"Transcript {now.strftime('%Y-%m-%d %H:%M:%S')}"
 
 
+TranscriptStatus = Literal[
+    "idle", "uploaded", "recording", "processing", "error", "ended"
+]
+
+
+class StrValue(BaseModel):
+    value: str
+
+
 class AudioWaveform(BaseModel):
     data: list[float]
 
@@ -147,14 +189,18 @@ 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"
+    status: TranscriptStatus = "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 +214,8 @@ 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 +316,12 @@ 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
+        # TODO don't import app in db
-        from reflector.views.transcripts import create_access_token
+        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 +382,6 @@ 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 +432,7 @@ 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 +442,7 @@ 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 +456,7 @@ 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 +474,7 @@ 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 +492,7 @@ 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 +545,52 @@ 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 +604,55 @@ 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 +665,7 @@ class TranscriptController:
         Append an event to a transcript
         """
         resp = transcript.add_event(event=event, data=data)
-        await self.update(
+        await self.update(transcript, {"events": transcript.events_dump()})
-            transcript,
-            {"events": transcript.events_dump()},
-            mutate=False,
-        )
         return resp
 
     async def upsert_topic(
@@ -574,11 +677,7 @@ class TranscriptController:
         Upsert topics to a transcript
         """
         transcript.upsert_topic(topic)
-        await self.update(
+        await self.update(transcript, {"topics": transcript.topics_dump()})
-            transcript,
-            {"topics": transcript.topics_dump()},
-            mutate=False,
-        )
 
     async def move_mp3_to_storage(self, transcript: Transcript):
         """
@@ -603,7 +702,8 @@ 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 +727,7 @@ class TranscriptController:
         Add/update a participant to a transcript
         """
         result = transcript.upsert_participant(participant)
-        await self.update(
+        await self.update(transcript, {"participants": transcript.participants_dump()})
-            transcript,
-            {"participants": transcript.participants_dump()},
-            mutate=False,
-        )
         return result
 
     async def delete_participant(
@@ -643,11 +739,29 @@ class TranscriptController:
         Delete a participant from a transcript
         """
         transcript.delete_participant(participant_id)
-        await self.update(
+        await self.update(transcript, {"participants": transcript.participants_dump()})
-            transcript,
+
-            {"participants": transcript.participants_dump()},
+    async def set_status(
-            mutate=False,
+        self, transcript_id: str, status: TranscriptStatus
+    ) -> TranscriptEvent | None:
+        """
+        Update the status of a transcript
+
+        Will add an event STATUS + update the status field of transcript
+        """
+        async with self.transaction():
+            transcript = await self.get_by_id(transcript_id)
+            if not transcript:
+                raise Exception(f"Transcript {transcript_id} not found")
+            if transcript.status == status:
+                return
+            resp = await self.append_event(
+                transcript=transcript,
+                event="STATUS",
+                data=StrValue(value=status),
             )
+            await self.update(transcript, {"status": status})
+        return resp
 
 
 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 +1,421 @@
+"""
+File-based processing pipeline
+==============================
+
+Optimized pipeline for processing complete audio/video files.
+Uses parallel processing for transcription, diarization, and waveform generation.
+"""
+
+import asyncio
+import uuid
+from pathlib import Path
+
+import av
+import structlog
+from celery import shared_task
+
+from reflector.asynctask import asynctask
+from reflector.db.rooms import rooms_controller
+from reflector.db.transcripts import (
+    SourceKind,
+    Transcript,
+    TranscriptStatus,
+    transcripts_controller,
+)
+from reflector.logger import logger
+from reflector.pipelines.main_live_pipeline import (
+    PipelineMainBase,
+    broadcast_to_sockets,
+)
+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
+from reflector.worker.webhook import send_transcript_webhook
+
+
+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,
+            )
+
+    @broadcast_to_sockets
+    async def set_status(self, transcript_id: str, status: TranscriptStatus):
+        async with self.lock_transaction():
+            return await transcripts_controller.set_status(transcript_id, status)
+
+    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()
+
+        # Clear transcript as we're going to regenerate everything
+        async with self.transaction():
+            await transcripts_controller.update(
+                transcript,
+                {
+                    "events": [],
+                    "topics": [],
+                },
+            )
+
+        # 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")
+
+        await transcripts_controller.set_status(transcript.id, "ended")
+
+    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")
+
+    pipeline = PipelineMainFile(transcript_id=transcript_id)
+    try:
+        await pipeline.set_status(transcript_id, "processing")
+
+        # 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")
+
+        await pipeline.process(audio_file)
+
+    except Exception:
+        await pipeline.set_status(transcript_id, "error")
+        raise
+
+    # Trigger webhook if this is a room recording with webhook configured
+    if transcript.source_kind == SourceKind.ROOM and transcript.room_id:
+        room = await rooms_controller.get_by_id(transcript.room_id)
+        if room and room.webhook_url:
+            logger.info(
+                "Dispatching webhook task",
+                transcript_id=transcript_id,
+                room_id=room.id,
+                webhook_url=room.webhook_url,
+            )
+            send_transcript_webhook.delay(
+                transcript_id, room.id, event_id=uuid.uuid4().hex
+            )

