dockerfile healthcheck

* Identify action items

* Add action items to mock summary

* Add action items validator

* Remove final prefix from action items

* Make on action items callback required

* Don't mutation action items response

* Assign action items to none on error

* Use timeout constant

* Exclude action items from transcript list

.github/workflows/db_migrations.yml

@@ -2,6 +2,8 @@ name: Test Database Migrations
 
 on:
   push:
+    branches:
+      - main
     paths:
       - "server/migrations/**"
       - "server/reflector/db/**"
@@ -17,6 +19,9 @@ on:
 jobs:
   test-migrations:
     runs-on: ubuntu-latest
+    concurrency:
+      group: db-ubuntu-latest-${{ github.ref }}
+      cancel-in-progress: true
     services:
       postgres:
         image: postgres:17

.github/workflows/deploy.yml

@@ -1,90 +0,0 @@
-name: Deploy to Amazon ECS
-
-on: [workflow_dispatch]
-
-env:
-  # 950402358378.dkr.ecr.us-east-1.amazonaws.com/reflector
-  AWS_REGION: us-east-1
-  ECR_REPOSITORY: reflector
-
-jobs:
-  build:
-    strategy:
-      matrix:
-        include:
-          - platform: linux/amd64
-            runner: linux-amd64
-            arch: amd64
-          - platform: linux/arm64
-            runner: linux-arm64
-            arch: arm64
-
-    runs-on: ${{ matrix.runner }}
-
-    permissions:
-      contents: read
-
-    outputs:
-      registry: ${{ steps.login-ecr.outputs.registry }}
-
-    steps:
-      - uses: actions/checkout@v4
-
-      - 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
-        id: login-ecr
-        uses: aws-actions/amazon-ecr-login@v2
-
-      - name: Set up Docker Buildx
-        uses: docker/setup-buildx-action@v3
-
-      - name: Build and push ${{ matrix.arch }}
-        uses: docker/build-push-action@v5
-        with:
-          context: server
-          platforms: ${{ matrix.platform }}
-          push: true
-          tags: ${{ steps.login-ecr.outputs.registry }}/${{ env.ECR_REPOSITORY }}:latest-${{ matrix.arch }}
-          cache-from: type=gha,scope=${{ matrix.arch }}
-          cache-to: type=gha,mode=max,scope=${{ matrix.arch }}
-          github-token: ${{ secrets.GHA_CACHE_TOKEN }}
-          provenance: false
-
-  create-manifest:
-    runs-on: ubuntu-latest
-    needs: [build]
-
-    permissions:
-      deployments: write
-      contents: read
-
-    steps:
-      - name: Configure AWS credentials
-        uses: aws-actions/configure-aws-credentials@v4
-        with:
-          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
-          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
-          aws-region: ${{ env.AWS_REGION }}
-
-      - name: Login to Amazon ECR
-        uses: aws-actions/amazon-ecr-login@v2
-
-      - name: Create and push multi-arch manifest
-        run: |
-          # Get the registry URL (since we can't easily access job outputs in matrix)
-          ECR_REGISTRY=$(aws ecr describe-registry --query 'registryId' --output text).dkr.ecr.${{ env.AWS_REGION }}.amazonaws.com
-
-          docker manifest create \
-            $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest \
-            $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest-amd64 \
-            $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest-arm64
-
-          docker manifest push $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest
-
-          echo "✅ Multi-arch manifest pushed: $ECR_REGISTRY/${{ env.ECR_REPOSITORY }}:latest"

.github/workflows/dockerhub-backend.yml

@@ -0,0 +1,53 @@
+name: Build and Push Backend Docker Image (Docker Hub)
+
+on:
+  push:
+    tags:
+      - "v*"
+  workflow_dispatch:
+
+env:
+  REGISTRY: docker.io
+  IMAGE_NAME: monadicalsas/reflector-backend
+
+jobs:
+  build-and-push:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Log in to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: monadicalsas
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      - name: Extract metadata
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+          tags: |
+            type=ref,event=branch
+            type=ref,event=tag
+            type=raw,value=latest,enable={{is_default_branch}}
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v5
+        with:
+          context: ./server
+          file: ./server/Dockerfile
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
+          platforms: linux/amd64,linux/arm64

.github/workflows/dockerhub-frontend.yml

@@ -0,0 +1,70 @@
+name: Build and Push Frontend Docker Image
+
+on:
+  push:
+    tags:
+      - "v*"
+  workflow_dispatch:
+
+env:
+  REGISTRY: docker.io
+  IMAGE_NAME: monadicalsas/reflector-frontend
+
+jobs:
+  build-and-push:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: read
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Log in to Docker Hub
+        uses: docker/login-action@v3
+        with:
+          registry: ${{ env.REGISTRY }}
+          username: monadicalsas
+          password: ${{ secrets.DOCKERHUB_TOKEN }}
+
+      - name: Extract metadata
+        id: meta
+        uses: docker/metadata-action@v5
+        with:
+          images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
+          tags: |
+            type=ref,event=branch
+            type=ref,event=tag
+            type=raw,value=latest,enable={{is_default_branch}}
+          github-token: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Build and push Docker image
+        uses: docker/build-push-action@v5
+        with:
+          context: ./www
+          file: ./www/Dockerfile
+          push: true
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+          cache-from: type=gha
+          cache-to: type=gha,mode=max
+          platforms: linux/amd64,linux/arm64
+
+  deploy:
+    needs: build-and-push
+    runs-on: ubuntu-latest
+    if: success()
+    strategy:
+      matrix:
+        environment: [reflector-monadical, reflector-media]
+    environment: ${{ matrix.environment }}
+    steps:
+      - name: Trigger Coolify deployment
+        run: |
+          curl -X POST "${{ secrets.COOLIFY_WEBHOOK_URL }}" \
+            -H "Content-Type: application/json" \
+            -H "Authorization: Bearer ${{ secrets.COOLIFY_WEBHOOK_TOKEN }}" \
+            -f || (echo "Failed to trigger Coolify deployment for ${{ matrix.environment }}" && exit 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
@@ -30,6 +35,9 @@ jobs:
 
   docker-amd64:
     runs-on: linux-amd64
+    concurrency:
+      group: docker-amd64-${{ github.ref }}
+      cancel-in-progress: true
     steps:
       - uses: actions/checkout@v4
       - name: Set up Docker Buildx
@@ -45,6 +53,9 @@ jobs:
 
   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

.gitignore

@@ -1,6 +1,7 @@
 .DS_Store
 server/.env
 .env
+Caddyfile
 server/exportdanswer
 .vercel
 .env*.local
@@ -14,4 +15,9 @@ data/
 www/REFACTOR.md
 www/reload-frontend
 server/test.sqlite
-CLAUDE.local.md
+CLAUDE.local.md
+www/.env.development
+www/.env.production
+.playwright-mcp
+docs/pnpm-lock.yaml
+.secrets

.gitleaksignore

@@ -0,0 +1,5 @@
+b9d891d3424f371642cb032ecfd0e2564470a72c:server/tests/test_transcripts_recording_deletion.py:generic-api-key:15
+docs/docs/installation/auth-setup.md:curl-auth-header:250
+docs/docs/installation/daily-setup.md:curl-auth-header:277
+gpu/self_hosted/DEV_SETUP.md:curl-auth-header:74
+gpu/self_hosted/DEV_SETUP.md:curl-auth-header:83

.pre-commit-config.yaml

@@ -27,3 +27,8 @@ repos:
         files: ^server/
       - id: ruff-format
         files: ^server/
+
+  - repo: https://github.com/gitleaks/gitleaks
+    rev: v8.28.0
+    hooks:
+      - id: gitleaks

.secrets.example

@@ -0,0 +1,24 @@
+# Example secrets file for GitHub Actions workflows
+# Copy this to .secrets and fill in your values
+# These secrets should be configured in GitHub repository settings:
+# Settings > Secrets and variables > Actions
+
+# DockerHub Configuration (required for frontend and backend deployment)
+# Create a Docker Hub access token at https://hub.docker.com/settings/security
+# Username: monadicalsas
+DOCKERHUB_TOKEN=your-dockerhub-access-token
+
+# GitHub Token (required for frontend and backend deployment)
+# Used by docker/metadata-action for extracting image metadata
+# Can use the default GITHUB_TOKEN or create a personal access token
+GITHUB_TOKEN=your-github-token-or-use-default-GITHUB_TOKEN
+
+# Coolify Deployment Webhook (required for frontend deployment)
+# Used to trigger automatic deployment after image push
+# Configure these secrets in GitHub Environments:
+# Each environment should have:
+#   - COOLIFY_WEBHOOK_URL: The webhook URL for that specific deployment
+#   - COOLIFY_WEBHOOK_TOKEN: The webhook token (can be the same for both if using same token)
+
+# Optional: GitHub Actions Cache Token (for local testing with act)
+GHA_CACHE_TOKEN=your-github-token-or-empty

CHANGELOG.md

@@ -1,5 +1,275 @@
 # Changelog
 
+## [0.23.2](https://github.com/Monadical-SAS/reflector/compare/v0.23.1...v0.23.2) (2025-12-11)
+
+
+### Bug Fixes
+
+* build on push tags ([#785](https://github.com/Monadical-SAS/reflector/issues/785)) ([d7f140b](https://github.com/Monadical-SAS/reflector/commit/d7f140b7d1f4660d5da7a0da1357f68869e0b5cd))
+
+## [0.23.1](https://github.com/Monadical-SAS/reflector/compare/v0.23.0...v0.23.1) (2025-12-11)
+
+
+### Bug Fixes
+
+* populate room_name in transcript GET endpoint ([#783](https://github.com/Monadical-SAS/reflector/issues/783)) ([0eba147](https://github.com/Monadical-SAS/reflector/commit/0eba1470181c7b9e0a79964a1ef28c09bcbdd9d7))
+
+## [0.23.0](https://github.com/Monadical-SAS/reflector/compare/v0.22.4...v0.23.0) (2025-12-10)
+
+
+### Features
+
+* dockerhub ci ([#772](https://github.com/Monadical-SAS/reflector/issues/772)) ([00549f1](https://github.com/Monadical-SAS/reflector/commit/00549f153ade922cf4cb6c5358a7d11a39c426d2))
+* llm retries ([#739](https://github.com/Monadical-SAS/reflector/issues/739)) ([61f0e29](https://github.com/Monadical-SAS/reflector/commit/61f0e29d4c51eab54ee67af92141fbb171e8ccaa))
+
+
+### Bug Fixes
+
+* celery inspect bug sidestep in restart script ([#766](https://github.com/Monadical-SAS/reflector/issues/766)) ([ec17ed7](https://github.com/Monadical-SAS/reflector/commit/ec17ed7b587cf6ee143646baaee67a7c017044d4))
+* deploy frontend to coolify ([#779](https://github.com/Monadical-SAS/reflector/issues/779)) ([91650ec](https://github.com/Monadical-SAS/reflector/commit/91650ec65f65713faa7ee0dcfb75af427b7c4ba0))
+* hide rooms settings instead of disabling ([#763](https://github.com/Monadical-SAS/reflector/issues/763)) ([3ad78be](https://github.com/Monadical-SAS/reflector/commit/3ad78be7628c0d029296b301a0e87236c76b7598))
+* return participant emails from transcript endpoint ([#769](https://github.com/Monadical-SAS/reflector/issues/769)) ([d3a5cd1](https://github.com/Monadical-SAS/reflector/commit/d3a5cd12d2d0d9c32af2d5bd9322e030ef69b85d))
+
+## [0.22.4](https://github.com/Monadical-SAS/reflector/compare/v0.22.3...v0.22.4) (2025-12-02)
+
+
+### Bug Fixes
+
+* Multitrack mixdown optimisation 2 ([#764](https://github.com/Monadical-SAS/reflector/issues/764)) ([bd5df1c](https://github.com/Monadical-SAS/reflector/commit/bd5df1ce2ebf35d7f3413b295e56937a9a28ef7b))
+
+## [0.22.3](https://github.com/Monadical-SAS/reflector/compare/v0.22.2...v0.22.3) (2025-12-02)
+
+
+### Bug Fixes
+
+* align daily room settings ([#759](https://github.com/Monadical-SAS/reflector/issues/759)) ([28f87c0](https://github.com/Monadical-SAS/reflector/commit/28f87c09dc459846873d0dde65b03e3d7b2b9399))
+
+## [0.22.2](https://github.com/Monadical-SAS/reflector/compare/v0.22.1...v0.22.2) (2025-12-02)
+
+
+### Bug Fixes
+
+* daily auto refresh fix ([#755](https://github.com/Monadical-SAS/reflector/issues/755)) ([fe47c46](https://github.com/Monadical-SAS/reflector/commit/fe47c46489c5aa0cc538109f7559cc9accb35c01))
+* Skip mixdown for multitrack ([#760](https://github.com/Monadical-SAS/reflector/issues/760)) ([b51b7aa](https://github.com/Monadical-SAS/reflector/commit/b51b7aa9176c1a53ba57ad99f5e976c804a1e80c))
+
+## [0.22.1](https://github.com/Monadical-SAS/reflector/compare/v0.22.0...v0.22.1) (2025-11-27)
+
+
+### Bug Fixes
+
+* participants update from daily ([#749](https://github.com/Monadical-SAS/reflector/issues/749)) ([7f0b728](https://github.com/Monadical-SAS/reflector/commit/7f0b728991c1b9f9aae702c96297eae63b561ef5))
+
+## [0.22.0](https://github.com/Monadical-SAS/reflector/compare/v0.21.0...v0.22.0) (2025-11-26)
+
+
+### Features
+
+* Multitrack segmentation ([#747](https://github.com/Monadical-SAS/reflector/issues/747)) ([d63040e](https://github.com/Monadical-SAS/reflector/commit/d63040e2fdc07e7b272e85a39eb2411cd6a14798))
+
+## [0.21.0](https://github.com/Monadical-SAS/reflector/compare/v0.20.0...v0.21.0) (2025-11-26)
+
+
+### Features
+
+* add transcript format parameter to GET endpoint ([#709](https://github.com/Monadical-SAS/reflector/issues/709)) ([f6ca075](https://github.com/Monadical-SAS/reflector/commit/f6ca07505f34483b02270a2ef3bd809e9d2e1045))
+
+## [0.20.0](https://github.com/Monadical-SAS/reflector/compare/v0.19.0...v0.20.0) (2025-11-25)
+
+
+### Features
+
+* link transcript participants ([#737](https://github.com/Monadical-SAS/reflector/issues/737)) ([9bec398](https://github.com/Monadical-SAS/reflector/commit/9bec39808fc6322612d8b87e922a6f7901fc01c1))
+* transcript restart script ([#742](https://github.com/Monadical-SAS/reflector/issues/742)) ([86d5e26](https://github.com/Monadical-SAS/reflector/commit/86d5e26224bb55a0f1cc785aeda52065bb92ee6f))
+
+## [0.19.0](https://github.com/Monadical-SAS/reflector/compare/v0.18.0...v0.19.0) (2025-11-25)
+
+
+### Features
+
+* dailyco api module ([#725](https://github.com/Monadical-SAS/reflector/issues/725)) ([4287f8b](https://github.com/Monadical-SAS/reflector/commit/4287f8b8aeee60e51db7539f4dcbda5f6e696bd8))
+* dailyco poll ([#730](https://github.com/Monadical-SAS/reflector/issues/730)) ([8e438ca](https://github.com/Monadical-SAS/reflector/commit/8e438ca285152bd48fdc42767e706fb448d3525c))
+* multitrack cli ([#735](https://github.com/Monadical-SAS/reflector/issues/735)) ([11731c9](https://github.com/Monadical-SAS/reflector/commit/11731c9d38439b04e93b1c3afbd7090bad11a11f))
+
+
+### Bug Fixes
+
+* default platform fix ([#736](https://github.com/Monadical-SAS/reflector/issues/736)) ([c442a62](https://github.com/Monadical-SAS/reflector/commit/c442a627873ca667656eeaefb63e54ab10b8d19e))
+* parakeet vad not getting the end timestamp ([#728](https://github.com/Monadical-SAS/reflector/issues/728)) ([18ed713](https://github.com/Monadical-SAS/reflector/commit/18ed7133693653ef4ddac6c659a8c14b320d1657))
+* start raw tracks recording ([#729](https://github.com/Monadical-SAS/reflector/issues/729)) ([3e47c2c](https://github.com/Monadical-SAS/reflector/commit/3e47c2c0573504858e0d2e1798b6ed31f16b4a5d))
+
+## [0.18.0](https://github.com/Monadical-SAS/reflector/compare/v0.17.0...v0.18.0) (2025-11-14)
+
+
+### Features
+
+* daily QOL: participants dictionary ([#721](https://github.com/Monadical-SAS/reflector/issues/721)) ([b20cad7](https://github.com/Monadical-SAS/reflector/commit/b20cad76e69fb6a76405af299a005f1ddcf60eae))
+
+
+### Bug Fixes
+
+* add proccessing page to file upload and reprocessing ([#650](https://github.com/Monadical-SAS/reflector/issues/650)) ([28a7258](https://github.com/Monadical-SAS/reflector/commit/28a7258e45317b78e60e6397be2bc503647eaace))
+* copy transcript ([#674](https://github.com/Monadical-SAS/reflector/issues/674)) ([a9a4f32](https://github.com/Monadical-SAS/reflector/commit/a9a4f32324f66c838e081eee42bb9502f38c1db1))
+
+## [0.17.0](https://github.com/Monadical-SAS/reflector/compare/v0.16.0...v0.17.0) (2025-11-13)
+
+
+### Features
+
+* add API key management UI ([#716](https://github.com/Monadical-SAS/reflector/issues/716)) ([372202b](https://github.com/Monadical-SAS/reflector/commit/372202b0e1a86823900b0aa77be1bfbc2893d8a1))
+* daily.co support as alternative to whereby ([#691](https://github.com/Monadical-SAS/reflector/issues/691)) ([1473fd8](https://github.com/Monadical-SAS/reflector/commit/1473fd82dc472c394cbaa2987212ad662a74bcac))
+
+## [0.16.0](https://github.com/Monadical-SAS/reflector/compare/v0.15.0...v0.16.0) (2025-10-24)
+
+
+### Features
+
+* search date filter ([#710](https://github.com/Monadical-SAS/reflector/issues/710)) ([962c40e](https://github.com/Monadical-SAS/reflector/commit/962c40e2b6428ac42fd10aea926782d7a6f3f902))
+
+## [0.15.0](https://github.com/Monadical-SAS/reflector/compare/v0.14.0...v0.15.0) (2025-10-20)
+
+
+### Features
+
+* api tokens  ([#705](https://github.com/Monadical-SAS/reflector/issues/705)) ([9a258ab](https://github.com/Monadical-SAS/reflector/commit/9a258abc0209b0ac3799532a507ea6a9125d703a))
+
+## [0.14.0](https://github.com/Monadical-SAS/reflector/compare/v0.13.1...v0.14.0) (2025-10-08)
+
+
+### Features
+
+* Add calendar event data to transcript webhook payload ([#689](https://github.com/Monadical-SAS/reflector/issues/689)) ([5f6910e](https://github.com/Monadical-SAS/reflector/commit/5f6910e5131b7f28f86c9ecdcc57fed8412ee3cd))
+* container build for www / github ([#672](https://github.com/Monadical-SAS/reflector/issues/672)) ([969bd84](https://github.com/Monadical-SAS/reflector/commit/969bd84fcc14851d1a101412a0ba115f1b7cde82))
+* docker-compose for production frontend ([#664](https://github.com/Monadical-SAS/reflector/issues/664)) ([5bf64b5](https://github.com/Monadical-SAS/reflector/commit/5bf64b5a41f64535e22849b4bb11734d4dbb4aae))
+
+
+### Bug Fixes
+
+* restore feature boolean logic ([#671](https://github.com/Monadical-SAS/reflector/issues/671)) ([3660884](https://github.com/Monadical-SAS/reflector/commit/36608849ec64e953e3be456172502762e3c33df9))
+* security review ([#656](https://github.com/Monadical-SAS/reflector/issues/656)) ([5d98754](https://github.com/Monadical-SAS/reflector/commit/5d98754305c6c540dd194dda268544f6d88bfaf8))
+* update transcript list on reprocess ([#676](https://github.com/Monadical-SAS/reflector/issues/676)) ([9a71af1](https://github.com/Monadical-SAS/reflector/commit/9a71af145ee9b833078c78d0c684590ab12e9f0e))
+* upgrade nemo toolkit ([#678](https://github.com/Monadical-SAS/reflector/issues/678)) ([eef6dc3](https://github.com/Monadical-SAS/reflector/commit/eef6dc39037329b65804297786d852dddb0557f9))
+
+## [0.13.1](https://github.com/Monadical-SAS/reflector/compare/v0.13.0...v0.13.1) (2025-09-22)
+
+
+### Bug Fixes
+
+* TypeError on not all arguments converted during string formatting in logger ([#667](https://github.com/Monadical-SAS/reflector/issues/667)) ([565a629](https://github.com/Monadical-SAS/reflector/commit/565a62900f5a02fc946b68f9269a42190ed70ab6))
+
+## [0.13.0](https://github.com/Monadical-SAS/reflector/compare/v0.12.1...v0.13.0) (2025-09-19)
+
+
+### Features
+
+* room form edit with enter ([#662](https://github.com/Monadical-SAS/reflector/issues/662)) ([47716f6](https://github.com/Monadical-SAS/reflector/commit/47716f6e5ddee952609d2fa0ffabdfa865286796))
+
+
+### Bug Fixes
+
+* invalid cleanup call ([#660](https://github.com/Monadical-SAS/reflector/issues/660)) ([0abcebf](https://github.com/Monadical-SAS/reflector/commit/0abcebfc9491f87f605f21faa3e53996fafedd9a))
+
+## [0.12.1](https://github.com/Monadical-SAS/reflector/compare/v0.12.0...v0.12.1) (2025-09-17)
+
+
+### Bug Fixes
+
+* production blocked because having existing meeting with room_id null ([#657](https://github.com/Monadical-SAS/reflector/issues/657)) ([870e860](https://github.com/Monadical-SAS/reflector/commit/870e8605171a27155a9cbee215eeccb9a8d6c0a2))
+
+## [0.12.0](https://github.com/Monadical-SAS/reflector/compare/v0.11.0...v0.12.0) (2025-09-17)
+
+
+### Features
+
+* calendar integration ([#608](https://github.com/Monadical-SAS/reflector/issues/608)) ([6f680b5](https://github.com/Monadical-SAS/reflector/commit/6f680b57954c688882c4ed49f40f161c52a00a24))
+* self-hosted gpu api ([#636](https://github.com/Monadical-SAS/reflector/issues/636)) ([ab859d6](https://github.com/Monadical-SAS/reflector/commit/ab859d65a6bded904133a163a081a651b3938d42))
+
+
+### Bug Fixes
+
+* ignore player hotkeys for text inputs ([#646](https://github.com/Monadical-SAS/reflector/issues/646)) ([fa049e8](https://github.com/Monadical-SAS/reflector/commit/fa049e8d068190ce7ea015fd9fcccb8543f54a3f))
+
+## [0.11.0](https://github.com/Monadical-SAS/reflector/compare/v0.10.0...v0.11.0) (2025-09-16)
+
+
+### Features
+
+* remove profanity filter that was there for conference ([#652](https://github.com/Monadical-SAS/reflector/issues/652)) ([b42f7cf](https://github.com/Monadical-SAS/reflector/commit/b42f7cfc606783afcee792590efcc78b507468ab))
+
+
+### Bug Fixes
+
+* zulip and consent handler on the file pipeline ([#645](https://github.com/Monadical-SAS/reflector/issues/645)) ([5f143fe](https://github.com/Monadical-SAS/reflector/commit/5f143fe3640875dcb56c26694254a93189281d17))
+* zulip stream and topic selection in share dialog ([#644](https://github.com/Monadical-SAS/reflector/issues/644)) ([c546e69](https://github.com/Monadical-SAS/reflector/commit/c546e69739e68bb74fbc877eb62609928e5b8de6))
+
+## [0.10.0](https://github.com/Monadical-SAS/reflector/compare/v0.9.0...v0.10.0) (2025-09-11)
+
+
+### Features
+
+* replace nextjs-config with environment variables ([#632](https://github.com/Monadical-SAS/reflector/issues/632)) ([369ecdf](https://github.com/Monadical-SAS/reflector/commit/369ecdff13f3862d926a9c0b87df52c9d94c4dde))
+
+
+### Bug Fixes
+
+* anonymous users transcript permissions ([#621](https://github.com/Monadical-SAS/reflector/issues/621)) ([f81fe99](https://github.com/Monadical-SAS/reflector/commit/f81fe9948a9237b3e0001b2d8ca84f54d76878f9))
+* auth post ([#624](https://github.com/Monadical-SAS/reflector/issues/624)) ([cde99ca](https://github.com/Monadical-SAS/reflector/commit/cde99ca2716f84ba26798f289047732f0448742e))
+* auth post ([#626](https://github.com/Monadical-SAS/reflector/issues/626)) ([3b85ff3](https://github.com/Monadical-SAS/reflector/commit/3b85ff3bdf4fb053b103070646811bc990c0e70a))
+* auth post ([#627](https://github.com/Monadical-SAS/reflector/issues/627)) ([962038e](https://github.com/Monadical-SAS/reflector/commit/962038ee3f2a555dc3c03856be0e4409456e0996))
+* missing follow_redirects=True on modal endpoint ([#630](https://github.com/Monadical-SAS/reflector/issues/630)) ([fc363bd](https://github.com/Monadical-SAS/reflector/commit/fc363bd49b17b075e64f9186e5e0185abc325ea7))
+* sync backend and frontend token refresh logic ([#614](https://github.com/Monadical-SAS/reflector/issues/614)) ([5a5b323](https://github.com/Monadical-SAS/reflector/commit/5a5b3233820df9536da75e87ce6184a983d4713a))
+
+## [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)
 
 

CLAUDE.md

@@ -66,7 +66,6 @@ pnpm install
 
 # Copy configuration templates
 cp .env_template .env
-cp config-template.ts config.ts
 ```
 
 **Development:**
@@ -152,7 +151,7 @@ All endpoints prefixed `/v1/`:
 
 **Frontend** (`www/.env`):
 - `NEXTAUTH_URL`, `NEXTAUTH_SECRET` - Authentication configuration
-- `NEXT_PUBLIC_REFLECTOR_API_URL` - Backend API endpoint
+- `REFLECTOR_API_URL` - Backend API endpoint
 - `REFLECTOR_DOMAIN_CONFIG` - Feature flags and domain settings
 
 ## Testing Strategy

Caddyfile.example

@@ -0,0 +1,22 @@
+# Reflector Caddyfile
+# Replace example.com with your actual domains
+# CORS is handled by the backend - Caddy just proxies
+#
+# For environment variable substitution, set:
+#   FRONTEND_DOMAIN=app.example.com
+#   API_DOMAIN=api.example.com
+#   AUTHENTIK_DOMAIN=authentik.example.com  (optional, for authentication)
+# Or edit this file directly with your domains.
+
+{$FRONTEND_DOMAIN:app.example.com} {
+	reverse_proxy web:3000
+}
+
+{$API_DOMAIN:api.example.com} {
+	reverse_proxy server:1250
+}
+
+# Uncomment if using Authentik for authentication (see auth-setup.md)
+# {$AUTHENTIK_DOMAIN:authentik.example.com} {
+# 	reverse_proxy authentik-server-1:9000
+# }

README.md

@@ -1,43 +1,60 @@
 <div align="center">
+<img width="100" alt="image" src="https://github.com/user-attachments/assets/66fb367b-2c89-4516-9912-f47ac59c6a7f"/>
 
 # Reflector
 
-Reflector Audio Management and Analysis is a cutting-edge web application under development by Monadical. It utilizes AI to record meetings, providing a permanent record with transcripts, translations, and automated summaries.
+Reflector is an AI-powered audio transcription and meeting analysis platform that provides real-time transcription, speaker diarization, translation and summarization for audio content and live meetings. It works 100% with local models (whisper/parakeet, pyannote, seamless-m4t, and your local llm like phi-4).
 
-[![Tests](https://github.com/monadical-sas/reflector/actions/workflows/pytests.yml/badge.svg?branch=main&event=push)](https://github.com/monadical-sas/reflector/actions/workflows/pytests.yml)
+[![Tests](https://github.com/monadical-sas/reflector/actions/workflows/test_server.yml/badge.svg?branch=main&event=push)](https://github.com/monadical-sas/reflector/actions/workflows/test_server.yml)
 [![License: MIT](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/licenses/MIT)
 </div>
-
-## Screenshots
+</div>
 <table>
   <tr>
     <td>
-      <a href="https://github.com/user-attachments/assets/3a976930-56c1-47ef-8c76-55d3864309e3">
-        <img width="700" alt="image" src="https://github.com/user-attachments/assets/3a976930-56c1-47ef-8c76-55d3864309e3" />
+      <a href="https://github.com/user-attachments/assets/21f5597c-2930-4899-a154-f7bd61a59e97">
+        <img width="700" alt="image" src="https://github.com/user-attachments/assets/21f5597c-2930-4899-a154-f7bd61a59e97" />
       </a>
     </td>
     <td>
-      <a href="https://github.com/user-attachments/assets/bfe3bde3-08af-4426-a9a1-11ad5cd63b33">
-        <img width="700" alt="image" src="https://github.com/user-attachments/assets/bfe3bde3-08af-4426-a9a1-11ad5cd63b33" />
+      <a href="https://github.com/user-attachments/assets/f6b9399a-5e51-4bae-b807-59128d0a940c">
+        <img width="700" alt="image" src="https://github.com/user-attachments/assets/f6b9399a-5e51-4bae-b807-59128d0a940c" />
       </a>
     </td>
     <td>
-      <a href="https://github.com/user-attachments/assets/7b60c9d0-efe4-474f-a27b-ea13bd0fabdc">
-        <img width="700" alt="image" src="https://github.com/user-attachments/assets/7b60c9d0-efe4-474f-a27b-ea13bd0fabdc" />
+      <a href="https://github.com/user-attachments/assets/a42ce460-c1fd-4489-a995-270516193897">
+        <img width="700" alt="image" src="https://github.com/user-attachments/assets/a42ce460-c1fd-4489-a995-270516193897" />
+      </a>
+    </td>
+    <td>
+      <a href="https://github.com/user-attachments/assets/21929f6d-c309-42fe-9c11-f1299e50fbd4">
+        <img width="700" alt="image" src="https://github.com/user-attachments/assets/21929f6d-c309-42fe-9c11-f1299e50fbd4" />
       </a>
     </td>
   </tr>
 </table>
 
+## What is Reflector?
+
+Reflector is a web application that utilizes 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`.
@@ -80,11 +99,10 @@ Start with `cd www`.
 
