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  ---
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  license: cc-by-sa-4.0
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  license: cc-by-sa-4.0
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+ task_categories:
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+ - audio-classification
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+ pretty_name: Engine Sound Windows (YouTube-derived, metadata-only)
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+ tags:
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+ - audio
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+ - automotive
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+ - engine-sounds
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+ - youtube
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+ - weak-supervision
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+ size_categories:
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+ - 1M<n<10M
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+ configs:
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+ - config_name: default
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+ data_files:
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+ - split: train
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+ path: data/train.parquet
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+ - split: test
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+ path: data/test.parquet
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+ dataset_info:
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+ features:
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+ - name: window_id
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+ dtype: string
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+ - name: youtube_id
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+ dtype: string
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+ - name: engine_class
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+ dtype: string
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+ - name: start_sec
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+ dtype: float64
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+ - name: end_sec
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+ dtype: float64
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+ - name: contains_target
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+ dtype: bool
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+ - name: quality_flag
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+ dtype: bool
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+ - name: split
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+ dtype: string
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+ splits:
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+ - name: train
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+ num_examples: 1508084
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+ - name: test
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+ num_examples: 375639
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+ download_size: 14615747
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+ dataset_size: 14615747
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  ---
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+
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+ # Engine Sound Windows (YouTube-derived, metadata-only)
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+
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+ Timestamps and weak labels for training an engine-configuration audio classifier (v-twin vs.
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+ inline-4 vs. flat-6, etc.) from short audio windows. **This dataset does not contain audio.**
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+ Each row points at a public YouTube video id plus a `(start_sec, end_sec)` window; you fetch
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+ and slice the audio yourself (see [Reconstructing audio](#reconstructing-audio) below).
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+
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+ ## Why metadata-only
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+
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+ The source audio was collected by searching YouTube (via [`yt-dlp`](https://github.com/yt-dlp/yt-dlp))
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+ for engine-sound terms and downloading matching videos. The dataset author does not hold
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+ redistribution rights to that audio, so — following the precedent set by
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+ [AudioSet](https://research.google.com/audioset/), [MusicCaps](https://huggingface.co/datasets/google/MusicCaps),
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+ and [FSD50K](https://zenodo.org/records/4060432) for exactly this situation — only the video id,
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+ window timestamps, and derived labels are published here, not audio bytes. This also means the
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+ `cc-by-sa-4.0` license above covers only this repository's metadata (ids, timestamps, labels); the
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+ underlying YouTube videos remain under their original creators' copyright and are not
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+ relicensed or redistributed by this dataset in any form.
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+
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+ ## Dataset structure
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+
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+ | Column | Type | Meaning |
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+ | --- | --- | --- |
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+ | `window_id` | string | Unique id for this window (`winNNN`), stable across the whole corpus |
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+ | `youtube_id` | string | 11-character YouTube video id (`https://www.youtube.com/watch?v=<youtube_id>`) |
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+ | `engine_class` | string | Engine configuration label, e.g. `v8_flat`, `i4_diesel`, `single_two_stroke` |
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+ | `start_sec` / `end_sec` | float | Window bounds within the source video, in seconds |
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+ | `contains_target` | bool | See [Label semantics](#label-semantics) |
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+ | `quality_flag` | bool | See [Label semantics](#label-semantics) |
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+ | `split` | string | `train` or `test` — assigned per source video, so every window from one video stays in the same split |
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+
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+ Windows are 2.0 seconds long with 1.0 second of step between consecutive windows (50% overlap),
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+ confirmed directly from the underlying manifest's timestamps.
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+
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+ ## Label semantics
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+
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+ `contains_target` and `quality_flag` are **model-derived, not human-verified** — they come from
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+ running [`panns_inference`](https://github.com/qiuqiangkong/audioset_tagging_cnn)'s
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+ `AudioTagging` model (trained on AudioSet) over each window and thresholding two sets of its 527
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+ class scores:
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+
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+ - `contains_target = True` when the window's max score across a set of engine/vehicle AudioSet
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+ classes exceeds `0.585` — i.e. an engine sound was likely detected.
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+ - `quality_flag = True` when the window's max score across a set of background-noise/contamination
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+ AudioSet classes exceeds `0.2` — i.e. contamination was likely detected.
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+
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+ **`quality_flag = True` is a caution flag, not an endorsement** — despite the name, it does not
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+ mean the window is good quality. Treat both columns as weak, noisy supervision (useful for
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+ filtering or as auxiliary features) rather than ground truth.
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+
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+ `engine_class`, by contrast, comes from which search query the source video was found under —
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+ also not independently verified per-video (see [Known limitations](#known-limitations)).
