latest changes feature parity

CHANGELOG.md

@@ -1,12 +1,5 @@
 # Changelog
 
-## [0.24.1](https://github.com/Monadical-SAS/reflector/compare/v0.24.0...v0.24.1) (2025-12-19)
-
-
-### Bug Fixes
-
-* logout redirect ([#802](https://github.com/Monadical-SAS/reflector/issues/802)) ([f0ee7b5](https://github.com/Monadical-SAS/reflector/commit/f0ee7b531a0911f214ccbb84d399e9a6c9b700c0))
-
 ## [0.24.0](https://github.com/Monadical-SAS/reflector/compare/v0.23.2...v0.24.0) (2025-12-18)
 
 

docker-compose.yml

@@ -34,6 +34,20 @@ services:
     environment:
       ENTRYPOINT: beat
 
+  hatchet-worker:
+    build:
+      context: server
+    volumes:
+      - ./server/:/app/
+      - /app/.venv
+    env_file:
+      - ./server/.env
+    environment:
+      ENTRYPOINT: hatchet-worker
+    depends_on:
+      hatchet:
+        condition: service_healthy
+
   redis:
     image: redis:7.2
     ports:
@@ -55,6 +69,7 @@ services:
 
   postgres:
     image: postgres:17
+    command: postgres -c 'max_connections=200'
     ports:
       - 5432:5432
     environment:
@@ -63,6 +78,42 @@ services:
       POSTGRES_DB: reflector
     volumes:
       - ./data/postgres:/var/lib/postgresql/data
+      - ./server/docker/init-hatchet-db.sql:/docker-entrypoint-initdb.d/init-hatchet-db.sql:ro
+    healthcheck:
+      test: ["CMD-SHELL", "pg_isready -d reflector -U reflector"]
+      interval: 10s
+      timeout: 10s
+      retries: 5
+      start_period: 10s
+
+  hatchet:
+    image: ghcr.io/hatchet-dev/hatchet/hatchet-lite:latest
+    ports:
+      - "8889:8888"
+      - "7078:7077"
+    depends_on:
+      postgres:
+        condition: service_healthy
+    environment:
+      DATABASE_URL: "postgresql://reflector:reflector@postgres:5432/hatchet?sslmode=disable"
+      SERVER_AUTH_COOKIE_DOMAIN: localhost
+      SERVER_AUTH_COOKIE_INSECURE: "t"
+      SERVER_GRPC_BIND_ADDRESS: "0.0.0.0"
+      SERVER_GRPC_INSECURE: "t"
+      SERVER_GRPC_BROADCAST_ADDRESS: hatchet:7077
+      SERVER_GRPC_PORT: "7077"
+      SERVER_URL: http://localhost:8889
+      SERVER_AUTH_SET_EMAIL_VERIFIED: "t"
+      # SERVER_DEFAULT_ENGINE_VERSION: "V1"  # default
+      SERVER_INTERNAL_CLIENT_INTERNAL_GRPC_BROADCAST_ADDRESS: hatchet:7077
+    volumes:
+      - ./data/hatchet-config:/config
+    healthcheck:
+      test: ["CMD", "curl", "-f", "http://localhost:8888/api/live"]
+      interval: 30s
+      timeout: 10s
+      retries: 5
+      start_period: 30s
 
 networks:
   default:

server/README.md

@@ -53,6 +53,36 @@ response = sqs.receive_message(QueueUrl=queue_url, ...)
 uv run /app/requeue_uploaded_file.py TRANSCRIPT_ID
 ```
 
+## Hatchet Setup (Fresh DB)
+
+After resetting the Hatchet database:
+
+### Option A: Automatic (CLI)
+
+```bash
+# Get default tenant ID and create token in one command
+TENANT_ID=$(docker compose exec -T postgres psql -U reflector -d hatchet -t -c \
+  "SELECT id FROM \"Tenant\" WHERE slug = 'default';" | tr -d ' \n') && \
+TOKEN=$(docker compose exec -T hatchet /hatchet-admin token create \
+  --config /config --tenant-id "$TENANT_ID" 2>/dev/null | tr -d '\n') && \
+echo "HATCHET_CLIENT_TOKEN=$TOKEN"
+```
+
+Copy the output to `server/.env`.
+
+### Option B: Manual (UI)
+
+1. Create API token at http://localhost:8889 → Settings → API Tokens
+2. Update `server/.env`: `HATCHET_CLIENT_TOKEN=<new-token>`
+
+### Then restart workers
+
+```bash
+docker compose restart server hatchet-worker
+```
+
+Workflows register automatically when hatchet-worker starts.
+
 ## Pipeline Management
 
 ### Continue stuck pipeline from final summaries (identify_participants) step:

server/docker/init-hatchet-db.sql

@@ -0,0 +1,2 @@
+-- Create hatchet database for Hatchet workflow engine
+CREATE DATABASE hatchet;

server/migrations/versions/0f943fede0e0_add_workflow_run_id_to_transcript.py

@@ -0,0 +1,28 @@
+"""add workflow_run_id to transcript
+
+Revision ID: 0f943fede0e0
+Revises: 05f8688d6895
+Create Date: 2025-12-16 01:54:13.855106
+
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision: str = "0f943fede0e0"
+down_revision: Union[str, None] = "05f8688d6895"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("transcript", schema=None) as batch_op:
+        batch_op.add_column(sa.Column("workflow_run_id", sa.String(), nullable=True))
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("transcript", schema=None) as batch_op:
+        batch_op.drop_column("workflow_run_id")

server/migrations/versions/bd3a729bb379_add_use_hatchet_to_room.py

@@ -0,0 +1,35 @@
+"""add use_hatchet to room
+
+Revision ID: bd3a729bb379
+Revises: 0f943fede0e0
+Create Date: 2025-12-16 16:34:03.594231
+
+"""
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+
+# revision identifiers, used by Alembic.
+revision: str = "bd3a729bb379"
+down_revision: Union[str, None] = "0f943fede0e0"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    with op.batch_alter_table("room", schema=None) as batch_op:
+        batch_op.add_column(
+            sa.Column(
+                "use_hatchet",
+                sa.Boolean(),
+                server_default=sa.text("false"),
+                nullable=False,
+            )
+        )
+
+
+def downgrade() -> None:
+    with op.batch_alter_table("room", schema=None) as batch_op:
+        batch_op.drop_column("use_hatchet")

server/pyproject.toml

@@ -39,6 +39,7 @@ dependencies = [
     "pytest-env>=1.1.5",
     "webvtt-py>=0.5.0",
     "icalendar>=6.0.0",
+    "hatchet-sdk>=0.47.0",
 ]
 
 [dependency-groups]

server/reflector/dailyco_api/client.py

@@ -47,7 +47,7 @@ class DailyApiError(Exception):
         )
 
         super().__init__(
-            f"Daily.co API error: {operation} failed with status {self.status_code}"
+            f"Daily.co API error: {operation} failed with status {self.status_code}: {response.text}"
         )
 
 

server/reflector/db/rooms.py

@@ -57,6 +57,12 @@ rooms = sqlalchemy.Table(
         sqlalchemy.String,
         nullable=False,
     ),
+    sqlalchemy.Column(
+        "use_hatchet",
+        sqlalchemy.Boolean,
+        nullable=False,
+        server_default=false(),
+    ),
     sqlalchemy.Index("idx_room_is_shared", "is_shared"),
     sqlalchemy.Index("idx_room_ics_enabled", "ics_enabled"),
 )
@@ -85,6 +91,7 @@ class Room(BaseModel):
     ics_last_sync: datetime | None = None
     ics_last_etag: str | None = None
     platform: Platform = Field(default_factory=lambda: settings.DEFAULT_VIDEO_PLATFORM)
+    use_hatchet: bool = False
 
 
 class RoomController:

server/reflector/db/transcripts.py

@@ -84,6 +84,8 @@ transcripts = sqlalchemy.Table(
     sqlalchemy.Column("audio_deleted", sqlalchemy.Boolean),
     sqlalchemy.Column("room_id", sqlalchemy.String),
     sqlalchemy.Column("webvtt", sqlalchemy.Text),
+    # Hatchet workflow run ID for resumption of failed workflows
+    sqlalchemy.Column("workflow_run_id", sqlalchemy.String),
     sqlalchemy.Index("idx_transcript_recording_id", "recording_id"),
     sqlalchemy.Index("idx_transcript_user_id", "user_id"),
     sqlalchemy.Index("idx_transcript_created_at", "created_at"),
@@ -223,6 +225,7 @@ class Transcript(BaseModel):
     zulip_message_id: int | None = None
     audio_deleted: bool | None = None
     webvtt: str | None = None
+    workflow_run_id: str | None = None  # Hatchet workflow run ID for resumption
 
     @field_serializer("created_at", when_used="json")
     def serialize_datetime(self, dt: datetime) -> str:

server/reflector/hatchet/__init__.py

@@ -0,0 +1,5 @@
+"""Hatchet workflow orchestration for Reflector."""
+
+from reflector.hatchet.client import HatchetClientManager
+
+__all__ = ["HatchetClientManager"]

server/reflector/hatchet/broadcast.py

@@ -0,0 +1,98 @@
+"""WebSocket broadcasting helpers for Hatchet workflows.
+
+DUPLICATION NOTE: To be kept when Celery is deprecated. Currently dupes Celery logic.
+
+Provides WebSocket broadcasting for Hatchet that matches Celery's @broadcast_to_sockets
+decorator behavior. Events are broadcast to transcript rooms and user rooms.
+"""
+
+from typing import Any
+
+import structlog
+
+from reflector.db.transcripts import Transcript, TranscriptEvent, transcripts_controller
+from reflector.utils.string import NonEmptyString
+from reflector.ws_manager import get_ws_manager
+
+# Events that should also be sent to user room (matches Celery behavior)
+USER_ROOM_EVENTS = {"STATUS", "FINAL_TITLE", "DURATION"}
+
+
+async def broadcast_event(
+    transcript_id: NonEmptyString,
+    event: TranscriptEvent,
+    logger: structlog.BoundLogger,
+) -> None:
+    """Broadcast a TranscriptEvent to WebSocket subscribers.
+
+    Fire-and-forget: errors are logged but don't interrupt workflow execution.
+    """
+    logger.info(
+        "Broadcasting event",
+        transcript_id=transcript_id,
+        event_type=event.event,
+    )
+    try:
+        ws_manager = get_ws_manager()
+
+        await ws_manager.send_json(
+            room_id=f"ts:{transcript_id}",
+            message=event.model_dump(mode="json"),
+        )
+        logger.info(
+            "Event sent to transcript room",
+            transcript_id=transcript_id,
+            event_type=event.event,
+        )
+
+        if event.event in USER_ROOM_EVENTS:
+            transcript = await transcripts_controller.get_by_id(transcript_id)
+            if transcript and transcript.user_id:
+                await ws_manager.send_json(
+                    room_id=f"user:{transcript.user_id}",
+                    message={
+                        "event": f"TRANSCRIPT_{event.event}",
+                        "data": {"id": transcript_id, **event.data},
+                    },
+                )
+    except Exception as e:
+        logger.warning(
+            "Failed to broadcast event",
+            error=str(e),
+            transcript_id=transcript_id,
+            event_type=event.event,
+        )
+
+
+async def set_status_and_broadcast(
+    transcript_id: NonEmptyString,
+    status: str,
+    logger: structlog.BoundLogger,
+) -> None:
+    """Set transcript status and broadcast to WebSocket.
+
+    Wrapper around transcripts_controller.set_status that adds WebSocket broadcasting.
+    """
+    event = await transcripts_controller.set_status(transcript_id, status)
+    if event:
+        await broadcast_event(transcript_id, event, logger=logger)
+
+
+async def append_event_and_broadcast(
+    transcript_id: NonEmptyString,
+    transcript: Transcript,
+    event_name: str,
+    data: Any,
+    logger: structlog.BoundLogger,
+) -> TranscriptEvent:
+    """Append event to transcript and broadcast to WebSocket.
+
+    Wrapper around transcripts_controller.append_event that adds WebSocket broadcasting.
+    """
+    event = await transcripts_controller.append_event(
+        transcript=transcript,
+        event=event_name,
+        data=data,
+    )
+    await broadcast_event(transcript_id, event, logger=logger)
+    return event

server/reflector/hatchet/client.py

@@ -0,0 +1,111 @@
+"""Hatchet Python client wrapper.
+
+Uses singleton pattern because:
+1. Hatchet client maintains persistent gRPC connections for workflow registration
+2. Creating multiple clients would cause registration conflicts and resource leaks
+3. The SDK is designed for a single client instance per process
+4. Tests use `HatchetClientManager.reset()` to isolate state between tests
+"""
+
+import logging
+import threading
+
+from hatchet_sdk import ClientConfig, Hatchet
+from hatchet_sdk.clients.rest.models import V1TaskStatus
+
+from reflector.logger import logger
+from reflector.settings import settings
+
+
+class HatchetClientManager:
+    """Singleton manager for Hatchet client connections.
+
+    See module docstring for rationale. For test isolation, use `reset()`.
+    """
+
+    _instance: Hatchet | None = None
+    _lock = threading.Lock()
+
+    @classmethod
+    def get_client(cls) -> Hatchet:
+        """Get or create the Hatchet client (thread-safe singleton)."""
+        if cls._instance is None:
+            with cls._lock:
+                if cls._instance is None:
+                    if not settings.HATCHET_CLIENT_TOKEN:
+                        raise ValueError("HATCHET_CLIENT_TOKEN must be set")
+
+                    # Pass root logger to Hatchet so workflow logs appear in dashboard
+                    root_logger = logging.getLogger()
+                    cls._instance = Hatchet(
+                        debug=settings.HATCHET_DEBUG,
+                        config=ClientConfig(logger=root_logger),
+                    )
+        return cls._instance
+
+    @classmethod
+    async def start_workflow(
+        cls,
+        workflow_name: str,
+        input_data: dict,
+        additional_metadata: dict | None = None,
+    ) -> str:
+        """Start a workflow and return the workflow run ID.
+
+        Args:
+            workflow_name: Name of the workflow to trigger.
+            input_data: Input data for the workflow run.
+            additional_metadata: Optional metadata for filtering in dashboard
+                (e.g., transcript_id, recording_id).
+        """
+        client = cls.get_client()
+        result = await client.runs.aio_create(
+            workflow_name,
+            input_data,
+            additional_metadata=additional_metadata,
+        )
+        return result.run.metadata.id
+
+    @classmethod
+    async def get_workflow_run_status(cls, workflow_run_id: str) -> V1TaskStatus:
+        client = cls.get_client()
+        return await client.runs.aio_get_status(workflow_run_id)
+
+    @classmethod
+    async def cancel_workflow(cls, workflow_run_id: str) -> None:
+        client = cls.get_client()
+        await client.runs.aio_cancel(workflow_run_id)
+        logger.info("[Hatchet] Cancelled workflow", workflow_run_id=workflow_run_id)
+
+    @classmethod
+    async def replay_workflow(cls, workflow_run_id: str) -> None:
+        client = cls.get_client()
+        await client.runs.aio_replay(workflow_run_id)
+        logger.info("[Hatchet] Replaying workflow", workflow_run_id=workflow_run_id)
+
+    @classmethod
+    async def can_replay(cls, workflow_run_id: str) -> bool:
+        """Check if workflow can be replayed (is FAILED)."""
+        try:
+            status = await cls.get_workflow_run_status(workflow_run_id)
+            return status == V1TaskStatus.FAILED or status == V1TaskStatus.CANCELLED
+        except Exception as e:
+            logger.warning(
+                "[Hatchet] Failed to check replay status",
+                workflow_run_id=workflow_run_id,
+                error=str(e),
+            )
+            return False
+
+    @classmethod
+    async def get_workflow_status(cls, workflow_run_id: str) -> dict:
+        """Get the full workflow run details as dict."""
