chore(main): release 0.7.2 (#559)

On ARM64, the docker iamge crash because torch cannot load libgomp.so.1
-- Look like pytorch does not install the same packages depending the
platform.

AMD64:

/app/.venv/lib/python3.12/site-packages/torch/lib/libgomp.so.1
/app/.venv/lib/python3.12/site-packages/ctranslate2.libs/libgomp-a34b3233.so.1.0.0
/app/.venv/lib/python3.12/site-packages/scikit_learn.libs/libgomp-a34b3233.so.1.0.0

ARM64:

/app/.venv/lib/python3.12/site-packages/ctranslate2.libs/libgomp-d22c30c5.so.1.0.0
/app/.venv/lib/python3.12/site-packages/scikit_learn.libs/libgomp-947d5fa1.so.1.0.0
/app/.venv/lib/python3.12/site-packages/torch.libs/libgomp-947d5fa1.so.1.0.0

.github/workflows/deploy.yml

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

.github/workflows/test_server.yml

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

CHANGELOG.md

@@ -1,5 +1,37 @@
 # Changelog
 
+## [0.7.2](https://github.com/Monadical-SAS/reflector/compare/v0.7.1...v0.7.2) (2025-08-21)
+
+
+### Bug Fixes
+
+* docker image not loading libgomp.so.1 for torch ([#560](https://github.com/Monadical-SAS/reflector/issues/560)) ([773fccd](https://github.com/Monadical-SAS/reflector/commit/773fccd93e887c3493abc2e4a4864dddce610177))
+* include shared rooms to search ([#558](https://github.com/Monadical-SAS/reflector/issues/558)) ([499eced](https://github.com/Monadical-SAS/reflector/commit/499eced3360b84fb3a90e1c8a3b554290d21adc2))
+
+## [0.7.1](https://github.com/Monadical-SAS/reflector/compare/v0.7.0...v0.7.1) (2025-08-21)
+
+
+### Bug Fixes
+
+* webvtt db null expectation mismatch ([#556](https://github.com/Monadical-SAS/reflector/issues/556)) ([e67ad1a](https://github.com/Monadical-SAS/reflector/commit/e67ad1a4a2054467bfeb1e0258fbac5868aaaf21))
+
+## [0.7.0](https://github.com/Monadical-SAS/reflector/compare/v0.6.1...v0.7.0) (2025-08-21)
+
+
+### Features
+
+* delete recording with transcript ([#547](https://github.com/Monadical-SAS/reflector/issues/547)) ([99cc984](https://github.com/Monadical-SAS/reflector/commit/99cc9840b3f5de01e0adfbfae93234042d706d13))
+* pipeline improvement with file processing, parakeet, silero-vad ([#540](https://github.com/Monadical-SAS/reflector/issues/540)) ([bcc29c9](https://github.com/Monadical-SAS/reflector/commit/bcc29c9e0050ae215f89d460e9d645aaf6a5e486))
+* postgresql migration and removal of sqlite in pytest ([#546](https://github.com/Monadical-SAS/reflector/issues/546)) ([cd1990f](https://github.com/Monadical-SAS/reflector/commit/cd1990f8f0fe1503ef5069512f33777a73a93d7f))
+* search backend ([#537](https://github.com/Monadical-SAS/reflector/issues/537)) ([5f9b892](https://github.com/Monadical-SAS/reflector/commit/5f9b89260c9ef7f3c921319719467df22830453f))
+* search frontend  ([#551](https://github.com/Monadical-SAS/reflector/issues/551)) ([3657242](https://github.com/Monadical-SAS/reflector/commit/365724271ca6e615e3425125a69ae2b46ce39285))
+
+
+### Bug Fixes
+
+* evaluation cli event wrap ([#536](https://github.com/Monadical-SAS/reflector/issues/536)) ([941c3db](https://github.com/Monadical-SAS/reflector/commit/941c3db0bdacc7b61fea412f3746cc5a7cb67836))
+* use structlog not logging ([#550](https://github.com/Monadical-SAS/reflector/issues/550)) ([27e2f81](https://github.com/Monadical-SAS/reflector/commit/27e2f81fda5232e53edc729d3e99c5ef03adbfe9))
+
 ## [0.6.1](https://github.com/Monadical-SAS/reflector/compare/v0.6.0...v0.6.1) (2025-08-06)
 
 

server/.gitignore

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

server/Dockerfile

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

server/README.md

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

server/gpu/modal_deployments/README.md

@@ -4,7 +4,8 @@ This repository hold an API for the GPU implementation of the Reflector API serv
 and use [Modal.com](https://modal.com)
 
 - `reflector_diarizer.py` - Diarization API
-- `reflector_transcriber.py` - Transcription API
+- `reflector_transcriber.py` - Transcription API (Whisper)
+- `reflector_transcriber_parakeet.py` - Transcription API (NVIDIA Parakeet)
 - `reflector_translator.py` - Translation API
 
 ## Modal.com deployment
@@ -19,6 +20,10 @@ $ modal deploy reflector_transcriber.py
 ...
 └── 🔨 Created web => https://xxxx--reflector-transcriber-web.modal.run
 
+$ modal deploy reflector_transcriber_parakeet.py
+...
+└── 🔨 Created web => https://xxxx--reflector-transcriber-parakeet-web.modal.run
+
 $ modal deploy reflector_llm.py
 ...
 └── 🔨 Created web => https://xxxx--reflector-llm-web.modal.run
@@ -68,6 +73,86 @@ Authorization: bearer <REFLECTOR_APIKEY>
 
 ### Transcription
 
+#### Parakeet Transcriber (`reflector_transcriber_parakeet.py`)
+
+NVIDIA Parakeet is a state-of-the-art ASR model optimized for real-time transcription with superior word-level timestamps.
+
+**GPU Configuration:**
+- **A10G GPU** - Used for `/v1/audio/transcriptions` endpoint (small files, live transcription)
+  - Higher concurrency (max_inputs=10)
+  - Optimized for multiple small audio files
+  - Supports batch processing for efficiency
+
+- **L40S GPU** - Used for `/v1/audio/transcriptions-from-url` endpoint (large files)
+  - Lower concurrency but more powerful processing
+  - Optimized for single large audio files
+  - VAD-based chunking for long-form audio
+
+##### `/v1/audio/transcriptions` - Small file transcription
+
+**request** (multipart/form-data)
+- `file` or `files[]` - audio file(s) to transcribe
+- `model` - model name (default: `nvidia/parakeet-tdt-0.6b-v2`)
+- `language` - language code (default: `en`)
+- `batch` - whether to use batch processing for multiple files (default: `true`)
+
+**response**
+```json
+{
+    "text": "transcribed text",
+    "words": [
+        {"word": "hello", "start": 0.0, "end": 0.5},
+        {"word": "world", "start": 0.5, "end": 1.0}
+    ],
+    "filename": "audio.mp3"
+}
+```
+
+For multiple files with batch=true:
+```json
+{
+    "results": [
+        {
+            "filename": "audio1.mp3",
+            "text": "transcribed text",
+            "words": [...]
+        },
+        {
+            "filename": "audio2.mp3",
+            "text": "transcribed text",
+            "words": [...]
+        }
+    ]
+}
+```
+
+##### `/v1/audio/transcriptions-from-url` - Large file transcription
+
+**request** (application/json)
+```json
+{
+    "audio_file_url": "https://example.com/audio.mp3",
+    "model": "nvidia/parakeet-tdt-0.6b-v2",
+    "language": "en",
+    "timestamp_offset": 0.0
+}
+```
+
+**response**
+```json
+{
+    "text": "transcribed text from large file",
+    "words": [
+        {"word": "hello", "start": 0.0, "end": 0.5},
+        {"word": "world", "start": 0.5, "end": 1.0}
+    ]
+}
+```
+
+**Supported file types:** mp3, mp4, mpeg, mpga, m4a, wav, webm
+
+#### Whisper Transcriber (`reflector_transcriber.py`)
+
 `POST /transcribe`
 
 **request** (multipart/form-data)

server/gpu/modal_deployments/reflector_diarizer.py

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

server/gpu/modal_deployments/reflector_transcriber_parakeet.py

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

server/migrations/versions/0ab2d7ffaa16_add_long_summary_to_search_vector.py

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

server/migrations/versions/b1c33bd09963_add_search_optimization_indexes.py

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

server/pyproject.toml

@@ -32,7 +32,6 @@ dependencies = [
     "redis>=5.0.1",
     "python-jose[cryptography]>=3.3.0",
     "python-multipart>=0.0.6",
-    "faster-whisper>=0.10.0",
     "transformers>=4.36.2",
     "jsonschema>=4.23.0",
     "openai>=1.59.7",
@@ -57,6 +56,7 @@ tests = [
     "httpx-ws>=0.4.1",
     "pytest-httpx>=0.23.1",
     "pytest-celery>=0.0.0",
+    "pytest-recording>=0.13.4",
     "pytest-docker>=3.2.3",
     "asgi-lifespan>=2.1.0",
 ]
@@ -67,6 +67,15 @@ evaluation = [
     "tqdm>=4.66.0",
     "pydantic>=2.1.1",
 ]
+local = [
+    "pyannote-audio>=3.3.2",
+    "faster-whisper>=0.10.0",
+]
+silero-vad = [
+    "silero-vad>=5.1.2",
+    "torch>=2.8.0",
+    "torchaudio>=2.8.0",
+]
 
 [tool.uv]
 default-groups = [
@@ -74,6 +83,21 @@ default-groups = [
     "tests",
     "aws",
     "evaluation",
+    "local",
+    "silero-vad"
+]
+
+[[tool.uv.index]]
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+explicit = true
+
+[tool.uv.sources]
+torch = [
+  { index = "pytorch-cpu" },
+]
+torchaudio = [
+  { index = "pytorch-cpu" },
 ]
 
 [build-system]
@@ -94,6 +118,9 @@ DATABASE_URL = "postgresql://test_user:test_password@localhost:15432/reflector_t
 addopts = "-ra -q --disable-pytest-warnings --cov --cov-report html -v"
 testpaths = ["tests"]
 asyncio_mode = "auto"
+markers = [
+    "gpu_modal: mark test to run only with GPU Modal endpoints (deselect with '-m \"not gpu_modal\"')",
+]
 
 [tool.ruff.lint]
 select = [

server/reflector/db/search.py

@@ -1,24 +1,37 @@
 """Search functionality for transcripts and other entities."""
 
+import itertools
+from dataclasses import dataclass
 from datetime import datetime
 from io import StringIO
-from typing import Annotated, Any, Dict
+from typing import Annotated, Any, Dict, Iterator
 
 import sqlalchemy
 import webvtt
-from pydantic import BaseModel, Field, constr, field_serializer
+from fastapi import HTTPException
+from pydantic import (
+    BaseModel,
+    Field,
+    NonNegativeFloat,
+    NonNegativeInt,
+    ValidationError,
+    constr,
+    field_serializer,
+)
 
 from reflector.db import get_database
+from reflector.db.rooms import rooms
 from reflector.db.transcripts import SourceKind, transcripts
 from reflector.db.utils import is_postgresql
 from reflector.logger import logger
 
 DEFAULT_SEARCH_LIMIT = 20
 SNIPPET_CONTEXT_LENGTH = 50  # Characters before/after match to include
-DEFAULT_SNIPPET_MAX_LENGTH = 150
-DEFAULT_MAX_SNIPPETS = 3
+DEFAULT_SNIPPET_MAX_LENGTH = NonNegativeInt(150)
+DEFAULT_MAX_SNIPPETS = NonNegativeInt(3)
+LONG_SUMMARY_MAX_SNIPPETS = 2
 
-SearchQueryBase = constr(min_length=1, strip_whitespace=True)
+SearchQueryBase = constr(min_length=0, strip_whitespace=True)
 SearchLimitBase = Annotated[int, Field(ge=1, le=100)]
 SearchOffsetBase = Annotated[int, Field(ge=0)]
 SearchTotalBase = Annotated[int, Field(ge=0)]
@@ -32,6 +45,82 @@ SearchTotal = Annotated[
     SearchTotalBase, Field(description="Total number of search results")
 ]
 
+WEBVTT_SPEC_HEADER = "WEBVTT"
+
+WebVTTContent = Annotated[
+    str,
+    Field(min_length=len(WEBVTT_SPEC_HEADER), description="WebVTT content"),
+]
+
+
+class WebVTTProcessor:
+    """Stateless processor for WebVTT content operations."""
+
+    @staticmethod
+    def parse(raw_content: str) -> WebVTTContent:
+        """Parse WebVTT content and return it as a string."""
+        if not raw_content.startswith(WEBVTT_SPEC_HEADER):
+            raise ValueError(f"Invalid WebVTT content, no header {WEBVTT_SPEC_HEADER}")
+        return raw_content
+
+    @staticmethod
+    def extract_text(webvtt_content: WebVTTContent) -> str:
+        """Extract plain text from WebVTT content using webvtt library."""
+        try:
+            buffer = StringIO(webvtt_content)
+            vtt = webvtt.read_buffer(buffer)
+            return " ".join(caption.text for caption in vtt if caption.text)
+        except webvtt.errors.MalformedFileError as e:
+            logger.warning(f"Malformed WebVTT content: {e}")
+            return ""
+        except (UnicodeDecodeError, ValueError) as e:
+            logger.warning(f"Failed to decode WebVTT content: {e}")
+            return ""
+        except AttributeError as e:
+            logger.error(
+                f"WebVTT parsing error - unexpected format: {e}", exc_info=True
+            )
+            return ""
+        except Exception as e:
+            logger.error(f"Unexpected error parsing WebVTT: {e}", exc_info=True)
+            return ""
+
+    @staticmethod
+    def generate_snippets(
+        webvtt_content: WebVTTContent,
+        query: str,
+        max_snippets: NonNegativeInt = DEFAULT_MAX_SNIPPETS,
+    ) -> list[str]:
+        """Generate snippets from WebVTT content."""
+        return SnippetGenerator.generate(
+            WebVTTProcessor.extract_text(webvtt_content),
+            query,
+            max_snippets=max_snippets,
+        )
+
+
+@dataclass(frozen=True)
+class SnippetCandidate:
+    """Represents a candidate snippet with its position."""
+
+    _text: str
+    start: NonNegativeInt
+    _original_text_length: int
+
+    @property
+    def end(self) -> NonNegativeInt:
+        """Calculate end position from start and raw text length."""
+        return self.start + len(self._text)
+
+    def text(self) -> str:
+        """Get display text with ellipses added if needed."""
+        result = self._text.strip()
+        if self.start > 0:
+            result = "..." + result
+        if self.end < self._original_text_length:
+            result = result + "..."
+        return result
+
 
 class SearchParameters(BaseModel):
     """Validated search parameters for full-text search."""
@@ -41,6 +130,7 @@ class SearchParameters(BaseModel):
     offset: SearchOffset = 0
     user_id: str | None = None
     room_id: str | None = None
+    source_kind: SourceKind | None = None
 
 
 class SearchResultDB(BaseModel):
@@ -64,13 +154,18 @@ class SearchResult(BaseModel):
     title: str | None = None
     user_id: str | None = None
     room_id: str | None = None
+    room_name: str | None = None
+    source_kind: SourceKind
     created_at: datetime
     status: str = Field(..., min_length=1)
     rank: float = Field(..., ge=0, le=1)
-    duration: float | None = Field(..., ge=0, description="Duration in seconds")
+    duration: NonNegativeFloat | None = Field(..., description="Duration in seconds")
     search_snippets: list[str] = Field(
         description="Text snippets around search matches"
     )
+    total_match_count: NonNegativeInt = Field(
+        default=0, description="Total number of matches found in the transcript"
+    )
 
     @field_serializer("created_at", when_used="json")
     def serialize_datetime(self, dt: datetime) -> str:
@@ -79,84 +174,153 @@ class SearchResult(BaseModel):
         return dt.isoformat()
 
 
-class SearchController:
-    """Controller for search operations across different entities."""
+class SnippetGenerator:
+    """Stateless generator for text snippets and match operations."""
 
     @staticmethod
-    def _extract_webvtt_text(webvtt_content: str) -> str:
-        """Extract plain text from WebVTT content using webvtt library."""
-        if not webvtt_content:
-            return ""
+    def find_all_matches(text: str, query: str) -> Iterator[int]:
+        """Generate all match positions for a query in text."""
+        if not text:
+            logger.warning("Empty text for search query in find_all_matches")
+            return
+        if not query:
+            logger.warning("Empty query for search text in find_all_matches")
+            return
 
-        try:
-            buffer = StringIO(webvtt_content)
-            vtt = webvtt.read_buffer(buffer)
-            return " ".join(caption.text for caption in vtt if caption.text)
-        except (webvtt.errors.MalformedFileError, UnicodeDecodeError, ValueError) as e:
-            logger.warning(f"Failed to parse WebVTT content: {e}", exc_info=e)
-            return ""
-        except AttributeError as e:
-            logger.warning(f"WebVTT parsing error - unexpected format: {e}", exc_info=e)
-            return ""
+        text_lower = text.lower()
+        query_lower = query.lower()
+        start = 0
+        prev_start = start
+        while (pos := text_lower.find(query_lower, start)) != -1:
+            yield pos
+            start = pos + len(query_lower)
+            if start <= prev_start:
+                raise ValueError("panic! find_all_matches is not incremental")
+            prev_start = start
 
     @staticmethod
-    def _generate_snippets(
+    def count_matches(text: str, query: str) -> NonNegativeInt:
+        """Count total number of matches for a query in text."""
+        ZERO = NonNegativeInt(0)
+        if not text:
+            logger.warning("Empty text for search query in count_matches")
+            return ZERO
+        if not query:
+            logger.warning("Empty query for search text in count_matches")
+            return ZERO
+        return NonNegativeInt(
+            sum(1 for _ in SnippetGenerator.find_all_matches(text, query))
+        )
+
+    @staticmethod
+    def create_snippet(
+        text: str, match_pos: int, max_length: int = DEFAULT_SNIPPET_MAX_LENGTH
+    ) -> SnippetCandidate:
+        """Create a snippet from a match position."""
+        snippet_start = NonNegativeInt(max(0, match_pos - SNIPPET_CONTEXT_LENGTH))
+        snippet_end = min(len(text), match_pos + max_length - SNIPPET_CONTEXT_LENGTH)
+
+        snippet_text = text[snippet_start:snippet_end]
+
+        return SnippetCandidate(
+            _text=snippet_text, start=snippet_start, _original_text_length=len(text)
+        )
+
+    @staticmethod
+    def filter_non_overlapping(
+        candidates: Iterator[SnippetCandidate],
+    ) -> Iterator[str]:
+        """Filter out overlapping snippets and return only display text."""
+        last_end = 0
+        for candidate in candidates:
+            display_text = candidate.text()
+            # it means that next overlapping snippets simply don't get included
+            # it's fine as simplistic logic and users probably won't care much because they already have their search results just fin
+            if candidate.start >= last_end and display_text:
+                yield display_text
+                last_end = candidate.end
+
+    @staticmethod
+    def generate(
         text: str,
-        q: SearchQuery,
-        max_length: int = DEFAULT_SNIPPET_MAX_LENGTH,
-        max_snippets: int = DEFAULT_MAX_SNIPPETS,
+        query: str,
+        max_length: NonNegativeInt = DEFAULT_SNIPPET_MAX_LENGTH,
+        max_snippets: NonNegativeInt = DEFAULT_MAX_SNIPPETS,
     ) -> list[str]:
-        """Generate multiple snippets around all occurrences of search term."""
-        if not text or not q:
+        """Generate snippets from text."""
+        if not text or not query:
+            logger.warning("Empty text or query for generate_snippets")
             return []
 
-        snippets = []
-        lower_text = text.lower()
-        search_lower = q.lower()
+        candidates = (
+            SnippetGenerator.create_snippet(text, pos, max_length)
+            for pos in SnippetGenerator.find_all_matches(text, query)
+        )
+        filtered = SnippetGenerator.filter_non_overlapping(candidates)
+        snippets = list(itertools.islice(filtered, max_snippets))
 
-        last_snippet_end = 0
-        start_pos = 0
-
-        while len(snippets) < max_snippets:
-            match_pos = lower_text.find(search_lower, start_pos)
-
-            if match_pos == -1:
-                if not snippets and search_lower.split():
-                    first_word = search_lower.split()[0]
-                    match_pos = lower_text.find(first_word, start_pos)
-                    if match_pos == -1:
-                        break
-                else:
-                    break
-
-            snippet_start = max(0, match_pos - SNIPPET_CONTEXT_LENGTH)
-            snippet_end = min(
-                len(text), match_pos + max_length - SNIPPET_CONTEXT_LENGTH
-            )
-
-            if snippet_start < last_snippet_end:
-                start_pos = match_pos + len(search_lower)
-                continue
-
-            snippet = text[snippet_start:snippet_end]
-
-            if snippet_start > 0:
-                snippet = "..." + snippet
-            if snippet_end < len(text):
-                snippet = snippet + "..."
-
-            snippet = snippet.strip()
-
-            if snippet:
-                snippets.append(snippet)
-                last_snippet_end = snippet_end
-
-            start_pos = match_pos + len(search_lower)
-            if start_pos >= len(text):
-                break
+        # Fallback to first word search if no full matches
+        # it's another assumption: proper snippet logic generation is quite complicated and tied to db logic, so simplification is used here
+        if not snippets and " " in query:
+            first_word = query.split()[0]
+            return SnippetGenerator.generate(text, first_word, max_length, max_snippets)
 
         return snippets
 
+    @staticmethod
+    def from_summary(
+        summary: str,
+        query: str,
+        max_snippets: NonNegativeInt = LONG_SUMMARY_MAX_SNIPPETS,
+    ) -> list[str]:
+        """Generate snippets from summary text."""
+        return SnippetGenerator.generate(summary, query, max_snippets=max_snippets)
+
+    @staticmethod
+    def combine_sources(
+        summary: str | None,
+        webvtt: WebVTTContent | None,
+        query: str,
+        max_total: NonNegativeInt = DEFAULT_MAX_SNIPPETS,
+    ) -> tuple[list[str], NonNegativeInt]:
+        """Combine snippets from multiple sources and return total match count.
+
+        Returns (snippets, total_match_count) tuple.
+
+        snippets can be empty for real in case of e.g. title match
+        """
+        webvtt_matches = 0
+        summary_matches = 0
+
+        if webvtt:
+            webvtt_text = WebVTTProcessor.extract_text(webvtt)
+            webvtt_matches = SnippetGenerator.count_matches(webvtt_text, query)
+
+        if summary:
+            summary_matches = SnippetGenerator.count_matches(summary, query)
+
+        total_matches = NonNegativeInt(webvtt_matches + summary_matches)
+
+        summary_snippets = (
+            SnippetGenerator.from_summary(summary, query) if summary else []
+        )
+
+        if len(summary_snippets) >= max_total:
+            return summary_snippets[:max_total], total_matches
+
+        remaining = max_total - len(summary_snippets)
+        webvtt_snippets = (
+            WebVTTProcessor.generate_snippets(webvtt, query, remaining)
+            if webvtt
+            else []
+        )
+
+        return summary_snippets + webvtt_snippets, total_matches
+
+
+class SearchController:
+    """Controller for search operations across different entities."""
+
     @classmethod
     async def search_transcripts(
         cls, params: SearchParameters
@@ -172,39 +336,70 @@ class SearchController:
             )
             return [], 0
 
-        search_query = sqlalchemy.func.websearch_to_tsquery(
-            "english", params.query_text
+        base_columns = [
+            transcripts.c.id,
+            transcripts.c.title,
+            transcripts.c.created_at,
+            transcripts.c.duration,
+            transcripts.c.status,
+            transcripts.c.user_id,
+            transcripts.c.room_id,
+            transcripts.c.source_kind,
+            transcripts.c.webvtt,
+            transcripts.c.long_summary,
+            sqlalchemy.case(
+                (
+                    transcripts.c.room_id.isnot(None) & rooms.c.id.is_(None),
+                    "Deleted Room",
+                ),
+                else_=rooms.c.name,
+            ).label("room_name"),
+        ]
+
+        if params.query_text:
+            search_query = sqlalchemy.func.websearch_to_tsquery(
+                "english", params.query_text
+            )
+            rank_column = sqlalchemy.func.ts_rank(
+                transcripts.c.search_vector_en,
+                search_query,
+                32,  # normalization flag: rank/(rank+1) for 0-1 range
+            ).label("rank")
+        else:
+            rank_column = sqlalchemy.cast(1.0, sqlalchemy.Float).label("rank")
+
+        columns = base_columns + [rank_column]
+        base_query = sqlalchemy.select(columns).select_from(
+            transcripts.join(rooms, transcripts.c.room_id == rooms.c.id, isouter=True)
         )
 
-        base_query = sqlalchemy.select(
-            [
-                transcripts.c.id,
-                transcripts.c.title,
-                transcripts.c.created_at,
-                transcripts.c.duration,
-                transcripts.c.status,
-                transcripts.c.user_id,
-                transcripts.c.room_id,
-                transcripts.c.source_kind,
-                transcripts.c.webvtt,
-                sqlalchemy.func.ts_rank(
-                    transcripts.c.search_vector_en,
-                    search_query,
-                    32,  # normalization flag: rank/(rank+1) for 0-1 range
-                ).label("rank"),
-            ]
-        ).where(transcripts.c.search_vector_en.op("@@")(search_query))
+        if params.query_text:
+            base_query = base_query.where(
+                transcripts.c.search_vector_en.op("@@")(search_query)
+            )
 
         if params.user_id:
-            base_query = base_query.where(transcripts.c.user_id == params.user_id)
+            base_query = base_query.where(
+                sqlalchemy.or_(
+                    transcripts.c.user_id == params.user_id, rooms.c.is_shared
+                )
+            )
+        else:
+            base_query = base_query.where(rooms.c.is_shared)
         if params.room_id:
             base_query = base_query.where(transcripts.c.room_id == params.room_id)
+        if params.source_kind:
+            base_query = base_query.where(
+                transcripts.c.source_kind == params.source_kind
+            )
+
+        if params.query_text:
+            order_by = sqlalchemy.desc(sqlalchemy.text("rank"))
+        else:
+            order_by = sqlalchemy.desc(transcripts.c.created_at)
+
+        query = base_query.order_by(order_by).limit(params.limit).offset(params.offset)
 
-        query = (
-            base_query.order_by(sqlalchemy.desc(sqlalchemy.text("rank")))
-            .limit(params.limit)
-            .offset(params.offset)
-        )
         rs = await get_database().fetch_all(query)
 
         count_query = sqlalchemy.select([sqlalchemy.func.count()]).select_from(
@@ -214,18 +409,40 @@ class SearchController:
 
         def _process_result(r) -> SearchResult:
             r_dict: Dict[str, Any] = dict(r)
-            webvtt: str | None = r_dict.pop("webvtt", None)
+            webvtt_raw: str | None = r_dict.pop("webvtt", None)
+            if webvtt_raw:
+                webvtt = WebVTTProcessor.parse(webvtt_raw)
+            else:
+                webvtt = None
+            long_summary: str | None = r_dict.pop("long_summary", None)
+            room_name: str | None = r_dict.pop("room_name", None)
             db_result = SearchResultDB.model_validate(r_dict)
 
-            snippets = []
-            if webvtt:
-                plain_text = cls._extract_webvtt_text(webvtt)
-                snippets = cls._generate_snippets(plain_text, params.query_text)
+            snippets, total_match_count = SnippetGenerator.combine_sources(
+                long_summary, webvtt, params.query_text, DEFAULT_MAX_SNIPPETS
+            )
 
-            return SearchResult(**db_result.model_dump(), search_snippets=snippets)
+            return SearchResult(
+                **db_result.model_dump(),
+                room_name=room_name,
+                search_snippets=snippets,
+                total_match_count=total_match_count,
+            )
+
+        try:
+            results = [_process_result(r) for r in rs]
+        except ValidationError as e:
+            logger.error(f"Invalid search result data: {e}", exc_info=True)
+            raise HTTPException(
+                status_code=500, detail="Internal search result data consistency error"
+            )
+        except Exception as e:
+            logger.error(f"Error processing search results: {e}", exc_info=True)
+            raise
 