 ```bash
 pnpm install
-cp .env_template .env
-cp config-template.ts config.ts
+cp .env.example .env
 ```
 
-Then, fill in the environment variables in `.env` and the configuration in `config.ts` as needed. If you are unsure on how to proceed, ask in Zulip.
+Then, fill in the environment variables in `.env` as needed. If you are unsure on how to proceed, ask in Zulip.
 
 **Run in development mode**
 
@@ -149,3 +167,47 @@ You can manually process an audio file by calling the process tool:
 ```bash
 uv run python -m reflector.tools.process path/to/audio.wav
 ```
+
+## Reprocessing any transcription
+
+```bash
+uv run -m reflector.tools.process_transcript 81ec38d1-9dd7-43d2-b3f8-51f4d34a07cd --sync
+```
+
+## Build-time env variables
+
+Next.js projects are more used to NEXT_PUBLIC_ prefixed buildtime vars. We don't have those for the reason we need to serve a ccustomizable prebuild docker container.
+
+Instead, all the variables are runtime. Variables needed to the frontend are served to the frontend app at initial render.
+
+It also means there's no static prebuild and no static files to serve for js/html.
+
+## Feature Flags
+
+Reflector uses environment variable-based feature flags to control application functionality. These flags allow you to enable or disable features without code changes.
+
+### Available Feature Flags
+
+| Feature Flag | Environment Variable |
+|-------------|---------------------|
+| `requireLogin` | `FEATURE_REQUIRE_LOGIN` |
+| `privacy` | `FEATURE_PRIVACY` |
+| `browse` | `FEATURE_BROWSE` |
+| `sendToZulip` | `FEATURE_SEND_TO_ZULIP` |
+| `rooms` | `FEATURE_ROOMS` |
+
+### Setting Feature Flags
+
+Feature flags are controlled via environment variables using the pattern `FEATURE_{FEATURE_NAME}` where `{FEATURE_NAME}` is the SCREAMING_SNAKE_CASE version of the feature name.
+
+**Examples:**
+```bash
+# Enable user authentication requirement
+FEATURE_REQUIRE_LOGIN=true
+
+# Disable browse functionality
+FEATURE_BROWSE=false
+
+# Enable Zulip integration
+FEATURE_SEND_TO_ZULIP=true
+```

docker-compose.prod.yml

@@ -0,0 +1,113 @@
+# Production Docker Compose configuration
+# Usage: docker compose -f docker-compose.prod.yml up -d
+#
+# Prerequisites:
+# 1. Copy .env.example to .env and configure for both server/ and www/
+# 2. Copy Caddyfile.example to Caddyfile and edit with your domains
+# 3. Deploy Modal GPU functions (see gpu/modal_deployments/deploy-all.sh)
+
+services:
+  web:
+    image: monadicalsas/reflector-frontend:latest
+    restart: unless-stopped
+    env_file:
+      - ./www/.env
+    pull_policy: always
+    environment:
+      - KV_URL=redis://redis:6379
+    depends_on:
+      - redis
+
+  server:
+    image: monadicalsas/reflector-backend:latest
+    restart: unless-stopped
+    env_file:
+      - ./server/.env
+    environment:
+      ENTRYPOINT: server
+    depends_on:
+      - postgres
+      - redis
+    volumes:
+      - server_data:/app/data
+      - ./server/reflector/auth/jwt/keys:/app/reflector/auth/jwt/keys:ro
+
+  worker:
+    image: monadicalsas/reflector-backend:latest
+    restart: unless-stopped
+    env_file:
+      - ./server/.env
+    environment:
+      ENTRYPOINT: worker
+    depends_on:
+      - postgres
+      - redis
+    volumes:
+      - server_data:/app/data
+      - ./server/reflector/auth/jwt/keys:/app/reflector/auth/jwt/keys:ro
+
+  beat:
+    image: monadicalsas/reflector-backend:latest
+    restart: unless-stopped
+    env_file:
+      - ./server/.env
+    environment:
+      ENTRYPOINT: beat
+    depends_on:
+      - postgres
+      - redis
+
+  redis:
+    image: redis:7.2-alpine
+    restart: unless-stopped
+    healthcheck:
+      test: ["CMD", "redis-cli", "ping"]
+      interval: 30s
+      timeout: 3s
+      retries: 3
+    volumes:
+      - redis_data:/data
+
+  postgres:
+    image: postgres:17-alpine
+    restart: unless-stopped
+    environment:
+      POSTGRES_USER: reflector
+      POSTGRES_PASSWORD: reflector
+      POSTGRES_DB: reflector
+    volumes:
+      - postgres_data:/var/lib/postgresql/data
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -U reflector"]
+      interval: 30s
+      timeout: 3s
+      retries: 3
+
+  caddy:
+    image: caddy:2-alpine
+    restart: unless-stopped
+    ports:
+      - "80:80"
+      - "443:443"
+    volumes:
+      - ./Caddyfile:/etc/caddy/Caddyfile:ro
+      - caddy_data:/data
+      - caddy_config:/config
+    depends_on:
+      - web
+      - server
+
+  docs:
+    build: ./docs
+    restart: unless-stopped
+
+volumes:
+  redis_data:
+  postgres_data:
+  server_data:
+  caddy_data:
+  caddy_config:
+
+networks:
+  default:
+    attachable: true

docker-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:
@@ -36,7 +39,7 @@ services:
     ports:
       - 6379:6379
   web:
-    image: node:18
+    image: node:22-alpine
     ports:
       - "3000:3000"
     command: sh -c "corepack enable && pnpm install && pnpm dev"
@@ -47,6 +50,8 @@ services:
       - /app/node_modules
     env_file:
       - ./www/.env.local
+    environment:
+      - NODE_ENV=development
 
   postgres:
     image: postgres:17

docs/.gitignore

@@ -0,0 +1,20 @@
+# Dependencies
+/node_modules
+
+# Production
+/build
+
+# Generated files
+.docusaurus
+.cache-loader
+
+# Misc
+.DS_Store
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+npm-debug.log*
+yarn-debug.log*
+yarn-error.log*

docs/Dockerfile

@@ -0,0 +1,39 @@
+FROM node:18-alpine AS builder
+WORKDIR /app
+
+# Install curl for fetching OpenAPI spec
+RUN apk add --no-cache curl
+
+# Copy package files
+COPY package*.json ./
+
+# Install dependencies
+RUN npm ci
+
+# Copy source
+COPY . .
+
+# Fetch OpenAPI spec from production API
+ARG OPENAPI_URL=https://api-reflector.monadical.com/openapi.json
+RUN mkdir -p ./static && curl -sf "${OPENAPI_URL}" -o ./static/openapi.json || echo '{}' > ./static/openapi.json
+
+# Fix docusaurus config: change onBrokenLinks to 'warn' for Docker build
+RUN sed -i "s/onBrokenLinks: 'throw'/onBrokenLinks: 'warn'/g" docusaurus.config.ts
+
+# Build static site (skip prebuild hook by calling docusaurus directly)
+RUN npx docusaurus build
+
+# Production image
+FROM nginx:alpine
+
+# Copy built static files
+COPY --from=builder /app/build /usr/share/nginx/html
+
+# Healthcheck for container orchestration
+HEALTHCHECK --interval=30s --timeout=3s --start-period=5s --retries=3 \
+  CMD wget --no-verbose --tries=1 --spider http://localhost/ || exit 1
+
+# Expose port
+EXPOSE 80
+
+CMD ["nginx", "-g", "daemon off;"]

docs/README.md

@@ -0,0 +1,41 @@
+# Website
+
+This website is built using [Docusaurus](https://docusaurus.io/), a modern static website generator.
+
+### Installation
+
+```
+$ yarn
+```
+
+### Local Development
+
+```
+$ yarn start
+```
+
+This command starts a local development server and opens up a browser window. Most changes are reflected live without having to restart the server.
+
+### Build
+
+```
+$ yarn build
+```
+
+This command generates static content into the `build` directory and can be served using any static contents hosting service.
+
+### Deployment
+
+Using SSH:
+
+```
+$ USE_SSH=true yarn deploy
+```
+
+Not using SSH:
+
+```
+$ GIT_USER=<Your GitHub username> yarn deploy
+```
+
+If you are using GitHub pages for hosting, this command is a convenient way to build the website and push to the `gh-pages` branch.

docs/TODO.md

@@ -0,0 +1,170 @@
+# Documentation TODO List
+
+This file tracks information needed from the user to complete the documentation.
+
+## Required Information
+
+### Processing Times & Costs
+
+Please provide actual numbers for:
+
+- [ ] **Modal.com GPU Costs**
+  - Cost per hour of audio for Whisper transcription
+  - Cost per hour of audio for Pyannote diarization
+  - Cost per hour of audio for Seamless-M4T translation
+  - Typical GPU instance used (T4, A10, etc.)
+
+- [ ] **RunPod LLM Costs**
+  - Cost per 1000 tokens for summarization
+  - Model used (phi-4-unsloth-bnb-4bit)
+  - RTX 4000 Ada instance cost per hour
+
+- [ ] **AWS S3 Storage**
+  - Cost per GB per month
+  - Data transfer costs
+  - Typical storage requirements per hour of audio
+
+- [ ] **Whereby API**
+  - Monthly cost structure
+  - API call limits
+  - Room participant limits
+
+- [ ] **Actual Processing Times**
+  - Whisper tiny model: X minutes per hour of audio
+  - Whisper base model: X minutes per hour of audio
+  - Whisper large-v3 model: X minutes per hour of audio
+  - Diarization: X minutes per hour of audio
+  - Translation: X minutes per hour of audio
+
+### Screenshots Needed
+
+Location: `/docs/static/screenshots/`
+
+Please provide screenshots of:
+
+- [ ] **Dashboard Overview** - Main dashboard showing recent transcripts
+- [ ] **Live Transcription** - Active transcription in progress
+- [ ] **Meeting Room Interface** - Whereby room with participants
+- [ ] **Transcript with Diarization** - Showing speaker labels
+- [ ] **Settings Page** - Configuration options
+- [ ] **API Documentation** - OpenAPI/Swagger interface
+- [ ] **File Upload Interface** - Drag and drop upload
+- [ ] **Translation View** - Showing original and translated text
+- [ ] **Summary View** - Generated summary and topics
+
+### Setup Screenshots
+
+Please provide step-by-step screenshots for:
+
+- [ ] **Modal.com Setup**
+  - Creating account
+  - Getting API keys
+  - Deploying functions
+
+- [ ] **Whereby Configuration**
+  - Creating developer account
+  - Getting API credentials
+  - Setting up rooms
+
+- [ ] **AWS S3 Setup**
+  - Creating bucket
+  - Setting permissions
+  - Getting access keys
+
+- [ ] **Authentik Integration**
+  - Adding application
+  - Configuring OAuth
+  - Setting up users
+
+### Technical Details
+
+Please provide specific values for:
+
+- [ ] **WebRTC Configuration**
+  - Exact UDP port range used (e.g., 10000-20000)
+  - STUN server configuration (if any)
+  - ICE candidate gathering timeout
+  - https://docs.daily.co/guides/privacy-and-security/corporate-firewalls-nats-allowed-ip-list
+
+- [ ] **Worker Configuration**
+  - Default Celery worker count
+  - Worker memory limits
+  - Queue priorities
+
+- [ ] **Redis Requirements**
+  - Typical memory usage
+  - Persistence configuration
+  - Eviction policies
+
+- [ ] **PostgreSQL**
+  - Expected database growth (MB per hour of audio)
+  - Recommended connection pool size
+  - Backup strategy
+
+- [ ] **Performance Metrics**
+  - Average transcription accuracy (WER)
+  - Average diarization accuracy (DER)
+  - Translation quality scores
+  - Typical latency for live streaming
+
+### Configuration Examples
+
+Please provide real-world examples for:
+
+- [ ] **Production .env file** (sanitized)
+- [ ] **Caddy configuration** for production
+- [ ] **Docker compose** for production deployment
+- [ ] **Nginx configuration** (if alternative to Caddy)
+
+### API Examples
+
+Please provide:
+
+- [ ] **Sample API requests** for common operations
+- [ ] **WebSocket message examples**
+- [ ] **Webhook payload examples**
+- [ ] **Error response examples**
+
+## How to Add Information
+
+1. **For text information**: Edit the relevant markdown files in `/docs/docs/`
+2. **For screenshots**: Add to `/docs/static/screenshots/` and reference in docs
+3. **For code examples**: Add to documentation with proper syntax highlighting
+
+## Priority Items
+
+High priority (blocks documentation completeness):
+1. Modal.com costs and setup steps
+2. Basic screenshots (dashboard, transcription)
+3. Docker deployment configuration
+
+Medium priority (enhances documentation):
+1. Performance metrics
+2. Advanced configuration examples
+3. Troubleshooting scenarios
+
+Low priority (nice to have):
+1. Video tutorials
+2. Architecture diagrams
+3. Benchmark comparisons
+
+## Documentation Structure
+
+Once information is provided, update these files:
+- `/docs/docs/installation/modal-setup.md` - Add Modal.com setup screenshots
+- `/docs/docs/installation/whereby-setup.md` - Add Whereby configuration steps
+- `/docs/docs/reference/configuration.md` - Add environment variable details
+- `/docs/docs/pipelines/file-pipeline.md` - Add actual processing times
+- `/docs/docs/pipelines/live-pipeline.md` - Add latency measurements
+
+## Notes
+
+- Replace placeholder values with actual data
+- Ensure all sensitive information is sanitized
+- Test all configuration examples before documenting
+- Verify all costs are up-to-date
+
+---
+
+Last updated: 2025-08-20
+Contact: [Your Email]

docs/create-docs.sh

@@ -0,0 +1,777 @@
+#!/bin/bash
+
+# Create directory structure
+mkdir -p docs/concepts
+mkdir -p docs/installation
+mkdir -p docs/pipelines
+mkdir -p docs/reference/architecture
+mkdir -p docs/reference/processors
+mkdir -p docs/reference/api
+
+# Create all documentation files with content
+echo "Creating documentation files..."
+
+# Concepts - Modes
+cat > docs/concepts/modes.md << 'EOF'
+---
+sidebar_position: 2
+title: Operating Modes
+---
+
+# Operating Modes
+
+Reflector operates in two distinct modes to accommodate different use cases and security requirements.
+
+## Public Mode
+
+Public mode provides immediate access to core transcription features without requiring authentication.
+
+### Features Available
+- **File Upload**: Process audio files up to 2GB
+- **Live Transcription**: Stream audio from microphone
+- **Basic Processing**: Transcription and diarization
+- **Temporary Storage**: Results available for 24 hours
+
+### Limitations
+- No persistent storage
+- No meeting rooms
+- Limited to single-user sessions
+- No team collaboration features
+
+### Use Cases
+- Quick transcription needs
+- Testing and evaluation
+- Individual users
+- Public demonstrations
+
+## Private Mode
+
+Private mode unlocks the full potential of Reflector with authentication and persistent storage.
+
+### Additional Features
+- **Virtual Meeting Rooms**: Whereby integration
+- **Team Collaboration**: Share transcripts with team
+- **Persistent Storage**: Long-term transcript archive
+- **Advanced Analytics**: Meeting insights and trends
+- **Custom Integration**: Webhooks and API access
+- **User Management**: Role-based access control
+
+### Authentication Options
+
+#### Authentik Integration
+Enterprise-grade SSO with support for:
+- SAML 2.0
+- OAuth 2.0 / OIDC
+- LDAP / Active Directory
+- Multi-factor authentication
+
+#### JWT Authentication
+Stateless token-based auth for:
+- API access
+- Service-to-service communication
+- Mobile applications
+
+### Room Management
+
+Virtual rooms provide dedicated spaces for meetings:
+- **Persistent URLs**: Same link for recurring meetings
+- **Access Control**: Invite-only or open rooms
+- **Recording Consent**: Automatic consent management
+- **Custom Settings**: Per-room configuration
+
+## Mode Selection
+
+The mode is determined by your deployment configuration:
+
+```yaml
+# Public Mode (no authentication)
+REFLECTOR_AUTH_BACKEND=none
+
+# Private Mode (with authentication)
+REFLECTOR_AUTH_BACKEND=jwt
+# or
+REFLECTOR_AUTH_BACKEND=authentik
+```
+
+## Feature Comparison
+
+| Feature | Public Mode | Private Mode |
+|---------|------------|--------------|
+| File Upload | ✅ | ✅ |
+| Live Transcription | ✅ | ✅ |
+| Speaker Diarization | ✅ | ✅ |
+| Translation | ✅ | ✅ |
+| Summarization | ✅ | ✅ |
+| Meeting Rooms | ❌ | ✅ |
+| Persistent Storage | ❌ | ✅ |
+| Team Collaboration | ❌ | ✅ |
+| API Access | Limited | Full |
+| User Management | ❌ | ✅ |
+| Custom Branding | ❌ | ✅ |
+| Analytics | ❌ | ✅ |
+| Webhooks | ❌ | ✅ |
+
+## Security Considerations
+
+### Public Mode Security
+- Rate limiting to prevent abuse
+- File size restrictions
+- Automatic cleanup of old data
+- No PII storage
+
+### Private Mode Security
+- Encrypted data storage
+- Audit logging
+- Session management
+- Access control lists
+- Data retention policies
+
+## Choosing the Right Mode
+
+### Choose Public Mode if:
+- You need quick, one-time transcriptions
+- You're evaluating Reflector
+- You don't need persistent storage
+- You're processing non-sensitive content
+
+### Choose Private Mode if:
+- You need team collaboration
+- You require persistent storage
+- You're processing sensitive content
+- You need meeting room functionality
+- You want advanced analytics
+EOF
+
+# Concepts - Independence
+cat > docs/concepts/independence.md << 'EOF'
+---
+sidebar_position: 3
+title: Data Independence
+---
+
+# Data Independence & Privacy
+
+Reflector is designed with privacy and data independence as core principles, giving you complete control over your data and processing.
+
+## Privacy by Design
+
+### No Third-Party Data Sharing
+
+Your audio and transcripts are never shared with third parties:
+- **Local Processing**: All ML models can run on your infrastructure
+- **No Training on User Data**: Your content is never used to improve models
+- **Isolated Processing**: Each transcript is processed in isolation
+- **No Analytics Tracking**: No usage analytics sent to external services
+
+### Data Ownership
+
+You maintain complete ownership of all data:
+- **Export Anytime**: Download all your transcripts and audio
+- **Delete on Demand**: Permanent deletion with no recovery
+- **API Access**: Full programmatic access to your data
+- **No Vendor Lock-in**: Standard formats for easy migration
+
+## Processing Transparency
+
+### What Happens to Your Audio
+
+1. **Upload/Stream**: Audio received by your server
+2. **Temporary Storage**: Stored only for processing duration
+3. **Processing**: ML models process audio locally or on Modal
+4. **Results Storage**: Transcripts stored in your database
+5. **Cleanup**: Original audio deleted (unless configured otherwise)
+
+### Local vs Cloud Processing
+
+#### Local Processing
+When configured for local processing:
+- All models run on your hardware
+- No data leaves your infrastructure
+- Complete air-gap capability
+- Higher hardware requirements
+
+#### Modal.com Processing
+When using Modal for GPU acceleration:
+- Audio chunks sent to Modal for processing
+- Processed immediately and deleted
+- No long-term storage on Modal
+- Modal's security: SOC 2 Type II compliant
+
+### Data Retention
+
+Default retention policies:
+- **Public Mode**: 24 hours then automatic deletion
+- **Private Mode**: Configurable (default: indefinite)
+- **Audio Files**: Deleted after processing (configurable)
+- **Transcripts**: Retained based on policy
+
+## Compliance Features
+
+### GDPR Compliance
+
+- **Right to Access**: Export all user data
+- **Right to Deletion**: Permanent data removal
+- **Data Portability**: Standard export formats
+- **Privacy by Default**: Minimal data collection
+
+### HIPAA Considerations
+
+For healthcare deployments:
+- **Self-hosted Option**: Complete infrastructure control
+- **Encryption**: At rest and in transit
+- **Audit Logging**: Complete access trail
+- **Access Controls**: Role-based permissions
+
+### Industry Standards
+
+- **TLS 1.3**: Modern encryption for data in transit
+- **AES-256**: Encryption for data at rest
+- **JWT Tokens**: Secure, stateless authentication
+- **OWASP Guidelines**: Security best practices
+
+## Self-Hosted Deployment
+
+### Complete Independence
+
+Self-hosting provides maximum control:
+- **Your Infrastructure**: Run on your servers
+- **Your Network**: No external connections required
+- **Your Policies**: Implement custom retention
+- **Your Compliance**: Meet specific requirements
+
+### Air-Gap Capability
+
+Reflector can run completely offline:
+1. Download all models during setup
+2. Configure for local processing only
+3. Disable all external integrations
+4. Run in isolated network environment
+
+## Data Flow Control
+
+### Configurable Processing
+
+Control where each step happens:
+
+```yaml
+# All local processing
+TRANSCRIPT_BACKEND=local
+DIARIZATION_BACKEND=local
+TRANSLATION_BACKEND=local
+
+# Hybrid approach
+TRANSCRIPT_BACKEND=modal  # Fast GPU processing
+DIARIZATION_BACKEND=local # Sensitive speaker data
+TRANSLATION_BACKEND=modal  # Non-sensitive translation
+```
+
+### Storage Options
+
+Choose where data is stored:
+- **Local Filesystem**: Complete control
+- **PostgreSQL**: Self-hosted database
+- **S3-Compatible**: MinIO or AWS with encryption
+- **Hybrid**: Different storage for different data types
+
+## Security Architecture
+
+### Defense in Depth
+
+Multiple layers of security:
+1. **Network Security**: Firewalls and VPNs
+2. **Application Security**: Input validation and sanitization
+3. **Data Security**: Encryption and access controls
+4. **Operational Security**: Logging and monitoring
+
+### Zero Trust Principles
+
+- **Verify Everything**: All requests authenticated
+- **Least Privilege**: Minimal permissions granted
+- **Assume Breach**: Design for compromise containment
+- **Encrypt Everything**: No plaintext transmission
+
+## Audit and Compliance
+
+### Audit Logging
+
+Comprehensive logging of:
+- **Access Events**: Who accessed what and when
+- **Processing Events**: What was processed and how
+- **Configuration Changes**: System modifications
+- **Security Events**: Failed authentication attempts
+
+### Compliance Reporting
+
+Generate reports for:
+- **Data Processing**: What data was processed
+- **Data Access**: Who accessed the data
+- **Data Retention**: What was retained or deleted
+- **Security Events**: Security-related incidents
+
+## Best Practices
+
+### For Maximum Privacy
+
+1. **Self-host** all components
+2. **Use local processing** for all models
+3. **Implement short retention** periods
+4. **Encrypt all storage** at rest
+5. **Use VPN** for all connections
+6. **Regular audits** of access logs
+
+### For Balanced Approach
+
+1. **Self-host core services** (database, API)
+2. **Use Modal for processing** (faster, cost-effective)
+3. **Implement encryption** everywhere
+4. **Regular backups** with encryption
+5. **Monitor access** patterns
+EOF
+
+# Concepts - Pipeline
+cat > docs/concepts/pipeline.md << 'EOF'
+---
+sidebar_position: 4
+title: Processing Pipeline
+---
+
+# Processing Pipeline
+
+Reflector uses a sophisticated pipeline architecture to process audio efficiently and accurately.
+
+## Pipeline Overview
+
+The processing pipeline consists of modular components that can be combined and configured based on your needs:
+
+```mermaid
+graph LR
+    A[Audio Input] --> B[Pre-processing]
+    B --> C[Chunking]
+    C --> D[Transcription]
+    D --> E[Diarization]
+    E --> F[Alignment]
+    F --> G[Post-processing]
+    G --> H[Output]
+```
+
+## Pipeline Components
+
+### Audio Input
+
+Accepts various input sources:
+- **File Upload**: MP3, WAV, M4A, WebM, MP4
+- **WebRTC Stream**: Live browser audio
+- **Recording Integration**: Whereby recordings
+- **API Upload**: Direct API submission
+
+### Pre-processing
+
+Prepares audio for optimal processing:
+- **Format Conversion**: Convert to 16kHz mono WAV
+- **Normalization**: Adjust volume to -23 LUFS
+- **Noise Reduction**: Optional background noise removal
+- **Validation**: Check duration and quality
+
+### Chunking
+
+Splits audio for parallel processing:
+- **Fixed Size**: 30-second chunks by default
+- **Overlap**: 1-second overlap for continuity
+- **Smart Boundaries**: Attempt to split at silence
+- **Metadata**: Track chunk positions
+
+### Transcription
+
+Converts speech to text:
+- **Model Selection**: Whisper or Parakeet
+- **Language Detection**: Automatic or specified
+- **Timestamp Generation**: Word-level timing
+- **Confidence Scores**: Quality indicators
+
+### Diarization
+
+Identifies different speakers:
+- **Voice Activity Detection**: Find speech segments
+- **Speaker Embedding**: Extract voice characteristics
+- **Clustering**: Group similar voices
+- **Label Assignment**: Assign speaker IDs
+
+### Alignment
+
+Merges all processing results:
+- **Chunk Assembly**: Combine transcription chunks
+- **Speaker Mapping**: Align speakers with text
+- **Overlap Resolution**: Handle chunk boundaries
+- **Timeline Creation**: Build unified timeline
+
+### Post-processing
+
+Enhances the final output:
+- **Formatting**: Apply punctuation and capitalization
+- **Translation**: Convert to target languages
+- **Summarization**: Generate concise summaries
+- **Topic Extraction**: Identify key themes
+- **Action Items**: Extract tasks and decisions
+
+## Processing Modes
+
+### Batch Processing
+
+For uploaded files:
+- Optimized for throughput
+- Parallel chunk processing
+- Higher accuracy models
+- Complete file analysis
+
+### Stream Processing
+
+For live audio:
+- Optimized for latency
+- Sequential processing
+- Real-time feedback
+- Progressive results
+
+### Hybrid Processing
+
+For meetings:
+- Stream during meeting
+- Batch after completion
+- Best of both modes
+- Maximum accuracy
+
+## Pipeline Configuration
+
+### Model Selection
+
+Choose models based on requirements:
+
+```python
+# High accuracy (slower)
+config = {
+    "transcription_model": "whisper-large-v3",
+    "diarization_model": "pyannote-3.1",
+    "translation_model": "seamless-m4t-large"
+}
+
+# Balanced (default)
+config = {
+    "transcription_model": "whisper-base",
+    "diarization_model": "pyannote-3.1",
+    "translation_model": "seamless-m4t-medium"
+}
+
+# Fast processing
+config = {
+    "transcription_model": "whisper-tiny",
+    "diarization_model": "pyannote-3.1-fast",
+    "translation_model": "seamless-m4t-small"
+}
+```
+
+### Processing Options
+
+Customize pipeline behavior:
+
+```yaml
+# Parallel processing
+max_parallel_chunks: 10
+chunk_size_seconds: 30
+chunk_overlap_seconds: 1
+
+# Quality settings
+enable_noise_reduction: true
+enable_normalization: true
+min_speech_confidence: 0.5
+
+# Post-processing
+enable_translation: true
+target_languages: ["es", "fr", "de"]
+enable_summarization: true
+summary_length: "medium"
+```
+
+## Performance Characteristics
+
+### Processing Times
+
+For 1 hour of audio:
+
+| Pipeline Config | Processing Time | Accuracy |
+|----------------|-----------------|----------|
+| Fast | 2-3 minutes | 85-90% |
+| Balanced | 5-8 minutes | 92-95% |
+| High Accuracy | 15-20 minutes | 95-98% |
+
+### Resource Usage
+
+| Component | CPU Usage | Memory | GPU |
+|-----------|-----------|---------|-----|
+| Transcription | Medium | 2-4 GB | Required |
+| Diarization | High | 4-8 GB | Required |
+| Translation | Low | 2-3 GB | Optional |
+| Post-processing | Low | 1-2 GB | Not needed |
+
+## Pipeline Orchestration
+
+### Celery Task Chain
+
+The pipeline is orchestrated using Celery:
+
+```python
+chain = (
+    chunk_audio.s(audio_id) |
+    group(transcribe_chunk.s(chunk) for chunk in chunks) |
+    merge_transcriptions.s() |
+    diarize_audio.s() |
+    align_speakers.s() |
+    post_process.s()
+)
+```
+
+### Error Handling
+
+Robust error recovery:
+- **Automatic Retry**: Failed tasks retry up to 3 times
+- **Partial Recovery**: Continue with successful chunks
+- **Fallback Models**: Use alternative models on failure
+- **Error Reporting**: Detailed error messages
+
+### Progress Tracking
+
+Real-time progress updates:
+- **Chunk Progress**: Track individual chunk processing
+- **Overall Progress**: Percentage completion
+- **ETA Calculation**: Estimated completion time
+- **WebSocket Updates**: Live progress to clients
+
+## Optimization Strategies
+
+### GPU Utilization
+
+Maximize GPU efficiency:
+- **Batch Processing**: Process multiple chunks together
+- **Model Caching**: Keep models loaded in memory
+- **Dynamic Batching**: Adjust batch size based on GPU memory
+- **Multi-GPU Support**: Distribute across available GPUs
+
+### Memory Management
+
+Efficient memory usage:
+- **Streaming Processing**: Process large files in chunks
+- **Garbage Collection**: Clean up after each chunk
+- **Memory Limits**: Prevent out-of-memory errors
+- **Disk Caching**: Use disk for large intermediate results
+
+### Network Optimization
+
+Minimize network overhead:
+- **Compression**: Compress audio before transfer
+- **CDN Integration**: Use CDN for static assets
+- **Connection Pooling**: Reuse network connections
+- **Parallel Uploads**: Multiple concurrent uploads
+
+## Quality Assurance
+
+### Accuracy Metrics
+
+Monitor processing quality:
+- **Word Error Rate (WER)**: Transcription accuracy
+- **Diarization Error Rate (DER)**: Speaker identification accuracy
+- **Translation BLEU Score**: Translation quality
+- **Summary Coherence**: Summary quality metrics
+
+### Validation Steps
+
+Ensure output quality:
+- **Confidence Thresholds**: Filter low-confidence segments
+- **Consistency Checks**: Verify timeline consistency
+- **Language Validation**: Ensure correct language detection
+- **Format Validation**: Check output format compliance
+
+## Advanced Features
+
+### Custom Models
+
+Use your own models:
+- **Fine-tuned Whisper**: Domain-specific models
+- **Custom Diarization**: Trained on your speakers
+- **Specialized Post-processing**: Industry-specific formatting
+
+### Pipeline Extensions
+
+Add custom processing steps:
+- **Sentiment Analysis**: Analyze emotional tone
