| # Dataset preparation |
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|
| LTX's trainer takes a dataset; it does not help you *build* one. For an audio LoRA the dataset is the challange, the model learns a transformation, so |
| every item must be the same performance twice, once dry and once through a real |
| acoustic treatment, aligned and gain-matched. I also used two iterations of each dry sound source so model can learn on variety. |
| These are the scripts that built the AKUSPACE set (266 pairs, 14 sources × 19 cells). |
|
|
| Set `AKUSPACE_DATASET` to your dataset root, or run from the repo and the |
| relative default applies. |
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| | Script | Does | |
| |---|---| |
| | `build_v5_rooms.py` | dry/wet room pairs at three levels per room | |
| | `build_v5_outdoor.py` | outdoor beds; projects the dry out before rebuilding | |
| | `build_v5_sfx.py` | Eurorack modular-delay pairs | |
| | `build_eurorack_dry.py` | assembles the dry side fed through the hardware | |
| | `make_manifest_v5.py` | writes the training CSV with captions and splits | |
| | `verify_dataset.py` | checks every pair: length, rate, ceiling, alignment | |
| | `check_limiting.py` | catches limiting/clipping introduced by a render chain | |
| | `check_clips.py` | duration and sample-rate conformance | |
| | `wetdry.py` | measures the wet/dry relationship of a rendered pair | |
| | `slice_ableton_stems.py` | cuts long recordings into per-source clips | |
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|
| ## The rules these encode |
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| Each one cost real time to learn. |
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|
| **Never normalise dry and wet independently.** Apply the same gain to both, or |
| the pair no longer describes a transformation and the model learns loudness |
| instead of acoustics. |
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|
| **Normalise by active loudness, not peak.** Sparse material — handclaps |
| especially — reads ~13 dB quiet on peak-based normalisation and ends up |
| mismatched against sustained sources. |
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|
| **An ambience bed is not a reverb tail.** A tail is part of the signal and |
| scales cleanly; a bed is a separate recording, so boosting it amplifies its own |
| noise floor. Scale beds down only, and only a little. This is why the outdoor |
| cells ship two levels while rooms and SFX ship three. |
|
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| **Check the dry fit before trusting any ambience measurement.** `R = wet − dry` |
| assumes the render carries the dry at exactly 1.00×. When it does not, a |
| negative ghost of the dry hides inside R and dominates every derived number — |
| which produced months of misleading ambience statistics here before |
| `verify_dataset.py` caught it. Confirm fit ≈ 1.000 first. |
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|
| **The trigger word belongs in preprocessing, not the manifest.** It is prepended |
| to training captions by the preprocessor and must appear in inference prompts; |
| storing it in the CSV double-applies it. |
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|