-        results = [_process_result(r) for r in rs]
         return results, total
 
 
 search_controller = SearchController()
+webvtt_processor = WebVTTProcessor()
+snippet_generator = SnippetGenerator()

server/reflector/db/transcripts.py

@@ -88,6 +88,8 @@ transcripts = sqlalchemy.Table(
     sqlalchemy.Index("idx_transcript_created_at", "created_at"),
     sqlalchemy.Index("idx_transcript_user_id_recording_id", "user_id", "recording_id"),
     sqlalchemy.Index("idx_transcript_room_id", "room_id"),
+    sqlalchemy.Index("idx_transcript_source_kind", "source_kind"),
+    sqlalchemy.Index("idx_transcript_room_id_created_at", "room_id", "created_at"),
 )
 
 # Add PostgreSQL-specific full-text search column
@@ -99,7 +101,8 @@ if is_postgresql():
             TSVECTOR,
             sqlalchemy.Computed(
                 "setweight(to_tsvector('english', coalesce(title, '')), 'A') || "
-                "setweight(to_tsvector('english', coalesce(webvtt, '')), 'B')",
+                "setweight(to_tsvector('english', coalesce(long_summary, '')), 'B') || "
+                "setweight(to_tsvector('english', coalesce(webvtt, '')), 'C')",
                 persisted=True,
             ),
         )

server/reflector/pipelines/main_file_pipeline.py

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

server/reflector/pipelines/main_live_pipeline.py

@@ -147,15 +147,18 @@ class StrValue(BaseModel):
 
 
 class PipelineMainBase(PipelineRunner[PipelineMessage], Generic[PipelineMessage]):
-    transcript_id: str
-    ws_room_id: str | None = None
-    ws_manager: WebsocketManager | None = None
-
-    def prepare(self):
-        # prepare websocket
+    def __init__(self, transcript_id: str):
+        super().__init__()
         self._lock = asyncio.Lock()
+        self.transcript_id = transcript_id
         self.ws_room_id = f"ts:{self.transcript_id}"
-        self.ws_manager = get_ws_manager()
+        self._ws_manager = None
+
+    @property
+    def ws_manager(self) -> WebsocketManager:
+        if self._ws_manager is None:
+            self._ws_manager = get_ws_manager()
+        return self._ws_manager
 
     async def get_transcript(self) -> Transcript:
         # fetch the transcript
@@ -355,7 +358,6 @@ class PipelineMainLive(PipelineMainBase):
     async def create(self) -> Pipeline:
         # create a context for the whole rtc transaction
         # add a customised logger to the context
-        self.prepare()
         transcript = await self.get_transcript()
 
         processors = [
@@ -376,6 +378,7 @@ class PipelineMainLive(PipelineMainBase):
         pipeline.set_pref("audio:target_language", transcript.target_language)
         pipeline.logger.bind(transcript_id=transcript.id)
         pipeline.logger.info("Pipeline main live created")
+        pipeline.describe()
 
         return pipeline
 
@@ -394,7 +397,6 @@ class PipelineMainDiarization(PipelineMainBase[AudioDiarizationInput]):
     async def create(self) -> Pipeline:
         # create a context for the whole rtc transaction
         # add a customised logger to the context
-        self.prepare()
         pipeline = Pipeline(
             AudioDiarizationAutoProcessor(callback=self.on_topic),
         )
@@ -435,8 +437,6 @@ class PipelineMainFromTopics(PipelineMainBase[TitleSummaryWithIdProcessorType]):
         raise NotImplementedError
 
     async def create(self) -> Pipeline:
-        self.prepare()
-
         # get transcript
         self._transcript = transcript = await self.get_transcript()
 

server/reflector/pipelines/runner.py

@@ -18,22 +18,14 @@ During its lifecycle, it will emit the following status:
 import asyncio
 from typing import Generic, TypeVar
 
-from pydantic import BaseModel, ConfigDict
-
 from reflector.logger import logger
 from reflector.processors import Pipeline
 
 PipelineMessage = TypeVar("PipelineMessage")
 
 
-class PipelineRunner(BaseModel, Generic[PipelineMessage]):
-    model_config = ConfigDict(arbitrary_types_allowed=True)
-
-    status: str = "idle"
-    pipeline: Pipeline | None = None
-
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
+class PipelineRunner(Generic[PipelineMessage]):
+    def __init__(self):
         self._task = None
         self._q_cmd = asyncio.Queue(maxsize=4096)
         self._ev_done = asyncio.Event()
@@ -42,6 +34,8 @@ class PipelineRunner(BaseModel, Generic[PipelineMessage]):
             runner=id(self),
             runner_cls=self.__class__.__name__,
         )
+        self.status = "idle"
+        self.pipeline: Pipeline | None = None
 
     async def create(self) -> Pipeline:
         """

server/reflector/processors/__init__.py

@@ -11,6 +11,13 @@ from .base import (  # noqa: F401
     Processor,
     ThreadedProcessor,
 )
+from .file_diarization import FileDiarizationProcessor  # noqa: F401
+from .file_diarization_auto import FileDiarizationAutoProcessor  # noqa: F401
+from .file_transcript import FileTranscriptProcessor  # noqa: F401
+from .file_transcript_auto import FileTranscriptAutoProcessor  # noqa: F401
+from .transcript_diarization_assembler import (
+    TranscriptDiarizationAssemblerProcessor,  # noqa: F401
+)
 from .transcript_final_summary import TranscriptFinalSummaryProcessor  # noqa: F401
 from .transcript_final_title import TranscriptFinalTitleProcessor  # noqa: F401
 from .transcript_liner import TranscriptLinerProcessor  # noqa: F401

server/reflector/processors/audio_chunker.py

@@ -1,28 +1,340 @@
+from typing import Optional
+
 import av
+import numpy as np
+import torch
+from silero_vad import VADIterator, load_silero_vad
 
 from reflector.processors.base import Processor
 
 
 class AudioChunkerProcessor(Processor):
     """
-    Assemble audio frames into chunks
+    Assemble audio frames into chunks with VAD-based speech detection
     """
 
     INPUT_TYPE = av.AudioFrame
     OUTPUT_TYPE = list[av.AudioFrame]
 
-    def __init__(self, max_frames=256):
+    def __init__(
+        self,
+        block_frames=256,
+        max_frames=1024,
+        vad_threshold=0.5,
+        use_onnx=False,
+        min_frames=2,
+    ):
         super().__init__()
         self.frames: list[av.AudioFrame] = []
+        self.block_frames = block_frames
         self.max_frames = max_frames
+        self.vad_threshold = vad_threshold
+        self.min_frames = min_frames
+
+        # Initialize Silero VAD
+        self._init_vad(use_onnx)
+
+    def _init_vad(self, use_onnx=False):
+        """Initialize Silero VAD model"""
+        try:
+            torch.set_num_threads(1)
+            self.vad_model = load_silero_vad(onnx=use_onnx)
+            self.vad_iterator = VADIterator(self.vad_model, sampling_rate=16000)
+            self.logger.info("Silero VAD initialized successfully")
+
+        except Exception as e:
+            self.logger.error(f"Failed to initialize Silero VAD: {e}")
+            self.vad_model = None
+            self.vad_iterator = None
 
     async def _push(self, data: av.AudioFrame):
         self.frames.append(data)
-        if len(self.frames) >= self.max_frames:
-            await self.flush()
+        # print("timestamp", data.pts * data.time_base * 1000)
+
+        # Check for speech segments every 32 frames (~1 second)
+        if len(self.frames) >= 32 and len(self.frames) % 32 == 0:
+            await self._process_block()
+
+        # Safety fallback - emit if we hit max frames
+        elif len(self.frames) >= self.max_frames:
+            self.logger.warning(
+                f"AudioChunkerProcessor: Reached max frames ({self.max_frames}), "
+                f"emitting first {self.max_frames // 2} frames"
+            )
+            frames_to_emit = self.frames[: self.max_frames // 2]
+            self.frames = self.frames[self.max_frames // 2 :]
+            if len(frames_to_emit) >= self.min_frames:
+                await self.emit(frames_to_emit)
+            else:
+                self.logger.debug(
+                    f"Ignoring fallback segment with {len(frames_to_emit)} frames "
+                    f"(< {self.min_frames} minimum)"
+                )
+
+    async def _process_block(self):
+        # Need at least 32 frames for VAD detection (~1 second)
+        if len(self.frames) < 32 or self.vad_iterator is None:
+            return
+
+        # Processing block with current buffer size
+        # print(f"Processing block: {len(self.frames)} frames in buffer")
+
+        try:
+            # Convert frames to numpy array for VAD
+            audio_array = self._frames_to_numpy(self.frames)
+
+            if audio_array is None:
+                # Fallback: emit all frames if conversion failed
+                frames_to_emit = self.frames[:]
+                self.frames = []
+                if len(frames_to_emit) >= self.min_frames:
+                    await self.emit(frames_to_emit)
+                else:
+                    self.logger.debug(
+                        f"Ignoring conversion-failed segment with {len(frames_to_emit)} frames "
+                        f"(< {self.min_frames} minimum)"
+                    )
+                return
+
+            # Find complete speech segments in the buffer
+            speech_end_frame = self._find_speech_segment_end(audio_array)
+
+            if speech_end_frame is None or speech_end_frame <= 0:
+                # No speech found but buffer is getting large
+                if len(self.frames) > 512:
+                    # Check if it's all silence and can be discarded
+                    # No speech segment found, buffer at {len(self.frames)} frames
+
+                    # Could emit silence or discard old frames here
+                    # For now, keep first 256 frames and discard older silence
+                    if len(self.frames) > 768:
+                        self.logger.debug(
+                            f"Discarding {len(self.frames) - 256} old frames (likely silence)"
+                        )
+                        self.frames = self.frames[-256:]
+                return
+
+            # Calculate segment timing information
+            frames_to_emit = self.frames[:speech_end_frame]
+
+            # Get timing from av.AudioFrame
+            if frames_to_emit:
+                first_frame = frames_to_emit[0]
+                last_frame = frames_to_emit[-1]
+                sample_rate = first_frame.sample_rate
+
+                # Calculate duration
+                total_samples = sum(f.samples for f in frames_to_emit)
+                duration_seconds = total_samples / sample_rate if sample_rate > 0 else 0
+
+                # Get timestamps if available
+                start_time = (
+                    first_frame.pts * first_frame.time_base if first_frame.pts else 0
+                )
+                end_time = (
+                    last_frame.pts * last_frame.time_base if last_frame.pts else 0
+                )
+
+                # Convert to HH:MM:SS format for logging
+                def format_time(seconds):
+                    if not seconds:
+                        return "00:00:00"
+                    total_seconds = int(float(seconds))
+                    hours = total_seconds // 3600
+                    minutes = (total_seconds % 3600) // 60
+                    secs = total_seconds % 60
+                    return f"{hours:02d}:{minutes:02d}:{secs:02d}"
+
+                start_formatted = format_time(start_time)
+                end_formatted = format_time(end_time)
+
+                # Keep remaining frames for next processing
+                remaining_after = len(self.frames) - speech_end_frame
+
+                # Single structured log line
+                self.logger.info(
+                    "Speech segment found",
+                    start=start_formatted,
+                    end=end_formatted,
+                    frames=speech_end_frame,
+                    duration=round(duration_seconds, 2),
+                    buffer_before=len(self.frames),
+                    remaining=remaining_after,
+                )
+
+            # Keep remaining frames for next processing
+            self.frames = self.frames[speech_end_frame:]
+
+            # Filter out segments with too few frames
+            if len(frames_to_emit) >= self.min_frames:
+                await self.emit(frames_to_emit)
+            else:
+                self.logger.debug(
+                    f"Ignoring segment with {len(frames_to_emit)} frames "
+                    f"(< {self.min_frames} minimum)"
+                )
+
+        except Exception as e:
+            self.logger.error(f"Error in VAD processing: {e}")
+            # Fallback to simple chunking
+            if len(self.frames) >= self.block_frames:
+                frames_to_emit = self.frames[: self.block_frames]
+                self.frames = self.frames[self.block_frames :]
+                if len(frames_to_emit) >= self.min_frames:
+                    await self.emit(frames_to_emit)
+                else:
+                    self.logger.debug(
+                        f"Ignoring exception-fallback segment with {len(frames_to_emit)} frames "
+                        f"(< {self.min_frames} minimum)"
+                    )
+
+    def _frames_to_numpy(self, frames: list[av.AudioFrame]) -> Optional[np.ndarray]:
+        """Convert av.AudioFrame list to numpy array for VAD processing"""
+        if not frames:
+            return None
+
+        try:
+            first_frame = frames[0]
+            original_sample_rate = first_frame.sample_rate
+
+            audio_data = []
+            for frame in frames:
+                frame_array = frame.to_ndarray()
+
+                # Handle stereo -> mono conversion
+                if len(frame_array.shape) == 2 and frame_array.shape[0] > 1:
+                    frame_array = np.mean(frame_array, axis=0)
+                elif len(frame_array.shape) == 2:
+                    frame_array = frame_array.flatten()
+
+                audio_data.append(frame_array)
+
+            if not audio_data:
+                return None
+
+            combined_audio = np.concatenate(audio_data)
+
+            # Resample from 48kHz to 16kHz if needed
+            if original_sample_rate != 16000:
+                combined_audio = self._resample_audio(
+                    combined_audio, original_sample_rate, 16000
+                )
+
+            # Ensure float32 format
+            if combined_audio.dtype == np.int16:
+                # Normalize int16 audio to float32 in range [-1.0, 1.0]
+                combined_audio = combined_audio.astype(np.float32) / 32768.0
+            elif combined_audio.dtype != np.float32:
+                combined_audio = combined_audio.astype(np.float32)
+
+            return combined_audio
+
+        except Exception as e:
+            self.logger.error(f"Error converting frames to numpy: {e}")
+
+        return None
+
+    def _resample_audio(
+        self, audio: np.ndarray, from_sr: int, to_sr: int
+    ) -> np.ndarray:
+        """Simple linear resampling from from_sr to to_sr"""
+        if from_sr == to_sr:
+            return audio
+
+        try:
+            # Simple linear interpolation resampling
+            ratio = to_sr / from_sr
+            new_length = int(len(audio) * ratio)
+
+            # Create indices for interpolation
+            old_indices = np.linspace(0, len(audio) - 1, new_length)
+            resampled = np.interp(old_indices, np.arange(len(audio)), audio)
+
+            return resampled.astype(np.float32)
+
+        except Exception as e:
+            self.logger.error("Resampling error", exc_info=e)
+            # Fallback: simple decimation/repetition
+            if from_sr > to_sr:
+                # Downsample by taking every nth sample
+                step = from_sr // to_sr
+                return audio[::step]
+            else:
+                # Upsample by repeating samples
+                repeat = to_sr // from_sr
+                return np.repeat(audio, repeat)
+
+    def _find_speech_segment_end(self, audio_array: np.ndarray) -> Optional[int]:
+        """Find complete speech segments and return frame index at segment end"""
+        if self.vad_iterator is None or len(audio_array) == 0:
+            return None
+
+        try:
+            # Process audio in 512-sample windows for VAD
+            window_size = 512
+            min_silence_windows = 3  # Require 3 windows of silence after speech
+
+            # Track speech state
+            in_speech = False
+            speech_start = None
+            speech_end = None
+            silence_count = 0
+
+            for i in range(0, len(audio_array), window_size):
+                chunk = audio_array[i : i + window_size]
+                if len(chunk) < window_size:
+                    chunk = np.pad(chunk, (0, window_size - len(chunk)))
+
+                # Detect if this window has speech
+                speech_dict = self.vad_iterator(chunk, return_seconds=True)
+
+                # VADIterator returns dict with 'start' and 'end' when speech segments are detected
+                if speech_dict:
+                    if not in_speech:
+                        # Speech started
+                        speech_start = i
+                        in_speech = True
+                        # Debug: print(f"Speech START at sample {i}, VAD: {speech_dict}")
+                    silence_count = 0  # Reset silence counter
+                    continue
+
+                if not in_speech:
+                    continue
+
+                # We're in speech but found silence
+                silence_count += 1
+                if silence_count < min_silence_windows:
+                    continue
+
+                # Found end of speech segment
+                speech_end = i - (min_silence_windows - 1) * window_size
+                # Debug: print(f"Speech END at sample {speech_end}")
+
+                # Convert sample position to frame index
+                samples_per_frame = self.frames[0].samples if self.frames else 1024
+                # Account for resampling: we process at 16kHz but frames might be 48kHz
+                resample_ratio = 48000 / 16000  # 3x
+                actual_sample_pos = int(speech_end * resample_ratio)
+                frame_index = actual_sample_pos // samples_per_frame
+
+                # Ensure we don't exceed buffer
+                frame_index = min(frame_index, len(self.frames))
+                return frame_index
+
+            return None
+
+        except Exception as e:
+            self.logger.error(f"Error finding speech segment: {e}")
+            return None
 
     async def _flush(self):
         frames = self.frames[:]
         self.frames = []
         if frames:
-            await self.emit(frames)
+            if len(frames) >= self.min_frames:
+                await self.emit(frames)
+            else:
+                self.logger.debug(
+                    f"Ignoring flush segment with {len(frames)} frames "
+                    f"(< {self.min_frames} minimum)"
+                )

server/reflector/processors/audio_diarization.py

@@ -1,6 +1,7 @@
 from reflector.processors.base import Processor
 from reflector.processors.types import (
     AudioDiarizationInput,
+    DiarizationSegment,
     TitleSummary,
     Word,
 )
@@ -37,18 +38,21 @@ class AudioDiarizationProcessor(Processor):
     async def _diarize(self, data: AudioDiarizationInput):
         raise NotImplementedError
 
-    def assign_speaker(self, words: list[Word], diarization: list[dict]):
-        self._diarization_remove_overlap(diarization)
-        self._diarization_remove_segment_without_words(words, diarization)
-        self._diarization_merge_same_speaker(words, diarization)
-        self._diarization_assign_speaker(words, diarization)
+    @classmethod
+    def assign_speaker(cls, words: list[Word], diarization: list[DiarizationSegment]):
+        cls._diarization_remove_overlap(diarization)
+        cls._diarization_remove_segment_without_words(words, diarization)
+        cls._diarization_merge_same_speaker(diarization)
+        cls._diarization_assign_speaker(words, diarization)
 
-    def iter_words_from_topics(self, topics: TitleSummary):
+    @staticmethod
+    def iter_words_from_topics(topics: list[TitleSummary]):
         for topic in topics:
             for word in topic.transcript.words:
                 yield word
 
-    def is_word_continuation(self, word_prev, word):
+    @staticmethod
+    def is_word_continuation(word_prev, word):
         """
         Return True if the word is a continuation of the previous word
         by checking if the previous word is ending with a punctuation
@@ -61,7 +65,8 @@ class AudioDiarizationProcessor(Processor):
             return False
         return True
 
-    def _diarization_remove_overlap(self, diarization: list[dict]):
+    @staticmethod
+    def _diarization_remove_overlap(diarization: list[DiarizationSegment]):
         """
         Remove overlap in diarization results
 
@@ -86,8 +91,9 @@ class AudioDiarizationProcessor(Processor):
             else:
                 diarization_idx += 1
 
+    @staticmethod
     def _diarization_remove_segment_without_words(
-        self, words: list[Word], diarization: list[dict]
+        words: list[Word], diarization: list[DiarizationSegment]
     ):
         """
         Remove diarization segments without words
@@ -116,9 +122,8 @@ class AudioDiarizationProcessor(Processor):
             else:
                 diarization_idx += 1
 
-    def _diarization_merge_same_speaker(
-        self, words: list[Word], diarization: list[dict]
-    ):
+    @staticmethod
+    def _diarization_merge_same_speaker(diarization: list[DiarizationSegment]):
         """
         Merge diarization contigous segments with the same speaker
 
@@ -135,7 +140,10 @@ class AudioDiarizationProcessor(Processor):
             else:
                 diarization_idx += 1
 
-    def _diarization_assign_speaker(self, words: list[Word], diarization: list[dict]):
+    @classmethod
+    def _diarization_assign_speaker(
+        cls, words: list[Word], diarization: list[DiarizationSegment]
+    ):
         """
         Assign speaker to words based on diarization
 
@@ -143,7 +151,7 @@ class AudioDiarizationProcessor(Processor):
         """
 
         word_idx = 0
-        last_speaker = None
+        last_speaker = 0
         for d in diarization:
             start = d["start"]
             end = d["end"]
@@ -158,7 +166,7 @@ class AudioDiarizationProcessor(Processor):
                     # If it's a continuation, assign with the last speaker
                     is_continuation = False
                     if word_idx > 0 and word_idx < len(words) - 1:
-                        is_continuation = self.is_word_continuation(
+                        is_continuation = cls.is_word_continuation(
                             *words[word_idx - 1 : word_idx + 1]
                         )
                     if is_continuation:

server/reflector/processors/audio_diarization_pyannote.py

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

server/reflector/processors/audio_merge.py

@@ -3,11 +3,24 @@ from time import monotonic_ns
 from uuid import uuid4
 
 import av
+from av.audio.resampler import AudioResampler
 
 from reflector.processors.base import Processor
 from reflector.processors.types import AudioFile
 
 
+def copy_frame(frame: av.AudioFrame) -> av.AudioFrame:
+    frame_copy = frame.from_ndarray(
+        frame.to_ndarray(),
+        format=frame.format.name,
+        layout=frame.layout.name,
+    )
+    frame_copy.sample_rate = frame.sample_rate
+    frame_copy.pts = frame.pts
+    frame_copy.time_base = frame.time_base
+    return frame_copy
+
+
 class AudioMergeProcessor(Processor):
     """
     Merge audio frame into a single file
@@ -16,37 +29,92 @@ class AudioMergeProcessor(Processor):
     INPUT_TYPE = list[av.AudioFrame]
     OUTPUT_TYPE = AudioFile
 
+    def __init__(self, downsample_to_16k_mono: bool = True, **kwargs):
+        super().__init__(**kwargs)
+        self.downsample_to_16k_mono = downsample_to_16k_mono
+
     async def _push(self, data: list[av.AudioFrame]):
         if not data:
             return
 
         # get audio information from first frame
         frame = data[0]
-        channels = len(frame.layout.channels)
-        sample_rate = frame.sample_rate
-        sample_width = frame.format.bytes
+        original_channels = len(frame.layout.channels)
+        original_sample_rate = frame.sample_rate
+        original_sample_width = frame.format.bytes
+
+        # determine if we need processing
+        needs_processing = self.downsample_to_16k_mono and (
+            original_sample_rate != 16000 or original_channels != 1
+        )
+
+        # determine output parameters
+        if self.downsample_to_16k_mono:
+            output_sample_rate = 16000
+            output_channels = 1
+            output_sample_width = 2  # 16-bit = 2 bytes
+        else:
+            output_sample_rate = original_sample_rate
+            output_channels = original_channels
+            output_sample_width = original_sample_width
 
         # create audio file
         uu = uuid4().hex
         fd = io.BytesIO()
 
-        out_container = av.open(fd, "w", format="wav")
-        out_stream = out_container.add_stream("pcm_s16le", rate=sample_rate)
-        for frame in data:
-            for packet in out_stream.encode(frame):
+        if needs_processing:
+            # Process with PyAV resampler
+            out_container = av.open(fd, "w", format="wav")
+            out_stream = out_container.add_stream("pcm_s16le", rate=16000)
+            out_stream.layout = "mono"
+
+            # Create resampler if needed
+            resampler = None
+            if original_sample_rate != 16000 or original_channels != 1:
+                resampler = AudioResampler(format="s16", layout="mono", rate=16000)
+
+            for frame in data:
+                if resampler:
+                    # Resample and convert to mono
+                    # XXX for an unknown reason, if we don't use a copy of the frame, we get
+                    # Invalid Argumment from resample. Debugging indicate that when a previous processor
+                    # already used the frame (like AudioFileWriter), it make it invalid argument here.
+                    resampled_frames = resampler.resample(copy_frame(frame))
+                    for resampled_frame in resampled_frames:
+                        for packet in out_stream.encode(resampled_frame):
+                            out_container.mux(packet)
+                else:
+                    # Direct encoding without resampling
+                    for packet in out_stream.encode(frame):
+                        out_container.mux(packet)
+
+            # Flush the encoder
+            for packet in out_stream.encode(None):
                 out_container.mux(packet)
-        for packet in out_stream.encode(None):
-            out_container.mux(packet)
-        out_container.close()
+            out_container.close()
+        else:
+            # Use PyAV for original frames (no processing needed)
+            out_container = av.open(fd, "w", format="wav")
+            out_stream = out_container.add_stream("pcm_s16le", rate=output_sample_rate)
+            out_stream.layout = "mono" if output_channels == 1 else frame.layout
+
+            for frame in data:
+                for packet in out_stream.encode(frame):
+                    out_container.mux(packet)
+
+            for packet in out_stream.encode(None):
+                out_container.mux(packet)
+            out_container.close()
+
         fd.seek(0)
 
         # emit audio file
         audiofile = AudioFile(
             name=f"{monotonic_ns()}-{uu}.wav",
             fd=fd,
-            sample_rate=sample_rate,
-            channels=channels,
-            sample_width=sample_width,
+            sample_rate=output_sample_rate,
+            channels=output_channels,
+            sample_width=output_sample_width,
             timestamp=data[0].pts * data[0].time_base,
         )
 

server/reflector/processors/audio_transcript_modal.py

@@ -12,6 +12,9 @@ API will be a POST request to TRANSCRIPT_URL:
 
 """
 
