Datasets:

laion
/

ProgrammaticAudio

---
license: cc0-1.0
pretty_name: ProgrammaticAudio
language:
- en
task_categories:
- audio-classification
- text-to-audio
tags:
- audio
- synthetic
- sound-synthesis
- audio-captioning
- webdataset
size_categories:
- 100K<n<1M
configs:
- config_name: default
  data_files:
  - split: train
    path: train/*.tar
---
# ProgrammaticAudio

ProgrammaticAudio is a synthetic audio-text dataset: 103,038 clips (304.4 hours), each with an English caption.
All audio is computed with numpy and scipy. No recordings, samples or learned models produce the audio. The dataset
covers 123 signal types in 14 categories, from basic waveforms (sine, square, sawtooth), coloured noise and synthesis
techniques to filters, effects, drum sounds, test signals and digital silence. It was built as a training source for
contrastive language-audio models, so that they see clean, precisely described synthetic sounds with known
parameters (frequency, note, filter, effect).

| | |
|---|---|
| Clips | 103,038 |
| Duration | 304.4 h, 2–20 s per clip (mean 10.6 s) |
| Audio | FLAC, 48 kHz, 16-bit, mono |
| Captions | one per clip, English: 94,997 generator templates, 8,041 model rewrites (see below) |
| Shards | 26 WebDataset tars, `train/0001.tar` … `train/0026.tar`, 4,096 clips per tar (the last one 638) |
| License | CC0 1.0 |

## Format

Each tar holds pairs `prog_NNNNNN.flac` + `prog_NNNNNN.json`. The JSON fields are:

| Field | Meaning |
|---|---|
| `__key__` | sample key, `prog_` + the index in the generation plan |
| `__url__` | the tar the sample is in |
| `text` | the caption |
| `original_text` | only on the 8,041 rewritten captions: the template caption from the generator |
| `generation_method` | the generator function, e.g. `gen_karplus_strong / numpy+scipy` |
| `category` | one of the 14 categories below |

Example:

```json
{"__key__": "prog_000011", "__url__": "ProgrammaticAudio/train/0001.tar", "text": "A very loud, pure 2.9 kHz sine wave",
 "generation_method": "gen_sine / numpy+scipy", "category": "basic_waveforms"}
```

Loading:

```python
from datasets import load_dataset
ds = load_dataset("laion/ProgrammaticAudio", split="train", streaming=True)
sample = next(iter(ds))
print(sample["json"]["text"], sample["flac"]["sampling_rate"])
```