+        client = cls.get_client()
+        run = await client.runs.aio_get(workflow_run_id)
+        return run.to_dict()
+
+    @classmethod
+    def reset(cls) -> None:
+        """Reset the client instance (for testing)."""
+        with cls._lock:
+            cls._instance = None

server/reflector/hatchet/run_workers.py

@@ -0,0 +1,63 @@
+"""
+Run Hatchet workers for the diarization pipeline.
+Runs as a separate process, just like Celery workers.
+
+Usage:
+    uv run -m reflector.hatchet.run_workers
+
+    # Or via docker:
+    docker compose exec server uv run -m reflector.hatchet.run_workers
+"""
+
+import signal
+import sys
+
+from reflector.logger import logger
+from reflector.settings import settings
+
+
+def main() -> None:
+    """Start Hatchet worker polling."""
+    if not settings.HATCHET_ENABLED:
+        logger.error("HATCHET_ENABLED is False, not starting workers")
+        sys.exit(1)
+
+    if not settings.HATCHET_CLIENT_TOKEN:
+        logger.error("HATCHET_CLIENT_TOKEN is not set")
+        sys.exit(1)
+
+    logger.info(
+        "Starting Hatchet workers",
+        debug=settings.HATCHET_DEBUG,
+    )
+
+    # Import here (not top-level) - workflow modules call HatchetClientManager.get_client()
+    # at module level because Hatchet SDK decorators (@workflow.task) bind at import time.
+    # Can't use lazy init: decorators need the client object when function is defined.
+    from reflector.hatchet.client import HatchetClientManager  # noqa: PLC0415
+    from reflector.hatchet.workflows import (  # noqa: PLC0415
+        diarization_pipeline,
+        track_workflow,
+    )
+
+    hatchet = HatchetClientManager.get_client()
+
+    worker = hatchet.worker(
+        "reflector-diarization-worker",
+        workflows=[diarization_pipeline, track_workflow],
+    )
+
+    def shutdown_handler(signum: int, frame) -> None:
+        logger.info("Received shutdown signal, stopping workers...")
+        # Worker cleanup happens automatically on exit
+        sys.exit(0)
+
+    signal.signal(signal.SIGINT, shutdown_handler)
+    signal.signal(signal.SIGTERM, shutdown_handler)
+
+    logger.info("Starting Hatchet worker polling...")
+    worker.start()
+
+
+if __name__ == "__main__":
+    main()

server/reflector/hatchet/workflows/__init__.py

@@ -0,0 +1,14 @@
+"""Hatchet workflow definitions."""
+
+from reflector.hatchet.workflows.diarization_pipeline import (
+    PipelineInput,
+    diarization_pipeline,
+)
+from reflector.hatchet.workflows.track_processing import TrackInput, track_workflow
+
+__all__ = [
+    "diarization_pipeline",
+    "track_workflow",
+    "PipelineInput",
+    "TrackInput",
+]

server/reflector/hatchet/workflows/diarization_pipeline.py

@@ -0,0 +1,961 @@
+"""
+Hatchet main workflow: DiarizationPipeline
+
+Multitrack diarization pipeline for Daily.co recordings.
+Orchestrates the full processing flow from recording metadata to final transcript.
+
+Note: This file uses deferred imports (inside functions/tasks) intentionally.
+Hatchet workers run in forked processes; fresh imports per task ensure DB connections
+are not shared across forks, avoiding connection pooling issues.
+"""
+
+import asyncio
+import functools
+import tempfile
+from contextlib import asynccontextmanager
+from datetime import timedelta
+from pathlib import Path
+from typing import Callable
+
+import httpx
+from hatchet_sdk import Context
+from pydantic import BaseModel
+
+from reflector.dailyco_api.client import DailyApiClient
+from reflector.hatchet.broadcast import (
+    append_event_and_broadcast,
+    set_status_and_broadcast,
+)
+from reflector.hatchet.client import HatchetClientManager
+from reflector.hatchet.workflows.models import (
+    ConsentResult,
+    FinalizeResult,
+    MixdownResult,
+    PaddedTrackInfo,
+    ParticipantsResult,
+    ProcessTracksResult,
+    RecordingResult,
+    SummaryResult,
+    TitleResult,
+    TopicsResult,
+    WaveformResult,
+    WebhookResult,
+    ZulipResult,
+)
+from reflector.hatchet.workflows.track_processing import TrackInput, track_workflow
+from reflector.logger import logger
+from reflector.pipelines import topic_processing
+from reflector.processors import AudioFileWriterProcessor
+from reflector.processors.types import (
+    TitleSummary,
+    TitleSummaryWithId,
+    Word,
+)
+from reflector.processors.types import (
+    Transcript as TranscriptType,
+)
+from reflector.settings import settings
+from reflector.storage.storage_aws import AwsStorage
+from reflector.utils.audio_constants import (
+    PRESIGNED_URL_EXPIRATION_SECONDS,
+    WAVEFORM_SEGMENTS,
+)
+from reflector.utils.audio_mixdown import (
+    detect_sample_rate_from_tracks,
+    mixdown_tracks_pyav,
+)
+from reflector.utils.audio_waveform import get_audio_waveform
+from reflector.utils.daily import (
+    filter_cam_audio_tracks,
+    parse_daily_recording_filename,
+)
+from reflector.utils.string import NonEmptyString, assert_non_none_and_non_empty
+from reflector.zulip import post_transcript_notification
+
+
+class PipelineInput(BaseModel):
+    """Input to trigger the diarization pipeline."""
+
+    recording_id: NonEmptyString
+    tracks: list[dict]  # List of {"s3_key": str}
+    bucket_name: NonEmptyString
+    transcript_id: NonEmptyString
+    room_id: NonEmptyString | None = None
+
+
+hatchet = HatchetClientManager.get_client()
+
+diarization_pipeline = hatchet.workflow(
+    name="DiarizationPipeline", input_validator=PipelineInput
+)
+
+
+@asynccontextmanager
+async def fresh_db_connection():
+    """Context manager for database connections in Hatchet workers.
+    TECH DEBT: Made to make connection fork-aware without changing db code too much.
+    The real fix would be making the db module fork-aware instead of bypassing it.
+    Current pattern is acceptable given Hatchet's process model.
+    """
+    import databases  # noqa: PLC0415
+
+    from reflector.db import _database_context  # noqa: PLC0415
+
+    _database_context.set(None)
+    db = databases.Database(settings.DATABASE_URL)
+    _database_context.set(db)
+    await db.connect()
+    try:
+        yield db
+    finally:
+        await db.disconnect()
+        _database_context.set(None)
+
+
+async def set_workflow_error_status(transcript_id: NonEmptyString) -> bool:
+    """Set transcript status to 'error' on workflow failure.
+
+    Returns:
+        True if status was set successfully, False if failed.
+        Failure is logged as CRITICAL since it means transcript may be stuck.
+    """
+    try:
+        async with fresh_db_connection():
+            await set_status_and_broadcast(transcript_id, "error", logger=logger)
+            return True
+    except Exception as e:
+        logger.critical(
+            "[Hatchet] CRITICAL: Failed to set error status - transcript may be stuck in 'processing'",
+            transcript_id=transcript_id,
+            error=str(e),
+            exc_info=True,
+        )
+        return False
+
+
+def _spawn_storage():
+    """Create fresh storage instance."""
+    return AwsStorage(
+        aws_bucket_name=settings.TRANSCRIPT_STORAGE_AWS_BUCKET_NAME,
+        aws_region=settings.TRANSCRIPT_STORAGE_AWS_REGION,
+        aws_access_key_id=settings.TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID,
+        aws_secret_access_key=settings.TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY,
+    )
+
+
+def with_error_handling(step_name: str, set_error_status: bool = True) -> Callable:
+    """Decorator that handles task failures uniformly.
+
+    Args:
+        step_name: Name of the step for logging and progress tracking.
+        set_error_status: Whether to set transcript status to 'error' on failure.
+    """
+
+    def decorator(func: Callable) -> Callable:
+        @functools.wraps(func)
+        async def wrapper(input: PipelineInput, ctx: Context):
+            try:
+                return await func(input, ctx)
+            except Exception as e:
+                logger.error(
+                    f"[Hatchet] {step_name} failed",
+                    transcript_id=input.transcript_id,
+                    error=str(e),
+                    exc_info=True,
+                )
+                if set_error_status:
+                    await set_workflow_error_status(input.transcript_id)
+                raise
+
+        return wrapper
+
+    return decorator
+
+
+@diarization_pipeline.task(execution_timeout=timedelta(seconds=60), retries=3)
+@with_error_handling("get_recording")
+async def get_recording(input: PipelineInput, ctx: Context) -> RecordingResult:
+    """Fetch recording metadata from Daily.co API."""
+    ctx.log(f"get_recording: recording_id={input.recording_id}")
+
+    # Set transcript status to "processing" at workflow start (broadcasts to WebSocket)
+    async with fresh_db_connection():
+        from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if transcript:
+            await set_status_and_broadcast(
+                input.transcript_id, "processing", logger=logger
+            )
+            ctx.log(f"Set transcript status to processing: {input.transcript_id}")
+
+    if not settings.DAILY_API_KEY:
+        raise ValueError("DAILY_API_KEY not configured")
+
+    async with DailyApiClient(api_key=settings.DAILY_API_KEY) as client:
+        recording = await client.get_recording(input.recording_id)
+
+    ctx.log(
+        f"get_recording complete: room={recording.room_name}, duration={recording.duration}s"
+    )
+
+    return RecordingResult(
+        id=recording.id,
+        mtg_session_id=recording.mtgSessionId,
+        duration=recording.duration,
+    )
+
+
+@diarization_pipeline.task(
+    parents=[get_recording], execution_timeout=timedelta(seconds=60), retries=3
+)
+@with_error_handling("get_participants")
+async def get_participants(input: PipelineInput, ctx: Context) -> ParticipantsResult:
+    """Fetch participant list from Daily.co API and update transcript in database."""
+    ctx.log(f"get_participants: transcript_id={input.transcript_id}")
+
+    recording = ctx.task_output(get_recording)
+    mtg_session_id = recording.mtg_session_id
+
+    async with fresh_db_connection():
+        from reflector.db.transcripts import (  # noqa: PLC0415
+            TranscriptParticipant,
+            transcripts_controller,
+        )
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if transcript:
+            # Note: title NOT cleared - preserves existing titles
+            await transcripts_controller.update(
+                transcript,
+                {
+                    "events": [],
+                    "topics": [],
+                    "participants": [],
+                },
+            )
+
+        mtg_session_id = assert_non_none_and_non_empty(
+            mtg_session_id, "mtg_session_id is required"
+        )
+        daily_api_key = assert_non_none_and_non_empty(
+            settings.DAILY_API_KEY, "DAILY_API_KEY is required"
+        )
+
+        async with DailyApiClient(api_key=daily_api_key) as client:
+            participants = await client.get_meeting_participants(mtg_session_id)
+
+        id_to_name = {}
+        id_to_user_id = {}
+        for p in participants.data:
+            if p.user_name:
+                id_to_name[p.participant_id] = p.user_name
+            if p.user_id:
+                id_to_user_id[p.participant_id] = p.user_id
+
+        track_keys = [t["s3_key"] for t in input.tracks]
+        cam_audio_keys = filter_cam_audio_tracks(track_keys)
+
+        participants_list = []
+        for idx, key in enumerate(cam_audio_keys):
+            try:
+                parsed = parse_daily_recording_filename(key)
+                participant_id = parsed.participant_id
+            except ValueError as e:
+                logger.error(
+                    "Failed to parse Daily recording filename",
+                    error=str(e),
+                    key=key,
+                )
+                continue
+
+            default_name = f"Speaker {idx}"
+            name = id_to_name.get(participant_id, default_name)
+            user_id = id_to_user_id.get(participant_id)
+
+            participant = TranscriptParticipant(
+                id=participant_id, speaker=idx, name=name, user_id=user_id
+            )
+            await transcripts_controller.upsert_participant(transcript, participant)
+            participants_list.append(
+                {
+                    "participant_id": participant_id,
+                    "user_name": name,
+                    "speaker": idx,
+                }
+            )
+
+        ctx.log(f"get_participants complete: {len(participants_list)} participants")
+
+    return ParticipantsResult(
+        participants=participants_list,
+        num_tracks=len(input.tracks),
+        source_language=transcript.source_language if transcript else "en",
+        target_language=transcript.target_language if transcript else "en",
+    )
+
+
+@diarization_pipeline.task(
+    parents=[get_participants], execution_timeout=timedelta(seconds=600), retries=3
+)
+@with_error_handling("process_tracks")
+async def process_tracks(input: PipelineInput, ctx: Context) -> ProcessTracksResult:
+    """Spawn child workflows for each track (dynamic fan-out)."""
+    ctx.log(f"process_tracks: spawning {len(input.tracks)} track workflows")
+
+    participants_result = ctx.task_output(get_participants)
+    source_language = participants_result.source_language
+
+    child_coroutines = [
+        track_workflow.aio_run(
+            TrackInput(
+                track_index=i,
+                s3_key=track["s3_key"],
+                bucket_name=input.bucket_name,
+                transcript_id=input.transcript_id,
+                language=source_language,
+            )
+        )
+        for i, track in enumerate(input.tracks)
+    ]
+
+    results = await asyncio.gather(*child_coroutines)
+
+    target_language = participants_result.target_language
+
+    track_words = []
+    padded_tracks = []
+    created_padded_files = set()
+
+    for result in results:
+        transcribe_result = result.get("transcribe_track", {})
+        track_words.append(transcribe_result.get("words", []))
+
+        pad_result = result.get("pad_track", {})
+        padded_key = pad_result.get("padded_key")
+        bucket_name = pad_result.get("bucket_name")
+
+        # Store S3 key info (not presigned URL) - consumer tasks presign on demand
+        if padded_key:
+            padded_tracks.append(
+                PaddedTrackInfo(key=padded_key, bucket_name=bucket_name)
+            )
+
+        track_index = pad_result.get("track_index")
+        if pad_result.get("size", 0) > 0 and track_index is not None:
+            storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{track_index}.webm"
+            created_padded_files.add(storage_path)
+
+    all_words = [word for words in track_words for word in words]
+    all_words.sort(key=lambda w: w.get("start", 0))
+
+    ctx.log(
+        f"process_tracks complete: {len(all_words)} words from {len(input.tracks)} tracks"
+    )
+
+    return ProcessTracksResult(
+        all_words=all_words,
+        padded_tracks=padded_tracks,
+        word_count=len(all_words),
+        num_tracks=len(input.tracks),
+        target_language=target_language,
+        created_padded_files=list(created_padded_files),
+    )
+
+
+@diarization_pipeline.task(
+    parents=[process_tracks], execution_timeout=timedelta(seconds=300), retries=3
+)
+@with_error_handling("mixdown_tracks")
+async def mixdown_tracks(input: PipelineInput, ctx: Context) -> MixdownResult:
+    """Mix all padded tracks into single audio file using PyAV (same as Celery)."""
