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license: cc-by-4.0
language:
- en
- fr
pretty_name: skycmd ear questions (s6-ear)
task_categories:
- audio-classification
tags:
- skycmd
configs:
- config_name: clips_mswc
data_files: data/clips_mswc.parquet
- config_name: clips_smart-turn
data_files: data/clips_smart-turn.parquet
- config_name: clips_speech-commands
data_files: data/clips_speech-commands.parquet
- config_name: keyword
data_files: data/keyword.parquet
- config_name: keyword_zs
data_files: data/keyword_zs.parquet
- config_name: mimi_train
data_files: data/mimi_train.parquet
- config_name: mimi_val
data_files: data/mimi_val.parquet
- config_name: turn_end
data_files: data/turn_end.parquet
---
# skycmd ear questions (s6-ear)
The audio questions of s6-ear (turn end, keyword spotting, zero-shot keywords), the index of every clip and the Mimi codes of its audio pretraining.
Part of [skycmd](https://github.com/maxgfr/skycmd), tiny decision models trained from scratch in Rust that run in the
browser. Written by `sky-data fetch-audio and sky-data audio-jev` at commit [`84c6d854`](https://github.com/maxgfr/skycmd/commit/84c6d854cdc2a9f6d951ebb55780060a01f38ca6).
Licences of every set: [DATA_LICENSES.md](https://github.com/maxgfr/skycmd/blob/main/DATA_LICENSES.md).
- the question configs (`turn_end`, `keyword`, `keyword_zs`) have the typed-decision schema of
skycmd-decisions (`src`, `t`, `ins`, `opts`, `state`, `target`, `split`) plus `lang` and `clip`;
`state` is the path of the clip's float16 log-mel features (`.npy`) relative to the question file;
- the `clips_*` configs index every clip: `id`, `set`, `split`, `lang`, `mel` (features path),
`frames`, `label`, and `wav` for the test clips;
- the `mimi_*` configs give, per LibriSpeech utterance, its features path and its Mimi codes.
**The audio is not in this repository** (31 GB of log-mel features and 16 kHz test WAVs): it is
rebuilt from the upstream sets by `sky-data fetch-audio`, deterministically, with the same paths.
## Sources and licences
Each config is one source and keeps **its source's licence** (share-alike sources stay share-alike).
Attribution goes to the upstream authors; the conversion is ours.
| config | upstream | licence | how it is used | rows |
|---|---|---|---|---:|
| `clips_mswc` | MLCommons/ml_spoken_words | cc-by-4.0 | clip index | 360,000 |
| `clips_smart-turn` | pipecat-ai/smart-turn-data-v3-train and v3.2-test | cc-by-4.0 | clip index: id, split, language, frames, label | 258,552 |
| `clips_speech-commands` | Speech Commands v0.02 | cc-by-4.0 | clip index | 50,289 |
| `keyword` | Speech Commands v0.02 (Warden 2018), official splits and test archive | cc-by-4.0 | `keyword` choice, the 12-class benchmark | 50,289 |
| `keyword_zs` | MLCommons/ml_spoken_words (en, fr; first archive of each split) | cc-by-4.0 | `keyword_zs` choice, 50 test words per language held out | 224,287 |
| `mimi_train` | openslr/librispeech_asr (`clean`, `train.100`) and shangeth/librispeech-mimi-codes | cc-by-4.0 | Mimi codebook-0 ids (12.5 Hz) per utterance, the audio pretraining target | 28,539 |
| `mimi_val` | openslr/librispeech_asr (`clean`, `validation`) and shangeth/librispeech-mimi-codes | cc-by-4.0 | same, validation | 2,703 |
| `turn_end` | pipecat-ai/smart-turn-data-v3-train (train, val) and pipecat-ai/smart-turn-data-v3.2-test (test) | cc-by-4.0 | `turn_end` noul questions | 258,552 |
## Files
| file | rows |
|---|---:|
| `data/clips_mswc.parquet` | 360,000 |
| `data/clips_smart-turn.parquet` | 258,552 |
| `data/clips_speech-commands.parquet` | 50,289 |
| `data/keyword.parquet` | 50,289 |
| `data/keyword_zs.parquet` | 224,287 |
| `data/mimi_train.parquet` | 28,539 |
| `data/mimi_val.parquet` | 2,703 |
| `data/turn_end.parquet` | 258,552 |
```python
from datasets import load_dataset
ds = load_dataset("maxgfr/skycmd-ear", "clips_mswc")
```
skycmd wrote no row with a language model: the targets come from the upstream labels (some upstream
sets carry their own teacher soft labels, see their cards), the games' oracles or our generators.
|