## Categories

| Category | Clips | Generators (`gen_<name>`) |
|---|---:|---|
| basic_waveforms | 13,000 | sine, square, sawtooth, triangle, pulse_pwm, supersaw, wavetable, sample_hold, bit_noise, unison_detune, drone |
| noise | 6,000 | white_noise, pink_noise, brown_noise, blue_noise, violet_noise, grey_noise, vinyl_crackle, tape_hiss, filtered_noise |
| synthesis_techniques | 11,500 | fm, am, ring_mod, additive, subtractive, granular, karplus_strong, physical_model |
| musical_constructs | 9,438 | chord, arpeggio, scale, interval, melody, harmonic_series, dtmf |
| pitch_tuning | 5,000 | vibrato, pitch_bend, glide, octaver, harmonizer, detune, formant_shift |
| filters | 8,000 | lowpass, highpass, bandpass, notch, peaking_eq, shelf_eq, comb_filter, allpass, formant_vowel, filter_sweep, wah, resonance |
| dynamics | 5,000 | compressor, limiter, gate, sidechain_pump, transient_shaper, ducker, amplitude_sweep |
| distortion | 6,100 | hard_clip, soft_clip, overdrive, fuzz, tape_saturation, waveshaper, bitcrush, sample_rate_reduce, rectify, wow_flutter, vinyl_lofi |
| time_effects | 6,000 | delay, slapback, pingpong, multitap, tape_delay, reverb_room, reverb_hall, reverb_plate, reverb_spring, gated_reverb, shimmer_reverb, reverse_reverb |
| modulation | 5,000 | chorus, flanger, phaser, tremolo, autopan, rotary, freq_shifter, lfo_filter |
| percussive | 8,000 | kick, snare, hihat, click_pop, clap, drum_pattern |
| special | 4,000 | silence, near_silence, dc_offset, clipping_max, nyquist, sub_bass, impulse, test_tone, sweep, beating, chirp |
| editing | 5,000 | reverse, stutter_glitch, beat_repeat, freeze, spectral_blur, vocoder, time_stretch, varispeed, chop_gate |
| combinations | 11,000 | layered, sequence, with_effects, crossfade, sound_design |
The `combinations` generators layer, sequence, crossfade or process the outputs of the other generators.
## How it was generated
The full generator is in [`generator/`](generator/) (`generate.py`, one module per category in `generators/`,
shared helpers in `utils.py`, caption vocabularies in `constants.py`). It ran once on a 48-core CPU node:
`python generate.py --workers 92 --seed 42`.
1. **Plan.** Each generator is registered with a clip count. The plan lists 103,100 samples; sample `i` gets the key
   `prog_{i:06d}` and its own random generator `numpy.random.default_rng(42 + i)`. All parameters (duration
   uniform in 2–20 s, frequencies, notes, chord types, filter cutoffs, effect settings, ...) are drawn from it.
2. **Signal.** The generator function returns the signal at 48 kHz, a list of descriptor words and a core phrase
   (e.g. `["resonant"]`, `"Karplus-Strong plucked string at G#4"`). Pitched sounds name the frequency or note,
   filters their cutoff, rhythmic sounds their BPM.
3. **Level.** The clip is peak-normalised to a random amplitude, uniform in 0.05–1.0. The amplitude sets a volume
   word: below 0.15 "very quiet", below 0.35 "quiet" or "soft", below 0.65 none, below 0.85 "loud", otherwise
   "very loud". A 10 ms linear fade is applied at both ends, the signal is clipped to [-1, 1] and written as
   16-bit FLAC.
4. **Caption.** The template is `A {volume}, {descriptors} {core}`, for example "A soft, resonant Karplus-Strong
   plucked string at G#4" or "A 112 BPM shuffle drum pattern with kick, snare, and hi-hat".
5. **Failures.** 62 `gen_melody` samples raised an error and were dropped, so `musical_constructs` has 9,438 clips
   instead of 9,500.
Tars are filled in completion order across worker processes, so keys inside a tar are not contiguous.
**Reproducibility.** Running the generator again with seed 42 gives bit-identical audio. We regenerated 40 randomly
chosen clips and compared the decoded 16-bit PCM: all 40 are identical. The generator reads `TAR_DIR` (output root)
from the environment and needs numpy, scipy, soundfile and python-dotenv.

## Caption rewrites

After generation, a caption clean-up pass over our whole audio collection replaced captions that had at most 4 words,
more than 40 words, or were not detected as English by `langdetect`. In this dataset that affected 8,041 clips
(7.8 %): 2,339 templates with at most 4 words (e.g. "A pink noise", "A DC offset signal") and 5,702 English
templates that `langdetect` misclassified (e.g. "A quiet, buzzy 815 Hz square wave"). The new caption was written by
[Qwen3-Omni-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct), which received the audio and
the prompt "Write an audio caption for this audio in 1 sentence. For potential context, see original caption:
{template caption}". The template caption is kept in `original_text`. Use `original_text` when present if you want
the exact generator description for every clip.

## Known quirks

- The volume word comes from the random peak amplitude, independent of the generator. Every clip is peak-normalised,
  so `near_silence` clips (Gaussian noise floor) play at a normal level, and some captions combine contradicting
  words, e.g. "A very loud, barely audible, faint ambient noise floor".
- `silence` clips are all-zero samples. `dc_offset` clips are a constant value with 10 ms ramps at both ends.
- Captions are templates. Many clips of the same generator share the same caption except for the numbers.

## License

CC0 1.0. The audio and the template captions are fully synthetic and generated by our own code. The rewritten
captions were generated by Qwen3-Omni (Apache 2.0).

