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Initial release of SMP-FSAVC dataset

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  1. .gitattributes +20 -60
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  17. csv/AVE/fewshot.csv +0 -0
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  25. csv/VGGSound100/fewshot.csv +0 -0
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  28. csv/VGGSound100/pretrain_test.csv +0 -0
  29. metadata/checksums.sha256 +27 -0
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  33. requirements-build.txt +1 -0
  34. scripts/build_release.py +432 -0
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  36. viewer/AVE/fewshot.parquet +3 -0
  37. viewer/AVE/fewshot_test.parquet +3 -0
  38. viewer/AVE/pretrain.parquet +3 -0
  39. viewer/AVE/pretrain_test.parquet +3 -0
  40. viewer/Kinetics-Sounds/fewshot.parquet +3 -0
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  43. viewer/Kinetics-Sounds/pretrain_test.parquet +3 -0
  44. viewer/VGGSound100/fewshot.parquet +3 -0
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  46. viewer/VGGSound100/pretrain.parquet +3 -0
  47. viewer/VGGSound100/pretrain_test.parquet +3 -0
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+ cff-version: 1.2.0
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+ message: >-
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+ If you use these splits or semantic prompts, please cite the SMP-FSAVC
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+ article and the upstream datasets relevant to your experiment.
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+ title: SMP-FSAVC Dataset Splits and Semantic Prompts
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+ type: dataset
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+ version: 1.0.0
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+ date-released: 2026-08-14
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+ repository-code: https://github.com/DennisHgj/SMP_FSAVC_Dataset
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+ authors:
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+ - family-names: Huang
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+ given-names: Guanjie
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+ - family-names: Cui
14
+ given-names: Yawen
15
+ - family-names: Tsang
16
+ given-names: Danny H. K.
17
+ - family-names: Wang
18
+ given-names: Wenwu
19
+ - family-names: Liu
20
+ given-names: Li
21
+ preferred-citation:
22
+ type: article
23
+ title: Semantic Modulated Prompting for Few-Shot Audio-Visual Classification
24
+ authors:
25
+ - family-names: Huang
26
+ given-names: Guanjie
27
+ - family-names: Cui
28
+ given-names: Yawen
29
+ - family-names: Tsang
30
+ given-names: Danny H. K.
31
+ - family-names: Wang
32
+ given-names: Wenwu
33
+ - family-names: Liu
34
+ given-names: Li
35
+ journal: IEEE Transactions on Audio, Speech and Language Processing
36
+ volume: 34
37
+ start: 723
38
+ end: 736
39
+ year: 2026
40
+ doi: 10.1109/TASLPRO.2026.3654246
41
+ url: https://ieeexplore.ieee.org/abstract/document/11352954
DATA_REMOVAL.md ADDED
@@ -0,0 +1,27 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Correction and removal requests
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+
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+ This release contains clip identifiers, labels, and generated text captions,
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+ but no video, audio, or image files.
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+
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+ If you are a content owner, depicted person, upstream dataset maintainer, or
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+ other affected party and want an identifier or generated caption corrected or
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+ removed, open an issue at:
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+
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+ https://github.com/DennisHgj/SMP_FSAVC_Dataset/issues
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+
