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https://github.com/Monadical-SAS/reflector.git
synced 2026-02-05 02:16:46 +00:00
feat: modal padding (#837)
* Add Modal backend for audio padding - Create reflector_padding.py Modal deployment (CPU-based) - Add PaddingWorkflow with conditional Modal/local backend - Update deploy-all.sh to include padding deployment --------- Co-authored-by: Igor Loskutov <igor.loskutoff@gmail.com>
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165
server/reflector/hatchet/workflows/padding_workflow.py
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165
server/reflector/hatchet/workflows/padding_workflow.py
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"""
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Hatchet child workflow: PaddingWorkflow
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Handles individual audio track padding via Modal.com backend.
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"""
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from datetime import timedelta
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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.constants import TIMEOUT_AUDIO
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from reflector.hatchet.workflows.models import PadTrackResult
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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 extract_stream_start_time_from_container
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class PaddingInput(BaseModel):
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"""Input for individual track padding."""
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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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hatchet = HatchetClientManager.get_client()
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padding_workflow = hatchet.workflow(
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name="PaddingWorkflow", input_validator=PaddingInput
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)
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@padding_workflow.task(execution_timeout=timedelta(seconds=TIMEOUT_AUDIO), retries=3)
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async def pad_track(input: PaddingInput, ctx: Context) -> PadTrackResult:
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"""Pad audio track with silence based on WebM container start_time."""
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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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# Extract start_time to determine if padding needed
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with av.open(source_url) as in_container:
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if in_container.duration:
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try:
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duration = timedelta(seconds=in_container.duration // 1_000_000)
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ctx.log(
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f"pad_track: track {input.track_index}, duration={duration}"
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)
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except (ValueError, TypeError, OverflowError) as e:
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ctx.log(
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f"pad_track: track {input.track_index}, duration error: {str(e)}"
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)
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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 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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storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
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# Presign PUT URL for output (Modal will upload directly)
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output_url = await storage.get_file_url(
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storage_path,
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operation="put_object",
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expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
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)
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import httpx # noqa: PLC0415
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from reflector.processors.audio_padding_modal import ( # noqa: PLC0415
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AudioPaddingModalProcessor,
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)
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try:
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processor = AudioPaddingModalProcessor()
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result = await processor.pad_track(
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track_url=source_url,
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output_url=output_url,
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start_time_seconds=start_time_seconds,
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track_index=input.track_index,
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)
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file_size = result.size
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ctx.log(f"pad_track: Modal returned size={file_size}")
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except httpx.HTTPStatusError as e:
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error_detail = e.response.text if hasattr(e.response, "text") else str(e)
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logger.error(
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"[Hatchet] Modal padding HTTP error",
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transcript_id=input.transcript_id,
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track_index=input.track_index,
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status_code=e.response.status_code if hasattr(e, "response") else None,
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error=error_detail,
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exc_info=True,
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)
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raise Exception(
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f"Modal padding failed: HTTP {e.response.status_code}"
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) from e
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except httpx.TimeoutException as e:
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logger.error(
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"[Hatchet] Modal padding timeout",
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transcript_id=input.transcript_id,
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track_index=input.track_index,
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error=str(e),
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exc_info=True,
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)
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raise Exception("Modal padding timeout") from e
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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 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(
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"[Hatchet] pad_track failed",
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transcript_id=input.transcript_id,
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track_index=input.track_index,
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error=str(e),
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exc_info=True,
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)
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raise
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@@ -14,9 +14,7 @@ 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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@@ -27,10 +25,7 @@ from reflector.hatchet.constants import TIMEOUT_AUDIO, TIMEOUT_HEAVY
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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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from reflector.utils.audio_padding import extract_stream_start_time_from_container
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class TrackInput(BaseModel):
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@@ -83,63 +78,44 @@ async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
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)
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with av.open(source_url) as in_container:
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if in_container.duration:
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try:
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duration = timedelta(seconds=in_container.duration // 1_000_000)
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ctx.log(
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f"pad_track: track {input.track_index}, duration={duration}"
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)
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except Exception:
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ctx.log(f"pad_track: track {input.track_index}, duration=ERROR")
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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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# 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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storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
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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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# Presign PUT URL for output (Modal uploads directly)
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output_url = await storage.get_file_url(
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storage_path,
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operation="put_object",
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expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
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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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from reflector.processors.audio_padding_modal import ( # noqa: PLC0415
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AudioPaddingModalProcessor,
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)
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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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processor = AudioPaddingModalProcessor()
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result = await processor.pad_track(
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track_url=source_url,
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output_url=output_url,
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start_time_seconds=start_time_seconds,
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track_index=input.track_index,
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)
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file_size = result.size
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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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