+- **Entity Extraction**: Identify people, places, organizations
+- **Custom Metrics**: Calculate domain-specific metrics
+- **Integration Hooks**: Call external services
+EOF
+
+# Create installation documentation
+cat > docs/installation/overview.md << 'EOF'
+---
+sidebar_position: 1
+title: Installation Overview
+---
+
+# Installation Overview
+
+Reflector is designed for self-hosted deployment, giving you complete control over your infrastructure and data.
+
+## Deployment Options
+
+### Docker Deployment (Recommended)
+
+The easiest way to deploy Reflector:
+- Pre-configured containers
+- Automated dependency management
+- Consistent environment
+- Easy updates
+
+### Manual Installation
+
+For custom deployments:
+- Greater control over configuration
+- Integration with existing infrastructure
+- Custom optimization options
+- Development environments
+
+## Requirements
+
+### System Requirements
+
+**Minimum Requirements:**
+- CPU: 4 cores
+- RAM: 8 GB
+- Storage: 50 GB
+- OS: Ubuntu 20.04+ or similar Linux
+
+**Recommended Requirements:**
+- CPU: 8+ cores
+- RAM: 16 GB
+- Storage: 100 GB SSD
+- GPU: NVIDIA GPU with 8GB+ VRAM (for local processing)
+
+### Network Requirements
+
+- Public IP address (for WebRTC)
+- Ports: 80, 443, 8000, 3000
+- Domain name (for SSL)
+- SSL certificate (Let's Encrypt supported)
+
+## Required Services
+
+### Core Services
+
+These services are required for basic operation:
+
+1. **PostgreSQL** - Primary database
+2. **Redis** - Message broker and cache
+3. **Docker** - Container runtime
+
+### GPU Processing
+
+Choose one:
+- **Modal.com** - Serverless GPU (recommended)
+- **Local GPU** - Self-hosted GPU processing
+
+### Optional Services
+
+Enhance functionality with:
+- **AWS S3** - Long-term storage
+- **Whereby** - Video conferencing rooms
+- **Authentik** - Enterprise authentication
+- **Zulip** - Chat integration
+
+## Quick Start
+
+### Using Docker Compose
+
+1. Clone the repository:
+```bash
+git clone https://github.com/monadical-sas/reflector.git
+cd reflector
+```
+
+2. Navigate to docker directory:
+```bash
+cd docker
+```
+
+3. Copy and configure environment:
+```bash
+cp .env.example .env
+# Edit .env with your settings
+```
+
+4. Start services:
+```bash
+docker compose up -d
+```
+
+5. Access Reflector:
+- Frontend: https://your-domain.com
+- API: https://your-domain.com/api
+
+## Configuration Overview
+
+### Essential Configuration
+
+```env
+# Database
+DATABASE_URL=postgresql://user:pass@localhost/reflector
+
+# Redis
+REDIS_URL=redis://localhost:6379
+
+# Modal.com (for GPU processing)
+TRANSCRIPT_MODAL_API_KEY=your-key
+DIARIZATION_MODAL_API_KEY=your-key
+
+# Domain
+DOMAIN=your-domain.com
+```
+
+### Security Configuration
+
+```env
+# Authentication
+REFLECTOR_AUTH_BACKEND=jwt
+NEXTAUTH_SECRET=generate-strong-secret
+
+# SSL (handled by Caddy)
+# Automatic with Let's Encrypt
+```
+
+## Service Architecture
+
+```mermaid
+graph TD
+    A[Caddy Reverse Proxy] --> B[Frontend - Next.js]
+    A --> C[Backend - FastAPI]
+    C --> D[PostgreSQL]
+    C --> E[Redis]
+    C --> F[Celery Workers]
+    F --> G[Modal.com GPU]
+```
+
+## Next Steps
+
+1. **Review Requirements**: [System Requirements](./requirements)
+2. **Docker Setup**: [Docker Deployment Guide](./docker-setup)
+3. **Configure Services**:
+   - [Modal.com Setup](./modal-setup)
+   - [Whereby Setup](./whereby-setup)
+   - [AWS S3 Setup](./aws-setup)
+4. **Optional Services**:
+   - [Authentik Setup](./authentik-setup)
+   - [Zulip Setup](./zulip-setup)
+
+## Getting Help
+
+- [Troubleshooting Guide](../reference/troubleshooting)
+- [GitHub Issues](https://github.com/monadical-sas/reflector/issues)
+- [Community Discord](#)
+EOF
+
+chmod +x create-docs.sh
+echo "Documentation creation script ready. Run ./create-docs.sh to generate all docs."

docs/docs/concepts/modes.md

@@ -0,0 +1,125 @@
+---
+sidebar_position: 2
+title: Operating Modes
+---
+
+# Operating Modes
+
+Reflector operates in two distinct modes to accommodate different use cases and security requirements.
+
+## Public Mode
+
+Public mode provides immediate access to core transcription features without requiring authentication.
+
+### Features Available
+- **File Upload**: Process audio files up to 2GB
+- **Live Transcription**: Stream audio from microphone
+- **Basic Processing**: Transcription and diarization
+- **Temporary Storage**: Results available for 24 hours
+
+### Limitations
+- No persistent storage
+- No meeting rooms
+- Limited to single-user sessions
+- No team collaboration features
+
+### Use Cases
+- Quick transcription needs
+- Testing and evaluation
+- Individual users
+- Public demonstrations
+
+## Private Mode
+
+Private mode unlocks the full potential of Reflector with authentication and persistent storage.
+
+### Additional Features
+- **Virtual Meeting Rooms**: Whereby and Daily.co integration
+- **Team Collaboration**: Share transcripts with team
+- **Persistent Storage**: Long-term transcript archive
+- **Advanced Analytics**: Meeting insights and trends
+- **Custom Integration**: Webhooks and API access
+- **User Management**: Role-based access control
+
+### Authentication Options
+
+#### Authentik Integration
+Enterprise-grade SSO with support for:
+- SAML 2.0
+- OAuth 2.0 / OIDC
+- LDAP / Active Directory
+- Multi-factor authentication
+
+#### JWT Authentication
+Stateless token-based auth for:
+- API access
+- Service-to-service communication
+- Mobile applications
+
+### Room Management
+
+Virtual rooms provide dedicated spaces for meetings:
+- **Persistent URLs**: Same link for recurring meetings
+- **Access Control**: Invite-only or open rooms
+- **Recording Consent**: Automatic consent management
+- **Custom Settings**: Per-room configuration
+
+## Mode Selection
+
+The mode is determined by your deployment configuration:
+
+```yaml
+# Public Mode (no authentication)
+REFLECTOR_AUTH_BACKEND=none
+
+# Private Mode (with authentication)
+REFLECTOR_AUTH_BACKEND=jwt
+# or
+REFLECTOR_AUTH_BACKEND=authentik
+```
+
+## Feature Comparison
+
+| Feature | Public Mode | Private Mode |
+|---------|------------|--------------|
+| File Upload | ✅ | ✅ |
+| Live Transcription | ✅ | ✅ |
+| Speaker Diarization | ✅ | ✅ |
+| Translation | ✅ | ✅ |
+| Summarization | ✅ | ✅ |
+| Meeting Rooms | ❌ | ✅ |
+| Persistent Storage | ❌ | ✅ |
+| Team Collaboration | ❌ | ✅ |
+| API Access | Limited | Full |
+| User Management | ❌ | ✅ |
+| Custom Branding | ❌ | ✅ |
+| Analytics | ❌ | ✅ |
+| Webhooks | ❌ | ✅ |
+
+## Security Considerations
+
+### Public Mode Security
+- File size restrictions
+- Automatic cleanup of old data
+
+### Private Mode Security
+- Encrypted data storage
+- Audit logging
+- Session management
+- Access control lists
+- Data retention policies
+
+## Choosing the Right Mode
+
+### Choose Public Mode if:
+- You need quick, one-time transcriptions
+- You're evaluating Reflector
+- You don't need persistent storage
+- You're processing non-sensitive content
+
+### Choose Private Mode if:
+- You need team collaboration
+- You require persistent storage
+- You're processing sensitive content
+- You need meeting room functionality
+- You want advanced analytics

docs/docs/concepts/overview.md

@@ -0,0 +1,194 @@
+---
+sidebar_position: 1
+title: Architecture Overview
+---
+
+# Architecture Overview
+
+Reflector is built as a modern, scalable, microservices-based application designed to handle audio processing workloads efficiently while maintaining data privacy and control.
+
+## System Components
+
+### Frontend Application
+
+The user interface is built with **Next.js 14** using the App Router pattern, providing:
+
+- Server-side rendering for optimal performance
+- Real-time WebSocket connections for live transcription
+- WebRTC support for audio streaming and live meetings
+- Responsive design with Chakra UI components
+
+### Backend API Server
+
+The core API is powered by **FastAPI**, a modern Python framework that provides:
+
+- High-performance async request handling
+- Automatic OpenAPI documentation generation
+- Type safety with Pydantic models
+- WebSocket support for real-time updates
+
+### Processing Pipeline
+
+Audio processing is handled through a modular pipeline architecture:
+
+```
+Audio Input → Chunking → Transcription → Diarization → Post-Processing → Storage
+```
+
+Each step can run independently and in parallel, allowing for:
+- Scalable processing of large files
+- Real-time streaming capabilities
+- Fault tolerance and retry mechanisms
+
+### Worker Architecture
+
+Background tasks are managed by **Celery** workers with **Redis** as the message broker:
+
+- Distributed task processing
+- Priority queues for time-sensitive operations
+- Automatic retry on failure
+- Progress tracking and notifications
+
+### GPU Acceleration
+
+ML models run on GPU-accelerated infrastructure:
+
+- **Modal.com** for serverless GPU processing
+- Support for local GPU deployment (coming soon)
+- Automatic scaling based on demand
+- Cost-effective pay-per-use model
+
+## Data Flow
+
+### File Processing Flow
+
+1. **Upload**: User uploads audio file through web interface
+2. **Storage**: File stored temporarily or in S3
+3. **Queue**: Processing job added to Celery queue
+4. **Chunking**: Audio split into 30-second segments
+5. **Parallel Processing**: Chunks processed simultaneously
+6. **Assembly**: Results merged and aligned
+7. **Post-Processing**: Summary, topics, translation
+8. **Delivery**: Results stored and user notified
+
+### Live Streaming Flow
+
+1. **WebRTC Connection**: Browser establishes peer connection
+2. **Audio Capture**: Microphone audio streamed to server
+3. **Buffering**: Audio buffered for processing
+4. **VAD**: Voice activity detection segments speech
+5. **Real-time Processing**: Segments transcribed immediately
+6. **WebSocket Updates**: Results streamed back to client
+7. **Continuous Assembly**: Full transcript built progressively
+
+## Deployment Architecture
+
+### Container-Based Deployment
+
+All components are containerized for consistent deployment:
+
+```yaml
+services:
+  frontend:    # Next.js application
+  backend:     # FastAPI server
+  worker:      # Celery workers
+  redis:       # Message broker
+  postgres:    # Database
+  caddy:       # Reverse proxy
+```
+
+### Networking
+
+- **Host Network Mode**: Required for WebRTC/ICE compatibility
+- **Caddy Reverse Proxy**: Handles SSL termination and routing
+- **WebSocket Upgrade**: Supports real-time connections
+
+## Scalability Considerations
+
+### Horizontal Scaling
+
+- **Stateless Backend**: Multiple API server instances
+- **Worker Pools**: Add workers based on queue depth
+- **Database Pooling**: Connection management for concurrent access
+
+### Vertical Scaling
+
+- **GPU Workers**: Scale up for faster model inference
+- **Memory Optimization**: Efficient audio buffering
+- **CPU Optimization**: Multi-threaded processing where applicable
+
+## Security Architecture
+
+### Authentication & Authorization
+
+- **JWT Tokens**: Stateless authentication
+- **Authentik Integration**: Enterprise SSO support
+- **Role-Based Access**: Granular permissions
+
+### Data Protection
+
+- **Encryption at Rest**: Database and S3 encryption
+- **Encryption in Transit**: TLS for all connections
+- **Temporary Storage**: Automatic cleanup of processed files
+
+### Privacy by Design
+
+- **Local Processing**: Option to process entirely on-premises
+- **No Training on User Data**: Models are pre-trained
+- **Data Isolation**: Multi-tenant data separation
+
+## Integration Points
+
+### External Services
+
+- **Modal.com**: GPU processing
+- **AWS S3**: Long-term storage
+- **Whereby**: Video conferencing rooms
+- **Zulip**: Chat integration (optional)
+
+### APIs and Webhooks
+
+- **RESTful API**: Standard CRUD operations
+- **WebSocket API**: Real-time updates
+- **Webhook Notifications**: Processing completion events
+- **OpenAPI Specification**: Machine-readable API definition
+
+## Performance Optimization
+
+### Caching Strategy
+
+- **Redis Cache**: Frequently accessed data
+- **CDN**: Static asset delivery
+- **Browser Cache**: Client-side optimization
+
+### Database Optimization
+
+- **Indexed Queries**: Fast search and retrieval
+- **Connection Pooling**: Efficient resource usage
+- **Query Optimization**: N+1 query prevention
+
+### Processing Optimization
+
+- **Batch Processing**: Efficient GPU utilization
+- **Parallel Execution**: Multi-core CPU usage
+- **Stream Processing**: Reduced memory footprint
+
+## Monitoring and Observability
+
+### Metrics Collection
+
+- **Application Metrics**: Request rates, response times
+- **System Metrics**: CPU, memory, disk usage
+- **Business Metrics**: Transcription accuracy, processing times
+
+### Logging
+
+- **Structured Logging**: JSON format for analysis
+- **Log Aggregation**: Centralized log management
+- **Error Tracking**: Sentry integration
+
+### Health Checks
+
+- **Liveness Probes**: Component availability
+- **Readiness Probes**: Service readiness
+- **Dependency Checks**: External service status

docs/docs/concepts/pipeline.md

@@ -0,0 +1,272 @@
+---
+sidebar_position: 4
+title: Processing Pipeline
+---
+
+# Processing Pipeline
+
+Reflector uses a modular pipeline architecture to process audio efficiently and accurately.
+
+## Pipeline Overview
+
+The processing pipeline consists of modular components that can be combined and configured based on your needs:
+
+```mermaid
+graph LR
+    A[Audio Input] --> B[Pre-processing]
+    B --> C[Chunking]
+    C --> D[Transcription]
+    D --> E[Diarization]
+    E --> F[Alignment]
+    F --> G[Post-processing]
+    G --> H[Output]
+```
+
+## Pipeline Components
+
+### Audio Input
+
+Accepts various input sources:
+- **File Upload**: MP3, WAV, M4A, WebM, MP4
+- **WebRTC Stream**: Live browser audio
+- **Recording Integration**: Whereby recordings
+- **API Upload**: Direct API submission
+
+### Pre-processing
+
+Prepares audio for optimal processing:
+- **Format Conversion**: Convert to 16kHz mono WAV
+- **Noise Reduction**: Optional background noise removal
+- **Validation**: Check duration and quality
+
+### Chunking
+
+Splits audio for parallel processing:
+- **Fixed Size**: 30-second chunks by default
+- **Overlap**: 1-second overlap for continuity
+- **Silence Detection**: Attempt to split at silence
+- **Metadata**: Track chunk positions
+
+### Transcription
+
+Converts speech to text:
+- **Model Selection**: Whisper or Parakeet
+- **Language Detection**: Automatic or specified
+- **Timestamp Generation**: Word-level timing
+- **Confidence Scores**: Quality indicators
+
+### Diarization
+
+Identifies different speakers:
+- **Voice Activity Detection**: Find speech segments
+- **Speaker Embedding**: Extract voice characteristics
+- **Clustering**: Group similar voices
+- **Label Assignment**: Assign speaker IDs
+
+### Alignment
+
+Merges all processing results:
+- **Chunk Assembly**: Combine transcription chunks
+- **Speaker Mapping**: Align speakers with text
+- **Overlap Resolution**: Handle chunk boundaries
+- **Timeline Creation**: Build unified timeline
+
+### Post-processing
+
+Enhances the final output:
+- **Formatting**: Apply punctuation and capitalization
+- **Translation**: Convert to target languages
+- **Summarization**: Generate concise summaries
+- **Topic Extraction**: Identify key themes
+- **Action Items**: Extract tasks and decisions
+
+## Processing Modes
+
+### Batch Processing
+
+For uploaded files:
+- Optimized for throughput
+- Parallel chunk processing
+- Higher accuracy models
+- Complete file analysis
+
+### Stream Processing
+
+For live audio:
+- Optimized for latency
+- Sequential processing
+- Real-time feedback
+- Progressive results
+
+### Hybrid Processing
+
+For meetings:
+- Stream during meeting
+- Batch after completion
+- Best of both modes
+- Maximum accuracy
+
+## Pipeline Configuration
+
+### Model Selection
+
+Choose models based on requirements:
+
+```python
+# High accuracy (slower)
+config = {
+    "transcription_model": "whisper-large-v3",
+    "diarization_model": "pyannote-3.1",
+    "translation_model": "seamless-m4t-large"
+}
+
+# Balanced (default)
+config = {
+    "transcription_model": "whisper-base",
+    "diarization_model": "pyannote-3.1",
+    "translation_model": "seamless-m4t-medium"
+}
+
+# Fast processing
+config = {
+    "transcription_model": "whisper-tiny",
+    "diarization_model": "pyannote-3.1-fast",
+    "translation_model": "seamless-m4t-small"
+}
+```
+
+### Processing Options
+
+Customize pipeline behavior:
+
+```yaml
+# Parallel processing
+max_parallel_chunks: 10
+chunk_size_seconds: 30
+chunk_overlap_seconds: 1
+
+# Quality settings
+enable_noise_reduction: true
+min_speech_confidence: 0.5
+
+# Post-processing
+enable_translation: true
+target_languages: ["es", "fr", "de"]
+enable_summarization: true
+summary_length: "medium"
+```
+
+## Performance Characteristics
+
+### Processing Times
+
+For 1 hour of audio:
+
+| Pipeline Config | Processing Time | Accuracy |
+|----------------|-----------------|----------|
+| Fast | 2-3 minutes | 85-90% |
+| Balanced | 5-8 minutes | 92-95% |
+| High Accuracy | 15-20 minutes | 95-98% |
+
+### Resource Usage
+
+| Component | CPU Usage | Memory | GPU |
+|-----------|-----------|---------|-----|
+| Transcription | Medium | 2-4 GB | Required |
+| Diarization | High | 4-8 GB | Required |
+| Translation | Low | 2-3 GB | Optional |
+| Post-processing | Low | 1-2 GB | Not needed |
+
+## Pipeline Orchestration
+
+### Celery Task Chain
+
+The pipeline is orchestrated using Celery:
+
+```python
+chain = (
+    chunk_audio.s(audio_id) |
+    group(transcribe_chunk.s(chunk) for chunk in chunks) |
+    merge_transcriptions.s() |
+    diarize_audio.s() |
+    align_speakers.s() |
+    post_process.s()
+)
+```
+
+### Error Handling
+
+Error recovery:
+- **Automatic Retry**: Failed tasks retry up to 3 times
+- **Partial Recovery**: Continue with successful chunks
+- **Fallback Models**: Use alternative models on failure
+- **Error Reporting**: Detailed error messages
+
+### Progress Tracking
+
+Real-time progress updates:
+- **Chunk Progress**: Track individual chunk processing
+- **Overall Progress**: Percentage completion
+- **ETA Calculation**: Estimated completion time
+- **WebSocket Updates**: Live progress to clients
+
+## Optimization Strategies
+
+### GPU Utilization
+
+Maximize GPU efficiency:
+- **Batch Processing**: Process multiple chunks together
+- **Model Caching**: Keep models loaded in memory
+- **Dynamic Batching**: Adjust batch size based on GPU memory
+- **Multi-GPU Support**: Distribute across available GPUs
+
+### Memory Management
+
+Efficient memory usage:
+- **Streaming Processing**: Process large files in chunks
+- **Garbage Collection**: Clean up after each chunk
+- **Memory Limits**: Prevent out-of-memory errors
+- **Disk Caching**: Use disk for large intermediate results
+
+### Network Optimization
+
+Minimize network overhead:
+- **Compression**: Compress audio before transfer
+- **CDN Integration**: Use CDN for static assets
+- **Connection Pooling**: Reuse network connections
+- **Parallel Uploads**: Multiple concurrent uploads
+
+## Quality Assurance
+
+### Accuracy Metrics
+
+Monitor processing quality:
+- **Word Error Rate (WER)**: Transcription accuracy
+- **Diarization Error Rate (DER)**: Speaker identification accuracy
+- **Translation BLEU Score**: Translation quality
+- **Summary Coherence**: Summary quality metrics
+
+### Validation Steps
+
+Ensure output quality:
+- **Confidence Thresholds**: Filter low-confidence segments
+- **Consistency Checks**: Verify timeline consistency
+- **Language Validation**: Ensure correct language detection
+- **Format Validation**: Check output format compliance
+
+## Advanced Features
+
+### Custom Models
+
+Use your own models:
+- **Fine-tuned Whisper**: Domain-specific models
+- **Custom Diarization**: Trained on your speakers
+- **Specialized Post-processing**: Industry-specific formatting
+
+### Pipeline Extensions
+
+Add custom processing steps:
+- **Sentiment Analysis**: Analyze emotional tone
+- **Entity Extraction**: Identify people, places, organizations
+- **Custom Metrics**: Calculate domain-specific metrics
+- **Integration Hooks**: Call external services

docs/docs/installation/auth-setup.md

@@ -0,0 +1,285 @@
+---
+sidebar_position: 5
+title: Authentication Setup
+---
+
+# Authentication Setup
+
+This page covers authentication setup in detail. For the complete deployment guide, see [Deployment Guide](./overview).
+
+Reflector uses [Authentik](https://goauthentik.io/) for OAuth/OIDC authentication. This guide walks you through setting up Authentik and connecting it to Reflector.
+
+The guide simplistically sets Authentic on the same server as Reflector. You can use your own Authentic instance instead.
+
+## Overview
+
+Reflector's authentication flow:
+1. User clicks "Sign In" on frontend
+2. Frontend redirects to Authentik login page
+3. User authenticates with Authentik
+4. Authentik redirects back with OAuth tokens
+5. Frontend stores tokens, backends verify JWT signature
+
+## Option 1: Self-Hosted Authentik (Same Server)
+
+This setup runs Authentik on the same server as Reflector, with Caddy proxying to both.
+
+### Deploy Authentik
+
+```bash
+# Create directory for Authentik
+mkdir -p ~/authentik && cd ~/authentik
+
+# Download docker-compose file
+curl -O https://goauthentik.io/docker-compose.yml
+
+# Generate secrets and bootstrap credentials
+cat > .env << 'EOF'
+PG_PASS=$(openssl rand -base64 36 | tr -d '\n')
+AUTHENTIK_SECRET_KEY=$(openssl rand -base64 60 | tr -d '\n')
+# Privacy-focused choice for self-hosted deployments
+AUTHENTIK_ERROR_REPORTING__ENABLED=false
+AUTHENTIK_BOOTSTRAP_PASSWORD=YourSecurePassword123
+AUTHENTIK_BOOTSTRAP_EMAIL=admin@example.com
+EOF
+
+# Start Authentik
+sudo docker compose up -d
+```
+
+Authentik takes ~2 minutes to run migrations and apply blueprints on first start.
+
+### Connect Authentik to Reflector's Network
+
+If Authentik runs in a separate Docker Compose project, connect it to Reflector's network so Caddy can proxy to it:
+
+```bash
+# Wait for Authentik to be healthy
+# Connect Authentik server to Reflector's network
+sudo docker network connect reflector_default authentik-server-1
+```
+
+**Important:** This step must be repeated if you restart Authentik with `docker compose down`. Add it to your deployment scripts or use `docker compose up -d` (which preserves containers) instead of down/up.
+
+### Add Authentik to Caddy
+
+Uncomment the Authentik section in your `Caddyfile` and set your domain:
+
+```bash
+nano Caddyfile
+```
+
+Uncomment and edit:
+```
+{$AUTHENTIK_DOMAIN:authentik.example.com} {
+    reverse_proxy authentik-server-1:9000
+}
+```
+
+Reload Caddy:
+```bash
+docker compose -f docker-compose.prod.yml exec caddy caddy reload --config /etc/caddy/Caddyfile
+```
+
+### Create OAuth2 Provider in Authentik
+
+**Option A: Automated Setup (Recommended)**
+
+**Location: Reflector server**
+
+Run the setup script from the Reflector repository:
+
+```bash
+ssh user@your-server-ip
+cd ~/reflector
+./scripts/setup-authentik-oauth.sh https://authentik.example.com YourSecurePassword123 https://app.example.com
+```
+
+**Important:** The script must be run from the `~/reflector` directory on your server, as it creates files using relative paths.
+
+The script will output the configuration values to add to your `.env` files. Skip to "Update docker-compose.prod.yml".
+
+**Option B: Manual Setup**
+
+1. **Login to Authentik Admin** at `https://authentik.example.com/`
+   - Username: `akadmin`
+   - Password: The `AUTHENTIK_BOOTSTRAP_PASSWORD` you set in .env
+
+2. **Create OAuth2 Provider:**
+   - Go to **Applications > Providers > Create**
+   - Select **OAuth2/OpenID Provider**
+   - Configure:
+     - **Name**: `Reflector`
+     - **Authorization flow**: `default-provider-authorization-implicit-consent`
+     - **Client type**: `Confidential`
+     - **Client ID**: Note this value (auto-generated)
+     - **Client Secret**: Note this value (auto-generated)
+     - **Redirect URIs**: Add entry with:
+       ```
+       https://app.example.com/api/auth/callback/authentik
+       ```
+   - Scroll down to **Advanced protocol settings**
+   - In **Scopes**, add these three mappings:
+     - `authentik default OAuth Mapping: OpenID 'email'`
+     - `authentik default OAuth Mapping: OpenID 'openid'`
+     - `authentik default OAuth Mapping: OpenID 'profile'`
+   - Click **Finish**
+
+3. **Create Application:**
+   - Go to **Applications > Applications > Create**
+   - Configure:
+     - **Name**: `Reflector`
+     - **Slug**: `reflector` (auto-filled)
+     - **Provider**: Select the `Reflector` provider you just created
+   - Click **Create**
+
+### Get Public Key for JWT Verification
+
+**Location: Reflector server**
+
+Extract the public key from Authentik's JWKS endpoint:
+
+```bash
+mkdir -p ~/reflector/server/reflector/auth/jwt/keys
+curl -s https://authentik.example.com/application/o/reflector/jwks/ | \
+  jq -r '.keys[0].x5c[0]' | base64 -d | openssl x509 -pubkey -noout \
+  > ~/reflector/server/reflector/auth/jwt/keys/authentik_public.pem
+```
+
+### Update docker-compose.prod.yml
+
+**Location: Reflector server**
+
+**Note:** This step is already done in the current `docker-compose.prod.yml`. Verify the volume mounts exist:
+
+```yaml
+server:
+  image: monadicalsas/reflector-backend:latest
+  # ... other config ...
+  volumes:
+    - server_data:/app/data
+    - ./server/reflector/auth/jwt/keys:/app/reflector/auth/jwt/keys:ro
+
+worker:
+  image: monadicalsas/reflector-backend:latest
+  # ... other config ...
+  volumes:
+    - server_data:/app/data
+    - ./server/reflector/auth/jwt/keys:/app/reflector/auth/jwt/keys:ro
+```
+
+### Configure Reflector Backend
+
+**Location: Reflector server**
+
+Update `server/.env`:
+```env
+# Authentication
+AUTH_BACKEND=jwt
+AUTH_JWT_PUBLIC_KEY=authentik_public.pem
+AUTH_JWT_AUDIENCE=<your-client-id>
+CORS_ALLOW_CREDENTIALS=true
+```
+
+Replace `<your-client-id>` with the Client ID from previous steps.
+
+### Configure Reflector Frontend
+
+**Location: Reflector server**
+
+Update `www/.env`:
+```env
+# Authentication
+FEATURE_REQUIRE_LOGIN=true
+
+# Authentik OAuth
+AUTHENTIK_ISSUER=https://authentik.example.com/application/o/reflector
+AUTHENTIK_REFRESH_TOKEN_URL=https://authentik.example.com/application/o/token/
+AUTHENTIK_CLIENT_ID=<your-client-id>
+AUTHENTIK_CLIENT_SECRET=<your-client-secret>
+
+# NextAuth
+NEXTAUTH_SECRET=<generate-with-openssl-rand-hex-32>
+```
+
+### Restart Services
+
+**Location: Reflector server**
+
+```bash
+cd ~/reflector
+sudo docker compose -f docker-compose.prod.yml up -d --force-recreate server worker web
+```
+
+### Verify Authentication
+
+1. Visit `https://app.example.com`
+2. Click "Log in" or navigate to `/api/auth/signin`
+3. Click "Sign in with Authentik"
+4. Login with your Authentik credentials
+5. You should be redirected back and see "Log out" in the header
+
+## Option 2: Disable Authentication
+
+For testing or internal deployments where authentication isn't needed:
+
+**Backend `server/.env`:**
+```env
+AUTH_BACKEND=none
+```
+
+**Frontend `www/.env`:**
+```env
+FEATURE_REQUIRE_LOGIN=false
+```
+
+**Note:** The pre-built Docker images have `FEATURE_REQUIRE_LOGIN=true` baked in. To disable auth, you'll need to rebuild the frontend image with the env var set at build time, or set up Authentik.
+
+## Troubleshooting
+
+### "Invalid redirect URI" error
+- Verify the redirect URI in Authentik matches exactly:
+  ```
+  https://app.example.com/api/auth/callback/authentik
+  ```
+- Check for trailing slashes - they must match exactly
+
+### "Invalid audience" JWT error
+- Ensure `AUTH_JWT_AUDIENCE` in `server/.env` matches the Client ID from Authentik
+- The audience value is the OAuth Client ID, not the issuer URL
+
+### "JWT verification failed" error
+- Verify the public key file is mounted in the container
+- Check `AUTH_JWT_PUBLIC_KEY` points to the correct filename
+- Ensure the key was extracted from the correct provider's JWKS endpoint
+
+### Caddy returns 503 for Authentik
+- Verify Authentik container is connected to Reflector's network:
+  ```bash
+  sudo docker network connect reflector_default authentik-server-1
+  ```
+- Check Authentik is healthy: `cd ~/authentik && sudo docker compose ps`
+
+### Users can't access protected pages
+- Verify `FEATURE_REQUIRE_LOGIN=true` in frontend
+- Check `AUTH_BACKEND=jwt` in backend
+- Verify CORS settings allow credentials
+
+### Token refresh errors
+- Ensure Redis is running (frontend uses Redis for token caching)
+- Verify `KV_URL` is set correctly in frontend env
+- Check `AUTHENTIK_REFRESH_TOKEN_URL` is correct
+
+## API Key Authentication
+
+For programmatic access (scripts, integrations), users can generate API keys:
+
+1. Login to Reflector