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.asynctask import asynctask
 from reflector.db.meetings import meeting_consent_controller, meetings_controller
 from reflector.db.recordings import recordings_controller
 from reflector.db.rooms import rooms_controller
@@ -29,16 +32,18 @@ from reflector.db.transcripts import (
     TranscriptFinalLongSummary,
     TranscriptFinalShortSummary,
     TranscriptFinalTitle,
+    TranscriptStatus,
     TranscriptText,
     TranscriptTopic,
     TranscriptWaveform,
     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 +52,7 @@ from reflector.processors import (
     TranscriptFinalTitleProcessor,
     TranscriptLinerProcessor,
     TranscriptTopicDetectorProcessor,
-    TranscriptTranslatorProcessor,
+    TranscriptTranslatorAutoProcessor,
 )
 from reflector.processors.audio_waveform_processor import AudioWaveformProcessor
 from reflector.processors.types import AudioDiarizationInput
@@ -65,30 +70,6 @@ from reflector.zulip import (
 )
 
 
-def asynctask(f):
-    @functools.wraps(f)
-    def wrapper(*args, **kwargs):
-        async def run_with_db():
-            from reflector.db import database
-
-            await database.connect()
-            try:
-                return await f(*args, **kwargs)
-            finally:
-                await database.disconnect()
-
-        coro = run_with_db()
-        try:
-            loop = asyncio.get_running_loop()
-        except RuntimeError:
-            loop = None
-        if loop and loop.is_running():
-            return loop.run_until_complete(coro)
-        return asyncio.run(coro)
-
-    return wrapper
-
-
 def broadcast_to_sockets(func):
     """
     Decorator to broadcast transcript event to websockets
@@ -144,16 +125,19 @@ class StrValue(BaseModel):
     value: str
 
 
-class PipelineMainBase(PipelineRunner):
+class PipelineMainBase(PipelineRunner[PipelineMessage], Generic[PipelineMessage]):
-    transcript_id: str
+    def __init__(self, transcript_id: str):
-    ws_room_id: str | None = None
+        super().__init__()
-    ws_manager: WebsocketManager | None = None
-
-    def prepare(self):
-        # prepare websocket
         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 +148,11 @@ 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,
@@ -174,12 +162,19 @@ class PipelineMainBase(PipelineRunner):
                 duration=topic.duration,
                 transcript=TranscriptProcessorType(words=topic.words),
             )
-            for topic in transcript.topics
+            for topic in topics
         ]
 
     @asynccontextmanager
-    async def transaction(self):
+    async def lock_transaction(self):
+        # This lock is to prevent multiple processor starting adding
+        # into event array at the same time
         async with self._lock:
+            yield
+
+    @asynccontextmanager
+    async def transaction(self):
+        async with self.lock_transaction():
             async with transcripts_controller.transaction():
                 yield
 