+    ctx.log("mixdown_tracks: mixing padded tracks into single audio file")
+
+    track_result = ctx.task_output(process_tracks)
+    padded_tracks = track_result.padded_tracks
+
+    # TODO think of NonEmpty type to avoid those checks, e.g. sized.NonEmpty from https://github.com/antonagestam/phantom-types/
+    if not padded_tracks:
+        raise ValueError("No padded tracks to mixdown")
+
+    storage = _spawn_storage()
+
+    # Presign URLs on demand (avoids stale URLs on workflow replay)
+    padded_urls = []
+    for track_info in padded_tracks:
+        if track_info.key:
+            url = await storage.get_file_url(
+                track_info.key,
+                operation="get_object",
+                expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
+                bucket=track_info.bucket_name,
+            )
+            padded_urls.append(url)
+
+    valid_urls = [url for url in padded_urls if url]
+    if not valid_urls:
+        raise ValueError("No valid padded tracks to mixdown")
+
+    target_sample_rate = detect_sample_rate_from_tracks(valid_urls, logger=logger)
+    if not target_sample_rate:
+        logger.error("Mixdown failed - no decodable audio frames found")
+        raise ValueError("No decodable audio frames in any track")
+
+    output_path = tempfile.mktemp(suffix=".mp3")
+    duration_ms_callback_capture_container = [0.0]
+
+    async def capture_duration(d):
+        duration_ms_callback_capture_container[0] = d
+
+    writer = AudioFileWriterProcessor(path=output_path, on_duration=capture_duration)
+
+    await mixdown_tracks_pyav(
+        valid_urls,
+        writer,
+        target_sample_rate,
+        offsets_seconds=None,
+        logger=logger,
+    )
+    await writer.flush()
+
+    file_size = Path(output_path).stat().st_size
+    storage_path = f"{input.transcript_id}/audio.mp3"
+
+    with open(output_path, "rb") as mixed_file:
+        await storage.put_file(storage_path, mixed_file)
+
+    Path(output_path).unlink(missing_ok=True)
+
+    async with fresh_db_connection():
+        from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if transcript:
+            await transcripts_controller.update(
+                transcript, {"audio_location": "storage"}
+            )
+
+    ctx.log(f"mixdown_tracks complete: uploaded {file_size} bytes to {storage_path}")
+
+    return MixdownResult(
+        audio_key=storage_path,
+        duration=duration_ms_callback_capture_container[0],
+        tracks_mixed=len(valid_urls),
+    )
+
+
+@diarization_pipeline.task(
+    parents=[mixdown_tracks], execution_timeout=timedelta(seconds=120), retries=3
+)
+@with_error_handling("generate_waveform")
+async def generate_waveform(input: PipelineInput, ctx: Context) -> WaveformResult:
+    """Generate audio waveform visualization using AudioWaveformProcessor (matches Celery)."""
+    ctx.log(f"generate_waveform: transcript_id={input.transcript_id}")
+
+    from reflector.db.transcripts import (  # noqa: PLC0415
+        TranscriptWaveform,
+        transcripts_controller,
+    )
+
+    mixdown_result = ctx.task_output(mixdown_tracks)
+    audio_key = mixdown_result.audio_key
+
+    storage = _spawn_storage()
+    audio_url = await storage.get_file_url(
+        audio_key,
+        operation="get_object",
+        expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
+    )
+
+    # Download MP3 to temp file (AudioWaveformProcessor needs local file)
+    with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as temp_file:
+        temp_path = temp_file.name
+
+    try:
+        async with httpx.AsyncClient() as client:
+            response = await client.get(audio_url, timeout=120)
+            response.raise_for_status()
+            with open(temp_path, "wb") as f:
+                f.write(response.content)
+
+        waveform = get_audio_waveform(
+            path=Path(temp_path), segments_count=WAVEFORM_SEGMENTS
+        )
+
+        async with fresh_db_connection():
+            transcript = await transcripts_controller.get_by_id(input.transcript_id)
+            if transcript:
+                waveform_data = TranscriptWaveform(waveform=waveform)
+                await append_event_and_broadcast(
+                    input.transcript_id,
+                    transcript,
+                    "WAVEFORM",
+                    waveform_data,
+                    logger=logger,
+                )
+
+    finally:
+        Path(temp_path).unlink(missing_ok=True)
+
+    ctx.log("generate_waveform complete")
+
+    return WaveformResult(waveform_generated=True)
+
+
+@diarization_pipeline.task(
+    parents=[mixdown_tracks], execution_timeout=timedelta(seconds=300), retries=3
+)
+@with_error_handling("detect_topics")
+async def detect_topics(input: PipelineInput, ctx: Context) -> TopicsResult:
+    """Detect topics using LLM and save to database (matches Celery on_topic callback)."""
+    ctx.log("detect_topics: analyzing transcript for topics")
+
+    track_result = ctx.task_output(process_tracks)
+    words = track_result.all_words
+    target_language = track_result.target_language
+
+    from reflector.db.transcripts import (  # noqa: PLC0415
+        TranscriptTopic,
+        transcripts_controller,
+    )
+
+    word_objects = [Word(**w) for w in words]
+    transcript_type = TranscriptType(words=word_objects)
+
+    empty_pipeline = topic_processing.EmptyPipeline(logger=logger)
+
+    async with fresh_db_connection():
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+
+        async def on_topic_callback(data):
+            topic = TranscriptTopic(
+                title=data.title,
+                summary=data.summary,
+                timestamp=data.timestamp,
+                transcript=data.transcript.text,
+                words=data.transcript.words,
+            )
+            if isinstance(
+                data, TitleSummaryWithId
+            ):  # Celery parity: main_live_pipeline.py
+                topic.id = data.id
+            await transcripts_controller.upsert_topic(transcript, topic)
+            await append_event_and_broadcast(
+                input.transcript_id, transcript, "TOPIC", topic, logger=logger
+            )
+
+        topics = await topic_processing.detect_topics(
+            transcript_type,
+            target_language,
+            on_topic_callback=on_topic_callback,
+            empty_pipeline=empty_pipeline,
+        )
+
+    topics_list = [t.model_dump() for t in topics]
+
+    ctx.log(f"detect_topics complete: found {len(topics_list)} topics")
+
+    return TopicsResult(topics=topics_list)
+
+
+@diarization_pipeline.task(
+    parents=[detect_topics], execution_timeout=timedelta(seconds=120), retries=3
+)
+@with_error_handling("generate_title")
+async def generate_title(input: PipelineInput, ctx: Context) -> TitleResult:
+    """Generate meeting title using LLM and save to database (matches Celery on_title callback)."""
+    ctx.log("generate_title: generating title from topics")
+
+    topics_result = ctx.task_output(detect_topics)
+    topics = topics_result.topics
+
+    from reflector.db.transcripts import (  # noqa: PLC0415
+        TranscriptFinalTitle,
+        transcripts_controller,
+    )
+
+    topic_objects = [TitleSummary(**t) for t in topics]
+
+    empty_pipeline = topic_processing.EmptyPipeline(logger=logger)
+    title_result = None
+
+    async with fresh_db_connection():
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+
+        async def on_title_callback(data):
+            nonlocal title_result
+            title_result = data.title
+            final_title = TranscriptFinalTitle(title=data.title)
+            if not transcript.title:
+                await transcripts_controller.update(
+                    transcript,
+                    {"title": final_title.title},
+                )
+            await append_event_and_broadcast(
+                input.transcript_id,
+                transcript,
+                "FINAL_TITLE",
+                final_title,
+                logger=logger,
+            )
+
+        await topic_processing.generate_title(
+            topic_objects,
+            on_title_callback=on_title_callback,
+            empty_pipeline=empty_pipeline,
+            logger=logger,
+        )
+
+    ctx.log(f"generate_title complete: '{title_result}'")
+
+    return TitleResult(title=title_result)
+
+
+@diarization_pipeline.task(
+    parents=[detect_topics], execution_timeout=timedelta(seconds=300), retries=3
+)
+@with_error_handling("generate_summary")
+async def generate_summary(input: PipelineInput, ctx: Context) -> SummaryResult:
+    """Generate meeting summary using LLM and save to database (matches Celery callbacks)."""
+    ctx.log("generate_summary: generating long and short summaries")
+
+    topics_result = ctx.task_output(detect_topics)
+    topics = topics_result.topics
+
+    from reflector.db.transcripts import (  # noqa: PLC0415
+        TranscriptActionItems,
+        TranscriptFinalLongSummary,
+        TranscriptFinalShortSummary,
+        transcripts_controller,
+    )
+
+    topic_objects = [TitleSummary(**t) for t in topics]
+
+    empty_pipeline = topic_processing.EmptyPipeline(logger=logger)
+    summary_result = None
+    short_summary_result = None
+    action_items_result = None
+
+    async with fresh_db_connection():
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+
+        async def on_long_summary_callback(data):
+            nonlocal summary_result
+            summary_result = data.long_summary
+            final_long_summary = TranscriptFinalLongSummary(
+                long_summary=data.long_summary
+            )
+            await transcripts_controller.update(
+                transcript,
+                {"long_summary": final_long_summary.long_summary},
+            )
+            await append_event_and_broadcast(
+                input.transcript_id,
+                transcript,
+                "FINAL_LONG_SUMMARY",
+                final_long_summary,
+                logger=logger,
+            )
+
+        async def on_short_summary_callback(data):
+            nonlocal short_summary_result
+            short_summary_result = data.short_summary
+            final_short_summary = TranscriptFinalShortSummary(
+                short_summary=data.short_summary
+            )
+            await transcripts_controller.update(
+                transcript,
+                {"short_summary": final_short_summary.short_summary},
+            )
+            await append_event_and_broadcast(
+                input.transcript_id,
+                transcript,
+                "FINAL_SHORT_SUMMARY",
+                final_short_summary,
+                logger=logger,
+            )
+
+        async def on_action_items_callback(data):
+            nonlocal action_items_result
+            action_items_result = data.action_items
+            action_items = TranscriptActionItems(action_items=data.action_items)
+            await transcripts_controller.update(
+                transcript,
+                {"action_items": action_items.action_items},
+            )
+            await append_event_and_broadcast(
+                input.transcript_id,
+                transcript,
+                "ACTION_ITEMS",
+                action_items,
+                logger=logger,
+            )
+
+        await topic_processing.generate_summaries(
+            topic_objects,
+            transcript,
+            on_long_summary_callback=on_long_summary_callback,
+            on_short_summary_callback=on_short_summary_callback,
+            on_action_items_callback=on_action_items_callback,
+            empty_pipeline=empty_pipeline,
+            logger=logger,
+        )
+
+    ctx.log("generate_summary complete")
+
+    return SummaryResult(
+        summary=summary_result,
+        short_summary=short_summary_result,
+        action_items=action_items_result,
+    )
+
+
+@diarization_pipeline.task(
+    parents=[generate_waveform, generate_title, generate_summary],
+    execution_timeout=timedelta(seconds=60),
+    retries=3,
+)
+@with_error_handling("finalize")
+async def finalize(input: PipelineInput, ctx: Context) -> FinalizeResult:
+    """Finalize transcript: save words, emit TRANSCRIPT event, set status to 'ended'.
+
+    Matches Celery's on_transcript + set_status behavior.
+    Note: Title and summaries are already saved by their respective task callbacks.
+    """
+    ctx.log("finalize: saving transcript and setting status to 'ended'")
+
+    mixdown_result = ctx.task_output(mixdown_tracks)
+    track_result = ctx.task_output(process_tracks)
+
+    duration = mixdown_result.duration
+    all_words = track_result.all_words
+
+    # Cleanup temporary padded S3 files (deferred until finalize for semantic parity with Celery)
+    created_padded_files = track_result.created_padded_files
+    if created_padded_files:
+        ctx.log(f"Cleaning up {len(created_padded_files)} temporary S3 files")
+        storage = _spawn_storage()
+        cleanup_results = await asyncio.gather(
+            *[storage.delete_file(path) for path in created_padded_files],
+            return_exceptions=True,
+        )
+        for storage_path, result in zip(created_padded_files, cleanup_results):
+            if isinstance(result, Exception):
+                logger.warning(
+                    "[Hatchet] Failed to cleanup temporary padded track",
+                    storage_path=storage_path,
+                    error=str(result),
+                )
+
+    async with fresh_db_connection():
+        from reflector.db.transcripts import (  # noqa: PLC0415
+            TranscriptDuration,
+            TranscriptText,
+            transcripts_controller,
+        )
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if transcript is None:
+            raise ValueError(f"Transcript {input.transcript_id} not found in database")
+
+        word_objects = [Word(**w) for w in all_words]
+        merged_transcript = TranscriptType(words=word_objects, translation=None)
+
+        await append_event_and_broadcast(
+            input.transcript_id,
+            transcript,
+            "TRANSCRIPT",
+            TranscriptText(
+                text=merged_transcript.text,
+                translation=merged_transcript.translation,
+            ),
+            logger=logger,
+        )
+
+        # Save duration and clear workflow_run_id (workflow completed successfully)
+        # Note: title/long_summary/short_summary already saved by their callbacks
+        await transcripts_controller.update(
+            transcript,
+            {
+                "duration": duration,
+                "workflow_run_id": None,  # Clear on success - no need to resume
+            },
+        )
+
+        duration_data = TranscriptDuration(duration=duration)
+        await append_event_and_broadcast(
+            input.transcript_id, transcript, "DURATION", duration_data, logger=logger
+        )
+
+        await set_status_and_broadcast(input.transcript_id, "ended", logger=logger)
+
+        ctx.log(
+            f"finalize complete: transcript {input.transcript_id} status set to 'ended'"
+        )
+
+    return FinalizeResult(status="COMPLETED")
+
+
+@diarization_pipeline.task(
+    parents=[finalize], execution_timeout=timedelta(seconds=60), retries=3
+)
+@with_error_handling("cleanup_consent", set_error_status=False)
+async def cleanup_consent(input: PipelineInput, ctx: Context) -> ConsentResult:
+    """Check consent and delete audio files if any participant denied."""
+    ctx.log(f"cleanup_consent: transcript_id={input.transcript_id}")
+
+    async with fresh_db_connection():
+        from reflector.db.meetings import (  # noqa: PLC0415
+            meeting_consent_controller,
+            meetings_controller,
+        )
+        from reflector.db.recordings import recordings_controller  # noqa: PLC0415
+        from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+        from reflector.storage import get_transcripts_storage  # noqa: PLC0415
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if not transcript:
+            ctx.log("cleanup_consent: transcript not found")
+            return ConsentResult()
+
+        consent_denied = False
+        if transcript.meeting_id:
+            meeting = await meetings_controller.get_by_id(transcript.meeting_id)
+            if meeting:
+                consent_denied = await meeting_consent_controller.has_any_denial(
+                    meeting.id
+                )
+
+        if not consent_denied:
+            ctx.log("cleanup_consent: consent approved, keeping all files")
+            return ConsentResult()
+
+        ctx.log("cleanup_consent: consent denied, deleting audio files")
+
+        input_track_keys = set(t["s3_key"] for t in input.tracks)
+
+        # Detect if recording.track_keys was manually modified after workflow started
+        if transcript.recording_id:
+            recording = await recordings_controller.get_by_id(transcript.recording_id)
+            if recording and recording.track_keys:
+                db_track_keys = set(filter_cam_audio_tracks(recording.track_keys))
+
+                if input_track_keys != db_track_keys:
+                    added = db_track_keys - input_track_keys
+                    removed = input_track_keys - db_track_keys
+                    logger.warning(
+                        "[Hatchet] Track keys mismatch: DB changed since workflow start",
+                        transcript_id=input.transcript_id,
+                        recording_id=transcript.recording_id,
+                        input_count=len(input_track_keys),
+                        db_count=len(db_track_keys),
+                        added_in_db=list(added) if added else None,
+                        removed_from_db=list(removed) if removed else None,
+                    )
+                    ctx.log(
+                        f"WARNING: track_keys mismatch - "
+                        f"input has {len(input_track_keys)}, DB has {len(db_track_keys)}. "
+                        f"Using input tracks for deletion."