+from typing import List
+
+import aiohttp
 from openai import AsyncOpenAI
 
 from reflector.processors.audio_transcript import AudioTranscriptProcessor
@@ -21,7 +24,9 @@ from reflector.settings import settings
 
 
 class AudioTranscriptModalProcessor(AudioTranscriptProcessor):
-    def __init__(self, modal_api_key: str | None = None, **kwargs):
+    def __init__(
+        self, modal_api_key: str | None = None, batch_enabled: bool = True, **kwargs
+    ):
         super().__init__()
         if not settings.TRANSCRIPT_URL:
             raise Exception(
@@ -30,6 +35,126 @@ class AudioTranscriptModalProcessor(AudioTranscriptProcessor):
         self.transcript_url = settings.TRANSCRIPT_URL + "/v1"
         self.timeout = settings.TRANSCRIPT_TIMEOUT
         self.modal_api_key = modal_api_key
+        self.max_batch_duration = 10.0
+        self.max_batch_files = 15
+        self.batch_enabled = batch_enabled
+        self.pending_files: List[AudioFile] = []  # Files waiting to be processed
+
+    @classmethod
+    def _calculate_duration(cls, audio_file: AudioFile) -> float:
+        """Calculate audio duration in seconds from AudioFile metadata"""
+        # Duration = total_samples / sample_rate
+        # We need to estimate total samples from the file data
+        import wave
+
+        try:
+            # Try to read as WAV file to get duration
+            audio_file.fd.seek(0)
+            with wave.open(audio_file.fd, "rb") as wav_file:
+                frames = wav_file.getnframes()
+                sample_rate = wav_file.getframerate()
+                duration = frames / sample_rate
+                return duration
+        except Exception:
+            # Fallback: estimate from file size and audio parameters
+            audio_file.fd.seek(0, 2)  # Seek to end
+            file_size = audio_file.fd.tell()
+            audio_file.fd.seek(0)  # Reset to beginning
+
+            # Estimate: file_size / (sample_rate * channels * sample_width)
+            bytes_per_second = (
+                audio_file.sample_rate
+                * audio_file.channels
+                * (audio_file.sample_width // 8)
+            )
+            estimated_duration = (
+                file_size / bytes_per_second if bytes_per_second > 0 else 0
+            )
+            return max(0, estimated_duration)
+
+    def _create_batches(self, audio_files: List[AudioFile]) -> List[List[AudioFile]]:
+        """Group audio files into batches with maximum 30s total duration"""
+        batches = []
+        current_batch = []
+        current_duration = 0.0
+
+        for audio_file in audio_files:
+            duration = self._calculate_duration(audio_file)
+
+            # If adding this file exceeds max duration, start a new batch
+            if current_duration + duration > self.max_batch_duration and current_batch:
+                batches.append(current_batch)
+                current_batch = [audio_file]
+                current_duration = duration
+            else:
+                current_batch.append(audio_file)
+                current_duration += duration
+
+        # Add the last batch if not empty
+        if current_batch:
+            batches.append(current_batch)
+
+        return batches
+
+    async def _transcript_batch(self, audio_files: List[AudioFile]) -> List[Transcript]:
+        """Transcribe a batch of audio files using the parakeet backend"""
+        if not audio_files:
+            return []
+
+        self.logger.debug(f"Batch transcribing {len(audio_files)} files")
+
+        # Prepare form data for batch request
+        data = aiohttp.FormData()
+        data.add_field("language", self.get_pref("audio:source_language", "en"))
+        data.add_field("batch", "true")
+
+        for i, audio_file in enumerate(audio_files):
+            audio_file.fd.seek(0)
+            data.add_field(
+                "files",
+                audio_file.fd,
+                filename=f"{audio_file.name}",
+                content_type="audio/wav",
+            )
+
+        # Make batch request
+        headers = {"Authorization": f"Bearer {self.modal_api_key}"}
+
+        async with aiohttp.ClientSession(
+            timeout=aiohttp.ClientTimeout(total=self.timeout)
+        ) as session:
+            async with session.post(
+                f"{self.transcript_url}/audio/transcriptions",
+                data=data,
+                headers=headers,
+            ) as response:
+                if response.status != 200:
+                    error_text = await response.text()
+                    raise Exception(
+                        f"Batch transcription failed: {response.status} {error_text}"
+                    )
+
+                result = await response.json()
+
+        # Process batch results
+        transcripts = []
+        results = result.get("results", [])
+
+        for i, (audio_file, file_result) in enumerate(zip(audio_files, results)):
+            transcript = Transcript(
+                words=[
+                    Word(
+                        text=word_info["word"],
+                        start=word_info["start"],
+                        end=word_info["end"],
+                    )
+                    for word_info in file_result.get("words", [])
+                ]
+            )
+            transcript.add_offset(audio_file.timestamp)
+            transcripts.append(transcript)
+
+        return transcripts
 
     async def _transcript(self, data: AudioFile):
         async with AsyncOpenAI(
@@ -62,5 +187,96 @@ class AudioTranscriptModalProcessor(AudioTranscriptProcessor):
 
         return transcript
 
+    async def transcript_multiple(
+        self, audio_files: List[AudioFile]
+    ) -> List[Transcript]:
+        """Transcribe multiple audio files using batching"""
+        if len(audio_files) == 1:
+            # Single file, use existing method
+            return [await self._transcript(audio_files[0])]
+
+        # Create batches with max 30s duration each
+        batches = self._create_batches(audio_files)
+
+        self.logger.debug(
+            f"Processing {len(audio_files)} files in {len(batches)} batches"
+        )
+
+        # Process all batches concurrently
+        all_transcripts = []
+
+        for batch in batches:
+            batch_transcripts = await self._transcript_batch(batch)
+            all_transcripts.extend(batch_transcripts)
+
+        return all_transcripts
+
+    async def _push(self, data: AudioFile):
+        """Override _push to support batching"""
+        if not self.batch_enabled:
+            # Use parent implementation for single file processing
+            return await super()._push(data)
+
+        # Add file to pending batch
+        self.pending_files.append(data)
+        self.logger.debug(
+            f"Added file to batch: {data.name}, batch size: {len(self.pending_files)}"
+        )
+
+        # Calculate total duration of pending files
+        total_duration = sum(self._calculate_duration(f) for f in self.pending_files)
+
+        # Process batch if it reaches max duration or has multiple files ready for optimization
+        should_process_batch = (
+            total_duration >= self.max_batch_duration
+            or len(self.pending_files) >= self.max_batch_files
+        )
+
+        if should_process_batch:
+            await self._process_pending_batch()
+
+    async def _process_pending_batch(self):
+        """Process all pending files as batches"""
+        if not self.pending_files:
+            return
+
+        self.logger.debug(f"Processing batch of {len(self.pending_files)} files")
+
+        try:
+            # Create batches respecting duration limit
+            batches = self._create_batches(self.pending_files)
+
+            # Process each batch
+            for batch in batches:
+                self.m_transcript_call.inc()
+                try:
+                    with self.m_transcript.time():
+                        # Use batch transcription
+                        transcripts = await self._transcript_batch(batch)
+
+                    self.m_transcript_success.inc()
+
+                    # Emit each transcript
+                    for transcript in transcripts:
+                        if transcript:
+                            await self.emit(transcript)
+
+                except Exception:
+                    self.m_transcript_failure.inc()
+                    raise
+                finally:
+                    # Release audio files
+                    for audio_file in batch:
+                        audio_file.release()
+
+        finally:
+            # Clear pending files
+            self.pending_files.clear()
+
+    async def _flush(self):
+        """Process any remaining files when flushing"""
+        await self._process_pending_batch()
+        await super()._flush()
+
 
 AudioTranscriptAutoProcessor.register("modal", AudioTranscriptModalProcessor)

server/reflector/processors/base.py

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

server/reflector/processors/file_diarization.py

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

server/reflector/processors/file_diarization_auto.py

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

server/reflector/processors/file_diarization_modal.py

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

server/reflector/processors/file_transcript.py

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

server/reflector/processors/file_transcript_auto.py

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

server/reflector/processors/file_transcript_modal.py

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

server/reflector/processors/transcript_diarization_assembler.py

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

server/reflector/processors/types.py

@@ -2,13 +2,22 @@ import io
 import re
 import tempfile
 from pathlib import Path
-from typing import Annotated
+from typing import Annotated, TypedDict
 
 from profanityfilter import ProfanityFilter
 from pydantic import BaseModel, Field, PrivateAttr
 
 from reflector.redis_cache import redis_cache
 
+
+class DiarizationSegment(TypedDict):
+    """Type definition for diarization segment containing speaker information"""
+
+    start: float
+    end: float
+    speaker: int
+
+
 PUNC_RE = re.compile(r"[.;:?!…]")
 
 profanity_filter = ProfanityFilter()

server/reflector/settings.py

@@ -26,6 +26,7 @@ class Settings(BaseSettings):
     TRANSCRIPT_BACKEND: str = "whisper"
     TRANSCRIPT_URL: str | None = None
     TRANSCRIPT_TIMEOUT: int = 90
+    TRANSCRIPT_FILE_TIMEOUT: int = 600
 
     # Audio Transcription: modal backend
     TRANSCRIPT_MODAL_API_KEY: str | None = None
@@ -66,10 +67,14 @@ class Settings(BaseSettings):
     DIARIZATION_ENABLED: bool = True
     DIARIZATION_BACKEND: str = "modal"
     DIARIZATION_URL: str | None = None
+    DIARIZATION_FILE_TIMEOUT: int = 600
 
     # Diarization: modal backend
     DIARIZATION_MODAL_API_KEY: str | None = None
 
+    # Diarization: local pyannote.audio
+    DIARIZATION_PYANNOTE_AUTH_TOKEN: str | None = None
+
     # Sentry
     SENTRY_DSN: str | None = None
 

server/reflector/tools/process.py

@@ -1,10 +1,23 @@
+"""
+Process audio file with diarization support
+===========================================
+
+Extended version of process.py that includes speaker diarization.
+This tool processes audio files locally without requiring the full server infrastructure.
+"""
+
 import asyncio
+import tempfile
+import uuid
+from pathlib import Path
+from typing import List
 
 import av
 
 from reflector.logger import logger
 from reflector.processors import (
     AudioChunkerProcessor,
+    AudioFileWriterProcessor,
     AudioMergeProcessor,
     AudioTranscriptAutoProcessor,
     Pipeline,
@@ -15,7 +28,43 @@ from reflector.processors import (
     TranscriptTopicDetectorProcessor,
     TranscriptTranslatorAutoProcessor,
 )
-from reflector.processors.base import BroadcastProcessor
+from reflector.processors.base import BroadcastProcessor, Processor
+from reflector.processors.types import (
+    AudioDiarizationInput,
+    TitleSummary,
+    TitleSummaryWithId,
+)
+
+
+class TopicCollectorProcessor(Processor):
+    """Collect topics for diarization"""
+
+    INPUT_TYPE = TitleSummary
+    OUTPUT_TYPE = TitleSummary
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.topics: List[TitleSummaryWithId] = []
+        self._topic_id = 0
+
+    async def _push(self, data: TitleSummary):
+        # Convert to TitleSummaryWithId and collect
+        self._topic_id += 1
+        topic_with_id = TitleSummaryWithId(
+            id=str(self._topic_id),
+            title=data.title,
+            summary=data.summary,
+            timestamp=data.timestamp,
+            duration=data.duration,
+            transcript=data.transcript,
+        )
+        self.topics.append(topic_with_id)
+
+        # Pass through the original topic
+        await self.emit(data)
+
+    def get_topics(self) -> List[TitleSummaryWithId]:
+        return self.topics
 
 
 async def process_audio_file(
@@ -24,18 +73,40 @@ async def process_audio_file(
     only_transcript=False,
     source_language="en",
     target_language="en",
+    enable_diarization=True,
+    diarization_backend="pyannote",
 ):
-    # build pipeline for audio processing
-    processors = [
+    # Create temp file for audio if diarization is enabled
+    audio_temp_path = None
+    if enable_diarization:
+        audio_temp_file = tempfile.NamedTemporaryFile(suffix=".wav", delete=False)
+        audio_temp_path = audio_temp_file.name
+        audio_temp_file.close()
+
+    # Create processor for collecting topics
+    topic_collector = TopicCollectorProcessor()
+
+    # Build pipeline for audio processing
+    processors = []
+
+    # Add audio file writer at the beginning if diarization is enabled
+    if enable_diarization:
+        processors.append(AudioFileWriterProcessor(audio_temp_path))
+
+    # Add the rest of the processors
+    processors += [
         AudioChunkerProcessor(),
         AudioMergeProcessor(),
         AudioTranscriptAutoProcessor.as_threaded(),
         TranscriptLinerProcessor(),
         TranscriptTranslatorAutoProcessor.as_threaded(),
     ]
+
     if not only_transcript:
         processors += [
             TranscriptTopicDetectorProcessor.as_threaded(),
+            # Collect topics for diarization
+            topic_collector,
             BroadcastProcessor(
                 processors=[
                     TranscriptFinalTitleProcessor.as_threaded(),
@@ -44,14 +115,14 @@ async def process_audio_file(
             ),
         ]
 
-    # transcription output
+    # Create main pipeline
     pipeline = Pipeline(*processors)
     pipeline.set_pref("audio:source_language", source_language)
     pipeline.set_pref("audio:target_language", target_language)
     pipeline.describe()
     pipeline.on(event_callback)
 
-    # start processing audio
+    # Start processing audio
     logger.info(f"Opening {filename}")
     container = av.open(filename)
     try:
@@ -62,43 +133,242 @@ async def process_audio_file(
         logger.info("Flushing the pipeline")
         await pipeline.flush()
 
-    logger.info("All done !")
+    # Run diarization if enabled and we have topics
+    if enable_diarization and not only_transcript and audio_temp_path:
+        topics = topic_collector.get_topics()
+
+        if topics:
+            logger.info(f"Starting diarization with {len(topics)} topics")
+
+            try:
+                from reflector.processors import AudioDiarizationAutoProcessor
+
+                diarization_processor = AudioDiarizationAutoProcessor(
+                    name=diarization_backend
+                )
+
+                diarization_processor.set_pipeline(pipeline)
+
+                # For Modal backend, we need to upload the file to S3 first
+                if diarization_backend == "modal":
+                    from datetime import datetime
+
+                    from reflector.storage import get_transcripts_storage
+                    from reflector.utils.s3_temp_file import S3TemporaryFile
+
+                    storage = get_transcripts_storage()
+
+                    # Generate a unique filename in evaluation folder
+                    timestamp = datetime.utcnow().strftime("%Y%m%d_%H%M%S")
+                    audio_filename = f"evaluation/diarization_temp/{timestamp}_{uuid.uuid4().hex}.wav"
+
+                    # Use context manager for automatic cleanup
+                    async with S3TemporaryFile(storage, audio_filename) as s3_file:
+                        # Read and upload the audio file
+                        with open(audio_temp_path, "rb") as f:
+                            audio_data = f.read()
+
+                        audio_url = await s3_file.upload(audio_data)
+                        logger.info(f"Uploaded audio to S3: {audio_filename}")
+
+                        # Create diarization input with S3 URL
+                        diarization_input = AudioDiarizationInput(
+                            audio_url=audio_url, topics=topics
+                        )
+
+                        # Run diarization
+                        await diarization_processor.push(diarization_input)
+                        await diarization_processor.flush()
+
+                        logger.info("Diarization complete")
+                        # File will be automatically cleaned up when exiting the context
+                else:
+                    # For local backend, use local file path
+                    audio_url = audio_temp_path
+
+                    # Create diarization input
+                    diarization_input = AudioDiarizationInput(
+                        audio_url=audio_url, topics=topics
+                    )
+
+                    # Run diarization
+                    await diarization_processor.push(diarization_input)
+                    await diarization_processor.flush()
+
+                    logger.info("Diarization complete")
+
+            except ImportError as e:
+                logger.error(f"Failed to import diarization dependencies: {e}")
+                logger.error(
+                    "Install with: uv pip install pyannote.audio torch torchaudio"
+                )
+                logger.error(
+                    "And set HF_TOKEN environment variable for pyannote models"
+                )
+                raise SystemExit(1)
+            except Exception as e:
+                logger.error(f"Diarization failed: {e}")
+                raise SystemExit(1)
+        else:
+            logger.warning("Skipping diarization: no topics available")
+
+    # Clean up temp file
+    if audio_temp_path:
+        try:
+            Path(audio_temp_path).unlink()
+        except Exception as e:
+            logger.warning(f"Failed to clean up temp file {audio_temp_path}: {e}")
+
+    logger.info("All done!")
+
+
+async def process_file_pipeline(
+    filename: str,
+    event_callback,
+    source_language="en",
+    target_language="en",
+    enable_diarization=True,
+    diarization_backend="modal",
+):
+    """Process audio/video file using the optimized file pipeline"""
+    try:
+        from reflector.db import database
+        from reflector.db.transcripts import SourceKind, transcripts_controller
+        from reflector.pipelines.main_file_pipeline import PipelineMainFile
+
+        await database.connect()
+        try:
+            # Create a temporary transcript for processing
+            transcript = await transcripts_controller.add(
+                "",
+                source_kind=SourceKind.FILE,
+                source_language=source_language,
+                target_language=target_language,
+            )
+
+            # Process the file
+            pipeline = PipelineMainFile(transcript_id=transcript.id)
+            await pipeline.process(Path(filename))
+
+            logger.info("File pipeline processing complete")
+
+        finally:
+            await database.disconnect()
+    except ImportError as e:
+        logger.error(f"File pipeline not available: {e}")
+        logger.info("Falling back to stream pipeline")
+        # Fall back to stream pipeline
+        await process_audio_file(
+            filename,
+            event_callback,
+            only_transcript=False,
+            source_language=source_language,
+            target_language=target_language,
+            enable_diarization=enable_diarization,
+            diarization_backend=diarization_backend,
+        )
 
 
 if __name__ == "__main__":
     import argparse
+    import os
 
-    parser = argparse.ArgumentParser()
+    parser = argparse.ArgumentParser(
+        description="Process audio files with optional speaker diarization"
+    )
     parser.add_argument("source", help="Source file (mp3, wav, mp4...)")
-    parser.add_argument("--only-transcript", "-t", action="store_true")
-    parser.add_argument("--source-language", default="en")
-    parser.add_argument("--target-language", default="en")
+    parser.add_argument(
+        "--stream",
+        action="store_true",
+        help="Use streaming pipeline (original frame-based processing)",
+    )
+    parser.add_argument(
+        "--only-transcript",
+        "-t",
+        action="store_true",
+        help="Only generate transcript without topics/summaries",
+    )
+    parser.add_argument(
+        "--source-language", default="en", help="Source language code (default: en)"
+    )
+    parser.add_argument(
+        "--target-language", default="en", help="Target language code (default: en)"
+    )
     parser.add_argument("--output", "-o", help="Output file (output.jsonl)")
+    parser.add_argument(
+        "--enable-diarization",
+        "-d",
+        action="store_true",
+        help="Enable speaker diarization",
+    )
+    parser.add_argument(
+        "--diarization-backend",
+        default="pyannote",
+        choices=["pyannote", "modal"],
+        help="Diarization backend to use (default: pyannote)",
+    )
     args = parser.parse_args()
 
+    if "REDIS_HOST" not in os.environ:
+        os.environ["REDIS_HOST"] = "localhost"
+
     output_fd = None
     if args.output:
         output_fd = open(args.output, "w")
 
     async def event_callback(event: PipelineEvent):
         processor = event.processor
-        # ignore some processor
-        if processor in ("AudioChunkerProcessor", "AudioMergeProcessor"):
+        data = event.data
+
+        # Ignore internal processors
+        if processor in (
+            "AudioChunkerProcessor",
+            "AudioMergeProcessor",
+            "AudioFileWriterProcessor",
+            "TopicCollectorProcessor",
+            "BroadcastProcessor",
+        ):
             return
-        logger.info(f"Event: {event}")
+
+        # If diarization is enabled, skip the original topic events from the pipeline
+        # The diarization processor will emit the same topics but with speaker info
+        if processor == "TranscriptTopicDetectorProcessor" and args.enable_diarization:
+            return
+
+        # Log all events
+        logger.info(f"Event: {processor} - {type(data).__name__}")
+
+        # Write to output
         if output_fd:
             output_fd.write(event.model_dump_json())
             output_fd.write("\n")
+            output_fd.flush()
 
-    asyncio.run(
-        process_audio_file(
-            args.source,
-            event_callback,
-            only_transcript=args.only_transcript,
-            source_language=args.source_language,
-            target_language=args.target_language,
+    if args.stream:
+        # Use original streaming pipeline
+        asyncio.run(
+            process_audio_file(
+                args.source,
+                event_callback,
+                only_transcript=args.only_transcript,
+                source_language=args.source_language,
+                target_language=args.target_language,
+                enable_diarization=args.enable_diarization,
+                diarization_backend=args.diarization_backend,
+            )
+        )
+    else:
+        # Use optimized file pipeline (default)
+        asyncio.run(
+            process_file_pipeline(
+                args.source,
+                event_callback,
+                source_language=args.source_language,
+                target_language=args.target_language,
+                enable_diarization=args.enable_diarization,
+                diarization_backend=args.diarization_backend,
+            )
         )
-    )
 
     if output_fd:
         output_fd.close()

server/reflector/views/transcripts.py

@@ -160,6 +160,7 @@ async def transcripts_search(
     limit: SearchLimitParam = DEFAULT_SEARCH_LIMIT,
     offset: SearchOffsetParam = 0,
     room_id: Optional[str] = None,
+    source_kind: Optional[SourceKind] = None,
     user: Annotated[
         Optional[auth.UserInfo], Depends(auth.current_user_optional)
     ] = None,
@@ -173,7 +174,12 @@ async def transcripts_search(
     user_id = user["sub"] if user else None
 
     search_params = SearchParameters(
-        query_text=q, limit=limit, offset=offset, user_id=user_id, room_id=room_id
+        query_text=q,
+        limit=limit,
+        offset=offset,
+        user_id=user_id,
+        room_id=room_id,
+        source_kind=source_kind,
     )
 
     results, total = await search_controller.search_transcripts(search_params)

server/reflector/worker/process.py

@@ -14,7 +14,8 @@ from reflector.db.meetings import meetings_controller
 from reflector.db.recordings import Recording, recordings_controller
 from reflector.db.rooms import rooms_controller
 from reflector.db.transcripts import SourceKind, transcripts_controller
-from reflector.pipelines.main_live_pipeline import asynctask, task_pipeline_process
+from reflector.pipelines.main_file_pipeline import task_pipeline_file_process
+from reflector.pipelines.main_live_pipeline import asynctask
 from reflector.settings import settings
 from reflector.whereby import get_room_sessions
 
@@ -140,7 +141,7 @@ async def process_recording(bucket_name: str, object_key: str):
 
     await transcripts_controller.update(transcript, {"status": "uploaded"})
 
-    task_pipeline_process.delay(transcript_id=transcript.id)
+    task_pipeline_file_process.delay(transcript_id=transcript.id)
 
 
 @shared_task

server/tests/cassettes/test_processors_modal/test_file_diarization_modal_processor.yaml

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

server/tests/cassettes/test_processors_modal/test_file_transcript_modal_processor.yaml

@@ -0,0 +1,46 @@
+interactions:
+- request:
+    body: '{"audio_file_url": "https://reflector-github-pytest.s3.us-east-1.amazonaws.com/test_mathieu_hello.mp3",
+      "language": "en", "batch": true}'
+    headers:
+      accept:
+      - '*/*'
+      accept-encoding:
+      - gzip, deflate
+      authorization:
+      - DUMMY_API_KEY
+      connection:
+      - keep-alive
+      content-length:
+      - '136'
+      content-type:
+      - application/json
+      host:
+      - monadical-sas--reflector-transcriber-parakeet-web.modal.run
+      user-agent:
+      - python-httpx/0.27.2
+    method: POST
+    uri: https://monadical-sas--reflector-transcriber-parakeet-web.modal.run/v1/audio/transcriptions-from-url
+  response:
+    body:
+      string: '{"text":"Hi there everyone. Today I want to share my incredible experience
+        with Reflector. a Q teenage product that revolutionizes audio processing.
+        With reflector, I can easily convert any audio into accurate transcription.
+        saving me hours of tedious manual work.","words":[{"word":"Hi","start":0.87,"end":1.19},{"word":"there","start":1.19,"end":1.35},{"word":"everyone.","start":1.51,"end":1.83},{"word":"Today","start":2.63,"end":2.87},{"word":"I","start":3.36,"end":3.52},{"word":"want","start":3.6,"end":3.76},{"word":"to","start":3.76,"end":3.92},{"word":"share","start":3.92,"end":4.16},{"word":"my","start":4.16,"end":4.4},{"word":"incredible","start":4.32,"end":4.96},{"word":"experience","start":4.96,"end":5.44},{"word":"with","start":5.44,"end":5.68},{"word":"Reflector.","start":5.68,"end":6.24},{"word":"a","start":6.93,"end":7.01},{"word":"Q","start":7.01,"end":7.17},{"word":"teenage","start":7.25,"end":7.65},{"word":"product","start":7.89,"end":8.29},{"word":"that","start":8.29,"end":8.61},{"word":"revolutionizes","start":8.61,"end":9.65},{"word":"audio","start":9.65,"end":10.05},{"word":"processing.","start":10.05,"end":10.53},{"word":"With","start":11.27,"end":11.43},{"word":"reflector,","start":11.51,"end":12.15},{"word":"I","start":12.31,"end":12.39},{"word":"can","start":12.39,"end":12.55},{"word":"easily","start":12.55,"end":12.95},{"word":"convert","start":12.95,"end":13.43},{"word":"any","start":13.43,"end":13.67},{"word":"audio","start":13.67,"end":13.99},{"word":"into","start":14.98,"end":15.06},{"word":"accurate","start":15.22,"end":15.54},{"word":"transcription.","start":15.7,"end":16.34},{"word":"saving","start":16.99,"end":17.15},{"word":"me","start":17.31,"end":17.47},{"word":"hours","start":17.47,"end":17.87},{"word":"of","start":17.87,"end":18.11},{"word":"tedious","start":18.11,"end":18.67},{"word":"manual","start":18.67,"end":19.07},{"word":"work.","start":19.07,"end":19.31}]}'
+    headers:
+      Alt-Svc:
+      - h3=":443"; ma=2592000
+      Content-Length:
+      - '1933'
+      Content-Type:
+      - application/json
+      Date:
+      - Wed, 13 Aug 2025 18:26:59 GMT
+      Modal-Function-Call-Id:
+      - fc-01K2JAWC7GAMKX4DSJ21WV31NG
+      Vary:
+      - accept-encoding
+    status:
+      code: 200
+      message: OK
+version: 1

server/tests/cassettes/test_processors_modal/test_full_modal_pipeline_integration.yaml

@@ -0,0 +1,84 @@
+interactions:
+- request:
+    body: '{"audio_file_url": "https://reflector-github-pytest.s3.us-east-1.amazonaws.com/test_mathieu_hello.mp3",
+      "language": "en", "batch": true}'
+    headers:
+      accept:
+      - '*/*'
+      accept-encoding:
+      - gzip, deflate
+      authorization:
+      - DUMMY_API_KEY
+      connection:
+      - keep-alive
+      content-length:
+      - '136'
+      content-type:
+      - application/json
+      host:
+      - monadical-sas--reflector-transcriber-parakeet-web.modal.run
+      user-agent:
+      - python-httpx/0.27.2
+    method: POST
+    uri: https://monadical-sas--reflector-transcriber-parakeet-web.modal.run/v1/audio/transcriptions-from-url
+  response:
+    body:
+      string: '{"text":"Hi there everyone. Today I want to share my incredible experience
+        with Reflector. a Q teenage product that revolutionizes audio processing.
+        With reflector, I can easily convert any audio into accurate transcription.
+        saving me hours of tedious manual work.","words":[{"word":"Hi","start":0.87,"end":1.19},{"word":"there","start":1.19,"end":1.35},{"word":"everyone.","start":1.51,"end":1.83},{"word":"Today","start":2.63,"end":2.87},{"word":"I","start":3.36,"end":3.52},{"word":"want","start":3.6,"end":3.76},{"word":"to","start":3.76,"end":3.92},{"word":"share","start":3.92,"end":4.16},{"word":"my","start":4.16,"end":4.4},{"word":"incredible","start":4.32,"end":4.96},{"word":"experience","start":4.96,"end":5.44},{"word":"with","start":5.44,"end":5.68},{"word":"Reflector.","start":5.68,"end":6.24},{"word":"a","start":6.93,"end":7.01},{"word":"Q","start":7.01,"end":7.17},{"word":"teenage","start":7.25,"end":7.65},{"word":"product","start":7.89,"end":8.29},{"word":"that","start":8.29,"end":8.61},{"word":"revolutionizes","start":8.61,"end":9.65},{"word":"audio","start":9.65,"end":10.05},{"word":"processing.","start":10.05,"end":10.53},{"word":"With","start":11.27,"end":11.43},{"word":"reflector,","start":11.51,"end":12.15},{"word":"I","start":12.31,"end":12.39},{"word":"can","start":12.39,"end":12.55},{"word":"easily","start":12.55,"end":12.95},{"word":"convert","start":12.95,"end":13.43},{"word":"any","start":13.43,"end":13.67},{"word":"audio","start":13.67,"end":13.99},{"word":"into","start":14.98,"end":15.06},{"word":"accurate","start":15.22,"end":15.54},{"word":"transcription.","start":15.7,"end":16.34},{"word":"saving","start":16.99,"end":17.15},{"word":"me","start":17.31,"end":17.47},{"word":"hours","start":17.47,"end":17.87},{"word":"of","start":17.87,"end":18.11},{"word":"tedious","start":18.11,"end":18.67},{"word":"manual","start":18.67,"end":19.07},{"word":"work.","start":19.07,"end":19.31}]}'
+    headers:
+      Alt-Svc:
+      - h3=":443"; ma=2592000
+      Content-Length:
+      - '1933'
+      Content-Type:
+      - application/json
+      Date:
+      - Wed, 13 Aug 2025 18:27:02 GMT
+      Modal-Function-Call-Id:
+      - fc-01K2JAYZ1AR2HE422VJVKBWX9Z
+      Vary:
+      - accept-encoding
+    status:
+      code: 200
+      message: OK
+- request:
+    body: ''
+    headers:
+      accept:
+      - '*/*'
+      accept-encoding:
+      - gzip, deflate
+      authorization:
+      - DUMMY_API_KEY
+      connection:
+      - keep-alive
+      content-length:
+      - '0'
+      host:
+      - monadical-sas--reflector-diarizer-web.modal.run
+      user-agent:
+      - python-httpx/0.27.2
+    method: POST
+    uri: https://monadical-sas--reflector-diarizer-web.modal.run/diarize?audio_file_url=https%3A%2F%2Freflector-github-pytest.s3.us-east-1.amazonaws.com%2Ftest_mathieu_hello.mp3&timestamp=0
+  response:
+    body:
+      string: '{"diarization":[{"start":0.823,"end":1.91,"speaker":0},{"start":2.572,"end":6.409,"speaker":0},{"start":6.783,"end":10.62,"speaker":0},{"start":11.231,"end":14.168,"speaker":0},{"start":14.796,"end":19.295,"speaker":0}]}'
+    headers:
+      Alt-Svc:
+      - h3=":443"; ma=2592000
+      Content-Length:
+      - '220'
+      Content-Type:
+      - application/json
+      Date:
+      - Wed, 13 Aug 2025 18:27:18 GMT
+      Modal-Function-Call-Id:
+      - fc-01K2JAZ1M34NQRJK03CCFK95D6
+      Vary:
+      - accept-encoding
+    status:
+      code: 200
+      message: OK
+version: 1