+2. Go to Settings > API Keys
+3. Click "Generate New Key"
+4. Use the key in requests:
+   ```bash
+   curl -H "X-API-Key: your-api-key" https://api.example.com/v1/transcripts
+   ```
+
+API keys are stored hashed and can be revoked at any time.

docs/docs/installation/daily-setup.md

@@ -0,0 +1,165 @@
+---
+sidebar_position: 6
+title: Daily.co Setup
+---
+
+# Daily.co Setup
+
+This page covers Daily.co video platform setup for live meeting rooms. For the complete deployment guide, see [Deployment Guide](./overview).
+
+Daily.co enables live video meetings with automatic recording and transcription.
+
+## What You'll Set Up
+
+```
+User joins meeting → Daily.co video room → Recording to S3 → [Webhook] → Reflector transcribes
+```
+
+## Prerequisites
+
+- [ ] **Daily.co account** - Free tier at https://dashboard.daily.co
+- [ ] **AWS account** - For S3 storage
+- [ ] **Reflector deployed** - Complete steps from [Deployment Guide](./overview)
+
+---
+
+## Create Daily.co Account
+
+1. Visit https://dashboard.daily.co and sign up
+2. Verify your email
+3. Note your subdomain (e.g., `yourname.daily.co` → subdomain is `yourname`)
+
+---
+
+## Get Daily.co API Key
+
+1. In Daily.co dashboard, go to **Developers**
+2. Click **API Keys**
+3. Click **Create API Key**
+4. Copy the key (starts with a long string)
+
+Save this for later.
+
+---
+
+## Create AWS S3 Bucket
+
+Daily.co needs somewhere to store recordings before Reflector processes them.
+
+```bash
+# Choose a unique bucket name
+BUCKET_NAME="reflector-dailyco-yourname" # -yourname is not a requirement, you can name the bucket as you wish
+AWS_REGION="us-east-1"
+
+# Create bucket
+aws s3 mb s3://$BUCKET_NAME --region $AWS_REGION
+
+# Enable versioning (required)
+aws s3api put-bucket-versioning \
+  --bucket $BUCKET_NAME \
+  --versioning-configuration Status=Enabled
+```
+
+---
+
+## Create IAM Role for Daily.co
+
+Daily.co needs permission to write recordings to your S3 bucket.
+
+Follow the guide https://docs.daily.co/guides/products/live-streaming-recording/storing-recordings-in-a-custom-s3-bucket
+
+Save the role ARN - you'll need it soon.
+
+It looks like: `arn:aws:iam::123456789012:role/DailyCo`
+
+Shortly, you'll need to set up a role and give this role your s3 bucket access
+
+No additional setup is required from Daily.co settings website side: the app code takes care of letting Daily know where to save the recordings.
+
+---
+
+## Configure Reflector
+
+**Location: Reflector server**
+
+Add to `server/.env`:
+
+```env
+# Daily.co Configuration
+DEFAULT_VIDEO_PLATFORM=daily
+DAILY_API_KEY=<your-api-key-from-daily-setup>
+DAILY_SUBDOMAIN=<your-subdomain-from-daily-setup>
+
+# S3 Storage for Daily.co recordings
+DAILYCO_STORAGE_AWS_BUCKET_NAME=<your-bucket-from-daily-setup>
+DAILYCO_STORAGE_AWS_REGION=us-east-1
+DAILYCO_STORAGE_AWS_ROLE_ARN=<your-role-arn-from-daily-setup>
+
+# Transcript storage (should already be configured from main setup)
+# TRANSCRIPT_STORAGE_BACKEND=aws
+# TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID=<your-key>
+# TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY=<your-secret>
+# TRANSCRIPT_STORAGE_AWS_BUCKET_NAME=<your-bucket-name>
+# TRANSCRIPT_STORAGE_AWS_REGION=<your-bucket-region>
+```
+
+---
+
+## Restart Services
+
+After changing `.env` files, reload with `up -d`:
+
+```bash
+sudo docker compose -f docker-compose.prod.yml up -d server worker
+```
+
+**Note**: `docker compose up -d` detects env changes and recreates containers automatically.
+
+---
+
+## Test Live Room
+
+1. Visit your Reflector frontend: `https://app.example.com`
+2. Go to **Rooms**
+3. Click **Create Room**
+4. Select **Daily** as the platform
+5. Allow camera/microphone access
+6. You should see Daily.co video interface
+7. Speak for 10-20 seconds
+8. Leave the meeting
+9. Recording should appear in **Transcripts** within 5 minutes (if webhooks aren't set up yet, see [Webhook Configuration](#webhook-configuration-optional) below)
+
+---
+
+## Troubleshooting
+
+### Recording doesn't appear in S3
+
+1. Check Daily.co dashboard → **Logs** for errors
+2. Verify IAM role trust policy has correct Daily.co account ID and your Daily.co subdomain
+3. Verify that the bucket has
+
+### Recording in S3 but not transcribed
+
+1. Check webhook is configured (Reflector should auto-create it)
+2. Check worker logs:
+   ```bash
+   docker compose -f docker-compose.prod.yml logs worker --tail 50
+   ```
+3. Verify `DAILYCO_STORAGE_AWS_*` vars in `server/.env`
+
+### "Access Denied" when Daily.co tries to write to S3
+
+1. Double-check IAM role ARN in Daily.co settings
+2. Verify bucket name matches exactly
+3. Check IAM policy has `s3:PutObject` permission
+
+---
+
+## Webhook Configuration [optional]
+
+`manage_daily_webhook.py` script guides you through creating a webhook for Daily recordings.
+
+The webhook isn't required - polling mechanism is the default and performed automatically.
+
+This guide won't go deep into webhook setup.

docs/docs/installation/docker-setup.md

@@ -0,0 +1,192 @@
+---
+sidebar_position: 3
+title: Docker Reference
+---
+
+# Docker Reference
+
+This page documents the Docker Compose configuration for Reflector. For the complete deployment guide, see [Deployment Guide](./overview).
+
+## Services
+
+The `docker-compose.prod.yml` includes these services:
+
+| Service | Image | Purpose |
+|---------|-------|---------|
+| `web` | `monadicalsas/reflector-frontend` | Next.js frontend |
+| `server` | `monadicalsas/reflector-backend` | FastAPI backend |
+| `worker` | `monadicalsas/reflector-backend` | Celery worker for background tasks |
+| `beat` | `monadicalsas/reflector-backend` | Celery beat scheduler |
+| `redis` | `redis:7.2-alpine` | Message broker and cache |
+| `postgres` | `postgres:17-alpine` | Primary database |
+| `caddy` | `caddy:2-alpine` | Reverse proxy with auto-SSL |
+
+## Environment Files
+
+Reflector uses two separate environment files:
+
+### Backend (`server/.env`)
+
+Used by: `server`, `worker`, `beat`
+
+Key variables:
+```env
+# Database connection
+DATABASE_URL=postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
+
+# Redis
+REDIS_HOST=redis
+CELERY_BROKER_URL=redis://redis:6379/1
+CELERY_RESULT_BACKEND=redis://redis:6379/1
+
+# API domain and CORS
+BASE_URL=https://api.example.com
+CORS_ORIGIN=https://app.example.com
+
+# Modal GPU processing
+TRANSCRIPT_BACKEND=modal
+TRANSCRIPT_URL=https://...
+TRANSCRIPT_MODAL_API_KEY=...
+```
+
+### Frontend (`www/.env`)
+
+Used by: `web`
+
+Key variables:
+```env
+# Domain configuration
+SITE_URL=https://app.example.com
+API_URL=https://api.example.com
+WEBSOCKET_URL=wss://api.example.com
+SERVER_API_URL=http://server:1250
+
+# Authentication
+NEXTAUTH_URL=https://app.example.com
+NEXTAUTH_SECRET=...
+```
+
+Note: `API_URL` is used client-side (browser), `SERVER_API_URL` is used server-side (SSR).
+
+## Volumes
+
+| Volume | Purpose |
+|--------|---------|
+| `redis_data` | Redis persistence |
+| `postgres_data` | PostgreSQL data |
+| `server_data` | Uploaded files, local storage |
+| `caddy_data` | SSL certificates |
+| `caddy_config` | Caddy configuration |
+
+## Network
+
+All services share the default network. The network is marked `attachable: true` to allow external containers (like Authentik) to join.
+
+## Common Commands
+
+### Start all services
+```bash
+docker compose -f docker-compose.prod.yml up -d
+```
+
+### View logs
+```bash
+# All services
+docker compose -f docker-compose.prod.yml logs -f
+
+# Specific service
+docker compose -f docker-compose.prod.yml logs server --tail 50
+```
+
+### Restart a service
+```bash
+# Quick restart (doesn't reload .env changes)
+docker compose -f docker-compose.prod.yml restart server
+
+# Reload .env and restart
+docker compose -f docker-compose.prod.yml up -d server
+```
+
+### Run database migrations
+```bash
+docker compose -f docker-compose.prod.yml exec server uv run alembic upgrade head
+```
+
+### Access database
+```bash
+docker compose -f docker-compose.prod.yml exec postgres psql -U reflector
+```
+
+### Pull latest images
+```bash
+docker compose -f docker-compose.prod.yml pull
+docker compose -f docker-compose.prod.yml up -d
+```
+
+### Stop all services
+```bash
+docker compose -f docker-compose.prod.yml down
+```
+
+### Full reset (WARNING: deletes data)
+```bash
+docker compose -f docker-compose.prod.yml down -v
+```
+
+## Customization
+
+### Using a different database
+
+To use an external PostgreSQL:
+
+1. Remove `postgres` service from compose file
+2. Update `DATABASE_URL` in `server/.env`:
+   ```env
+   DATABASE_URL=postgresql+asyncpg://user:pass@external-host:5432/reflector
+   ```
+
+### Using external Redis
+
+1. Remove `redis` service from compose file
+2. Update Redis settings in `server/.env`:
+   ```env
+   REDIS_HOST=external-redis-host
+   CELERY_BROKER_URL=redis://external-redis-host:6379/1
+   ```
+
+### Adding Authentik
+
+To add Authentik for authentication, see [Authentication Setup](./auth-setup). Quick steps:
+
+1. Deploy Authentik separately
+2. Connect to Reflector's network:
+   ```bash
+   docker network connect reflector_default authentik-server-1
+   ```
+3. Add to Caddyfile:
+   ```
+   authentik.example.com {
+       reverse_proxy authentik-server-1:9000
+   }
+   ```
+
+## Caddyfile Reference
+
+The Caddyfile supports environment variable substitution:
+
+```
+{$FRONTEND_DOMAIN:app.example.com} {
+    reverse_proxy web:3000
+}
+
+{$API_DOMAIN:api.example.com} {
+    reverse_proxy server:1250
+}
+```
+
+Set `FRONTEND_DOMAIN` and `API_DOMAIN` environment variables, or edit the file directly.
+
+### Reload Caddy after changes
+```bash
+docker compose -f docker-compose.prod.yml exec caddy caddy reload --config /etc/caddy/Caddyfile
+```

docs/docs/installation/docs-deployment.md

@@ -0,0 +1,139 @@
+---
+sidebar_position: 10
+title: Docs Website Deployment
+---
+
+# Docs Website Deployment
+
+This guide covers deploying the Reflector documentation website. **This is optional and intended for internal/experimental use only.**
+
+## Overview
+
+The documentation is built using Docusaurus and deployed as a static nginx-served site.
+
+## Prerequisites
+
+- Reflector already deployed (Steps 1-7 from [Deployment Guide](./overview))
+- DNS A record for docs subdomain (e.g., `docs.example.com`)
+
+## Deployment Steps
+
+### Step 1: Pre-fetch OpenAPI Spec
+
+The docs site includes API reference from your running backend. Fetch it before building:
+
+```bash
+cd ~/reflector
+docker compose -f docker-compose.prod.yml exec server curl -s http://localhost:1250/openapi.json > docs/static/openapi.json
+```
+
+This creates `docs/static/openapi.json` (should be ~70KB) which will be copied during Docker build.
+
+**Why not fetch during build?** Docker build containers are network-isolated and can't access the running backend services.
+
+### Step 2: Verify Dockerfile
+
+The Dockerfile is already in `docs/Dockerfile`:
+
+```dockerfile
+FROM node:18-alpine AS builder
+WORKDIR /app
+
+# Copy package files
+COPY package*.json ./
+
+# Inshall dependencies
+RUN npm ci
+
+# Copy source (includes static/openapi.json if pre-fetched)
+COPY . .
+
+# Fix docusaurus config: change onBrokenLinks to 'warn' for Docker build
+RUN sed -i "s/onBrokenLinks: 'throw'/onBrokenLinks: 'warn'/g" docusaurus.config.ts
+
+# Build static site
+RUN npx docusaurus build
+
+FROM nginx:alpine
+COPY --from=builder /app/build /usr/share/nginx/html
+EXPOSE 80
+CMD ["nginx", "-g", "daemon off;"]
+```
+
+### Step 3: Add Docs Service to docker-compose.prod.yml
+
+Add this service to `docker-compose.prod.yml`:
+
+```yaml
+docs:
+  build: ./docs
+  restart: unless-stopped
+  networks:
+    - default
+```
+
+### Step 4: Add Caddy Route
+
+Add to `Caddyfile`:
+
+```
+{$DOCS_DOMAIN:docs.example.com} {
+    reverse_proxy docs:80
+}
+```
+
+### Step 5: Build and Deploy
+
+```bash
+cd ~/reflector
+docker compose -f docker-compose.prod.yml up -d --build docs
+docker compose -f docker-compose.prod.yml exec caddy caddy reload --config /etc/caddy/Caddyfile
+```
+
+### Step 6: Verify
+
+```bash
+# Check container status
+docker compose -f docker-compose.prod.yml ps docs
+# Should show "Up"
+
+# Test URL
+curl -I https://docs.example.com
+# Should return HTTP/2 200
+```
+
+Visit `https://docs.example.com` in your browser
+
+## Updating Documentation
+
+When docs are updated:
+
+```bash
+cd ~/reflector
+git pull
+
+# Refresh OpenAPI spec from backend
+docker compose -f docker-compose.prod.yml exec server curl -s http://localhost:1250/openapi.json > docs/static/openapi.json
+
+# Rebuild docs
+docker compose -f docker-compose.prod.yml up -d --build docs
+```
+
+## Troubleshooting
+
+### Missing openapi.json during build
+- Make sure you ran the pre-fetch step first (Step 1)
+- Verify `docs/static/openapi.json` exists and is ~70KB
+- Re-run: `docker compose exec server curl -s http://localhost:1250/openapi.json > docs/static/openapi.json`
+
+### Build fails with "Docusaurus found broken links"
+- This happens if `onBrokenLinks: 'throw'` is set in docusaurus.config.ts
+- Solution is already in Dockerfile: uses `sed` to change to `'warn'` during build
+
+### 404 on all pages
+- Docusaurus baseUrl might be wrong - should be `/` for custom domain
+- Check `docs/docusaurus.config.ts`: `baseUrl: '/'`
+
+### Docs not updating after rebuild
+- Force rebuild: `docker compose -f docker-compose.prod.yml build --no-cache docs`
+- Then: `docker compose -f docker-compose.prod.yml up -d docs`

docs/docs/installation/modal-setup.md

@@ -0,0 +1,171 @@
+---
+sidebar_position: 4
+title: Modal.com Setup
+---
+
+# Modal.com Setup
+
+This page covers Modal.com GPU setup in detail. For the complete deployment guide, see [Deployment Guide](./overview).
+
+Reflector uses [Modal.com](https://modal.com) for GPU-accelerated audio processing. This guide walks you through deploying the required GPU functions.
+
+## What is Modal.com?
+
+Modal is a serverless GPU platform. You deploy Python code that runs on their GPUs, and pay only for actual compute time. Reflector uses Modal for:
+
+- **Transcription**: Whisper model for speech-to-text
+- **Diarization**: Pyannote model for speaker identification
+
+## Prerequisites
+
+1. **Modal.com account** - Sign up at https://modal.com (free tier available)
+2. **HuggingFace account** - Required for Pyannote diarization models:
+   - Create account at https://huggingface.co
+   - Accept **both** Pyannote licenses:
+     - https://huggingface.co/pyannote/speaker-diarization-3.1
+     - https://huggingface.co/pyannote/segmentation-3.0
+   - Generate access token at https://huggingface.co/settings/tokens
+
+## Deployment
+
+**Location: YOUR LOCAL COMPUTER (laptop/desktop)**
+
+Modal CLI requires browser authentication, so this must run on a machine with a browser - not on a headless server.
+
+### Install Modal CLI
+
+```bash
+pip install modal
+```
+
+### Authenticate with Modal
+
+```bash
+modal setup
+```
+
+This opens your browser for authentication. Complete the login flow.
+
+### Clone Repository and Deploy
+
+```bash
+git clone https://github.com/monadical-sas/reflector.git
+cd reflector/gpu/modal_deployments
+./deploy-all.sh --hf-token YOUR_HUGGINGFACE_TOKEN
+```
+
+Or run interactively (script will prompt for token):
+```bash
+./deploy-all.sh
+```
+
+### What the Script Does
+
+1. **Prompts for HuggingFace token** - Needed to download the Pyannote diarization model
+2. **Generates API key** - Creates a secure random key for authenticating requests to GPU functions
+3. **Creates Modal secrets**:
+   - `hf_token` - Your HuggingFace token
+   - `reflector-gpu` - The generated API key
+4. **Deploys GPU functions** - Transcriber (Whisper) and Diarizer (Pyannote)
+5. **Outputs configuration** - Prints URLs and API key to console
+
+### Example Output
+
+```
+==========================================
+Reflector GPU Functions Deployment
+==========================================
+
+Generating API key for GPU services...
+Creating Modal secrets...
+  -> Creating secret: hf_token
+  -> Creating secret: reflector-gpu
+
+Deploying transcriber (Whisper)...
+  -> https://yourname--reflector-transcriber-web.modal.run
+
+Deploying diarizer (Pyannote)...
+  -> https://yourname--reflector-diarizer-web.modal.run
+
+==========================================
+Deployment complete!
+==========================================
+
+Copy these values to your server's server/.env file:
+
+# --- Modal GPU Configuration ---
+TRANSCRIPT_BACKEND=modal
+TRANSCRIPT_URL=https://yourname--reflector-transcriber-web.modal.run
+TRANSCRIPT_MODAL_API_KEY=abc123...
+
+DIARIZATION_BACKEND=modal
+DIARIZATION_URL=https://yourname--reflector-diarizer-web.modal.run
+DIARIZATION_MODAL_API_KEY=abc123...
+# --- End Modal Configuration ---
+```
+
+Copy the output and paste it into your `server/.env` file on your server.
+
+## Costs
+
+Modal charges based on GPU compute time:
+- Functions scale to zero when not in use (no cost when idle)
+- You only pay for actual processing time
+- Free tier includes $30/month of credits
+
+Typical costs for audio processing:
+- Transcription: ~$0.01-0.05 per minute of audio
+- Diarization: ~$0.02-0.10 per minute of audio
+
+## Troubleshooting
+
+### "Modal CLI not installed"
+```bash
+pip install modal
+```
+
+### "Not authenticated with Modal"
+```bash
+modal setup
+# Complete browser authentication
+```
+
+### "Failed to create secret hf_token"
+- Verify your HuggingFace token is valid
+- Ensure you've accepted the Pyannote license
+- Token needs `read` permission
+
+### Deployment fails
+Check the Modal dashboard for detailed error logs:
+- Visit https://modal.com/apps
+- Click on the failed function
+- View build and runtime logs
+
+### Re-running deployment
+The script is safe to re-run. It will:
+- Update existing secrets if they exist
+- Redeploy functions with latest code
+- Output new configuration (API key stays the same if secret exists)
+
+## Manual Deployment (Advanced)
+
+If you prefer to deploy functions individually:
+
+```bash
+cd gpu/modal_deployments
+
+# Create secrets manually
+modal secret create hf_token HF_TOKEN=your-hf-token
+modal secret create reflector-gpu REFLECTOR_GPU_APIKEY=$(openssl rand -hex 32)
+
+# Deploy each function
+modal deploy reflector_transcriber.py
+modal deploy reflector_diarizer.py
+```
+
+## Monitoring
+
+View your deployed functions and their usage:
+- **Modal Dashboard**: https://modal.com/apps
+- **Function logs**: Click on any function to view logs
+- **Usage**: View compute time and costs in the dashboard

docs/docs/installation/overview.md

@@ -0,0 +1,401 @@
+---
+sidebar_position: 1
+title: Deployment Guide
+---
+
+# Deployment Guide
+
+This guide walks you through deploying Reflector from scratch. Follow these steps in order.
+
+## What You'll Set Up
+
+```mermaid
+flowchart LR
+    User --> Caddy["Caddy (auto-SSL)"]
+    Caddy --> Frontend["Frontend (Next.js)"]
+    Caddy --> Backend["Backend (FastAPI)"]
+    Backend --> PostgreSQL
+    Backend --> Redis
+    Backend --> Workers["Celery Workers"]
+    Workers --> PostgreSQL
+    Workers --> Redis
+    Workers --> GPU["GPU Processing<br/>(Modal.com OR Self-hosted)"]
+```
+
+## Prerequisites
+
+Before starting, you need:
+
+- **Production server** -  4+ cores, 8GB+ RAM, public IP
+- **Two domain names** - e.g., `app.example.com` (frontend) and `api.example.com` (backend)
+- **GPU processing** - Choose one:
+  - Modal.com account, OR
+  - GPU server with NVIDIA GPU (8GB+ VRAM)
+- **HuggingFace account** - Free at https://huggingface.co
+  - Accept both Pyannote licenses (required for speaker diarization):
+    - https://huggingface.co/pyannote/speaker-diarization-3.1
+    - https://huggingface.co/pyannote/segmentation-3.0
+- **LLM API** - For summaries and topic detection. Choose one:
+  - OpenAI API key at https://platform.openai.com/account/api-keys, OR
+  - Any OpenAI-compatible endpoint (vLLM, LiteLLM, Ollama, etc.)
+- **AWS S3 bucket** - For storing audio files and transcripts (see [S3 Setup](#create-s3-bucket-for-transcript-storage) below)
+
+### Optional (for live meeting rooms)
+
+- [ ] **Daily.co account** - Free tier at https://dashboard.daily.co
+- [ ] **AWS S3 bucket + IAM Role** - For Daily.co recording storage (separate from transcript storage)
+
+---
+
+## Configure DNS
+
+```
+Type: A    Name: app    Value: <your-server-ip>
+Type: A    Name: api    Value: <your-server-ip>
+```
+
+---
+
+## Deploy GPU Processing
+
+Reflector requires GPU processing for transcription and speaker diarization. Choose one option:
+
+| | **Modal.com (Cloud)** | **Self-Hosted GPU** |
+|---|---|---|
+| **Best for** | No GPU hardware, zero maintenance | Own GPU server, full control |
+| **Pricing** | Pay-per-use | Fixed infrastructure cost |
+
+### Option A: Modal.com (Serverless Cloud GPU)
+
+#### Accept HuggingFace Licenses
+
+Visit both pages and click "Accept":
+- https://huggingface.co/pyannote/speaker-diarization-3.1
+- https://huggingface.co/pyannote/segmentation-3.0
+
+Generate a token at https://huggingface.co/settings/tokens
+
+#### Deploy to Modal
+
+There's an install script to help with this setup. It's using modal API to set all necessary moving parts.
+
+As an alternative, all those operations that script does could be performed in modal settings in modal UI.
+
+```bash
+pip install modal
+modal setup  # opens browser for authentication
+
+git clone https://github.com/monadical-sas/reflector.git
+cd reflector/gpu/modal_deployments
+./deploy-all.sh --hf-token YOUR_HUGGINGFACE_TOKEN
+```
+
+**Save the output** - copy the configuration block, you'll need it soon.
+
+See [Modal Setup](./modal-setup) for troubleshooting and details.
+
+### Option B: Self-Hosted GPU
+
+**Location: YOUR GPU SERVER**
+
+Requires: NVIDIA GPU with 8GB+ VRAM, Ubuntu 22.04+, 40-50GB disk (Docker) or 25-30GB (Systemd).
+
+See [Self-Hosted GPU Setup](./self-hosted-gpu-setup) for complete instructions. Quick summary:
+
+1. Install NVIDIA drivers and Docker (or uv for systemd)
+2. Clone repository: `git clone https://github.com/monadical-sas/reflector.git`
+3. Configure `.env` with HuggingFace token
+4. Start service (Docker compose or systemd)
+5. Set up Caddy reverse proxy for HTTPS
+
+**Save your API key and HTTPS URL** - you'll need them soon.
+
+---
+
+## Prepare Server
+
+**Location: dedicated reflector server**
+
+### Install Docker
+
+```bash
+ssh user@your-server-ip
+
+curl -fsSL https://get.docker.com | sh
+sudo usermod -aG docker $USER
+
+# Log out and back in for group changes
+exit
+ssh user@your-server-ip
+
+docker --version  # verify
+```
+
+### Firewall
+
+Ensure ports 80 (HTTP) and 443 (HTTPS) are open for inbound traffic. The method varies by cloud provider and OS configuration.
+
+### Clone Repository
+
+The Docker images contain all application code. You clone the repository for configuration files and the compose definition:
+
+```bash
+git clone https://github.com/monadical-sas/reflector.git
+cd reflector
+```
+
+---
+
+## Create S3 Bucket for Transcript Storage
+
+Reflector requires AWS S3 to store audio files during processing.
+
+### Create Bucket
+
+```bash
+# Choose a unique bucket name
+BUCKET_NAME="reflector-transcripts-yourname"
+AWS_REGION="us-east-1"
+
+# Create bucket
+aws s3 mb s3://$BUCKET_NAME --region $AWS_REGION
+```
+
+### Create IAM User
+
+Create an IAM user with S3 access for Reflector:
+
+1. Go to AWS IAM Console → Users → Create User
+2. Name: `reflector-transcripts`
+3. Attach policy: `AmazonS3FullAccess` (or create a custom policy for just your bucket)
+4. Create access key (Access key ID + Secret access key)
+
+Save these credentials - you'll need them in the next step.
+
+---
+
+## Configure Environment
+
+Reflector has two env files:
+- `server/.env` - Backend configuration
+- `www/.env` - Frontend configuration
+
+### Backend Configuration
+
+```bash
+cp server/.env.example server/.env
+nano server/.env
+```
+
+**Required settings:**
+```env
+# Database (defaults work with docker-compose.prod.yml)
+DATABASE_URL=postgresql+asyncpg://reflector:reflector@postgres:5432/reflector
+
+# Redis
+REDIS_HOST=redis
+CELERY_BROKER_URL=redis://redis:6379/1
+CELERY_RESULT_BACKEND=redis://redis:6379/1
+
+# Your domains
+BASE_URL=https://api.example.com
+CORS_ORIGIN=https://app.example.com
+CORS_ALLOW_CREDENTIALS=true
+
+# Secret key - generate with: openssl rand -hex 32
+SECRET_KEY=<your-generated-secret>
+
+# GPU Processing - choose ONE option:
+
+# Option A: Modal.com (paste from deploy-all.sh output)
+TRANSCRIPT_BACKEND=modal
+TRANSCRIPT_URL=https://yourname--reflector-transcriber-web.modal.run
+TRANSCRIPT_MODAL_API_KEY=<from-deploy-all.sh-output>
+DIARIZATION_BACKEND=modal
+DIARIZATION_URL=https://yourname--reflector-diarizer-web.modal.run
+DIARIZATION_MODAL_API_KEY=<from-deploy-all.sh-output>
+
+# Option B: Self-hosted GPU (use your GPU server URL and API key)
+# TRANSCRIPT_BACKEND=modal
+# TRANSCRIPT_URL=https://gpu.example.com
+# TRANSCRIPT_MODAL_API_KEY=<your-generated-api-key>
+# DIARIZATION_BACKEND=modal
+# DIARIZATION_URL=https://gpu.example.com
+# DIARIZATION_MODAL_API_KEY=<your-generated-api-key>
+
+# Storage - where to store audio files and transcripts (requires AWS S3)
+TRANSCRIPT_STORAGE_BACKEND=aws
+TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID=your-aws-access-key
+TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY=your-aws-secret-key
+TRANSCRIPT_STORAGE_AWS_BUCKET_NAME=reflector-media
+TRANSCRIPT_STORAGE_AWS_REGION=us-east-1
+
+# LLM - for generating titles, summaries, and topics
+LLM_API_KEY=sk-your-openai-api-key
+LLM_MODEL=gpt-4o-mini
+# LLM_URL=https://api.openai.com/v1  # Optional: custom endpoint (vLLM, LiteLLM, Ollama, etc.)
+
+# Auth - disable for initial setup (see a dedicated step for authentication)
+AUTH_BACKEND=none
+```
+
+### Frontend Configuration
+
+```bash
+cp www/.env.example www/.env
+nano www/.env
+```
+
+**Required settings:**
+```env
+# Your domains
+SITE_URL=https://app.example.com
+API_URL=https://api.example.com
+WEBSOCKET_URL=wss://api.example.com
+SERVER_API_URL=http://server:1250
+
+# NextAuth
+NEXTAUTH_URL=https://app.example.com
+NEXTAUTH_SECRET=<generate-with-openssl-rand-hex-32>
+
+# Disable login requirement for initial setup
+FEATURE_REQUIRE_LOGIN=false
+```
+
+---
+
+## Configure Caddy
+
+```bash
+cp Caddyfile.example Caddyfile
+nano Caddyfile
+```
+
+Replace `example.com` with your domains. The `{$VAR:default}` syntax uses Caddy's env var substitution - you can either edit the file directly or set `FRONTEND_DOMAIN` and `API_DOMAIN` environment variables.
+
+```
+{$FRONTEND_DOMAIN:app.example.com} {
+    reverse_proxy web:3000
+}
+
+{$API_DOMAIN:api.example.com} {
+    reverse_proxy server:1250
+}
+```
+
+---
+
+## Start Services
+
+```bash
+docker compose -f docker-compose.prod.yml up -d
+```
+
+Wait for PostgreSQL to be ready, then run migrations:
+
+```bash
+# Wait for postgres to be healthy (may take 30-60 seconds on first run)
+docker compose -f docker-compose.prod.yml exec postgres pg_isready -U reflector
+
+# Run database migrations
+docker compose -f docker-compose.prod.yml exec server uv run alembic upgrade head
+```
+
+---
+
+## Verify Deployment
+
+### Check services
+```bash
+docker compose -f docker-compose.prod.yml ps
+# All should show "Up"
+```
+
+### Test API
+```bash
+curl https://api.example.com/health
+# Should return: {"status":"healthy"}
+```
+
+### Test Frontend
+- Visit https://app.example.com
+- You should see the Reflector interface
+- Try uploading an audio file to test transcription
+
+---
+
+## Enable Authentication (Required for Live Rooms)
+
+By default, Reflector is open (no login required). **Authentication is required if you want to use Live Meeting Rooms.**
+
+See [Authentication Setup](./auth-setup) for full Authentik OAuth configuration.
+
+Quick summary:
+1. Deploy Authentik on your server
+2. Create OAuth provider in Authentik
+3. Extract public key for JWT verification
+4. Update `server/.env`: `AUTH_BACKEND=jwt` + `AUTH_JWT_AUDIENCE`
+5. Update `www/.env`: `FEATURE_REQUIRE_LOGIN=true` + Authentik credentials
+6. Mount JWT keys volume and restart services
+
+---
+
+## Enable Live Meeting Rooms
+
+**Requires: Authentication Step**
+
+Live rooms require Daily.co and AWS S3. See [Daily.co Setup](./daily-setup) for complete S3/IAM configuration instructions.
+
+Note that Reflector also supports Whereby as a call provider - this doc doesn't cover its setup yet.
+
+Quick config - Add to `server/.env`:
+
+```env
+DEFAULT_VIDEO_PLATFORM=daily
+DAILY_API_KEY=<from-daily.co-dashboard>
+DAILY_SUBDOMAIN=<your-daily-subdomain>
+
+# S3 for recording storage
+DAILYCO_STORAGE_AWS_BUCKET_NAME=<your-bucket>
+DAILYCO_STORAGE_AWS_REGION=us-east-1
+DAILYCO_STORAGE_AWS_ROLE_ARN=<arn:aws:iam::ACCOUNT:role/DailyCo>
+```
+
+Reload env and restart:
+```bash
+docker compose -f docker-compose.prod.yml up -d server worker
+```
+
+---
+
+## Troubleshooting
+
+### Check logs for errors
