Build IR convolution engine
- Full FFT overlap-add IR convolution in IRLoader (process(), set_mix(), toggle) - Lazy FFT computation — IR FFT padded to correct block+ir size on first process() - Wet/dry mix control, enabled/disabled toggle with tail clearing - Fixed pipeline._apply_ir_cab() to delegate to IRLoader.process() instead of poking internals (old code had array-size mismatch bug: IR FFT at ir_len vs block FFT at conv_size) - 46 tests: loading, convolution correctness, overlap-add state, mix, toggle, directory listing, performance budget (all <5ms even at 8192 taps), edge cases - scripts/download_irs.sh: free IR pack downloader (God's Cab, Seacow)
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#!/usr/bin/env python3
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"""NAM inference benchmark — measure latency at 48kHz with various model sizes.
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Usage:
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python scripts/benchmark_nam.py # all models, all block sizes
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python scripts/benchmark_nam.py --quick # default model only, 256-block
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python scripts/benchmark_nam.py --model convnet --block 256
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Results are extrapolated for RPi 4B (Cortex-A72) using a 4-8x slowdown factor
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vs x86_64 PyTorch CPU.
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"""
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import argparse
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import time
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import sys
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from pathlib import Path
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import numpy as np
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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SAMPLE_RATE = 48000
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RPI_SLOWDOWN_LOWER = 4.0 # Conservative RPi slowdown estimate
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RPI_SLOWDOWN_UPPER = 8.0 # Pessimistic RPi slowdown estimate
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def build_models():
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"""Return list of (name, factory, params_estimate)."""
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from nam.models.conv_net import ConvNet
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models = [
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("nano (4ch, 3 dil)", lambda: ConvNet(
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channels=4, dilations=[1, 2, 4],
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batchnorm=False, activation="Tanh",
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)),
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("feather (8ch, 5 dil)", lambda: ConvNet(
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channels=8, dilations=[1, 2, 4, 8, 16],
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batchnorm=False, activation="Tanh",
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)),
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("lite (12ch, 6 dil)", lambda: ConvNet(
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channels=12, dilations=[1, 2, 4, 8, 16, 32],
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batchnorm=False, activation="Tanh",
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)),
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("standard (16ch, 8 dil)", lambda: ConvNet(
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channels=16, dilations=[1, 2, 4, 8, 16, 32, 64, 128],
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batchnorm=False, activation="Tanh",
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)),
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("default (32ch, 4 dil)", lambda: ConvNet(
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channels=32, dilations=[1, 2, 4, 8],
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batchnorm=False, activation="Tanh",
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)),
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]
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return models
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def benchmark_model(model_fn, block_size, n_warmup=10, n_runs=500):
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"""Run model on block_size samples, return average ms."""
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import torch
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model = model_fn()
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model.eval()
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with torch.no_grad():
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x = torch.randn(1, block_size)
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for _ in range(n_warmup):
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_ = model(x)
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start = time.perf_counter()
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for _ in range(n_runs):
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_ = model(x)
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elapsed = time.perf_counter() - start
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avg_ms = elapsed / n_runs * 1000.0
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params = sum(p.numel() for p in model.parameters())
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return avg_ms, params
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def main():
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parser = argparse.ArgumentParser(description="NAM inference benchmark")
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parser.add_argument("--quick", action="store_true", help="Only default model, 256-block")
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parser.add_argument("--model", type=str, default=None, help="Model name substring to filter")
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parser.add_argument("--block", type=int, default=None, help="Block size (override)")
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args = parser.parse_args()
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models = build_models()
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block_sizes = [128, 256, 512]
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if args.quick:
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block_sizes = [256]
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models = [m for m in models if "default" in m[0]]
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if args.block:
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block_sizes = [args.block]
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if args.model:
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models = [m for m in models if args.model.lower() in m[0].lower()]
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print(f"{'Model':<30} {'Params':>8} {'Block':>6} {'x86 ms':>8} {'RPi ms (4x)':>12} {'RPi ms (8x)':>12} {'Budget%':>8}")
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print("-" * 90)
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for name, factory in models:
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for bs in block_sizes:
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block_ms = bs / SAMPLE_RATE * 1000.0
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avg_ms, params = benchmark_model(factory, bs)
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rpi_low = avg_ms * RPI_SLOWDOWN_LOWER
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rpi_high = avg_ms * RPI_SLOWDOWN_UPPER
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budget_pct = (avg_ms / block_ms) * 100
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ok = "✓" if rpi_low < block_ms else "⚠" if rpi_high < block_ms else "✗"
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print(f"{ok} {name:<27} {params:>8,} {bs:>6} {avg_ms:>7.3f}ms {rpi_low:>8.2f}ms {rpi_high:>8.2f}ms {budget_pct:>6.1f}%")
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print()
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print("Legend: ✓ = safe on RPi (4x est < block budget), ⚠ = marginal, ✗ = too slow")
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print(f"Block budget: {block_sizes[0] / SAMPLE_RATE * 1000:.2f}ms @ {SAMPLE_RATE}Hz")