@@ -188,14 +183,14 @@ class PipelineMainBase(PipelineRunner):
         # if it's the first part, update the status of the transcript
         # but do not set the ended status yet.
         if isinstance(self, PipelineMainLive):
-            status_mapping = {
+            status_mapping: dict[str, TranscriptStatus] = {
                 "started": "recording",
                 "push": "recording",
                 "flush": "processing",
                 "error": "error",
             }
         elif isinstance(self, PipelineMainFinalSummaries):
-            status_mapping = {
+            status_mapping: dict[str, TranscriptStatus] = {
                 "push": "processing",
                 "flush": "processing",
                 "error": "error",
@@ -211,22 +206,8 @@ class PipelineMainBase(PipelineRunner):
             return
 
         # when the status of the pipeline changes, update the transcript
-        async with self.transaction():
+        async with self._lock:
-            transcript = await self.get_transcript()
+            return await transcripts_controller.set_status(self.transcript_id, status)
-            if status == transcript.status:
-                return
-            resp = await transcripts_controller.append_event(
-                transcript=transcript,
-                event="STATUS",
-                data=StrValue(value=status),
-            )
-            await transcripts_controller.update(
-                transcript,
-                {
-                    "status": status,
-                },
-            )
-            return resp
 
     @broadcast_to_sockets
     async def on_transcript(self, data):
@@ -349,7 +330,6 @@ 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 +337,12 @@ 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 +351,7 @@ 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 +362,7 @@ 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 +370,6 @@ 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 +385,10 @@ 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 +401,7 @@ class PipelineMainDiarization(PipelineMainBase):
         return pipeline
 
 
-class PipelineMainFromTopics(PipelineMainBase):
+class PipelineMainFromTopics(PipelineMainBase[TitleSummaryWithIdProcessorType]):
     """
     Pseudo class for generating a pipeline from topics
     """
@@ -430,8 +410,6 @@ class PipelineMainFromTopics(PipelineMainBase):
         raise NotImplementedError
 
     async def create(self) -> Pipeline:
-        self.prepare()
-
         # get transcript
         self._transcript = transcript = await self.get_transcript()
 
@@ -443,7 +421,7 @@ 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 +502,6 @@ 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 +580,7 @@ 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 +603,7 @@ 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 +616,7 @@ 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 +625,7 @@ 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")
 
@@ -789,13 +765,11 @@ def pipeline_post(*, transcript_id: str):
         chain_final_summaries,
     ) | task_pipeline_post_to_zulip.si(transcript_id=transcript_id)
 
-    chain.delay()
+    return chain.delay()
 
 
 @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 typing import Generic, TypeVar
-from pydantic import BaseModel, ConfigDict
 
 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"
+class PipelineRunner(Generic[PipelineMessage]):
-    pipeline: Pipeline | None = None
+    def __init__(self):
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
         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.
@@ -143,6 +144,9 @@ class PipelineRunner(BaseModel):
                 cmd, data = await self._q_cmd.get()
                 func = getattr(self, f"cmd_{cmd.lower()}")
                 if func:
+                    if cmd.upper() == "FLUSH":
+                        await func()
+                    else:
                         await func(data)
                 else:
                     raise Exception(f"Unknown command {cmd}")
@@ -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):
+    m_chunk = Histogram(
-        super().__init__()
+        "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)
+        """Process incoming audio frame"""
-        if len(self.frames) >= self.max_frames:
+        # Validate audio format on first frame
-            await self.flush()
+        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[:]
+        """Flush any remaining frames when processing ends"""
-        self.frames = []
+        raise NotImplementedError
-        if frames:
-            await self.emit(frames)