+```bash
+docker compose -f docker-compose.prod.yml logs server --tail 20
+docker compose -f docker-compose.prod.yml logs worker --tail 20
+```
+
+### Services won't start
+```bash
+docker compose -f docker-compose.prod.yml logs
+```
+
+### CORS errors in browser
+- Verify `CORS_ORIGIN` in `server/.env` matches your frontend domain exactly (including `https://`)
+- Reload env: `docker compose -f docker-compose.prod.yml up -d server`
+
+### SSL certificate errors
+- Caddy auto-provisions Let's Encrypt certificates
+- Ensure ports 80 and 443 are open
+- Check: `docker compose -f docker-compose.prod.yml logs caddy`
+
+### Transcription not working
+- Check Modal dashboard: https://modal.com/apps
+- Verify URLs in `server/.env` match deployed functions
+- Check worker logs: `docker compose -f docker-compose.prod.yml logs worker`
+
+### "Login required" but auth not configured
+- Set `FEATURE_REQUIRE_LOGIN=false` in `www/.env`
+- Rebuild frontend: `docker compose -f docker-compose.prod.yml up -d --force-recreate web`
+

docs/docs/installation/requirements.md

@@ -0,0 +1,60 @@
+---
+sidebar_position: 2
+title: System Requirements
+---
+
+# System Requirements
+
+This page lists hardware and software requirements. For the complete deployment guide, see [Deployment Guide](./overview).
+
+## Server Requirements
+
+### Minimum Requirements
+
+- **CPU**: 4 cores
+- **RAM**: 8 GB
+- **Storage**: 50 GB SSD
+- **OS**: Ubuntu 22.04+ or compatible Linux
+- **Network**: Public IP address
+
+### Recommended Requirements
+
+- **CPU**: 8+ cores
+- **RAM**: 16 GB
+- **Storage**: 100 GB SSD
+- **Network**: 1 Gbps connection
+
+## Software Requirements
+
+- Docker Engine 20.10+
+- Docker Compose 2.0+
+
+## External Services
+
+### Required
+
+- **Two domain names** - One for frontend (e.g., `app.example.com`), one for API (e.g., `api.example.com`)
+- **Modal.com account** - For GPU-accelerated transcription and diarization (free tier available)
+- **HuggingFace account** - For Pyannote diarization model access
+- **OpenAI API key** - For generating summaries and topic detection (https://platform.openai.com/account/api-keys)
+
+### Required for Live Meeting Rooms
+
+- **Daily.co account** - For video conferencing (free tier available at https://dashboard.daily.co)
+- **AWS S3 bucket + IAM Role** - For Daily.co to store recordings
+- **Another AWS S3 bucket (optional, can reuse the one above)** - For Reflector to store "compiled" mp3 files and transient diarization process temporary files
+
+### Optional
+
+- **AWS S3** - For cloud storage of recordings and transcripts
+- **Authentik** - For SSO/OIDC authentication
+- **Sentry** - For error tracking
+
+## Development Requirements
+
+For local development only (not required for production deployment):
+
+- Node.js 22+ (for frontend development)
+- Python 3.12+ (for backend development)
+- pnpm (for frontend package management)
+- uv (for Python package management)

docs/docs/installation/self-hosted-gpu-setup.md

@@ -0,0 +1,465 @@
+---
+sidebar_position: 5
+title: Self-Hosted GPU Setup
+---
+
+# Self-Hosted GPU Setup
+
+This guide covers deploying Reflector's GPU processing on your own server instead of Modal.com. For the complete deployment guide, see [Deployment Guide](./overview).
+
+## When to Use Self-Hosted GPU
+
+**Choose self-hosted GPU if you:**
+- Have GPU hardware available (NVIDIA required)
+- Want full control over processing
+- Prefer fixed infrastructure costs over pay-per-use
+- Have privacy or data locality requirements
+- Need to process audio without external API calls
+
+**Choose Modal.com instead if you:**
+- Don't have GPU hardware
+- Want zero infrastructure management
+- Prefer pay-per-use pricing
+- Need instant scaling for variable workloads
+
+See [Modal.com Setup](./modal-setup) for cloud GPU deployment.
+
+## What Gets Deployed
+
+The self-hosted GPU service provides the same API endpoints as Modal:
+- `POST /v1/audio/transcriptions` - Whisper transcription
+- `POST /diarize` - Pyannote speaker diarization
+
+Your main Reflector server connects to this service exactly like it connects to Modal - only the URL changes.
+
+## Prerequisites
+
+### Hardware
+- **GPU**: NVIDIA GPU with 8GB+ VRAM (tested on Tesla T4 with 15GB)
+- **CPU**: 4+ cores recommended
+- **RAM**: 8GB minimum, 16GB recommended
+- **Disk**:
+  - Docker method: 40-50GB minimum
+  - Systemd method: 25-30GB minimum
+
+### Software
+- Public IP address
+- Domain name with DNS A record pointing to server
+
+### Accounts
+- **HuggingFace account** with accepted Pyannote licenses:
+  - https://huggingface.co/pyannote/speaker-diarization-3.1
+  - https://huggingface.co/pyannote/segmentation-3.0
+- **HuggingFace access token** from https://huggingface.co/settings/tokens
+
+## Choose Deployment Method
+
+---
+
+## Docker Deployment
+
+### Step 1: Install NVIDIA Driver
+
+```bash
+sudo apt update
+sudo apt install -y nvidia-driver-535
+
+# Load kernel modules
+sudo modprobe nvidia
+
+# Verify installation
+nvidia-smi
+```
+
+Expected output: GPU details with driver version and CUDA version.
+
+### Step 2: Install Docker
+
+```bash
+curl -fsSL https://get.docker.com | sudo sh
+sudo usermod -aG docker $USER
+
+# Log out and back in for group changes
+exit
+# SSH back in
+```
+
+### Step 3: Install NVIDIA Container Toolkit
+
+```bash
+# Add NVIDIA repository
+curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | \
+  sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
+
+curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
+  sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
+  sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
+
+# Install toolkit
+sudo apt-get update
+sudo apt-get install -y nvidia-container-toolkit
+
+# Configure Docker runtime
+sudo nvidia-ctk runtime configure --runtime=docker
+sudo systemctl restart docker
+```
+
+### Step 4: Clone Repository and Configure
+
+```bash
+git clone https://github.com/monadical-sas/reflector.git
+cd reflector/gpu/self_hosted
+
+# Create environment file
+cat > .env << EOF
+REFLECTOR_GPU_APIKEY=$(openssl rand -hex 16)
+HF_TOKEN=your_huggingface_token_here
+EOF
+
+# Note the generated API key - you'll need it for main server config
+cat .env
+```
+
+### Step 5: Create Docker Compose File
+
+```bash
+cat > compose.yml << 'EOF'
+services:
+  reflector_gpu:
+    build:
+      context: .
+    ports:
+      - "8000:8000"
+    env_file:
+      - .env
+    volumes:
+      - ./cache:/root/.cache
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: all
+              capabilities: [gpu]
+    restart: unless-stopped
+EOF
+```
+
+### Step 6: Build and Start
+
+```bash
+# Build image (takes ~5 minutes, downloads ~10GB)
+sudo docker compose build
+
+# Start service
+sudo docker compose up -d
+
+# Wait for startup and verify
+sleep 30
+sudo docker compose logs
+```
+
+Look for: `INFO: Application startup complete. Uvicorn running on http://0.0.0.0:8000`
+
+### Step 7: Verify GPU Access
+
+```bash
+# Check GPU is accessible from container
+sudo docker exec $(sudo docker ps -q) nvidia-smi
+```
+
+Should show GPU with ~3GB VRAM used (models loaded).
+
+---
+
+## Systemd Deployment
+
+### Step 1: Install NVIDIA Driver
+
+```bash
+sudo apt update
+sudo apt install -y nvidia-driver-535
+
+# Load kernel modules
+sudo modprobe nvidia
+
+# Verify installation
+nvidia-smi
+```
+
+### Step 2: Install Dependencies
+
+```bash
+# Install ffmpeg
+sudo apt install -y ffmpeg
+
+# Install uv package manager
+curl -LsSf https://astral.sh/uv/install.sh | sh
+source ~/.local/bin/env
+
+# Clone repository
+git clone https://github.com/monadical-sas/reflector.git
+cd reflector/gpu/self_hosted
+```
+
+### Step 3: Configure Environment
+
+```bash
+# Create environment file
+cat > .env << EOF
+REFLECTOR_GPU_APIKEY=$(openssl rand -hex 16)
+HF_TOKEN=your_huggingface_token_here
+EOF
+
+# Note the generated API key
+cat .env
+```
+
+### Step 4: Install Python Packages
+
+```bash
+# Install dependencies (~3GB download)
+uv sync
+```
+
+### Step 5: Create Systemd Service
+
+```bash
+# Generate library paths for NVIDIA packages
+export NVIDIA_LIBS=$(find ~/reflector/gpu/self_hosted/.venv/lib/python3.12/site-packages/nvidia -name lib -type d | tr '\n' ':')
+
+# Load environment variables
+source ~/reflector/gpu/self_hosted/.env
+
+# Create service file
+sudo tee /etc/systemd/system/reflector-gpu.service << EOFSVC
+[Unit]
+Description=Reflector GPU Service (Transcription & Diarization)
+After=network.target
+
+[Service]
+Type=simple
+User=$USER
+WorkingDirectory=$HOME/reflector/gpu/self_hosted
+Environment="PATH=$HOME/.local/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin"
+Environment="HF_TOKEN=${HF_TOKEN}"
+Environment="REFLECTOR_GPU_APIKEY=${REFLECTOR_GPU_APIKEY}"
+Environment="LD_LIBRARY_PATH=${NVIDIA_LIBS}"
+ExecStart=$HOME/reflector/gpu/self_hosted/.venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
+Restart=always
+RestartSec=10
+
+[Install]
+WantedBy=multi-user.target
+EOFSVC
+
+# Enable and start
+sudo systemctl daemon-reload
+sudo systemctl enable reflector-gpu
+sudo systemctl start reflector-gpu
+```
+
+### Step 6: Verify Service
+
+```bash
+# Check status
+sudo systemctl status reflector-gpu
+
+# View logs
+sudo journalctl -u reflector-gpu -f
+```
+
+Look for: `INFO: Application startup complete.`
+
+---
+
+## Configure HTTPS with Caddy
+
+Both deployment methods need HTTPS for production. Caddy handles SSL automatically.
+
+### Install Caddy
+
+```bash
+sudo apt install -y debian-keyring debian-archive-keyring apt-transport-https curl
+
+curl -1sLf 'https://dl.cloudsmith.io/public/caddy/stable/gpg.key' | \
+  sudo gpg --dearmor -o /usr/share/keyrings/caddy-stable-archive-keyring.gpg
+
+curl -1sLf 'https://dl.cloudsmith.io/public/caddy/stable/debian.deb.txt' | \
+  sudo tee /etc/apt/sources.list.d/caddy-stable.list
+
+sudo apt update
+sudo apt install -y caddy
+```
+
+### Configure Reverse Proxy
+
+```bash
+sudo tee /etc/caddy/Caddyfile << 'EOF'
+gpu.example.com {
+    reverse_proxy localhost:8000
+}
+EOF
+
+# Reload Caddy (auto-provisions SSL certificate)
+sudo systemctl reload caddy
+```
+
+Replace `gpu.example.com` with your domain.
+
+### Verify HTTPS
+
+```bash
+curl -I https://gpu.example.com/docs
+# Should return HTTP/2 200
+```
+
+---
+
+## Configure Main Reflector Server
+
+On your main Reflector server, update `server/.env`:
+
+```env
+# GPU Processing - Self-hosted
+TRANSCRIPT_BACKEND=modal
+TRANSCRIPT_URL=https://gpu.example.com
+TRANSCRIPT_MODAL_API_KEY=<your-generated-api-key>
+
+DIARIZATION_BACKEND=modal
+DIARIZATION_URL=https://gpu.example.com
+DIARIZATION_MODAL_API_KEY=<your-generated-api-key>
+```
+
+**Note:** The backend type is `modal` because the self-hosted GPU service implements the same API contract as Modal.com. This allows you to switch between cloud and self-hosted GPU processing by only changing the URL and API key.
+
+Restart services to apply:
+
+```bash
+docker compose -f docker-compose.prod.yml restart server worker
+```
+
+---
+
+## Service Management
+
+All commands in this section assume you're in `~/reflector/gpu/self_hosted/`.
+
+### Docker
+
+```bash
+# View logs
+sudo docker compose logs -f
+
+# Restart service
+sudo docker compose restart
+
+# Stop service
+sudo docker compose down
+
+# Check status
+sudo docker compose ps
+```
+
+### Systemd
+
+```bash
+# View logs
+sudo journalctl -u reflector-gpu -f
+
+# Restart service
+sudo systemctl restart reflector-gpu
+
+# Stop service
+sudo systemctl stop reflector-gpu
+
+# Check status
+sudo systemctl status reflector-gpu
+```
+
+### Monitor GPU
+
+```bash
+# Check GPU usage
+nvidia-smi
+
+# Watch in real-time
+watch -n 1 nvidia-smi
+```
+
+**Typical GPU memory usage:**
+- Idle (models loaded): ~3GB VRAM
+- During transcription: ~4-5GB VRAM
+
+---
+
+## Troubleshooting
+
+### nvidia-smi fails after driver install
+
+```bash
+# Manually load kernel modules
+sudo modprobe nvidia
+nvidia-smi
+```
+
+### Service fails with "Could not download pyannote pipeline"
+
+1. Verify HF_TOKEN is valid: `echo $HF_TOKEN`
+2. Check model access at https://huggingface.co/pyannote/speaker-diarization-3.1
+3. Regenerate service/compose with correct token
+4. Restart service
+
+### cuDNN library loading errors (Systemd only)
+
+Symptom: `Unable to load libcudnn_cnn.so`
+
+Regenerate the systemd service file - the `LD_LIBRARY_PATH` must include all NVIDIA package directories.
+
+### Cannot connect to HTTPS endpoint
+
+1. Verify DNS resolves: `dig +short gpu.example.com`
+2. Check firewall: `sudo ufw status` (ports 80, 443 must be open)
+3. Check Caddy: `sudo systemctl status caddy`
+4. View Caddy logs: `sudo journalctl -u caddy -n 50`
+
+### SSL certificate not provisioning
+
+Requirements for Let's Encrypt:
+- Ports 80 and 443 publicly accessible
+- DNS resolves to server's public IP
+- Valid domain (not localhost or private IP)
+
+### Docker container won't start
+
+```bash
+# Check logs
+sudo docker compose logs
+
+# Common issues:
+# - Port 8000 already in use
+# - GPU not accessible (nvidia-ctk not configured)
+# - Missing .env file
+```
+
+---
+
+## Updating
+
+### Docker
+
+```bash
+cd ~/reflector/gpu/self_hosted
+git pull
+sudo docker compose build
+sudo docker compose up -d
+```
+
+### Systemd
+
+```bash
+cd ~/reflector/gpu/self_hosted
+git pull
+uv sync
+sudo systemctl restart reflector-gpu
+```

docs/docs/installation/whereby-setup.md

@@ -0,0 +1,7 @@
+---
+title: whereby setup
+---
+
+# whereby setup
+
+Documentation coming soon. See [TODO.md](/docs/TODO) for required information.

docs/docs/installation/zulip-setup.md

@@ -0,0 +1,7 @@
+---
+title: zulip setup
+---
+
+# zulip setup
+
+Documentation coming soon. See [TODO.md](/docs/TODO) for required information.

docs/docs/intro.md

@@ -0,0 +1,61 @@
+---
+sidebar_position: 1
+title: Introduction
+---
+
+# Welcome to Reflector
+
+Reflector is a privacy-focused, self-hosted AI-powered audio transcription and meeting analysis platform that provides real-time transcription, speaker diarization, translation, and summarization for audio content and live meetings. With complete control over your data and infrastructure, you can run models on your own hardware (roadmap - currently supports Modal.com for GPU processing).
+
+## What is Reflector?
+
+Reflector is a web application that utilizes AI to process audio content, providing:
+
+- **Real-time Transcription**: Convert speech to text using [Whisper](https://github.com/openai/whisper) (multi-language) or [Parakeet](https://github.com/NVIDIA/NeMo) (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 100+ 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
+
+## Features
+
+| Feature | Public Mode | Private Mode |
+|---------|------------|--------------|
+| **Authentication** | None required | Required |
+| **Audio Upload** | ✅ | ✅ |
+| **Live Microphone Streaming** | ✅ | ✅ |
+| **Transcription** | ✅ | ✅ |
+| **Speaker Diarization** | ✅ | ✅ |
+| **Translation** | ✅ | ✅ |
+| **Topic Detection** | ✅ | ✅ |
+| **Summarization** | ✅ | ✅ |
+| **Virtual Meeting Rooms (Whereby)** | ❌ | ✅ |
+| **Browse Transcripts Page** | ❌ | ✅ |
+| **Search Functionality** | ❌ | ✅ |
+| **Persistent Storage** | ❌ | ✅ |
+
+## Architecture Overview
+
+Reflector consists of three main components:
+
+- **Frontend**: React application built with Next.js 14
+- **Backend**: Python server using FastAPI
+- **Processing**: Scalable GPU workers for ML inference (Modal.com or local)
+
+## Getting Started
+
+Ready to deploy Reflector? Head over to our [Installation Guide](./installation/overview) to set up your own instance.
+
+For a quick overview of how Reflector processes audio, check out our [Pipeline Documentation](./pipelines/overview).
+
+## Open Source
+
+Reflector is open source software developed by [Monadical](https://monadical.com) and licensed under the **MIT License**. We welcome contributions from the community!
+
+- [GitHub Repository](https://github.com/monadical-sas/reflector)
+- [Issue Tracker](https://github.com/monadical-sas/reflector/issues)
+- [Pull Requests](https://github.com/monadical-sas/reflector/pulls)
+
+## Support
+
+Need help? Reach out to the community through GitHub Discussions.

docs/docs/pipelines/file-pipeline.md

@@ -0,0 +1,343 @@
+---
+sidebar_position: 2
+title: File Processing Pipeline
+---
+
+# File Processing Pipeline
+
+The file processing pipeline handles uploaded audio files, optimizing for accuracy and throughput.
+
+## Pipeline Stages
+
+### 1. Input Stage
+
+**Accepted Formats:**
+- MP3 (most common)
+- WAV (uncompressed)
+- M4A (Apple format)
+- WebM (browser recordings)
+- MP4 (video with audio track)
+
+**File Validation:**
+- Maximum size: 2GB (configurable)
+- Minimum duration: 5 seconds
+- Maximum duration: 6 hours
+- Sample rate: Any (will be resampled)
+
+### 2. Pre-processing
+
+**Audio Normalization:**
+```yaml
+# Convert to standard format
+- Sample rate: 16kHz (Whisper requirement)
+- Channels: Mono
+- Bit depth: 16-bit
+- Format: WAV
+```
+
+**Noise Reduction (Optional):**
+- Background noise removal
+- Echo cancellation
+- High-pass filter for rumble
+
+### 3. Chunking Strategy
+
+**Default Configuration:**
+```yaml
+chunk_size: 30  # seconds
+overlap: 1      # seconds
+max_parallel: 10
+silence_detection: true
+```
+
+**Chunking with Silence Detection:**
+- Detects silence periods
+- Attempts to break at natural pauses
+- Maintains context with overlap
+- Preserves sentence boundaries
+
+**Chunk Metadata:**
+```json
+{
+  "chunk_id": "chunk_001",
+  "start_time": 0.0,
+  "end_time": 30.0,
+  "duration": 30.0,
+  "has_speech": true,
+  "audio_hash": "sha256:..."
+}
+```
+
+### 4. Transcription Processing
+
+**Whisper Models:**
+
+| Model | Size | Speed | Accuracy | Use Case |
+|-------|------|-------|----------|----------|
+| tiny | 39M | Very Fast | 85% | Quick drafts |
+| base | 74M | Fast | 89% | Good balance |
+| small | 244M | Medium | 91% | Better accuracy |
+| medium | 769M | Slow | 93% | High quality |
+| large-v3 | 1550M | Very Slow | 96% | Best quality |
+
+**Processing Configuration:**
+```python
+transcription_config = {
+    "model": "whisper-base",
+    "language": "auto",  # or specify: "en", "es", etc.
+    "task": "transcribe",  # or "translate"
+    "temperature": 0,  # deterministic
+    "compression_ratio_threshold": 2.4,
+    "no_speech_threshold": 0.6,
+    "condition_on_previous_text": True,
+    "initial_prompt": None,  # optional context
+}
+```
+
+**Parallel Processing:**
+- Each chunk processed independently
+- GPU batching for efficiency
+- Automatic load balancing
+- Failure isolation
+
+### 5. Diarization (Speaker Identification)
+
+**Pyannote 3.1 Pipeline:**
+
+1. **Voice Activity Detection (VAD)**
+   - Identifies speech segments
+   - Filters out silence and noise
+   - Precision: 95%+
+
+2. **Speaker Embedding**
+   - Extracts voice characteristics
+   - 256-dimensional vectors
+   - Speaker-invariant features
+
+3. **Clustering**
+   - Groups similar voice embeddings
+   - Agglomerative clustering
+   - Automatic speaker count detection
+
+4. **Segmentation**
+   - Assigns speaker labels to time segments
+   - Handles overlapping speech
+   - Minimum segment duration: 0.5s
+
+**Configuration:**
+```python
+diarization_config = {
+    "min_speakers": 1,
+    "max_speakers": 10,
+    "min_duration": 0.5,
+    "clustering": "AgglomerativeClustering",
+    "embedding_model": "speechbrain/spkrec-ecapa-voxceleb",
+}
+```
+
+### 6. Alignment & Merging
+
+**Chunk Assembly:**
+```python
+# Merge overlapping segments
+for chunk in chunks:
+    # Remove overlap duplicates
+    if chunk.start < previous.end:
+        chunk.text = resolve_overlap(previous, chunk)
+
+    # Maintain timeline
+    merged_transcript.append(chunk)
+```
+
+**Speaker Alignment:**
+- Map diarization timeline to transcript
+- Resolve speaker changes mid-sentence
+- Handle multiple speakers per segment
+
+**Quality Checks:**
+- Timeline consistency
+- No gaps in transcript
+- Speaker label continuity
+- Confidence score validation
+
+### 7. Post-processing Chain
+
+**Text Formatting:**
+- Sentence capitalization
+- Punctuation restoration
+- Number formatting
+- Acronym detection
+
+**Translation (Optional):**
+```python
+translation_config = {
+    "model": "facebook/seamless-m4t-medium",
+    "source_lang": "auto",
+    "target_langs": ["es", "fr", "de"],
+    "preserve_formatting": True
+}
+```
+
+**Topic Detection:**
+- LLM-based analysis
+- Extract 3-5 key topics
+- Keyword extraction
+- Entity recognition
+
+**Summarization:**
+```python
+summary_config = {
+    "model": "openai-compatible",
+    "max_length": 500,
+    "style": "bullets",  # or "paragraph"
+    "include_action_items": True,
+    "include_decisions": True
+}
+```
+
+### 8. Storage & Delivery
+
+**Database Storage:**
+```sql
+-- Main transcript record
+INSERT INTO transcripts (
+    id, title, duration, language,
+    transcript_text, transcript_json,
+    speakers, topics, summary,
+    created_at, processing_time
+) VALUES (...);
+
+-- Processing metadata
+INSERT INTO processing_metadata (
+    transcript_id, model_versions,
+    chunk_count, total_chunks,
+    error_count, warnings
+) VALUES (...);
+```
+
+**File Storage:**
+- Original audio: S3 (optional)
+- Processed chunks: Temporary (24h)
+- Transcript exports: JSON, SRT, VTT, TXT
+
+**Notification:**
+```json
+{
+  "type": "webhook",
+  "url": "https://your-app.com/webhook",
+  "payload": {
+    "transcript_id": "...",
+    "status": "completed",
+    "duration": 3600,
+    "processing_time": 180
+  }
+}
+```
+
+## Processing Times
+
+**Estimated times for 1 hour of audio:**
+
+| Component | Fast Mode | Balanced | High Quality |
+|-----------|-----------|----------|--------------|
+| Pre-processing | 10s | 10s | 10s |
+| Transcription | 60s | 180s | 600s |
+| Diarization | 30s | 60s | 120s |
+| Post-processing | 20s | 30s | 60s |
+| **Total** | **2 min** | **5 min** | **13 min** |
+
+## Error Handling
+
+### Retry Strategy
+
+```python
+@celery.task(
+    bind=True,
+    max_retries=3,
+    default_retry_delay=60,
+    retry_backoff=True
+)
+def process_chunk(self, chunk_id):
+    try:
+        # Process chunk
+        result = transcribe(chunk_id)
+    except Exception as exc:
+        # Exponential backoff
+        raise self.retry(exc=exc)
+```
+
+### Partial Recovery
+
+- Continue with successful chunks
+- Mark failed chunks in output
+- Provide partial transcript
+- Report processing issues
+
+### Fallback Options
+
+1. **Model Fallback:**
+   - If large model fails, try medium
+   - If GPU fails, try CPU
+   - If Modal fails, try local
+
+2. **Quality Degradation:**
+   - Reduce chunk size
+   - Disable post-processing
+   - Skip diarization if needed
+
+## Optimization Tips
+
+### For Speed
+
+1. Use smaller models (tiny/base)
+2. Increase parallel chunks
+3. Disable diarization
+4. Skip post-processing
+5. Use GPU acceleration
+
+### For Accuracy
+
+1. Use larger models (medium/large)
+2. Enable all pre-processing
+3. Reduce chunk size
+4. Enable silence detection
+5. Multiple pass processing
+
+### For Cost
+
+1. Use Modal spot instances
+2. Batch multiple files
+3. Cache common phrases
+4. Optimize chunk size
+5. Selective post-processing
+
+## Monitoring
+
+### Metrics to Track
+
+```python
+metrics = {
+    "processing_time": histogram,
+    "chunk_success_rate": gauge,
+    "model_accuracy": histogram,
+    "queue_depth": gauge,
+    "gpu_utilization": gauge,
+    "cost_per_hour": counter
+}
+```
+
+### Quality Metrics
+
+- Word Error Rate (WER)
+- Diarization Error Rate (DER)
+- Confidence scores
+- Processing speed
+- User feedback
+
+### Alerts
+
+- Processing time > 30 minutes
+- Error rate > 5%
+- Queue depth > 100
+- GPU memory > 90%
+- Cost spike detected

docs/docs/pipelines/live-pipeline.md

@@ -0,0 +1,7 @@
+---
+title: Live pipeline
+---
+
+# Live pipeline
+
+Documentation coming soon.

docs/docs/pipelines/overview.md

@@ -0,0 +1,7 @@
+---
+title: overview
+---
+
+# overview
+
+Documentation coming soon. See [TODO.md](/docs/TODO) for required information.

docs/docs/reference/api.md

@@ -0,0 +1,441 @@
+---
+title: API Reference
+---
+
+# API Reference
+
+The Reflector API provides a comprehensive RESTful interface for audio transcription, meeting management, and real-time streaming capabilities.
+
+## Base URL
+
+```
+http://localhost:8000/v1
+```
+
+All API endpoints are prefixed with `/v1/` for versioning.
+
+## Authentication
+
+Reflector supports multiple authentication modes:
+
+- **No Authentication** (Public Mode): Basic transcription and upload functionality
+- **JWT Authentication** (Private Mode): Full feature access including meeting rooms and persistent storage
+- **OAuth/OIDC via Authentik**: Enterprise single sign-on integration
+
+## Core Endpoints
+
+### Transcripts
+
+Manage audio transcriptions and their associated metadata.
+
+#### List Transcripts
+```http
+GET /v1/transcripts/
+```
+
+Returns a paginated list of transcripts with filtering options.
+
+#### Create Transcript
+```http
+POST /v1/transcripts/
+```
+
+Create a new transcript from uploaded audio or initialize for streaming.
+
+#### Get Transcript
+```http
+GET /v1/transcripts/{transcript_id}
+```
+
+Retrieve detailed information about a specific transcript.
+
+#### Update Transcript
+```http
+PATCH /v1/transcripts/{transcript_id}
+```
+
+Update transcript metadata, summary, or processing status.
+
+#### Delete Transcript
+```http
+DELETE /v1/transcripts/{transcript_id}
+```
+
+Remove a transcript and its associated data.
+
+### Audio Processing
+
+#### Upload Audio
+```http
+POST /v1/transcripts_audio/{transcript_id}/upload
+```
+
+Upload an audio file for transcription processing.
+
+**Supported formats:**
+- WAV, MP3, M4A, FLAC, OGG
+- Maximum file size: 500MB
+- Sample rates: 8kHz - 48kHz
+
+#### Download Audio
+```http
+GET /v1/transcripts_audio/{transcript_id}/download
+```
+
+Download the original or processed audio file.
+
+#### Stream Audio
+```http
+GET /v1/transcripts_audio/{transcript_id}/stream
+```
+
+Stream audio content with range support for progressive playback.
+
+### WebRTC Streaming
+
+Real-time audio streaming via WebRTC for live transcription.
+
+#### Initialize WebRTC Session
+```http
+POST /v1/transcripts_webrtc/{transcript_id}/offer
+```
+
+Create a WebRTC offer for establishing a peer connection.
+
+#### Complete WebRTC Handshake
+```http
+POST /v1/transcripts_webrtc/{transcript_id}/answer
+```
+
+Submit the WebRTC answer to complete connection setup.
+
+### WebSocket Streaming
+
+Real-time updates and live transcription via WebSocket.
+
+#### WebSocket Endpoint
+```ws
+ws://localhost:8000/v1/transcripts_websocket/{transcript_id}
+```
+
+Receive real-time transcription updates, speaker changes, and processing status.
+
+**Message Types:**
+- `transcription`: New transcribed text segments
+- `diarization`: Speaker identification updates
+- `status`: Processing status changes
+- `error`: Error notifications
+
+### Meetings
+
+Manage virtual meeting rooms and recordings.
+
+#### List Meetings
+```http
+GET /v1/meetings/
+```
+
+Get all meetings for the authenticated user.
+
+#### Create Meeting
+```http
+POST /v1/meetings/
+```
+
+Initialize a new meeting room with Whereby integration.
+
+#### Join Meeting
+```http
+POST /v1/meetings/{meeting_id}/join
+```
+
+Join an existing meeting and start recording.
+
+#### End Meeting
+```http
+POST /v1/meetings/{meeting_id}/end
+```
+
+End the meeting and finalize the recording.
+
+### Rooms
+
+Virtual meeting room configuration and management.
+
+#### List Rooms
+```http
+GET /v1/rooms/
+```
+
+Get available meeting rooms.
+
+#### Create Room
+```http
+POST /v1/rooms/
+```
+
+Create a new persistent meeting room.
+
+#### Update Room Settings
+```http
+PATCH /v1/rooms/{room_id}
+```
+
+Modify room configuration and permissions.
+
+## Response Formats
+
+### Success Response
+```json
+{
+  "id": "uuid",
+  "created_at": "2025-01-20T10:00:00Z",
+  "updated_at": "2025-01-20T10:30:00Z",
+  "data": {...}
+}
+```
+
+### Error Response
+```json
+{
+  "error": {
+    "code": "ERROR_CODE",
+    "message": "Human-readable error message",
+    "details": {...}
+  }
+}
+```
+
+### Status Codes
+
+- `200 OK`: Successful request
+- `201 Created`: Resource created successfully
+- `204 No Content`: Successful deletion
+- `400 Bad Request`: Invalid request parameters
+- `401 Unauthorized`: Authentication required
+- `403 Forbidden`: Insufficient permissions
+- `404 Not Found`: Resource not found
+- `409 Conflict`: Resource conflict
+- `422 Unprocessable Entity`: Validation error
+- `429 Too Many Requests`: Rate limit exceeded
+- `500 Internal Server Error`: Server error
+
+## WebSocket Protocol
+
+The WebSocket connection provides real-time updates during transcription processing. The server sends structured messages to communicate different events and data updates.
+
+### Connection
+```javascript
