| --- |
| 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. |
|
|