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+ Please include:
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+
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+ 1. the dataset name and `clip_id`;
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+ 2. the affected file or split, if known;
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+ 3. whether you request correction, redaction, or removal;
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+ 4. enough information to verify the request without posting unnecessary
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+ personal data publicly.
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+
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+ For a private request, contact the corresponding author through the contact
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+ information on the [published paper](https://ieeexplore.ieee.org/abstract/document/11352954).
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+ Do not include identity documents, private account credentials, or other
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+ sensitive material in a public issue.
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+
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+ Removal from this annotation repository does not remove a source video from
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+ YouTube or an upstream dataset. Requests concerning source media should also
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+ be directed to the hosting platform and the relevant upstream dataset owner.
LICENSE_CODE ADDED
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LICENSE_DATA.md ADDED
@@ -0,0 +1,49 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Data and annotation licensing
2
+
3
+ This repository intentionally does **not** redistribute source videos, audio,
4
+ or extracted frames.
5
+
6
+ ## Release-authored material
7
+
8
+ To the extent that the SMP-FSAVC authors hold the necessary rights, the
9
+ following release-authored material is made available under the
10
+ [Creative Commons Attribution 4.0 International license](https://creativecommons.org/licenses/by/4.0/):
11
+
12
+ - the few-shot source/target split definitions;
13
+ - the released mPLUG-2-generated semantic prompts;
14
+ - release-specific label maps, statistics, documentation, and metadata.
15
+
16
+ Attribution should identify the SMP-FSAVC authors and cite the published
17
+ article listed in `README.md`.
18
+
19
+ ## Upstream material and identifiers
20
+
21
+ Clip identifiers, category names, and source annotations originate from or
22
+ refer to AVE, Kinetics/Kinetics-Sounds, and VGGSound. They remain subject to
23
+ the applicable upstream terms and are not relicensed by this repository.
24
+ Users are responsible for reviewing and complying with those terms before
25
+ obtaining or using the corresponding media.
26
+
27
+ - AVE project: https://github.com/YapengTian/AVE-ECCV18
28
+ - Kinetics downloader and annotations: https://github.com/cvdfoundation/kinetics-dataset
29
+ - VGGSound dataset: https://www.robots.ox.ac.uk/~vgg/data/vggsound/
30
+
31
+ The VGGSound project states that its dataset is available under CC BY 4.0,
32
+ while copyright in each source video remains with its original owner. This
33
+ repository makes no claim of ownership over any source media.
34
+
35
+ ## Model and code dependencies
36
+
37
+ The captions were generated with mPLUG-2. mPLUG-2 code and model artifacts are
38
+ governed by their own upstream licenses and terms:
39
+ https://github.com/X-PLUG/mPLUG-2
40
+
41
+ The small release-maintenance scripts in `scripts/` are provided under the
42
+ same GNU GPL v3 terms as the SMP code repository. See [LICENSE_CODE](LICENSE_CODE).
43
+
44
+ ## No additional rights
45
+
46
+ Nothing in this notice grants rights to source media, personal likenesses,
47
+ trademarks, or other third-party content. If an upstream license or platform
48
+ term conflicts with this notice, the upstream restriction controls for that
49
+ material.
README.md ADDED
@@ -0,0 +1,303 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ pretty_name: SMP-FSAVC Splits and Semantic Prompts