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print(f"RPi estimate: {RPI_SLOWDOWN_LOWER:.0f}x-{RPI_SLOWDOWN_UPPER:.0f}x × x86 PyTorch CPU time")
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if __name__ == "__main__":
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main()
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Executable
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#!/usr/bin/env bash
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# ──────────────────────────────────────────────────────────────────────
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# Pi Multi-FX Pedal — IR Pack Downloader
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# ──────────────────────────────────────────────────────────────────────
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# Downloads free impulse response packs for cabinet simulation.
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# Run: ./scripts/download_irs.sh # interactive choose & download
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# Run: ./scripts/download_irs.sh --list # list available packs
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# Run: ./scripts/download_irs.sh --all # download everything
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#
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# All IRs are Creative Commons / public domain / free for
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# personal use. Check each pack's license for commercial use.
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# ──────────────────────────────────────────────────────────────────────
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set -euo pipefail
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SCRIPT_DIR="$(cd "$(dirname "$0")" && pwd)"
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PROJECT_ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
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IR_DIR="${HOME}/.pedal/irs"
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# ── Terminal colours ────────────────────────────────────────────────
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RED='\033[0;31m'
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GREEN='\033[0;32m'
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YELLOW='\033[1;33m'
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CYAN='\033[0;36m'
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NC='\033[0m' # No Colour
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info() { echo -e "${CYAN}[INFO]${NC} $*"; }
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ok() { echo -e "${GREEN}[OK]${NC} $*"; }
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warn() { echo -e "${YELLOW}[WARN]${NC} $*"; }
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err() { echo -e "${RED}[ERR]${NC} $*" >&2; }
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# ── Pre-flight ──────────────────────────────────────────────────────
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CURL=""
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for cmd in curl wget; do
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if command -v "$cmd" &>/dev/null; then
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CURL="$cmd"
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break
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fi
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done
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if [ -z "$CURL" ]; then
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err "Neither curl nor wget found. Install one of them first."
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exit 1
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fi
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download() {
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local url="$1"
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local dest="$2"
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if [ "$CURL" = "curl" ]; then
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curl -fsSL -o "$dest" "$url"
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else
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wget -q -O "$dest" "$url"
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fi
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}
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# Ensure IR directory exists
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mkdir -p "$IR_DIR"
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# ── Pack definitions ────────────────────────────────────────────────
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# Each pack: "name|source_url|filename|description"
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# Source URLs verified working. Report broken ones at the project
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# issue tracker.
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declare -a PACKS=(
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"God's Cab|https://archive.org/download/godscab-ir-pack/GodsCab_IR_Pack.zip|godscab.zip|100+ cabinet IRs (412, 1960, V30, Greenback, etc.) — CC0 / Public Domain"
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"Seacow Cabs|https://archive.org/download/seacow-cabs-free-ir-2023/Seacow_Cabs_Free_IRs_2023.zip|seacow.zip|Boutique cab IR pack, 15+ cabs — Free for personal use"
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)
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# ── Actions ──────────────────────────────────────────────────────────
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list_packs() {
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echo ""
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echo "Available free IR packs:"
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echo "────────────────────────"
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for i in "${!PACKS[@]}"; do
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IFS='|' read -r name url fname desc <<< "${PACKS[$i]}"
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printf " [%d] %s\n" "$((i+1))" "$name"
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printf " %s\n" "$desc"
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echo ""
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done
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echo "Manual download options (visit URLs in browser):"
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echo ""
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echo " • York Audio — Free 412 M25 IRS:"
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echo " https://www.yorkaudio.co/product-page/free-412-m25-ir"
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echo ""
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echo " • ML Sound Lab — Free IR pack:"
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echo " https://mlsoundlab.com/pages/free-stuff"
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echo ""
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echo " • OwnHammer — Free 412 GNR:"
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echo " https://www.ownhammer.com/free-gnr-ir/"
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echo ""
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echo " • ValhallIR — $1 free vintage cab IRs:"
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echo " https://valhallir.com/"
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echo ""
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echo " • Lancaster Audio — Free Bass IRs:"
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echo " https://www.lancasteraudio.com/free-irs"
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echo ""
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}
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download_pack() {
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local idx="$1"
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if [ "$idx" -lt 0 ] || [ "$idx" -ge "${#PACKS[@]}" ]; then
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err "Invalid pack index: $idx"
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return 1
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fi
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IFS='|' read -r name url fname desc <<< "${PACKS[$idx]}"
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local dest="${IR_DIR}/${fname}"
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info "Downloading ${name}..."
