Datasets:
File size: 3,241 Bytes
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license: cc-by-4.0
task_categories:
- audio-classification
tags:
- audio
- synthesizer
- audio-effects
- parameter-estimation
- synthetic
pretty_name: StemFlipper synth/effects parameter-estimation scaffold
configs:
- config_name: synth
data_files:
- split: train
path: synth/train-*
- config_name: effects
data_files:
- split: train
path: effects/train-*
---
# StemFlipper — synth & effects parameter-estimation dataset (scaffold)
A synthetic **(audio → parameters)** dataset for the inverse problems StemFlipper
targets: recover a synth patch from its sound, and recover an effect chain from
wet audio. No public dataset pairs real audio with the synth-patch / effect-chain
parameters that produced it — this fills that gap with deterministic synthetic
generation. **The moat is the generator + seeds, not stored audio**: every example
here regenerates bit-for-bit from its seed.
## Configs
- **`synth`** — torchsynth `Voice` renders `(audio, params)`; `params` is the
78-dim normalized parameter vector (`adsr_1.attack`, `vco_1.tuning`, …).
- **`effects`** — a known `dasp-pytorch` chain (`parametric_eq, compressor, distortion`)
applied to clean synth voices → `(wet audio, params)`; `param_names` labels each
value.
```python
from datasets import load_dataset
synth = load_dataset("nakas/stemflipper-dataset", "synth", split="train")
fx = load_dataset("nakas/stemflipper-dataset", "effects", split="train")
```
## Reproduce / extend from seeds
The published splits are a small demo. Regenerate or scale up deterministically:
```python
from dataset.synth_gen import SynthGenConfig, iter_examples
list(iter_examples(SynthGenConfig(batch_indices=[0, 1, 2]))) # 3 batches
```
Generation spec (seeds):
```json
{
"sample_rate": 44100,
"synth": {
"generator": "dataset/synth_gen.py",
"batch_size": 32,
"batch_indices": [
0,
1
],
"param_names_count": 78
},
"effects": {
"generator": "dataset/effects_gen.py",
"chain": [
"parametric_eq",
"compressor",
"distortion"
],
"seeds": [
0,
1,
2,
3
],
"n_dry": 8,
"param_names": [
"parametric_eq.low_shelf_gain_db",
"parametric_eq.low_shelf_cutoff_freq",
"parametric_eq.low_shelf_q_factor",
"parametric_eq.band0_gain_db",
"parametric_eq.band0_cutoff_freq",
"parametric_eq.band0_q_factor",
"parametric_eq.band1_gain_db",
"parametric_eq.band1_cutoff_freq",
"parametric_eq.band1_q_factor",
"parametric_eq.band2_gain_db",
"parametric_eq.band2_cutoff_freq",
"parametric_eq.band2_q_factor",
"parametric_eq.band3_gain_db",
"parametric_eq.band3_cutoff_freq",
"parametric_eq.band3_q_factor",
"parametric_eq.high_shelf_gain_db",
"parametric_eq.high_shelf_cutoff_freq",
"parametric_eq.high_shelf_q_factor",
"compressor.threshold_db",
"compressor.ratio",
"compressor.attack_ms",
"compressor.release_ms",
"compressor.knee_db",
"compressor.makeup_gain_db",
"distortion.drive_db"
]
}
}
```
Generated by `dataset/build.py` in the [StemFlipper](https://huggingface.co/spaces/nakas/stemflipper) repo.
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