mirror of
https://github.com/Monadical-SAS/reflector.git
synced 2026-02-04 09:56:47 +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>
This commit is contained in:
GitHub
1
.gitignore
vendored
1
.gitignore
vendored
@@ -1,5 +1,6 @@
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.DS_Store
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server/.env
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server/.env.production
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.env
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Caddyfile
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server/exportdanswer
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@@ -131,6 +131,15 @@ if [ -z "$DIARIZER_URL" ]; then
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fi
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echo " -> $DIARIZER_URL"
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echo ""
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echo "Deploying padding (CPU audio processing via Modal SDK)..."
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modal deploy reflector_padding.py
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if [ $? -ne 0 ]; then
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echo "Error: Failed to deploy padding. Check Modal dashboard for details."
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exit 1
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fi
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echo " -> reflector-padding.pad_track (Modal SDK function)"
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# --- Output Configuration ---
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echo ""
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echo "=========================================="
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@@ -147,4 +156,6 @@ echo ""
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echo "DIARIZATION_BACKEND=modal"
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echo "DIARIZATION_URL=$DIARIZER_URL"
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echo "DIARIZATION_MODAL_API_KEY=$API_KEY"
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echo ""
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echo "# Padding uses Modal SDK (requires MODAL_TOKEN_ID/SECRET in worker containers)"
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echo "# --- End Modal Configuration ---"
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277
gpu/modal_deployments/reflector_padding.py
Normal file
277
gpu/modal_deployments/reflector_padding.py
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@@ -0,0 +1,277 @@
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"""
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Reflector GPU backend - audio padding
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======================================
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CPU-intensive audio padding service for adding silence to audio tracks.
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Uses PyAV filter graph (adelay) for precise track synchronization.
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IMPORTANT: This padding logic is duplicated from server/reflector/utils/audio_padding.py
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for Modal deployment isolation (Modal can't import from server/reflector/). If you modify
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the PyAV filter graph or padding algorithm, you MUST update both:
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- gpu/modal_deployments/reflector_padding.py (this file)
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- server/reflector/utils/audio_padding.py
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Constants duplicated from server/reflector/utils/audio_constants.py for same reason.
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"""
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import os
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import tempfile
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from fractions import Fraction
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import math
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import asyncio
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import modal
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S3_TIMEOUT = 60 # happens 2 times
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PADDING_TIMEOUT = 600 + (S3_TIMEOUT * 2)
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SCALEDOWN_WINDOW = 60 # The maximum duration (in seconds) that individual containers can remain idle when scaling down.
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DISCONNECT_CHECK_INTERVAL = 2 # Check for client disconnect
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app = modal.App("reflector-padding")
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# CPU-based image
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image = (
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modal.Image.debian_slim(python_version="3.12")
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.apt_install("ffmpeg") # Required by PyAV
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.pip_install(
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"av==13.1.0", # PyAV for audio processing
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"requests==2.32.3", # HTTP for presigned URL downloads/uploads
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"fastapi==0.115.12", # API framework
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)
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)
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# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
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OPUS_STANDARD_SAMPLE_RATE = 48000
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# ref B0F71CE8-FC59-4AA5-8414-DAFB836DB711
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OPUS_DEFAULT_BIT_RATE = 128000
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@app.function(
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cpu=2.0,
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timeout=PADDING_TIMEOUT,
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scaledown_window=SCALEDOWN_WINDOW,
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image=image,
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)
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@modal.asgi_app()
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def web():
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from fastapi import FastAPI, Request, HTTPException
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from pydantic import BaseModel
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class PaddingRequest(BaseModel):
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track_url: str
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output_url: str
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start_time_seconds: float
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track_index: int
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class PaddingResponse(BaseModel):
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size: int
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cancelled: bool = False
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web_app = FastAPI()
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@web_app.post("/pad")
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async def pad_track_endpoint(request: Request, req: PaddingRequest) -> PaddingResponse:
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"""Modal web endpoint for padding audio tracks with disconnect detection.
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"""
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import logging
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logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
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logger = logging.getLogger(__name__)
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if not req.track_url:
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raise HTTPException(status_code=400, detail="track_url cannot be empty")
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if not req.output_url:
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raise HTTPException(status_code=400, detail="output_url cannot be empty")
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if req.start_time_seconds <= 0:
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raise HTTPException(status_code=400, detail=f"start_time_seconds must be positive, got {req.start_time_seconds}")
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if req.start_time_seconds > 18000:
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raise HTTPException(status_code=400, detail=f"start_time_seconds exceeds maximum 18000s (5 hours)")
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logger.info(f"Padding request: track {req.track_index}, delay={req.start_time_seconds}s")
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# Thread-safe cancellation flag shared between async disconnect checker and blocking thread
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import threading
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cancelled = threading.Event()
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async def check_disconnect():
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"""Background task to check for client disconnect every 2 seconds."""