3
+ language:
4
+ - en
5
+ license: other
6
+ task_categories:
7
+ - video-classification
8
+ - audio-classification
9
+ size_categories:
10
+ - 10K<n<100K
11
+ tags:
12
+ - audio-visual
13
+ - few-shot-learning
14
+ - semantic-prompting
15
+ - video-captioning
16
+ configs:
17
+ - config_name: ave
18
+ default: true
19
+ data_files:
20
+ - split: source_train
21
+ path: viewer/AVE/pretrain.parquet
22
+ - split: source_test
23
+ path: viewer/AVE/pretrain_test.parquet
24
+ - split: target_train_pool
25
+ path: viewer/AVE/fewshot.parquet
26
+ - split: target_test
27
+ path: viewer/AVE/fewshot_test.parquet
28
+ - config_name: kinetics-sounds
29
+ data_files:
30
+ - split: source_train
31
+ path: viewer/Kinetics-Sounds/pretrain.parquet
32
+ - split: source_test
33
+ path: viewer/Kinetics-Sounds/pretrain_test.parquet
34
+ - split: target_train_pool
35
+ path: viewer/Kinetics-Sounds/fewshot.parquet
36
+ - split: target_test
37
+ path: viewer/Kinetics-Sounds/fewshot_test.parquet
38
+ - config_name: vggsound100
39
+ data_files:
40
+ - split: source_train
41
+ path: viewer/VGGSound100/pretrain.parquet
42
+ - split: source_test
43
+ path: viewer/VGGSound100/pretrain_test.parquet
44
+ - split: target_train_pool
45
+ path: viewer/VGGSound100/fewshot.parquet
46
+ - split: target_test
47
+ path: viewer/VGGSound100/fewshot_test.parquet
48
+ ---
49
+
50
+ # SMP-FSAVC splits and semantic prompts
51
+
52
+ [中文说明](README.zh-CN.md) · [SMP code](https://github.com/DennisHgj/SMP_FSAVC) · [Paper](https://ieeexplore.ieee.org/abstract/document/11352954) · [DOI](https://doi.org/10.1109/TASLPRO.2026.3654246)
53
+
54
+ This repository releases the source/target class partitions, train/test sample
55
+ lists, and mPLUG-2-generated semantic prompts used by **Semantic Modulated
56
+ Prompting for Few-Shot Audio-Visual Classification** (SMP-FSAVC). It is ready
57
+ to host on both GitHub and the Hugging Face Hub: the original headerless CSV
58
+ files are preserved for the training code, while equivalent Parquet files
59
+ power the Hugging Face Dataset Viewer.
60
+
61
+ No video, audio, or extracted frame is distributed here. Users must obtain
62
+ media from the respective upstream datasets and follow their terms.
63
+
64
+ ## What is included
65
+
66
+ | Dataset | Released rows | Source classes | Target classes | Notes |
67
+ | --- | ---: | ---: | ---: | --- |
68
+ | AVE | 4,051 | 16 | 12 | 28 defined classes |
69
+ | Kinetics-Sounds | 22,908 | 19 | 13 | 32 defined classes |
70
+ | VGGSound100 | 59,795 | 60 defined / 59 available | 40 | Source label 14 has no obtainable media in this snapshot |
71
+ | **Total** | **86,754** | | | |
72
+
73
+ The VGGSound100 files named `train_caption.csv` and `test_caption.csv` in the
74
+ research workspace are not included because they are exact unions of the four
75
+ released split files and would duplicate content.
76
+
77
+ ## Split semantics
78
+
79
+ | File | Hugging Face split | Meaning |
80
+ | --- | --- | --- |
81
+ | `pretrain.csv` | `source_train` | Source-class pretraining set |
82
+ | `pretrain_test.csv` | `source_test` | Source-class evaluation set |
83
+ | `fewshot.csv` | `target_train_pool` | Target-class pool from which N-way K-shot support sets are sampled |
84
+ | `fewshot_test.csv` | `target_test` | Target-class query/evaluation set |
85
+
86
+ `fewshot.csv` is a sampling pool, not one fixed K-shot episode. The SMP
87
+ protocol repeatedly samples support examples from it.
88
+
89
+ ## Repository layout
90
+
91
+ ```text
92
+ .
93
+ |-- csv/ # original headerless files used by SMP
94
+ | |-- AVE/
95
+ | |-- Kinetics-Sounds/
96
+ | `-- VGGSound100/
97
+ |-- viewer/ # equivalent Parquet files for Dataset Viewer
98
+ |-- metadata/
99
+ | |-- dataset_statistics.json
100
+ | |-- generation_config.yaml
101
+ | |-- label_maps.json
102
+ | `-- checksums.sha256
103
+ |-- scripts/
104
+ | |-- build_release.py
105
+ | `-- verify_release.py
106
+ |-- LICENSE_DATA.md
107
+ |-- LICENSE_CODE
108
+ `-- DATA_REMOVAL.md
109
+ ```
110
+
111
+ ## Data schema
112
+
113
+ The raw files have no header. AVE rows contain three fields:
114
+
115