+const ws = new WebSocket('ws://localhost:8000/v1/transcripts_websocket/{transcript_id}');
+```
+
+### Message Types and Formats
+
+#### Transcription Update
+Sent when new text is transcribed from the audio stream.
+```json
+{
+  "type": "transcription",
+  "data": {
+    "text": "The transcribed text segment",
+    "speaker": "Speaker 1",
+    "timestamp": 1705745623.456,
+    "confidence": 0.95,
+    "segment_id": "seg_001",
+    "is_final": true
+  }
+}
+```
+
+#### Diarization Update
+Sent when speaker changes are detected or speaker labels are updated.
+```json
+{
+  "type": "diarization",
+  "data": {
+    "speaker": "Speaker 2",
+    "speaker_id": "spk_002",
+    "start_time": 1705745620.123,
+    "end_time": 1705745625.456,
+    "confidence": 0.87
+  }
+}
+```
+
+#### Processing Status
+Sent to indicate changes in the processing pipeline status.
+```json
+{
+  "type": "status",
+  "data": {
+    "status": "processing",
+    "stage": "transcription",
+    "progress": 45.5,
+    "message": "Processing audio chunk 12 of 26"
+  }
+}
+```
+
+Status values:
+- `initializing`: Setting up processing pipeline
+- `processing`: Active transcription/diarization
+- `completed`: Processing finished successfully
+- `failed`: Processing encountered an error
+- `paused`: Processing temporarily suspended
+
+#### Summary Update
+Sent when AI-generated summaries or topics are available.
+```json
+{
+  "type": "summary",
+  "data": {
+    "summary": "Brief summary of the conversation",
+    "topics": ["topic1", "topic2", "topic3"],
+    "action_items": ["action 1", "action 2"],
+    "key_points": ["point 1", "point 2"]
+  }
+}
+```
+
+#### Error Messages
+Sent when errors occur during processing.
+```json
+{
+  "type": "error",
+  "data": {
+    "code": "AUDIO_FORMAT_ERROR",
+    "message": "Unsupported audio format",
+    "details": {
+      "format": "unknown",
+      "sample_rate": 0
+    },
+    "recoverable": false
+  }
+}
+```
+
+#### Heartbeat/Keepalive
+Sent periodically to maintain the connection.
+```json
+{
+  "type": "ping",
+  "data": {
+    "timestamp": 1705745630.000
+  }
+}
+```
+
+### Client-to-Server Messages
+
+Clients can send control messages to the server:
+
+#### Start/Resume Processing
+```json
+{
+  "action": "start",
+  "params": {}
+}
+```
+
+#### Pause Processing
+```json
+{
+  "action": "pause",
+  "params": {}
+}
+```
+
+#### Request Status
+```json
+{
+  "action": "get_status",
+  "params": {}
+}
+```
+
+## OpenAPI Specification
+
+The complete OpenAPI 3.0 specification is available at:
+
+```
+http://localhost:8000/v1/openapi.json
+```
+
+You can import this specification into tools like:
+- Postman
+- Insomnia
+- Swagger UI
+- OpenAPI Generator (for client SDK generation)
+
+## SDK Support
+
+While Reflector doesn't provide official SDKs, you can generate client libraries using the OpenAPI specification with tools like:
+
+- **Python**: `openapi-python-client`
+- **TypeScript**: `openapi-typescript-codegen`
+- **Go**: `oapi-codegen`
+- **Java**: `openapi-generator`
+
+## Example Usage
+
+### Python Example
+```python
+import requests
+
+# Upload and transcribe audio
+with open('meeting.mp3', 'rb') as f:
+    response = requests.post(
+        'http://localhost:8000/v1/transcripts/',
+        files={'file': f}
+    )
+    transcript_id = response.json()['id']
+
+# Check transcription status
+status = requests.get(
+    f'http://localhost:8000/v1/transcripts/{transcript_id}'
+).json()
+
+print(f"Transcription status: {status['status']}")
+```
+
+### JavaScript WebSocket Example
+```javascript
+// Connect to WebSocket for real-time transcription updates
+const ws = new WebSocket(`ws://localhost:8000/v1/transcripts_websocket/${transcriptId}`);
+
+ws.onopen = () => {
+    console.log('Connected to transcription WebSocket');
+};
+
+ws.onmessage = (event) => {
+    const message = JSON.parse(event.data);
+
+    switch(message.type) {
+        case 'transcription':
+            console.log(`[${message.data.speaker}]: ${message.data.text}`);
+            break;
+        case 'diarization':
+            console.log(`Speaker change: ${message.data.speaker}`);
+            break;
+        case 'status':
+            console.log(`Status: ${message.data.status}`);
+            break;
+        case 'error':
+            console.error(`Error: ${message.data.message}`);
+            break;
+    }
+};
+
+ws.onerror = (error) => {
+    console.error('WebSocket error:', error);
+};
+
+ws.onclose = () => {
+    console.log('WebSocket connection closed');
+};
+```
+
+## Need Help?
+
+- Review [example implementations](https://github.com/monadical-sas/reflector/tree/main/examples)
+- Open an issue on [GitHub](https://github.com/monadical-sas/reflector/issues)

docs/docs/reference/api/overview.md

@@ -0,0 +1,7 @@
+---
+title: overview
+---
+
+# overview
+
+Documentation coming soon. See [TODO.md](/docs/TODO) for required information.

docs/docs/reference/architecture/backend.md

@@ -0,0 +1,7 @@
+---
+title: backend
+---
+
+# backend
+
+Documentation coming soon. See [TODO.md](/docs/TODO) for required information.

docs/docs/reference/architecture/database.md

@@ -0,0 +1,7 @@
+---
+title: database
+---
+
+# database
+
+Documentation coming soon. See [TODO.md](/docs/TODO) for required information.

docs/docs/reference/architecture/frontend.md

@@ -0,0 +1,7 @@
+---
+title: frontend
+---
+
+# frontend
+
+Documentation coming soon. See [TODO.md](/docs/TODO) for required information.

docs/docs/reference/architecture/overview.md

@@ -0,0 +1,7 @@
+---
+title: overview
+---
+
+# overview
+
+Documentation coming soon. See [TODO.md](/docs/TODO) for required information.

docs/docs/reference/architecture/workers.md

@@ -0,0 +1,7 @@
+---
+title: workers
+---
+
+# workers
+
+Documentation coming soon. See [TODO.md](/docs/TODO) for required information.

docs/docs/reference/configuration.md

@@ -0,0 +1,7 @@
+---
+title: configuration
+---
+
+# configuration
+
+Documentation coming soon. See [TODO.md](/docs/TODO) for required information.

docs/docs/reference/processors/analysis.md

@@ -0,0 +1,7 @@
+---
+title: analysis
+---
+
+# analysis
+
+Documentation coming soon. See [TODO.md](/docs/TODO) for required information.

docs/docs/reference/processors/diarization.md

@@ -0,0 +1,7 @@
+---
+title: diarization
+---
+
+# diarization
+
+Documentation coming soon. See [TODO.md](/docs/TODO) for required information.

docs/docs/reference/processors/transcription.md

@@ -0,0 +1,7 @@
+---
+title: transcription
+---
+
+# transcription
+
+Documentation coming soon. See [TODO.md](/docs/TODO) for required information.

docs/docs/reference/processors/translation.md

@@ -0,0 +1,7 @@
+---
+title: translation
+---
+
+# translation
+
+Documentation coming soon. See [TODO.md](/docs/TODO) for required information.

docs/docs/roadmap.md

@@ -0,0 +1,139 @@
+---
+sidebar_position: 100
+title: Roadmap
+---
+
+# Product Roadmap
+
+Our development roadmap for Reflector, focusing on expanding capabilities while maintaining privacy and performance.
+
+## Planned Features
+
+### 🌍 Multi-Language Support Enhancement
+
+**Current State:**
+- Whisper supports 99+ languages for transcription
+- Parakeet supports English only with high accuracy
+- Translation available to 100+ languages
+
+**Planned Improvements:**
+- Default language selection per room/user
+- Automatic language detection improvements
+- Multi-language diarization support
+- RTL (Right-to-Left) language UI support
+- Language-specific post-processing rules
+
+### 🏠 Self-Hosted Room Providers
+
+**Jitsi Integration**
+
+Moving beyond Whereby to support self-hosted video conferencing:
+
+- No API keys required
+- Complete control over video infrastructure
+- Custom branding and configuration
+- Lower operational costs
+- Enhanced privacy with self-hosted video
+
+**Implementation Plan:**
+- WebRTC bridge for Jitsi Meet
+- Room management API integration
+- Recording synchronization
+- Participant tracking
+
+### 📅 Calendar Integration
+
+**Planned Capabilities:**
+- Google Calendar synchronization
+- Microsoft Outlook integration
+- Automatic meeting room creation
+- Pre-meeting document preparation
+- Post-meeting transcript delivery
+- Recurring meeting support
+
+**Features:**
+- Auto-join scheduled meetings
+- Calendar-based access control
+- Meeting agenda import
+- Action item export to calendar
+
+### 🖥️ Self-Hosted GPU Service
+
+**For organizations with dedicated GPU hardware (H100, A100, RTX 4090):**
+
+**Docker GPU Worker Image:**
+- Self-contained processing service
+- CUDA 11/12 support
+- Pre-loaded models:
+  - Whisper (all sizes)
+  - Pyannote diarization
+  - Seamless-M4T translation
+- Automatic model management
+
+**Deployment Options:**
+- Kubernetes GPU operators
+- Docker Compose with nvidia-docker
+- Bare metal installation
+- Hybrid cloud/on-premise
+
+**Benefits:**
+- No Modal.com dependency
+- Complete data isolation
+- Predictable costs
+- Maximum performance
+- Custom model support
+
+## Future Considerations
+
+### Enhanced Analytics
+- Meeting insights dashboard
+- Speaker participation metrics
+- Topic trends over time
+- Team collaboration patterns
+
+### Advanced AI Features
+- Real-time sentiment analysis
+- Emotion detection
+- Meeting quality scores
+- Automated coaching suggestions
+
+### Integration Ecosystem
+- Slack/Teams notifications
+- CRM integration (Salesforce, HubSpot)
+- Project management tools (Jira, Asana)
+- Knowledge bases (Notion, Confluence)
+
+### Performance Improvements
+- WebAssembly for client-side processing
+- Edge computing support
+- 5G network optimization
+- Blockchain for transcript verification
+
+## Contributing
+
+We welcome community contributions! Areas where you can help:
+
+1. **Language Support**: Add support for your language
+2. **Integrations**: Connect with your favorite tools
+3. **Models**: Fine-tune models for specific domains
+4. **Documentation**: Improve guides and examples
+
+See our [Contributing Guide](https://github.com/monadical-sas/reflector/blob/main/CONTRIBUTING.md) for details.
+
+## Timeline
+
+We don't provide specific dates as development depends on community contributions and priorities. Features are generally released when they're ready and properly tested.
+
+## Feature Requests
+
+Have an idea for Reflector? We'd love to hear it!
+
+- [Open a GitHub Issue](https://github.com/monadical-sas/reflector/issues/new)
+- [Join our Discord](#)
+- [Email us](mailto:reflector@monadical.com)
+
+## Stay Updated
+
+- Watch our [GitHub repository](https://github.com/monadical-sas/reflector)
+- Follow our [blog](#)
+- Subscribe to our [newsletter](#)

docs/docusaurus.config.ts

@@ -0,0 +1,163 @@
+import {themes as prismThemes} from 'prism-react-renderer';
+import type {Config} from '@docusaurus/types';
+import type * as Preset from '@docusaurus/preset-classic';
+import type * as OpenApiPlugin from 'docusaurus-plugin-openapi-docs';
+
+const config: Config = {
+  title: 'Reflector',
+  tagline: 'AI-powered audio transcription and meeting analysis platform',
+  favicon: 'img/favicon.ico',
+
+  url: 'https://monadical-sas.github.io',
+  baseUrl: '/',
+
+  organizationName: 'monadical-sas',
+  projectName: 'reflector',
+
+  onBrokenLinks: 'throw',
+  onBrokenMarkdownLinks: 'warn',
+
+  markdown: {
+    mermaid: true,
+  },
+
+  i18n: {
+    defaultLocale: 'en',
+    locales: ['en'],
+  },
+
+  presets: [
+    [
+      'classic',
+      {
+        docs: {
+          sidebarPath: './sidebars.ts',
+          editUrl: 'https://github.com/monadical-sas/reflector/tree/main/docs/',
+        },
+        blog: false,
+        theme: {
+          customCss: './src/css/custom.css',
+        },
+      } satisfies Preset.Options,
+    ],
+  ],
+
+  plugins: [
+    [
+      'docusaurus-plugin-openapi-docs',
+      {
+        id: 'openapi',
+        docsPluginId: 'classic',
+        config: {
+          reflectorapi: {
+            specPath: 'static/openapi.json', // Use local file fetched by script
+            outputDir: 'docs/reference/api-generated',
+            sidebarOptions: {
+              groupPathsBy: 'tag',
+              categoryLinkSource: 'tag',
+            },
+            downloadUrl: '/openapi.json',
+            hideSendButton: false,
+            showExtensions: true,
+          },
+        } satisfies OpenApiPlugin.Options,
+      },
+    ],
+  ],
+
+  themes: ['docusaurus-theme-openapi-docs', '@docusaurus/theme-mermaid'],
+
+  themeConfig: {
+    image: 'img/reflector-social-card.jpg',
+    colorMode: {
+      defaultMode: 'light',
+      disableSwitch: false,
+      respectPrefersColorScheme: true,
+    },
+    navbar: {
+      title: 'Reflector',
+      logo: {
+        alt: 'Reflector Logo',
+        src: 'img/reflector-logo.svg',
+      },
+      items: [
+        {
+          type: 'docSidebar',
+          sidebarId: 'tutorialSidebar',
+          position: 'left',
+          label: 'Documentation',
+        },
+        {
+          to: '/docs/reference/api',
+          label: 'API',
+          position: 'left',
+        },
+        {
+          href: 'https://github.com/monadical-sas/reflector',
+          label: 'GitHub',
+          position: 'right',
+        },
+      ],
+    },
+    footer: {
+      style: 'dark',
+      links: [
+        {
+          title: 'Documentation',
+          items: [
+            {
+              label: 'Introduction',
+              to: '/docs/intro',
+            },
+            {
+              label: 'Installation',
+              to: '/docs/installation/overview',
+            },
+            {
+              label: 'API Reference',
+              to: '/docs/reference/api',
+            },
+          ],
+        },
+        {
+          title: 'Resources',
+          items: [
+            {
+              label: 'Architecture',
+              to: '/docs/reference/architecture/overview',
+            },
+            {
+              label: 'Pipelines',
+              to: '/docs/pipelines/overview',
+            },
+            {
+              label: 'Roadmap',
+              to: '/docs/roadmap',
+            },
+          ],
+        },
+        {
+          title: 'More',
+          items: [
+            {
+              label: 'GitHub',
+              href: 'https://github.com/monadical-sas/reflector',
+            },
+            {
+              label: 'Docker Hub',
+              href: 'https://hub.docker.com/r/reflector/backend',
+            },
+          ],
+        },
+      ],
+      copyright: `Copyright © ${new Date().getFullYear()} <a href="https://monadical.com" target="_blank" rel="noopener noreferrer">Monadical</a>. Licensed under MIT. Built with Docusaurus.`,
+    },
+    prism: {
+      theme: prismThemes.github,
+      darkTheme: prismThemes.dracula,
+      additionalLanguages: ['python', 'bash', 'docker', 'yaml'],
+    },
+  } satisfies Preset.ThemeConfig,
+};
+
+export default config;

docs/package.json

@@ -0,0 +1,53 @@
+{
+  "name": "docs",
+  "version": "0.0.0",
+  "private": true,
+  "scripts": {
+    "docusaurus": "docusaurus",
+    "start": "docusaurus start",
+    "build": "docusaurus build",
+    "swizzle": "docusaurus swizzle",
+    "deploy": "docusaurus deploy",
+    "clear": "docusaurus clear",
+    "serve": "docusaurus serve",
+    "write-translations": "docusaurus write-translations",
+    "write-heading-ids": "docusaurus write-heading-ids",
+    "typecheck": "tsc",
+    "fetch-openapi": "./scripts/fetch-openapi.sh",
+    "gen-api-docs": "npm run fetch-openapi && docusaurus gen-api-docs reflector",
+    "prebuild": "npm run fetch-openapi"
+  },
+  "dependencies": {
+    "@docusaurus/core": "3.6.3",
+    "@docusaurus/preset-classic": "3.6.3",
+    "@mdx-js/react": "^3.0.0",
+    "clsx": "^2.0.0",
+    "docusaurus-plugin-openapi-docs": "^4.5.1",
+    "docusaurus-theme-openapi-docs": "^4.5.1",
+    "@docusaurus/theme-mermaid": "3.6.3",
+    "prism-react-renderer": "^2.3.0",
+    "react": "^18.0.0",
+    "react-dom": "^18.0.0"
+  },
+  "devDependencies": {
+    "@docusaurus/module-type-aliases": "3.6.3",
+    "@docusaurus/tsconfig": "3.6.3",
+    "@docusaurus/types": "3.6.3",
+    "typescript": "~5.6.2"
+  },
+  "browserslist": {
+    "production": [
+      ">0.5%",
+      "not dead",
+      "not op_mini all"
+    ],
+    "development": [
+      "last 3 chrome version",
+      "last 3 firefox version",
+      "last 5 safari version"
+    ]
+  },
+  "engines": {
+    "node": ">=18.0"
+  }
+}

docs/scripts/fetch-openapi.sh

@@ -0,0 +1,115 @@
+#!/bin/bash
+
+# Script to fetch OpenAPI specification from FastAPI backend
+# Used during documentation build process
+
+set -e
+
+echo "📡 Fetching OpenAPI specification from FastAPI backend..."
+
+# Colors for output
+RED='\033[0;31m'
+GREEN='\033[0;32m'
+YELLOW='\033[1;33m'
+NC='\033[0m' # No Color
+
+# Configuration
+BACKEND_DIR="../server"
+OPENAPI_OUTPUT="./static/openapi.json"
+SERVER_PORT=1250  # Reflector uses port 1250 by default
+MAX_WAIT=30
+
+# Check if backend directory exists
+if [ ! -d "$BACKEND_DIR" ]; then
+    echo -e "${RED}Error: Backend directory not found at $BACKEND_DIR${NC}"
+    exit 1
+fi
+
+# Function to check if server is running
+check_server() {
+    curl -s -o /dev/null -w "%{http_code}" "http://localhost:${SERVER_PORT}/openapi.json" 2>/dev/null
+}
+
+# Function to cleanup on exit
+cleanup() {
+    if [ ! -z "$SERVER_PID" ]; then
+        echo -e "\n${YELLOW}Stopping FastAPI server (PID: $SERVER_PID)...${NC}"
+        kill $SERVER_PID 2>/dev/null || true
+        wait $SERVER_PID 2>/dev/null || true
+    fi
+}
+
+# Set trap to cleanup on exit
+trap cleanup EXIT INT TERM
+
+# Change to backend directory
+cd "$BACKEND_DIR"
+
+# Check if uv is installed
+if ! command -v uv &> /dev/null; then
+    echo -e "${YELLOW}uv not found, checking for python...${NC}"
+    if command -v python3 &> /dev/null; then
+        PYTHON_CMD="python3"
+    elif command -v python &> /dev/null; then
+        PYTHON_CMD="python"
+    else
+        echo -e "${RED}Error: Neither uv nor python found${NC}"
+        exit 1
+    fi
+    RUN_CMD="$PYTHON_CMD -m"
+else
+    RUN_CMD="uv run -m"
+fi
+
+# Start the FastAPI server in the background (let it use default port 1250)
+echo -e "${YELLOW}Starting FastAPI server...${NC}"
+$RUN_CMD reflector.app > /dev/null 2>&1 &
+SERVER_PID=$!
+
+# Wait for server to be ready
+echo -n "Waiting for server to be ready"
+WAITED=0
+while [ $WAITED -lt $MAX_WAIT ]; do
+    if [ "$(check_server)" = "200" ]; then
+        echo -e " ${GREEN}✓${NC}"
+        break
+    fi
+    echo -n "."
+    sleep 1
+    WAITED=$((WAITED + 1))
+done
+
+if [ $WAITED -ge $MAX_WAIT ]; then
+    echo -e " ${RED}✗${NC}"
+    echo -e "${RED}Error: Server failed to start within ${MAX_WAIT} seconds${NC}"
+    exit 1
+fi
+
+# Change back to docs directory
+cd - > /dev/null
+
+# Create static directory if it doesn't exist
+mkdir -p "$(dirname "$OPENAPI_OUTPUT")"
+
+# Fetch the OpenAPI specification
+echo -e "${YELLOW}Fetching OpenAPI specification...${NC}"
+if curl -s "http://localhost:${SERVER_PORT}/openapi.json" -o "$OPENAPI_OUTPUT"; then
+    echo -e "${GREEN}✓ OpenAPI specification saved to $OPENAPI_OUTPUT${NC}"
+
+    # Validate JSON
+    if command -v jq &> /dev/null; then
+        if jq empty "$OPENAPI_OUTPUT" 2>/dev/null; then
+            echo -e "${GREEN}✓ OpenAPI specification is valid JSON${NC}"
+            # Pretty print the JSON
+            jq . "$OPENAPI_OUTPUT" > "${OPENAPI_OUTPUT}.tmp" && mv "${OPENAPI_OUTPUT}.tmp" "$OPENAPI_OUTPUT"
+        else
+            echo -e "${RED}Error: Invalid JSON in OpenAPI specification${NC}"
+            exit 1
+        fi
+    fi
+else
+    echo -e "${RED}Error: Failed to fetch OpenAPI specification${NC}"
+    exit 1
+fi
+
+echo -e "${GREEN}✅ OpenAPI specification successfully fetched!${NC}"

docs/sidebars.ts

@@ -0,0 +1,95 @@
+import type {SidebarsConfig} from '@docusaurus/plugin-content-docs';
+
+const sidebars: SidebarsConfig = {
+  tutorialSidebar: [
+    'intro',
+    {
+      type: 'category',
+      label: 'Concepts',
+      collapsed: false,
+      items: [
+        'concepts/overview',
+        'concepts/modes',
+        'concepts/pipeline',
+      ],
+    },
+    {
+      type: 'category',
+      label: 'Installation',
+      collapsed: false,
+      items: [
+        'installation/overview',
+        'installation/requirements',
+        'installation/docker-setup',
+        'installation/modal-setup',
+        'installation/self-hosted-gpu-setup',
+        'installation/auth-setup',
+        'installation/daily-setup',
+        {
+          type: 'category',
+          label: 'Other Integrations',
+          collapsed: true,
+          items: [
+            'installation/whereby-setup',
+            'installation/zulip-setup',
+          ],
+        },
+      ],
+    },
+    {
+      type: 'category',
+      label: 'Pipelines',
+      items: [
+        'pipelines/overview',
+        'pipelines/file-pipeline',
+        'pipelines/live-pipeline',
+      ],
+    },
+    {
+      type: 'category',
+      label: 'Reference',
+      items: [
+        {
+          type: 'category',
+          label: 'Architecture',
+          items: [
+            'reference/architecture/overview',
+            'reference/architecture/backend',
+            'reference/architecture/frontend',
+            'reference/architecture/workers',
+            'reference/architecture/database',
+          ],
+        },
+        {
+          type: 'category',
+          label: 'Processors',
+          items: [
+            'reference/processors/transcription',
+            'reference/processors/diarization',
+            'reference/processors/translation',
+            'reference/processors/analysis',
+          ],
+        },
+        {
+          type: 'category',
+          label: 'API',
+          items: [
+            {
+              type: 'doc',
+              id: 'reference/api/overview',
+            },
+            {
+              type: 'link',
+              label: 'OpenAPI Reference',
+              href: '/docs/reference/api',
+            },
+          ],
+        },
+        'reference/configuration',
+      ],
+    },
+    'roadmap',
+  ],
+};
+
+export default sidebars;

docs/src/components/HomepageFeatures/index.tsx

@@ -0,0 +1,70 @@
+import clsx from 'clsx';
+import Heading from '@theme/Heading';
+import styles from './styles.module.css';
+
+type FeatureItem = {
+  title: string;
+  Svg: React.ComponentType<React.ComponentProps<'svg'>>;
+  description: JSX.Element;
+};
+
+const FeatureList: FeatureItem[] = [
+  {
+    title: 'Easy to Use',
+    Svg: require('@site/static/img/undraw_docusaurus_mountain.svg').default,
+    description: (
+      <>
+        Docusaurus was designed from the ground up to be easily installed and
+        used to get your website up and running quickly.
+      </>
+    ),
+  },
+  {
+    title: 'Focus on What Matters',
+    Svg: require('@site/static/img/undraw_docusaurus_tree.svg').default,
+    description: (
+      <>
+        Docusaurus lets you focus on your docs, and we&apos;ll do the chores. Go
+        ahead and move your docs into the <code>docs</code> directory.
+      </>
+    ),
+  },
+  {
+    title: 'Powered by React',
+    Svg: require('@site/static/img/undraw_docusaurus_react.svg').default,
+    description: (
+      <>
+        Extend or customize your website layout by reusing React. Docusaurus can
+        be extended while reusing the same header and footer.
+      </>
+    ),
+  },
+];
+
+function Feature({title, Svg, description}: FeatureItem) {
+  return (
+    <div className={clsx('col col--4')}>
+      <div className="text--center">
+        <Svg className={styles.featureSvg} role="img" />
+      </div>
+      <div className="text--center padding-horiz--md">
+        <Heading as="h3">{title}</Heading>
+        <p>{description}</p>
+      </div>
+    </div>
+  );
+}
+
+export default function HomepageFeatures(): JSX.Element {
+  return (
+    <section className={styles.features}>
+      <div className="container">
+        <div className="row">
+          {FeatureList.map((props, idx) => (
+            <Feature key={idx} {...props} />
+          ))}
+        </div>
+      </div>
+    </section>
+  );
+}

docs/src/components/HomepageFeatures/styles.module.css

@@ -0,0 +1,11 @@
+.features {
+  display: flex;
+  align-items: center;
+  padding: 2rem 0;
+  width: 100%;
+}
+
+.featureSvg {
+  height: 200px;
+  width: 200px;
+}

docs/src/css/custom.css

@@ -0,0 +1,46 @@
+/**
+ * Reflector Documentation Theme
+ * Based on frontend colors from www/app/styles/theme.ts
+ */
+
+@import url('https://fonts.googleapis.com/css2?family=Poppins:wght@300;400;500;600;700&display=swap');
+
+:root {
+  --ifm-color-primary: #3158E2;
+  --ifm-color-primary-dark: #2847C9;
+  --ifm-color-primary-darker: #2442BF;
+  --ifm-color-primary-darkest: #1D369C;
+  --ifm-color-primary-light: #4A6FE5;
+  --ifm-color-primary-lighter: #5F81E8;
+  --ifm-color-primary-lightest: #8DA6F0;
+
+  --ifm-background-color: #FFFFFF;
+  --ifm-background-surface-color: #F4F4F4;
+  --ifm-font-color-base: #1A202C;
+  --ifm-font-color-secondary: #838383;
+
+  --ifm-code-font-size: 95%;
+  --docusaurus-highlighted-code-line-bg: rgba(49, 88, 226, 0.1);
+
+  --ifm-font-family-base: 'Poppins', system-ui, -apple-system, sans-serif;
+  --ifm-font-family-monospace: 'Fira Code', 'Monaco', 'Consolas', monospace;
+  --ifm-navbar-background-color: #FFFFFF;
+  --ifm-heading-font-weight: 600;
+}
+
+[data-theme='dark'] {
+  --ifm-color-primary: #B1CBFF;
+  --ifm-color-primary-dark: #91B3FF;
+  --ifm-color-primary-darker: #81A7FF;
+  --ifm-color-primary-darkest: #5189FF;
+  --ifm-color-primary-light: #D1DFFF;
+  --ifm-color-primary-lighter: #E1EBFF;
+  --ifm-color-primary-lightest: #F0F5FF;
+
+  --ifm-background-color: #0C0D0E;
+  --ifm-background-surface-color: #1A202C;
+  --ifm-font-color-base: #E2E8F0;
+  --ifm-font-color-secondary: #A0AEC0;
+  --docusaurus-highlighted-code-line-bg: rgba(177, 203, 255, 0.1);
+  --ifm-navbar-background-color: #1A202C;
+}

docs/src/pages/index.module.css

@@ -0,0 +1,23 @@
+/**
+ * CSS files with the .module.css suffix will be treated as CSS modules
+ * and scoped locally.
+ */
+
+.heroBanner {
+  padding: 4rem 0;
+  text-align: center;
+  position: relative;
+  overflow: hidden;
+}
+
+@media screen and (max-width: 996px) {
+  .heroBanner {
+    padding: 2rem;
+  }
+}
+
+.buttons {
+  display: flex;
+  align-items: center;
+  justify-content: center;
+}

docs/src/pages/index.tsx

@@ -0,0 +1,7 @@
+import React from 'react';
+import { Redirect } from '@docusaurus/router';
+import useBaseUrl from '@docusaurus/useBaseUrl';
+
+export default function Home(): JSX.Element {
+  return <Redirect to={useBaseUrl('/docs/intro')} />;
+}

docs/src/pages/markdown-page.md

@@ -0,0 +1,7 @@
+---
+title: Markdown page example
+---
+
+# Markdown page example
+
+You don't need React to write simple standalone pages.

docs/static/img/logo.svg

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+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 27.9.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 500 500" style="enable-background:new 0 0 500 500;" xml:space="preserve">
+<style type="text/css">
+	.st0{fill:#B6B6B6;}
+	.st1{fill:#4A4A4A;}
+</style>
+<g>
+	<polygon class="st0" points="227.5,51.5 86.5,150.1 100.8,383.9 244.3,249.8 	"/>
+	<polygon class="st1" points="305.4,421.4 423.9,286 244.3,249.8 100.8,383.9 	"/>
+</g>
+<image style="overflow:visible;" width="1504" height="1128" xlink:href="Ref/original-12843059d855efa50c3a12db8586ced7.jpg"  transform="matrix(1 0 0 1 1857.8739 723.9433)">
+</image>
+<image style="overflow:visible;" width="1504" height="1128" xlink:href="Ref/original-f72ce8039f760337a51b47d045b477b8.jpg"  transform="matrix(1 0 0 1 1857.8739 -512.4843)">
+</image>
+</svg>

docs/static/img/reflector-logo.svg

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+<?xml version="1.0" encoding="utf-8"?>
+<!-- Generator: Adobe Illustrator 27.9.0, SVG Export Plug-In . SVG Version: 6.00 Build 0)  -->
+<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
+	 viewBox="0 0 500 500" style="enable-background:new 0 0 500 500;" xml:space="preserve">
+<style type="text/css">
+	.st0{fill:#B6B6B6;}
+	.st1{fill:#4A4A4A;}
+</style>
+<g>
+	<polygon class="st0" points="227.5,51.5 86.5,150.1 100.8,383.9 244.3,249.8 	"/>
+	<polygon class="st1" points="305.4,421.4 423.9,286 244.3,249.8 100.8,383.9 	"/>
+</g>
+<image style="overflow:visible;" width="1504" height="1128" xlink:href="Ref/original-12843059d855efa50c3a12db8586ced7.jpg"  transform="matrix(1 0 0 1 1857.8739 723.9433)">
+</image>
+<image style="overflow:visible;" width="1504" height="1128" xlink:href="Ref/original-f72ce8039f760337a51b47d045b477b8.jpg"  transform="matrix(1 0 0 1 1857.8739 -512.4843)">
+</image>
+</svg>

docs/static/img/undraw_docusaurus_mountain.svg

