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feat: modal padding (#837)

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* 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>
This commit is contained in:
Igor Monadical
2026-01-30 13:11:51 -05:00
committed by GitHub
parent 2ca624f052
commit 7fde64e252
11 changed files with 625 additions and 82 deletions
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1
.gitignore vendored
View File

@@ -1,5 +1,6 @@
.DS_Store
server/.env
server/.env.production
.env
Caddyfile
server/exportdanswer

11
gpu/modal_deployments/deploy-all.sh
View File

@@ -131,6 +131,15 @@ if [ -z "$DIARIZER_URL" ]; then
fi
echo " -> $DIARIZER_URL"
echo ""
echo "Deploying padding (CPU audio processing via Modal SDK)..."
modal deploy reflector_padding.py
if [ $? -ne 0 ]; then
echo "Error: Failed to deploy padding. Check Modal dashboard for details."
exit 1
fi
echo " -> reflector-padding.pad_track (Modal SDK function)"
# --- Output Configuration ---
echo ""
echo "=========================================="
@@ -147,4 +156,6 @@ echo ""
echo "DIARIZATION_BACKEND=modal"
echo "DIARIZATION_URL=$DIARIZER_URL"
echo "DIARIZATION_MODAL_API_KEY=$API_KEY"
echo ""
echo "# Padding uses Modal SDK (requires MODAL_TOKEN_ID/SECRET in worker containers)"
echo "# --- End Modal Configuration ---"