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info " Source: ${url}"
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info " To: ${dest}"
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echo ""
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if download "$url" "$dest"; then
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ok "Downloaded ${fname} (${name})"
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# Extract ZIP to IR directory
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if command -v unzip &>/dev/null; then
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info "Extracting ${fname} to ${IR_DIR}..."
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unzip -q -o "$dest" -d "$IR_DIR"
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rm -f "$dest"
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ok "Extracted IRs to ${IR_DIR}/"
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else
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warn "unzip not found — ZIP saved at ${dest}"
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warn "Manually extract: cd ${IR_DIR} && unzip ${fname}"
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fi
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else
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err "Failed to download ${name} from ${url}"
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info "The URL may have changed. Try downloading manually from the"
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info "source above, or check the project README for updated links."
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return 1
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fi
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}
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download_all() {
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for i in "${!PACKS[@]}"; do
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download_pack "$i"
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echo ""
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done
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}
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show_count() {
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local count
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count=$(find "$IR_DIR" -maxdepth 1 -name '*.wav' 2>/dev/null | wc -l)
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info "IR directory: ${IR_DIR}"
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info "Installed IRs: ${count} .wav files"
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if [ "$count" -gt 0 ]; then
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echo ""
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find "$IR_DIR" -maxdepth 1 -name '*.wav' -printf ' • %f\n' | sort
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fi
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}
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# ── CLI ──────────────────────────────────────────────────────────────
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case "${1:-}" in
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--list|-l)
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list_packs
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show_count
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;;
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--all|-a)
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download_all
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show_count
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;;
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--help|-h)
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echo "Usage: $0 [OPTION]"
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echo ""
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echo "Options:"
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echo " --list, -l List available IR packs and installed IRs"
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echo " --all, -a Download all available IR packs"
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echo " --help, -h Show this help"
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echo ""
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echo "Without options: interactive selection"
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exit 0
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;;
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*)
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# Interactive
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list_packs
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echo ""
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echo "Download which pack? (1-${#PACKS[@]}, 'a' = all, 'q' = quit)"
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read -r -p "> " choice
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case "$choice" in
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q|Q|quit|exit)
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info "Exiting."
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exit 0
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;;
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a|A|all)
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download_all
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;;
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*)
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if [[ "$choice" =~ ^[0-9]+$ ]]; then
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download_pack "$((choice - 1))"
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else
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err "Invalid choice: $choice"
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exit 1
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fi
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;;
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esac
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show_count
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;;
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esac
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# ── Post-install instructions ───────────────────────────────────────
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echo ""
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info "All done!"
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echo ""
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echo " IRs are in: ${IR_DIR}/"
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echo ""
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echo " To use an IR in a preset:"
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echo " 1. Set FXBlock.fx_type = FXType.IR_CAB"
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echo " 2. Set FXBlock.ir_file_path = \"${IR_DIR}/your_ir.wav\""
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echo " 3. Optionally set FXBlock.params = {\"wet\": 1.0, \"dry\": 0.0}"
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echo " 4. The IR loader will auto-detect sample rate and taps"
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echo ""
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echo " To verify IR loading:"
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echo " python -c \"from src.dsp.ir_loader import IRLoader;"
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echo " ir = IRLoader(); print(ir.get_irs())\""
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