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while not cancelled.is_set():
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await asyncio.sleep(DISCONNECT_CHECK_INTERVAL)
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if await request.is_disconnected():
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logger.warning("Client disconnected, setting cancellation flag")
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cancelled.set()
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break
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# Start disconnect checker in background
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disconnect_task = asyncio.create_task(check_disconnect())
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try:
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result = await asyncio.get_event_loop().run_in_executor(
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None, _pad_track_blocking, req, cancelled, logger
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)
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return PaddingResponse(**result)
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finally:
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cancelled.set()
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disconnect_task.cancel()
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try:
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await disconnect_task
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except asyncio.CancelledError:
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pass
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def _pad_track_blocking(req, cancelled, logger) -> dict:
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"""Blocking CPU-bound padding work with periodic cancellation checks.
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Args:
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cancelled: threading.Event for thread-safe cancellation signaling
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"""
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import av
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import requests
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from av.audio.resampler import AudioResampler
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import time
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temp_dir = tempfile.mkdtemp()
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input_path = None
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output_path = None
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last_check = time.time()
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try:
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logger.info("Downloading track for padding")
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response = requests.get(req.track_url, stream=True, timeout=S3_TIMEOUT)
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response.raise_for_status()
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input_path = os.path.join(temp_dir, "track.webm")
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total_bytes = 0
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chunk_count = 0
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with open(input_path, "wb") as f:
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for chunk in response.iter_content(chunk_size=8192):
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if chunk:
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f.write(chunk)
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total_bytes += len(chunk)
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chunk_count += 1
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# Check for cancellation every arbitrary amount of chunks
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if chunk_count % 12 == 0:
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now = time.time()
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if now - last_check >= DISCONNECT_CHECK_INTERVAL:
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if cancelled.is_set():
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logger.info("Cancelled during download, exiting early")
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return {"size": 0, "cancelled": True}
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last_check = now
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logger.info(f"Track downloaded: {total_bytes} bytes")
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if cancelled.is_set():
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logger.info("Cancelled after download, exiting early")
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return {"size": 0, "cancelled": True}
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# Apply padding using PyAV
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output_path = os.path.join(temp_dir, "padded.webm")
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delay_ms = math.floor(req.start_time_seconds * 1000)
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logger.info(f"Padding track {req.track_index} with {delay_ms}ms delay using PyAV")
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in_container = av.open(input_path)
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in_stream = next((s for s in in_container.streams if s.type == "audio"), None)
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if in_stream is None:
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raise ValueError("No audio stream in input")
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with av.open(output_path, "w", format="webm") as out_container:
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out_stream = out_container.add_stream("libopus", rate=OPUS_STANDARD_SAMPLE_RATE)
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out_stream.bit_rate = OPUS_DEFAULT_BIT_RATE
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graph = av.filter.Graph()
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abuf_args = (
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f"time_base=1/{OPUS_STANDARD_SAMPLE_RATE}:"
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f"sample_rate={OPUS_STANDARD_SAMPLE_RATE}:"
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f"sample_fmt=s16:"
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f"channel_layout=stereo"
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)
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src = graph.add("abuffer", args=abuf_args, name="src")
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aresample_f = graph.add("aresample", args="async=1", name="ares")
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delays_arg = f"{delay_ms}|{delay_ms}"
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adelay_f = graph.add("adelay", args=f"delays={delays_arg}:all=1", name="delay")