+                    )
+
+        deletion_errors = []
+
+        if input_track_keys and input.bucket_name:
+            master_storage = get_transcripts_storage()
+            for key in input_track_keys:
+                try:
+                    await master_storage.delete_file(key, bucket=input.bucket_name)
+                    ctx.log(f"Deleted recording file: {input.bucket_name}/{key}")
+                except Exception as e:
+                    error_msg = f"Failed to delete {key}: {e}"
+                    logger.error(error_msg, exc_info=True)
+                    deletion_errors.append(error_msg)
+
+        if transcript.audio_location == "storage":
+            storage = get_transcripts_storage()
+            try:
+                await storage.delete_file(transcript.storage_audio_path)
+                ctx.log(f"Deleted processed audio: {transcript.storage_audio_path}")
+            except Exception as e:
+                error_msg = f"Failed to delete processed audio: {e}"
+                logger.error(error_msg, exc_info=True)
+                deletion_errors.append(error_msg)
+
+        if deletion_errors:
+            logger.warning(
+                "[Hatchet] cleanup_consent completed with errors",
+                transcript_id=input.transcript_id,
+                error_count=len(deletion_errors),
+                errors=deletion_errors,
+            )
+            ctx.log(f"cleanup_consent completed with {len(deletion_errors)} errors")
+        else:
+            await transcripts_controller.update(transcript, {"audio_deleted": True})
+            ctx.log("cleanup_consent: all audio deleted successfully")
+
+    return ConsentResult()
+
+
+@diarization_pipeline.task(
+    parents=[cleanup_consent], execution_timeout=timedelta(seconds=60), retries=5
+)
+@with_error_handling("post_zulip", set_error_status=False)
+async def post_zulip(input: PipelineInput, ctx: Context) -> ZulipResult:
+    """Post notification to Zulip."""
+    ctx.log(f"post_zulip: transcript_id={input.transcript_id}")
+
+    if not settings.ZULIP_REALM:
+        ctx.log("post_zulip skipped (Zulip not configured)")
+        return ZulipResult(zulip_message_id=None, skipped=True)
+
+    async with fresh_db_connection():
+        from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+        if transcript:
+            message_id = await post_transcript_notification(transcript)
+            ctx.log(f"post_zulip complete: zulip_message_id={message_id}")
+        else:
+            message_id = None
+
+    return ZulipResult(zulip_message_id=message_id)
+
+
+@diarization_pipeline.task(
+    parents=[post_zulip], execution_timeout=timedelta(seconds=120), retries=30
+)
+@with_error_handling("send_webhook", set_error_status=False)
+async def send_webhook(input: PipelineInput, ctx: Context) -> WebhookResult:
+    """Send completion webhook to external service."""
+    ctx.log(f"send_webhook: transcript_id={input.transcript_id}")
+
+    if not input.room_id:
+        ctx.log("send_webhook skipped (no room_id)")
+        return WebhookResult(webhook_sent=False, skipped=True)
+
+    async with fresh_db_connection():
+        from reflector.db.rooms import rooms_controller  # noqa: PLC0415
+        from reflector.db.transcripts import transcripts_controller  # noqa: PLC0415
+
+        room = await rooms_controller.get_by_id(input.room_id)
+        transcript = await transcripts_controller.get_by_id(input.transcript_id)
+
+        if room and room.webhook_url and transcript:
+            webhook_payload = {
+                "event": "transcript.completed",
+                "transcript_id": input.transcript_id,
+                "title": transcript.title,
+                "duration": transcript.duration,
+            }
+
+            async with httpx.AsyncClient() as client:
+                response = await client.post(
+                    room.webhook_url, json=webhook_payload, timeout=30
+                )
+                response.raise_for_status()
+
+            ctx.log(f"send_webhook complete: status_code={response.status_code}")
+
+            return WebhookResult(webhook_sent=True, response_code=response.status_code)
+
+    return WebhookResult(webhook_sent=False, skipped=True)

server/reflector/hatchet/workflows/models.py

@@ -0,0 +1,124 @@
+"""
+Pydantic models for Hatchet workflow task return types.
+
+Provides static typing for all task outputs, enabling type checking
+and better IDE support.
+"""
+
+from typing import Any
+
+from pydantic import BaseModel
+
+from reflector.utils.string import NonEmptyString
+
+
+class PadTrackResult(BaseModel):
+    """Result from pad_track task."""
+
+    padded_key: NonEmptyString  # S3 key (not presigned URL) - presign on demand to avoid stale URLs on replay
+    bucket_name: (
+        NonEmptyString | None
+    )  # None means use default transcript storage bucket
+    size: int
+    track_index: int
+
+
+class TranscribeTrackResult(BaseModel):
+    """Result from transcribe_track task."""
+
+    words: list[dict[str, Any]]
+    track_index: int
+
+
+class RecordingResult(BaseModel):
+    """Result from get_recording task."""
+
+    id: NonEmptyString | None
+    mtg_session_id: NonEmptyString | None
+    duration: float
+
+
+class ParticipantsResult(BaseModel):
+    """Result from get_participants task."""
+
+    participants: list[dict[str, Any]]
+    num_tracks: int
+    source_language: NonEmptyString
+    target_language: NonEmptyString
+
+
+class PaddedTrackInfo(BaseModel):
+    """Info for a padded track - S3 key + bucket for on-demand presigning."""
+
+    key: NonEmptyString
+    bucket_name: NonEmptyString | None  # None = use default storage bucket
+
+
+class ProcessTracksResult(BaseModel):
+    """Result from process_tracks task."""
+
+    all_words: list[dict[str, Any]]
+    padded_tracks: list[PaddedTrackInfo]  # S3 keys, not presigned URLs
+    word_count: int
+    num_tracks: int
+    target_language: NonEmptyString
+    created_padded_files: list[NonEmptyString]
+
+
+class MixdownResult(BaseModel):
+    """Result from mixdown_tracks task."""
+
+    audio_key: NonEmptyString
+    duration: float
+    tracks_mixed: int
+
+
+class WaveformResult(BaseModel):
+    """Result from generate_waveform task."""
+
+    waveform_generated: bool
+
+
+class TopicsResult(BaseModel):
+    """Result from detect_topics task."""
+
+    topics: list[dict[str, Any]]
+
+
+class TitleResult(BaseModel):
+    """Result from generate_title task."""
+
+    title: str | None
+
+
+class SummaryResult(BaseModel):
+    """Result from generate_summary task."""
+
+    summary: str | None
+    short_summary: str | None
+    action_items: dict | None = None
+
+
+class FinalizeResult(BaseModel):
+    """Result from finalize task."""
+
+    status: NonEmptyString
+
+
+class ConsentResult(BaseModel):
+    """Result from cleanup_consent task."""
+
+
+class ZulipResult(BaseModel):
+    """Result from post_zulip task."""
+
+    zulip_message_id: int | None = None
+    skipped: bool = False
+
+
+class WebhookResult(BaseModel):
+    """Result from send_webhook task."""
+
+    webhook_sent: bool
+    skipped: bool = False
+    response_code: int | None = None

server/reflector/hatchet/workflows/track_processing.py

@@ -0,0 +1,222 @@
+"""
+Hatchet child workflow: TrackProcessing
+
+Handles individual audio track processing: padding and transcription.
+Spawned dynamically by the main diarization pipeline for each track.
+
+Architecture note: This is a separate workflow (not inline tasks in DiarizationPipeline)
+because Hatchet workflow DAGs are defined statically, but the number of tracks varies
+at runtime. Child workflow spawning via `aio_run()` + `asyncio.gather()` is the
+standard pattern for dynamic fan-out. See `process_tracks` in diarization_pipeline.py.
+
+Note: This file uses deferred imports (inside tasks) intentionally.
+Hatchet workers run in forked processes; fresh imports per task ensure
+storage/DB connections are not shared across forks.
+"""
+
+import tempfile
+from datetime import timedelta
+from pathlib import Path
+
+import av
+from hatchet_sdk import Context
+from pydantic import BaseModel
+
+from reflector.hatchet.client import HatchetClientManager
+from reflector.hatchet.workflows.models import PadTrackResult, TranscribeTrackResult
+from reflector.logger import logger
+from reflector.utils.audio_constants import PRESIGNED_URL_EXPIRATION_SECONDS
+from reflector.utils.audio_padding import (
+    apply_audio_padding_to_file,
+    extract_stream_start_time_from_container,
+)
+
+
+class TrackInput(BaseModel):
+    """Input for individual track processing."""
+
+    track_index: int
+    s3_key: str
+    bucket_name: str
+    transcript_id: str
+    language: str = "en"
+
+
+hatchet = HatchetClientManager.get_client()
+
+track_workflow = hatchet.workflow(name="TrackProcessing", input_validator=TrackInput)
+
+
+@track_workflow.task(execution_timeout=timedelta(seconds=300), retries=3)
+async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
+    """Pad single audio track with silence for alignment.
+
+    Extracts stream.start_time from WebM container metadata and applies
+    silence padding using PyAV filter graph (adelay).
+    """
+    ctx.log(f"pad_track: track {input.track_index}, s3_key={input.s3_key}")
+    logger.info(
+        "[Hatchet] pad_track",
+        track_index=input.track_index,
+        s3_key=input.s3_key,
+        transcript_id=input.transcript_id,
+    )
+
+    try:
+        # Create fresh storage instance to avoid aioboto3 fork issues
+        from reflector.settings import settings  # noqa: PLC0415
+        from reflector.storage.storage_aws import AwsStorage  # noqa: PLC0415
+
+        storage = AwsStorage(
+            aws_bucket_name=settings.TRANSCRIPT_STORAGE_AWS_BUCKET_NAME,
+            aws_region=settings.TRANSCRIPT_STORAGE_AWS_REGION,
+            aws_access_key_id=settings.TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID,
+            aws_secret_access_key=settings.TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY,
+        )
+
+        source_url = await storage.get_file_url(
+            input.s3_key,
+            operation="get_object",
+            expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
+            bucket=input.bucket_name,
+        )
+
+        with av.open(source_url) as in_container:
+            start_time_seconds = extract_stream_start_time_from_container(
+                in_container, input.track_index, logger=logger
+            )
+
+            # If no padding needed, return original S3 key
+            if start_time_seconds <= 0:
+                logger.info(
+                    f"Track {input.track_index} requires no padding",
+                    track_index=input.track_index,
+                )
+                return PadTrackResult(
+                    padded_key=input.s3_key,
+                    bucket_name=input.bucket_name,
+                    size=0,
+                    track_index=input.track_index,
+                )
+
+            with tempfile.NamedTemporaryFile(suffix=".webm", delete=False) as temp_file:
+                temp_path = temp_file.name
+
+            try:
+                apply_audio_padding_to_file(
+                    in_container,
+                    temp_path,
+                    start_time_seconds,
+                    input.track_index,
+                    logger=logger,
+                )
+
+                file_size = Path(temp_path).stat().st_size
+                storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
+
+                logger.info(
+                    f"About to upload padded track",
+                    key=storage_path,
+                    size=file_size,
+                )
+
+                with open(temp_path, "rb") as padded_file:
+                    await storage.put_file(storage_path, padded_file)
+
+                logger.info(
+                    f"Uploaded padded track to S3",
+                    key=storage_path,
+                    size=file_size,
+                )
+            finally:
+                Path(temp_path).unlink(missing_ok=True)
+
+        ctx.log(f"pad_track complete: track {input.track_index} -> {storage_path}")
+        logger.info(
+            "[Hatchet] pad_track complete",
+            track_index=input.track_index,
+            padded_key=storage_path,
+        )
+
+        # Return S3 key (not presigned URL) - consumer tasks presign on demand
+        # This avoids stale URLs when workflow is replayed
+        return PadTrackResult(
+            padded_key=storage_path,
+            bucket_name=None,  # None = use default transcript storage bucket
+            size=file_size,
+            track_index=input.track_index,
+        )
+
+    except Exception as e:
+        logger.error("[Hatchet] pad_track failed", error=str(e), exc_info=True)
+        raise
+
+
+@track_workflow.task(
+    parents=[pad_track], execution_timeout=timedelta(seconds=600), retries=3
+)
+async def transcribe_track(input: TrackInput, ctx: Context) -> TranscribeTrackResult:
+    """Transcribe audio track using GPU (Modal.com) or local Whisper."""
+    ctx.log(f"transcribe_track: track {input.track_index}, language={input.language}")
+    logger.info(
+        "[Hatchet] transcribe_track",
+        track_index=input.track_index,
+        language=input.language,
+    )
+
+    try:
+        pad_result = ctx.task_output(pad_track)
+        padded_key = pad_result.padded_key
+        bucket_name = pad_result.bucket_name
+
+        if not padded_key:
+            raise ValueError("Missing padded_key from pad_track")
+
+        # Presign URL on demand (avoids stale URLs on workflow replay)
+        from reflector.settings import settings  # noqa: PLC0415
+        from reflector.storage.storage_aws import AwsStorage  # noqa: PLC0415
+
+        storage = AwsStorage(
+            aws_bucket_name=settings.TRANSCRIPT_STORAGE_AWS_BUCKET_NAME,
+            aws_region=settings.TRANSCRIPT_STORAGE_AWS_REGION,
+            aws_access_key_id=settings.TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID,
+            aws_secret_access_key=settings.TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY,
+        )
+
+        audio_url = await storage.get_file_url(
+            padded_key,
+            operation="get_object",
+            expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
+            bucket=bucket_name,
+        )
+
+        from reflector.pipelines.transcription_helpers import (  # noqa: PLC0415
+            transcribe_file_with_processor,
+        )
+
+        transcript = await transcribe_file_with_processor(audio_url, input.language)
+
+        # Tag all words with speaker index
+        words = []
+        for word in transcript.words:
+            word_dict = word.model_dump()
+            word_dict["speaker"] = input.track_index
+            words.append(word_dict)
+
+        ctx.log(
+            f"transcribe_track complete: track {input.track_index}, {len(words)} words"
+        )
+        logger.info(
+            "[Hatchet] transcribe_track complete",
+            track_index=input.track_index,
+            word_count=len(words),
+        )
+
+        return TranscribeTrackResult(
+            words=words,
+            track_index=input.track_index,
+        )
+
+    except Exception as e:
+        logger.error("[Hatchet] transcribe_track failed", error=str(e), exc_info=True)
+        raise

server/reflector/pipelines/main_file_pipeline.py

@@ -97,13 +97,8 @@ class PipelineMainFile(PipelineMainBase):
                 },
             )
 
-        # 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,
@@ -197,7 +192,6 @@ class PipelineMainFile(PipelineMainBase):
         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):
@@ -212,7 +206,6 @@ class PipelineMainFile(PipelineMainBase):
             transcript=transcript_result, diarization=diarization_result or []
         )
 
-        # Store result for retrieval
         diarized_transcript: Transcript | None = None
 
         async def capture_result(transcript):
@@ -349,7 +342,6 @@ async def task_pipeline_file_process(*, transcript_id: str):
     try:
         await pipeline.set_status(transcript_id, "processing")
 
-        # Find the file to process
         audio_file = next(transcript.data_path.glob("upload.*"), None)
         if not audio_file:
             audio_file = next(transcript.data_path.glob("audio.*"), None)

server/reflector/pipelines/main_multitrack_pipeline.py

@@ -1,11 +1,8 @@
 import asyncio
-import math
 import tempfile
-from fractions import Fraction
 from pathlib import Path
 
 import av
-from av.audio.resampler import AudioResampler
 from celery import chain, shared_task
 
 from reflector.asynctask import asynctask
@@ -32,6 +29,15 @@ from reflector.processors.audio_waveform_processor import AudioWaveformProcessor
 from reflector.processors.types import TitleSummary
 from reflector.processors.types import Transcript as TranscriptType
 from reflector.storage import Storage, get_transcripts_storage
+from reflector.utils.audio_constants import PRESIGNED_URL_EXPIRATION_SECONDS
+from reflector.utils.audio_mixdown import (
+    detect_sample_rate_from_tracks,
+    mixdown_tracks_pyav,
+)
+from reflector.utils.audio_padding import (
+    apply_audio_padding_to_file,
+    extract_stream_start_time_from_container,
+)
 from reflector.utils.daily import (
     filter_cam_audio_tracks,
     parse_daily_recording_filename,
@@ -39,13 +45,6 @@ from reflector.utils.daily import (
 from reflector.utils.string import NonEmptyString
 from reflector.video_platforms.factory import create_platform_client
 
-# Audio encoding constants
-OPUS_STANDARD_SAMPLE_RATE = 48000
-OPUS_DEFAULT_BIT_RATE = 128000
-
-# Storage operation constants
-PRESIGNED_URL_EXPIRATION_SECONDS = 7200  # 2 hours
-
 
 class PipelineMainMultitrack(PipelineMainBase):
     def __init__(self, transcript_id: str):
@@ -125,8 +124,8 @@ class PipelineMainMultitrack(PipelineMainBase):
         try:
             # PyAV streams input from S3 URL efficiently (2-5MB fixed overhead for codec/filters)
             with av.open(track_url) as in_container:
-                start_time_seconds = self._extract_stream_start_time_from_container(
+                start_time_seconds = extract_stream_start_time_from_container(
-                    in_container, track_idx
+                    in_container, track_idx, logger=self.logger
                 )
 
                 if start_time_seconds <= 0:
@@ -144,8 +143,12 @@ class PipelineMainMultitrack(PipelineMainBase):
                     temp_path = temp_file.name
 
                 try:
-                    self._apply_audio_padding_to_file(
+                    apply_audio_padding_to_file(
-                        in_container, temp_path, start_time_seconds, track_idx
+                        in_container,
+                        temp_path,
+                        start_time_seconds,
+                        track_idx,
+                        logger=self.logger,
                     )
 
                     storage_path = (
@@ -156,7 +159,6 @@ class PipelineMainMultitrack(PipelineMainBase):
                     with open(temp_path, "rb") as padded_file:
                         await storage.put_file(storage_path, padded_file)
                 finally:
-                    # Clean up temp file
                     Path(temp_path).unlink(missing_ok=True)
 
                 padded_url = await storage.get_file_url(
@@ -186,316 +188,27 @@ class PipelineMainMultitrack(PipelineMainBase):
                 f"Track {track_idx} padding failed - transcript would have incorrect timestamps"
             ) from e
 
-    def _extract_stream_start_time_from_container(
-        self, container, track_idx: int
-    ) -> float:
-        """
-        Extract meeting-relative start time from WebM stream metadata.