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]):
+    @classmethod
-        self._diarization_remove_overlap(diarization)
+    def assign_speaker(cls, words: list[Word], diarization: list[DiarizationSegment]):
-        self._diarization_remove_segment_without_words(words, diarization)
+        cls._diarization_remove_overlap(diarization)
-        self._diarization_merge_same_speaker(words, diarization)
+        cls._diarization_remove_segment_without_words(words, diarization)
-        self._diarization_assign_speaker(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(
+    @staticmethod
-        self, words: list[Word], diarization: list[dict]
+    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 = {
+        self.modal_api_key = modal_api_key
-            "Authorization": f"Bearer {settings.LLM_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)
+        output_channels = len(frame.layout.channels)
-        sample_rate = frame.sample_rate
+        output_sample_rate = frame.sample_rate
-        sample_width = frame.format.bytes
+        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,
+            sample_rate=output_sample_rate,
-            channels=channels,
+            channels=output_channels,
-            sample_width=sample_width,
+            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):
         """
@@ -240,14 +241,15 @@ 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.queue = asyncio.Queue(maxsize=50)
-        self.task = asyncio.get_running_loop().create_task(self.loop())
+        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):
+        try:
             while True:
                 data = await self.queue.get()
                 self.m_processor_queue.set(self.queue.qsize())
@@ -265,8 +267,19 @@ class ThreadedProcessor(Processor):
                             )
                     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 +1,77 @@
+"""
+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", [])
+        ]
+
+        # words come not in order
+        words.sort(key=lambda w: w.start)
+
+        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.processors.types import Transcript
-from reflector.settings import settings
-from reflector.utils.retry import retry
 
 
 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:
+    async def _translate(self, text: str) -> str | None:
-        # FIXME this should be a processor after, as each user may want
+        raise NotImplementedError
-        # 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 _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
+    start: Seconds
-    end: float
+    end: Seconds
     speaker: int = 0
 
 
 class TranscriptSegment(BaseModel):
     text: str
-    start: float
+    start: Seconds
-    end: float
+    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
+        return words_to_segments(self.words)
-        # 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
 
 
 class TitleSummary(BaseModel):

server/reflector/settings.py

@@ -1,3 +1,4 @@
+from pydantic.types import PositiveInt
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
 
@@ -14,18 +15,25 @@ 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 +45,23 @@ 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 +72,13 @@ 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
@@ -63,9 +91,8 @@ class Settings(BaseSettings):
     AUTH_JWT_PUBLIC_KEY: str | None = "authentik.monadical.com_public.pem"
     AUTH_JWT_AUDIENCE: str | None = None
 
-    # API public mode
-    # if set, all anonymous record will be public
     PUBLIC_MODE: bool = False
+    PUBLIC_DATA_RETENTION_DAYS: PositiveInt = 7
 
     # Min transcript length to generate topic + summary
     MIN_TRANSCRIPT_LENGTH: int = 750
@@ -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/cleanup_old_data.py

@@ -0,0 +1,72 @@
+#!/usr/bin/env python
+"""
+Manual cleanup tool for old public data.
+Uses the same implementation as the Celery worker task.
+"""
+
+import argparse
+import asyncio
+import sys
+
+import structlog
+
+from reflector.settings import settings
+from reflector.worker.cleanup import _cleanup_old_public_data
+
+logger = structlog.get_logger(__name__)
+
+
+async def cleanup_old_data(days: int = 7):
+    logger.info(
+        "Starting manual cleanup",
+        retention_days=days,
+        public_mode=settings.PUBLIC_MODE,
+    )
+
+    if not settings.PUBLIC_MODE:
+        logger.critical(
+            "WARNING: PUBLIC_MODE is False. "
+            "This tool is intended for public instances only."
+        )
+        raise Exception("Tool intended for public instances only")
+
+    result = await _cleanup_old_public_data(days=days)
+
+    if result:
+        logger.info(
+            "Cleanup completed",
+            transcripts_deleted=result.get("transcripts_deleted", 0),
+            meetings_deleted=result.get("meetings_deleted", 0),
+            recordings_deleted=result.get("recordings_deleted", 0),
+            errors_count=len(result.get("errors", [])),
+        )
+        if result.get("errors"):
+            logger.warning(
+                "Errors encountered during cleanup:", errors=result["errors"][:10]
+            )
+    else:
+        logger.info("Cleanup skipped or completed without results")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Clean up old transcripts and meetings"
+    )
+    parser.add_argument(
+        "--days",
+        type=int,
+        default=7,
+        help="Number of days to keep data (default: 7)",
+    )
+
+    args = parser.parse_args()
+
+    if args.days < 1:
+        logger.error("Days must be at least 1")
+        sys.exit(1)
+
+    asyncio.run(cleanup_old_data(days=args.days))
+
+
+if __name__ == "__main__":
+    main()