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sink = graph.add("abuffersink", name="sink")
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src.link_to(aresample_f)
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aresample_f.link_to(adelay_f)
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adelay_f.link_to(sink)
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graph.configure()
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resampler = AudioResampler(
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format="s16", layout="stereo", rate=OPUS_STANDARD_SAMPLE_RATE
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)
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for frame in in_container.decode(in_stream):
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# Check for cancellation periodically
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now = time.time()
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if now - last_check >= DISCONNECT_CHECK_INTERVAL:
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if cancelled.is_set():
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logger.info("Cancelled during processing, exiting early")
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in_container.close()
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return {"size": 0, "cancelled": True}
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last_check = now
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out_frames = resampler.resample(frame) or []
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for rframe in out_frames:
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rframe.sample_rate = OPUS_STANDARD_SAMPLE_RATE
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rframe.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
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src.push(rframe)
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while True:
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try:
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f_out = sink.pull()
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except Exception:
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break
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f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
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f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
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for packet in out_stream.encode(f_out):
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out_container.mux(packet)
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# Flush filter graph
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src.push(None)
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while True:
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try:
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f_out = sink.pull()
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except Exception:
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break
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f_out.sample_rate = OPUS_STANDARD_SAMPLE_RATE
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f_out.time_base = Fraction(1, OPUS_STANDARD_SAMPLE_RATE)
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for packet in out_stream.encode(f_out):
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out_container.mux(packet)
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# Flush encoder
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for packet in out_stream.encode(None):
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out_container.mux(packet)
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in_container.close()
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file_size = os.path.getsize(output_path)
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logger.info(f"Padding complete: {file_size} bytes")
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logger.info("Uploading padded track to S3")
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with open(output_path, "rb") as f:
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upload_response = requests.put(req.output_url, data=f, timeout=S3_TIMEOUT)
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upload_response.raise_for_status()
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logger.info(f"Upload complete: {file_size} bytes")
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return {"size": file_size}
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finally:
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if input_path and os.path.exists(input_path):
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try:
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os.unlink(input_path)
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except Exception as e:
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logger.warning(f"Failed to cleanup input file: {e}")
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if output_path and os.path.exists(output_path):
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try:
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os.unlink(output_path)
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except Exception as e:
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logger.warning(f"Failed to cleanup output file: {e}")
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try:
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os.rmdir(temp_dir)
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except Exception as e:
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logger.warning(f"Failed to cleanup temp directory: {e}")
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return web_app
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@@ -8,7 +8,7 @@ readme = "README.md"
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dependencies = [
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"aiohttp>=3.9.0",
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"aiohttp-cors>=0.7.0",
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"av>=10.0.0",
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"av>=15.0.0",
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"requests>=2.31.0",
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"aiortc>=1.5.0",
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"sortedcontainers>=2.4.0",
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@@ -37,5 +37,5 @@ LLM_RATE_LIMIT_PER_SECOND = 10
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TIMEOUT_SHORT = 60 # Quick operations: API calls, DB updates
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TIMEOUT_MEDIUM = 120 # Single LLM calls, waveform generation
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TIMEOUT_LONG = 180 # Action items (larger context LLM)
