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feat: durable (#794)
* durable (no-mistakes) * hatchet no-mistake * hatchet no-mistake * hatchet no-mistake, better logging * remove conductor and add hatchet tests (no-mistakes) * self-review (no-mistakes) * hatched logs * remove shadow mode for hatchet * and add hatchet processor setting to room * . * cleanup * hatchet init db * self-review (no-mistakes) * self-review (no-mistakes) * hatchet: restore zullip report * self-review round * self-review round * self-review round * dry hatchet with celery * dry hatched with celery - 2 * self-review round * more NES instead of str * self-review wip * self-review round * self-review round * self-review round * can_replay cancelled * add forgotten file * pr autoreviewer fixes * better log webhook events * durable_started return * migration sync * latest changes feature parity * migration merge * pr review --------- Co-authored-by: Igor Loskutov <igor.loskutoff@gmail.com>
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222
server/reflector/hatchet/workflows/track_processing.py
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222
server/reflector/hatchet/workflows/track_processing.py
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"""
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Hatchet child workflow: TrackProcessing
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Handles individual audio track processing: padding and transcription.
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Spawned dynamically by the main diarization pipeline for each track.
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Architecture note: This is a separate workflow (not inline tasks in DiarizationPipeline)
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because Hatchet workflow DAGs are defined statically, but the number of tracks varies
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at runtime. Child workflow spawning via `aio_run()` + `asyncio.gather()` is the
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standard pattern for dynamic fan-out. See `process_tracks` in diarization_pipeline.py.
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Note: This file uses deferred imports (inside tasks) intentionally.
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Hatchet workers run in forked processes; fresh imports per task ensure
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storage/DB connections are not shared across forks.
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"""
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import tempfile
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from datetime import timedelta
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from pathlib import Path
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import av
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from hatchet_sdk import Context
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from pydantic import BaseModel
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from reflector.hatchet.client import HatchetClientManager
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from reflector.hatchet.workflows.models import PadTrackResult, TranscribeTrackResult
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from reflector.logger import logger
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from reflector.utils.audio_constants import PRESIGNED_URL_EXPIRATION_SECONDS
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from reflector.utils.audio_padding import (
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apply_audio_padding_to_file,
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extract_stream_start_time_from_container,
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)
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class TrackInput(BaseModel):
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"""Input for individual track processing."""
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track_index: int
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s3_key: str
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bucket_name: str
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transcript_id: str
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language: str = "en"
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hatchet = HatchetClientManager.get_client()
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track_workflow = hatchet.workflow(name="TrackProcessing", input_validator=TrackInput)
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@track_workflow.task(execution_timeout=timedelta(seconds=300), retries=3)
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async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
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"""Pad single audio track with silence for alignment.
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Extracts stream.start_time from WebM container metadata and applies
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silence padding using PyAV filter graph (adelay).