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+<svg xmlns="http://www.w3.org/2000/svg" width="1088" height="687.962" viewBox="0 0 1088 687.962">
+  <title>Easy to Use</title>
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+  <path d="M344.38682,555.65836h53.08286V529.11693H344.38682Z" transform="translate(-35.5 -118.5)" fill="#3ecc5f" fill-rule="evenodd" />
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+</svg>

docs/transcript.md

@@ -0,0 +1,241 @@
+# Transcript Formats
+
+The Reflector API provides multiple output formats for transcript data through the `transcript_format` query parameter on the GET `/v1/transcripts/{id}` endpoint.
+
+## Overview
+
+When retrieving a transcript, you can specify the desired format using the `transcript_format` query parameter. The API supports four formats optimized for different use cases:
+
+- **text** - Plain text with speaker names (default)
+- **text-timestamped** - Timestamped text with speaker names
+- **webvtt-named** - WebVTT subtitle format with participant names
+- **json** - Structured JSON segments with full metadata
+
+All formats include participant information when available, resolving speaker IDs to actual names.
+
+## Query Parameter Usage
+
+```
+GET /v1/transcripts/{id}?transcript_format={format}
+```
+
+### Parameters
+
+- `transcript_format` (optional): The desired output format
+  - Type: `"text" | "text-timestamped" | "webvtt-named" | "json"`
+  - Default: `"text"`
+
+## Format Descriptions
+
+### Text Format (`text`)
+
+**Use case:** Simple, human-readable transcript for display or export.
+
+**Format:** Speaker names followed by their dialogue, one line per segment.
+
+**Example:**
+```
+John Smith: Hello everyone
+Jane Doe: Hi there
+John Smith: How are you today?
+```
+
+**Request:**
+```bash
+GET /v1/transcripts/{id}?transcript_format=text
+```
+
+**Response:**
+```json
+{
+  "id": "transcript_123",
+  "name": "Meeting Recording",
+  "transcript_format": "text",
+  "transcript": "John Smith: Hello everyone\nJane Doe: Hi there\nJohn Smith: How are you today?",
+  "participants": [
+    {"id": "p1", "speaker": 0, "name": "John Smith"},
+    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
+  ],
+  ...
+}
+```
+
+### Text Timestamped Format (`text-timestamped`)
+
+**Use case:** Transcript with timing information for navigation or reference.
+
+**Format:** `[MM:SS]` timestamp prefix before each speaker and dialogue.
+
+**Example:**
+```
+[00:00] John Smith: Hello everyone
+[00:05] Jane Doe: Hi there
+[00:12] John Smith: How are you today?
+```
+
+**Request:**
+```bash
+GET /v1/transcripts/{id}?transcript_format=text-timestamped
+```
+
+**Response:**
+```json
+{
+  "id": "transcript_123",
+  "name": "Meeting Recording",
+  "transcript_format": "text-timestamped",
+  "transcript": "[00:00] John Smith: Hello everyone\n[00:05] Jane Doe: Hi there\n[00:12] John Smith: How are you today?",
+  "participants": [
+    {"id": "p1", "speaker": 0, "name": "John Smith"},
+    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
+  ],
+  ...
+}
+```
+
+### WebVTT Named Format (`webvtt-named`)
+
+**Use case:** Subtitle files for video players, accessibility tools, or video editing.
+
+**Format:** Standard WebVTT subtitle format with voice tags using participant names.
+
+**Example:**
+```
+WEBVTT
+
+00:00:00.000 --> 00:00:05.000
+<v John Smith>Hello everyone
+
+00:00:05.000 --> 00:00:12.000
+<v Jane Doe>Hi there
+
+00:00:12.000 --> 00:00:18.000
+<v John Smith>How are you today?
+```
+
+**Request:**
+```bash
+GET /v1/transcripts/{id}?transcript_format=webvtt-named
+```
+
+**Response:**
+```json
+{
+  "id": "transcript_123",
+  "name": "Meeting Recording",
+  "transcript_format": "webvtt-named",
+  "transcript": "WEBVTT\n\n00:00:00.000 --> 00:00:05.000\n<v John Smith>Hello everyone\n\n...",
+  "participants": [
+    {"id": "p1", "speaker": 0, "name": "John Smith"},
+    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
+  ],
+  ...
+}
+```
+
+### JSON Format (`json`)
+
+**Use case:** Programmatic access with full timing and speaker metadata.
+
+**Format:** Array of segment objects with speaker information, text content, and precise timing.
+
+**Example:**
+```json
+[
+  {
+    "speaker": 0,
+    "speaker_name": "John Smith",
+    "text": "Hello everyone",
+    "start": 0.0,
+    "end": 5.0
+  },
+  {
+    "speaker": 1,
+    "speaker_name": "Jane Doe",
+    "text": "Hi there",
+    "start": 5.0,
+    "end": 12.0
+  },
+  {
+    "speaker": 0,
+    "speaker_name": "John Smith",
+    "text": "How are you today?",
+    "start": 12.0,
+    "end": 18.0
+  }
+]
+```
+
+**Request:**
+```bash
+GET /v1/transcripts/{id}?transcript_format=json
+```
+
+**Response:**
+```json
+{
+  "id": "transcript_123",
+  "name": "Meeting Recording",
+  "transcript_format": "json",
+  "transcript": [
+    {
+      "speaker": 0,
+      "speaker_name": "John Smith",
+      "text": "Hello everyone",
+      "start": 0.0,
+      "end": 5.0
+    },
+    {
+      "speaker": 1,
+      "speaker_name": "Jane Doe",
+      "text": "Hi there",
+      "start": 5.0,
+      "end": 12.0
+    }
+  ],
+  "participants": [
+    {"id": "p1", "speaker": 0, "name": "John Smith"},
+    {"id": "p2", "speaker": 1, "name": "Jane Doe"}
+  ],
+  ...
+}
+```
+
+## Response Structure
+
+All formats return the same base transcript metadata with an additional `transcript_format` field and format-specific `transcript` field:
+
+### Common Fields
+
+- `id`: Transcript identifier
+- `user_id`: Owner user ID (if authenticated)
+- `name`: Transcript name
+- `status`: Processing status
+- `locked`: Whether transcript is locked for editing
+- `duration`: Total duration in seconds
+- `title`: Auto-generated or custom title
+- `short_summary`: Brief summary
+- `long_summary`: Detailed summary
+- `created_at`: Creation timestamp
+- `share_mode`: Access control setting
+- `source_language`: Original audio language
+- `target_language`: Translation target language
+- `reviewed`: Whether transcript has been reviewed
+- `meeting_id`: Associated meeting ID (if applicable)
+- `source_kind`: Source type (live, file, room)
+- `room_id`: Associated room ID (if applicable)
+- `audio_deleted`: Whether audio has been deleted
+- `participants`: Array of participant objects with speaker mappings
+
+### Format-Specific Fields
+
+- `transcript_format`: The format identifier (discriminator field)
+- `transcript`: The formatted transcript content (string for text/webvtt formats, array for json format)
+
+## Speaker Name Resolution
+
+All formats resolve speaker IDs to participant names when available:
+
+- If a participant exists for the speaker ID, their name is used
+- If no participant exists, a default name like "Speaker 0" is generated
+- Speaker IDs are integers (0, 1, 2, etc.) assigned during diarization

docs/tsconfig.json

@@ -0,0 +1,8 @@
+{
+  // This file is not used in compilation. It is here just for a nice editor experience.
+  "extends": "@docusaurus/tsconfig",
+  "compilerOptions": {
+    "baseUrl": "."
+  },
+  "exclude": [".docusaurus", "build"]
+}

gpu/modal_deployments/.gitignore

@@ -0,0 +1,33 @@
+# OS / Editor
+.DS_Store
+.vscode/
+.idea/
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Logs
+*.log
+
+# Env and secrets
+.env
+.env.*
+*.env
+*.secret
+
+# Build / dist
+build/
+dist/
+.eggs/
+*.egg-info/
+
+# Coverage / test
+.pytest_cache/
+.coverage*
+htmlcov/
+
+# Modal local state (if any)
+modal_mounts/
+.modal_cache/

gpu/modal_deployments/deploy-all.sh

@@ -0,0 +1,150 @@
+#!/bin/bash
+set -e
+
+# --- Usage ---
+usage() {
+    echo "Usage: $0 [OPTIONS]"
+    echo ""
+    echo "Options:"
+    echo "  --hf-token TOKEN    HuggingFace token"
+    echo "  --help              Show this help message"
+    echo ""
+    echo "Examples:"
+    echo "  $0                              # Interactive mode"
+    echo "  $0 --hf-token hf_xxxxx          # Non-interactive mode"
+    echo ""
+    exit 0
+}
+
+# --- Parse Arguments ---
+HF_TOKEN=""
+while [[ $# -gt 0 ]]; do
+    case $1 in
+        --hf-token)
+            HF_TOKEN="$2"
+            shift 2
+            ;;
+        --help)
+            usage
+            ;;
+        *)
+            echo "Unknown option: $1"
+            usage
+            ;;
+    esac
+done
+
+echo "=========================================="
+echo "Reflector GPU Functions Deployment"
+echo "=========================================="
+echo ""
+
+# --- Check Dependencies ---
+if ! command -v modal &> /dev/null; then
+    echo "Error: Modal CLI not installed."
+    echo "  Install with: pip install modal"
+    exit 1
+fi
+
+if ! command -v openssl &> /dev/null; then
+    echo "Error: openssl not found."
+    echo "  Mac: brew install openssl"
+    echo "  Ubuntu: sudo apt-get install openssl"
+    exit 1
+fi
+
+# Check Modal authentication
+if ! modal profile current &> /dev/null; then
+    echo "Error: Not authenticated with Modal."
+    echo "  Run: modal setup"
+    exit 1
+fi
+
+# --- HuggingFace Token Setup ---
+if [ -z "$HF_TOKEN" ]; then
+    echo "HuggingFace token required for Pyannote diarization model."
+    echo "1. Create account at https://huggingface.co"
+    echo "2. Accept license at https://huggingface.co/pyannote/speaker-diarization-3.1"
+    echo "3. Generate token at https://huggingface.co/settings/tokens"
+    echo ""
+    read -p "Enter your HuggingFace token: " HF_TOKEN
+fi
+
+if [ -z "$HF_TOKEN" ]; then
+    echo "Error: HuggingFace token is required for diarization"
+    exit 1
+fi
+
+# Basic token format validation
+if [[ ! "$HF_TOKEN" =~ ^hf_ ]]; then
+    echo "Warning: HuggingFace tokens usually start with 'hf_'"
+    if [ -t 0 ]; then
+        read -p "Continue anyway? (y/n): " confirm
+        if [ "$confirm" != "y" ]; then
+            exit 1
+        fi
+    else
+        echo "Non-interactive mode: proceeding anyway"
+    fi
+fi
+
+# --- Auto-generate reflector<->GPU API Key ---
+echo ""
+echo "Generating API key for GPU services..."
+API_KEY=$(openssl rand -hex 32)
+
+# --- Create Modal Secrets ---
+echo "Creating Modal secrets..."
+
+# Create or update hf_token secret (delete first if exists)
+if modal secret list 2>/dev/null | grep -q "hf_token"; then
+    echo "  -> Recreating secret: hf_token"
+    modal secret delete hf_token --yes 2>/dev/null || true
+fi
+echo "  -> Creating secret: hf_token"
+modal secret create hf_token HF_TOKEN="$HF_TOKEN"
+
+# Create or update reflector-gpu secret (delete first if exists)
+if modal secret list 2>/dev/null | grep -q "reflector-gpu"; then
+    echo "  -> Recreating secret: reflector-gpu"
+    modal secret delete reflector-gpu --yes 2>/dev/null || true
+fi
+echo "  -> Creating secret: reflector-gpu"
+modal secret create reflector-gpu REFLECTOR_GPU_APIKEY="$API_KEY"
+
+# --- Deploy Functions ---
+echo ""
+echo "Deploying transcriber (Whisper)..."
+TRANSCRIBER_URL=$(modal deploy reflector_transcriber.py 2>&1 | grep -o 'https://[^ ]*web.modal.run' | head -1)
+if [ -z "$TRANSCRIBER_URL" ]; then
+    echo "Error: Failed to deploy transcriber. Check Modal dashboard for details."
+    exit 1
+fi
+echo "  -> $TRANSCRIBER_URL"
+
+echo ""
+echo "Deploying diarizer (Pyannote)..."
+DIARIZER_URL=$(modal deploy reflector_diarizer.py 2>&1 | grep -o 'https://[^ ]*web.modal.run' | head -1)
+if [ -z "$DIARIZER_URL" ]; then
+    echo "Error: Failed to deploy diarizer. Check Modal dashboard for details."
+    exit 1
+fi
+echo "  -> $DIARIZER_URL"
+
+# --- Output Configuration ---
+echo ""
+echo "=========================================="
+echo "Deployment complete!"
+echo "=========================================="
+echo ""
+echo "Copy these values to your server's server/.env file:"
+echo ""
+echo "# --- Modal GPU Configuration ---"
+echo "TRANSCRIPT_BACKEND=modal"
+echo "TRANSCRIPT_URL=$TRANSCRIBER_URL"
+echo "TRANSCRIPT_MODAL_API_KEY=$API_KEY"
+echo ""
+echo "DIARIZATION_BACKEND=modal"
+echo "DIARIZATION_URL=$DIARIZER_URL"
+echo "DIARIZATION_MODAL_API_KEY=$API_KEY"
+echo "# --- End Modal Configuration ---"

gpu/modal_deployments/reflector_diarizer.py

@@ -24,6 +24,12 @@ app = modal.App(name="reflector-diarizer")
 upload_volume = modal.Volume.from_name("diarizer-uploads", create_if_missing=True)
 
 
+# IMPORTANT: This function is duplicated in multiple files for deployment isolation.
+# If you modify the audio format detection logic, you MUST update all copies:
+#   - gpu/self_hosted/app/utils.py
+#   - gpu/modal_deployments/reflector_transcriber.py (2 copies)
+#   - gpu/modal_deployments/reflector_transcriber_parakeet.py
+#   - gpu/modal_deployments/reflector_diarizer.py (this file)
 def detect_audio_format(url: str, headers: Mapping[str, str]) -> AudioFileExtension:
     parsed_url = urlparse(url)
     url_path = parsed_url.path
@@ -39,6 +45,8 @@ def detect_audio_format(url: str, headers: Mapping[str, str]) -> AudioFileExtens
         return AudioFileExtension("wav")
     if "audio/mp4" in content_type:
         return AudioFileExtension("mp4")
+    if "audio/webm" in content_type or "video/webm" in content_type:
+        return AudioFileExtension("webm")
 
     raise ValueError(
         f"Unsupported audio format for URL: {url}. "
@@ -105,7 +113,7 @@ def download_pyannote_audio():
 
 
 diarizer_image = (
-    modal.Image.debian_slim(python_version="3.10.8")
+    modal.Image.debian_slim(python_version="3.10")
     .pip_install(
         "pyannote.audio==3.1.0",
         "requests",
@@ -116,7 +124,7 @@ diarizer_image = (
         "transformers==4.34.0",
         "sentencepiece",
         "protobuf",
-        "numpy",
+        "numpy<2",
         "huggingface_hub",
         "hf-transfer",
     )

gpu/modal_deployments/reflector_transcriber.py

@@ -0,0 +1,634 @@
+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 = "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,
+}
+
+
+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
+
+    model_cache.reload()
+
+    download_model(MODEL_NAME, cache_dir=CACHE_PATH)
+
+    model_cache.commit()
+
+
+image = (
+    modal.Image.debian_slim(python_version="3.12")
+    .env(
+        {
+            "HF_HUB_ENABLE_HF_TRANSFER": "1",
+            "LD_LIBRARY_PATH": (
+                "/usr/local/lib/python3.12/site-packages/nvidia/cudnn/lib/:"
+                "/opt/conda/lib/python3.12/site-packages/nvidia/cublas/lib/"
+            ),
+        }
+    )
+    .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",
+        "python-multipart",
+        "requests",
+        "librosa==0.10.1",
+        "numpy<2",
+        "silero-vad==5.1.0",
+    )
+    .run_function(download_model, volumes={CACHE_PATH: model_cache})
+)
+
+
+# IMPORTANT: This function is duplicated in multiple files for deployment isolation.
+# If you modify the audio format detection logic, you MUST update all copies:
+#   - gpu/self_hosted/app/utils.py
+#   - gpu/modal_deployments/reflector_transcriber.py (this file - 2 copies!)
+#   - gpu/modal_deployments/reflector_transcriber_parakeet.py
+#   - gpu/modal_deployments/reflector_diarizer.py
+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")
+    if "audio/webm" in content_type or "video/webm" in content_type:
+        return AudioFileExtension("webm")
+
+    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,
+    image=image,
+    volumes={CACHE_PATH: model_cache, UPLOADS_PATH: upload_volume},
+)
+@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
+        import torch
+
+        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,
+        )
+        print(f"Model is on device: {self.device}")
+
+    @modal.method()
+    def transcribe_segment(
+        self,
+        filename: str,
+        language: str = "en",
+    ):
+        """Transcribe a single uploaded audio file by filename."""
+        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(
+                    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(segment.text for segment in segments).strip()
+        words = [
+            {
+                "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
+
+        # IMPORTANT: This VAD segment logic is duplicated in multiple files for deployment isolation.
+        # If you modify this function, you MUST update all copies:
+        #   - gpu/modal_deployments/reflector_transcriber.py (this file)
+        #   - gpu/modal_deployments/reflector_transcriber_parakeet.py
+        #   - gpu/self_hosted/app/services/transcriber.py
+        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)
+            audio_duration = len(audio_array) / float(SAMPLERATE)
+            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
+            # Handle case where audio ends while speech is still active
+            if start is not None:
+                yield TimeSegment(start / float(SAMPLERATE), audio_duration)
+            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}
+
+
+# IMPORTANT: This function is duplicated in multiple files for deployment isolation.
+# If you modify the audio format detection logic, you MUST update all copies:
+#   - gpu/self_hosted/app/utils.py
+#   - gpu/modal_deployments/reflector_transcriber.py (this file - 2 copies!)
+#   - gpu/modal_deployments/reflector_transcriber_parakeet.py
+#   - gpu/modal_deployments/reflector_diarizer.py
+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"
+    if "audio/webm" in content_type or "video/webm" in content_type:
+        return "webm"
+
+    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=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():
+    from fastapi import (
+        Body,
+        Depends,
+        FastAPI,
+        Form,
+        HTTPException,
+        UploadFile,
+        status,
+    )
+    from fastapi.security import OAuth2PasswordBearer
+
+    transcriber_live = TranscriberWhisperLive()
+    transcriber_file = TranscriberWhisperFile()
+
+    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(dict):
+        pass
+
+    @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),
+    ):
+        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
+
+        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()

gpu/modal_deployments/reflector_transcriber_parakeet.py

@@ -3,7 +3,7 @@ import os
 import sys
 import threading
 import uuid
-from typing import Mapping, NewType
+from typing import Generator, Mapping, NamedTuple, NewType, TypedDict
 from urllib.parse import urlparse
 
 import modal
@@ -14,10 +14,7 @@ SAMPLERATE = 16000
 UPLOADS_PATH = "/uploads"
 CACHE_PATH = "/cache"
 VAD_CONFIG = {
-    "max_segment_duration": 30.0,
-    "batch_max_files": 10,
-    "batch_max_duration": 5.0,
-    "min_segment_duration": 0.02,
+    "batch_max_duration": 30.0,
     "silence_padding": 0.5,
     "window_size": 512,
 }
@@ -25,6 +22,37 @@ VAD_CONFIG = {
 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
@@ -49,19 +77,25 @@ image = (
     .pip_install(
         "hf_transfer==0.1.9",
         "huggingface_hub[hf-xet]==0.31.2",
-        "nemo_toolkit[asr]==2.3.0",
+        "nemo_toolkit[asr]==2.5.0",
         "cuda-python==12.8.0",
         "fastapi==0.115.12",
         "numpy<2",
-        "librosa==0.10.1",
+        "librosa==0.11.0",
         "requests",
-        "silero-vad==5.1.0",
+        "silero-vad==6.2.0",
         "torch",
     )
     .entrypoint([])  # silence chatty logs by container on start
 )
 
 
+# IMPORTANT: This function is duplicated in multiple files for deployment isolation.
+# If you modify the audio format detection logic, you MUST update all copies:
+#   - gpu/self_hosted/app/utils.py
+#   - gpu/modal_deployments/reflector_transcriber.py (2 copies)
+#   - gpu/modal_deployments/reflector_transcriber_parakeet.py (this file)
+#   - gpu/modal_deployments/reflector_diarizer.py
 def detect_audio_format(url: str, headers: Mapping[str, str]) -> AudioFileExtension:
     parsed_url = urlparse(url)
     url_path = parsed_url.path
@@ -77,6 +111,8 @@ def detect_audio_format(url: str, headers: Mapping[str, str]) -> AudioFileExtens
         return AudioFileExtension("wav")
     if "audio/mp4" in content_type:
         return AudioFileExtension("mp4")
+    if "audio/webm" in content_type or "video/webm" in content_type:
+        return AudioFileExtension("webm")
 
     raise ValueError(
         f"Unsupported audio format for URL: {url}. "
@@ -170,12 +206,14 @@ class TranscriberParakeetLive:
                 (output,) = self.model.transcribe([padded_audio], timestamps=True)
 
         text = output.text.strip()
-        words = [
-            {
-                "word": word_info["word"],
-                "start": round(word_info["start"], 2),
-                "end": round(word_info["end"], 2),
-            }
+        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"]
         ]
 
@@ -211,12 +249,12 @@ class TranscriberParakeetLive:
         for i, (filename, output) in enumerate(zip(filenames, outputs)):
             text = output.text.strip()
 
-            words = [
-                {
-                    "word": word_info["word"],
-                    "start": round(word_info["start"], 2),
-                    "end": round(word_info["end"], 2),
-                }
+            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"]
             ]
 
@@ -271,9 +309,17 @@ class TranscriberParakeetFile:
             audio_array, sample_rate = librosa.load(file_path, sr=SAMPLERATE, mono=True)
             return audio_array
 
-        def vad_segment_generator(audio_array):
+        # IMPORTANT: This VAD segment logic is duplicated in multiple files for deployment isolation.
+        # If you modify this function, you MUST update all copies:
+        #   - gpu/modal_deployments/reflector_transcriber.py
+        #   - gpu/modal_deployments/reflector_transcriber_parakeet.py (this file)
+        #   - gpu/self_hosted/app/services/transcriber.py
+        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)
+            audio_duration = len(audio_array) / float(SAMPLERATE)
             window_size = VAD_CONFIG["window_size"]
             start = None
 
@@ -297,107 +343,125 @@ class TranscriberParakeetFile:
                     start_time = start / float(SAMPLERATE)
                     end_time = end / float(SAMPLERATE)
 
-                    # Extract the actual audio segment
-                    audio_segment = audio_array[start:end]
-
-                    yield (start_time, end_time, audio_segment)
+                    yield TimeSegment(start_time, end_time)
                     start = None
 
+            if start is not None:
+                start_time = start / float(SAMPLERATE)
+                yield TimeSegment(start_time, audio_duration)
+
             vad_iterator.reset_states()
 
-        def vad_segment_filter(segments):
-            """Filter VAD segments by duration and chunk large segments"""
-            min_dur = VAD_CONFIG["min_segment_duration"]
-            max_dur = VAD_CONFIG["max_segment_duration"]
+        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]
 
-            for start_time, end_time, audio_segment in segments:
-                segment_duration = end_time - start_time
+                                  ↓ (max_duration=10)
 
-                # Skip very small segments
-                if segment_duration < min_dur:
+              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
 
-                # If segment is within max duration, yield as-is
-                if segment_duration <= max_dur:
-                    yield (start_time, end_time, audio_segment)
+                total_duration = end_time - batch_start_time
+
+                if total_duration <= max_duration:
+                    batch_end_time = end_time
                     continue
 
-                # Chunk large segments into smaller pieces
-                chunk_samples = int(max_dur * SAMPLERATE)
-                current_start = start_time
+                yield TimeSegment(batch_start_time, batch_end_time)
+                batch_start_time = start_time
+                batch_end_time = end_time
 
-                for chunk_offset in range(0, len(audio_segment), chunk_samples):
-                    chunk_audio = audio_segment[
-                        chunk_offset : chunk_offset + chunk_samples
-                    ]
-                    if len(chunk_audio) == 0:
-                        break
+            if batch_start_time is None or batch_end_time is None:
+                return
 
-                    chunk_duration = len(chunk_audio) / float(SAMPLERATE)
-                    chunk_end = current_start + chunk_duration
+            yield TimeSegment(batch_start_time, batch_end_time)
 
-                    # Only yield chunks that meet minimum duration
-                    if chunk_duration >= min_dur:
-                        yield (current_start, chunk_end, chunk_audio)
+        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.
 
-                    current_start = chunk_end
+            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]
 
-        def batch_segments(segments, max_files=10, max_duration=5.0):
-            batch = []
-            batch_duration = 0.0
+                padded_segment = pad_audio(audio_segment, SAMPLERATE)
 
-            for start_time, end_time, audio_segment in segments:
-                segment_duration = end_time - start_time
+                yield AudioSegment(start_time, end_time, padded_segment)
 
-                if segment_duration < VAD_CONFIG["silence_padding"]:
-                    silence_samples = int(
-                        (VAD_CONFIG["silence_padding"] - segment_duration) * SAMPLERATE
-                    )
-                    padding = np.zeros(silence_samples, dtype=np.float32)
-                    audio_segment = np.concatenate([audio_segment, padding])
-                    segment_duration = VAD_CONFIG["silence_padding"]
-
-                batch.append((start_time, end_time, audio_segment))
-                batch_duration += segment_duration
-
-                if len(batch) >= max_files or batch_duration >= max_duration:
-                    yield batch
-                    batch = []
-                    batch_duration = 0.0
-
-            if batch:
-                yield batch
-
-        def transcribe_batch(model, audio_segments):
+        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,
-            segments_info,
-            batch_index,
-            total_batches,
-        ):
+            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, (start_time, end_time, _)) in enumerate(
-                zip(results, segments_info)
-            ):
+            for i, (output, segment) in enumerate(zip(results, segments_info)):
+                start_time, end_time = segment.start, segment.end
                 text = output.text.strip()
-                words = [
-                    {
-                        "word": word_info["word"],
-                        "start": round(
+                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
-                        ),
-                    }
+                        end=round(word_info["end"] + start_time + timestamp_offset, 2),
+                    )
                     for word_info in output.timestamp["word"]
                 ]
 
-                yield text, words
+                yield TranscriptResult(text, words)
 
         upload_volume.reload()
 
@@ -407,41 +471,31 @@ class TranscriberParakeetFile:
 
         audio_array = load_and_convert_audio(file_path)
         total_duration = len(audio_array) / float(SAMPLERATE)
-        processed_duration = 0.0
 
-        all_text_parts = []
-        all_words = []
+        all_text_parts: list[str] = []
+        all_words: list[WordTiming] = []
 
         raw_segments = vad_segment_generator(audio_array)
-        filtered_segments = vad_segment_filter(raw_segments)
-        batches = batch_segments(
-            filtered_segments,
-            VAD_CONFIG["batch_max_files"],
+        speech_segments = batch_speech_segments(
+            raw_segments,
             VAD_CONFIG["batch_max_duration"],
         )
+        audio_segments = batch_segment_to_audio_segment(speech_segments, audio_array)
 
-        batch_index = 0
-        total_batches = max(
-            1, int(total_duration / VAD_CONFIG["batch_max_duration"]) + 1
-        )
+        for batch in audio_segments:
+            audio_segment = batch.audio
+            results = transcribe_batch(self.model, [audio_segment])
 
-        for batch in batches:
-            batch_index += 1
-            audio_segments = [seg[2] for seg in batch]
-            results = transcribe_batch(self.model, audio_segments)
-
-            for text, words in emit_results(
+            for result in emit_results(
                 results,
-                batch,
-                batch_index,
-                total_batches,
+                [batch],
             ):
-                if not text:
+                if not result.text:
                     continue
-                all_text_parts.append(text)
