From 1ebc42863e9e3be60896aa731954c333a9d0a2e4 Mon Sep 17 00:00:00 2001
From: Lotus CI <ci@lotusguild.org>
Date: Thu, 29 Oct 2026 21:35:20 -0400
Subject: [PATCH] fix(settings): measured CPU and download figures for the
 noise-suppression models

The CPU column was guessed (DeepFilterNet "25-50%", DTLN "10-20%") with no
unit, so it read like Task Manager's whole-CPU figure and looked wildly off.
Measured with each model running exactly as Lotus Call runs it (real-time
speech, browser process CPU time from /proc minus a no-model baseline,
Ryzen 7 PRO 4750GE): DeepFilterNet ~30%, DTLN ~9%, RNNoise ~3%, Speex ~2%
of one core. Label it "one core" and explain the Task Manager relation.
DTLN's download was listed as ~4 MB but fetches ~20 MB (17 MB worklet with
the runtime inlined, plus 4 MB of models); DFN's ~18 MB is right (17.6 MB).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_017PCb59uY3UKqDqt7B6hzd5
---
 src/app/features/settings/general/General.tsx |  4 ++--
 src/app/utils/lotusDenoiseUtils.ts            | 13 ++++++++-----
 2 files changed, 10 insertions(+), 7 deletions(-)

diff --git a/src/app/features/settings/general/General.tsx b/src/app/features/settings/general/General.tsx
index 7b4d33c69..624fdf17f 100644
--- a/src/app/features/settings/general/General.tsx
+++ b/src/app/features/settings/general/General.tsx
@@ -2037,7 +2037,7 @@ export function Calls() {
                 <Text size="L400">Model</Text>
                 <SettingTile
                   title="ML model"
-                  description="Choose the machine learning model used for noise removal. Heavier models clean more aggressively at a higher CPU cost."
+                  description="Choose the machine learning model used for noise removal. Heavier models clean more aggressively at a higher CPU cost. CPU figures are the share of one processor core during a call on a mid-range desktop; Task Manager shows that divided by your number of cores, and slower computers use more."
                   after={
                     <SettingsSelect<DenoiseModelId>
                       value={callDenoiseModel}
@@ -2064,7 +2064,7 @@ export function Calls() {
                     <Box direction="Row" gap="400" wrap="Wrap">
                       {(
                         [
-                          { label: 'CPU', value: selectedDenoiseModel.cpuUsage },
+                          { label: 'CPU (one core)', value: selectedDenoiseModel.cpuUsage },
                           { label: 'Voice quality', value: selectedDenoiseModel.voiceQuality },
                           { label: 'Transients', value: selectedDenoiseModel.transients },
                           { label: 'Download', value: selectedDenoiseModel.binarySize },
diff --git a/src/app/utils/lotusDenoiseUtils.ts b/src/app/utils/lotusDenoiseUtils.ts
index 8d9e0ee08..0f3d5327e 100644
--- a/src/app/utils/lotusDenoiseUtils.ts
+++ b/src/app/utils/lotusDenoiseUtils.ts
@@ -7,6 +7,9 @@
 
 import { DenoiseModelId } from '../state/settings';
 
+// CPU = share of ONE core while in a call, measured with each model running
+// exactly as Lotus Call runs it (Ryzen 7 PRO 4750GE, Chromium); download = bytes
+// actually fetched on first use. Re-measure when a model or its settings change.
 export type DenoiseModel = {
   id: DenoiseModelId;
   name: string;
@@ -24,7 +27,7 @@ export const DENOISE_MODELS: DenoiseModel[] = [
     name: 'DeepFilterNet 3 (beta)',
     description:
       'Studio-grade deep-learning model (48 kHz fullband, ONNX). Best quality; highest CPU and a larger one-time download.',
-    cpuUsage: '25-50%',
+    cpuUsage: '~30%',
     binarySize: '~18 MB',
     transients: 'Excellent',
     voiceQuality: 'Very High',
@@ -34,8 +37,8 @@ export const DENOISE_MODELS: DenoiseModel[] = [
     name: 'DTLN (beta)',
     description:
       'Dual-signal deep-learning model (16 kHz). Strong on transient noise; moderate CPU.',
-    cpuUsage: '10-20%',
-    binarySize: '~4 MB',
+    cpuUsage: '~9%',
+    binarySize: '~20 MB',
     transients: 'Excellent',
     voiceQuality: 'High',
   },
@@ -44,7 +47,7 @@ export const DENOISE_MODELS: DenoiseModel[] = [
     name: 'RNNoise',
     description:
       'Lightweight hybrid model (48 kHz). Very low CPU; good for steady noise like fans, but can sound processed at full strength.',
-    cpuUsage: '< 5%',
+    cpuUsage: '~3%',
     binarySize: '< 1 MB',
     transients: 'Good',
     voiceQuality: 'Moderate',
@@ -54,7 +57,7 @@ export const DENOISE_MODELS: DenoiseModel[] = [
     name: 'Speex (Legacy)',
     description:
       'Classic DSP noise suppressor. Minimal CPU, gentlest on voice; weakest suppression.',
-    cpuUsage: '< 2%',
+    cpuUsage: '~2%',
     binarySize: '< 1 MB',
     transients: 'Poor',
     voiceQuality: 'Moderate',
-- 
2.47.3