-        Uses PyAV to read stream.start_time from WebM container.
-        More accurate than filename timestamps by ~209ms due to network/encoding delays.
-        """
-        start_time_seconds = 0.0
-        try:
-            audio_streams = [s for s in container.streams if s.type == "audio"]
-            stream = audio_streams[0] if audio_streams else container.streams[0]
-
-            # 1) Try stream-level start_time (most reliable for Daily.co tracks)
-            if stream.start_time is not None and stream.time_base is not None:
-                start_time_seconds = float(stream.start_time * stream.time_base)
-
-            # 2) Fallback to container-level start_time (in av.time_base units)
-            if (start_time_seconds <= 0) and (container.start_time is not None):
-                start_time_seconds = float(container.start_time * av.time_base)
-
-            # 3) Fallback to first packet DTS in stream.time_base
-            if start_time_seconds <= 0:
-                for packet in container.demux(stream):
-                    if packet.dts is not None:
-                        start_time_seconds = float(packet.dts * stream.time_base)
-                        break
-        except Exception as e:
-            self.logger.warning(
-                "PyAV metadata read failed; assuming 0 start_time",
-                track_idx=track_idx,
-                error=str(e),
-            )
-            start_time_seconds = 0.0
-
-        self.logger.info(
-            f"Track {track_idx} stream metadata: start_time={start_time_seconds:.3f}s",
-            track_idx=track_idx,
-        )
-        return start_time_seconds
-
-    def _apply_audio_padding_to_file(
-        self,
-        in_container,
-        output_path: str,
-        start_time_seconds: float,
-        track_idx: int,
-    ) -> None:
-        """Apply silence padding to audio track using PyAV filter graph, writing to file"""
-        delay_ms = math.floor(start_time_seconds * 1000)
-
-        self.logger.info(
-            f"Padding track {track_idx} with {delay_ms}ms delay using PyAV",
-            track_idx=track_idx,
-            delay_ms=delay_ms,
-        )
-
-        try:
-            with av.open(output_path, "w", format="webm") as out_container:
-                in_stream = next(
-                    (s for s in in_container.streams if s.type == "audio"), None
-                )
-                if in_stream is None:
-                    raise Exception("No audio stream in input")
-
-                out_stream = out_container.add_stream(
-                    "libopus", rate=OPUS_STANDARD_SAMPLE_RATE
-                )
-                out_stream.bit_rate = OPUS_DEFAULT_BIT_RATE
-                graph = av.filter.Graph()
-
-                abuf_args = (
-                    f"time_base=1/{OPUS_STANDARD_SAMPLE_RATE}:"
-                    f"sample_rate={OPUS_STANDARD_SAMPLE_RATE}:"
-                    f"sample_fmt=s16:"
-                    f"channel_layout=stereo"
-                )
-                src = graph.add("abuffer", args=abuf_args, name="src")
-                aresample_f = graph.add("aresample", args="async=1", name="ares")
-                # adelay requires one delay value per channel separated by '|'
-                delays_arg = f"{delay_ms}|{delay_ms}"
-                adelay_f = graph.add(
-                    "adelay", args=f"delays={delays_arg}:all=1", name="delay"
-                )
-                sink = graph.add("abuffersink", name="sink")
-
-                src.link_to(aresample_f)
-                aresample_f.link_to(adelay_f)
-                adelay_f.link_to(sink)
-                graph.configure()
-
-                resampler = AudioResampler(
-                    format="s16", layout="stereo", rate=OPUS_STANDARD_SAMPLE_RATE
-                )
-                # Decode -> resample -> push through graph -> encode Opus
-                for frame in in_container.decode(in_stream):
-                    out_frames = resampler.resample(frame) or []
-                    for rframe in out_frames:
-                        rframe.sample_rate = OPUS_STANDARD_SAMPLE_RATE
-                        rframe.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
-                        src.push(rframe)
-
-                        while True:
-                            try:
-                                f_out = sink.pull()
-                            except Exception:
-                                break
-                            f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
-                            f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
-                            for packet in out_stream.encode(f_out):
-                                out_container.mux(packet)
-
-                src.push(None)
-                while True:
-                    try:
-                        f_out = sink.pull()
-                    except Exception:
-                        break
-                    f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
-                    f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
-                    for packet in out_stream.encode(f_out):
-                        out_container.mux(packet)
-
-                for packet in out_stream.encode(None):
-                    out_container.mux(packet)
-        except Exception as e:
-            self.logger.error(
-                "PyAV padding failed for track",
-                track_idx=track_idx,
-                delay_ms=delay_ms,
-                error=str(e),
-                exc_info=True,
-            )
-            raise
-
     async def mixdown_tracks(
         self,
         track_urls: list[str],
         writer: AudioFileWriterProcessor,
         offsets_seconds: list[float] | None = None,
     ) -> None:
-        """Multi-track mixdown using PyAV filter graph (amix), reading from S3 presigned URLs"""
+        """Multi-track mixdown using PyAV filter graph (amix), reading from S3 presigned URLs."""
-
+        target_sample_rate = detect_sample_rate_from_tracks(
-        target_sample_rate: int | None = None
+            track_urls, logger=self.logger
-        for url in track_urls:
+        )
-            if not url:
-                continue
-            container = None
-            try:
-                container = av.open(url)
-                for frame in container.decode(audio=0):
-                    target_sample_rate = frame.sample_rate
-                    break
-            except Exception:
-                continue
-            finally:
-                if container is not None:
-                    container.close()
-            if target_sample_rate:
-                break
-
         if not target_sample_rate:
             self.logger.error("Mixdown failed - no decodable audio frames found")
             raise Exception("Mixdown failed: No decodable audio frames in any track")
-        # Build PyAV filter graph:
+
-        # N abuffer (s32/stereo)
+        await mixdown_tracks_pyav(
-        #   -> optional adelay per input (for alignment)
+            track_urls,
-        #   -> amix (s32)
+            writer,
-        #   -> aformat(s16)
+            target_sample_rate,
-        #   -> sink
+            offsets_seconds=offsets_seconds,
-        graph = av.filter.Graph()
+            logger=self.logger,
-        inputs = []
-        valid_track_urls = [url for url in track_urls if url]
-        input_offsets_seconds = None
-        if offsets_seconds is not None:
-            input_offsets_seconds = [
-                offsets_seconds[i] for i, url in enumerate(track_urls) if url
-            ]
-        for idx, url in enumerate(valid_track_urls):
-            args = (
-                f"time_base=1/{target_sample_rate}:"
-                f"sample_rate={target_sample_rate}:"
-                f"sample_fmt=s32:"
-                f"channel_layout=stereo"
         )
-            in_ctx = graph.add("abuffer", args=args, name=f"in{idx}")
-            inputs.append(in_ctx)
-
-        if not inputs:
-            self.logger.error("Mixdown failed - no valid inputs for graph")
-            raise Exception("Mixdown failed: No valid inputs for filter graph")
-
-        mixer = graph.add("amix", args=f"inputs={len(inputs)}:normalize=0", name="mix")
-
-        fmt = graph.add(
-            "aformat",
-            args=(
-                f"sample_fmts=s32:channel_layouts=stereo:sample_rates={target_sample_rate}"
-            ),
-            name="fmt",
-        )
-
-        sink = graph.add("abuffersink", name="out")
-
-        # Optional per-input delay before mixing
-        delays_ms: list[int] = []
-        if input_offsets_seconds is not None:
-            base = min(input_offsets_seconds) if input_offsets_seconds else 0.0
-            delays_ms = [
-                max(0, int(round((o - base) * 1000))) for o in input_offsets_seconds
-            ]
-        else:
-            delays_ms = [0 for _ in inputs]
-
-        for idx, in_ctx in enumerate(inputs):
-            delay_ms = delays_ms[idx] if idx < len(delays_ms) else 0
-            if delay_ms > 0:
-                # adelay requires one value per channel; use same for stereo
-                adelay = graph.add(
-                    "adelay",
-                    args=f"delays={delay_ms}|{delay_ms}:all=1",
-                    name=f"delay{idx}",
-                )
-                in_ctx.link_to(adelay)
-                adelay.link_to(mixer, 0, idx)
-            else:
-                in_ctx.link_to(mixer, 0, idx)
-        mixer.link_to(fmt)
-        fmt.link_to(sink)
-        graph.configure()
-
-        containers = []
-        try:
-            # Open all containers with cleanup guaranteed
-            for i, url in enumerate(valid_track_urls):
-                try:
-                    c = av.open(
-                        url,
-                        options={
-                            # it's trying to stream from s3 by default
-                            "reconnect": "1",
-                            "reconnect_streamed": "1",
-                            "reconnect_delay_max": "5",
-                        },
-                    )
-                    containers.append(c)
-                except Exception as e:
-                    self.logger.warning(
-                        "Mixdown: failed to open container from URL",
-                        input=i,
-                        url=url,
-                        error=str(e),
-                    )
-
-            if not containers:
-                self.logger.error("Mixdown failed - no valid containers opened")
-                raise Exception("Mixdown failed: Could not open any track containers")
-
-            decoders = [c.decode(audio=0) for c in containers]
-            active = [True] * len(decoders)
-            resamplers = [
-                AudioResampler(format="s32", layout="stereo", rate=target_sample_rate)
-                for _ in decoders
-            ]
-
-            while any(active):
-                for i, (dec, is_active) in enumerate(zip(decoders, active)):
-                    if not is_active:
-                        continue
-                    try:
-                        frame = next(dec)
-                    except StopIteration:
-                        active[i] = False
-                        # causes stream to move on / unclogs memory
-                        inputs[i].push(None)
-                        continue
-
-                    if frame.sample_rate != target_sample_rate:
-                        continue
-                    out_frames = resamplers[i].resample(frame) or []
-                    for rf in out_frames:
-                        rf.sample_rate = target_sample_rate
-                        rf.time_base = Fraction(1, target_sample_rate)
-                        inputs[i].push(rf)
-
-                    while True:
-                        try:
-                            mixed = sink.pull()
-                        except Exception:
-                            break
-                        mixed.sample_rate = target_sample_rate
-                        mixed.time_base = Fraction(1, target_sample_rate)
-                        await writer.push(mixed)
-
-            while True:
-                try:
-                    mixed = sink.pull()
-                except Exception:
-                    break
-                mixed.sample_rate = target_sample_rate
-                mixed.time_base = Fraction(1, target_sample_rate)
-                await writer.push(mixed)
-        finally:
-            # Cleanup all containers, even if processing failed
-            for c in containers:
-                if c is not None:
-                    try:
-                        c.close()
-                    except Exception:
-                        pass  # Best effort cleanup
 
     @broadcast_to_sockets
     async def set_status(self, transcript_id: str, status: TranscriptStatus):

server/reflector/services/transcript_process.py

@@ -11,13 +11,19 @@ from typing import Literal, Union, assert_never
 
 import celery
 from celery.result import AsyncResult
+from hatchet_sdk.clients.rest.exceptions import ApiException
+from hatchet_sdk.clients.rest.models import V1TaskStatus
 
 from reflector.db.recordings import recordings_controller
-from reflector.db.transcripts import Transcript
+from reflector.db.rooms import rooms_controller
+from reflector.db.transcripts import Transcript, transcripts_controller
+from reflector.hatchet.client import HatchetClientManager
+from reflector.logger import logger
 from reflector.pipelines.main_file_pipeline import task_pipeline_file_process
 from reflector.pipelines.main_multitrack_pipeline import (
     task_pipeline_multitrack_process,
 )
+from reflector.settings import settings
 from reflector.utils.string import NonEmptyString
 
 
@@ -37,6 +43,8 @@ class MultitrackProcessingConfig:
     transcript_id: NonEmptyString
     bucket_name: NonEmptyString
     track_keys: list[str]
+    recording_id: NonEmptyString | None = None
+    room_id: NonEmptyString | None = None
     mode: Literal["multitrack"] = "multitrack"
 
 
@@ -49,6 +57,7 @@ class ValidationOk:
     # transcript currently doesnt always have recording_id
     recording_id: NonEmptyString | None
     transcript_id: NonEmptyString
+    room_id: NonEmptyString | None = None
 
 
 @dataclass
@@ -96,6 +105,7 @@ async def validate_transcript_for_processing(
     if transcript.status == "idle":
         return ValidationNotReady(detail="Recording is not ready for processing")
 
+    # Check Celery tasks
     if task_is_scheduled_or_active(
         "reflector.pipelines.main_file_pipeline.task_pipeline_file_process",
         transcript_id=transcript.id,
@@ -105,8 +115,25 @@ async def validate_transcript_for_processing(
     ):
         return ValidationAlreadyScheduled(detail="already running")
 
+    # Check Hatchet workflows (if enabled)
+    if settings.HATCHET_ENABLED and transcript.workflow_run_id:
+        try:
+            status = await HatchetClientManager.get_workflow_run_status(
+                transcript.workflow_run_id
+            )
+            # If workflow is running or queued, don't allow new processing
+            if status in (V1TaskStatus.RUNNING, V1TaskStatus.QUEUED):
+                return ValidationAlreadyScheduled(
+                    detail="Hatchet workflow already running"
+                )
+        except ApiException:
+            # Workflow might be gone (404) or API issue - allow processing
+            pass
+
     return ValidationOk(
-        recording_id=transcript.recording_id, transcript_id=transcript.id
+        recording_id=transcript.recording_id,
+        transcript_id=transcript.id,
+        room_id=transcript.room_id,
     )
 
 
@@ -116,6 +143,7 @@ async def prepare_transcript_processing(validation: ValidationOk) -> PrepareResu
     """
     bucket_name: str | None = None
     track_keys: list[str] | None = None
+    recording_id: str | None = validation.recording_id
 
     if validation.recording_id:
         recording = await recordings_controller.get_by_id(validation.recording_id)
@@ -137,6 +165,8 @@ async def prepare_transcript_processing(validation: ValidationOk) -> PrepareResu
             bucket_name=bucket_name,  # type: ignore (validated above)
             track_keys=track_keys,
             transcript_id=validation.transcript_id,
+            recording_id=recording_id,
+            room_id=validation.room_id,
         )
 
     return FileProcessingConfig(
@@ -144,8 +174,104 @@ async def prepare_transcript_processing(validation: ValidationOk) -> PrepareResu
     )
 
 
-def dispatch_transcript_processing(config: ProcessingConfig) -> AsyncResult:
+async def dispatch_transcript_processing(
+    config: ProcessingConfig, force: bool = False
+) -> AsyncResult | None:
+    """Dispatch transcript processing to appropriate backend (Hatchet or Celery).