-                all_words.extend(words)
+                all_text_parts.append(result.text)
+                all_words.extend(result.words)
 
-            processed_duration += sum(len(seg[2]) / float(SAMPLERATE) for seg in batch)
+        all_words = enforce_word_timing_constraints(all_words)
 
         combined_text = " ".join(all_text_parts)
         return {"text": combined_text, "words": all_words}

gpu/modal_deployments/reflector_translator.py

@@ -103,7 +103,7 @@ def configure_seamless_m4t():
 
 
 transcriber_image = (
-    Image.debian_slim(python_version="3.10.8")
+    Image.debian_slim(python_version="3.10")
     .apt_install("git")
     .apt_install("wget")
     .apt_install("libsndfile-dev")
@@ -119,6 +119,7 @@ transcriber_image = (
         "fairseq2",
         "pyyaml",
         "hf-transfer~=0.1",
+        "pydantic",
     )
     .run_function(install_seamless_communication)
     .run_function(download_seamlessm4t_model)

gpu/self_hosted/.env.example

@@ -0,0 +1,2 @@
+REFLECTOR_GPU_APIKEY=
+HF_TOKEN=

gpu/self_hosted/.gitignore

@@ -0,0 +1,38 @@
+cache/
+
+# OS / Editor
+.DS_Store
+.vscode/
+.idea/
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+
+# Env and secrets
+.env
+*.env
+*.secret
+HF_TOKEN
+REFLECTOR_GPU_APIKEY
+
+# Virtual env / uv
+.venv/
+venv/
+ENV/
+uv/
+
+# Build / dist
+build/
+dist/
+.eggs/
+*.egg-info/
+
+# Coverage / test
+.pytest_cache/
+.coverage*
+htmlcov/
+
+# Logs
+*.log

gpu/self_hosted/DEV_SETUP.md

@@ -0,0 +1,137 @@
+# Local Development GPU Setup
+
+Run transcription and diarization locally for development/testing.
+
+> **For production deployment**, see the [Self-Hosted GPU Setup Guide](../../docs/docs/installation/self-hosted-gpu-setup.md).
+
+## Prerequisites
+
+1. **Python 3.12+** and **uv** package manager
+2. **FFmpeg** installed and on PATH
+3. **HuggingFace account** with access to pyannote models
+
+### Accept Pyannote Licenses (Required)
+
+Before first run, accept licenses for these gated models (logged into HuggingFace):
+- https://hf.co/pyannote/speaker-diarization-3.1
+- https://hf.co/pyannote/segmentation-3.0
+
+## Quick Start
+
+### 1. Install dependencies
+
+```bash
+cd gpu/self_hosted
+uv sync
+```
+
+### 2. Start the GPU service
+
+```bash
+cd gpu/self_hosted
+HF_TOKEN=<your-huggingface-token> \
+REFLECTOR_GPU_APIKEY=dev-key-12345 \
+.venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
+```
+
+Note: The `.env` file is NOT auto-loaded. Pass env vars explicitly or use:
+```bash
+export HF_TOKEN=<your-token>
+export REFLECTOR_GPU_APIKEY=dev-key-12345
+.venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
+```
+
+### 3. Configure Reflector to use local GPU
+
+Edit `server/.env`:
+
+```bash
+# Transcription - local GPU service
+TRANSCRIPT_BACKEND=modal
+TRANSCRIPT_URL=http://host.docker.internal:8000
+TRANSCRIPT_MODAL_API_KEY=dev-key-12345
+
+# Diarization - local GPU service
+DIARIZATION_BACKEND=modal
+DIARIZATION_URL=http://host.docker.internal:8000
+DIARIZATION_MODAL_API_KEY=dev-key-12345
+```
+
+Note: Use `host.docker.internal` because Reflector server runs in Docker.
+
+### 4. Restart Reflector server
+
+```bash
+cd server
+docker compose restart server worker
+```
+
+## Testing
+
+### Test transcription
+
+```bash
+curl -s -X POST http://localhost:8000/v1/audio/transcriptions \
+  -H "Authorization: Bearer dev-key-12345" \
+  -F "file=@/path/to/audio.wav" \
+  -F "language=en"
+```
+
+### Test diarization
+
+```bash
+curl -s -X POST "http://localhost:8000/diarize?audio_file_url=<audio-url>" \
+  -H "Authorization: Bearer dev-key-12345"
+```
+
+## Platform Notes
+
+### macOS (ARM)
+
+Docker build fails - CUDA packages are x86_64 only. Use local Python instead:
+```bash
+uv sync
+HF_TOKEN=xxx REFLECTOR_GPU_APIKEY=xxx .venv/bin/uvicorn main:app --host 0.0.0.0 --port 8000
+```
+
+### Linux with NVIDIA GPU
+
+Docker works with CUDA acceleration:
+```bash
+docker compose up -d
+```
+
+### CPU-only
+
+Works on any platform, just slower. PyTorch auto-detects and falls back to CPU.
+
+## Switching Back to Modal.com
+
+Edit `server/.env`:
+
+```bash
+TRANSCRIPT_BACKEND=modal
+TRANSCRIPT_URL=https://monadical-sas--reflector-transcriber-parakeet-web.modal.run
+TRANSCRIPT_MODAL_API_KEY=<modal-api-key>
+
+DIARIZATION_BACKEND=modal
+DIARIZATION_URL=https://monadical-sas--reflector-diarizer-web.modal.run
+DIARIZATION_MODAL_API_KEY=<modal-api-key>
+```
+
+## Troubleshooting
+
+### "Could not download pyannote pipeline"
+- Accept model licenses at HuggingFace (see Prerequisites)
+- Verify HF_TOKEN is set and valid
+
+### Service won't start
+- Check port 8000 is free: `lsof -i :8000`
+- Kill orphan processes if needed
+
+### Transcription returns empty text
+- Ensure audio contains speech (not just tones/silence)
+- Check audio format is supported (wav, mp3, etc.)
+
+### Deprecation warnings from torchaudio/pyannote
+- Safe to ignore - doesn't affect functionality

gpu/self_hosted/Dockerfile

@@ -0,0 +1,46 @@
+FROM python:3.12-slim
+
+ENV PYTHONUNBUFFERED=1 \
+    UV_LINK_MODE=copy \
+    UV_NO_CACHE=1
+
+WORKDIR /tmp
+RUN apt-get update \
+ && apt-get install -y \
+    ffmpeg \
+    curl \
+    ca-certificates \
+    gnupg \
+    wget \
+ && apt-get clean
+# Add NVIDIA CUDA repo for Debian 12 (bookworm) and install cuDNN 9 for CUDA 12
+ADD https://developer.download.nvidia.com/compute/cuda/repos/debian12/x86_64/cuda-keyring_1.1-1_all.deb /cuda-keyring.deb
+RUN dpkg -i /cuda-keyring.deb \
+ && rm /cuda-keyring.deb \
+ && apt-get update \
+ && apt-get install -y --no-install-recommends \
+    cuda-cudart-12-6 \
+    libcublas-12-6 \
+    libcudnn9-cuda-12 \
+    libcudnn9-dev-cuda-12 \
+ && apt-get clean \
+ && rm -rf /var/lib/apt/lists/*
+ADD https://astral.sh/uv/install.sh /uv-installer.sh
+RUN sh /uv-installer.sh && rm /uv-installer.sh
+ENV PATH="/root/.local/bin/:$PATH"
+ENV LD_LIBRARY_PATH="/usr/local/cuda/lib64:/usr/lib/x86_64-linux-gnu:$LD_LIBRARY_PATH"
+
+RUN mkdir -p /app
+WORKDIR /app
+COPY pyproject.toml uv.lock /app/
+
+
+COPY ./app /app/app
+COPY ./main.py /app/
+COPY ./runserver.sh /app/
+
+EXPOSE 8000
+
+CMD ["sh", "/app/runserver.sh"]
+
+

gpu/self_hosted/README.md

@@ -0,0 +1,77 @@
+# Self-hosted Model API
+
+Run transcription, translation, and diarization services compatible with Reflector's GPU Model API. Works on CPU or GPU.
+
+Environment variables
+
+- REFLECTOR_GPU_APIKEY: Optional Bearer token. If unset, auth is disabled.
+- HF_TOKEN: Optional. Required for diarization to download pyannote pipelines
+
+Requirements
+
+- FFmpeg must be installed and on PATH (used for URL-based and segmented transcription)
+- Python 3.12+
+- NVIDIA GPU optional. If available, it will be used automatically
+
+Local run
+Set env vars in self_hosted/.env file
+uv sync
+
+uv run uvicorn main:app --host 0.0.0.0 --port 8000
+
+Authentication
+
+- If REFLECTOR_GPU_APIKEY is set, include header: Authorization: Bearer <key>
+
+Endpoints
+
+- POST /v1/audio/transcriptions
+
+  - multipart/form-data
+  - fields: file (single file) OR files[] (multiple files), language, batch (true/false)
+  - response: single { text, words, filename } or { results: [ ... ] }
+
+- POST /v1/audio/transcriptions-from-url
+
+  - application/json
+  - body: { audio_file_url, language, timestamp_offset }
+  - response: { text, words }
+
+- POST /translate
+
+  - text: query parameter
+  - body (application/json): { source_language, target_language }
+  - response: { text: { <src>: original, <tgt>: translated } }
+
+- POST /diarize
+  - query parameters: audio_file_url, timestamp (optional)
+  - requires HF_TOKEN to be set (for pyannote)
+  - response: { diarization: [ { start, end, speaker } ] }
+
+OpenAPI docs
+
+- Visit /docs when the server is running
+
+Docker
+
+- Not yet provided in this directory. A Dockerfile will be added later. For now, use Local run above
+
+# Setup
+
+[SETUP.md](SETUP.md)
+
+# Conformance tests
+
+## From this directory
+
+TRANSCRIPT_URL=http://localhost:8000 \
+TRANSCRIPT_API_KEY=dev-key \
+uv run -m pytest -m model_api --no-cov ../../server/tests/test_model_api_transcript.py
+
+TRANSLATION_URL=http://localhost:8000 \
+TRANSLATION_API_KEY=dev-key \
+uv run -m pytest -m model_api --no-cov ../../server/tests/test_model_api_translation.py
+
+DIARIZATION_URL=http://localhost:8000 \
+DIARIZATION_API_KEY=dev-key \
+uv run -m pytest -m model_api --no-cov ../../server/tests/test_model_api_diarization.py

gpu/self_hosted/app/auth.py

@@ -0,0 +1,19 @@
+import os
+
+from fastapi import Depends, HTTPException, status
+from fastapi.security import OAuth2PasswordBearer
+
+oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
+
+
+def apikey_auth(apikey: str = Depends(oauth2_scheme)):
+    required_key = os.environ.get("REFLECTOR_GPU_APIKEY")
+    if not required_key:
+        return
+    if apikey == required_key:
+        return
+    raise HTTPException(
+        status_code=status.HTTP_401_UNAUTHORIZED,
+        detail="Invalid API key",
+        headers={"WWW-Authenticate": "Bearer"},
+    )

gpu/self_hosted/app/config.py

@@ -0,0 +1,12 @@
+from pathlib import Path
+
+SUPPORTED_FILE_EXTENSIONS = ["mp3", "mp4", "mpeg", "mpga", "m4a", "wav", "webm"]
+SAMPLE_RATE = 16000
+VAD_CONFIG = {
+    "batch_max_duration": 30.0,
+    "silence_padding": 0.5,
+    "window_size": 512,
+}
+
+# App-level paths
+UPLOADS_PATH = Path("/tmp/whisper-uploads")

gpu/self_hosted/app/factory.py

@@ -0,0 +1,30 @@
+from contextlib import asynccontextmanager
+
+from fastapi import FastAPI
+
+from .routers.diarization import router as diarization_router
+from .routers.transcription import router as transcription_router
+from .routers.translation import router as translation_router
+from .services.transcriber import WhisperService
+from .services.diarizer import PyannoteDiarizationService
+from .utils import ensure_dirs
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    ensure_dirs()
+    whisper_service = WhisperService()
+    whisper_service.load()
+    app.state.whisper = whisper_service
+    diarization_service = PyannoteDiarizationService()
+    diarization_service.load()
+    app.state.diarizer = diarization_service
+    yield
+
+
+def create_app() -> FastAPI:
+    app = FastAPI(lifespan=lifespan)
+    app.include_router(transcription_router)
+    app.include_router(translation_router)
+    app.include_router(diarization_router)
+    return app

gpu/self_hosted/app/routers/diarization.py

@@ -0,0 +1,30 @@
+from typing import List
+
+from fastapi import APIRouter, Depends, Request
+from pydantic import BaseModel
+
+from ..auth import apikey_auth
+from ..services.diarizer import PyannoteDiarizationService
+from ..utils import download_audio_file
+
+router = APIRouter(tags=["diarization"])
+
+
+class DiarizationSegment(BaseModel):
+    start: float
+    end: float
+    speaker: int
+
+
+class DiarizationResponse(BaseModel):
+    diarization: List[DiarizationSegment]
+
+
+@router.post(
+    "/diarize", dependencies=[Depends(apikey_auth)], response_model=DiarizationResponse
+)
+def diarize(request: Request, audio_file_url: str, timestamp: float = 0.0):
+    with download_audio_file(audio_file_url) as (file_path, _ext):
+        file_path = str(file_path)
+        diarizer: PyannoteDiarizationService = request.app.state.diarizer
+        return diarizer.diarize_file(file_path, timestamp=timestamp)

gpu/self_hosted/app/routers/transcription.py

@@ -0,0 +1,109 @@
+import uuid
+from typing import Optional, Union
+
+from fastapi import APIRouter, Body, Depends, Form, HTTPException, Request, UploadFile
+from pydantic import BaseModel
+from pathlib import Path
+from ..auth import apikey_auth
+from ..config import SUPPORTED_FILE_EXTENSIONS, UPLOADS_PATH
+from ..services.transcriber import MODEL_NAME
+from ..utils import cleanup_uploaded_files, download_audio_file
+
+router = APIRouter(prefix="/v1/audio", tags=["transcription"])
+
+
+class WordTiming(BaseModel):
+    word: str
+    start: float
+    end: float
+
+
+class TranscriptResult(BaseModel):
+    text: str
+    words: list[WordTiming]
+    filename: Optional[str] = None
+
+
+class TranscriptBatchResponse(BaseModel):
+    results: list[TranscriptResult]
+
+
+@router.post(
+    "/transcriptions",
+    dependencies=[Depends(apikey_auth)],
+    response_model=Union[TranscriptResult, TranscriptBatchResponse],
+)
+def transcribe(
+    request: Request,
+    file: UploadFile = None,
+    files: list[UploadFile] | None = None,
+    model: str = Form(MODEL_NAME),
+    language: str = Form("en"),
+    batch: bool = Form(False),
+):
+    service = request.app.state.whisper
+    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
+
+    uploaded_paths: list[Path] = []
+    with cleanup_uploaded_files(uploaded_paths):
+        for upload_file in upload_files:
+            audio_suffix = upload_file.filename.split(".")[-1].lower()
+            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 = UPLOADS_PATH / unique_filename
+            with open(file_path, "wb") as f:
+                content = upload_file.file.read()
+                f.write(content)
+            uploaded_paths.append(file_path)
+
+        if batch and len(upload_files) > 1:
+            results = []
+            for path in uploaded_paths:
+                result = service.transcribe_file(str(path), language=language)
+                result["filename"] = path.name
+                results.append(result)
+            return {"results": results}
+
+        results = []
+        for path in uploaded_paths:
+            result = service.transcribe_file(str(path), language=language)
+            result["filename"] = path.name
+            results.append(result)
+
+        return {"results": results} if len(results) > 1 else results[0]
+
+
+@router.post(
+    "/transcriptions-from-url",
+    dependencies=[Depends(apikey_auth)],
+    response_model=TranscriptResult,
+)
+def transcribe_from_url(
+    request: Request,
+    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),
+):
+    service = request.app.state.whisper
+    with download_audio_file(audio_file_url) as (file_path, _ext):
+        file_path = str(file_path)
+        result = service.transcribe_vad_url_segment(
+            file_path=file_path, timestamp_offset=timestamp_offset, language=language
+        )
+        return result

gpu/self_hosted/app/routers/translation.py

@@ -0,0 +1,28 @@
+from typing import Dict
+
+from fastapi import APIRouter, Body, Depends
+from pydantic import BaseModel
+
+from ..auth import apikey_auth
+from ..services.translator import TextTranslatorService
+
+router = APIRouter(tags=["translation"])
+
+translator = TextTranslatorService()
+
+
+class TranslationResponse(BaseModel):
+    text: Dict[str, str]
+
+
+@router.post(
+    "/translate",
+    dependencies=[Depends(apikey_auth)],
+    response_model=TranslationResponse,
+)
+def translate(
+    text: str,
+    source_language: str = Body("en"),
+    target_language: str = Body("fr"),
+):
+    return translator.translate(text, source_language, target_language)

gpu/self_hosted/app/services/diarizer.py

@@ -0,0 +1,42 @@
+import os
+import threading
+
+import torch
+import torchaudio
+from pyannote.audio import Pipeline
+
+
+class PyannoteDiarizationService:
+    def __init__(self):
+        self._pipeline = None
+        self._device = "cpu"
+        self._lock = threading.Lock()
+
+    def load(self):
+        self._device = "cuda" if torch.cuda.is_available() else "cpu"
+        self._pipeline = Pipeline.from_pretrained(
+            "pyannote/speaker-diarization-3.1",
+            use_auth_token=os.environ.get("HF_TOKEN"),
+        )
+        self._pipeline.to(torch.device(self._device))
+
+    def diarize_file(self, file_path: str, timestamp: float = 0.0) -> dict:
+        if self._pipeline is None:
+            self.load()
+        waveform, sample_rate = torchaudio.load(file_path)
+        with self._lock:
+            diarization = self._pipeline(
+                {"waveform": waveform, "sample_rate": sample_rate}
+            )
+        words = []
+        for diarization_segment, _, speaker in diarization.itertracks(yield_label=True):
+            words.append(
+                {
+                    "start": round(timestamp + diarization_segment.start, 3),
+                    "end": round(timestamp + diarization_segment.end, 3),
+                    "speaker": int(speaker[-2:])
+                    if speaker and speaker[-2:].isdigit()
+                    else 0,
+                }
+            )
+        return {"diarization": words}

gpu/self_hosted/app/services/transcriber.py

@@ -0,0 +1,217 @@
+import os
+import shutil
+import subprocess
+import threading
+from typing import Generator
+
+import faster_whisper
+import librosa
+import numpy as np
+import torch
+from fastapi import HTTPException
+from silero_vad import VADIterator, load_silero_vad
+
+from ..config import SAMPLE_RATE, VAD_CONFIG
+
+# Whisper configuration (service-local defaults)
+MODEL_NAME = "large-v2"
+# None delegates compute type to runtime: float16 on CUDA, int8 on CPU
+MODEL_COMPUTE_TYPE = None
+MODEL_NUM_WORKERS = 1
+CACHE_PATH = os.path.join(os.path.expanduser("~"), ".cache", "reflector-whisper")
+from ..utils import NoStdStreams
+
+
+class WhisperService:
+    def __init__(self):
+        self.model = None
+        self.device = "cpu"
+        self.lock = threading.Lock()
+
+    def load(self):
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+        compute_type = MODEL_COMPUTE_TYPE or (
+            "float16" if self.device == "cuda" else "int8"
+        )
+        self.model = faster_whisper.WhisperModel(
+            MODEL_NAME,
+            device=self.device,
+            compute_type=compute_type,
+            num_workers=MODEL_NUM_WORKERS,
+            download_root=CACHE_PATH,
+        )
+
+    def pad_audio(self, audio_array, sample_rate: int = SAMPLE_RATE):
+        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
+
+    def enforce_word_timing_constraints(self, words: list[dict]) -> list[dict]:
+        if len(words) <= 1:
+            return words
+        enforced: list[dict] = []
+        for i, word in enumerate(words):
+            current = dict(word)
+            if i < len(words) - 1:
+                next_start = words[i + 1]["start"]
+                if current["end"] > next_start:
+                    current["end"] = next_start
+            enforced.append(current)
+        return enforced
+
+    def transcribe_file(self, file_path: str, language: str = "en") -> dict:
+        input_for_model: str | "object" = file_path
+        try:
+            audio_array, _sample_rate = librosa.load(
+                file_path, sr=SAMPLE_RATE, mono=True
+            )
+            if len(audio_array) / float(SAMPLE_RATE) < VAD_CONFIG["silence_padding"]:
+                input_for_model = self.pad_audio(audio_array, SAMPLE_RATE)
+        except Exception:
+            pass
+
+        with self.lock:
+            with NoStdStreams():
+                segments, _ = self.model.transcribe(
+                    input_for_model,
+                    language=language,
+                    beam_size=5,
+                    word_timestamps=True,
+                    vad_filter=True,
+                    vad_parameters={"min_silence_duration_ms": 500},
+                )
+
+        segments = list(segments)
+        text = "".join(segment.text for segment in segments).strip()
+        words = [
+            {
+                "word": word.word,
+                "start": round(float(word.start), 2),
+                "end": round(float(word.end), 2),
+            }
+            for segment in segments
+            for word in segment.words
+        ]
+        words = self.enforce_word_timing_constraints(words)
+        return {"text": text, "words": words}
+
+    def transcribe_vad_url_segment(
+        self, file_path: str, timestamp_offset: float = 0.0, language: str = "en"
+    ) -> dict:
+        def load_audio_via_ffmpeg(input_path: str, sample_rate: int) -> np.ndarray:
+            ffmpeg_bin = shutil.which("ffmpeg") or "ffmpeg"
+            cmd = [
+                ffmpeg_bin,
+                "-nostdin",
+                "-threads",
+                "1",
+                "-i",
+                input_path,
+                "-f",
+                "f32le",
+                "-acodec",
+                "pcm_f32le",
+                "-ac",
+                "1",
+                "-ar",
+                str(sample_rate),
+                "pipe:1",
+            ]
+            try:
+                proc = subprocess.run(
+                    cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=True
+                )
+            except Exception as e:
+                raise HTTPException(status_code=400, detail=f"ffmpeg failed: {e}")
+            audio = np.frombuffer(proc.stdout, dtype=np.float32)
+            return audio
+
+        # IMPORTANT: This VAD segment logic is duplicated in multiple files for deployment isolation.
+        # If you modify this function, you MUST update all copies:
+        #   - gpu/modal_deployments/reflector_transcriber.py
+        #   - gpu/modal_deployments/reflector_transcriber_parakeet.py
+        #   - gpu/self_hosted/app/services/transcriber.py (this file)
+        def vad_segments(
+            audio_array,
+            sample_rate: int = SAMPLE_RATE,
+            window_size: int = VAD_CONFIG["window_size"],
+        ) -> Generator[tuple[float, float], None, None]:
+            vad_model = load_silero_vad(onnx=False)
+            iterator = VADIterator(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 (start / float(SAMPLE_RATE), end / float(SAMPLE_RATE))
+                    start = None
+            # Handle case where audio ends while speech is still active
+            if start is not None:
+                audio_duration = len(audio_array) / float(sample_rate)
+                yield (start / float(SAMPLE_RATE), audio_duration)
+            iterator.reset_states()
+
+        audio_array = load_audio_via_ffmpeg(file_path, SAMPLE_RATE)
+
+        merged_batches: list[tuple[float, float]] = []
+        batch_start = None
+        batch_end = None
+        max_duration = VAD_CONFIG["batch_max_duration"]
+        for seg_start, seg_end in vad_segments(audio_array):
+            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((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((batch_start, batch_end))
+
+        all_text = []
+        all_words = []
+        for start_time, end_time in merged_batches:
+            s_idx = int(start_time * SAMPLE_RATE)
+            e_idx = int(end_time * SAMPLE_RATE)
+            segment = audio_array[s_idx:e_idx]
+            segment = self.pad_audio(segment, SAMPLE_RATE)
+            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)
+
+        all_words = self.enforce_word_timing_constraints(all_words)
+        return {"text": " ".join(all_text), "words": all_words}

gpu/self_hosted/app/services/translator.py

@@ -0,0 +1,44 @@
+import threading
+
+from transformers import MarianMTModel, MarianTokenizer, pipeline
+
+
+class TextTranslatorService:
+    """Simple text-to-text translator using HuggingFace MarianMT models.
+
+    This mirrors the modal translator API shape but uses text translation only.
+    """
+
+    def __init__(self):
+        self._pipeline = None
+        self._lock = threading.Lock()
+
+    def load(self, source_language: str = "en", target_language: str = "fr"):
+        # Pick a default MarianMT model pair if available; fall back to Helsinki-NLP en->fr
+        model_name = self._resolve_model_name(source_language, target_language)
+        tokenizer = MarianTokenizer.from_pretrained(model_name)
+        model = MarianMTModel.from_pretrained(model_name)
+        self._pipeline = pipeline("translation", model=model, tokenizer=tokenizer)
+
+    def _resolve_model_name(self, src: str, tgt: str) -> str:
+        # Minimal mapping; extend as needed
+        pair = (src.lower(), tgt.lower())
+        mapping = {
+            ("en", "fr"): "Helsinki-NLP/opus-mt-en-fr",
+            ("fr", "en"): "Helsinki-NLP/opus-mt-fr-en",
+            ("en", "es"): "Helsinki-NLP/opus-mt-en-es",
+            ("es", "en"): "Helsinki-NLP/opus-mt-es-en",
+            ("en", "de"): "Helsinki-NLP/opus-mt-en-de",
+            ("de", "en"): "Helsinki-NLP/opus-mt-de-en",
+        }
+        return mapping.get(pair, "Helsinki-NLP/opus-mt-en-fr")
+
+    def translate(self, text: str, source_language: str, target_language: str) -> dict:
+        if self._pipeline is None:
+            self.load(source_language, target_language)
+        with self._lock:
+            results = self._pipeline(
+                text, src_lang=source_language, tgt_lang=target_language
+            )
+        translated = results[0]["translation_text"] if results else ""
+        return {"text": {source_language: text, target_language: translated}}

gpu/self_hosted/app/utils.py

@@ -0,0 +1,115 @@
+import logging
+import os
+import sys
+import uuid
+from contextlib import contextmanager
+from typing import Mapping
+from urllib.parse import urlparse
+from pathlib import Path
+
+import requests
+from fastapi import HTTPException
+
+from .config import SUPPORTED_FILE_EXTENSIONS, UPLOADS_PATH
+
+logger = logging.getLogger(__name__)
+
+
+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()
+
+
+def ensure_dirs():
+    UPLOADS_PATH.mkdir(parents=True, exist_ok=True)
+
+
+# IMPORTANT: This function is duplicated in multiple files for deployment isolation.
+# If you modify the audio format detection logic, you MUST update all copies:
+#   - gpu/self_hosted/app/utils.py (this file)
+#   - gpu/modal_deployments/reflector_transcriber.py (2 copies)
+#   - gpu/modal_deployments/reflector_transcriber_parakeet.py
+#   - gpu/modal_deployments/reflector_diarizer.py
+def detect_audio_format(url: str, headers: Mapping[str, str]) -> str:
+    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"
+    if "audio/webm" in content_type or "video/webm" in content_type:
+        return "webm"
+
+    raise HTTPException(
+        status_code=400,
+        detail=(
+            f"Unsupported audio format for URL. Supported extensions: {', '.join(SUPPORTED_FILE_EXTENSIONS)}"
+        ),
+    )
+
+
+def download_audio_to_uploads(audio_file_url: str) -> tuple[Path, str]:
+    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: Path = UPLOADS_PATH / unique_filename
+
+    with open(file_path, "wb") as f:
+        f.write(response.content)
+
+    return file_path, audio_suffix
+
+
+@contextmanager
+def download_audio_file(audio_file_url: str):
+    """Download an audio file to UPLOADS_PATH and remove it after use.
+
+    Yields (file_path: Path, audio_suffix: str).
+    """
+    file_path, audio_suffix = download_audio_to_uploads(audio_file_url)
+    try:
+        yield file_path, audio_suffix
+    finally:
+        try:
+            file_path.unlink(missing_ok=True)
+        except Exception as e:
+            logger.error("Error deleting temporary file %s: %s", file_path, e)
+
+
+@contextmanager
+def cleanup_uploaded_files(file_paths: list[Path]):
+    """Ensure provided file paths are removed after use.
+
+    The provided list can be populated inside the context; all present entries
+    at exit will be deleted.
+    """
+    try:
+        yield file_paths
+    finally:
+        for path in list(file_paths):
+            try:
+                path.unlink(missing_ok=True)
+            except Exception as e:
+                logger.error("Error deleting temporary file %s: %s", path, e)

gpu/self_hosted/compose.yml

@@ -0,0 +1,10 @@
+services:
+  reflector_gpu:
+    build:
+      context: .
+    ports:
+      - "8000:8000"
+    env_file:
+      - .env
+    volumes:
+      - ./cache:/root/.cache

gpu/self_hosted/main.py

@@ -0,0 +1,3 @@
+from app.factory import create_app
+
+app = create_app()

gpu/self_hosted/pyproject.toml

@@ -0,0 +1,19 @@
+[project]
+name = "reflector-gpu"
+version = "0.1.0"
+description = "Self-hosted GPU service for speech transcription, diarization, and translation via FastAPI."
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "fastapi[standard]>=0.116.1",
+    "uvicorn[standard]>=0.30.0",
+    "torch>=2.3.0",
+    "faster-whisper>=1.1.0",
+    "librosa==0.10.1",
+    "numpy<2",
+    "silero-vad==5.1.0",
+    "transformers>=4.35.0",
+    "sentencepiece",
+    "pyannote.audio==3.1.0",
+    "torchaudio>=2.3.0",
+]

gpu/self_hosted/runserver.sh

@@ -0,0 +1,17 @@
+#!/bin/sh
+set -e
+
+export PATH="/root/.local/bin:$PATH"
+cd /app
+
+# Install Python dependencies at runtime (first run or when FORCE_SYNC=1)
+if [ ! -d "/app/.venv" ] || [ "$FORCE_SYNC" = "1" ]; then
+  echo "[startup] Installing Python dependencies with uv..."
+  uv sync --compile-bytecode --locked
+else
+  echo "[startup] Using existing virtual environment at /app/.venv"
+fi
+
+exec uv run uvicorn main:app --host 0.0.0.0 --port 8000
+
+