+ ```text
116
+ clip_id,integer_label,semantic_prompt
117
+ ```
118
+
119
+ Kinetics-Sounds and VGGSound100 rows contain a fourth field:
120
+
121
+ ```text
122
+ clip_id,integer_label,semantic_prompt,class_name
123
+ ```
124
+
125
+ CSV quoting is used where captions or class names contain commas. The Parquet
126
+ mirrors expose the same four named columns for every dataset:
127
+
128
+ | Column | Type | Description |
129
+ | --- | --- | --- |
130
+ | `clip_id` | string | Upstream clip identifier; no media URL or media payload |
131
+ | `label` | int64 | Dataset-specific integer class label |
132
+ | `semantic_prompt` | string | mPLUG-2-generated video caption used as the semantic prompt |
133
+ | `class_name` | string | Human-readable class name; derived from the AVE identifier suffix for AVE |
134
+
135
+ Complete source/target label mappings and availability flags are in
136
+ [`metadata/label_maps.json`](metadata/label_maps.json).
137
+
138
+ ## Loading the data
139
+
140
+ The Parquet files require no custom loading script. After cloning this
141
+ repository:
142
+
143
+ ```python
144
+ from datasets import load_dataset
145
+
146
+ ave = load_dataset(
147
+ "parquet",
148
+ data_files={
149
+ "source_train": "viewer/AVE/pretrain.parquet",
150
+ "source_test": "viewer/AVE/pretrain_test.parquet",
151
+ "target_train_pool": "viewer/AVE/fewshot.parquet",
152
+ "target_test": "viewer/AVE/fewshot_test.parquet",
153
+ },
154
+ )
155
+ ```
156
+
157
+ When this tree is pushed to a Hugging Face dataset repository, the YAML
158
+ configuration at the top of this card automatically exposes the `ave`,
159
+ `kinetics-sounds`, and `vggsound100` configurations in Dataset Viewer.
160
+
161
+ For the [SMP training repository](https://github.com/DennisHgj/SMP_FSAVC),
162
+ point the source annotation root at a dataset's `csv/<dataset>/` directory for
163
+ pretraining. Point the few-shot root at the same directory for target training;
164
+ the scripts select the required filenames.
165
+
166
+ ## Semantic prompts
167
+
168
+ The released `semantic_prompt` values were generated from visual frames with
169
+ [mPLUG-2](https://github.com/X-PLUG/mPLUG-2), using its MSVD-finetuned video
170
+ captioning checkpoint. The retained settings use 16 RGB frames at 224×224,
171
+ ViT-L/14, beam size 5, output lengths 4–20, and seed 42. See
172
+ [`metadata/generation_config.yaml`](metadata/generation_config.yaml) for the
173
+ full reconstructed configuration and reproducibility caveats.
174
+
175
+ SMP also supports static prompts. To test that setting, replace each sample's
176
+ prompt with:
177
+
178
+ ```python
179
+ static_prompt = "a video of [label]"
180
+ ```
181
+
182
+ `[label]` is literal text: it is not substituted with the sample label or
183
+ class name. Every sample receives exactly the same string.
184
+
185
+ The published CSVs retain the generated captions; the static alternative is
186
+ not duplicated as another set of files.
187
+
188
+ ## Integrity and rebuilding
189
+
190
+ Verify the checked-in release artifacts with Python's standard library:
191
+
192
+ ```bash
193
+ python scripts/verify_release.py
194
+ ```
195
+
196
+ To validate the raw CSVs and rebuild all Parquet mirrors and metadata:
197
+
198
+ ```bash
199
+ python -m pip install -r requirements-build.txt
200
+ python scripts/build_release.py
201
+ ```
202
+
203
+ The build script checks row counts, column counts, blank fields, duplicate clip
204
+ IDs, label coverage, class-name consistency, and leakage across the four files.
205
+ It never rewrites the original CSVs.
206
+
207
+ ## Limitations and responsible use
208
+
209
+ - Semantic prompts are model-generated descriptions, not ground-truth
210
+ captions. They may hallucinate, omit audible/visible events, encode social
211
+ biases, or disagree with the class label.
212
+ - The released sample counts reflect the processed media snapshot available
213
+ during the research. YouTube-hosted source clips can disappear over time, so
214
+ later downloads may not reproduce the same media inventory.
215
+ - VGGSound100 source label `14` (`subway, metro`) has no available example in
216