+
+    Returns AsyncResult for Celery tasks, None for Hatchet workflows.
+    """
     if isinstance(config, MultitrackProcessingConfig):
+        # Check if room has use_hatchet=True (overrides env vars)
+        room_forces_hatchet = False
+        if config.room_id:
+            room = await rooms_controller.get_by_id(config.room_id)
+            room_forces_hatchet = room.use_hatchet if room else False
+
+        # Start durable workflow if enabled (Hatchet)
+        # or if room has use_hatchet=True
+        use_hatchet = settings.HATCHET_ENABLED or room_forces_hatchet
+
+        if room_forces_hatchet:
+            logger.info(
+                "Room forces Hatchet workflow",
+                room_id=config.room_id,
+                transcript_id=config.transcript_id,
+            )
+
+        if use_hatchet:
+            # First check if we can replay (outside transaction since it's read-only)
+            transcript = await transcripts_controller.get_by_id(config.transcript_id)
+            if transcript and transcript.workflow_run_id and not force:
+                can_replay = await HatchetClientManager.can_replay(
+                    transcript.workflow_run_id
+                )
+                if can_replay:
+                    await HatchetClientManager.replay_workflow(
+                        transcript.workflow_run_id
+                    )
+                    logger.info(
+                        "Replaying Hatchet workflow",
+                        workflow_id=transcript.workflow_run_id,
+                    )
+                    return None
+
+            # Force: cancel old workflow if exists
+            if force and transcript and transcript.workflow_run_id:
+                await HatchetClientManager.cancel_workflow(transcript.workflow_run_id)
+                logger.info(
+                    "Cancelled old workflow (--force)",
+                    workflow_id=transcript.workflow_run_id,
+                )
+                await transcripts_controller.update(
+                    transcript, {"workflow_run_id": None}
+                )
+
+            # Re-fetch and check for concurrent dispatch (optimistic approach).
+            # No database lock - worst case is duplicate dispatch, but Hatchet
+            # workflows are idempotent so this is acceptable.
+            transcript = await transcripts_controller.get_by_id(config.transcript_id)
+            if transcript and transcript.workflow_run_id:
+                # Another process started a workflow between validation and now
+                try:
+                    status = await HatchetClientManager.get_workflow_run_status(
+                        transcript.workflow_run_id
+                    )
+                    if status in (V1TaskStatus.RUNNING, V1TaskStatus.QUEUED):
+                        logger.info(
+                            "Concurrent workflow detected, skipping dispatch",
+                            workflow_id=transcript.workflow_run_id,
+                        )
+                        return None
+                except ApiException:
+                    # Workflow might be gone (404) or API issue - proceed with new workflow
+                    pass
+
+            workflow_id = await HatchetClientManager.start_workflow(
+                workflow_name="DiarizationPipeline",
+                input_data={
+                    "recording_id": config.recording_id,
+                    "tracks": [{"s3_key": k} for k in config.track_keys],
+                    "bucket_name": config.bucket_name,
+                    "transcript_id": config.transcript_id,
+                    "room_id": config.room_id,
+                },
+                additional_metadata={
+                    "transcript_id": config.transcript_id,
+                    "recording_id": config.recording_id,
+                    "daily_recording_id": config.recording_id,
+                },
+            )
+
+            if transcript:
+                await transcripts_controller.update(
+                    transcript, {"workflow_run_id": workflow_id}
+                )
+
+            logger.info("Hatchet workflow dispatched", workflow_id=workflow_id)
+            return None
+
+        # Celery pipeline (durable workflows disabled)
         return task_pipeline_multitrack_process.delay(
             transcript_id=config.transcript_id,
             bucket_name=config.bucket_name,

server/reflector/settings.py

@@ -153,5 +153,19 @@ class Settings(BaseSettings):
     ZULIP_API_KEY: str | None = None
     ZULIP_BOT_EMAIL: str | None = None
 
+    # Durable workflow orchestration
+    # Provider: "hatchet" (or "none" to disable)
+    DURABLE_WORKFLOW_PROVIDER: str = "none"
+
+    # Hatchet workflow orchestration
+    HATCHET_CLIENT_TOKEN: str | None = None
+    HATCHET_CLIENT_TLS_STRATEGY: str = "none"  # none, tls, mtls
+    HATCHET_DEBUG: bool = False
+
+    @property
+    def HATCHET_ENABLED(self) -> bool:
+        """True if Hatchet is the active provider."""
+        return self.DURABLE_WORKFLOW_PROVIDER == "hatchet"
+
 
 settings = Settings()

server/reflector/tools/process_transcript.py

@@ -15,8 +15,11 @@ import time
 from typing import Callable
 
 from celery.result import AsyncResult
+from hatchet_sdk.clients.rest.models import V1TaskStatus
 
+from reflector.db import get_database
 from reflector.db.transcripts import Transcript, transcripts_controller
+from reflector.hatchet.client import HatchetClientManager
 from reflector.services.transcript_process import (
     FileProcessingConfig,
     MultitrackProcessingConfig,
@@ -34,24 +37,26 @@ async def process_transcript_inner(
     transcript: Transcript,
     on_validation: Callable[[ValidationResult], None],
     on_preprocess: Callable[[PrepareResult], None],
-) -> AsyncResult:
+    force: bool = False,
+) -> AsyncResult | None:
     validation = await validate_transcript_for_processing(transcript)
     on_validation(validation)
     config = await prepare_transcript_processing(validation)
     on_preprocess(config)
-    return dispatch_transcript_processing(config)
+    return await dispatch_transcript_processing(config, force=force)
 
 
-async def process_transcript(transcript_id: str, sync: bool = False) -> None:
+async def process_transcript(
+    transcript_id: str, sync: bool = False, force: bool = False
+) -> None:
     """
     Process a transcript by ID, auto-detecting multitrack vs file pipeline.
 
     Args:
         transcript_id: The transcript UUID
         sync: If True, wait for task completion. If False, dispatch and exit.
+        force: If True, cancel old workflow and start new (latest code). If False, replay failed workflow.
     """
-    from reflector.db import get_database
-
     database = get_database()
     await database.connect()
 
@@ -82,10 +87,42 @@ async def process_transcript(transcript_id: str, sync: bool = False) -> None:
                 print(f"Dispatching file pipeline", file=sys.stderr)
 
         result = await process_transcript_inner(
-            transcript, on_validation=on_validation, on_preprocess=on_preprocess
+            transcript,
+            on_validation=on_validation,
+            on_preprocess=on_preprocess,
+            force=force,
         )
 
+        if result is None:
+            # Hatchet workflow dispatched
             if sync:
+                # Re-fetch transcript to get workflow_run_id
+                transcript = await transcripts_controller.get_by_id(transcript_id)
+                if not transcript or not transcript.workflow_run_id:
+                    print("Error: workflow_run_id not found", file=sys.stderr)
+                    sys.exit(1)
+
+                print("Waiting for Hatchet workflow...", file=sys.stderr)
+                while True:
+                    status = await HatchetClientManager.get_workflow_run_status(
+                        transcript.workflow_run_id
+                    )
+                    print(f"  Status: {status.value}", file=sys.stderr)
+
+                    if status == V1TaskStatus.COMPLETED:
+                        print("Workflow completed successfully", file=sys.stderr)
+                        break
+                    elif status in (V1TaskStatus.FAILED, V1TaskStatus.CANCELLED):
+                        print(f"Workflow failed: {status}", file=sys.stderr)
+                        sys.exit(1)
+
+                    await asyncio.sleep(5)
+            else:
+                print(
+                    "Task dispatched (use --sync to wait for completion)",
+                    file=sys.stderr,
+                )
+        elif sync:
             print("Waiting for task completion...", file=sys.stderr)
             while not result.ready():
                 print(f"  Status: {result.state}", file=sys.stderr)
@@ -118,9 +155,16 @@ def main():
         action="store_true",
         help="Wait for task completion instead of just dispatching",
     )
+    parser.add_argument(
+        "--force",
+        action="store_true",
+        help="Cancel old workflow and start new (uses latest code instead of replaying)",
+    )
 
     args = parser.parse_args()
-    asyncio.run(process_transcript(args.transcript_id, sync=args.sync))
+    asyncio.run(
+        process_transcript(args.transcript_id, sync=args.sync, force=args.force)
+    )
 
 
 if __name__ == "__main__":

server/reflector/utils/audio_constants.py

@@ -0,0 +1,15 @@
+"""
+Shared audio processing constants.
+
+Used by both Hatchet workflows and Celery pipelines for consistent audio encoding.
+"""
+
+# Opus codec settings
+OPUS_STANDARD_SAMPLE_RATE = 48000
+OPUS_DEFAULT_BIT_RATE = 128000  # 128kbps for good speech quality
+
+# S3 presigned URL expiration
+PRESIGNED_URL_EXPIRATION_SECONDS = 7200  # 2 hours
+
+# Waveform visualization
+WAVEFORM_SEGMENTS = 255

server/reflector/utils/audio_mixdown.py

@@ -0,0 +1,227 @@
+"""
+Audio track mixdown utilities.
+
+Shared PyAV-based functions for mixing multiple audio tracks into a single output.
+Used by both Hatchet workflows and Celery pipelines.
+"""
+
+from fractions import Fraction
+
+import av
+from av.audio.resampler import AudioResampler
+
+
+def detect_sample_rate_from_tracks(track_urls: list[str], logger=None) -> int | None:
+    """Detect sample rate from first decodable audio frame.
+
+    Args:
+        track_urls: List of URLs to audio files (S3 presigned or local)
+        logger: Optional logger instance
+
+    Returns:
+        Sample rate in Hz, or None if no decodable frames found
+    """
+    for url in track_urls:
+        if not url:
+            continue
+        container = None
+        try:
+            container = av.open(url)
+            for frame in container.decode(audio=0):
+                return frame.sample_rate
+        except Exception:
+            continue
+        finally:
+            if container is not None:
+                container.close()
+    return None
+
+
+async def mixdown_tracks_pyav(
+    track_urls: list[str],
+    writer,
+    target_sample_rate: int,
+    offsets_seconds: list[float] | None = None,
+    logger=None,
+) -> None:
+    """Multi-track mixdown using PyAV filter graph (amix).
+
+    Builds a filter graph: N abuffer -> optional adelay -> amix -> aformat -> sink
+    Reads from S3 presigned URLs or local files, pushes mixed frames to writer.
+
+    Args:
+        track_urls: List of URLs to audio tracks (S3 presigned or local)
+        writer: AudioFileWriterProcessor instance with async push() method
+        target_sample_rate: Sample rate for output (Hz)
+        offsets_seconds: Optional per-track delays in seconds for alignment.
+            If provided, must have same length as track_urls. Delays are relative
+            to the minimum offset (earliest track has delay=0).
+        logger: Optional logger instance
+
+    Raises:
+        ValueError: If offsets_seconds length doesn't match track_urls,
+            no valid tracks provided, or no containers can be opened
+    """
+    if offsets_seconds is not None and len(offsets_seconds) != len(track_urls):
+        raise ValueError(
+            f"offsets_seconds length ({len(offsets_seconds)}) must match track_urls ({len(track_urls)})"
+        )
+
+    valid_track_urls = [url for url in track_urls if url]
+    if not valid_track_urls:
+        if logger:
+            logger.error("Mixdown failed - no valid track URLs provided")
+        raise ValueError("Mixdown failed: No valid track URLs")
+
+    # Calculate per-input delays if offsets provided
+    input_offsets_seconds = None
+    if offsets_seconds is not None:
+        input_offsets_seconds = [
+            offsets_seconds[i] for i, url in enumerate(track_urls) if url
+        ]
+
+    # Build PyAV filter graph:
+    # N abuffer (s32/stereo)
+    #   -> optional adelay per input (for alignment)
+    #   -> amix (s32)
+    #   -> aformat(s16)
+    #   -> sink
+    graph = av.filter.Graph()
+    inputs = []
+
+    for idx, url in enumerate(valid_track_urls):
+        args = (
+            f"time_base=1/{target_sample_rate}:"
+            f"sample_rate={target_sample_rate}:"
+            f"sample_fmt=s32:"
+            f"channel_layout=stereo"
+        )
+        in_ctx = graph.add("abuffer", args=args, name=f"in{idx}")
+        inputs.append(in_ctx)
+
+    if not inputs:
+        if logger:
+            logger.error("Mixdown failed - no valid inputs for graph")
+        raise ValueError("Mixdown failed: No valid inputs for filter graph")
+
+    mixer = graph.add("amix", args=f"inputs={len(inputs)}:normalize=0", name="mix")
+
+    fmt = graph.add(
+        "aformat",
+        args=f"sample_fmts=s32:channel_layouts=stereo:sample_rates={target_sample_rate}",
+        name="fmt",
+    )
+
+    sink = graph.add("abuffersink", name="out")
+
+    # Optional per-input delay before mixing
+    delays_ms: list[int] = []
+    if input_offsets_seconds is not None:
+        base = min(input_offsets_seconds) if input_offsets_seconds else 0.0
+        delays_ms = [
+            max(0, int(round((o - base) * 1000))) for o in input_offsets_seconds
+        ]
+    else:
+        delays_ms = [0 for _ in inputs]
+
+    for idx, in_ctx in enumerate(inputs):
+        delay_ms = delays_ms[idx] if idx < len(delays_ms) else 0
+        if delay_ms > 0:
+            # adelay requires one value per channel; use same for stereo
+            adelay = graph.add(
+                "adelay",
+                args=f"delays={delay_ms}|{delay_ms}:all=1",
+                name=f"delay{idx}",
+            )
+            in_ctx.link_to(adelay)
+            adelay.link_to(mixer, 0, idx)
+        else:
+            in_ctx.link_to(mixer, 0, idx)
+
+    mixer.link_to(fmt)
+    fmt.link_to(sink)
+    graph.configure()
+
+    containers = []
+    try:
+        # Open all containers with cleanup guaranteed
+        for i, url in enumerate(valid_track_urls):
+            try:
+                c = av.open(
+                    url,
+                    options={
+                        # S3 streaming options
+                        "reconnect": "1",
+                        "reconnect_streamed": "1",
+                        "reconnect_delay_max": "5",
+                    },
+                )
+                containers.append(c)
+            except Exception as e:
+                if logger:
+                    logger.warning(
+                        "Mixdown: failed to open container from URL",
+                        input=i,
+                        url=url,
+                        error=str(e),
+                    )
+
+        if not containers:
+            if logger:
+                logger.error("Mixdown failed - no valid containers opened")
+            raise ValueError("Mixdown failed: Could not open any track containers")
+
+        decoders = [c.decode(audio=0) for c in containers]
+        active = [True] * len(decoders)
+        resamplers = [
+            AudioResampler(format="s32", layout="stereo", rate=target_sample_rate)
+            for _ in decoders
+        ]
+
+        while any(active):
+            for i, (dec, is_active) in enumerate(zip(decoders, active)):
+                if not is_active:
+                    continue
+                try:
+                    frame = next(dec)
+                except StopIteration:
+                    active[i] = False
+                    # Signal end of stream to filter graph
+                    inputs[i].push(None)
+                    continue
+
+                if frame.sample_rate != target_sample_rate:
+                    continue
+                out_frames = resamplers[i].resample(frame) or []
+                for rf in out_frames:
+                    rf.sample_rate = target_sample_rate
+                    rf.time_base = Fraction(1, target_sample_rate)
+                    inputs[i].push(rf)
+
+                while True:
+                    try:
+                        mixed = sink.pull()
+                    except Exception:
+                        break
+                    mixed.sample_rate = target_sample_rate
+                    mixed.time_base = Fraction(1, target_sample_rate)
+                    await writer.push(mixed)
+
+        # Flush remaining frames from filter graph
+        while True:
+            try:
+                mixed = sink.pull()
+            except Exception:
+                break
+            mixed.sample_rate = target_sample_rate
+            mixed.time_base = Fraction(1, target_sample_rate)
+            await writer.push(mixed)
+
+    finally:
+        # Cleanup all containers, even if processing failed
+        for c in containers:
+            if c is not None:
+                try:
+                    c.close()
+                except Exception:
+                    pass  # Best effort cleanup

server/reflector/utils/audio_padding.py

@@ -0,0 +1,186 @@
+"""
+Audio track padding utilities.