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TIMEOUT_AUDIO = 300 # Audio processing: padding, mixdown
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TIMEOUT_AUDIO = 720 # Audio processing: padding, mixdown
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TIMEOUT_HEAVY = 600 # Transcription, fan-out LLM tasks
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165
server/reflector/hatchet/workflows/padding_workflow.py
Normal file
165
server/reflector/hatchet/workflows/padding_workflow.py
Normal file
@@ -0,0 +1,165 @@
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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,
|
||||
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
|
||||
@@ -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):
|
||||
@@ -83,15 +78,6 @@ 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
|
||||
)
|
||||
@@ -109,37 +95,27 @@ async def pad_track(input: TrackInput, ctx: Context) -> PadTrackResult:
|
||||
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,
|
||||
# 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,
|
||||
)
|
||||
|
||||
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,
|
||||
from reflector.processors.audio_padding_modal import ( # noqa: PLC0415
|
||||
AudioPaddingModalProcessor,
|
||||
)
|
||||
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
112
server/reflector/processors/audio_padding_modal.py
Normal 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
|
||||
@@ -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
|
||||
|
||||
|
||||
@@ -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
45
server/uv.lock
generated
@@ -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" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/62/03/bc947d74c548e0c17cf94e5d5bdacaed0ee9e5b2bb7b8b8cf1ac7a7c01ec/aiortc-1.13.0.tar.gz", hash = "sha256:5d209975c22d0910fb5a0f0e2caa828f2da966c53580f7c7170ac3a16a871620", size = 1179894 }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/51/9c/4e027bfe0195de0442da301e2389329496745d40ae44d2d7c4571c4290ce/aiortc-1.14.0.tar.gz", hash = "sha256:adc8a67ace10a085721e588e06a00358ed8eaf5f6b62f0a95358ff45628dd762", size = 1180864 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/87/29/765633cab5f1888890f5f172d1d53009b9b14e079cdfa01a62d9896a9ea9/aiortc-1.13.0-py3-none-any.whl", hash = "sha256:9ccccec98796f6a96bd1c3dd437a06da7e0f57521c96bd56e4b965a91b03a0a0", size = 92910 },
|
||||
{ url = "https://files.pythonhosted.org/packages/57/ab/31646a49209568cde3b97eeade0d28bb78b400e6645c56422c101df68932/aiortc-1.14.0-py3-none-any.whl", hash = "sha256:4b244d7e482f4e1f67e685b3468269628eca1ec91fa5b329ab517738cfca086e", size = 93183 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@@ -327,28 +326,24 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "av"
|
||||
version = "14.4.0"
|
||||
version = "16.1.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/86/f6/0b473dab52dfdea05f28f3578b1c56b6c796ce85e76951bab7c4e38d5a74/av-14.4.0.tar.gz", hash = "sha256:3ecbf803a7fdf67229c0edada0830d6bfaea4d10bfb24f0c3f4e607cd1064b42", size = 3892203 }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/78/cd/3a83ffbc3cc25b39721d174487fb0d51a76582f4a1703f98e46170ce83d4/av-16.1.0.tar.gz", hash = "sha256:a094b4fd87a3721dacf02794d3d2c82b8d712c85b9534437e82a8a978c175ffd", size = 4285203 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/18/8a/d57418b686ffd05fabd5a0a9cfa97e63b38c35d7101af00e87c51c8cc43c/av-14.4.0-cp311-cp311-macosx_12_0_arm64.whl", hash = "sha256:5b21d5586a88b9fce0ab78e26bd1c38f8642f8e2aad5b35e619f4d202217c701", size = 19965048 },
|
||||
{ url = "https://files.pythonhosted.org/packages/f5/aa/3f878b0301efe587e9b07bb773dd6b47ef44ca09a3cffb4af50c08a170f3/av-14.4.0-cp311-cp311-macosx_12_0_x86_64.whl", hash = "sha256:cf8762d90b0f94a20c9f6e25a94f1757db5a256707964dfd0b1d4403e7a16835", size = 23750064 },
|
||||
{ url = "https://files.pythonhosted.org/packages/9a/b4/6fe94a31f9ed3a927daa72df67c7151968587106f30f9f8fcd792b186633/av-14.4.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c0ac9f08920c7bbe0795319689d901e27cb3d7870b9a0acae3f26fc9daa801a6", size = 33648775 },
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||||
{ url = "https://files.pythonhosted.org/packages/6c/f3/7f3130753521d779450c935aec3f4beefc8d4645471159f27b54e896470c/av-14.4.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:a56d9ad2afdb638ec0404e962dc570960aae7e08ae331ad7ff70fbe99a6cf40e", size = 32216915 },
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||||
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||||
{ url = "https://files.pythonhosted.org/packages/ad/11/7023ba0a2ca94a57aedf3114ab8cfcecb0819b50c30982a4c5be4d31df41/av-14.4.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d030c2d3647931e53d51f2f6e0fcf465263e7acf9ec6e4faa8dbfc77975318c3", size = 36294683 },
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||||
{ url = "https://files.pythonhosted.org/packages/3d/fa/b8ac9636bd5034e2b899354468bef9f4dadb067420a16d8a493a514b7817/av-14.4.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:1cc21582a4f606271d8c2036ec7a6247df0831050306c55cf8a905701d0f0474", size = 34552391 },
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||||
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||||
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||||
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||||
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||||
{ url = "https://files.pythonhosted.org/packages/f7/75/9a5f0e6bda5f513b62bafd1cff2b495441a8b07ab7fb7b8e62f0c0d1683f/av-14.4.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a3b316fed3597675fe2aacfed34e25fc9d5bb0196dc8c0b014ae5ed4adda48de", size = 33801401 },
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||||
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||||
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]
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||||
[[package]]
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||||
@@ -3267,7 +3262,7 @@ requires-dist = [
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||||
{ name = "aiohttp-cors", specifier = ">=0.7.0" },
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||||
{ name = "aiortc", specifier = ">=1.5.0" },
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{ name = "alembic", specifier = ">=1.11.3" },
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{ name = "av", specifier = ">=10.0.0" },
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{ name = "av", specifier = ">=15.0.0" },
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{ name = "celery", specifier = ">=5.3.4" },
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{ name = "databases", extras = ["aiosqlite", "asyncpg"], specifier = ">=0.7.0" },
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||||
{ name = "fastapi", extras = ["standard"], specifier = ">=0.100.1" },
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||||
|
||||
Reference in New Issue
Block a user