+ this release because the original videos could not be obtained. The numeric
217
+ label space remains `0..59` for compatibility.
218
+ - The original frame loader did not explicitly sort stored frame filenames
219
+ before uniform selection. The retained settings therefore document the
220
+ procedure but do not guarantee bit-for-bit caption regeneration on a new
221
+ filesystem snapshot.
222
+ - These annotations are intended for research on audio-visual learning and
223
+ few-shot classification. Inspect generated captions and upstream media for
224
+ suitability before using them in sensitive applications.
225
+
226
+ See [DATA_REMOVAL.md](DATA_REMOVAL.md) for correction or removal requests.
227
+
228
+ ## Licensing
229
+
230
+ The release contains derived annotations and identifiers, but no media. See
231
+ [LICENSE_DATA.md](LICENSE_DATA.md) for the layered licensing terms. In short,
232
+ release-authored split definitions, generated prompts, and metadata are
233
+ provided under CC BY 4.0 to the extent the authors hold rights; upstream
234
+ identifiers and labels remain subject to their source terms, and video
235
+ copyright remains with the original owners.
236
+
237
+ ## Citation
238
+
239
+ If you use these splits or semantic prompts, please cite the SMP article and
240
+ the upstream datasets relevant to your experiment.
241
+
242
+ ```bibtex
243
+ @ARTICLE{11352954,
244
+ author={Huang, Guanjie and Cui, Yawen and Tsang, Danny H.K. and Wang, Wenwu and Liu, Li},
245
+ journal={IEEE Transactions on Audio, Speech and Language Processing},
246
+ title={Semantic Modulated Prompting for Few-Shot Audio-Visual Classification},
247
+ year={2026},
248
+ volume={34},
249
+ pages={723-736},
250
+ doi={10.1109/TASLPRO.2026.3654246}
251
+ }
252
+ ```
253
+
254
+ Additional BibTeX entries for AVE, Kinetics/Kinetics-Sounds, VGGSound, and
255
+ mPLUG-2 are provided below.
256
+
257
+ <details>
258
+ <summary>Upstream dataset and caption-model citations</summary>
259
+
260
+ ```bibtex
261
+ @inproceedings{tian2018ave,
262
+ author={Tian, Yapeng and Shi, Jing and Li, Bochen and Duan, Zhiyao and Xu, Chenliang},
263
+ title={Audio-Visual Event Localization in Unconstrained Videos},
264
+ booktitle={European Conference on Computer Vision},
265
+ pages={247--263},
266
+ year={2018}
267
+ }
268
+
269
+ @inproceedings{carreira2017quo,
270
+ author={Carreira, Joao and Zisserman, Andrew},
271
+ title={Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset},
272
+ booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
273
+ pages={6299--6308},
274
+ year={2017}
275
+ }
276
+
277
+ @inproceedings{arandjelovic2017look,
278
+ author={Arandjelovic, Relja and Zisserman, Andrew},
279
+ title={Look, Listen and Learn},
280
+ booktitle={IEEE International Conference on Computer Vision},
281
+ pages={609--617},
282
+ year={2017}
283
+ }
284
+
285
+ @inproceedings{chen2020vggsound,
286
+ author={Chen, Honglie and Xie, Weidi and Vedaldi, Andrea and Zisserman, Andrew},
287
+ title={VGGSound: A Large-Scale Audio-Visual Dataset},
288
+ booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing},
289
+ pages={721--725},
290
+ year={2020}
291
+ }
292
+
293
+ @inproceedings{xu2023mplug2,
294
+ author={Xu, Haiyang and Ye, Qinghao and Yan, Ming and Shi, Yaya and Ye, Jiabo and Xu, Yuanhong and Li, Chenliang and Bi, Bin and Qian, Qi and Wang, Wei and Xu, Guohai and Zhang, Ji and Huang, Songfang and Huang, Fei and Zhou, Jingren},
295
+ title={mPLUG-2: A Modularized Multi-modal Foundation Model Across Text, Image and Video},
296
+ booktitle={Proceedings of the 40th International Conference on Machine Learning},
297
+ volume={202},
298
+ pages={38728--38748},
299
+ year={2023}
300
+ }
301
+ ```
302
+
303
+ </details>
README.zh-CN.md ADDED
@@ -0,0 +1,149 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # SMP-FSAVC 数据划分与语义提示
2
+
3
+ [English](README.md) · [SMP 代码](https://github.com/DennisHgj/SMP_FSAVC) · [论文](https://ieeexplore.ieee.org/abstract/document/11352954) · [DOI](https://doi.org/10.1109/TASLPRO.2026.3654246)
4
+
5
+ 本仓库公开论文 **Semantic Modulated Prompting for Few-Shot Audio-Visual
6
+ Classification**(SMP-FSAVC)使用的源域/目标域类别划分、训练测试样本列表,以及