server/tests/conftest.py

@@ -5,7 +5,29 @@ from unittest.mock import patch
 import pytest
 
 
-# Pytest-docker configuration
+@pytest.fixture(scope="session", autouse=True)
+def settings_configuration():
+    # theses settings are linked to monadical for pytest-recording
+    # if a fork is done, they have to provide their own url when cassettes needs to be updated
+    # modal api keys has to be defined by the user
+    from reflector.settings import settings
+
+    settings.TRANSCRIPT_BACKEND = "modal"
+    settings.TRANSCRIPT_URL = (
+        "https://monadical-sas--reflector-transcriber-parakeet-web.modal.run"
+    )
+    settings.DIARIZATION_BACKEND = "modal"
+    settings.DIARIZATION_URL = "https://monadical-sas--reflector-diarizer-web.modal.run"
+
+
+@pytest.fixture(scope="module")
+def vcr_config():
+    """VCR configuration to filter sensitive headers"""
+    return {
+        "filter_headers": [("authorization", "DUMMY_API_KEY")],
+    }
+
+
 @pytest.fixture(scope="session")
 def docker_compose_file(pytestconfig):
     return os.path.join(str(pytestconfig.rootdir), "tests", "docker-compose.test.yml")

server/tests/docker-compose.test.yml

@@ -1,7 +1,7 @@
-version: '3.8'
+version: "3.8"
 services:
   postgres_test:
-    image: postgres:15
+    image: postgres:17
     environment:
       POSTGRES_DB: reflector_test
       POSTGRES_USER: test_user
@@ -10,4 +10,4 @@ services:
       - "15432:5432"
     command: postgres -c fsync=off -c synchronous_commit=off -c full_page_writes=off
     tmpfs:
-      - /var/lib/postgresql/data:rw,noexec,nosuid,size=1g
+      - /var/lib/postgresql/data:rw,noexec,nosuid,size=1g

server/tests/test_gpu_modal_transcript.py

@@ -0,0 +1,330 @@
+"""
+Tests for GPU Modal transcription endpoints.
+
+These tests are marked with the "gpu-modal" group and will not run by default.
+Run them with: pytest -m gpu-modal tests/test_gpu_modal_transcript_parakeet.py
+
+Required environment variables:
+- TRANSCRIPT_URL: URL to the Modal.com endpoint (required)
+- TRANSCRIPT_MODAL_API_KEY: API key for authentication (optional)
+- TRANSCRIPT_MODEL: Model name to use (optional, defaults to nvidia/parakeet-tdt-0.6b-v2)
+
+Example with pytest (override default addopts to run ONLY gpu_modal tests):
+    TRANSCRIPT_URL=https://monadical-sas--reflector-transcriber-parakeet-web-dev.modal.run \
+    TRANSCRIPT_MODAL_API_KEY=your-api-key \
+    uv run -m pytest -m gpu_modal --no-cov tests/test_gpu_modal_transcript.py
+
+    # Or with completely clean options:
+    uv run -m pytest -m gpu_modal -o addopts="" tests/
+
+Running Modal locally for testing:
+    modal serve gpu/modal_deployments/reflector_transcriber_parakeet.py
+    # This will give you a local URL like https://xxxxx--reflector-transcriber-parakeet-web-dev.modal.run to test against
+"""
+
+import os
+import tempfile
+from pathlib import Path
+
+import httpx
+import pytest
+
+# Test audio file URL for testing
+TEST_AUDIO_URL = (
+    "https://reflector-github-pytest.s3.us-east-1.amazonaws.com/test_mathieu_hello.mp3"
+)
+
+
+def get_modal_transcript_url():
+    """Get and validate the Modal transcript URL from environment."""
+    url = os.environ.get("TRANSCRIPT_URL")
+    if not url:
+        pytest.skip(
+            "TRANSCRIPT_URL environment variable is required for GPU Modal tests"
+        )
+    return url
+
+
+def get_auth_headers():
+    """Get authentication headers if API key is available."""
+    api_key = os.environ.get("TRANSCRIPT_MODAL_API_KEY")
+    if api_key:
+        return {"Authorization": f"Bearer {api_key}"}
+    return {}
+
+
+def get_model_name():
+    """Get the model name from environment or use default."""
+    return os.environ.get("TRANSCRIPT_MODEL", "nvidia/parakeet-tdt-0.6b-v2")
+
+
+@pytest.mark.gpu_modal
+class TestGPUModalTranscript:
+    """Test suite for GPU Modal transcription endpoints."""
+
+    def test_transcriptions_from_url(self):
+        """Test the /v1/audio/transcriptions-from-url endpoint."""
+        url = get_modal_transcript_url()
+        headers = get_auth_headers()
+
+        with httpx.Client(timeout=60.0) as client:
+            response = client.post(
+                f"{url}/v1/audio/transcriptions-from-url",
+                json={
+                    "audio_file_url": TEST_AUDIO_URL,
+                    "model": get_model_name(),
+                    "language": "en",
+                    "timestamp_offset": 0.0,
+                },
+                headers=headers,
+            )
+
+            assert response.status_code == 200, f"Request failed: {response.text}"
+            result = response.json()
+
+            # Verify response structure
+            assert "text" in result
+            assert "words" in result
+            assert isinstance(result["text"], str)
+            assert isinstance(result["words"], list)
+
+            # Verify content is meaningful
+            assert len(result["text"]) > 0, "Transcript text should not be empty"
+            assert len(result["words"]) > 0, "Words list must not be empty"
+
+            # Verify word structure
+            for word in result["words"]:
+                assert "word" in word
+                assert "start" in word
+                assert "end" in word
+                assert isinstance(word["start"], (int, float))
+                assert isinstance(word["end"], (int, float))
+                assert word["start"] <= word["end"]
+
+    def test_transcriptions_single_file(self):
+        """Test the /v1/audio/transcriptions endpoint with a single file."""
+        url = get_modal_transcript_url()
+        headers = get_auth_headers()
+
+        # Download test audio file to upload
+        with httpx.Client(timeout=60.0) as client:
+            audio_response = client.get(TEST_AUDIO_URL)
+            audio_response.raise_for_status()
+
+            with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as tmp_file:
+                tmp_file.write(audio_response.content)
+                tmp_file_path = tmp_file.name
+
+            try:
+                # Upload the file for transcription
+                with open(tmp_file_path, "rb") as f:
+                    files = {"file": ("test_audio.mp3", f, "audio/mpeg")}
+                    data = {
+                        "model": get_model_name(),
+                        "language": "en",
+                        "batch": "false",
+                    }
+
+                    response = client.post(
+                        f"{url}/v1/audio/transcriptions",
+                        files=files,
+                        data=data,
+                        headers=headers,
+                    )
+
+                assert response.status_code == 200, f"Request failed: {response.text}"
+                result = response.json()
+
+                # Verify response structure for single file
+                assert "text" in result
+                assert "words" in result
+                assert "filename" in result
+                assert isinstance(result["text"], str)
+                assert isinstance(result["words"], list)
+
+                # Verify content
+                assert len(result["text"]) > 0, "Transcript text should not be empty"
+
+            finally:
+                Path(tmp_file_path).unlink(missing_ok=True)
+
+    def test_transcriptions_multiple_files(self):
+        """Test the /v1/audio/transcriptions endpoint with multiple files (non-batch mode)."""
+        url = get_modal_transcript_url()
+        headers = get_auth_headers()
+
+        # Create multiple test files (we'll use the same audio content for simplicity)
+        with httpx.Client(timeout=60.0) as client:
+            audio_response = client.get(TEST_AUDIO_URL)
+            audio_response.raise_for_status()
+            audio_content = audio_response.content
+
+            temp_files = []
+            try:
+                # Create 3 temporary files
+                for i in range(3):
+                    tmp_file = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False)
+                    tmp_file.write(audio_content)
+                    tmp_file.close()
+                    temp_files.append(tmp_file.name)
+
+                # Upload multiple files for transcription (non-batch)
+                files = [
+                    ("files", (f"test_audio_{i}.mp3", open(f, "rb"), "audio/mpeg"))
+                    for i, f in enumerate(temp_files)
+                ]
+                data = {
+                    "model": get_model_name(),
+                    "language": "en",
+                    "batch": "false",
+                }
+
+                response = client.post(
+                    f"{url}/v1/audio/transcriptions",
+                    files=files,
+                    data=data,
+                    headers=headers,
+                )
+
+                # Close file handles
+                for _, file_tuple in files:
+                    file_tuple[1].close()
+
+                assert response.status_code == 200, f"Request failed: {response.text}"
+                result = response.json()
+
+                # Verify response structure for multiple files (non-batch)
+                assert "results" in result
+                assert isinstance(result["results"], list)
+                assert len(result["results"]) == 3
+
+                for idx, file_result in enumerate(result["results"]):
+                    assert "text" in file_result
+                    assert "words" in file_result
+                    assert "filename" in file_result
+                    assert isinstance(file_result["text"], str)
+                    assert isinstance(file_result["words"], list)
+                    assert len(file_result["text"]) > 0
+
+            finally:
+                for f in temp_files:
+                    Path(f).unlink(missing_ok=True)
+
+    def test_transcriptions_multiple_files_batch(self):
+        """Test the /v1/audio/transcriptions endpoint with multiple files in batch mode."""
+        url = get_modal_transcript_url()
+        headers = get_auth_headers()
+
+        # Create multiple test files
+        with httpx.Client(timeout=60.0) as client:
+            audio_response = client.get(TEST_AUDIO_URL)
+            audio_response.raise_for_status()
+            audio_content = audio_response.content
+
+            temp_files = []
+            try:
+                # Create 3 temporary files
+                for i in range(3):
+                    tmp_file = tempfile.NamedTemporaryFile(suffix=".mp3", delete=False)
+                    tmp_file.write(audio_content)
+                    tmp_file.close()
+                    temp_files.append(tmp_file.name)
+
+                # Upload multiple files for batch transcription
+                files = [
+                    ("files", (f"test_audio_{i}.mp3", open(f, "rb"), "audio/mpeg"))
+                    for i, f in enumerate(temp_files)
+                ]
+                data = {
+                    "model": get_model_name(),
+                    "language": "en",
+                    "batch": "true",
+                }
+
+                response = client.post(
+                    f"{url}/v1/audio/transcriptions",
+                    files=files,
+                    data=data,
+                    headers=headers,
+                )
+
+                # Close file handles
+                for _, file_tuple in files:
+                    file_tuple[1].close()
+
+                assert response.status_code == 200, f"Request failed: {response.text}"
+                result = response.json()
+
+                # Verify response structure for batch mode
+                assert "results" in result
+                assert isinstance(result["results"], list)
+                assert len(result["results"]) == 3
+
+                for idx, batch_result in enumerate(result["results"]):
+                    assert "text" in batch_result
+                    assert "words" in batch_result
+                    assert "filename" in batch_result
+                    assert isinstance(batch_result["text"], str)
+                    assert isinstance(batch_result["words"], list)
+                    assert len(batch_result["text"]) > 0
+
+            finally:
+                for f in temp_files:
+                    Path(f).unlink(missing_ok=True)
+
+    def test_transcriptions_error_handling(self):
+        """Test error handling for invalid requests."""
+        url = get_modal_transcript_url()
+        headers = get_auth_headers()
+
+        with httpx.Client(timeout=60.0) as client:
+            # Test with unsupported language
+            response = client.post(
+                f"{url}/v1/audio/transcriptions-from-url",
+                json={
+                    "audio_file_url": TEST_AUDIO_URL,
+                    "model": get_model_name(),
+                    "language": "fr",  # Parakeet only supports English
+                    "timestamp_offset": 0.0,
+                },
+                headers=headers,
+            )
+
+            assert response.status_code == 400
+            assert "only supports English" in response.text
+
+    def test_transcriptions_with_timestamp_offset(self):
+        """Test transcription with timestamp offset parameter."""
+        url = get_modal_transcript_url()
+        headers = get_auth_headers()
+
+        with httpx.Client(timeout=60.0) as client:
+            # Test with timestamp offset
+            response = client.post(
+                f"{url}/v1/audio/transcriptions-from-url",
+                json={
+                    "audio_file_url": TEST_AUDIO_URL,
+                    "model": get_model_name(),
+                    "language": "en",
+                    "timestamp_offset": 10.0,  # Add 10 second offset
+                },
+                headers=headers,
+            )
+
+            assert response.status_code == 200, f"Request failed: {response.text}"
+            result = response.json()
+
+            # Verify response structure
+            assert "text" in result
+            assert "words" in result
+            assert len(result["words"]) > 0, "Words list must not be empty"
+
+            # Verify that timestamps have been offset
+            for word in result["words"]:
+                # All timestamps should be >= 10.0 due to offset
+                assert (
+                    word["start"] >= 10.0
+                ), f"Word start time {word['start']} should be >= 10.0"
+                assert (
+                    word["end"] >= 10.0
+                ), f"Word end time {word['end']} should be >= 10.0"

server/tests/test_pipeline_main_file.py

@@ -0,0 +1,633 @@
+"""
+Tests for PipelineMainFile - file-based processing pipeline
+
+This test verifies the complete file processing pipeline without mocking much,
+ensuring all processors are correctly invoked and the happy path works correctly.
+"""
+
+from pathlib import Path
+from unittest.mock import AsyncMock, MagicMock, patch
+from uuid import uuid4
+
+import pytest
+
+from reflector.pipelines.main_file_pipeline import PipelineMainFile
+from reflector.processors.file_diarization import FileDiarizationOutput
+from reflector.processors.types import (
+    DiarizationSegment,
+    TitleSummary,
+    Word,
+)
+from reflector.processors.types import (
+    Transcript as TranscriptType,
+)
+
+
+@pytest.fixture
+async def dummy_file_transcript():
+    """Mock FileTranscriptAutoProcessor for file processing"""
+    from reflector.processors.file_transcript import FileTranscriptProcessor
+
+    class TestFileTranscriptProcessor(FileTranscriptProcessor):
+        async def _transcript(self, data):
+            return TranscriptType(
+                text="Hello world. How are you today?",
+                words=[
+                    Word(start=0.0, end=0.5, text="Hello", speaker=0),
+                    Word(start=0.5, end=0.6, text=" ", speaker=0),
+                    Word(start=0.6, end=1.0, text="world", speaker=0),
+                    Word(start=1.0, end=1.1, text=".", speaker=0),
+                    Word(start=1.1, end=1.2, text=" ", speaker=0),
+                    Word(start=1.2, end=1.5, text="How", speaker=0),
+                    Word(start=1.5, end=1.6, text=" ", speaker=0),
+                    Word(start=1.6, end=1.8, text="are", speaker=0),
+                    Word(start=1.8, end=1.9, text=" ", speaker=0),
+                    Word(start=1.9, end=2.1, text="you", speaker=0),
+                    Word(start=2.1, end=2.2, text=" ", speaker=0),
+                    Word(start=2.2, end=2.5, text="today", speaker=0),
+                    Word(start=2.5, end=2.6, text="?", speaker=0),
+                ],
+            )
+
+    with patch(
+        "reflector.processors.file_transcript_auto.FileTranscriptAutoProcessor.__new__"
+    ) as mock_auto:
+        mock_auto.return_value = TestFileTranscriptProcessor()
+        yield
+
+
+@pytest.fixture
+async def dummy_file_diarization():
+    """Mock FileDiarizationAutoProcessor for file processing"""
+    from reflector.processors.file_diarization import FileDiarizationProcessor
+
+    class TestFileDiarizationProcessor(FileDiarizationProcessor):
+        async def _diarize(self, data):
+            return FileDiarizationOutput(
+                diarization=[
+                    DiarizationSegment(start=0.0, end=1.1, speaker=0),
+                    DiarizationSegment(start=1.2, end=2.6, speaker=1),
+                ]
+            )
+
+    with patch(
+        "reflector.processors.file_diarization_auto.FileDiarizationAutoProcessor.__new__"
+    ) as mock_auto:
+        mock_auto.return_value = TestFileDiarizationProcessor()
+        yield
+
+
+@pytest.fixture
+async def mock_transcript_in_db(tmpdir):
+    """Create a mock transcript in the database"""
+    from reflector.db.transcripts import Transcript
+    from reflector.settings import settings
+
+    # Set the DATA_DIR to our tmpdir
+    original_data_dir = settings.DATA_DIR
+    settings.DATA_DIR = str(tmpdir)
+
+    transcript_id = str(uuid4())
+    data_path = Path(tmpdir) / transcript_id
+    data_path.mkdir(parents=True, exist_ok=True)
+
+    # Create mock transcript object
+    transcript = Transcript(
+        id=transcript_id,
+        name="Test Transcript",
+        status="processing",
+        source_kind="file",
+        source_language="en",
+        target_language="en",
+    )
+
+    # Mock the controller to return our transcript
+    try:
+        with patch(
+            "reflector.pipelines.main_file_pipeline.transcripts_controller.get_by_id"
+        ) as mock_get:
+            mock_get.return_value = transcript
+            with patch(
+                "reflector.pipelines.main_live_pipeline.transcripts_controller.get_by_id"
+            ) as mock_get2:
+                mock_get2.return_value = transcript
+                with patch(
+                    "reflector.pipelines.main_live_pipeline.transcripts_controller.update"
+                ) as mock_update:
+                    mock_update.return_value = None
+                    yield transcript
+    finally:
+        # Restore original DATA_DIR
+        settings.DATA_DIR = original_data_dir
+
+
+@pytest.fixture
+async def mock_storage():
+    """Mock storage for file uploads"""
+    from reflector.storage.base import Storage
+
+    class TestStorage(Storage):
+        async def _put_file(self, path, data):
+            return None
+
+        async def _get_file_url(self, path):
+            return f"http://test-storage/{path}"
+
+        async def _get_file(self, path):
+            return b"test_audio_data"
+
+        async def _delete_file(self, path):
+            return None
+
+    storage = TestStorage()
+    # Add mock tracking for verification
+    storage._put_file = AsyncMock(side_effect=storage._put_file)
+    storage._get_file_url = AsyncMock(side_effect=storage._get_file_url)
+
+    with patch(
+        "reflector.pipelines.main_file_pipeline.get_transcripts_storage"
+    ) as mock_get:
+        mock_get.return_value = storage
+        yield storage
+
+
+@pytest.fixture
+async def mock_audio_file_writer():
+    """Mock AudioFileWriterProcessor to avoid actual file writing"""
+    with patch(
+        "reflector.pipelines.main_file_pipeline.AudioFileWriterProcessor"
+    ) as mock_writer_class:
+        mock_writer = AsyncMock()
+        mock_writer.push = AsyncMock()
+        mock_writer.flush = AsyncMock()
+        mock_writer_class.return_value = mock_writer
+        yield mock_writer
+
+
+@pytest.fixture
+async def mock_waveform_processor():
+    """Mock AudioWaveformProcessor"""
+    with patch(
+        "reflector.pipelines.main_file_pipeline.AudioWaveformProcessor"
+    ) as mock_waveform_class:
+        mock_waveform = AsyncMock()
+        mock_waveform.set_pipeline = MagicMock()
+        mock_waveform.flush = AsyncMock()
+        mock_waveform_class.return_value = mock_waveform
+        yield mock_waveform
+
+
+@pytest.fixture
+async def mock_topic_detector():
+    """Mock TranscriptTopicDetectorProcessor"""
+    with patch(
+        "reflector.pipelines.main_file_pipeline.TranscriptTopicDetectorProcessor"
+    ) as mock_topic_class:
+        mock_topic = AsyncMock()
+        mock_topic.set_pipeline = MagicMock()
+        mock_topic.push = AsyncMock()
+        mock_topic.flush_called = False
+
+        # When flush is called, simulate topic detection by calling the callback
+        async def flush_with_callback():
+            mock_topic.flush_called = True
+            if hasattr(mock_topic, "_callback"):
+                # Create a minimal transcript for the TitleSummary
+                test_transcript = TranscriptType(words=[], text="test transcript")
+                await mock_topic._callback(
+                    TitleSummary(
+                        title="Test Topic",
+                        summary="Test topic summary",
+                        timestamp=0.0,
+                        duration=10.0,
+                        transcript=test_transcript,
+                    )
+                )
+
+        mock_topic.flush = flush_with_callback
+
+        def init_with_callback(callback=None):
+            mock_topic._callback = callback
+            return mock_topic
+
+        mock_topic_class.side_effect = init_with_callback
+        yield mock_topic
+
+
+@pytest.fixture
+async def mock_title_processor():
+    """Mock TranscriptFinalTitleProcessor"""
+    with patch(
+        "reflector.pipelines.main_file_pipeline.TranscriptFinalTitleProcessor"
+    ) as mock_title_class:
+        mock_title = AsyncMock()
+        mock_title.set_pipeline = MagicMock()
+        mock_title.push = AsyncMock()
+        mock_title.flush_called = False
+
+        # When flush is called, simulate title generation by calling the callback
+        async def flush_with_callback():
+            mock_title.flush_called = True
+            if hasattr(mock_title, "_callback"):
+                from reflector.processors.types import FinalTitle
+
+                await mock_title._callback(FinalTitle(title="Test Title"))
+
+        mock_title.flush = flush_with_callback
+
+        def init_with_callback(callback=None):
+            mock_title._callback = callback
+            return mock_title
+
+        mock_title_class.side_effect = init_with_callback
+        yield mock_title
+
+
+@pytest.fixture
+async def mock_summary_processor():
+    """Mock TranscriptFinalSummaryProcessor"""
+    with patch(
+        "reflector.pipelines.main_file_pipeline.TranscriptFinalSummaryProcessor"
+    ) as mock_summary_class:
+        mock_summary = AsyncMock()
+        mock_summary.set_pipeline = MagicMock()
+        mock_summary.push = AsyncMock()
+        mock_summary.flush_called = False
+
+        # When flush is called, simulate summary generation by calling the callbacks
+        async def flush_with_callback():
+            mock_summary.flush_called = True
+            from reflector.processors.types import FinalLongSummary, FinalShortSummary
+
+            if hasattr(mock_summary, "_callback"):
+                await mock_summary._callback(
+                    FinalLongSummary(long_summary="Test long summary", duration=10.0)
+                )
+            if hasattr(mock_summary, "_on_short_summary"):
+                await mock_summary._on_short_summary(
+                    FinalShortSummary(short_summary="Test short summary", duration=10.0)
+                )
+
+        mock_summary.flush = flush_with_callback
+
+        def init_with_callback(transcript=None, callback=None, on_short_summary=None):
+            mock_summary._callback = callback
+            mock_summary._on_short_summary = on_short_summary
+            return mock_summary
+
+        mock_summary_class.side_effect = init_with_callback
+        yield mock_summary
+
+
+@pytest.mark.asyncio
+async def test_pipeline_main_file_process(
+    tmpdir,
+    mock_transcript_in_db,
+    dummy_file_transcript,
+    dummy_file_diarization,
+    mock_storage,
+    mock_audio_file_writer,
+    mock_waveform_processor,
+    mock_topic_detector,
+    mock_title_processor,
+    mock_summary_processor,
+):
+    """
+    Test the complete PipelineMainFile processing pipeline.
+
+    This test verifies:
+    1. Audio extraction and writing
+    2. Audio upload to storage
+    3. Parallel processing of transcription, diarization, and waveform
+    4. Assembly of transcript with diarization
+    5. Topic detection
+    6. Title and summary generation
+    """
+    # Create a test audio file
+    test_audio_path = Path(__file__).parent / "records" / "test_mathieu_hello.wav"
+
+    # Copy test audio to the transcript's data path as if it was uploaded
+    upload_path = mock_transcript_in_db.data_path / "upload.wav"
+    upload_path.write_bytes(test_audio_path.read_bytes())
+
+    # Also create the audio.mp3 file that would be created by AudioFileWriterProcessor
+    # Since we're mocking AudioFileWriterProcessor, we need to create this manually
+    mp3_path = mock_transcript_in_db.data_path / "audio.mp3"
+    mp3_path.write_bytes(b"mock_mp3_data")
+
+    # Track callback invocations
+    callback_marks = {
+        "on_status": [],
+        "on_duration": [],
+        "on_waveform": [],
+        "on_topic": [],
+        "on_title": [],
+        "on_long_summary": [],
+        "on_short_summary": [],
+    }
+
+    # Create pipeline with mocked callbacks
+    pipeline = PipelineMainFile(transcript_id=mock_transcript_in_db.id)
+
+    # Override callbacks to track invocations
+    async def track_callback(name, data):
+        callback_marks[name].append(data)
+        # Call the original callback
+        original = getattr(PipelineMainFile, name)
+        return await original(pipeline, data)
+
+    for callback_name in callback_marks.keys():
+        setattr(
+            pipeline,
+            callback_name,
+            lambda data, n=callback_name: track_callback(n, data),
+        )
+
+    # Mock av.open for audio processing
+    with patch("reflector.pipelines.main_file_pipeline.av.open") as mock_av:
+        # Mock container for checking video streams
+        mock_container = MagicMock()
+        mock_container.streams.video = []  # No video streams (audio only)
+        mock_container.close = MagicMock()
+
+        # Mock container for decoding audio frames
+        mock_decode_container = MagicMock()
+        mock_decode_container.decode.return_value = iter(
+            [MagicMock()]
+        )  # One mock audio frame
+        mock_decode_container.close = MagicMock()
+
+        # Return different containers for different calls
+        mock_av.side_effect = [mock_container, mock_decode_container]
+
+        # Run the pipeline
+        await pipeline.process(upload_path)
+
+    # Verify audio extraction and writing
+    assert mock_audio_file_writer.push.called
+    assert mock_audio_file_writer.flush.called
+
+    # Verify storage upload
+    assert mock_storage._put_file.called
+    assert mock_storage._get_file_url.called
+
+    # Verify waveform generation
+    assert mock_waveform_processor.flush.called
+    assert mock_waveform_processor.set_pipeline.called
+
+    # Verify topic detection
+    assert mock_topic_detector.push.called
+    assert mock_topic_detector.flush_called
+
+    # Verify title generation
+    assert mock_title_processor.push.called
+    assert mock_title_processor.flush_called
+
+    # Verify summary generation
+    assert mock_summary_processor.push.called
+    assert mock_summary_processor.flush_called
+
+    # Verify callbacks were invoked
+    assert len(callback_marks["on_topic"]) > 0, "Topic callback should be invoked"
+    assert len(callback_marks["on_title"]) > 0, "Title callback should be invoked"
+    assert (
+        len(callback_marks["on_long_summary"]) > 0
+    ), "Long summary callback should be invoked"
+    assert (
+        len(callback_marks["on_short_summary"]) > 0
+    ), "Short summary callback should be invoked"
+
+    print(f"Callback marks: {callback_marks}")
+
+    # Verify the pipeline completed successfully
+    assert pipeline.logger is not None
+    print("PipelineMainFile test completed successfully!")
+
+
+@pytest.mark.asyncio
+async def test_pipeline_main_file_with_video(
+    tmpdir,
+    mock_transcript_in_db,
+    dummy_file_transcript,
+    dummy_file_diarization,
+    mock_storage,
+    mock_audio_file_writer,
+    mock_waveform_processor,
+    mock_topic_detector,
+    mock_title_processor,
+    mock_summary_processor,
+):
+    """
+    Test PipelineMainFile with video input (verifies audio extraction).
+    """
+    # Create a test audio file
+    test_audio_path = Path(__file__).parent / "records" / "test_mathieu_hello.wav"
+
+    # Copy test audio to the transcript's data path as if it was a video upload
+    upload_path = mock_transcript_in_db.data_path / "upload.mp4"
+    upload_path.write_bytes(test_audio_path.read_bytes())
+
+    # Also create the audio.mp3 file that would be created by AudioFileWriterProcessor
+    mp3_path = mock_transcript_in_db.data_path / "audio.mp3"
+    mp3_path.write_bytes(b"mock_mp3_data")
+
+    # Create pipeline
+    pipeline = PipelineMainFile(transcript_id=mock_transcript_in_db.id)
+
+    # Mock av.open for video processing
+    with patch("reflector.pipelines.main_file_pipeline.av.open") as mock_av:
+        # Mock container for checking video streams
+        mock_container = MagicMock()
+        mock_container.streams.video = [MagicMock()]  # Has video streams
+        mock_container.close = MagicMock()
+
+        # Mock container for decoding audio frames
+        mock_decode_container = MagicMock()
+        mock_decode_container.decode.return_value = iter(
+            [MagicMock()]
+        )  # One mock audio frame
+        mock_decode_container.close = MagicMock()
+
+        # Return different containers for different calls
+        mock_av.side_effect = [mock_container, mock_decode_container]
+
+        # Run the pipeline
+        await pipeline.process(upload_path)
+
+    # Verify audio extraction from video
+    assert mock_audio_file_writer.push.called
+    assert mock_audio_file_writer.flush.called
+
+    # Verify the rest of the pipeline completed
+    assert mock_storage._put_file.called
+    assert mock_waveform_processor.flush.called
+    assert mock_topic_detector.push.called
+    assert mock_title_processor.push.called
+    assert mock_summary_processor.push.called
+
+    print("PipelineMainFile video test completed successfully!")
+
+
+@pytest.mark.asyncio
+async def test_pipeline_main_file_no_diarization(
+    tmpdir,
+    mock_transcript_in_db,
+    dummy_file_transcript,
+    mock_storage,
+    mock_audio_file_writer,
+    mock_waveform_processor,
+    mock_topic_detector,
+    mock_title_processor,
+    mock_summary_processor,
+):
+    """
+    Test PipelineMainFile with diarization disabled.
+    """
+    from reflector.settings import settings
+
+    # Disable diarization
+    with patch.object(settings, "DIARIZATION_BACKEND", None):
+        # Create a test audio file
+        test_audio_path = Path(__file__).parent / "records" / "test_mathieu_hello.wav"
+
+        # Copy test audio to the transcript's data path
+        upload_path = mock_transcript_in_db.data_path / "upload.wav"
+        upload_path.write_bytes(test_audio_path.read_bytes())
+
+        # Also create the audio.mp3 file
+        mp3_path = mock_transcript_in_db.data_path / "audio.mp3"
+        mp3_path.write_bytes(b"mock_mp3_data")
+
+        # Create pipeline
+        pipeline = PipelineMainFile(transcript_id=mock_transcript_in_db.id)
+
+        # Mock av.open for audio processing
+        with patch("reflector.pipelines.main_file_pipeline.av.open") as mock_av:
+            # Mock container for checking video streams
+            mock_container = MagicMock()
+            mock_container.streams.video = []  # No video streams
+            mock_container.close = MagicMock()
+
+            # Mock container for decoding audio frames
+            mock_decode_container = MagicMock()
+            mock_decode_container.decode.return_value = iter([MagicMock()])
+            mock_decode_container.close = MagicMock()
+
+            # Return different containers for different calls
+            mock_av.side_effect = [mock_container, mock_decode_container]
+
+            # Run the pipeline
+            await pipeline.process(upload_path)
+
+        # Verify the pipeline completed without diarization
+        assert mock_storage._put_file.called
+        assert mock_waveform_processor.flush.called
+        assert mock_topic_detector.push.called
+        assert mock_title_processor.push.called
+        assert mock_summary_processor.push.called
+
+        print("PipelineMainFile no-diarization test completed successfully!")
+
+
+@pytest.mark.asyncio
+async def test_task_pipeline_file_process(
+    tmpdir,
+    mock_transcript_in_db,
+    dummy_file_transcript,
+    dummy_file_diarization,
+    mock_storage,
+    mock_audio_file_writer,
+    mock_waveform_processor,
+    mock_topic_detector,
+    mock_title_processor,
+    mock_summary_processor,
+):
+    """
+    Test the Celery task entry point for file pipeline processing.
+    """
+    # Direct import of the underlying async function, bypassing the asynctask decorator
+
+    # Create a test audio file in the transcript's data path
+    test_audio_path = Path(__file__).parent / "records" / "test_mathieu_hello.wav"
+    upload_path = mock_transcript_in_db.data_path / "upload.wav"
+    upload_path.write_bytes(test_audio_path.read_bytes())
+
+    # Also create the audio.mp3 file
+    mp3_path = mock_transcript_in_db.data_path / "audio.mp3"
+    mp3_path.write_bytes(b"mock_mp3_data")
+
+    # Mock av.open for audio processing
+    with patch("reflector.pipelines.main_file_pipeline.av.open") as mock_av:
+        # Mock container for checking video streams
+        mock_container = MagicMock()
+        mock_container.streams.video = []  # No video streams
+        mock_container.close = MagicMock()
+
+        # Mock container for decoding audio frames
+        mock_decode_container = MagicMock()
+        mock_decode_container.decode.return_value = iter([MagicMock()])
+        mock_decode_container.close = MagicMock()
+
+        # Return different containers for different calls
+        mock_av.side_effect = [mock_container, mock_decode_container]
+
+        # Get the original async function without the asynctask decorator
+        # The function is wrapped, so we need to call it differently
+        # For now, we test the pipeline directly since the task is just a thin wrapper
+        from reflector.pipelines.main_file_pipeline import PipelineMainFile
+
+        pipeline = PipelineMainFile(transcript_id=mock_transcript_in_db.id)
+        await pipeline.process(upload_path)
+
+    # Verify the pipeline was executed through the task
+    assert mock_audio_file_writer.push.called
+    assert mock_audio_file_writer.flush.called
+    assert mock_storage._put_file.called
+    assert mock_waveform_processor.flush.called
+    assert mock_topic_detector.push.called
+    assert mock_title_processor.push.called
+    assert mock_summary_processor.push.called
+
+    print("task_pipeline_file_process test completed successfully!")
+
+
+@pytest.mark.asyncio
+async def test_pipeline_file_process_no_transcript():
+    """
+    Test the pipeline with a non-existent transcript.
+    """
+    from reflector.pipelines.main_file_pipeline import PipelineMainFile
+
+    # Mock the controller to return None (transcript not found)
+    with patch(
+        "reflector.pipelines.main_file_pipeline.transcripts_controller.get_by_id"
+    ) as mock_get:
+        mock_get.return_value = None
+
+        pipeline = PipelineMainFile(transcript_id=str(uuid4()))
+
+        # Should raise an exception for missing transcript when get_transcript is called
+        with pytest.raises(Exception, match="Transcript not found"):
+            await pipeline.get_transcript()
+
+
+@pytest.mark.asyncio
+async def test_pipeline_file_process_no_audio_file(
+    mock_transcript_in_db,
+):
+    """
+    Test the pipeline when no audio file is found.
+    """
+    from reflector.pipelines.main_file_pipeline import PipelineMainFile
+
+    # Don't create any audio files in the data path
+    # The pipeline's process should handle missing files gracefully
+
+    pipeline = PipelineMainFile(transcript_id=mock_transcript_in_db.id)
+
+    # Try to process a non-existent file
+    non_existent_path = mock_transcript_in_db.data_path / "nonexistent.wav"
+
+    # This should fail when trying to open the file with av
+    with pytest.raises(Exception):
+        await pipeline.process(non_existent_path)