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+
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+ ## Engine classes
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+
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+ 43 engine classes, 3,830 source videos, after exclusions below:
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+
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+ | Engine class | Files | Windows | Train | Test |
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+ | --- | --- | --- | --- | --- |
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+ | `2_rotor` | 18 | 3740 | 3372 | 368 |
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+ | `h12` | 10 | 4863 | 3981 | 882 |
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+ | `h2` | 92 | 56012 | 37044 | 18968 |
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+ | `h4` | 112 | 40657 | 34450 | 6207 |
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+ | `h6` | 97 | 84341 | 57384 | 26957 |
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+ | `i2_180` | 127 | 88771 | 66385 | 22386 |
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+ | `i2_180_two_stroke` | 41 | 9162 | 7007 | 2155 |
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+ | `i2_270` | 169 | 134456 | 104910 | 29546 |
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+ | `i2_360` | 63 | 24789 | 18457 | 6332 |
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+ | `i2_360_two_stroke` | 44 | 12124 | 10577 | 1547 |
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+ | `i3` | 133 | 57496 | 45440 | 12056 |
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+ | `i3_two_stroke` | 10 | 3219 | 2943 | 276 |
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+ | `i4` | 392 | 166630 | 131564 | 35066 |
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+ | `i4_crossplane` | 134 | 68221 | 48907 | 19314 |
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+ | `i4_diesel` | 75 | 31429 | 26944 | 4485 |
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+ | `i5` | 115 | 29789 | 24849 | 4940 |
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+ | `i5_diesel` | 52 | 21759 | 17435 | 4324 |
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+ | `i6` | 157 | 49570 | 35274 | 14296 |
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+ | `i6_diesel` | 52 | 29830 | 26165 | 3665 |
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+ | `single_four_stroke` | 101 | 42045 | 31663 | 10382 |
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+ | `single_two_stroke` | 94 | 44185 | 33465 | 10720 |
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+ | `v10_72` | 154 | 63020 | 46769 | 16251 |
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+ | `v10_90` | 14 | 11291 | 8406 | 2885 |
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+ | `v12` | 112 | 37882 | 35178 | 2704 |
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+ | `v16` | 8 | 4512 | 4512 | 0 |
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+ | `v2_45` | 138 | 165815 | 146284 | 19531 |
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+ | `v2_90` | 97 | 83045 | 73491 | 9554 |
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+ | `v2_two_stroke` | 11 | 2425 | 1915 | 510 |
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+ | `v4` | 80 | 31392 | 26760 | 4632 |
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+ | `v4_two_stroke` | 35 | 8369 | 6995 | 1374 |
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+ | `v6_120` | 72 | 31924 | 21840 | 10084 |
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+ | `v6_60` | 212 | 61043 | 47627 | 13416 |
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+ | `v6_90_even` | 58 | 36963 | 33490 | 3473 |
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+ | `v6_90_odd` | 5 | 3446 | 2629 | 817 |
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+ | `v6_diesel` | 16 | 3346 | 2502 | 844 |
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+ | `v8_60` | 18 | 6307 | 5173 | 1134 |
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+ | `v8_cross` | 264 | 139946 | 120989 | 18957 |
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+ | `v8_diesel` | 124 | 45184 | 35857 | 9327 |
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+ | `v8_flat` | 180 | 81189 | 68971 | 12218 |
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+ | `v8_voodoo` | 65 | 34937 | 27993 | 6944 |
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+ | `vr6` | 54 | 11260 | 6762 | 4498 |
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+ | `w12` | 19 | 8787 | 7173 | 1614 |
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+ | `w16` | 6 | 8552 | 8552 | 0 |
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+
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+ Class sizes are heavily imbalanced (5 to 392 files per class) — account for this when
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+ sampling/weighting during training.
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+
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+ ## Known limitations
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+
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+ - **Weak, auto-derived labels.** `contains_target`/`quality_flag` come from an AudioSet-trained
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+ tagger's thresholded scores, not human review (see [Label semantics](#label-semantics)).
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+ - **Class imbalance.** File counts per class range from 5 (`v6_90_odd`) to 392 (`i4`).
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+ - **Two classes have zero test windows.** `v16` and `w16` have only 8 and 6 source videos
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+ respectively; the per-video random 80/20 split happened to put every video from both classes
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+ into `train`. Don't evaluate on these classes without re-splitting.
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+ - **97 ambiguous videos were excluded.** Cross-referencing every video id against every
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+ engine_class it was scraped under found 97 YouTube videos that had been pulled into **more
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+ than one conflicting engine_class** (almost certainly multi-engine compilation/comparison
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+ videos caught by more than one search query, e.g. one video labeled both `v2_90` and `v4`,
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+ another labeled across `i4_diesel`/`i6_diesel`/`v10_90`/`v8_diesel` simultaneously). All
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+ windows sourced from any of these videos were dropped entirely (110,830 of 1,994,553 rows,
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+ 5.6%) rather than guessing which label was correct. This hit some already-small classes hard:
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+ `v2_two_stroke` went from 19 to 11 files, `h12` from 18 to 10, `v10_90` from 19 to 14. The
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+ class table above already reflects these counts.
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+ - **Link rot.** Since only YouTube ids are published (see [Why metadata-only](#why-metadata-only)),
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+ some fraction of source videos will become unavailable over time as creators delete or
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+ privatize them — unlike a self-hosted audio dataset, this one can shrink on its own.
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+ - **`engine_class` isn't independently verified per video** beyond the cross-class-conflict
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+ check above — a video could still be mislabeled by its original search query in a way that
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+ doesn't produce a detectable cross-class conflict (e.g. a single video mislabeled but never
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+ scraped under any other class).
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+
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+ ## Reconstructing audio
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+
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+ For a given row, download the source video's audio and trim to the window:
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+
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+ ```bash
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+ yt-dlp -f bestaudio -x --audio-format m4a \
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+ "https://www.youtube.com/watch?v=<youtube_id>" -o source.m4a
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+
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+ ffmpeg -i source.m4a -ss <start_sec> -to <end_sec> -c copy window.m4a
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+ ```
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+
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+ For batch reconstruction, group rows by `youtube_id` first so each video is downloaded once
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+ regardless of how many windows come from it.
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+
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+ ## License and usage
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+
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+ The labels, timestamps, and ids in this repository are released under `cc-by-sa-4.0`. This does
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+ **not** extend any rights to the underlying YouTube video content, which remains the property of
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+ its original creators — this dataset does not redistribute, host, or relicense that audio.
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+ Commercial users should independently verify their own right to use any audio they fetch via the
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+ ids in this dataset.