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"""
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ctx.log(f"pad_track: track {input.track_index}, s3_key={input.s3_key}")
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logger.info(
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"[Hatchet] pad_track",
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track_index=input.track_index,
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s3_key=input.s3_key,
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transcript_id=input.transcript_id,
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)
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try:
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# Create fresh storage instance to avoid aioboto3 fork issues
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from reflector.settings import settings # noqa: PLC0415
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from reflector.storage.storage_aws import AwsStorage # noqa: PLC0415
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storage = AwsStorage(
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aws_bucket_name=settings.TRANSCRIPT_STORAGE_AWS_BUCKET_NAME,
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aws_region=settings.TRANSCRIPT_STORAGE_AWS_REGION,
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aws_access_key_id=settings.TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID,
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aws_secret_access_key=settings.TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY,
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)
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source_url = await storage.get_file_url(
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input.s3_key,
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operation="get_object",
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expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
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bucket=input.bucket_name,
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)
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with av.open(source_url) as in_container:
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start_time_seconds = extract_stream_start_time_from_container(
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in_container, input.track_index, logger=logger
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)
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# If no padding needed, return original S3 key
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if start_time_seconds <= 0:
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logger.info(
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f"Track {input.track_index} requires no padding",
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track_index=input.track_index,
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)
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return PadTrackResult(
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padded_key=input.s3_key,
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bucket_name=input.bucket_name,
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size=0,
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track_index=input.track_index,
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)
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with tempfile.NamedTemporaryFile(suffix=".webm", delete=False) as temp_file:
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temp_path = temp_file.name
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try:
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apply_audio_padding_to_file(
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in_container,
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temp_path,
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start_time_seconds,
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input.track_index,
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logger=logger,
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)
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file_size = Path(temp_path).stat().st_size
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storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
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logger.info(
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f"About to upload padded track",
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key=storage_path,
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size=file_size,
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)
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with open(temp_path, "rb") as padded_file:
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await storage.put_file(storage_path, padded_file)
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logger.info(
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f"Uploaded padded track to S3",
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key=storage_path,
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size=file_size,
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)
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finally:
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Path(temp_path).unlink(missing_ok=True)
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ctx.log(f"pad_track complete: track {input.track_index} -> {storage_path}")
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logger.info(
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"[Hatchet] pad_track complete",
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track_index=input.track_index,
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padded_key=storage_path,
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)
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# Return S3 key (not presigned URL) - consumer tasks presign on demand
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# This avoids stale URLs when workflow is replayed
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return PadTrackResult(
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padded_key=storage_path,
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bucket_name=None, # None = use default transcript storage bucket
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size=file_size,
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track_index=input.track_index,
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)
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except Exception as e:
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logger.error("[Hatchet] pad_track failed", error=str(e), exc_info=True)
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raise
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@track_workflow.task(
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parents=[pad_track], execution_timeout=timedelta(seconds=600), retries=3
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)
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async def transcribe_track(input: TrackInput, ctx: Context) -> TranscribeTrackResult:
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"""Transcribe audio track using GPU (Modal.com) or local Whisper."""
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ctx.log(f"transcribe_track: track {input.track_index}, language={input.language}")
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logger.info(
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"[Hatchet] transcribe_track",
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track_index=input.track_index,
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language=input.language,
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)
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try:
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pad_result = ctx.task_output(pad_track)
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padded_key = pad_result.padded_key
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bucket_name = pad_result.bucket_name
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if not padded_key:
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raise ValueError("Missing padded_key from pad_track")
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# Presign URL on demand (avoids stale URLs on workflow replay)
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from reflector.settings import settings # noqa: PLC0415
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from reflector.storage.storage_aws import AwsStorage # noqa: PLC0415
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storage = AwsStorage(
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aws_bucket_name=settings.TRANSCRIPT_STORAGE_AWS_BUCKET_NAME,
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aws_region=settings.TRANSCRIPT_STORAGE_AWS_REGION,
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aws_access_key_id=settings.TRANSCRIPT_STORAGE_AWS_ACCESS_KEY_ID,
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aws_secret_access_key=settings.TRANSCRIPT_STORAGE_AWS_SECRET_ACCESS_KEY,
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)
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audio_url = await storage.get_file_url(
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padded_key,
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operation="get_object",
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expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
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bucket=bucket_name,
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)
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from reflector.pipelines.transcription_helpers import ( # noqa: PLC0415
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transcribe_file_with_processor,
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)
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transcript = await transcribe_file_with_processor(audio_url, input.language)
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# Tag all words with speaker index
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words = []
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for word in transcript.words:
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word_dict = word.model_dump()
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word_dict["speaker"] = input.track_index
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words.append(word_dict)
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ctx.log(
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f"transcribe_track complete: track {input.track_index}, {len(words)} words"
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)
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logger.info(
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"[Hatchet] transcribe_track complete",
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track_index=input.track_index,
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word_count=len(words),
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)
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return TranscribeTrackResult(
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words=words,
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track_index=input.track_index,
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)
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except Exception as e:
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logger.error("[Hatchet] transcribe_track failed", error=str(e), exc_info=True)
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raise
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