server/tests/test_processors_modal.py

@@ -0,0 +1,265 @@
+"""
+Tests for Modal-based processors using pytest-recording for HTTP recording/playbook
+
+Note: theses tests require full modal configuration to be able to record
+      vcr cassettes
+
+Configuration required for the first recording:
+- TRANSCRIPT_BACKEND=modal
+- TRANSCRIPT_URL=https://xxxxx--reflector-transcriber-parakeet-web.modal.run
+- TRANSCRIPT_MODAL_API_KEY=xxxxx
+- DIARIZATION_BACKEND=modal
+- DIARIZATION_URL=https://xxxxx--reflector-diarizer-web.modal.run
+- DIARIZATION_MODAL_API_KEY=xxxxx
+"""
+
+from unittest.mock import patch
+
+import pytest
+
+from reflector.processors.file_diarization import FileDiarizationInput
+from reflector.processors.file_diarization_modal import FileDiarizationModalProcessor
+from reflector.processors.file_transcript import FileTranscriptInput
+from reflector.processors.file_transcript_modal import FileTranscriptModalProcessor
+from reflector.processors.transcript_diarization_assembler import (
+    TranscriptDiarizationAssemblerInput,
+    TranscriptDiarizationAssemblerProcessor,
+)
+from reflector.processors.types import DiarizationSegment, Transcript, Word
+
+# Public test audio file hosted on S3 specifically for reflector pytests
+TEST_AUDIO_URL = (
+    "https://reflector-github-pytest.s3.us-east-1.amazonaws.com/test_mathieu_hello.mp3"
+)
+
+
+@pytest.mark.asyncio
+async def test_file_transcript_modal_processor_missing_url():
+    with patch("reflector.processors.file_transcript_modal.settings") as mock_settings:
+        mock_settings.TRANSCRIPT_URL = None
+        with pytest.raises(Exception, match="TRANSCRIPT_URL required"):
+            FileTranscriptModalProcessor(modal_api_key="test-api-key")
+
+
+@pytest.mark.asyncio
+async def test_file_diarization_modal_processor_missing_url():
+    with patch("reflector.processors.file_diarization_modal.settings") as mock_settings:
+        mock_settings.DIARIZATION_URL = None
+        with pytest.raises(Exception, match="DIARIZATION_URL required"):
+            FileDiarizationModalProcessor(modal_api_key="test-api-key")
+
+
+@pytest.mark.vcr()
+@pytest.mark.asyncio
+async def test_file_diarization_modal_processor(vcr):
+    """Test FileDiarizationModalProcessor using public audio URL and Modal API"""
+    from reflector.settings import settings
+
+    processor = FileDiarizationModalProcessor(
+        modal_api_key=settings.DIARIZATION_MODAL_API_KEY
+    )
+
+    test_input = FileDiarizationInput(audio_url=TEST_AUDIO_URL)
+    result = await processor._diarize(test_input)
+
+    # Verify the result structure
+    assert result is not None
+    assert hasattr(result, "diarization")
+    assert isinstance(result.diarization, list)
+
+    # Check structure of each diarization segment
+    for segment in result.diarization:
+        assert "start" in segment
+        assert "end" in segment
+        assert "speaker" in segment
+        assert isinstance(segment["start"], (int, float))
+        assert isinstance(segment["end"], (int, float))
+        assert isinstance(segment["speaker"], int)
+        # Basic sanity check - start should be before end
+        assert segment["start"] < segment["end"]
+
+
+@pytest.mark.vcr()
+@pytest.mark.asyncio
+async def test_file_transcript_modal_processor():
+    """Test FileTranscriptModalProcessor using public audio URL and Modal API"""
+    from reflector.settings import settings
+
+    processor = FileTranscriptModalProcessor(
+        modal_api_key=settings.TRANSCRIPT_MODAL_API_KEY
+    )
+
+    test_input = FileTranscriptInput(
+        audio_url=TEST_AUDIO_URL,
+        language="en",
+    )
+
+    # This will record the HTTP interaction on first run, replay on subsequent runs
+    result = await processor._transcript(test_input)
+
+    # Verify the result structure
+    assert result is not None
+    assert hasattr(result, "words")
+    assert isinstance(result.words, list)
+
+    # Check structure of each word if present
+    for word in result.words:
+        assert hasattr(word, "text")
+        assert hasattr(word, "start")
+        assert hasattr(word, "end")
+        assert isinstance(word.start, (int, float))
+        assert isinstance(word.end, (int, float))
+        assert isinstance(word.text, str)
+        # Basic sanity check - start should be before or equal to end
+        assert word.start <= word.end
+
+
+@pytest.mark.asyncio
+async def test_transcript_diarization_assembler_processor():
+    """Test TranscriptDiarizationAssemblerProcessor without VCR (no HTTP requests)"""
+    # Create test transcript with words
+    words = [
+        Word(text="Hello", start=0.0, end=1.0, speaker=0),
+        Word(text=" ", start=1.0, end=1.1, speaker=0),
+        Word(text="world", start=1.1, end=2.0, speaker=0),
+        Word(text=".", start=2.0, end=2.1, speaker=0),
+        Word(text=" ", start=2.1, end=2.2, speaker=0),
+        Word(text="How", start=2.2, end=2.8, speaker=0),
+        Word(text=" ", start=2.8, end=2.9, speaker=0),
+        Word(text="are", start=2.9, end=3.2, speaker=0),
+        Word(text=" ", start=3.2, end=3.3, speaker=0),
+        Word(text="you", start=3.3, end=3.8, speaker=0),
+        Word(text="?", start=3.8, end=3.9, speaker=0),
+    ]
+    transcript = Transcript(words=words)
+
+    # Create test diarization segments
+    diarization = [
+        DiarizationSegment(start=0.0, end=2.1, speaker=0),
+        DiarizationSegment(start=2.1, end=3.9, speaker=1),
+    ]
+
+    # Create processor and test input
+    processor = TranscriptDiarizationAssemblerProcessor()
+    test_input = TranscriptDiarizationAssemblerInput(
+        transcript=transcript, diarization=diarization
+    )
+
+    # Track emitted results
+    emitted_results = []
+
+    async def capture_result(result):
+        emitted_results.append(result)
+
+    processor.on(capture_result)
+
+    # Process the input
+    await processor.push(test_input)
+
+    # Verify result was emitted
+    assert len(emitted_results) == 1
+    result = emitted_results[0]
+
+    # Verify result structure
+    assert isinstance(result, Transcript)
+    assert len(result.words) == len(words)
+
+    # Verify speaker assignments were applied
+    # Words 0-3 (indices) should be speaker 0 (time 0.0-2.0)
+    # Words 4-10 (indices) should be speaker 1 (time 2.1-3.9)
+    for i in range(4):  # First 4 words (Hello, space, world, .)
+        assert (
+            result.words[i].speaker == 0
+        ), f"Word {i} '{result.words[i].text}' should be speaker 0, got {result.words[i].speaker}"
+
+    for i in range(4, 11):  # Remaining words (space, How, space, are, space, you, ?)
+        assert (
+            result.words[i].speaker == 1
+        ), f"Word {i} '{result.words[i].text}' should be speaker 1, got {result.words[i].speaker}"
+
+
+@pytest.mark.asyncio
+async def test_transcript_diarization_assembler_no_diarization():
+    """Test TranscriptDiarizationAssemblerProcessor with no diarization data"""
+    # Create test transcript
+    words = [Word(text="Hello", start=0.0, end=1.0, speaker=0)]
+    transcript = Transcript(words=words)
+
+    # Create processor and test input with empty diarization
+    processor = TranscriptDiarizationAssemblerProcessor()
+    test_input = TranscriptDiarizationAssemblerInput(
+        transcript=transcript, diarization=[]
+    )
+
+    # Track emitted results
+    emitted_results = []
+
+    async def capture_result(result):
+        emitted_results.append(result)
+
+    processor.on(capture_result)
+
+    # Process the input
+    await processor.push(test_input)
+
+    # Verify original transcript was returned unchanged
+    assert len(emitted_results) == 1
+    result = emitted_results[0]
+    assert result is transcript  # Should be the same object
+    assert result.words[0].speaker == 0  # Original speaker unchanged
+
+
+@pytest.mark.vcr()
+@pytest.mark.asyncio
+async def test_full_modal_pipeline_integration(vcr):
+    """Integration test: Transcription -> Diarization -> Assembly
+
+    This test demonstrates the full pipeline:
+    1. Run transcription via Modal
+    2. Run diarization via Modal
+    3. Assemble transcript with diarization
+    """
+    from reflector.settings import settings
+
+    # Step 1: Transcription
+    transcript_processor = FileTranscriptModalProcessor(
+        modal_api_key=settings.TRANSCRIPT_MODAL_API_KEY
+    )
+    transcript_input = FileTranscriptInput(audio_url=TEST_AUDIO_URL, language="en")
+    transcript = await transcript_processor._transcript(transcript_input)
+
+    # Step 2: Diarization
+    diarization_processor = FileDiarizationModalProcessor(
+        modal_api_key=settings.DIARIZATION_MODAL_API_KEY
+    )
+    diarization_input = FileDiarizationInput(audio_url=TEST_AUDIO_URL)
+    diarization_result = await diarization_processor._diarize(diarization_input)
+
+    # Step 3: Assembly
+    assembler = TranscriptDiarizationAssemblerProcessor()
+    assembly_input = TranscriptDiarizationAssemblerInput(
+        transcript=transcript, diarization=diarization_result.diarization
+    )
+
+    # Track assembled result
+    assembled_results = []
+
+    async def capture_result(result):
+        assembled_results.append(result)
+
+    assembler.on(capture_result)
+
+    await assembler.push(assembly_input)
+
+    # Verify the full pipeline worked
+    assert len(assembled_results) == 1
+    final_transcript = assembled_results[0]
+
+    # Verify the final transcript has the original words with updated speaker info
+    assert isinstance(final_transcript, Transcript)
+    assert len(final_transcript.words) == len(transcript.words)
+    assert len(final_transcript.words) > 0
+
+    # Verify some words have been assigned speakers from diarization
+    speakers_found = set(word.speaker for word in final_transcript.words)
+    assert len(speakers_found) > 0  # At least some speaker assignments

server/tests/test_processors_pipeline.py

@@ -2,10 +2,13 @@ import pytest
 
 
 @pytest.mark.asyncio
+@pytest.mark.parametrize("enable_diarization", [False, True])
 async def test_basic_process(
     dummy_transcript,
     dummy_llm,
     dummy_processors,
+    enable_diarization,
+    dummy_diarization,
 ):
     # goal is to start the server, and send rtc audio to it
     # validate the events received
@@ -28,12 +31,31 @@ async def test_basic_process(
 
     # invoke the process and capture events
     path = Path(__file__).parent / "records" / "test_mathieu_hello.wav"
-    await process_audio_file(path.as_posix(), event_callback)
-    print(marks)
+
+    if enable_diarization:
+        # Test with diarization - may fail if pyannote.audio is not installed
+        try:
+            await process_audio_file(
+                path.as_posix(), event_callback, enable_diarization=True
+            )
+        except SystemExit:
+            pytest.skip("pyannote.audio not installed - skipping diarization test")
+    else:
+        # Test without diarization - should always work
+        await process_audio_file(
+            path.as_posix(), event_callback, enable_diarization=False
+        )
+
+    print(f"Diarization: {enable_diarization}, Marks: {marks}")
 
     # validate the events
-    assert marks["TranscriptLinerProcessor"] == 1
-    assert marks["TranscriptTranslatorPassthroughProcessor"] == 1
+    # Each processor should be called for each audio segment processed
+    # The final processors (Topic, Title, Summary) should be called once at the end
+    assert marks["TranscriptLinerProcessor"] > 0
+    assert marks["TranscriptTranslatorPassthroughProcessor"] > 0
     assert marks["TranscriptTopicDetectorProcessor"] == 1
     assert marks["TranscriptFinalSummaryProcessor"] == 1
     assert marks["TranscriptFinalTitleProcessor"] == 1
+
+    if enable_diarization:
+        assert marks["TestAudioDiarizationProcessor"] == 1

server/tests/test_search.py

@@ -2,13 +2,18 @@
 
 import json
 from datetime import datetime, timezone
+from unittest.mock import AsyncMock, patch
 
 import pytest
-from pydantic import ValidationError
 
 from reflector.db import get_database
-from reflector.db.search import SearchParameters, search_controller
-from reflector.db.transcripts import transcripts
+from reflector.db.search import (
+    SearchController,
+    SearchParameters,
+    SearchResult,
+    search_controller,
+)
+from reflector.db.transcripts import SourceKind, transcripts
 
 
 @pytest.mark.asyncio
@@ -18,39 +23,137 @@ async def test_search_postgresql_only():
     assert results == []
     assert total == 0
 
-    try:
-        SearchParameters(query_text="")
-        assert False, "Should have raised validation error"
-    except ValidationError:
-        pass  # Expected
-
-    # Test that whitespace query raises validation error
-    try:
-        SearchParameters(query_text="   ")
-        assert False, "Should have raised validation error"
-    except ValidationError:
-        pass  # Expected
+    params_empty = SearchParameters(query_text="")
+    results_empty, total_empty = await search_controller.search_transcripts(
+        params_empty
+    )
+    assert isinstance(results_empty, list)
+    assert isinstance(total_empty, int)
 
 
 @pytest.mark.asyncio
-async def test_search_input_validation():
-    try:
-        SearchParameters(query_text="")
-        assert False, "Should have raised ValidationError"
-    except ValidationError:
-        pass  # Expected
+async def test_search_with_empty_query():
+    """Test that empty query returns all transcripts."""
+    params = SearchParameters(query_text="")
+    results, total = await search_controller.search_transcripts(params)
+
+    assert isinstance(results, list)
+    assert isinstance(total, int)
+    if len(results) > 1:
+        for i in range(len(results) - 1):
+            assert results[i].created_at >= results[i + 1].created_at
+
+
+@pytest.mark.asyncio
+async def test_empty_transcript_title_only_match():
+    """Test that transcripts with title-only matches return empty snippets."""
+    test_id = "test-empty-9b3f2a8d"
 
-    # Test that whitespace query raises validation error
     try:
-        SearchParameters(query_text="   \t\n  ")
-        assert False, "Should have raised ValidationError"
-    except ValidationError:
-        pass  # Expected
+        await get_database().execute(
+            transcripts.delete().where(transcripts.c.id == test_id)
+        )
+
+        test_data = {
+            "id": test_id,
+            "name": "Empty Transcript",
+            "title": "Empty Meeting",
+            "status": "completed",
+            "locked": False,
+            "duration": 0.0,
+            "created_at": datetime.now(timezone.utc),
+            "short_summary": None,
+            "long_summary": None,
+            "topics": json.dumps([]),
+            "events": json.dumps([]),
+            "participants": json.dumps([]),
+            "source_language": "en",
+            "target_language": "en",
+            "reviewed": False,
+            "audio_location": "local",
+            "share_mode": "private",
+            "source_kind": "room",
+            "webvtt": None,
+            "user_id": "test-user-1",
+        }
+
+        await get_database().execute(transcripts.insert().values(**test_data))
+
+        params = SearchParameters(query_text="empty", user_id="test-user-1")
+        results, total = await search_controller.search_transcripts(params)
+
+        assert total >= 1
+        found = next((r for r in results if r.id == test_id), None)
+        assert found is not None, "Should find transcript by title match"
+        assert found.search_snippets == []
+        assert found.total_match_count == 0
+
+    finally:
+        await get_database().execute(
+            transcripts.delete().where(transcripts.c.id == test_id)
+        )
+        await get_database().disconnect()
+
+
+@pytest.mark.asyncio
+async def test_search_with_long_summary():
+    """Test that long_summary content is searchable."""
+    test_id = "test-long-summary-8a9f3c2d"
+
+    try:
+        await get_database().execute(
+            transcripts.delete().where(transcripts.c.id == test_id)
+        )
+
+        test_data = {
+            "id": test_id,
+            "name": "Test Long Summary",
+            "title": "Regular Meeting",
+            "status": "completed",
+            "locked": False,
+            "duration": 1800.0,
+            "created_at": datetime.now(timezone.utc),
+            "short_summary": "Brief overview",
+            "long_summary": "Detailed discussion about quantum computing applications and blockchain technology integration",
+            "topics": json.dumps([]),
+            "events": json.dumps([]),
+            "participants": json.dumps([]),
+            "source_language": "en",
+            "target_language": "en",
+            "reviewed": False,
+            "audio_location": "local",
+            "share_mode": "private",
+            "source_kind": "room",
+            "webvtt": """WEBVTT
+
+00:00:00.000 --> 00:00:10.000
+Basic meeting content without special keywords.""",
+            "user_id": "test-user-2",
+        }
+
+        await get_database().execute(transcripts.insert().values(**test_data))
+
+        params = SearchParameters(query_text="quantum computing", user_id="test-user-2")
+        results, total = await search_controller.search_transcripts(params)
+
+        assert total >= 1
+        found = any(r.id == test_id for r in results)
+        assert found, "Should find transcript by long_summary content"
+
+        test_result = next((r for r in results if r.id == test_id), None)
+        assert test_result
+        assert len(test_result.search_snippets) > 0
+        assert "quantum computing" in test_result.search_snippets[0].lower()
+
+    finally:
+        await get_database().execute(
+            transcripts.delete().where(transcripts.c.id == test_id)
+        )
+        await get_database().disconnect()
 
 
 @pytest.mark.asyncio
 async def test_postgresql_search_with_data():
-    # collision is improbable
     test_id = "test-search-e2e-7f3a9b2c"
 
     try:
@@ -90,32 +193,31 @@ The search feature should support complex queries with ranking.
 
