# 24thFX Dites33 — NAM A2 Capture Report

## Deliverable

- Model: `Dites33_12oclock_A2.nam`
- Model SHA-256: `e6c26eb6cfbb96c56e86f3ab469eb8d4732b214f105dd20a329265bb4afd78cf`
- Format: NAM model version 0.7.0, 48 kHz, A2 `SlimmableContainer`
- Quality tiers: two packed WaveNet submodels (`channels_3` and `channels_8`)
- Pedal setting: engaged, single control at 12 o'clock

## Capture chain

`Clarett 8Pre Line Output 3 → Dites33 input → Dites33 output → Clarett Input 2`

- Cubase playback bus: Clarett Playback 5
- Focusrite Output 3: mono custom mix, Playback 5 at 0.0 dB
- Calibrated Output 3 drive: approximately -34.13 dBFS in the direct-loop test
- Return: Input 2 in Line mode, Air off, physical gain raised for the final passes
- Capture format: mono, 48 kHz, 24-bit PCM
- Four complete physical reamp passes were recorded and preserved

## Signal preparation

- Official NAM v3 input file: exact strong-hash match
- Input/output length: 190.000 seconds (9,120,000 samples)
- Cubase pre-record removed: 48,000 samples (1.000 second) per pass
- Inter-pass alignment: 0 samples across all four passes
- Four-pass mean used for the training region (approximately 6 dB reduction of uncorrelated noise)
- The eight physically recorded copies of the repeated nine-second v3 validation passage were averaged into one validation target
- Final training output peak: -9.95025 dBFS
- Final training output RMS: -35.11883 dBFS
- Final output SHA-256: `0a24908ff87f615f867fce558f3c1082c01ffa579e1972e92d03969f75ffbe83`

The untouched single-pass training output remains in `output_single_pass.wav`. Source paths, hashes, and per-pass levels are recorded in `capture_average_metadata.json`.

## Official NAM validation and training

- Trainer: Neural Amp Modeler 0.13.0
- Architecture: official packed A2 configuration, 22.8k trainable parameters
- Training: 100 epochs on Apple Metal (MPS)
- PyTorch Lightning: 2.6.1
- Automatic latency: -3 samples after the one-second Cubase pre-record trim
- NAM input, length, sample-rate, latency, repeatability, and dataset checks: passed
- Final aggregate validation ESR: 0.06852377
- Submodel validation ESRs at export: 0.03498 (`channels_3`) and 0.03355 (`channels_8`)

Both exported A2 submodels were independently parsed, instantiated, and run against a 48,000-sample input. Both produced finite output and reported the expected 6,347-sample receptive field.

## Loading the model

The capture is embedded directly in the dedicated **Dites33 A2** VST3, Audio
Unit, and standalone app. No separate NAM loader is required.

1. Insert **Dites33 A2** before the amp/preamp plug-in, matching the physical
   boost-pedal signal chain.
2. Leave **A2 ENGINE** on **FULL** for normal studio use; select **ECO** when CPU
   use matters more than the final increment of accuracy.
3. Use **INPUT** to change how hard the fixed 12-o'clock capture is driven and
   **OUTPUT** for final gain staging.

The original `Dites33_12oclock_A2.nam` remains included as a portable fallback
for standard NAM hosts. A2 is documented by the NAM project at
<https://www.neuralampmodeler.com/post/a2-is-released>.