	---
	license: cc0-1.0
	pretty_name: ProgrammaticAudio
	language:
	- en
	task_categories:
	- audio-classification
	- text-to-audio
	tags:
	- audio
	- synthetic
	- sound-synthesis
	- audio-captioning
	- webdataset
	size_categories:
	- 100K<n<1M
	configs:
	- config_name: default
	data_files:
	- split: train
	path: train/*.tar
	---

	# ProgrammaticAudio

	ProgrammaticAudio is a synthetic audio-text dataset: 103,038 clips (304.4 hours), each with an English caption.
	All audio is computed with numpy and scipy. No recordings, samples or learned models produce the audio. The dataset
	covers 123 signal types in 14 categories, from basic waveforms (sine, square, sawtooth), coloured noise and synthesis
	techniques to filters, effects, drum sounds, test signals and digital silence. It was built as a training source for
	contrastive language-audio models, so that they see clean, precisely described synthetic sounds with known
	parameters (frequency, note, filter, effect).

	\| \| \|
	\|---\|---\|
	\| Clips \| 103,038 \|
	\| Duration \| 304.4 h, 2–20 s per clip (mean 10.6 s) \|
	\| Audio \| FLAC, 48 kHz, 16-bit, mono \|
	\| Captions \| one per clip, English: 94,997 generator templates, 8,041 model rewrites (see below) \|
	\| Shards \| 26 WebDataset tars, `train/0001.tar` … `train/0026.tar`, 4,096 clips per tar (the last one 638) \|
	\| License \| CC0 1.0 \|

	## Format

	Each tar holds pairs `prog_NNNNNN.flac` + `prog_NNNNNN.json`. The JSON fields are:

	\| Field \| Meaning \|
	\|---\|---\|
	\| `__key__` \| sample key, `prog_` + the index in the generation plan \|
	\| `__url__` \| the tar the sample is in \|
	\| `text` \| the caption \|
	\| `original_text` \| only on the 8,041 rewritten captions: the template caption from the generator \|
	\| `generation_method` \| the generator function, e.g. `gen_karplus_strong / numpy+scipy` \|
	\| `category` \| one of the 14 categories below \|

	Example:

	```json
	{"__key__": "prog_000011", "__url__": "ProgrammaticAudio/train/0001.tar", "text": "A very loud, pure 2.9 kHz sine wave",
	"generation_method": "gen_sine / numpy+scipy", "category": "basic_waveforms"}
	```

	Loading:

	```python
	from datasets import load_dataset

	ds = load_dataset("laion/ProgrammaticAudio", split="train", streaming=True)
	sample = next(iter(ds))
	print(sample["json"]["text"], sample["flac"]["sampling_rate"])
	```

	## Categories

	\| Category \| Clips \| Generators (`gen_<name>`) \|
	\|---\|---:\|---\|
	\| basic_waveforms \| 13,000 \| sine, square, sawtooth, triangle, pulse_pwm, supersaw, wavetable, sample_hold, bit_noise, unison_detune, drone \|
	\| noise \| 6,000 \| white_noise, pink_noise, brown_noise, blue_noise, violet_noise, grey_noise, vinyl_crackle, tape_hiss, filtered_noise \|
	\| synthesis_techniques \| 11,500 \| fm, am, ring_mod, additive, subtractive, granular, karplus_strong, physical_model \|
	\| musical_constructs \| 9,438 \| chord, arpeggio, scale, interval, melody, harmonic_series, dtmf \|
	\| pitch_tuning \| 5,000 \| vibrato, pitch_bend, glide, octaver, harmonizer, detune, formant_shift \|
	\| filters \| 8,000 \| lowpass, highpass, bandpass, notch, peaking_eq, shelf_eq, comb_filter, allpass, formant_vowel, filter_sweep, wah, resonance \|
	\| dynamics \| 5,000 \| compressor, limiter, gate, sidechain_pump, transient_shaper, ducker, amplitude_sweep \|
	\| distortion \| 6,100 \| hard_clip, soft_clip, overdrive, fuzz, tape_saturation, waveshaper, bitcrush, sample_rate_reduce, rectify, wow_flutter, vinyl_lofi \|
	\| time_effects \| 6,000 \| delay, slapback, pingpong, multitap, tape_delay, reverb_room, reverb_hall, reverb_plate, reverb_spring, gated_reverb, shimmer_reverb, reverse_reverb \|
	\| modulation \| 5,000 \| chorus, flanger, phaser, tremolo, autopan, rotary, freq_shifter, lfo_filter \|
	\| percussive \| 8,000 \| kick, snare, hihat, click_pop, clap, drum_pattern \|
	\| special \| 4,000 \| silence, near_silence, dc_offset, clipping_max, nyquist, sub_bass, impulse, test_tone, sweep, beating, chirp \|
	\| editing \| 5,000 \| reverse, stutter_glitch, beat_repeat, freeze, spectral_blur, vocoder, time_stretch, varispeed, chop_gate \|
	\| combinations \| 11,000 \| layered, sequence, with_effects, crossfade, sound_design \|