277
gpu/modal_deployments/reflector_padding.py Normal file
View File

@@ -0,0 +1,277 @@
"""
Reflector GPU backend - audio padding
======================================
CPU-intensive audio padding service for adding silence to audio tracks.
Uses PyAV filter graph (adelay) for precise track synchronization.
IMPORTANT: This padding logic is duplicated from server/reflector/utils/audio_padding.py
for Modal deployment isolation (Modal can't import from server/reflector/). If you modify
the PyAV filter graph or padding algorithm, you MUST update both:
- gpu/modal_deployments/reflector_padding.py (this file)
- server/reflector/utils/audio_padding.py
Constants duplicated from server/reflector/utils/audio_constants.py for same reason.
"""
import os
import tempfile
from fractions import Fraction
import math
import asyncio
import modal
S3_TIMEOUT = 60 # happens 2 times
PADDING_TIMEOUT = 600 + (S3_TIMEOUT * 2)
SCALEDOWN_WINDOW = 60 # The maximum duration (in seconds) that individual containers can remain idle when scaling down.
DISCONNECT_CHECK_INTERVAL = 2 # Check for client disconnect
app = modal.App("reflector-padding")
# CPU-based image
image = (
modal.Image.debian_slim(python_version="3.12")
.apt_install("ffmpeg") # Required by PyAV
.pip_install(
"av==13.1.0", # PyAV for audio processing
"requests==2.32.3", # HTTP for presigned URL downloads/uploads
"fastapi==0.115.12", # API framework
)
)
# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
OPUS_STANDARD_SAMPLE_RATE = 48000
# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
OPUS_DEFAULT_BIT_RATE = 128000
@app.function(
cpu=2.0,
timeout=PADDING_TIMEOUT,
scaledown_window=SCALEDOWN_WINDOW,
image=image,
)
@modal.asgi_app()
def web():
from fastapi import FastAPI, Request, HTTPException
from pydantic import BaseModel
class PaddingRequest(BaseModel):
track_url: str
output_url: str
start_time_seconds: float
track_index: int
class PaddingResponse(BaseModel):
size: int
cancelled: bool = False
web_app = FastAPI()
@web_app.post("/pad")
async def pad_track_endpoint(request: Request, req: PaddingRequest) -> PaddingResponse:
"""Modal web endpoint for padding audio tracks with disconnect detection.
"""
import logging
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)
if not req.track_url:
raise HTTPException(status_code=400, detail="track_url cannot be empty")
if not req.output_url:
raise HTTPException(status_code=400, detail="output_url cannot be empty")
if req.start_time_seconds <= 0:
raise HTTPException(status_code=400, detail=f"start_time_seconds must be positive, got {req.start_time_seconds}")
if req.start_time_seconds > 18000:
raise HTTPException(status_code=400, detail=f"start_time_seconds exceeds maximum 18000s (5 hours)")
logger.info(f"Padding request: track {req.track_index}, delay={req.start_time_seconds}s")
# Thread-safe cancellation flag shared between async disconnect checker and blocking thread
import threading
cancelled = threading.Event()
async def check_disconnect():
"""Background task to check for client disconnect every 2 seconds."""
while not cancelled.is_set():
await asyncio.sleep(DISCONNECT_CHECK_INTERVAL)
if await request.is_disconnected():
logger.warning("Client disconnected, setting cancellation flag")
cancelled.set()
break
# Start disconnect checker in background
disconnect_task = asyncio.create_task(check_disconnect())
try:
result = await asyncio.get_event_loop().run_in_executor(
None, _pad_track_blocking, req, cancelled, logger
)
return PaddingResponse(**result)
finally:
cancelled.set()
disconnect_task.cancel()
try:
await disconnect_task
except asyncio.CancelledError:
pass
def _pad_track_blocking(req, cancelled, logger) -> dict:
"""Blocking CPU-bound padding work with periodic cancellation checks.
Args:
cancelled: threading.Event for thread-safe cancellation signaling
"""
import av
import requests
from av.audio.resampler import AudioResampler
import time
temp_dir = tempfile.mkdtemp()
input_path = None
output_path = None
last_check = time.time()
try:
logger.info("Downloading track for padding")
response = requests.get(req.track_url, stream=True, timeout=S3_TIMEOUT)
response.raise_for_status()
input_path = os.path.join(temp_dir, "track.webm")
total_bytes = 0
chunk_count = 0
with open(input_path, "wb") as f:
for chunk in response.iter_content(chunk_size=8192):
if chunk:
f.write(chunk)
total_bytes += len(chunk)
chunk_count += 1
# Check for cancellation every arbitrary amount of chunks
if chunk_count % 12 == 0:
now = time.time()
if now - last_check >= DISCONNECT_CHECK_INTERVAL:
if cancelled.is_set():
logger.info("Cancelled during download, exiting early")
return {"size": 0, "cancelled": True}
last_check = now
logger.info(f"Track downloaded: {total_bytes} bytes")
if cancelled.is_set():
logger.info("Cancelled after download, exiting early")
return {"size": 0, "cancelled": True}
# Apply padding using PyAV
output_path = os.path.join(temp_dir, "padded.webm")
delay_ms = math.floor(req.start_time_seconds * 1000)
logger.info(f"Padding track {req.track_index} with {delay_ms}ms delay using PyAV")
in_container = av.open(input_path)
in_stream = next((s for s in in_container.streams if s.type == "audio"), None)
if in_stream is None:
raise ValueError("No audio stream in input")
with av.open(output_path, "w", format="webm") as out_container:
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")
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
)
for frame in in_container.decode(in_stream):
# Check for cancellation periodically
now = time.time()
if now - last_check >= DISCONNECT_CHECK_INTERVAL:
if cancelled.is_set():
logger.info("Cancelled during processing, exiting early")
in_container.close()
return {"size": 0, "cancelled": True}
last_check = now
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 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)
in_container.close()
file_size = os.path.getsize(output_path)
logger.info(f"Padding complete: {file_size} bytes")
logger.info("Uploading padded track to S3")
with open(output_path, "rb") as f:
upload_response = requests.put(req.output_url, data=f, timeout=S3_TIMEOUT)
upload_response.raise_for_status()
logger.info(f"Upload complete: {file_size} bytes")
return {"size": file_size}
finally:
if input_path and os.path.exists(input_path):
try:
os.unlink(input_path)
except Exception as e:
logger.warning(f"Failed to cleanup input file: {e}")
if output_path and os.path.exists(output_path):
try:
os.unlink(output_path)
except Exception as e:
logger.warning(f"Failed to cleanup output file: {e}")
try:
os.rmdir(temp_dir)
except Exception as e:
logger.warning(f"Failed to cleanup temp directory: {e}")
return web_app

2
server/pyproject.toml
View File

@@ -8,7 +8,7 @@ readme = "README.md"
dependencies = [
"aiohttp>=3.9.0",
"aiohttp-cors>=0.7.0",
"av>=10.0.0",
"av>=15.0.0",
"requests>=2.31.0",
"aiortc>=1.5.0",
"sortedcontainers>=2.4.0",

2
server/reflector/hatchet/constants.py
View File

@@ -37,5 +37,5 @@ LLM_RATE_LIMIT_PER_SECOND = 10
TIMEOUT_SHORT = 60 # Quick operations: API calls, DB updates
TIMEOUT_MEDIUM = 120 # Single LLM calls, waveform generation
TIMEOUT_LONG = 180 # Action items (larger context LLM)
TIMEOUT_AUDIO = 300 # Audio processing: padding, mixdown
TIMEOUT_AUDIO = 720 # Audio processing: padding, mixdown
TIMEOUT_HEAVY = 600 # Transcription, fan-out LLM tasks