 00:00:30.000 --> 00:00:40.000
 We need to implement PostgreSQL tsvector for better performance.""",
+            "user_id": "test-user-3",
         }
 
         await get_database().execute(transcripts.insert().values(**test_data))
 
-        # Test 1: Search for a word in title
-        params = SearchParameters(query_text="planning")
+        params = SearchParameters(query_text="planning", user_id="test-user-3")
         results, total = await search_controller.search_transcripts(params)
         assert total >= 1
         found = any(r.id == test_id for r in results)
         assert found, "Should find test transcript by title word"
 
-        # Test 2: Search for a word in webvtt content
-        params = SearchParameters(query_text="tsvector")
+        params = SearchParameters(query_text="tsvector", user_id="test-user-3")
         results, total = await search_controller.search_transcripts(params)
         assert total >= 1
         found = any(r.id == test_id for r in results)
         assert found, "Should find test transcript by webvtt content"
 
-        # Test 3: Search with multiple words
-        params = SearchParameters(query_text="engineering planning")
+        params = SearchParameters(
+            query_text="engineering planning", user_id="test-user-3"
+        )
         results, total = await search_controller.search_transcripts(params)
         assert total >= 1
         found = any(r.id == test_id for r in results)
         assert found, "Should find test transcript by multiple words"
 
-        # Test 4: Verify SearchResult structure
         test_result = next((r for r in results if r.id == test_id), None)
         if test_result:
             assert test_result.title == "Engineering Planning Meeting Q4 2024"
@@ -123,15 +225,17 @@ We need to implement PostgreSQL tsvector for better performance.""",
             assert test_result.duration == 1800.0
             assert 0 <= test_result.rank <= 1, "Rank should be normalized to 0-1"
 
-        # Test 5: Search with OR operator
-        params = SearchParameters(query_text="tsvector OR nosuchword")
+        params = SearchParameters(
+            query_text="tsvector OR nosuchword", user_id="test-user-3"
+        )
         results, total = await search_controller.search_transcripts(params)
         assert total >= 1
         found = any(r.id == test_id for r in results)
         assert found, "Should find test transcript with OR query"
 
-        # Test 6: Quoted phrase search
-        params = SearchParameters(query_text='"full-text search"')
+        params = SearchParameters(
+            query_text='"full-text search"', user_id="test-user-3"
+        )
         results, total = await search_controller.search_transcripts(params)
         assert total >= 1
         found = any(r.id == test_id for r in results)
@@ -142,3 +246,240 @@ We need to implement PostgreSQL tsvector for better performance.""",
             transcripts.delete().where(transcripts.c.id == test_id)
         )
         await get_database().disconnect()
+
+
+@pytest.fixture
+def sample_search_params():
+    """Create sample search parameters for testing."""
+    return SearchParameters(
+        query_text="test query",
+        limit=20,
+        offset=0,
+        user_id="test-user",
+        room_id="room1",
+    )
+
+
+@pytest.fixture
+def mock_db_result():
+    """Create a mock database result."""
+    return {
+        "id": "test-transcript-id",
+        "title": "Test Transcript",
+        "created_at": datetime(2024, 6, 15, tzinfo=timezone.utc),
+        "duration": 3600.0,
+        "status": "completed",
+        "user_id": "test-user",
+        "room_id": "room1",
+        "source_kind": SourceKind.LIVE,
+        "webvtt": "WEBVTT\n\n00:00:00.000 --> 00:00:05.000\nThis is a test transcript",
+        "rank": 0.95,
+    }
+
+
+class TestSearchParameters:
+    """Test SearchParameters model validation and functionality."""
+
+    def test_search_parameters_with_available_filters(self):
+        """Test creating SearchParameters with currently available filter options."""
+        params = SearchParameters(
+            query_text="search term",
+            limit=50,
+            offset=10,
+            user_id="user123",
+            room_id="room1",
+        )
+
+        assert params.query_text == "search term"
+        assert params.limit == 50
+        assert params.offset == 10
+        assert params.user_id == "user123"
+        assert params.room_id == "room1"
+
+    def test_search_parameters_defaults(self):
+        """Test SearchParameters with default values."""
+        params = SearchParameters(query_text="test")
+
+        assert params.query_text == "test"
+        assert params.limit == 20
+        assert params.offset == 0
+        assert params.user_id is None
+        assert params.room_id is None
+
+
+class TestSearchControllerFilters:
+    """Test SearchController functionality with various filters."""
+
+    @pytest.mark.asyncio
+    async def test_search_with_source_kind_filter(self):
+        """Test search filtering by source_kind."""
+        controller = SearchController()
+        with (
+            patch("reflector.db.search.is_postgresql", return_value=True),
+            patch("reflector.db.search.get_database") as mock_db,
+        ):
+            mock_db.return_value.fetch_all = AsyncMock(return_value=[])
+            mock_db.return_value.fetch_val = AsyncMock(return_value=0)
+
+            params = SearchParameters(query_text="test", source_kind=SourceKind.LIVE)
+
+            results, total = await controller.search_transcripts(params)
+
+            assert results == []
+            assert total == 0
+
+            mock_db.return_value.fetch_all.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_search_with_single_room_id(self):
+        """Test search filtering by single room ID (currently supported)."""
+        controller = SearchController()
+        with (
+            patch("reflector.db.search.is_postgresql", return_value=True),
+            patch("reflector.db.search.get_database") as mock_db,
+        ):
+            mock_db.return_value.fetch_all = AsyncMock(return_value=[])
+            mock_db.return_value.fetch_val = AsyncMock(return_value=0)
+
+            params = SearchParameters(
+                query_text="test",
+                room_id="room1",
+            )
+
+            results, total = await controller.search_transcripts(params)
+
+            assert results == []
+            assert total == 0
+            mock_db.return_value.fetch_all.assert_called_once()
+
+    @pytest.mark.asyncio
+    async def test_search_result_includes_available_fields(self, mock_db_result):
+        """Test that search results include available fields like source_kind."""
+        controller = SearchController()
+        with (
+            patch("reflector.db.search.is_postgresql", return_value=True),
+            patch("reflector.db.search.get_database") as mock_db,
+        ):
+
+            class MockRow:
+                def __init__(self, data):
+                    self._data = data
+                    self._mapping = data
+
+                def __iter__(self):
+                    return iter(self._data.items())
+
+                def __getitem__(self, key):
+                    return self._data[key]
+
+                def keys(self):
+                    return self._data.keys()
+
+            mock_row = MockRow(mock_db_result)
+
+            mock_db.return_value.fetch_all = AsyncMock(return_value=[mock_row])
+            mock_db.return_value.fetch_val = AsyncMock(return_value=1)
+
+            params = SearchParameters(query_text="test")
+
+            results, total = await controller.search_transcripts(params)
+
+            assert total == 1
+            assert len(results) == 1
+
+            result = results[0]
+            assert isinstance(result, SearchResult)
+            assert result.id == "test-transcript-id"
+            assert result.title == "Test Transcript"
+            assert result.rank == 0.95
+
+
+class TestSearchEndpointParsing:
+    """Test parameter parsing in the search endpoint."""
+
+    def test_parse_comma_separated_room_ids(self):
+        """Test parsing comma-separated room IDs."""
+        room_ids_str = "room1,room2,room3"
+        parsed = [rid.strip() for rid in room_ids_str.split(",") if rid.strip()]
+        assert parsed == ["room1", "room2", "room3"]
+
+        room_ids_str = "room1, room2 , room3"
+        parsed = [rid.strip() for rid in room_ids_str.split(",") if rid.strip()]
+        assert parsed == ["room1", "room2", "room3"]
+
+        room_ids_str = "room1,,room3,"
+        parsed = [rid.strip() for rid in room_ids_str.split(",") if rid.strip()]
+        assert parsed == ["room1", "room3"]
+
+    def test_parse_source_kind(self):
+        """Test parsing source_kind values."""
+        for kind_str in ["live", "file", "room"]:
+            parsed = SourceKind(kind_str)
+            assert parsed == SourceKind(kind_str)
+
+        with pytest.raises(ValueError):
+            SourceKind("invalid_kind")
+
+
+class TestSearchResultModel:
+    """Test SearchResult model and serialization."""
+
+    def test_search_result_with_available_fields(self):
+        """Test SearchResult model with currently available fields populated."""
+        result = SearchResult(
+            id="test-id",
+            title="Test Title",
+            user_id="user-123",
+            room_id="room-456",
+            source_kind=SourceKind.ROOM,
+            created_at=datetime(2024, 6, 15, tzinfo=timezone.utc),
+            status="completed",
+            rank=0.85,
+            duration=1800.5,
+            search_snippets=["snippet 1", "snippet 2"],
+        )
+
+        assert result.id == "test-id"
+        assert result.title == "Test Title"
+        assert result.user_id == "user-123"
+        assert result.room_id == "room-456"
+        assert result.status == "completed"
+        assert result.rank == 0.85
+        assert result.duration == 1800.5
+        assert len(result.search_snippets) == 2
+
+    def test_search_result_with_optional_fields_none(self):
+        """Test SearchResult model with optional fields as None."""
+        result = SearchResult(
+            id="test-id",
+            source_kind=SourceKind.FILE,
+            created_at=datetime.now(timezone.utc),
+            status="processing",
+            rank=0.5,
+            search_snippets=[],
+            title=None,
+            user_id=None,
+            room_id=None,
+            duration=None,
+        )
+
+        assert result.title is None
+        assert result.user_id is None
+        assert result.room_id is None
+        assert result.duration is None
+
+    def test_search_result_datetime_field(self):
+        """Test that SearchResult accepts datetime field."""
+        result = SearchResult(
+            id="test-id",
+            source_kind=SourceKind.LIVE,
+            created_at=datetime(2024, 6, 15, 12, 30, 45, tzinfo=timezone.utc),
+            status="completed",
+            rank=0.9,
+            duration=None,
+            search_snippets=[],
+        )
+
+        assert result.created_at == datetime(
+            2024, 6, 15, 12, 30, 45, tzinfo=timezone.utc
+        )

server/tests/test_search_long_summary.py

@@ -0,0 +1,166 @@
+"""Tests for long_summary in search functionality."""
+
+import json
+from datetime import datetime, timezone
+
+import pytest
+
+from reflector.db import get_database
+from reflector.db.search import SearchParameters, search_controller
+from reflector.db.transcripts import transcripts
+
+
+@pytest.mark.asyncio
+async def test_long_summary_snippet_prioritization():
+    """Test that snippets from long_summary are prioritized over webvtt content."""
+    test_id = "test-snippet-priority-3f9a2b8c"
+
+    try:
+        # Clean up any existing test data
+        await get_database().execute(
+            transcripts.delete().where(transcripts.c.id == test_id)
+        )
+
+        test_data = {
+            "id": test_id,
+            "name": "Test Snippet Priority",
+            "title": "Meeting About Projects",
+            "status": "completed",
+            "locked": False,
+            "duration": 1800.0,
+            "created_at": datetime.now(timezone.utc),
+            "short_summary": "Project discussion",
+            "long_summary": (
+                "The team discussed advanced robotics applications including "
+                "autonomous navigation systems and sensor fusion techniques. "
+                "Robotics development will focus on real-time processing."
+            ),
+            "topics": json.dumps([]),
+            "events": json.dumps([]),
+            "participants": json.dumps([]),
+            "source_language": "en",
+            "target_language": "en",
+            "reviewed": False,
+            "audio_location": "local",
+            "share_mode": "private",
+            "source_kind": "room",
+            "webvtt": """WEBVTT
+
+00:00:00.000 --> 00:00:10.000
+We talked about many different topics today.
+
+00:00:10.000 --> 00:00:20.000
+The robotics project is making good progress.
+
+00:00:20.000 --> 00:00:30.000
+We need to consider various implementation approaches.""",
+            "user_id": "test-user-priority",
+        }
+
+        await get_database().execute(transcripts.insert().values(**test_data))
+
+        # Search for "robotics" which appears in both long_summary and webvtt
+        params = SearchParameters(query_text="robotics", user_id="test-user-priority")
+        results, total = await search_controller.search_transcripts(params)
+
+        assert total >= 1
+        test_result = next((r for r in results if r.id == test_id), None)
+        assert test_result, "Should find the test transcript"
+
+        snippets = test_result.search_snippets
+        assert len(snippets) > 0, "Should have at least one snippet"
+
+        # The first snippets should be from long_summary (more detailed content)
+        first_snippet = snippets[0].lower()
+        assert (
+            "advanced robotics" in first_snippet or "autonomous" in first_snippet
+        ), f"First snippet should be from long_summary with detailed content. Got: {snippets[0]}"
+
+        # With max 3 snippets, we should get both from long_summary and webvtt
+        assert len(snippets) <= 3, "Should respect max snippets limit"
+
+        # All snippets should contain the search term
+        for snippet in snippets:
+            assert (
+                "robotics" in snippet.lower()
+            ), f"Snippet should contain search term: {snippet}"
+
+    finally:
+        await get_database().execute(
+            transcripts.delete().where(transcripts.c.id == test_id)
+        )
+        await get_database().disconnect()
+
+
+@pytest.mark.asyncio
+async def test_long_summary_only_search():
+    """Test searching for content that only exists in long_summary."""
+    test_id = "test-long-only-8b3c9f2a"
+
+    try:
+        await get_database().execute(
+            transcripts.delete().where(transcripts.c.id == test_id)
+        )
+
+        test_data = {
+            "id": test_id,
+            "name": "Test Long Only",
+            "title": "Standard Meeting",
+            "status": "completed",
+            "locked": False,
+            "duration": 1800.0,
+            "created_at": datetime.now(timezone.utc),
+            "short_summary": "Team sync",
+            "long_summary": (
+                "Detailed analysis of cryptocurrency market trends and "
+                "decentralized finance protocols. Discussion included "
+                "yield farming strategies and liquidity pool mechanics."
+            ),
+            "topics": json.dumps([]),
+            "events": json.dumps([]),
+            "participants": json.dumps([]),
+            "source_language": "en",
+            "target_language": "en",
+            "reviewed": False,
+            "audio_location": "local",
+            "share_mode": "private",
+            "source_kind": "room",
+            "webvtt": """WEBVTT
+
+00:00:00.000 --> 00:00:10.000
+Team meeting about general project updates.
+
+00:00:10.000 --> 00:00:20.000
+Discussion of timeline and deliverables.""",
+            "user_id": "test-user-long",
+        }
+
+        await get_database().execute(transcripts.insert().values(**test_data))
+
+        # Search for terms only in long_summary
+        params = SearchParameters(query_text="cryptocurrency", user_id="test-user-long")
+        results, total = await search_controller.search_transcripts(params)
+
+        found = any(r.id == test_id for r in results)
+        assert found, "Should find transcript by long_summary-only content"
+
+        test_result = next((r for r in results if r.id == test_id), None)
+        assert test_result
+        assert len(test_result.search_snippets) > 0
+
+        # Verify the snippet is about cryptocurrency
+        snippet = test_result.search_snippets[0].lower()
+        assert "cryptocurrency" in snippet, "Snippet should contain the search term"
+
+        # Search for "yield farming" - a more specific term
+        params2 = SearchParameters(query_text="yield farming", user_id="test-user-long")
+        results2, total2 = await search_controller.search_transcripts(params2)
+
+        found2 = any(r.id == test_id for r in results2)
+        assert found2, "Should find transcript by specific long_summary phrase"
+
+    finally:
+        await get_database().execute(
+            transcripts.delete().where(transcripts.c.id == test_id)
+        )
+        await get_database().disconnect()

server/tests/test_search_snippets.py

@@ -1,6 +1,10 @@
 """Unit tests for search snippet generation."""
 
-from reflector.db.search import SearchController
+from reflector.db.search import (
+    SnippetCandidate,
+    SnippetGenerator,
+    WebVTTProcessor,
+)
 
 
 class TestExtractWebVTT:
@@ -16,7 +20,7 @@ class TestExtractWebVTT:
 00:00:10.000 --> 00:00:20.000
 <v Speaker1>Indeed it is a test of WebVTT parsing.
 """
-        result = SearchController._extract_webvtt_text(webvtt)
+        result = WebVTTProcessor.extract_text(webvtt)
         assert "Hello world, this is a test" in result
         assert "Indeed it is a test" in result
         assert "<v Speaker" not in result
@@ -25,12 +29,11 @@ class TestExtractWebVTT:
 
     def test_extract_empty_webvtt(self):
         """Test empty WebVTT returns empty string."""
-        assert SearchController._extract_webvtt_text("") == ""
-        assert SearchController._extract_webvtt_text(None) == ""
+        assert WebVTTProcessor.extract_text("") == ""
 
     def test_extract_malformed_webvtt(self):
         """Test malformed WebVTT returns empty string."""
-        result = SearchController._extract_webvtt_text("Not a valid WebVTT")
+        result = WebVTTProcessor.extract_text("Not a valid WebVTT")
         assert result == ""
 
 
@@ -39,8 +42,7 @@ class TestGenerateSnippets:
 
     def test_multiple_matches(self):
         """Test finding multiple occurrences of search term in long text."""
-        # Create text with Python mentions far apart to get separate snippets
-        separator = " This is filler text. " * 20  # ~400 chars of padding
+        separator = " This is filler text. " * 20
         text = (
             "Python is great for machine learning."
             + separator
@@ -51,18 +53,16 @@ class TestGenerateSnippets:
             + "The Python community is very supportive."
         )
 
-        snippets = SearchController._generate_snippets(text, "Python")
-        # With enough separation, we should get multiple snippets
-        assert len(snippets) >= 2  # At least 2 distinct snippets
+        snippets = SnippetGenerator.generate(text, "Python")
+        assert len(snippets) >= 2
 
-        # Each snippet should contain "Python"
         for snippet in snippets:
             assert "python" in snippet.lower()
 
     def test_single_match(self):
         """Test single occurrence returns one snippet."""
         text = "This document discusses artificial intelligence and its applications."
-        snippets = SearchController._generate_snippets(text, "artificial intelligence")
+        snippets = SnippetGenerator.generate(text, "artificial intelligence")
 
         assert len(snippets) == 1
         assert "artificial intelligence" in snippets[0].lower()
@@ -70,24 +70,22 @@ class TestGenerateSnippets:
     def test_no_matches(self):
         """Test no matches returns empty list."""
         text = "This is some random text without the search term."
-        snippets = SearchController._generate_snippets(text, "machine learning")
+        snippets = SnippetGenerator.generate(text, "machine learning")
 
         assert snippets == []
 
     def test_case_insensitive_search(self):
         """Test search is case insensitive."""
-        # Add enough text between matches to get separate snippets
         text = (
             "MACHINE LEARNING is important for modern applications. "
-            + "It requires lots of data and computational resources. " * 5  # Padding
+            + "It requires lots of data and computational resources. " * 5
             + "Machine Learning rocks and transforms industries. "
-            + "Deep learning is a subset of it. " * 5  # More padding
+            + "Deep learning is a subset of it. " * 5
             + "Finally, machine learning will shape our future."
         )
 
-        snippets = SearchController._generate_snippets(text, "machine learning")
+        snippets = SnippetGenerator.generate(text, "machine learning")
 
-        # Should find at least 2 (might be 3 if text is long enough)
         assert len(snippets) >= 2
         for snippet in snippets:
             assert "machine learning" in snippet.lower()
@@ -95,61 +93,55 @@ class TestGenerateSnippets:
     def test_partial_match_fallback(self):
         """Test fallback to first word when exact phrase not found."""
         text = "We use machine intelligence for processing."
-        snippets = SearchController._generate_snippets(text, "machine learning")
+        snippets = SnippetGenerator.generate(text, "machine learning")
 
-        # Should fall back to finding "machine"
         assert len(snippets) == 1
         assert "machine" in snippets[0].lower()
 
     def test_snippet_ellipsis(self):
         """Test ellipsis added for truncated snippets."""
-        # Long text where match is in the middle
         text = "a " * 100 + "TARGET_WORD special content here" + " b" * 100
-        snippets = SearchController._generate_snippets(text, "TARGET_WORD")
+        snippets = SnippetGenerator.generate(text, "TARGET_WORD")
 
         assert len(snippets) == 1
-        assert "..." in snippets[0]  # Should have ellipsis
+        assert "..." in snippets[0]
         assert "TARGET_WORD" in snippets[0]
 
     def test_overlapping_snippets_deduplicated(self):
         """Test overlapping matches don't create duplicate snippets."""
-        text = "test test test word" * 10  # Repeated pattern
-        snippets = SearchController._generate_snippets(text, "test")
+        text = "test test test word" * 10
+        snippets = SnippetGenerator.generate(text, "test")
 
-        # Should get unique snippets, not duplicates
         assert len(snippets) <= 3
-        assert len(snippets) == len(set(snippets))  # All unique
+        assert len(snippets) == len(set(snippets))
 
     def test_empty_inputs(self):
         """Test empty text or search term returns empty list."""
-        assert SearchController._generate_snippets("", "search") == []
-        assert SearchController._generate_snippets("text", "") == []
-        assert SearchController._generate_snippets("", "") == []
+        assert SnippetGenerator.generate("", "search") == []
+        assert SnippetGenerator.generate("text", "") == []
+        assert SnippetGenerator.generate("", "") == []
 
     def test_max_snippets_limit(self):
         """Test respects max_snippets parameter."""
-        # Create text with well-separated occurrences
-        separator = " filler " * 50  # Ensure snippets don't overlap
-        text = ("Python is amazing" + separator) * 10  # 10 occurrences
+        separator = " filler " * 50
+        text = ("Python is amazing" + separator) * 10
 
-        # Test with different limits
-        snippets_1 = SearchController._generate_snippets(text, "Python", max_snippets=1)
+        snippets_1 = SnippetGenerator.generate(text, "Python", max_snippets=1)
         assert len(snippets_1) == 1
 
-        snippets_2 = SearchController._generate_snippets(text, "Python", max_snippets=2)
+        snippets_2 = SnippetGenerator.generate(text, "Python", max_snippets=2)
         assert len(snippets_2) == 2
 
-        snippets_5 = SearchController._generate_snippets(text, "Python", max_snippets=5)
-        assert len(snippets_5) == 5  # Should get exactly 5 with enough separation
+        snippets_5 = SnippetGenerator.generate(text, "Python", max_snippets=5)
+        assert len(snippets_5) == 5
 
     def test_snippet_length(self):
         """Test snippet length is reasonable."""
-        text = "word " * 200  # Long text
-        snippets = SearchController._generate_snippets(text, "word")
+        text = "word " * 200
+        snippets = SnippetGenerator.generate(text, "word")
 
         for snippet in snippets:
-            # Default max_length is 150 + some context
-            assert len(snippet) <= 200  # Some buffer for ellipsis
+            assert len(snippet) <= 200
 
 
 class TestFullPipeline:
@@ -157,7 +149,6 @@ class TestFullPipeline:
 
     def test_webvtt_to_snippets_integration(self):
         """Test full pipeline from WebVTT to search snippets."""
-        # Create WebVTT with well-separated content for multiple snippets
         webvtt = (
             """WEBVTT
 
@@ -182,17 +173,362 @@ class TestFullPipeline:
 """
         )
 
-        # Extract and generate snippets
-        plain_text = SearchController._extract_webvtt_text(webvtt)
-        snippets = SearchController._generate_snippets(plain_text, "machine learning")
+        plain_text = WebVTTProcessor.extract_text(webvtt)
+        snippets = SnippetGenerator.generate(plain_text, "machine learning")
 
-        # Should find at least 2 snippets (text might still be close together)
-        assert len(snippets) >= 1  # At minimum one snippet containing matches
-        assert len(snippets) <= 3  # At most 3 by default
+        assert len(snippets) >= 1
+        assert len(snippets) <= 3
 
-        # No WebVTT artifacts in snippets
         for snippet in snippets:
             assert "machine learning" in snippet.lower()
             assert "<v Speaker" not in snippet
             assert "00:00" not in snippet
             assert "-->" not in snippet
+
+
+class TestMultiWordQueryBehavior:
+    """Tests for multi-word query behavior and exact phrase matching."""
+
+    def test_multi_word_query_snippet_behavior(self):
+        """Test that multi-word queries generate snippets based on exact phrase matching."""
+        sample_text = """This is a sample transcript where user Alice is talking.
+        Later in the conversation, jordan mentions something important.
+        The user jordan collaboration was successful.
+        Another user named Bob joins the discussion."""
+
+        user_snippets = SnippetGenerator.generate(sample_text, "user")
+        assert len(user_snippets) == 2, "Should find 2 snippets for 'user'"
+
+        jordan_snippets = SnippetGenerator.generate(sample_text, "jordan")
+        assert len(jordan_snippets) >= 1, "Should find at least 1 snippet for 'jordan'"
+
+        multi_word_snippets = SnippetGenerator.generate(sample_text, "user jordan")
+        assert len(multi_word_snippets) == 1, (
+            "Should return exactly 1 snippet for 'user jordan' "
+            "(only the exact phrase match, not individual word occurrences)"
+        )
+
+        snippet = multi_word_snippets[0]
+        assert (
+            "user jordan" in snippet.lower()
+        ), "The snippet should contain the exact phrase 'user jordan'"
+
+        assert (
+            "alice" not in snippet.lower()
+        ), "The snippet should not include the first standalone 'user' with Alice"
+
+    def test_multi_word_query_without_exact_match(self):
+        """Test snippet generation when exact phrase is not found."""
+        sample_text = """User Alice is here. Bob and jordan are talking.
+        Later jordan mentions something. The user is happy."""
+
+        snippets = SnippetGenerator.generate(sample_text, "user jordan")
+
+        assert (
+            len(snippets) >= 1
+        ), "Should find at least 1 snippet when falling back to first word"
+
+        all_snippets_text = " ".join(snippets).lower()
+        assert (
+            "user" in all_snippets_text
+        ), "Snippets should contain 'user' (the first word)"
+
+    def test_exact_phrase_at_text_boundaries(self):
+        """Test snippet generation when exact phrase appears at text boundaries."""
+
+        text_start = "user jordan started the meeting. Other content here."
+        snippets = SnippetGenerator.generate(text_start, "user jordan")
+        assert len(snippets) == 1
+        assert "user jordan" in snippets[0].lower()
+
+        text_end = "Other content here. The meeting ended with user jordan"
+        snippets = SnippetGenerator.generate(text_end, "user jordan")
+        assert len(snippets) == 1
+        assert "user jordan" in snippets[0].lower()
+
+    def test_multi_word_query_matches_words_appearing_separately_and_together(self):
+        """Test that multi-word queries prioritize exact phrase matches over individual word occurrences."""
+        sample_text = """This is a sample transcript where user Alice is talking.
+        Later in the conversation, jordan mentions something important.
+        The user jordan collaboration was successful.
+        Another user named Bob joins the discussion."""
+
+        search_query = "user jordan"
+        snippets = SnippetGenerator.generate(sample_text, search_query)
+
+        assert len(snippets) == 1, (
+            f"Expected exactly 1 snippet for '{search_query}' when exact phrase exists, "
+            f"got {len(snippets)}. Should ignore individual word occurrences."
+        )
+
+        snippet = snippets[0]
+
+        assert (
+            search_query in snippet.lower()
+        ), f"Snippet should contain the exact phrase '{search_query}'. Got: {snippet}"
+
+        assert (
+            "jordan mentions" in snippet.lower()
+        ), f"Snippet should include context before the exact phrase match. Got: {snippet}"
+
+        assert (
+            "alice" not in snippet.lower()
+        ), f"Snippet should not include separate occurrences of individual words. Got: {snippet}"
+
+        text_2 = """The alpha version was released.
+        Beta testing started yesterday.
+        The alpha beta integration is complete."""
+
+        snippets_2 = SnippetGenerator.generate(text_2, "alpha beta")
+        assert len(snippets_2) == 1, "Should return 1 snippet for exact phrase match"
+        assert "alpha beta" in snippets_2[0].lower(), "Should contain exact phrase"
+        assert (
+            "version" not in snippets_2[0].lower()
+        ), "Should not include first separate occurrence"
+
+
+class TestSnippetGenerationEnhanced:
+    """Additional snippet generation tests from test_search_enhancements.py."""
+
+    def test_snippet_generation_from_webvtt(self):
+        """Test snippet generation from WebVTT content."""
+        webvtt_content = """WEBVTT
+
+00:00:00.000 --> 00:00:05.000
+This is the beginning of the transcript
+
+00:00:05.000 --> 00:00:10.000
+The search term appears here in the middle
+
+00:00:10.000 --> 00:00:15.000
+And this is the end of the content"""
+
+        plain_text = WebVTTProcessor.extract_text(webvtt_content)
+        snippets = SnippetGenerator.generate(plain_text, "search term")
+
+        assert len(snippets) > 0
+        assert any("search term" in snippet.lower() for snippet in snippets)
+
+    def test_extract_webvtt_text_with_malformed_variations(self):
+        """Test WebVTT extraction with various malformed content."""
+        malformed_vtt = "This is not valid WebVTT content"
+        result = WebVTTProcessor.extract_text(malformed_vtt)
+        assert result == ""
+
+        partial_vtt = "WEBVTT\nNo timestamps here"
+        result = WebVTTProcessor.extract_text(partial_vtt)
+        assert result == "" or "No timestamps" not in result
+
+
+class TestPureFunctions:
+    """Test the pure functions extracted for functional programming."""
+
+    def test_find_all_matches(self):
+        """Test finding all match positions in text."""
+        text = "Python is great. Python is powerful. I love Python."
+        matches = list(SnippetGenerator.find_all_matches(text, "Python"))
+        assert matches == [0, 17, 44]
+
+        matches = list(SnippetGenerator.find_all_matches(text, "python"))
+        assert matches == [0, 17, 44]
+
+        matches = list(SnippetGenerator.find_all_matches(text, "Ruby"))
+        assert matches == []
+
+        matches = list(SnippetGenerator.find_all_matches("", "test"))
+        assert matches == []
+        matches = list(SnippetGenerator.find_all_matches("test", ""))
+        assert matches == []
+
+    def test_create_snippet(self):
+        """Test creating a snippet from a match position."""
+        text = "This is a long text with the word Python in the middle and more text after."
+
+        snippet = SnippetGenerator.create_snippet(text, 35, max_length=150)
+        assert "Python" in snippet.text()
+        assert snippet.start >= 0
+        assert snippet.end <= len(text)
+        assert isinstance(snippet, SnippetCandidate)
+
+        assert len(snippet.text()) > 0
+        assert snippet.start <= snippet.end
+
+        long_text = "A" * 200
+        snippet = SnippetGenerator.create_snippet(long_text, 100, max_length=50)
+        assert snippet.text().startswith("...")
+        assert snippet.text().endswith("...")
+
+        snippet = SnippetGenerator.create_snippet("short text", 0, max_length=100)
+        assert snippet.start == 0
+        assert "short text" in snippet.text()
+
+    def test_filter_non_overlapping(self):
+        """Test filtering overlapping snippets."""
+        candidates = [
+            SnippetCandidate(_text="First snippet", start=0, _original_text_length=100),
+            SnippetCandidate(_text="Overlapping", start=10, _original_text_length=100),
+            SnippetCandidate(
+                _text="Third snippet", start=40, _original_text_length=100
+            ),
+            SnippetCandidate(
+                _text="Fourth snippet", start=65, _original_text_length=100
+            ),
+        ]
+
+        filtered = list(SnippetGenerator.filter_non_overlapping(iter(candidates)))
+        assert filtered == [
+            "First snippet...",
+            "...Third snippet...",
+            "...Fourth snippet...",
+        ]
+
+        filtered = list(SnippetGenerator.filter_non_overlapping(iter([])))
+        assert filtered == []
+
+    def test_generate_integration(self):
+        """Test the main SnippetGenerator.generate function."""
+        text = "Machine learning is amazing. Machine learning transforms data. Learn machine learning today."
+
+        snippets = SnippetGenerator.generate(text, "machine learning")
+        assert len(snippets) <= 3
+        assert all("machine learning" in s.lower() for s in snippets)
+
+        snippets = SnippetGenerator.generate(text, "machine learning", max_snippets=2)
+        assert len(snippets) <= 2
+
+        snippets = SnippetGenerator.generate(text, "machine vision")
+        assert len(snippets) > 0
+        assert any("machine" in s.lower() for s in snippets)
+
+    def test_extract_webvtt_text_basic(self):
+        """Test WebVTT text extraction (basic test, full tests exist elsewhere)."""
+        webvtt = """WEBVTT
+
+00:00:00.000 --> 00:00:02.000
+Hello world
+
+00:00:02.000 --> 00:00:04.000
+This is a test"""
+
+        result = WebVTTProcessor.extract_text(webvtt)
+        assert "Hello world" in result
+        assert "This is a test" in result
+
+        # Test empty input
+        assert WebVTTProcessor.extract_text("") == ""
+        assert WebVTTProcessor.extract_text(None) == ""
+
+    def test_generate_webvtt_snippets(self):
+        """Test generating snippets from WebVTT content."""
+        webvtt = """WEBVTT
+
+00:00:00.000 --> 00:00:02.000
+Python programming is great
+
+00:00:02.000 --> 00:00:04.000
+Learn Python today"""
+
+        snippets = WebVTTProcessor.generate_snippets(webvtt, "Python")
+        assert len(snippets) > 0
+        assert any("Python" in s for s in snippets)
+
+        snippets = WebVTTProcessor.generate_snippets("", "Python")
+        assert snippets == []
+
+    def test_from_summary(self):
+        """Test generating snippets from summary text."""
+        summary = "This meeting discussed Python development and machine learning applications."
+
+        snippets = SnippetGenerator.from_summary(summary, "Python")
+        assert len(snippets) > 0
+        assert any("Python" in s for s in snippets)
+
+        long_summary = "Python " * 20
+        snippets = SnippetGenerator.from_summary(long_summary, "Python")
+        assert len(snippets) <= 2
+
+    def test_combine_sources(self):
+        """Test combining snippets from multiple sources."""
+        summary = "Python is a great programming language."
+        webvtt = """WEBVTT
+
+00:00:00.000 --> 00:00:02.000
+Learn Python programming
+
+00:00:02.000 --> 00:00:04.000
+Python is powerful"""
+
+        snippets, total_count = SnippetGenerator.combine_sources(
+            summary, webvtt, "Python", max_total=3
+        )
+        assert len(snippets) <= 3
+        assert len(snippets) > 0
+        assert total_count > 0
+
+        snippets, total_count = SnippetGenerator.combine_sources(
+            summary, None, "Python", max_total=3
+        )
+        assert len(snippets) > 0
+        assert all("Python" in s for s in snippets)
+        assert total_count == 1
+
+        snippets, total_count = SnippetGenerator.combine_sources(
+            None, webvtt, "Python", max_total=3
+        )
+        assert len(snippets) > 0
+        assert total_count == 2
+
+        long_summary = "Python " * 10
+        snippets, total_count = SnippetGenerator.combine_sources(
+            long_summary, webvtt, "Python", max_total=2
+        )
+        assert len(snippets) == 2
+        assert total_count >= 10
+
+    def test_match_counting_sum_logic(self):
+        """Test that match counting correctly sums matches from both sources."""
+        summary = "data science uses data analysis and data mining techniques"
+        webvtt = """WEBVTT
+
+00:00:00.000 --> 00:00:02.000
+Big data processing
+
+00:00:02.000 --> 00:00:04.000
+data visualization and data storage"""
+
+        snippets, total_count = SnippetGenerator.combine_sources(
+            summary, webvtt, "data", max_total=3
+        )
+        assert total_count == 6
+        assert len(snippets) <= 3
+
+        summary_snippets, summary_count = SnippetGenerator.combine_sources(
+            summary, None, "data", max_total=3
+        )
+        assert summary_count == 3
+
+        webvtt_snippets, webvtt_count = SnippetGenerator.combine_sources(
+            None, webvtt, "data", max_total=3
+        )
+        assert webvtt_count == 3
+
+        snippets_empty, count_empty = SnippetGenerator.combine_sources(
+            None, None, "data", max_total=3
+        )
+        assert snippets_empty == []
+        assert count_empty == 0
+
+    def test_edge_cases(self):
+        """Test edge cases for the pure functions."""
+        text = "Test with special: @#$%^&*() characters"
+        snippets = SnippetGenerator.generate(text, "@#$%")
+        assert len(snippets) > 0
+
+        long_query = "a" * 100
+        snippets = SnippetGenerator.generate("Some text", long_query)
+        assert snippets == []
+
+        text = "Unicode test: café, naïve, 日本語"
+        snippets = SnippetGenerator.generate(text, "café")
+        assert len(snippets) > 0
+        assert "café" in snippets[0]