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

@@ -1,105 +1,220 @@
+"""
+Process audio file with diarization support
+"""
+
+import argparse
 import asyncio
+import json
+import shutil
+import sys
+import time
+from pathlib import Path
+from typing import Any, Dict, List, Literal
 
-import av
+from reflector.db.transcripts import SourceKind, TranscriptTopic, transcripts_controller
-
 from reflector.logger import logger
-from reflector.processors import (
+from reflector.pipelines.main_file_pipeline import (
-    AudioChunkerProcessor,
+    task_pipeline_file_process as task_pipeline_file_process,
-    AudioMergeProcessor,
+)
-    AudioTranscriptAutoProcessor,
+from reflector.pipelines.main_live_pipeline import pipeline_post as live_pipeline_post
-    Pipeline,
+from reflector.pipelines.main_live_pipeline import (
-    PipelineEvent,
+    pipeline_process as live_pipeline_process,
-    TranscriptFinalSummaryProcessor,
-    TranscriptFinalTitleProcessor,
-    TranscriptLinerProcessor,
-    TranscriptTopicDetectorProcessor,
-    TranscriptTranslatorProcessor,
 )
-from reflector.processors.base import BroadcastProcessor
 
 
-async def process_audio_file(
+def serialize_topics(topics: List[TranscriptTopic]) -> List[Dict[str, Any]]:
-    filename,
+    """Convert TranscriptTopic objects to JSON-serializable dicts"""
-    event_callback,
+    serialized = []
-    only_transcript=False,
+    for topic in topics:
-    source_language="en",
+        topic_dict = topic.model_dump()
-    target_language="en",
+        serialized.append(topic_dict)
+    return serialized
+
+
+def debug_print_speakers(serialized_topics: List[Dict[str, Any]]) -> None:
+    """Print debug info about speakers found in topics"""
+    all_speakers = set()
+    for topic_dict in serialized_topics:
+        for word in topic_dict.get("words", []):
+            all_speakers.add(word.get("speaker", 0))
+
+    print(
+        f"Found {len(serialized_topics)} topics with speakers: {all_speakers}",
+        file=sys.stderr,
+    )
+
+
+TranscriptId = str
+
+
+# common interface for every flow: it needs an Entry in db with specific ceremony (file path + status + actual file in file system)
+# ideally we want to get rid of it at some point
+async def prepare_entry(
+    source_path: str,
+    source_language: str,
+    target_language: str,
+) -> TranscriptId:
+    file_path = Path(source_path)
+
+    transcript = await transcripts_controller.add(
+        file_path.name,
+        # note that the real file upload has SourceKind: LIVE for the reason of it's an error
+        source_kind=SourceKind.FILE,
+        source_language=source_language,
+        target_language=target_language,
+        user_id=None,
+    )
+
+    logger.info(
+        f"Created empty transcript {transcript.id} for file {file_path.name} because technically we need an empty transcript before we start transcript"
+    )
+
+    # pipelines expect files as upload.*
+
+    extension = file_path.suffix
+    upload_path = transcript.data_path / f"upload{extension}"
+    upload_path.parent.mkdir(parents=True, exist_ok=True)
+    shutil.copy2(source_path, upload_path)
+    logger.info(f"Copied {source_path} to {upload_path}")
+
+    # pipelines expect entity status "uploaded"
+    await transcripts_controller.update(transcript, {"status": "uploaded"})
+
+    return transcript.id
+
+
+# same reason as prepare_entry
+async def extract_result_from_entry(
+    transcript_id: TranscriptId, output_path: str
+) -> None:
+    post_final_transcript = await transcripts_controller.get_by_id(transcript_id)
+
+    # assert post_final_transcript.status == "ended"
+    # File pipeline doesn't set status to "ended", only live pipeline does https://github.com/Monadical-SAS/reflector/issues/582
+    topics = post_final_transcript.topics
+    if not topics:
+        raise RuntimeError(
+            f"No topics found for transcript {transcript_id} after processing"
+        )
+
+    serialized_topics = serialize_topics(topics)
+
+    if output_path:
+        # Write to JSON file
+        with open(output_path, "w") as f:
+            for topic_dict in serialized_topics:
+                json.dump(topic_dict, f)
+                f.write("\n")
+        print(f"Results written to {output_path}", file=sys.stderr)
+    else:
+        # Write to stdout as JSONL
+        for topic_dict in serialized_topics:
+            print(json.dumps(topic_dict))
+
+    debug_print_speakers(serialized_topics)
+
+
+async def process_live_pipeline(
+    transcript_id: TranscriptId,
 ):
-    # build pipeline for audio processing
+    """Process transcript_id with transcription and diarization"""
-    processors = [
-        AudioChunkerProcessor(),
-        AudioMergeProcessor(),
-        AudioTranscriptAutoProcessor.as_threaded(),
-        TranscriptLinerProcessor(),
-        TranscriptTranslatorProcessor.as_threaded(),
-    ]
-    if not only_transcript:
-        processors += [
-            TranscriptTopicDetectorProcessor.as_threaded(),
-            BroadcastProcessor(
-                processors=[
-                    TranscriptFinalTitleProcessor.as_threaded(),
-                    TranscriptFinalSummaryProcessor.as_threaded(),
-                ],
-            ),
-        ]
 