165
server/reflector/hatchet/workflows/padding_workflow.py Normal file
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@@ -0,0 +1,165 @@
"""
Hatchet child workflow: PaddingWorkflow
Handles individual audio track padding via Modal.com backend.
"""
from datetime import timedelta
import av
from hatchet_sdk import Context
from pydantic import BaseModel
from reflector.hatchet.client import HatchetClientManager
from reflector.hatchet.constants import TIMEOUT_AUDIO
from reflector.hatchet.workflows.models import PadTrackResult
from reflector.logger import logger
from reflector.utils.audio_constants import PRESIGNED_URL_EXPIRATION_SECONDS
from reflector.utils.audio_padding import extract_stream_start_time_from_container
class PaddingInput(BaseModel):
"""Input for individual track padding."""
track_index: int
s3_key: str
bucket_name: str
transcript_id: str
hatchet = HatchetClientManager.get_client()
padding_workflow = hatchet.workflow(
name="PaddingWorkflow", input_validator=PaddingInput
)
@padding_workflow.task(execution_timeout=timedelta(seconds=TIMEOUT_AUDIO), retries=3)
async def pad_track(input: PaddingInput, ctx: Context) -> PadTrackResult:
"""Pad audio track with silence based on WebM container start_time."""
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,
)
# Extract start_time to determine if padding needed
with av.open(source_url) as in_container:
if in_container.duration:
try:
duration = timedelta(seconds=in_container.duration // 1_000_000)
ctx.log(
f"pad_track: track {input.track_index}, duration={duration}"
)
except (ValueError, TypeError, OverflowError) as e:
ctx.log(
f"pad_track: track {input.track_index}, duration error: {str(e)}"
)
start_time_seconds = extract_stream_start_time_from_container(
in_container, input.track_index, logger=logger
)
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,
)
storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
# Presign PUT URL for output (Modal will upload directly)
output_url = await storage.get_file_url(
storage_path,
operation="put_object",
expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
)
import httpx # noqa: PLC0415
from reflector.processors.audio_padding_modal import ( # noqa: PLC0415
AudioPaddingModalProcessor,
)
try:
processor = AudioPaddingModalProcessor()
result = await processor.pad_track(
track_url=source_url,
output_url=output_url,
start_time_seconds=start_time_seconds,
track_index=input.track_index,
)
file_size = result.size
ctx.log(f"pad_track: Modal returned size={file_size}")
except httpx.HTTPStatusError as e:
error_detail = e.response.text if hasattr(e.response, "text") else str(e)
logger.error(
"[Hatchet] Modal padding HTTP error",
transcript_id=input.transcript_id,
track_index=input.track_index,
status_code=e.response.status_code if hasattr(e, "response") else None,
error=error_detail,
exc_info=True,
)
raise Exception(
f"Modal padding failed: HTTP {e.response.status_code}"
) from e
except httpx.TimeoutException as e:
logger.error(
"[Hatchet] Modal padding timeout",
transcript_id=input.transcript_id,
track_index=input.track_index,
error=str(e),
exc_info=True,
)
raise Exception("Modal padding timeout") from e
logger.info(
"[Hatchet] pad_track complete",
track_index=input.track_index,
padded_key=storage_path,
)
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",
transcript_id=input.transcript_id,
track_index=input.track_index,
error=str(e),
exc_info=True,
)
raise

86
server/reflector/hatchet/workflows/track_processing.py
View File

@@ -14,9 +14,7 @@ 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
@@ -27,10 +25,7 @@ from reflector.hatchet.constants import TIMEOUT_AUDIO, TIMEOUT_HEAVY
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,
)
from reflector.utils.audio_padding import extract_stream_start_time_from_container
class TrackInput(BaseModel):
@@ -83,63 +78,44 @@ async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
)
with av.open(source_url) as in_container:
if in_container.duration:
try:
duration = timedelta(seconds=in_container.duration // 1_000_000)
ctx.log(
f"pad_track: track {input.track_index}, duration={duration}"
)
except Exception:
ctx.log(f"pad_track: track {input.track_index}, duration=ERROR")
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,
)
# 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
storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
try:
apply_audio_padding_to_file(
in_container,
temp_path,
start_time_seconds,
input.track_index,
logger=logger,
)
# Presign PUT URL for output (Modal uploads directly)
output_url = await storage.get_file_url(
storage_path,
operation="put_object",
expires_in=PRESIGNED_URL_EXPIRATION_SECONDS,
)
file_size = Path(temp_path).stat().st_size
storage_path = f"file_pipeline_hatchet/{input.transcript_id}/tracks/padded_{input.track_index}.webm"
from reflector.processors.audio_padding_modal import ( # noqa: PLC0415
AudioPaddingModalProcessor,
)
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)
processor = AudioPaddingModalProcessor()
result = await processor.pad_track(
track_url=source_url,
output_url=output_url,
start_time_seconds=start_time_seconds,
track_index=input.track_index,
)
file_size = result.size
ctx.log(f"pad_track complete: track {input.track_index} -> {storage_path}")
logger.info(