www/app/(app)/browse/_components/Pagination.tsx

@@ -1,26 +1,67 @@
-import React from "react";
+import React, { useEffect } from "react";
 import { Pagination, IconButton, ButtonGroup } from "@chakra-ui/react";
 import { LuChevronLeft, LuChevronRight } from "react-icons/lu";
 
+// explicitly 1-based to prevent +/-1-confusion errors
+export const FIRST_PAGE = 1 as PaginationPage;
+export const parsePaginationPage = (
+  page: number,
+):
+  | {
+      value: PaginationPage;
+    }
+  | {
+      error: string;
+    } => {
+  if (page < FIRST_PAGE)
+    return {
+      error: "Page must be greater than 0",
+    };
+  if (!Number.isInteger(page))
+    return {
+      error: "Page must be an integer",
+    };
+  return {
+    value: page as PaginationPage,
+  };
+};
+export type PaginationPage = number & { __brand: "PaginationPage" };
+export const PaginationPage = (page: number): PaginationPage => {
+  const v = parsePaginationPage(page);
+  if ("error" in v) throw new Error(v.error);
+  return v.value;
+};
+
+export const paginationPageTo0Based = (page: PaginationPage): number =>
+  page - FIRST_PAGE;
+
 type PaginationProps = {
-  page: number;
-  setPage: (page: number) => void;
+  page: PaginationPage;
+  setPage: (page: PaginationPage) => void;
   total: number;
   size: number;
 };
 
+export const totalPages = (total: number, size: number) => {
+  return Math.ceil(total / size);
+};
+
 export default function PaginationComponent(props: PaginationProps) {
   const { page, setPage, total, size } = props;
-  const totalPages = Math.ceil(total / size);
-
-  if (totalPages <= 1) return null;
+  useEffect(() => {
+    if (page > totalPages(total, size)) {
+      console.error(
+        `Page number (${page}) is greater than total pages (${totalPages}) in pagination`,
+      );
+    }
+  }, [page, totalPages(total, size)]);
 
   return (
     <Pagination.Root
       count={total}
       pageSize={size}
       page={page}
-      onPageChange={(details) => setPage(details.page)}
+      onPageChange={(details) => setPage(PaginationPage(details.page))}
       style={{ display: "flex", justifyContent: "center" }}
     >
       <ButtonGroup variant="ghost" size="xs">

www/app/(app)/browse/_components/SearchBar.tsx

@@ -1,34 +0,0 @@
-import React, { useState } from "react";
-import { Flex, Input, Button } from "@chakra-ui/react";
-
-interface SearchBarProps {
-  onSearch: (searchTerm: string) => void;
-}
-
-export default function SearchBar({ onSearch }: SearchBarProps) {
-  const [searchInputValue, setSearchInputValue] = useState("");
-
-  const handleSearch = () => {
-    onSearch(searchInputValue);
-  };
-
-  const handleKeyDown = (event: React.KeyboardEvent) => {
-    if (event.key === "Enter") {
-      handleSearch();
-    }
-  };
-
-  return (
-    <Flex alignItems="center">
-      <Input
-        placeholder="Search transcriptions..."
-        value={searchInputValue}
-        onChange={(e) => setSearchInputValue(e.target.value)}
-        onKeyDown={handleKeyDown}
-      />
-      <Button ml={2} onClick={handleSearch}>
-        Search
-      </Button>
-    </Flex>
-  );
-}

www/app/(app)/browse/_components/TranscriptActionsMenu.tsx

@@ -4,8 +4,8 @@ import { LuMenu, LuTrash, LuRotateCw } from "react-icons/lu";
 
 interface TranscriptActionsMenuProps {
   transcriptId: string;
-  onDelete: (transcriptId: string) => (e: any) => void;
-  onReprocess: (transcriptId: string) => (e: any) => void;
+  onDelete: (transcriptId: string) => void;
+  onReprocess: (transcriptId: string) => void;
 }
 
 export default function TranscriptActionsMenu({
@@ -24,11 +24,17 @@ export default function TranscriptActionsMenu({
         <Menu.Content>
           <Menu.Item
             value="reprocess"
-            onClick={(e) => onReprocess(transcriptId)(e)}
+            onClick={() => onReprocess(transcriptId)}
           >
             <LuRotateCw /> Reprocess
           </Menu.Item>
-          <Menu.Item value="delete" onClick={(e) => onDelete(transcriptId)(e)}>
+          <Menu.Item
+            value="delete"
+            onClick={(e) => {
+              e.stopPropagation();
+              onDelete(transcriptId);
+            }}
+          >
             <LuTrash /> Delete
           </Menu.Item>
         </Menu.Content>

www/app/(app)/browse/_components/TranscriptCards.tsx

@@ -1,27 +1,290 @@
-import React from "react";
-import { Box, Stack, Text, Flex, Link, Spinner } from "@chakra-ui/react";
+import React, { useState } from "react";
+import {
+  Box,
+  Stack,
+  Text,
+  Flex,
+  Link,
+  Spinner,
+  Badge,
+  HStack,
+  VStack,
+} from "@chakra-ui/react";
 import NextLink from "next/link";
-import { GetTranscriptMinimal } from "../../../api";
 import { formatTimeMs, formatLocalDate } from "../../../lib/time";
 import TranscriptStatusIcon from "./TranscriptStatusIcon";
 import TranscriptActionsMenu from "./TranscriptActionsMenu";
+import {
+  highlightMatches,
+  generateTextFragment,
+} from "../../../lib/textHighlight";
+import { SearchResult } from "../../../api";
 
 interface TranscriptCardsProps {
-  transcripts: GetTranscriptMinimal[];
-  onDelete: (transcriptId: string) => (e: any) => void;
-  onReprocess: (transcriptId: string) => (e: any) => void;
-  loading?: boolean;
+  results: SearchResult[];
+  query: string;
+  isLoading?: boolean;
+  onDelete: (transcriptId: string) => void;
+  onReprocess: (transcriptId: string) => void;
+}
+
+function highlightText(text: string, query: string): React.ReactNode {
+  if (!query) return text;
+
+  const matches = highlightMatches(text, query);
+
+  if (matches.length === 0) return text;
+
+  // Sort matches by index to process them in order
+  const sortedMatches = [...matches].sort((a, b) => a.index - b.index);
+
+  const parts: React.ReactNode[] = [];
+  let lastIndex = 0;
+
+  sortedMatches.forEach((match, i) => {
+    // Add text before the match
+    if (match.index > lastIndex) {
+      parts.push(
+        <Text as="span" key={`text-${i}`} display="inline">
+          {text.slice(lastIndex, match.index)}
+        </Text>,
+      );
+    }
+
+    // Add the highlighted match
+    parts.push(
+      <Text
+        as="mark"
+        key={`match-${i}`}
+        bg="yellow.200"
+        px={0.5}
+        display="inline"
+      >
+        {match.match}
+      </Text>,
+    );
+
+    lastIndex = match.index + match.match.length;
+  });
+
+  // Add remaining text after last match
+  if (lastIndex < text.length) {
+    parts.push(
+      <Text as="span" key={`text-end`} display="inline">
+        {text.slice(lastIndex)}
+      </Text>,
+    );
+  }
+
+  return parts;
+}
+
+const transcriptHref = (
+  transcriptId: string,
+  mainSnippet: string,
+  query: string,
+): `/transcripts/${string}` => {
+  const urlTextFragment = mainSnippet
+    ? generateTextFragment(mainSnippet, query)
+    : null;
+  const urlTextFragmentWithHash = urlTextFragment
+    ? `#${urlTextFragment.k}=${encodeURIComponent(urlTextFragment.v)}`
+    : "";
+  return `/transcripts/${transcriptId}${urlTextFragmentWithHash}`;
+};
+
+// note that it's strongly tied to search logic - in case you want to use it independently, refactor
+function TranscriptCard({
+  result,
+  query,
+  onDelete,
+  onReprocess,
+}: {
+  result: SearchResult;
+  query: string;
+  onDelete: (transcriptId: string) => void;
+  onReprocess: (transcriptId: string) => void;
+}) {
+  const [isExpanded, setIsExpanded] = useState(false);
+
+  const mainSnippet = result.search_snippets[0];
+  const additionalSnippets = result.search_snippets.slice(1);
+  const totalMatches = result.total_match_count || 0;
+  const snippetsShown = result.search_snippets.length;
+  const remainingMatches = totalMatches - snippetsShown;
+  const hasAdditionalSnippets = additionalSnippets.length > 0;
+  const resultTitle = result.title || "Unnamed Transcript";
+
+  const formattedDuration = result.duration
+    ? formatTimeMs(result.duration)
+    : "N/A";
+  const formattedDate = formatLocalDate(result.created_at);
+  const source =
+    result.source_kind === "room"
+      ? result.room_name || result.room_id
+      : result.source_kind;
+
+  const handleExpandClick = (e: React.MouseEvent) => {
+    e.preventDefault();
+    e.stopPropagation();
+    setIsExpanded(!isExpanded);
+  };
+
+  return (
+    <Box borderWidth={1} p={4} borderRadius="md" fontSize="sm">
+      <Flex justify="space-between" alignItems="flex-start" gap="2">
+        <Box>
+          <TranscriptStatusIcon status={result.status} />
+        </Box>
+        <Box flex="1">
+          {/* Title with highlighting and text fragment for deep linking */}
+          <Link
+            as={NextLink}
+            href={transcriptHref(result.id, mainSnippet, query)}
+            fontWeight="600"
+            display="block"
+            mb={2}
+          >
+            {highlightText(resultTitle, query)}
+          </Link>
+
+          {/* Metadata - Horizontal on desktop, vertical on mobile */}
+          <Flex
+            direction={{ base: "column", md: "row" }}
+            gap={{ base: 1, md: 2 }}
+            fontSize="xs"
+            color="gray.600"
+            flexWrap="wrap"
+            align={{ base: "flex-start", md: "center" }}
+          >
+            <Flex align="center" gap={1}>
+              <Text fontWeight="medium" color="gray.500">
+                Source:
+              </Text>
+              <Text>{source}</Text>
+            </Flex>
+            <Text display={{ base: "none", md: "block" }} color="gray.400">
+              •
+            </Text>
+            <Flex align="center" gap={1}>
+              <Text fontWeight="medium" color="gray.500">
+                Date:
+              </Text>
+              <Text>{formattedDate}</Text>
+            </Flex>
+            <Text display={{ base: "none", md: "block" }} color="gray.400">
+              •
+            </Text>
+            <Flex align="center" gap={1}>
+              <Text fontWeight="medium" color="gray.500">
+                Duration:
+              </Text>
+              <Text>{formattedDuration}</Text>
+            </Flex>
+          </Flex>
+
+          {/* Search Results Section - only show when searching */}
+          {mainSnippet && (
+            <>
+              {/* Main Snippet */}
+              <Box
+                mt={3}
+                p={2}
+                bg="gray.50"
+                borderLeft="2px solid"
+                borderLeftColor="blue.400"
+                borderRadius="sm"
+                fontSize="xs"
+              >
+                <Text color="gray.700">
+                  {highlightText(mainSnippet, query)}
+                </Text>
+              </Box>
+
+              {hasAdditionalSnippets && (
+                <>
+                  <Flex
+                    mt={2}
+                    p={2}
+                    bg="blue.50"
+                    borderRadius="sm"
+                    cursor="pointer"
+                    onClick={handleExpandClick}
+                    _hover={{ bg: "blue.100" }}
+                    align="center"
+                    justify="space-between"
+                  >
+                    <HStack gap={2}>
+                      <Badge
+                        bg="blue.500"
+                        color="white"
+                        fontSize="xs"
+                        px={2}
+                        borderRadius="full"
+                      >
+                        {remainingMatches > 0
+                          ? `${additionalSnippets.length + remainingMatches}+`
+                          : additionalSnippets.length}
+                      </Badge>
+                      <Text fontSize="xs" color="blue.600" fontWeight="medium">
+                        more{" "}
+                        {additionalSnippets.length + remainingMatches === 1
+                          ? "match"
+                          : "matches"}
+                        {remainingMatches > 0 &&
+                          ` (${additionalSnippets.length} shown)`}
+                      </Text>
+                    </HStack>
+                    <Text fontSize="xs" color="blue.600">
+                      {isExpanded ? "▲" : "▼"}
+                    </Text>
+                  </Flex>
+
+                  {/* Additional Snippets */}
+                  {isExpanded && (
+                    <VStack align="stretch" gap={2} mt={2}>
+                      {additionalSnippets.map((snippet, index) => (
+                        <Box
+                          key={index}
+                          p={2}
+                          bg="gray.50"
+                          borderLeft="2px solid"
+                          borderLeftColor="gray.300"
+                          borderRadius="sm"
+                          fontSize="xs"
+                        >
+                          <Text color="gray.700">
+                            {highlightText(snippet, query)}
+                          </Text>
+                        </Box>
+                      ))}
+                    </VStack>
+                  )}
+                </>
+              )}
+            </>
+          )}
+        </Box>
+        <TranscriptActionsMenu
+          transcriptId={result.id}
+          onDelete={onDelete}
+          onReprocess={onReprocess}
+        />
+      </Flex>
+    </Box>
+  );
 }
 
 export default function TranscriptCards({
-  transcripts,
+  results,
+  query,
+  isLoading,
   onDelete,
   onReprocess,
-  loading,
 }: TranscriptCardsProps) {
   return (
-    <Box display={{ base: "block", lg: "none" }} position="relative">
-      {loading && (
+    <Box position="relative">
+      {isLoading && (
         <Flex
           position="absolute"
           top={0}
@@ -37,48 +300,19 @@ export default function TranscriptCards({
         </Flex>
       )}
       <Box
-        opacity={loading ? 0.9 : 1}
-        pointerEvents={loading ? "none" : "auto"}
+        opacity={isLoading ? 0.9 : 1}
+        pointerEvents={isLoading ? "none" : "auto"}
         transition="opacity 0.2s ease-in-out"
       >
-        <Stack gap={2}>
-          {transcripts.map((item) => (
-            <Box
-              key={item.id}
-              borderWidth={1}
-              p={4}
-              borderRadius="md"
-              fontSize="sm"
-            >
-              <Flex justify="space-between" alignItems="flex-start" gap="2">
-                <Box>
-                  <TranscriptStatusIcon status={item.status} />
-                </Box>
-                <Box flex="1">
-                  <Link
-                    as={NextLink}
-                    href={`/transcripts/${item.id}`}
-                    fontWeight="600"
-                    display="block"
-                  >
-                    {item.title || "Unnamed Transcript"}
-                  </Link>
-                  <Text>
-                    Source:{" "}
-                    {item.source_kind === "room"
-                      ? item.room_name
-                      : item.source_kind}
-                  </Text>
-                  <Text>Date: {formatLocalDate(item.created_at)}</Text>
-                  <Text>Duration: {formatTimeMs(item.duration)}</Text>
-                </Box>
-                <TranscriptActionsMenu
-                  transcriptId={item.id}
-                  onDelete={onDelete}
-                  onReprocess={onReprocess}
-                />
-              </Flex>
-            </Box>
+        <Stack gap={3}>
+          {results.map((result) => (
+            <TranscriptCard
+              key={result.id}
+              result={result}
+              query={query}
+              onDelete={onDelete}
+              onReprocess={onReprocess}
+            />
           ))}
         </Stack>
       </Box>

www/app/(app)/browse/_components/TranscriptTable.tsx

@@ -1,99 +0,0 @@
-import React from "react";
-import { Box, Table, Link, Flex, Spinner } from "@chakra-ui/react";
-import NextLink from "next/link";
-import { GetTranscriptMinimal } from "../../../api";
-import { formatTimeMs, formatLocalDate } from "../../../lib/time";
-import TranscriptStatusIcon from "./TranscriptStatusIcon";
-import TranscriptActionsMenu from "./TranscriptActionsMenu";
-
-interface TranscriptTableProps {
-  transcripts: GetTranscriptMinimal[];
-  onDelete: (transcriptId: string) => (e: any) => void;
-  onReprocess: (transcriptId: string) => (e: any) => void;
-  loading?: boolean;
-}
-
-export default function TranscriptTable({
-  transcripts,
-  onDelete,
-  onReprocess,
-  loading,
-}: TranscriptTableProps) {
-  return (
-    <Box display={{ base: "none", lg: "block" }} position="relative">
-      {loading && (
-        <Flex
-          position="absolute"
-          top={0}
-          left={0}
-          right={0}
-          bottom={0}
-          align="center"
-          justify="center"
-        >
-          <Spinner size="xl" color="gray.700" />
-        </Flex>
-      )}
-      <Box
-        opacity={loading ? 0.9 : 1}
-        pointerEvents={loading ? "none" : "auto"}
-        transition="opacity 0.2s ease-in-out"
-      >
-        <Table.Root>
-          <Table.Header>
-            <Table.Row>
-              <Table.ColumnHeader
-                width="16px"
-                fontWeight="600"
-              ></Table.ColumnHeader>
-              <Table.ColumnHeader width="400px" fontWeight="600">
-                Transcription Title
-              </Table.ColumnHeader>
-              <Table.ColumnHeader width="150px" fontWeight="600">
-                Source
-              </Table.ColumnHeader>
-              <Table.ColumnHeader width="200px" fontWeight="600">
-                Date
-              </Table.ColumnHeader>
-              <Table.ColumnHeader width="100px" fontWeight="600">
-                Duration
-              </Table.ColumnHeader>
-              <Table.ColumnHeader
-                width="50px"
-                fontWeight="600"
-              ></Table.ColumnHeader>
-            </Table.Row>
-          </Table.Header>
-          <Table.Body>
-            {transcripts.map((item) => (
-              <Table.Row key={item.id}>
-                <Table.Cell>
-                  <TranscriptStatusIcon status={item.status} />
-                </Table.Cell>
-                <Table.Cell>
-                  <Link as={NextLink} href={`/transcripts/${item.id}`}>
-                    {item.title || "Unnamed Transcript"}
-                  </Link>
-                </Table.Cell>
-                <Table.Cell>
-                  {item.source_kind === "room"
-                    ? item.room_name
-                    : item.source_kind}
-                </Table.Cell>
-                <Table.Cell>{formatLocalDate(item.created_at)}</Table.Cell>
-                <Table.Cell>{formatTimeMs(item.duration)}</Table.Cell>
-                <Table.Cell>
-                  <TranscriptActionsMenu
-                    transcriptId={item.id}
-                    onDelete={onDelete}
-                    onReprocess={onReprocess}
-                  />
-                </Table.Cell>
-              </Table.Row>
-            ))}
-          </Table.Body>
-        </Table.Root>
-      </Box>
-    </Box>
-  );
-}

www/app/(app)/browse/page.tsx

@@ -1,33 +1,264 @@
 "use client";
 import React, { useState, useEffect } from "react";
-import { Flex, Spinner, Heading, Text, Link } from "@chakra-ui/react";
-import useTranscriptList from "../transcripts/useTranscriptList";
+import {
+  Flex,
+  Spinner,
+  Heading,
+  Text,
+  Link,
+  Box,
+  Stack,
+  Input,
+  Button,
+  IconButton,
+} from "@chakra-ui/react";
+import {
+  useQueryState,
+  parseAsString,
+  parseAsInteger,
+  parseAsStringLiteral,
+} from "nuqs";
+import { LuX } from "react-icons/lu";
+import { useSearchTranscripts } from "../transcripts/useSearchTranscripts";
 import useSessionUser from "../../lib/useSessionUser";
-import { Room } from "../../api";
-import Pagination from "./_components/Pagination";
+import { Room, SourceKind, SearchResult, $SourceKind } from "../../api";
 import useApi from "../../lib/useApi";
 import { useError } from "../../(errors)/errorContext";
-import { SourceKind } from "../../api";
 import FilterSidebar from "./_components/FilterSidebar";
-import SearchBar from "./_components/SearchBar";
-import TranscriptTable from "./_components/TranscriptTable";
+import Pagination, {
+  FIRST_PAGE,
+  PaginationPage,
+  parsePaginationPage,
+  totalPages as getTotalPages,
+} from "./_components/Pagination";
 import TranscriptCards from "./_components/TranscriptCards";
 import DeleteTranscriptDialog from "./_components/DeleteTranscriptDialog";
 import { formatLocalDate } from "../../lib/time";
+import { RECORD_A_MEETING_URL } from "../../api/urls";
+
+const SEARCH_FORM_QUERY_INPUT_NAME = "query" as const;
+
+const usePrefetchRooms = (setRooms: (rooms: Room[]) => void): void => {
+  const { setError } = useError();
+  const api = useApi();
+  useEffect(() => {
+    if (!api) return;
+    api
+      .v1RoomsList({ page: 1 })
+      .then((rooms) => setRooms(rooms.items))
+      .catch((err) => setError(err, "There was an error fetching the rooms"));
+  }, [api, setError]);
+};
+
+const SearchForm: React.FC<{
+  setPage: (page: PaginationPage) => void;
+  sourceKind: SourceKind | null;
+  roomId: string | null;
+  setSourceKind: (sourceKind: SourceKind | null) => void;
+  setRoomId: (roomId: string | null) => void;
+  rooms: Room[];
+  searchQuery: string | null;
+  setSearchQuery: (query: string | null) => void;
+}> = ({
+  setPage,
+  sourceKind,
+  roomId,
+  setRoomId,
+  setSourceKind,
+  rooms,
+  searchQuery,
+  setSearchQuery,
+}) => {
+  // to keep the search input controllable + more fine grained control (urlSearchQuery is updated on submits)
+  const [searchInputValue, setSearchInputValue] = useState(searchQuery || "");
+  const handleSearchQuerySubmit = async (d: FormData) => {
+    await setSearchQuery((d.get(SEARCH_FORM_QUERY_INPUT_NAME) as string) || "");
+  };
+
+  const handleClearSearch = () => {
+    setSearchInputValue("");
+    setSearchQuery(null);
+    setPage(FIRST_PAGE);
+  };
+  return (
+    <Stack gap={2}>
+      <form action={handleSearchQuerySubmit}>
+        <Flex alignItems="center">
+          <Box position="relative" flex="1">
+            <Input
+              placeholder="Search transcriptions..."
+              value={searchInputValue}
+              onChange={(e) => setSearchInputValue(e.target.value)}
+              name={SEARCH_FORM_QUERY_INPUT_NAME}
+              pr={searchQuery ? "2.5rem" : undefined}
+            />
+            {searchQuery && (
+              <IconButton
+                aria-label="Clear search"
+                size="sm"
+                variant="ghost"
+                onClick={handleClearSearch}
+                position="absolute"
+                right="0.25rem"
+                top="50%"
+                transform="translateY(-50%)"
+                _hover={{ bg: "gray.100" }}
+              >
+                <LuX />
+              </IconButton>
+            )}
+          </Box>
+          <Button ml={2} type="submit">
+            Search
+          </Button>
+        </Flex>
+      </form>
+      <UnderSearchFormFilterIndicators
+        sourceKind={sourceKind}
+        roomId={roomId}
+        setSourceKind={setSourceKind}
+        setRoomId={setRoomId}
+        rooms={rooms}
+      />
+    </Stack>
+  );
+};
+
+const UnderSearchFormFilterIndicators: React.FC<{
+  sourceKind: SourceKind | null;
+  roomId: string | null;
+  setSourceKind: (sourceKind: SourceKind | null) => void;
+  setRoomId: (roomId: string | null) => void;
+  rooms: Room[];
+}> = ({ sourceKind, roomId, setRoomId, setSourceKind, rooms }) => {
+  return (
+    <>
+      {(sourceKind || roomId) && (
+        <Flex gap={2} flexWrap="wrap" align="center">
+          <Text fontSize="sm" color="gray.600">
+            Active filters:
+          </Text>
+          {sourceKind && (
+            <Flex
+              align="center"
+              px={2}
+              py={1}
+              bg="blue.100"
+              borderRadius="md"
+              fontSize="xs"
+              gap={1}
+            >
+              <Text>
+                {roomId
+                  ? `Room: ${
+                      rooms.find((r) => r.id === roomId)?.name || roomId
+                    }`
+                  : `Source: ${sourceKind}`}
+              </Text>
+              <Button
+                size="xs"
+                variant="ghost"
+                minW="auto"
+                h="auto"
+                p="1px"
+                onClick={() => {
+                  setSourceKind(null);
+                  // TODO questionable
+                  setRoomId(null);
+                }}
+                _hover={{ bg: "blue.200" }}
+                aria-label="Clear filter"
+              >
+                <LuX size={14} />
+              </Button>
+            </Flex>
+          )}
+        </Flex>
+      )}
+    </>
+  );
+};
+
+const EmptyResult: React.FC<{
+  searchQuery: string;
+}> = ({ searchQuery }) => {
+  return (
+    <Flex flexDir="column" alignItems="center" justifyContent="center" py={8}>
+      <Text textAlign="center">
+        {searchQuery
+          ? `No results found for "${searchQuery}". Try adjusting your search terms.`
+          : "No transcripts found, but you can "}
+        {!searchQuery && (
+          <>
+            <Link href={RECORD_A_MEETING_URL} color="blue.500">
+              record a meeting
+            </Link>
+            {" to get started."}
+          </>
+        )}
+      </Text>
+    </Flex>
+  );
+};
 
 export default function TranscriptBrowser() {
-  const [selectedSourceKind, setSelectedSourceKind] =
-    useState<SourceKind | null>(null);
-  const [selectedRoomId, setSelectedRoomId] = useState("");
-  const [rooms, setRooms] = useState<Room[]>([]);
-  const [page, setPage] = useState(1);
-  const [searchTerm, setSearchTerm] = useState("");
-  const { loading, response, refetch } = useTranscriptList(
-    page,
-    selectedSourceKind,
-    selectedRoomId,
-    searchTerm,
+  const [urlSearchQuery, setUrlSearchQuery] = useQueryState(
+    "q",
+    parseAsString.withDefault("").withOptions({ shallow: false }),
   );
+
+  const [urlSourceKind, setUrlSourceKind] = useQueryState(
+    "source",
+    parseAsStringLiteral($SourceKind.enum).withOptions({
+      shallow: false,
+    }),
+  );
+  const [urlRoomId, setUrlRoomId] = useQueryState(
+    "room",
+    parseAsString.withDefault("").withOptions({ shallow: false }),
+  );
+
+  const [urlPage, setPage] = useQueryState(
+    "page",
+    parseAsInteger.withDefault(1).withOptions({ shallow: false }),
+  );
+
+  const [page, _setSafePage] = useState(FIRST_PAGE);
+
+  // safety net
+  useEffect(() => {
+    const maybePage = parsePaginationPage(urlPage);
+    if ("error" in maybePage) {
+      setPage(FIRST_PAGE).then(() => {
+        /*may be called n times we dont care*/
+      });
+      return;
+    }
+    _setSafePage(maybePage.value);
+  }, [urlPage]);
+
+  const [rooms, setRooms] = useState<Room[]>([]);
+
+  const pageSize = 20;
+  const {
+    results,
+    totalCount: totalResults,
+    isLoading,
+    reload,
+  } = useSearchTranscripts(
+    urlSearchQuery,
+    {
+      roomIds: urlRoomId ? [urlRoomId] : null,
+      sourceKind: urlSourceKind,
+    },
+    {
+      pageSize,
+      page,
+    },
+  );
+
+  const totalPages = getTotalPages(totalResults, pageSize);
+
   const userName = useSessionUser().name;
   const [deletionLoading, setDeletionLoading] = useState(false);
   const api = useApi();
@@ -35,37 +266,73 @@ export default function TranscriptBrowser() {
   const cancelRef = React.useRef(null);
   const [transcriptToDeleteId, setTranscriptToDeleteId] =
     React.useState<string>();
-  const [deletedItemIds, setDeletedItemIds] = React.useState<string[]>();
 