-    # transcription output
+    print(f"Processing transcript_id {transcript_id}...", file=sys.stderr)
-    pipeline = Pipeline(*processors)
+    await live_pipeline_process(transcript_id=transcript_id)
-    pipeline.set_pref("audio:source_language", source_language)
+    print(f"Processing complete for transcript {transcript_id}", file=sys.stderr)
-    pipeline.set_pref("audio:target_language", target_language)
+
-    pipeline.describe()
+    pre_final_transcript = await transcripts_controller.get_by_id(transcript_id)
-    pipeline.on(event_callback)
+
+    # assert documented behaviour: after process, the pipeline isn't ended. this is the reason of calling pipeline_post
+    assert pre_final_transcript.status != "ended"
+
+    # at this point, diarization is running but we have no access to it. run diarization in parallel - one will hopefully win after polling
+    result = live_pipeline_post(transcript_id=transcript_id)
+
+    # result.ready() blocks even without await; it mutates result also
+    while not result.ready():
+        print(f"Status: {result.state}")
+        time.sleep(2)
+
+
+async def process_file_pipeline(
+    transcript_id: TranscriptId,
+):
+    """Process audio/video file using the optimized file pipeline"""
+
+    # task_pipeline_file_process is a Celery task, need to use .delay() for async execution
+    result = task_pipeline_file_process.delay(transcript_id=transcript_id)
+
+    # Wait for the Celery task to complete
+    while not result.ready():
+        print(f"File pipeline status: {result.state}", file=sys.stderr)
+        time.sleep(2)
+
+    logger.info("File pipeline processing complete")
+
+
+async def process(
+    source_path: str,
+    source_language: str,
+    target_language: str,
+    pipeline: Literal["live", "file"],
+    output_path: str = None,
+):
+    from reflector.db import get_database
+
+    database = get_database()
+    # db connect is a part of ceremony
+    await database.connect()
 
-    # start processing audio
-    logger.info(f"Opening {filename}")
-    container = av.open(filename)
     try:
-        logger.info("Start pushing audio into the pipeline")
+        transcript_id = await prepare_entry(
-        for frame in container.decode(audio=0):
+            source_path,
-            await pipeline.push(frame)
+            source_language,
-    finally:
+            target_language,
-        logger.info("Flushing the pipeline")
+        )
-        await pipeline.flush()
 