112
server/reflector/processors/audio_padding_modal.py Normal file
View File

@@ -0,0 +1,112 @@
"""
Modal.com backend for audio padding.
"""
import asyncio
import os
import httpx
from pydantic import BaseModel
from reflector.logger import logger
class PaddingResponse(BaseModel):
size: int
cancelled: bool = False
class AudioPaddingModalProcessor:
"""Audio padding processor using Modal.com CPU backend via HTTP."""
def __init__(
self, padding_url: str | None = None, modal_api_key: str | None = None
):
self.padding_url = padding_url or os.getenv("PADDING_URL")
if not self.padding_url:
raise ValueError(
"PADDING_URL required to use AudioPaddingModalProcessor. "
"Set PADDING_URL environment variable or pass padding_url parameter."
)
self.modal_api_key = modal_api_key or os.getenv("MODAL_API_KEY")
async def pad_track(
self,
track_url: str,
output_url: str,
start_time_seconds: float,
track_index: int,
) -> PaddingResponse:
"""Pad audio track with silence via Modal backend.
Args:
track_url: Presigned GET URL for source audio track
output_url: Presigned PUT URL for output WebM
start_time_seconds: Amount of silence to prepend
track_index: Track index for logging
"""
if not track_url:
raise ValueError("track_url cannot be empty")
if start_time_seconds <= 0:
raise ValueError(
f"start_time_seconds must be positive, got {start_time_seconds}"
)
log = logger.bind(track_index=track_index, padding_seconds=start_time_seconds)
log.info("Sending Modal padding HTTP request")
url = f"{self.padding_url}/pad"
headers = {}
if self.modal_api_key:
headers["Authorization"] = f"Bearer {self.modal_api_key}"
try:
async with httpx.AsyncClient() as client:
response = await client.post(
url,
headers=headers,
json={
"track_url": track_url,
"output_url": output_url,
"start_time_seconds": start_time_seconds,
"track_index": track_index,
},
follow_redirects=True,
)
if response.status_code != 200:
error_body = response.text
log.error(
"Modal padding API error",
status_code=response.status_code,
error_body=error_body,
)
response.raise_for_status()
result = response.json()
# Check if work was cancelled
if result.get("cancelled"):
log.warning("Modal padding was cancelled by disconnect detection")
raise asyncio.CancelledError(
"Padding cancelled due to client disconnect"
)
log.info("Modal padding complete", size=result["size"])
return PaddingResponse(**result)
except asyncio.CancelledError:
log.warning(
"Modal padding cancelled (Hatchet timeout, disconnect detected on Modal side)"
)
raise
except httpx.TimeoutException as e:
log.error("Modal padding timeout", error=str(e), exc_info=True)
raise Exception(f"Modal padding timeout: {e}") from e
except httpx.HTTPStatusError as e:
log.error("Modal padding HTTP error", error=str(e), exc_info=True)
raise Exception(f"Modal padding HTTP error: {e}") from e
except Exception as e:
log.error("Modal padding unexpected error", error=str(e), exc_info=True)
raise

4
server/reflector/settings.py
View File

@@ -98,6 +98,10 @@ class Settings(BaseSettings):
# Diarization: local pyannote.audio
DIARIZATION_PYANNOTE_AUTH_TOKEN: str | None = None
# Audio Padding (Modal.com backend)
PADDING_URL: str | None = None
PADDING_MODAL_API_KEY: str | None = None
# Sentry
SENTRY_DSN: str | None = None

2
server/reflector/utils/audio_constants.py
View File

@@ -5,7 +5,9 @@ Used by both Hatchet workflows and Celery pipelines for consistent audio encodin
"""
# Opus codec settings
# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
OPUS_STANDARD_SAMPLE_RATE = 48000
# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
OPUS_DEFAULT_BIT_RATE = 128000 # 128kbps for good speech quality
# S3 presigned URL expiration

45
server/uv.lock generated
View File

@@ -159,21 +159,20 @@ wheels = [
[[package]]
name = "aiortc"
version = "1.13.0"
version = "1.14.0"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "aioice" },
{ name = "av" },
{ name = "cffi" },
{ name = "cryptography" },
{ name = "google-crc32c" },
{ name = "pyee" },
{ name = "pylibsrtp" },
{ name = "pyopenssl" },
]
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