-  useEffect(() => {
-    setDeletedItemIds([]);
-  }, [page, response]);
-
-  useEffect(() => {
-    if (!api) return;
-    api
-      .v1RoomsList({ page: 1 })
-      .then((rooms) => setRooms(rooms.items))
-      .catch((err) => setError(err, "There was an error fetching the rooms"));
-  }, [api]);
+  usePrefetchRooms(setRooms);
 
   const handleFilterTranscripts = (
     sourceKind: SourceKind | null,
     roomId: string,
   ) => {
-    setSelectedSourceKind(sourceKind);
-    setSelectedRoomId(roomId);
+    setUrlSourceKind(sourceKind);
+    setUrlRoomId(roomId);
     setPage(1);
   };
 
-  const handleSearch = (searchTerm: string) => {
-    setPage(1);
-    setSearchTerm(searchTerm);
-    setSelectedSourceKind(null);
-    setSelectedRoomId("");
+  const onCloseDeletion = () => setTranscriptToDeleteId(undefined);
+
+  const confirmDeleteTranscript = (transcriptId: string) => {
+    if (!api || deletionLoading) return;
+    setDeletionLoading(true);
+    api
+      .v1TranscriptDelete({ transcriptId })
+      .then(() => {
+        setDeletionLoading(false);
+        onCloseDeletion();
+        reload();
+      })
+      .catch((err) => {
+        setDeletionLoading(false);
+        setError(err, "There was an error deleting the transcript");
+      });
   };
 
-  if (loading && !response)
+  const handleProcessTranscript = (transcriptId: string) => {
+    if (!api) {
+      console.error("API not available on handleProcessTranscript");
+      return;
+    }
+    api
+      .v1TranscriptProcess({ transcriptId })
+      .then((result) => {
+        const status =
+          result && typeof result === "object" && "status" in result
+            ? (result as { status: string }).status
+            : undefined;
+        if (status === "already running") {
+          setError(
+            new Error("Processing is already running, please wait"),
+            "Processing is already running, please wait",
+          );
+        }
+      })
+      .catch((err) => {
+        setError(err, "There was an error processing the transcript");
+      });
+  };
+
+  const transcriptToDelete = results?.find(
+    (i) => i.id === transcriptToDeleteId,
+  );
+  const dialogTitle = transcriptToDelete?.title || "Unnamed Transcript";
+  const dialogDate = transcriptToDelete?.created_at
+    ? formatLocalDate(transcriptToDelete.created_at)
+    : undefined;
+  const dialogSource =
+    transcriptToDelete?.source_kind === "room" && transcriptToDelete?.room_id
+      ? transcriptToDelete.room_name || transcriptToDelete.room_id
+      : transcriptToDelete?.source_kind;
+
+  if (isLoading && results.length === 0) {
     return (
       <Flex
         flexDir="column"
@@ -76,82 +343,7 @@ export default function TranscriptBrowser() {
         <Spinner size="xl" />
       </Flex>
     );
-
-  if (!loading && !response)
-    return (
-      <Flex
-        flexDir="column"
-        alignItems="center"
-        justifyContent="center"
-        h="100%"
-      >
-        <Text>
-          No transcripts found, but you can&nbsp;
-          <Link href="/transcripts/new" className="underline">
-            record a meeting
-          </Link>
-          &nbsp;to get started.
-        </Text>
-      </Flex>
-    );
-
-  const onCloseDeletion = () => setTranscriptToDeleteId(undefined);
-
-  const confirmDeleteTranscript = (transcriptId: string) => {
-    if (!api || deletionLoading) return;
-    setDeletionLoading(true);
-    api
-      .v1TranscriptDelete({ transcriptId })
-      .then(() => {
-        refetch();
-        setDeletionLoading(false);
-        onCloseDeletion();
-        setDeletedItemIds((prev) =>
-          prev ? [...prev, transcriptId] : [transcriptId],
-        );
-      })
-      .catch((err) => {
-        setDeletionLoading(false);
-        setError(err, "There was an error deleting the transcript");
-      });
-  };
-
-  const handleDeleteTranscript = (transcriptId: string) => (e: any) => {
-    e?.stopPropagation?.();
-    setTranscriptToDeleteId(transcriptId);
-  };
-
-  const handleProcessTranscript = (transcriptId) => (e) => {
-    if (api) {
-      api
-        .v1TranscriptProcess({ transcriptId })
-        .then((result) => {
-          const status = (result as any).status;
-          if (status === "already running") {
-            setError(
-              new Error("Processing is already running, please wait"),
-              "Processing is already running, please wait",
-            );
-          }
-        })
-        .catch((err) => {
-          setError(err, "There was an error processing the transcript");
-        });
-    }
-  };
-
-  const transcriptToDelete = response?.items?.find(
-    (i) => i.id === transcriptToDeleteId,
-  );
-  const dialogTitle = transcriptToDelete?.title || "Unnamed Transcript";
-  const dialogDate = transcriptToDelete?.created_at
-    ? formatLocalDate(transcriptToDelete.created_at)
-    : undefined;
-  const dialogSource = transcriptToDelete
-    ? transcriptToDelete.source_kind === "room"
-      ? transcriptToDelete.room_name || undefined
-      : transcriptToDelete.source_kind
-    : undefined;
+  }
 
   return (
     <Flex
@@ -168,15 +360,15 @@ export default function TranscriptBrowser() {
       >
         <Heading size="lg">
           {userName ? `${userName}'s Transcriptions` : "Your Transcriptions"}{" "}
-          {loading || (deletionLoading && <Spinner size="sm" />)}
+          {(isLoading || deletionLoading) && <Spinner size="sm" />}
         </Heading>
       </Flex>
 
       <Flex flexDir={{ base: "column", md: "row" }}>
         <FilterSidebar
           rooms={rooms}
-          selectedSourceKind={selectedSourceKind}
-          selectedRoomId={selectedRoomId}
+          selectedSourceKind={urlSourceKind}
+          selectedRoomId={urlRoomId}
           onFilterChange={handleFilterTranscripts}
         />
 
@@ -188,25 +380,37 @@ export default function TranscriptBrowser() {
           gap={4}
           px={{ base: 0, md: 4 }}
         >
-          <SearchBar onSearch={handleSearch} />
-          <Pagination
-            page={page}
+          <SearchForm
             setPage={setPage}
-            total={response?.total || 0}
-            size={response?.size || 0}
-          />
-          <TranscriptTable
-            transcripts={response?.items || []}
-            onDelete={handleDeleteTranscript}
-            onReprocess={handleProcessTranscript}
-            loading={loading}
+            sourceKind={urlSourceKind}
+            roomId={urlRoomId}
+            searchQuery={urlSearchQuery}
+            setSearchQuery={setUrlSearchQuery}
+            setSourceKind={setUrlSourceKind}
+            setRoomId={setUrlRoomId}
+            rooms={rooms}
           />
+
+          {totalPages > 1 ? (
+            <Pagination
+              page={page}
+              setPage={setPage}
+              total={totalResults}
+              size={pageSize}
+            />
+          ) : null}
+
           <TranscriptCards
-            transcripts={response?.items || []}
-            onDelete={handleDeleteTranscript}
+            results={results}
+            query={urlSearchQuery}
+            isLoading={isLoading}
+            onDelete={setTranscriptToDeleteId}
             onReprocess={handleProcessTranscript}
-            loading={loading}
           />
+
+          {!isLoading && results.length === 0 && (
+            <EmptyResult searchQuery={urlSearchQuery} />
+          )}
         </Flex>
       </Flex>
 

www/app/(app)/layout.tsx

@@ -5,6 +5,7 @@ import Image from "next/image";
 import About from "../(aboutAndPrivacy)/about";
 import Privacy from "../(aboutAndPrivacy)/privacy";
 import UserInfo from "../(auth)/userInfo";
+import { RECORD_A_MEETING_URL } from "../api/urls";
 
 export default async function AppLayout({
   children,
@@ -53,7 +54,7 @@ export default async function AppLayout({
           {/* Text link on the right */}
           <Link
             as={NextLink}
-            href="/transcripts/new"
+            href={RECORD_A_MEETING_URL}
             className="font-light px-2"
           >
             Create

www/app/(app)/rooms/page.tsx

@@ -19,6 +19,7 @@ import useApi from "../../lib/useApi";
 import useRoomList from "./useRoomList";
 import { ApiError, Room } from "../../api";
 import { RoomList } from "./_components/RoomList";
+import { PaginationPage } from "../browse/_components/Pagination";
 
 interface SelectOption {
   label: string;
@@ -75,8 +76,9 @@ export default function RoomsList() {
   const [isEditing, setIsEditing] = useState(false);
   const [editRoomId, setEditRoomId] = useState("");
   const api = useApi();
+  // TODO seems to be no setPage calls
   const [page, setPage] = useState<number>(1);
-  const { loading, response, refetch } = useRoomList(page);
+  const { loading, response, refetch } = useRoomList(PaginationPage(page));
   const [streams, setStreams] = useState<Stream[]>([]);
   const [topics, setTopics] = useState<Topic[]>([]);
   const [nameError, setNameError] = useState("");

www/app/(app)/rooms/useRoomList.tsx

@@ -2,6 +2,7 @@ import { useEffect, useState } from "react";
 import { useError } from "../../(errors)/errorContext";
 import useApi from "../../lib/useApi";
 import { Page_Room_ } from "../../api";
+import { PaginationPage } from "../browse/_components/Pagination";
 
 type RoomList = {
   response: Page_Room_ | null;
@@ -11,7 +12,7 @@ type RoomList = {
 };
 
 //always protected
-const useRoomList = (page: number): RoomList => {
+const useRoomList = (page: PaginationPage): RoomList => {
   const [response, setResponse] = useState<Page_Room_ | null>(null);
   const [loading, setLoading] = useState<boolean>(true);
   const [error, setErrorState] = useState<Error | null>(null);

www/app/(app)/transcripts/mockTopics.json

@@ -1,32 +0,0 @@
-[
-  {
-    "id": "27c07e49-d7a3-4b86-905c-f1a047366f91",
-    "title": "Issue one",
-    "summary": "The team discusses the first issue in the list",
-    "timestamp": 0.0,
-    "transcript": "",
-    "duration": 33,
-    "segments": [
-      {
-        "text": "Let's start with issue one, Alice you've been working on that, can you give an update ?",
-        "start": 0.0,
-        "speaker": 0
-      },
-      {
-        "text": "Yes, I've run into an issue with the task system but Bob helped me out and I have a POC ready, should I present it now ?",
-        "start": 0.38,
-        "speaker": 1
-      },
-      {
-        "text": "Yeah, I had to modify the task system because it didn't account for incoming blobs",
-        "start": 4.5,
-        "speaker": 2
-      },
-      {
-        "text": "Cool, yeah lets see it",
-        "start": 5.96,
-        "speaker": 0
-      }
-    ]
-  }
-]

www/app/(app)/transcripts/recorder.tsx

@@ -11,6 +11,7 @@ import useWebRTC from "./useWebRTC";
 import useAudioDevice from "./useAudioDevice";
 import { Box, Flex, IconButton, Menu, RadioGroup } from "@chakra-ui/react";
 import { LuScreenShare, LuMic, LuPlay, LuCircleStop } from "react-icons/lu";
+import { RECORD_A_MEETING_URL } from "../../api/urls";
 
 type RecorderProps = {
   transcriptId: string;
@@ -46,7 +47,7 @@ export default function Recorder(props: RecorderProps) {
           location.href = "";
           break;
         case ",":
-          location.href = "/transcripts/new";
+          location.href = RECORD_A_MEETING_URL;
           break;
         case "!":
           if (record.isRecording()) return;

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

@@ -0,0 +1,123 @@
+// this hook is not great, we want to substitute it with a proper state management solution that is also not re-invention
+
+import { useEffect, useRef, useState } from "react";
+import { SearchResult, SourceKind } from "../../api";
+import useApi from "../../lib/useApi";
+import {
+  PaginationPage,
+  paginationPageTo0Based,
+} from "../browse/_components/Pagination";
+
+interface SearchFilters {
+  roomIds: readonly string[] | null;
+  sourceKind: SourceKind | null;
+}
+
+const EMPTY_SEARCH_FILTERS: SearchFilters = {
+  roomIds: null,
+  sourceKind: null,
+};
+
+type UseSearchTranscriptsOptions = {
+  pageSize: number;
+  page: PaginationPage;
+};
+
+interface UseSearchTranscriptsReturn {
+  results: SearchResult[];
+  totalCount: number;
+  isLoading: boolean;
+  error: unknown;
+  reload: () => void;
+}
+
+function hashEffectFilters(filters: SearchFilters): string {
+  return JSON.stringify(filters);
+}
+
+export function useSearchTranscripts(
+  query: string = "",
+  filters: SearchFilters = EMPTY_SEARCH_FILTERS,
+  options: UseSearchTranscriptsOptions = {
+    pageSize: 20,
+    page: PaginationPage(1),
+  },
+): UseSearchTranscriptsReturn {
+  const { pageSize, page } = options;
+
+  const [reloadCount, setReloadCount] = useState(0);
+
+  const api = useApi();
+  const abortControllerRef = useRef<AbortController>();
+
+  const [data, setData] = useState<{ results: SearchResult[]; total: number }>({
+    results: [],
+    total: 0,
+  });
+  const [error, setError] = useState<any>();
+  const [isLoading, setIsLoading] = useState(false);
+
+  const filterHash = hashEffectFilters(filters);
+
+  useEffect(() => {
+    if (!api) {
+      setData({ results: [], total: 0 });
+      setError(undefined);
+      setIsLoading(false);
+      return;
+    }
+
+    if (abortControllerRef.current) {
+      abortControllerRef.current.abort();
+    }
+
+    const abortController = new AbortController();
+    abortControllerRef.current = abortController;
+
+    const performSearch = async () => {
+      setIsLoading(true);
+
+      try {
+        const response = await api.v1TranscriptsSearch({
+          q: query || "",
+          limit: pageSize,
+          offset: paginationPageTo0Based(page) * pageSize,
+          roomId: filters.roomIds?.[0],
+          sourceKind: filters.sourceKind || undefined,
+        });
+
+        if (abortController.signal.aborted) return;
+        setData(response);
+        setError(undefined);
+      } catch (err: unknown) {
+        if ((err as Error).name === "AbortError") {
+          return;
+        }
+        if (abortController.signal.aborted) {
+          console.error("Aborted search but error", err);
+          return;
+        }
+
+        setError(err);
+      } finally {
+        if (!abortController.signal.aborted) {
+          setIsLoading(false);
+        }
+      }
+    };
+
+    performSearch().then(() => {});
+
+    return () => {
+      abortController.abort();
+    };
+  }, [api, query, page, filterHash, pageSize, reloadCount]);
+
+  return {
+    results: data.results,
+    totalCount: data.total,
+    isLoading,
+    error,
+    reload: () => setReloadCount(reloadCount + 1),
+  };
+}

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

@@ -1,59 +0,0 @@
-import { useEffect, useState } from "react";
-import { useError } from "../../(errors)/errorContext";
-import useApi from "../../lib/useApi";
-import { Page_GetTranscriptMinimal_, SourceKind } from "../../api";
-
-type TranscriptList = {
-  response: Page_GetTranscriptMinimal_ | null;
-  loading: boolean;
-  error: Error | null;
-  refetch: () => void;
-};
-
-const useTranscriptList = (
-  page: number,
-  sourceKind: SourceKind | null,
-  roomId: string | null,
-  searchTerm: string | null,
-): TranscriptList => {
-  const [response, setResponse] = useState<Page_GetTranscriptMinimal_ | null>(
-    null,
-  );
-  const [loading, setLoading] = useState<boolean>(true);
-  const [error, setErrorState] = useState<Error | null>(null);
-  const { setError } = useError();
-  const api = useApi();
-  const [refetchCount, setRefetchCount] = useState(0);
-
-  const refetch = () => {
-    setLoading(true);
-    setRefetchCount(refetchCount + 1);
-  };
-
-  useEffect(() => {
-    if (!api) return;
-    setLoading(true);
-    api
-      .v1TranscriptsList({
-        page,
-        sourceKind,
-        roomId,
-        searchTerm,
-        size: 10,
-      })
-      .then((response) => {
-        setResponse(response);
-        setLoading(false);
-      })
-      .catch((err) => {
-        setResponse(null);
-        setLoading(false);
-        setError(err);
-        setErrorState(err);
-      });
-  }, [api, page, refetchCount, roomId, searchTerm, sourceKind]);
-
-  return { response, loading, error, refetch };
-};
-
-export default useTranscriptList;

www/app/api/schemas.gen.ts

@@ -1002,7 +1002,7 @@ export const $SearchResponse = {
     },
     query: {
       type: "string",
-      minLength: 1,
+      minLength: 0,
       title: "Query",
       description: "Search query text",
     },
@@ -1065,6 +1065,20 @@ export const $SearchResult = {
       ],
       title: "Room Id",
     },
+    room_name: {
+      anyOf: [
+        {
+          type: "string",
+        },
+        {
+          type: "null",
+        },
+      ],
+      title: "Room Name",
+    },
+    source_kind: {
+      $ref: "#/components/schemas/SourceKind",
+    },
     created_at: {
       type: "string",
       title: "Created At",
@@ -1101,10 +1115,18 @@ export const $SearchResult = {
       title: "Search Snippets",
       description: "Text snippets around search matches",
     },
+    total_match_count: {
+      type: "integer",
+      minimum: 0,
+      title: "Total Match Count",
+      description: "Total number of matches found in the transcript",
+      default: 0,
+    },
   },
   type: "object",
   required: [
     "id",
+    "source_kind",
     "created_at",
     "status",
     "rank",

www/app/api/services.gen.ts

@@ -286,6 +286,7 @@ export class DefaultService {
    * @param data.limit Results per page
    * @param data.offset Number of results to skip
    * @param data.roomId
+   * @param data.sourceKind
    * @returns SearchResponse Successful Response
    * @throws ApiError
    */
@@ -300,6 +301,7 @@ export class DefaultService {
         limit: data.limit,
         offset: data.offset,
         room_id: data.roomId,
+        source_kind: data.sourceKind,
       },
       errors: {
         422: "Validation Error",

www/app/api/types.gen.ts

@@ -209,6 +209,8 @@ export type SearchResult = {
   title?: string | null;
   user_id?: string | null;
   room_id?: string | null;
+  room_name?: string | null;
+  source_kind: SourceKind;
   created_at: string;
   status: string;
   rank: number;
@@ -220,6 +222,10 @@ export type SearchResult = {
    * Text snippets around search matches
    */
   search_snippets: Array<string>;
+  /**
+   * Total number of matches found in the transcript
+   */
+  total_match_count?: number;
 };
 
 export type SourceKind = "room" | "live" | "file";
@@ -407,6 +413,7 @@ export type V1TranscriptsSearchData = {
    */
   q: string;
   roomId?: string | null;
+  sourceKind?: SourceKind | null;
 };
 
 export type V1TranscriptsSearchResponse = SearchResponse;

www/app/api/urls.ts

@@ -0,0 +1,2 @@
+// TODO better connection with generated schema; it's duplication
+export const RECORD_A_MEETING_URL = "/transcripts/new" as const;

www/app/lib/textHighlight.tsx

@@ -0,0 +1,62 @@
+/**
+ * Text highlighting and text fragment generation utilities
+ * Used for search result highlighting and deep linking with Chrome Text Fragments
+ */
+
+import React from "react";
+
+export interface HighlightResult {
+  text: string;
+  matches: string[];
+}
+
+/**
+ * Escapes special regex characters in a string
+ */
+function escapeRegex(str: string): string {
+  return str.replace(/[.*+?^${}()|[\]\\]/g, "\\$&");
+}
+
+export const highlightMatches = (
+  text: string,
+  query: string,
+): { match: string; index: number }[] => {
+  if (!query || !text) {
+    return [];
+  }
+
+  const queryWords = query.trim().split(/\s+/);
+
+  const regex = new RegExp(
+    `(${queryWords.map((word) => escapeRegex(word)).join("|")})`,
+    "gi",
+  );
+
+  return Array.from(text.matchAll(regex)).map((result) => ({
+    match: result[0],
+    index: result.index!,
+  }));
+};
+
+export function findFirstHighlight(text: string, query: string): string | null {
+  const matches = highlightMatches(text, query);
+  if (matches.length === 0) {
+    return null;
+  }
+  return matches[0].match;
+}
+
+export function generateTextFragment(
+  text: string,
+  query: string,
+): {
+  k: ":~:text";
+  v: string;
+} | null {
+  const firstMatch = findFirstHighlight(text, query);
+  if (!firstMatch) return null;
+  return {
+    k: ":~:text",
+    v: firstMatch,
+  };
+}

www/app/lib/utils.ts

@@ -136,3 +136,10 @@ export function extractDomain(url) {
     return null;
   }
 }
+
+export function assertExists<T>(value: T | null | undefined, err?: string): T {
+  if (value === null || value === undefined) {
+    throw new Error(`Assertion failed: ${err ?? "value is null or undefined"}`);
+  }
+  return value;
+}

www/app/page.tsx

@@ -1,6 +1,7 @@
 "use client";
 import { redirect } from "next/navigation";
+import { RECORD_A_MEETING_URL } from "./api/urls";
 
 export default function Index() {
-  redirect("/transcripts/new");
+  redirect(RECORD_A_MEETING_URL);
 }

www/app/providers.tsx

@@ -5,14 +5,17 @@ import system from "./styles/theme";
 
 import { WherebyProvider } from "@whereby.com/browser-sdk/react";
 import { Toaster } from "./components/ui/toaster";
+import { NuqsAdapter } from "nuqs/adapters/next/app";
 
 export function Providers({ children }: { children: React.ReactNode }) {
   return (
-    <ChakraProvider value={system}>
-      <WherebyProvider>
-        {children}
-        <Toaster />
-      </WherebyProvider>
-    </ChakraProvider>
+    <NuqsAdapter>
+      <ChakraProvider value={system}>
+        <WherebyProvider>
+          {children}
+          <Toaster />
+        </WherebyProvider>
+      </ChakraProvider>
+    </NuqsAdapter>
   );
 }

www/package.json

@@ -31,6 +31,7 @@
     "next": "^14.2.30",
     "next-auth": "^4.24.7",
     "next-themes": "^0.4.6",
+    "nuqs": "^2.4.3",
     "postcss": "8.4.31",
     "prop-types": "^15.8.1",
     "react": "^18.2.0",

www/pnpm-lock.yaml

@@ -67,6 +67,9 @@ importers:
       next-themes:
         specifier: ^0.4.6
         version: 0.4.6(react-dom@18.3.1(react@18.3.1))(react@18.3.1)
+      nuqs:
+        specifier: ^2.4.3
+        version: 2.4.3(next@14.2.31(react-dom@18.3.1(react@18.3.1))(react@18.3.1)(sass@1.90.0))(react@18.3.1)
       postcss:
         specifier: 8.4.31
         version: 8.4.31
@@ -5436,6 +5439,12 @@ packages:
       }
     engines: { node: ">= 8" }
 
+  mitt@3.0.1:
+    resolution:
+      {
+        integrity: sha512-vKivATfr97l2/QBCYAkXYDbrIWPM2IIKEl7YPhjCvKlG3kE2gm+uBo6nEXK3M5/Ffh/FLpKExzOQ3JJoJGFKBw==,
+      }
+
   mkdirp@0.5.6:
     resolution:
       {
@@ -5660,6 +5669,27 @@ packages:
       }
     deprecated: This package is no longer supported.
 
+  nuqs@2.4.3:
+    resolution:
+      {
+        integrity: sha512-BgtlYpvRwLYiJuWzxt34q2bXu/AIS66sLU1QePIMr2LWkb+XH0vKXdbLSgn9t6p7QKzwI7f38rX3Wl9llTXQ8Q==,
+      }
+    peerDependencies:
+      "@remix-run/react": ">=2"
+      next: ">=14.2.0"
+      react: ">=18.2.0 || ^19.0.0-0"
+      react-router: ^6 || ^7
+      react-router-dom: ^6 || ^7
+    peerDependenciesMeta:
+      "@remix-run/react":
+        optional: true
+      next:
+        optional: true
+      react-router:
+        optional: true
+      react-router-dom:
+        optional: true
+
   nypm@0.5.4:
     resolution:
       {
@@ -11553,6 +11583,8 @@ snapshots:
       minipass: 3.3.6
       yallist: 4.0.0
 
+  mitt@3.0.1: {}
+
   mkdirp@0.5.6:
     dependencies:
       minimist: 1.2.8
@@ -11674,6 +11706,13 @@ snapshots:
       gauge: 3.0.2
       set-blocking: 2.0.0
 
+  nuqs@2.4.3(next@14.2.31(react-dom@18.3.1(react@18.3.1))(react@18.3.1)(sass@1.90.0))(react@18.3.1):
+    dependencies:
+      mitt: 3.0.1
+      react: 18.3.1
+    optionalDependencies:
+      next: 14.2.31(react-dom@18.3.1(react@18.3.1))(react@18.3.1)(sass@1.90.0)
+
   nypm@0.5.4:
     dependencies:
       citty: 0.1.6