+
+Shared PyAV-based functions for extracting stream metadata and applying
+silence padding to audio tracks. Used by both Hatchet workflows and Celery pipelines.
+"""
+
+import math
+from fractions import Fraction
+
+import av
+from av.audio.resampler import AudioResampler
+
+from reflector.utils.audio_constants import (
+    OPUS_DEFAULT_BIT_RATE,
+    OPUS_STANDARD_SAMPLE_RATE,
+)
+
+
+def extract_stream_start_time_from_container(
+    container,
+    track_idx: int,
+    logger=None,
+) -> float:
+    """Extract meeting-relative start time from WebM stream metadata.
+
+    Uses PyAV to read stream.start_time from WebM container.
+    More accurate than filename timestamps by ~209ms due to network/encoding delays.
+
+    Args:
+        container: PyAV container opened from audio file/URL
+        track_idx: Track index for logging context
+        logger: Optional logger instance (structlog or stdlib compatible)
+
+    Returns:
+        Start time in seconds (0.0 if extraction fails)
+    """
+    start_time_seconds = 0.0
+    try:
+        audio_streams = [s for s in container.streams if s.type == "audio"]
+        stream = audio_streams[0] if audio_streams else container.streams[0]
+
+        # 1) Try stream-level start_time (most reliable for Daily.co tracks)
+        if stream.start_time is not None and stream.time_base is not None:
+            start_time_seconds = float(stream.start_time * stream.time_base)
+
+        # 2) Fallback to container-level start_time (in av.time_base units)
+        if (start_time_seconds <= 0) and (container.start_time is not None):
+            start_time_seconds = float(container.start_time * av.time_base)
+
+        # 3) Fallback to first packet DTS in stream.time_base
+        if start_time_seconds <= 0:
+            for packet in container.demux(stream):
+                if packet.dts is not None:
+                    start_time_seconds = float(packet.dts * stream.time_base)
+                    break
+    except Exception as e:
+        if logger:
+            logger.warning(
+                "PyAV metadata read failed; assuming 0 start_time",
+                track_idx=track_idx,
+                error=str(e),
+            )
+        start_time_seconds = 0.0
+
+    if logger:
+        logger.info(
+            f"Track {track_idx} stream metadata: start_time={start_time_seconds:.3f}s",
+            track_idx=track_idx,
+        )
+    return start_time_seconds
+
+
+def apply_audio_padding_to_file(
+    in_container,
+    output_path: str,
+    start_time_seconds: float,
+    track_idx: int,
+    logger=None,
+) -> None:
+    """Apply silence padding to audio track using PyAV filter graph.
+
+    Uses adelay filter to prepend silence, aligning track to meeting start time.
+    Output is WebM/Opus format.
+
+    Args:
+        in_container: PyAV container opened from source audio
+        output_path: Path for output WebM file
+        start_time_seconds: Amount of silence to prepend (in seconds)
+        track_idx: Track index for logging context
+        logger: Optional logger instance (structlog or stdlib compatible)
+
+    Raises:
+        Exception: If no audio stream found or PyAV processing fails
+    """
+    delay_ms = math.floor(start_time_seconds * 1000)
+
+    if logger:
+        logger.info(
+            f"Padding track {track_idx} with {delay_ms}ms delay using PyAV",
+            track_idx=track_idx,
+            delay_ms=delay_ms,
+        )
+
+    try:
+        with av.open(output_path, "w", format="webm") as out_container:
+            in_stream = next(
+                (s for s in in_container.streams if s.type == "audio"), None
+            )
+            if in_stream is None:
+                raise Exception("No audio stream in input")
+
+            out_stream = out_container.add_stream(
+                "libopus", rate=OPUS_STANDARD_SAMPLE_RATE
+            )
+            out_stream.bit_rate = OPUS_DEFAULT_BIT_RATE
+            graph = av.filter.Graph()
+
+            abuf_args = (
+                f"time_base=1/{OPUS_STANDARD_SAMPLE_RATE}:"
+                f"sample_rate={OPUS_STANDARD_SAMPLE_RATE}:"
+                f"sample_fmt=s16:"
+                f"channel_layout=stereo"
+            )
+            src = graph.add("abuffer", args=abuf_args, name="src")
+            aresample_f = graph.add("aresample", args="async=1", name="ares")
+            # adelay requires one delay value per channel separated by '|'
+            delays_arg = f"{delay_ms}|{delay_ms}"
+            adelay_f = graph.add(
+                "adelay", args=f"delays={delays_arg}:all=1", name="delay"
+            )
+            sink = graph.add("abuffersink", name="sink")
+
+            src.link_to(aresample_f)
+            aresample_f.link_to(adelay_f)
+            adelay_f.link_to(sink)
+            graph.configure()
+
+            resampler = AudioResampler(
+                format="s16", layout="stereo", rate=OPUS_STANDARD_SAMPLE_RATE
+            )
+
+            # Decode -> resample -> push through graph -> encode Opus
+            for frame in in_container.decode(in_stream):
+                out_frames = resampler.resample(frame) or []
+                for rframe in out_frames:
+                    rframe.sample_rate = OPUS_STANDARD_SAMPLE_RATE
+                    rframe.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
+                    src.push(rframe)
+
+                    while True:
+                        try:
+                            f_out = sink.pull()
+                        except Exception:
+                            break
+                        f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
+                        f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
+                        for packet in out_stream.encode(f_out):
+                            out_container.mux(packet)
+
+            # Flush remaining frames from filter graph
+            src.push(None)
+            while True:
+                try:
+                    f_out = sink.pull()
+                except Exception:
+                    break
+                f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
+                f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
+                for packet in out_stream.encode(f_out):
+                    out_container.mux(packet)
+
+            # Flush encoder
+            for packet in out_stream.encode(None):
+                out_container.mux(packet)
+
+    except Exception as e:
+        if logger:
+            logger.error(
+                "PyAV padding failed for track",
+                track_idx=track_idx,
+                delay_ms=delay_ms,
+                error=str(e),
+                exc_info=True,
+            )
+        raise

server/reflector/utils/common.py

@@ -0,0 +1,4 @@
+def assert_not_none[T](value: T | None, message: str = "Value is None") -> T:
+    if value is None:
+        raise ValueError(message)
+    return value

server/reflector/utils/string.py

@@ -2,6 +2,17 @@ from typing import Annotated, TypeVar
 
 from pydantic import Field, TypeAdapter, constr
 
+T_NotNone = TypeVar("T_NotNone")
+
+
+def assert_not_none(
+    value: T_NotNone | None, message: str = "Value is None"
+) -> T_NotNone:
+    if value is None:
+        raise ValueError(message)
+    return value
+
+
 NonEmptyStringBase = constr(min_length=1, strip_whitespace=False)
 NonEmptyString = Annotated[
     NonEmptyStringBase,
@@ -23,10 +34,18 @@ def try_parse_non_empty_string(s: str) -> NonEmptyString | None:
     return parse_non_empty_string(s)
 
 
-T = TypeVar("T", bound=str)
+T_Str = TypeVar("T_Str", bound=str)
 
 
-def assert_equal[T](s1: T, s2: T) -> T:
+def assert_equal(s1: T_Str, s2: T_Str) -> T_Str:
     if s1 != s2:
         raise ValueError(f"assert_equal: {s1} != {s2}")
     return s1
+
+
+def assert_non_none_and_non_empty(
+    value: str | None, error: str | None = None
+) -> NonEmptyString:
+    return parse_non_empty_string(
+        assert_not_none(value, error or "Value is None"), error
+    )

server/reflector/views/transcripts_process.py

@@ -50,5 +50,5 @@ async def transcript_process(
     if isinstance(config, ProcessError):
         raise HTTPException(status_code=500, detail=config.detail)
     else:
-        dispatch_transcript_processing(config)
+        await dispatch_transcript_processing(config)
         return ProcessStatus(status="ok")

server/reflector/worker/process.py

@@ -24,6 +24,7 @@ from reflector.db.transcripts import (
     SourceKind,
     transcripts_controller,
 )
+from reflector.hatchet.client import HatchetClientManager
 from reflector.pipelines.main_file_pipeline import task_pipeline_file_process
 from reflector.pipelines.main_live_pipeline import asynctask
 from reflector.pipelines.main_multitrack_pipeline import (
@@ -286,6 +287,45 @@ async def _process_multitrack_recording_inner(
             room_id=room.id,
         )
 
+    # Start durable workflow if enabled (Hatchet) or room overrides it
+    durable_started = False
+    use_hatchet = settings.HATCHET_ENABLED or (room and room.use_hatchet)
+
+    if room and room.use_hatchet and not settings.HATCHET_ENABLED:
+        logger.info(
+            "Room forces Hatchet workflow",
+            room_id=room.id,
+            transcript_id=transcript.id,
+        )
+
+    if use_hatchet:
+        workflow_id = await HatchetClientManager.start_workflow(
+            workflow_name="DiarizationPipeline",
+            input_data={
+                "recording_id": recording_id,
+                "tracks": [{"s3_key": k} for k in filter_cam_audio_tracks(track_keys)],
+                "bucket_name": bucket_name,
+                "transcript_id": transcript.id,
+                "room_id": room.id,
+            },
+            additional_metadata={
+                "transcript_id": transcript.id,
+                "recording_id": recording_id,
+                "daily_recording_id": recording_id,
+            },
+        )
+        logger.info(
+            "Started Hatchet workflow",
+            workflow_id=workflow_id,
+            transcript_id=transcript.id,
+        )
+
+        await transcripts_controller.update(
+            transcript, {"workflow_run_id": workflow_id}
+        )
+        return
+
+    # Celery pipeline (runs when durable workflows disabled)
     task_pipeline_multitrack_process.delay(
         transcript_id=transcript.id,
         bucket_name=bucket_name,
@@ -772,6 +812,11 @@ async def reprocess_failed_daily_recordings():
                 )
                 continue
 
+            # Fetch room to check use_hatchet flag
+            room = None
+            if meeting.room_id:
+                room = await rooms_controller.get_by_id(meeting.room_id)
+
             transcript = None
             try:
                 transcript = await transcripts_controller.get_by_recording_id(
@@ -791,8 +836,49 @@ async def reprocess_failed_daily_recordings():
                 )
                 continue
 
+            use_hatchet = settings.HATCHET_ENABLED or (room and room.use_hatchet)
+
+            if use_hatchet:
+                # Hatchet requires a transcript for workflow_run_id tracking
+                if not transcript:
+                    logger.warning(
+                        "No transcript for Hatchet reprocessing, skipping",
+                        recording_id=recording.id,
+                    )
+                    continue
+
+                workflow_id = await HatchetClientManager.start_workflow(
+                    workflow_name="DiarizationPipeline",
+                    input_data={
+                        "recording_id": recording.id,
+                        "tracks": [
+                            {"s3_key": k}
+                            for k in filter_cam_audio_tracks(recording.track_keys)
+                        ],
+                        "bucket_name": bucket_name,
+                        "transcript_id": transcript.id,
+                        "room_id": room.id if room else None,
+                    },
+                    additional_metadata={
+                        "transcript_id": transcript.id,
+                        "recording_id": recording.id,
+                        "reprocess": True,
+                    },
+                )
+                await transcripts_controller.update(
+                    transcript, {"workflow_run_id": workflow_id}
+                )
+
                 logger.info(
-                "Queueing Daily recording for reprocessing",
+                    "Queued Daily recording for Hatchet reprocessing",
+                    recording_id=recording.id,
+                    workflow_id=workflow_id,
+                    room_name=meeting.room_name,
+                    track_count=len(recording.track_keys),
+                )
+            else:
+                logger.info(
+                    "Queueing Daily recording for Celery reprocessing",
                     recording_id=recording.id,
                     room_name=meeting.room_name,
                     track_count=len(recording.track_keys),
@@ -805,6 +891,7 @@ async def reprocess_failed_daily_recordings():
                     recording_id=recording.id,
                     track_keys=recording.track_keys,
                 )
+
             reprocessed_count += 1
 
     except Exception as e:

server/reflector/ws_manager.py

@@ -16,6 +16,7 @@ import threading
 import redis.asyncio as redis
 from fastapi import WebSocket
 
+from reflector.events import subscribers_shutdown
 from reflector.settings import settings
 
 
@@ -109,29 +110,30 @@ class WebsocketManager:
                     await socket.send_json(data)
 
 
+_ws_manager_instance: WebsocketManager | None = None
+_ws_manager_lock = threading.Lock()
+
+
 def get_ws_manager() -> WebsocketManager:
-    """
+    """Returns the WebsocketManager singleton instance."""
-    Returns the WebsocketManager instance for managing websockets.
+    global _ws_manager_instance
-
+    if _ws_manager_instance is None:
-    This function initializes and returns the WebsocketManager instance,
+        with _ws_manager_lock:
-    which is responsible for managing websockets and handling websocket
+            if _ws_manager_instance is None:
-    connections.
-
-    Returns:
-        WebsocketManager: The initialized WebsocketManager instance.
-
-    Raises:
-        ImportError: If the 'reflector.settings' module cannot be imported.
-        RedisConnectionError: If there is an error connecting to the Redis server.
-    """
-    local = threading.local()
-    if hasattr(local, "ws_manager"):
-        return local.ws_manager
-
                 pubsub_client = RedisPubSubManager(
                     host=settings.REDIS_HOST,
                     port=settings.REDIS_PORT,
                 )
-    ws_manager = WebsocketManager(pubsub_client=pubsub_client)
+                _ws_manager_instance = WebsocketManager(pubsub_client=pubsub_client)
-    local.ws_manager = ws_manager
+    return _ws_manager_instance
-    return ws_manager
+
+
+async def cleanup_ws_manager(_app=None) -> None:
+    """Cleanup WebsocketManager singleton on shutdown."""