-    logger.info("All done !")
+        pipeline_handlers = {
+            "live": process_live_pipeline,
+            "file": process_file_pipeline,
+        }
+
+        handler = pipeline_handlers.get(pipeline)
+        if not handler:
+            raise ValueError(f"Unknown pipeline type: {pipeline}")
+
+        await handler(transcript_id)
+
+        await extract_result_from_entry(transcript_id, output_path)
+    finally:
+        await database.disconnect()
 
 
 if __name__ == "__main__":
-    import argparse
+    parser = argparse.ArgumentParser(
-
+        description="Process audio files with speaker diarization"
-    parser = argparse.ArgumentParser()
+    )
     parser.add_argument("source", help="Source file (mp3, wav, mp4...)")
-    parser.add_argument("--only-transcript", "-t", action="store_true")
+    parser.add_argument(
-    parser.add_argument("--source-language", default="en")
+        "--pipeline",
-    parser.add_argument("--target-language", default="en")
+        required=True,
+        choices=["live", "file"],
+        help="Pipeline type to use for processing (live: streaming/incremental, file: batch/parallel)",
+    )
+    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)")
     args = parser.parse_args()
 
-    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"):
-            return
-        logger.info(f"Event: {event}")
-        if output_fd:
-            output_fd.write(event.model_dump_json())
-            output_fd.write("\n")
-
     asyncio.run(
-        process_audio_file(
+        process(
             args.source,
-            event_callback,
+            args.source_language,
-            only_transcript=args.only_transcript,
+            args.target_language,
-            source_language=args.source_language,
+            args.pipeline,
-            target_language=args.target_language,
+            args.output,
         )
     )
-
-    if output_fd:
-        output_fd.close()
-        logger.info(f"Output written to {args.output}")

server/reflector/tools/process_with_diarization.py

@@ -1,316 +0,0 @@
-"""
-@vibe-generated
-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,
-    AudioFileWriterProcessor,
-    AudioMergeProcessor,
-    AudioTranscriptAutoProcessor,
-    Pipeline,
-    PipelineEvent,
-    TranscriptFinalSummaryProcessor,
-    TranscriptFinalTitleProcessor,
-    TranscriptLinerProcessor,
-    TranscriptTopicDetectorProcessor,
-    TranscriptTranslatorProcessor,
-)
-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_with_diarization(
-    filename,
-    event_callback,
-    only_transcript=False,
-    source_language="en",
-    target_language="en",
-    enable_diarization=True,
-    diarization_backend="modal",
-):
-    # 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 += [
-        AudioChunkerProcessor(),
-        AudioMergeProcessor(),
-        AudioTranscriptAutoProcessor.as_threaded(),
-    ]
-
-    processors += [
-        TranscriptLinerProcessor(),
-        TranscriptTranslatorProcessor.as_threaded(),
-    ]
-
-    if not only_transcript:
-        processors += [
-            TranscriptTopicDetectorProcessor.as_threaded(),
-            # Collect topics for diarization
-            topic_collector,
-            BroadcastProcessor(
-                processors=[
-                    TranscriptFinalTitleProcessor.as_threaded(),
-                    TranscriptFinalSummaryProcessor.as_threaded(),
-                ],
-            ),
-        ]
-
-    # 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
-    logger.info(f"Opening {filename}")
-    container = av.open(filename)
-    try:
-        logger.info("Start pushing audio into the pipeline")
-        for frame in container.decode(audio=0):
-            await pipeline.push(frame)
-    finally:
-        logger.info("Flushing the pipeline")
-        await pipeline.flush()
-
-    # 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:
-                # Import diarization processor
-                from reflector.processors import AudioDiarizationAutoProcessor
-
-                # Create diarization processor
-                diarization_processor = AudioDiarizationAutoProcessor(
-                    name=diarization_backend
-                )
-                diarization_processor.on(event_callback)
-
-                # 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!")
-
-
-if __name__ == "__main__":
-    import argparse
-    import os
-
-    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",
-        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="modal",
-        choices=["modal"],
-        help="Diarization backend to use (default: modal)",
-    )
-    args = parser.parse_args()
-
-    # Set REDIS_HOST to localhost if not provided
-    if "REDIS_HOST" not in os.environ:
-        os.environ["REDIS_HOST"] = "localhost"
-        logger.info("REDIS_HOST not set, defaulting to localhost")
-
-    output_fd = None
-    if args.output:
-        output_fd = open(args.output, "w")
-
-    async def event_callback(event: PipelineEvent):
-        processor = event.processor
-        data = event.data
-
-        # Ignore internal processors
-        if processor in (
-            "AudioChunkerProcessor",
-            "AudioMergeProcessor",
-            "AudioFileWriterProcessor",
-            "TopicCollectorProcessor",
-            "BroadcastProcessor",
-        ):
-            return
-
-        # 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_with_diarization(
-            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,
-        )
-    )
-
-    if output_fd:
-        output_fd.close()
-        logger.info(f"Output written to {args.output}")

