docs: latency budget, thread cap, and Arc B580 CUDA mismatch

Also extend bench_enhance.py with --stereo --hop for isolated RTF.
This commit is contained in:
root
2026-08-28 06:21:02 +00:00
parent 36c4bbc956
commit f83f0f0683
2 changed files with 68 additions and 22 deletions
+14 -6
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@@ -74,13 +74,20 @@ re-run `--write` unless you intend to remap slots.
C920 mics are 16 kHz **stereo**. Each hop (default 200 ms) is kept C920 mics are 16 kHz **stereo**. Each hop (default 200 ms) is kept
stereo through a SpeechBrain preprocessor, then published as mono: stereo through a SpeechBrain preprocessor, then published as mono:
1. `AudioNormalizer(mix="keep")` at 16 kHz 1. Delay-and-Sum beamform (GCC-PHAT TDOA, frozen for
2. Delay-and-Sum beamform (GCC-PHAT TDOA) — the live path from the `BEAMFORM_TDOA_EVERY=5` hops) — live path from the SpeechBrain
SpeechBrain [multi-microphone beamforming tutorial](https://speechbrain.readthedocs.io/en/latest/tutorials/preprocessing/multi-microphone-beamforming.html) [multi-microphone beamforming tutorial](https://speechbrain.readthedocs.io/en/latest/tutorials/preprocessing/multi-microphone-beamforming.html)
(`speechbrain.lobes.beamform_multimic.DelaySum_Beamformer`) 2. `speechbrain/metricgan-plus-voicebank` neural enhancement on a 1 s
3. `speechbrain/metricgan-plus-voicebank` neural enhancement on a 1 s
rolling window (`ENHANCE_CHUNK_S=1.0`, `ENHANCE_HOP_S=0.2`) rolling window (`ENHANCE_CHUNK_S=1.0`, `ENHANCE_HOP_S=0.2`)
The 1 s window is **past context**, not +1 s delay: only the latest hop
is published. Algorithmic delay ≈ hop + compute.
Each worker caps Torch/BLAS to `TORCH_NUM_THREADS=1` (default) so 14–20
processes do not oversubscribe the i7-14700. This host has an Intel Arc
B580; the venv Torch is NVIDIA CUDA and **cannot** use it. Do not set
`run_opts={"device":"cuda"}`.
One worker process per camera loads the modules once and reuses them. One worker process per camera loads the modules once and reuses them.
`enhance_batch` must be called with `lengths=torch.tensor([1.0])` `enhance_batch` must be called with `lengths=torch.tensor([1.0])`
(relative, not sample count). `ENHANCE_MODE=off` (or a failed model (relative, not sample count). `ENHANCE_MODE=off` (or a failed model
@@ -89,7 +96,8 @@ instead of Delay-and-Sum. DSP high-pass + soft-clip is the last-resort
fallback so streams still work. fallback so streams still work.
Until the 1 s window is full, hops use the DSP fallback so LiveKit is Until the 1 s window is full, hops use the DSP fallback so LiveKit is
not silent on startup. not silent on startup. Workers log `process_ms` / `capture_ms` every 50
hops.
## Appliance boot / power button ## Appliance boot / power button
+54 -16
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@@ -1,5 +1,12 @@
#!/usr/bin/env python3 #!/usr/bin/env python3
"""RTF bench for hop/window speechbrain enhance.""" """RTF bench for hop/window speechbrain enhance.
Stop livekit-cameras.service first or the numbers fight the fleet.
.venv/bin/python scripts/bench_enhance.py
.venv/bin/python scripts/bench_enhance.py --stereo --hop 0.1
"""
import argparse
import sys import sys
import time import time
from pathlib import Path from pathlib import Path
@@ -10,18 +17,49 @@ import numpy as np
from audio_cleanup import AudioCleaner from audio_cleanup import AudioCleaner
c = AudioCleaner(mode="force", chunk_s=1.0, hop_s=0.2)
hop = 3200 def main() -> int:
pcm = (np.random.randn(hop) * 800).astype(np.int16) ap = argparse.ArgumentParser()
for _ in range(8): ap.add_argument("--stereo", action="store_true")
c.process(pcm) ap.add_argument("--hop", type=float, default=0.2)
n = 50 ap.add_argument("--chunk", type=float, default=1.0)
t0 = time.perf_counter() ap.add_argument("--n", type=int, default=50)
for _ in range(n): args = ap.parse_args()
c.process(pcm)
dt = time.perf_counter() - t0 c = AudioCleaner(
audio_s = n * 0.2 mode="force",
rtf = dt / audio_s beamform="force" if args.stereo else "off",
print(f"hops={n} wall={dt:.3f}s audio={audio_s:.1f}s rtf={rtf:.3f}") chunk_s=args.chunk,
if rtf > 0.8: hop_s=args.hop,
raise SystemExit("RTF too high for 20-way realtime") torch_num_threads=1,
)
hop_n = int(args.hop * 16000)
if args.stereo:
t = np.arange(hop_n)
ch0 = (np.sin(2 * np.pi * 440 * t / 16000) * 800).astype(np.int16)
pcm = np.stack([ch0, np.roll(ch0, 2)], axis=1)
else:
pcm = (np.random.randn(hop_n) * 800).astype(np.int16)
warmup = max(8, int(args.chunk / args.hop) + 2)
for _ in range(warmup):
c.process(pcm)
n = args.n
t0 = time.perf_counter()
for _ in range(n):
c.process(pcm)
dt = time.perf_counter() - t0
audio_s = n * args.hop
rtf = dt / audio_s
print(
f"backend={c.backend} stereo={args.stereo} hops={n} "
f"wall={dt:.3f}s audio={audio_s:.1f}s rtf={rtf:.3f} "
f"last_ms={c.last_dt_ms:.1f}"
)
limit = 0.5 if args.stereo else 0.8
if rtf > limit:
raise SystemExit(f"RTF too high for 20-way realtime (limit {limit})")
return 0
if __name__ == "__main__":
sys.exit(main())