+    global _ws_manager_instance
+    if _ws_manager_instance is not None:
+        await _ws_manager_instance.pubsub_client.disconnect()
+        _ws_manager_instance = None
+
+
+subscribers_shutdown.append(cleanup_ws_manager)

server/reflector/zulip.py

@@ -3,7 +3,8 @@ from urllib.parse import urlparse
 
 import httpx
 
-from reflector.db.transcripts import Transcript
+from reflector.db.rooms import rooms_controller
+from reflector.db.transcripts import Transcript, transcripts_controller
 from reflector.settings import settings
 
 
@@ -113,6 +114,49 @@ def get_zulip_message(transcript: Transcript, include_topics: bool):
     return message
 
 
+async def post_transcript_notification(transcript: Transcript) -> int | None:
+    """Post or update transcript notification in Zulip.
+
+    Uses transcript.room_id directly (Hatchet flow).
+    Celery's pipeline_post_to_zulip uses recording→meeting→room path instead.
+    DUPLICATION NOTE: This function will stay when we use Celery no more, and Celery one will be removed.
+    """
+    if not transcript.room_id:
+        return None
+
+    room = await rooms_controller.get_by_id(transcript.room_id)
+    if not room or not room.zulip_stream or not room.zulip_auto_post:
+        return None
+
+    message = get_zulip_message(transcript=transcript, include_topics=True)
+    message_updated = False
+
+    if transcript.zulip_message_id:
+        try:
+            await update_zulip_message(
+                transcript.zulip_message_id,
+                room.zulip_stream,
+                room.zulip_topic,
+                message,
+            )
+            message_updated = True
+        except Exception:
+            pass
+
+    if not message_updated:
+        response = await send_message_to_zulip(
+            room.zulip_stream, room.zulip_topic, message
+        )
+        message_id = response.get("id")
+        if message_id:
+            await transcripts_controller.update(
+                transcript, {"zulip_message_id": message_id}
+            )
+        return message_id
+
+    return transcript.zulip_message_id
+
+
 def extract_domain(url: str) -> str:
     return urlparse(url).netloc
 

server/runserver.sh

@@ -7,6 +7,8 @@ elif [ "${ENTRYPOINT}" = "worker" ]; then
     uv run celery -A reflector.worker.app worker --loglevel=info
 elif [ "${ENTRYPOINT}" = "beat" ]; then
     uv run celery -A reflector.worker.app beat --loglevel=info
+elif [ "${ENTRYPOINT}" = "hatchet-worker" ]; then
+    uv run python -m reflector.hatchet.run_workers
 else
     echo "Unknown command"
 fi

server/tests/conftest.py

@@ -527,6 +527,22 @@ def fake_mp3_upload():
         yield
 
 
+@pytest.fixture(autouse=True)
+def reset_hatchet_client():
+    """Reset HatchetClientManager singleton before and after each test.
+
+    This ensures test isolation - each test starts with a fresh client state.
+    The fixture is autouse=True so it applies to all tests automatically.
+    """
+    from reflector.hatchet.client import HatchetClientManager
+
+    # Reset before test
+    HatchetClientManager.reset()
+    yield
+    # Reset after test to clean up
+    HatchetClientManager.reset()
+
+
 @pytest.fixture
 async def fake_transcript_with_topics(tmpdir, client):
     import shutil

server/tests/test_hatchet_client.py

@@ -0,0 +1,54 @@
+"""
+Tests for HatchetClientManager error handling and validation.
+
+Only tests that catch real bugs - not mock verification tests.
+
+Note: The `reset_hatchet_client` fixture (autouse=True in conftest.py)
+automatically resets the singleton before and after each test.
+"""
+
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+
+
+@pytest.mark.asyncio
+async def test_hatchet_client_can_replay_handles_exception():
+    """Test can_replay returns False when status check fails.
+
+    Useful: Ensures network/API errors don't crash the system and
+    gracefully allow reprocessing when workflow state is unknown.
+    """
+    from reflector.hatchet.client import HatchetClientManager
+
+    with patch("reflector.hatchet.client.settings") as mock_settings:
+        mock_settings.HATCHET_CLIENT_TOKEN = "test-token"
+        mock_settings.HATCHET_DEBUG = False
+
+        with patch("reflector.hatchet.client.Hatchet") as mock_hatchet_class:
+            mock_client = MagicMock()
+            mock_hatchet_class.return_value = mock_client
+
+            mock_client.runs.aio_get_status = AsyncMock(
+                side_effect=Exception("Network error")
+            )
+
+            can_replay = await HatchetClientManager.can_replay("workflow-123")
+
+            # Should return False on error (workflow might be gone)
+            assert can_replay is False
+
+
+def test_hatchet_client_raises_without_token():
+    """Test that get_client raises ValueError without token.
+
+    Useful: Catches if someone removes the token validation,
+    which would cause cryptic errors later.
+    """
+    from reflector.hatchet.client import HatchetClientManager
+
+    with patch("reflector.hatchet.client.settings") as mock_settings:
+        mock_settings.HATCHET_CLIENT_TOKEN = None
+
+        with pytest.raises(ValueError, match="HATCHET_CLIENT_TOKEN must be set"):
+            HatchetClientManager.get_client()

server/tests/test_hatchet_dispatch.py

@@ -0,0 +1,398 @@
+"""
+Tests for Hatchet workflow dispatch and routing logic.
+
+These tests verify:
+1. Routing to Hatchet when HATCHET_ENABLED=True
+2. Replay logic for failed workflows
+3. Force flag to cancel and restart
+4. Validation prevents concurrent workflows
+"""
+
+from unittest.mock import AsyncMock, patch
+
+import pytest
+from hatchet_sdk.clients.rest.exceptions import ApiException
+from hatchet_sdk.clients.rest.models import V1TaskStatus
+
+from reflector.db.transcripts import Transcript
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_hatchet_validation_blocks_running_workflow():
+    """Test that validation blocks reprocessing when workflow is running."""
+    from reflector.services.transcript_process import (
+        ValidationAlreadyScheduled,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="processing",
+        source_kind="room",
+        workflow_run_id="running-workflow-123",
+    )
+
+    with patch("reflector.services.transcript_process.settings") as mock_settings:
+        mock_settings.HATCHET_ENABLED = True
+
+        with patch(
+            "reflector.services.transcript_process.HatchetClientManager"
+        ) as mock_hatchet:
+            mock_hatchet.get_workflow_run_status = AsyncMock(
+                return_value=V1TaskStatus.RUNNING
+            )
+
+            with patch(
+                "reflector.services.transcript_process.task_is_scheduled_or_active"
+            ) as mock_celery_check:
+                mock_celery_check.return_value = False
+
+                result = await validate_transcript_for_processing(mock_transcript)
+
+            assert isinstance(result, ValidationAlreadyScheduled)
+            assert "running" in result.detail.lower()
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_hatchet_validation_blocks_queued_workflow():
+    """Test that validation blocks reprocessing when workflow is queued."""
+    from reflector.services.transcript_process import (
+        ValidationAlreadyScheduled,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="processing",
+        source_kind="room",
+        workflow_run_id="queued-workflow-123",
+    )
+
+    with patch("reflector.services.transcript_process.settings") as mock_settings:
+        mock_settings.HATCHET_ENABLED = True
+
+        with patch(
+            "reflector.services.transcript_process.HatchetClientManager"
+        ) as mock_hatchet:
+            mock_hatchet.get_workflow_run_status = AsyncMock(
+                return_value=V1TaskStatus.QUEUED
+            )
+
+            with patch(
+                "reflector.services.transcript_process.task_is_scheduled_or_active"
+            ) as mock_celery_check:
+                mock_celery_check.return_value = False
+
+                result = await validate_transcript_for_processing(mock_transcript)
+
+            assert isinstance(result, ValidationAlreadyScheduled)
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_hatchet_validation_allows_failed_workflow():
+    """Test that validation allows reprocessing when workflow has failed."""
+    from reflector.services.transcript_process import (
+        ValidationOk,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="error",
+        source_kind="room",
+        workflow_run_id="failed-workflow-123",
+        recording_id="test-recording-id",
+    )
+
+    with patch("reflector.services.transcript_process.settings") as mock_settings:
+        mock_settings.HATCHET_ENABLED = True
+
+        with patch(
+            "reflector.services.transcript_process.HatchetClientManager"
+        ) as mock_hatchet:
+            mock_hatchet.get_workflow_run_status = AsyncMock(
+                return_value=V1TaskStatus.FAILED
+            )
+
+            with patch(
+                "reflector.services.transcript_process.task_is_scheduled_or_active"
+            ) as mock_celery_check:
+                mock_celery_check.return_value = False
+
+                result = await validate_transcript_for_processing(mock_transcript)
+
+            assert isinstance(result, ValidationOk)
+            assert result.transcript_id == "test-transcript-id"
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_hatchet_validation_allows_completed_workflow():
+    """Test that validation allows reprocessing when workflow has completed."""
+    from reflector.services.transcript_process import (
+        ValidationOk,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="ended",
+        source_kind="room",
+        workflow_run_id="completed-workflow-123",
+        recording_id="test-recording-id",
+    )
+
+    with patch("reflector.services.transcript_process.settings") as mock_settings:
+        mock_settings.HATCHET_ENABLED = True
+
+        with patch(
+            "reflector.services.transcript_process.HatchetClientManager"
+        ) as mock_hatchet:
+            mock_hatchet.get_workflow_run_status = AsyncMock(
+                return_value=V1TaskStatus.COMPLETED
+            )
+
+            with patch(
+                "reflector.services.transcript_process.task_is_scheduled_or_active"
+            ) as mock_celery_check:
+                mock_celery_check.return_value = False
+
+                result = await validate_transcript_for_processing(mock_transcript)
+
+            assert isinstance(result, ValidationOk)
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_hatchet_validation_allows_when_status_check_fails():
+    """Test that validation allows reprocessing when status check fails (workflow might be gone)."""
+    from reflector.services.transcript_process import (
+        ValidationOk,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="error",
+        source_kind="room",
+        workflow_run_id="old-workflow-123",
+        recording_id="test-recording-id",
+    )
+
+    with patch("reflector.services.transcript_process.settings") as mock_settings:
+        mock_settings.HATCHET_ENABLED = True
+
+        with patch(
+            "reflector.services.transcript_process.HatchetClientManager"
+        ) as mock_hatchet:
+            # Status check fails (workflow might be deleted)
+            mock_hatchet.get_workflow_run_status = AsyncMock(
+                side_effect=ApiException("Workflow not found")
+            )
+
+            with patch(
+                "reflector.services.transcript_process.task_is_scheduled_or_active"
+            ) as mock_celery_check:
+                mock_celery_check.return_value = False
+
+                result = await validate_transcript_for_processing(mock_transcript)
+
+            # Should allow processing when we can't get status
+            assert isinstance(result, ValidationOk)
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_hatchet_validation_skipped_when_no_workflow_id():
+    """Test that Hatchet validation is skipped when transcript has no workflow_run_id."""
+    from reflector.services.transcript_process import (
+        ValidationOk,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="uploaded",
+        source_kind="room",
+        workflow_run_id=None,  # No workflow yet
+        recording_id="test-recording-id",
+    )
+
+    with patch("reflector.services.transcript_process.settings") as mock_settings:
+        mock_settings.HATCHET_ENABLED = True
+
+        with patch(
+            "reflector.services.transcript_process.HatchetClientManager"
+        ) as mock_hatchet:
+            # Should not be called
+            mock_hatchet.get_workflow_run_status = AsyncMock()
+
+            with patch(
+                "reflector.services.transcript_process.task_is_scheduled_or_active"
+            ) as mock_celery_check:
+                mock_celery_check.return_value = False
+
+                result = await validate_transcript_for_processing(mock_transcript)
+
+            # Should not check Hatchet status
+            mock_hatchet.get_workflow_run_status.assert_not_called()
+            assert isinstance(result, ValidationOk)
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_hatchet_validation_skipped_when_disabled():
+    """Test that Hatchet validation is skipped when HATCHET_ENABLED is False."""
+    from reflector.services.transcript_process import (
+        ValidationOk,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="uploaded",
+        source_kind="room",
+        workflow_run_id="some-workflow-123",
+        recording_id="test-recording-id",
+    )
+
+    with patch("reflector.services.transcript_process.settings") as mock_settings:
+        mock_settings.HATCHET_ENABLED = False  # Hatchet disabled
+
+        with patch(
+            "reflector.services.transcript_process.task_is_scheduled_or_active"
+        ) as mock_celery_check:
+            mock_celery_check.return_value = False
+
+            result = await validate_transcript_for_processing(mock_transcript)
+
+        # Should not check Hatchet at all
+        assert isinstance(result, ValidationOk)
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_validation_locked_transcript():
+    """Test that validation rejects locked transcripts."""
+    from reflector.services.transcript_process import (
+        ValidationLocked,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="ended",
+        source_kind="room",
+        locked=True,
+    )
+
+    result = await validate_transcript_for_processing(mock_transcript)
+
+    assert isinstance(result, ValidationLocked)
+    assert "locked" in result.detail.lower()
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_validation_idle_transcript():
+    """Test that validation rejects idle transcripts (not ready)."""
+    from reflector.services.transcript_process import (
+        ValidationNotReady,
+        validate_transcript_for_processing,
+    )
+
+    mock_transcript = Transcript(
+        id="test-transcript-id",
+        name="Test",
+        status="idle",
+        source_kind="room",
+    )
+
+    result = await validate_transcript_for_processing(mock_transcript)
+
+    assert isinstance(result, ValidationNotReady)
+    assert "not ready" in result.detail.lower()
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_prepare_multitrack_config():
+    """Test preparing multitrack processing config."""
+    from reflector.db.recordings import Recording
+    from reflector.services.transcript_process import (
+        MultitrackProcessingConfig,
+        ValidationOk,
+        prepare_transcript_processing,
+    )
+
+    validation = ValidationOk(
+        recording_id="test-recording-id",
+        transcript_id="test-transcript-id",
+    )
+
+    mock_recording = Recording(
+        id="test-recording-id",
+        bucket_name="test-bucket",
+        object_key="recordings/test",
+        recorded_at="2024-01-01T00:00:00Z",
+        track_keys=["track1.webm", "track2.webm"],
+    )
+
+    with patch(
+        "reflector.services.transcript_process.recordings_controller"
+    ) as mock_rc:
+        mock_rc.get_by_id = AsyncMock(return_value=mock_recording)
+
+        result = await prepare_transcript_processing(validation)
+
+    assert isinstance(result, MultitrackProcessingConfig)
+    assert result.bucket_name == "test-bucket"
+    assert result.track_keys == ["track1.webm", "track2.webm"]
+    assert result.transcript_id == "test-transcript-id"
+    assert result.room_id is None  # ValidationOk didn't specify room_id
+
+
+@pytest.mark.usefixtures("setup_database")
+@pytest.mark.asyncio
+async def test_prepare_file_config():
+    """Test preparing file processing config (no track keys)."""
+    from reflector.db.recordings import Recording
+    from reflector.services.transcript_process import (
+        FileProcessingConfig,
+        ValidationOk,
+        prepare_transcript_processing,
+    )
+
+    validation = ValidationOk(
+        recording_id="test-recording-id",
+        transcript_id="test-transcript-id",
+    )
+
+    mock_recording = Recording(
+        id="test-recording-id",
+        bucket_name="test-bucket",
+        object_key="recordings/test.mp4",
+        recorded_at="2024-01-01T00:00:00Z",
+        track_keys=None,  # No track keys = file pipeline
+    )
+
+    with patch(
+        "reflector.services.transcript_process.recordings_controller"
+    ) as mock_rc:
+        mock_rc.get_by_id = AsyncMock(return_value=mock_recording)
+
+        result = await prepare_transcript_processing(validation)
+
+    assert isinstance(result, FileProcessingConfig)
+    assert result.transcript_id == "test-transcript-id"