7
+ 由 mPLUG-2 生成的语义提示。仓库可以同时发布到 GitHub 和 Hugging Face Hub:
8
+ 保留无表头的原始 CSV 供训练代码直接读取,并提供等价的 Parquet 文件用于
9
+ Hugging Face Dataset Viewer 在线预览。
10
+
11
+ 本仓库不包含视频、音频或抽取后的图像帧。使用者需要从各原始数据集获取媒体,
12
+ 并遵守其许可和使用条款。
13
+
14
+ ## 数据规模
15
+
16
+ | 数据集 | 样本数 | 源域类别 | 目标域类别 | 说明 |
17
+ | --- | ---: | ---: | ---: | --- |
18
+ | AVE | 4,051 | 16 | 12 | 共定义 28 类 |
19
+ | Kinetics-Sounds | 22,908 | 19 | 13 | 共定义 32 类 |
20
+ | VGGSound100 | 59,795 | 定义 60 类 / 实际 59 类 | 40 | 源域标签 14 在本快照中无可获取媒体 |
21
+ | **合计** | **86,754** | | | |
22
+
23
+ 研究目录中的 VGGSound100 `train_caption.csv` 和 `test_caption.csv` 分别是四个
24
+ 正式划分文件的精确并集,因此不再重复发布。
25
+
26
+ ## 划分含义
27
+
28
+ | CSV 文件 | Hugging Face split | 用途 |
29
+ | --- | --- | --- |
30
+ | `pretrain.csv` | `source_train` | 源域预训练 |
31
+ | `pretrain_test.csv` | `source_test` | 源域测试 |
32
+ | `fewshot.csv` | `target_train_pool` | 目标域支持集采样池 |
33
+ | `fewshot_test.csv` | `target_test` | 目标域查询/测试集 |
34
+
35
+ `fewshot.csv` 不是一个固定的 K-shot episode;论文协议会从该池中多次抽取
36
+ N-way K-shot 支持集。
37
+
38
+ ## 文件结构与格式
39
+
40
+ `csv/` 保存原始无表头 CSV;`viewer/` 保存逐行等价的 Parquet;`metadata/`
41
+ 保存标签映射、统计、caption 生成配置和 SHA-256 校验值。
42
+
43
+ AVE 的 CSV 每行三列:
44
+
45
+ ```text
46
+ clip_id,integer_label,semantic_prompt
47
+ ```
48
+
49
+ Kinetics-Sounds 和 VGGSound100 每行四列:
50
+
51
+ ```text
52
+ clip_id,integer_label,semantic_prompt,class_name
53
+ ```
54
+
55
+ 当 caption 或类别名包含逗号时,CSV 使用标准引号转义。Parquet 统一提供
56
+ `clip_id`、`label`、`semantic_prompt`、`class_name` 四个具名字段;AVE 的
57
+ `class_name` 由 `clip_id` 的类别后缀推导。完整类别映射见
58
+ [`metadata/label_maps.json`](metadata/label_maps.json)。
59
+
60
+ ## 语义提示
61
+
62
+ 公开的 `semantic_prompt` 使用
63
+ [mPLUG-2](https://github.com/X-PLUG/mPLUG-2) 的 MSVD 视频 caption checkpoint
64
+ 从视觉帧生成。保留的主要设置为:16 帧、224×224、ViT-L/14、beam size 5、
65
+ 输出长度 4–20、随机种子 42。完整的重建配置及复现限制见
66
+ [`metadata/generation_config.yaml`](metadata/generation_config.yaml)。
67
+
68
+ SMP 同时允许使用静态提示。使用者可将每条样本的第三列替换为:
69
+
70
+ ```python
71
+ static_prompt = "a video of [label]"
72
+ ```
73
+
74
+ 这里的 `[label]` 是固定的字面文本,不会替换成样本标签或类别名;每个样本使用的
75
+ 字符串完全相同。
76
+
77
+ 为避免数据重复,本仓库只发布论文实验使用的生成式 caption,不另存一份静态提示文件。
78
+
79
+ ## 加载与校验
80
+
81
+ 克隆仓库后,可直接加载 Parquet:
82
+
83
+ ```python
84
+ from datasets import load_dataset
85
+
86
+ ave = load_dataset(
87
+ "parquet",
88
+ data_files={
89
+ "source_train": "viewer/AVE/pretrain.parquet",
90
+ "source_test": "viewer/AVE/pretrain_test.parquet",
91
+ "target_train_pool": "viewer/AVE/fewshot.parquet",
92
+ "target_test": "viewer/AVE/fewshot_test.parquet",
93
+ },
94
+ )
95
+ ```
96
+
97
+ 校验所有发布文件:
98
+
99
+ ```bash
100
+ python scripts/verify_release.py
101
+ ```
102
+
103
+ 重新验证 CSV 并生成 Parquet 和元数据:
104
+
105
+ ```bash
106
+ python -m pip install -r requirements-build.txt
107
+ python scripts/build_release.py
108
+ ```
109
+
110
+ 构建脚本会检查行数、列数、空字段、重复 ID、标签覆盖、类别名一致性及四个划分之间
111
+ 的数据泄漏;脚本不会改写原始 CSV。
112
+
113
+ ## 局限性
114
+
115
+ - caption 是模型生成的描述,不是真值标注,可能出现幻觉、遗漏、偏见,或与类别标签不一致。
116
+ - 样本数反映研究时可获得并完成处理的媒体快照。YouTube 视频可能失效,后来重新下载时不一定能得到相同媒体集合。
117
+ - VGGSound100 源域标签 `14`(`subway, metro`)因原始视频无法获取而没有样本;
118
+ 为兼容训练代码,源域标签空间仍保留为 `0..59`。
119
+ - 原始 caption 数据加载器在均匀选帧前没有显式排序帧文件名,因此公开的配置能说明流程,
120
+ 但不能保证在新的文件系统快照上逐字节复现 caption。
121
+ - 在敏感应用中使用前,请人工检查模型生成文本与原始媒体是否适用。
122
+
123
+ 纠错或移除请求请参见 [DATA_REMOVAL.md](DATA_REMOVAL.md)。
124
+
125
+ ## 许可
126
+
127
+ 本仓库只发布派生标注和标识符,不发布媒体。完整的分层许可说明见
128
+ [LICENSE_DATA.md](LICENSE_DATA.md):在作者拥有权利的范围内,发布者创建的数据划分、
129
+ 生成式提示与元数据采用 CC BY 4.0;原始标识符和标签仍受上游数据集条款约束,视频版权
130
+ 仍属于原始权利人。
131
+
132
+ ## 引用
133
+
134
+ 如使用这些划分或语义提示,请引用 SMP 论文,并引用实验涉及的原始数据集。
135
+
136
+ ```bibtex
137
+ @ARTICLE{11352954,
138
+ author={Huang, Guanjie and Cui, Yawen and Tsang, Danny H.K. and Wang, Wenwu and Liu, Li},
139
+ journal={IEEE Transactions on Audio, Speech and Language Processing},
140
+ title={Semantic Modulated Prompting for Few-Shot Audio-Visual Classification},
141
+ year={2026},
142
+ volume={34},
143
+ pages={723-736},
144
+ doi={10.1109/TASLPRO.2026.3654246}
145
+ }
146
+ ```
147
+
148
+ AVE、Kinetics/Kinetics-Sounds、VGGSound 和 mPLUG-2 的 BibTeX 已集中放在
149
+ [英文 README](README.md#citation) 中。
csv/AVE/fewshot.csv ADDED
Binary file (74.5 kB). View file
 