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/tools/test_diarization.py

@@ -1,96 +0,0 @@
-#!/usr/bin/env python3
-"""
-@vibe-generated
-Test script for the diarization CLI tool
-=========================================
-
-This script helps test the diarization functionality with sample audio files.
-"""
-
-import asyncio
-import sys
-from pathlib import Path
-
-from reflector.logger import logger
-
-
-async def test_diarization(audio_file: str):
-    """Test the diarization functionality"""
-
-    # Import the processing function
-    from process_with_diarization import process_audio_file_with_diarization
-
-    # Collect events
-    events = []
-
-    async def event_callback(event):
-        events.append({"processor": event.processor, "data": event.data})
-        logger.info(f"Event from {event.processor}")
-
-    # Process the audio file
-    logger.info(f"Processing audio file: {audio_file}")
-
-    try:
-        await process_audio_file_with_diarization(
-            audio_file,
-            event_callback,
-            only_transcript=False,
-            source_language="en",
-            target_language="en",
-            enable_diarization=True,
-            diarization_backend="modal",
-        )
-
-        # Analyze results
-        logger.info(f"Processing complete. Received {len(events)} events")
-
-        # Look for diarization results
-        diarized_topics = []
-        for event in events:
-            if "TitleSummary" in event["processor"]:
-                # Check if words have speaker information
-                if hasattr(event["data"], "transcript") and event["data"].transcript:
-                    words = event["data"].transcript.words
-                    if words and hasattr(words[0], "speaker"):
-                        speakers = set(
-                            w.speaker for w in words if hasattr(w, "speaker")
-                        )
-                        logger.info(
-                            f"Found {len(speakers)} speakers in topic: {event['data'].title}"
-                        )
-                        diarized_topics.append(event["data"])
-
-        if diarized_topics:
-            logger.info(f"Successfully diarized {len(diarized_topics)} topics")
-
-            # Print sample output
-            sample_topic = diarized_topics[0]
-            logger.info("Sample diarized output:")
-            for i, word in enumerate(sample_topic.transcript.words[:10]):
-                logger.info(f"  Word {i}: '{word.text}' - Speaker {word.speaker}")
-        else:
-            logger.warning("No diarization results found in output")
-
-        return events
-
-    except Exception as e:
-        logger.error(f"Error during processing: {e}")
-        raise
-
-
-def main():
-    if len(sys.argv) < 2:
-        print("Usage: python test_diarization.py <audio_file>")
-        sys.exit(1)
-
-    audio_file = sys.argv[1]
-    if not Path(audio_file).exists():
-        print(f"Error: Audio file '{audio_file}' not found")
-        sys.exit(1)
-
-    # Run the test
-    asyncio.run(test_diarization(audio_file))
-
-
-if __name__ == "__main__":
-    main()

server/reflector/utils/string.py

@@ -0,0 +1,20 @@
+from typing import Annotated
+
+from pydantic import Field, TypeAdapter, constr
+
+NonEmptyStringBase = constr(min_length=1, strip_whitespace=False)
+NonEmptyString = Annotated[
+    NonEmptyStringBase,
+    Field(description="A non-empty string", min_length=1),
+]
+non_empty_string_adapter = TypeAdapter(NonEmptyString)
+
+
+def parse_non_empty_string(s: str) -> NonEmptyString:
+    return non_empty_string_adapter.validate_python(s)
+
+
+def try_parse_non_empty_string(s: str) -> NonEmptyString | None:
+    if not s:
+        return None
+    return parse_non_empty_string(s)

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