	The `combinations` generators layer, sequence, crossfade or process the outputs of the other generators.

	## How it was generated

	The full generator is in [`generator/`](generator/) (`generate.py`, one module per category in `generators/`,
	shared helpers in `utils.py`, caption vocabularies in `constants.py`). It ran once on a 48-core CPU node:
	`python generate.py --workers 92 --seed 42`.

	1. Plan. Each generator is registered with a clip count. The plan lists 103,100 samples; sample `i` gets the key
	`prog_{i:06d}` and its own random generator `numpy.random.default_rng(42 + i)`. All parameters (duration
	uniform in 2–20 s, frequencies, notes, chord types, filter cutoffs, effect settings, ...) are drawn from it.
	2. Signal. The generator function returns the signal at 48 kHz, a list of descriptor words and a core phrase
	(e.g. `["resonant"]`, `"Karplus-Strong plucked string at G#4"`). Pitched sounds name the frequency or note,
	filters their cutoff, rhythmic sounds their BPM.
	3. Level. The clip is peak-normalised to a random amplitude, uniform in 0.05–1.0. The amplitude sets a volume
	word: below 0.15 "very quiet", below 0.35 "quiet" or "soft", below 0.65 none, below 0.85 "loud", otherwise
	"very loud". A 10 ms linear fade is applied at both ends, the signal is clipped to [-1, 1] and written as
	16-bit FLAC.
	4. Caption. The template is `A {volume}, {descriptors} {core}`, for example "A soft, resonant Karplus-Strong
	plucked string at G#4" or "A 112 BPM shuffle drum pattern with kick, snare, and hi-hat".
	5. Failures. 62 `gen_melody` samples raised an error and were dropped, so `musical_constructs` has 9,438 clips
	instead of 9,500.

	Tars are filled in completion order across worker processes, so keys inside a tar are not contiguous.

	Reproducibility. Running the generator again with seed 42 gives bit-identical audio. We regenerated 40 randomly
	chosen clips and compared the decoded 16-bit PCM: all 40 are identical. The generator reads `TAR_DIR` (output root)
	from the environment and needs numpy, scipy, soundfile and python-dotenv.

	## Caption rewrites

	After generation, a caption clean-up pass over our whole audio collection replaced captions that had at most 4 words,
	more than 40 words, or were not detected as English by `langdetect`. In this dataset that affected 8,041 clips
	(7.8 %): 2,339 templates with at most 4 words (e.g. "A pink noise", "A DC offset signal") and 5,702 English
	templates that `langdetect` misclassified (e.g. "A quiet, buzzy 815 Hz square wave"). The new caption was written by
	[Qwen3-Omni-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Omni-30B-A3B-Instruct), which received the audio and
	the prompt "Write an audio caption for this audio in 1 sentence. For potential context, see original caption:
	{template caption}". The template caption is kept in `original_text`. Use `original_text` when present if you want
	the exact generator description for every clip.

	## Known quirks

	- The volume word comes from the random peak amplitude, independent of the generator. Every clip is peak-normalised,
	so `near_silence` clips (Gaussian noise floor) play at a normal level, and some captions combine contradicting
	words, e.g. "A very loud, barely audible, faint ambient noise floor".
	- `silence` clips are all-zero samples. `dc_offset` clips are a constant value with 10 ms ramps at both ends.
	- Captions are templates. Many clips of the same generator share the same caption except for the numbers.

	## License

	CC0 1.0. The audio and the template captions are fully synthetic and generated by our own code. The rewritten
	captions were generated by Qwen3-Omni (Apache 2.0).