csv/AVE/fewshot_test.csv ADDED
Binary file (8.05 kB). View file
 
csv/AVE/pretrain.csv ADDED
Binary file (150 kB). View file
 
csv/AVE/pretrain_test.csv ADDED
Binary file (16.1 kB). View file
 
csv/Kinetics-Sounds/fewshot.csv ADDED
Binary file (429 kB). View file
 
csv/Kinetics-Sounds/fewshot_test.csv ADDED
Binary file (63.4 kB). View file
 
csv/Kinetics-Sounds/pretrain.csv ADDED
Binary file (828 kB). View file
 
csv/Kinetics-Sounds/pretrain_test.csv ADDED
Binary file (101 kB). View file
 
csv/VGGSound100/fewshot.csv ADDED
Binary file (1.73 MB). View file
 
csv/VGGSound100/fewshot_test.csv ADDED
Binary file (145 kB). View file
 
csv/VGGSound100/pretrain.csv ADDED
Binary file (2.24 MB). View file
 
csv/VGGSound100/pretrain_test.csv ADDED
Binary file (211 kB). View file
 
metadata/checksums.sha256 ADDED
@@ -0,0 +1,27 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ 74004b99f7cae6dc465ee6cdbb388506ef6ca765a3a4c0be7098e53b95c7441c csv/AVE/fewshot.csv
2
+ 4695a61969c400d9efb26c7568864b53f6e2239f6efc5631bef301d8ef58345f csv/AVE/fewshot_test.csv
3
+ afd4ea2abec757c9af424a39a69605f85050c2a5e2a5e731ff192429194b881f csv/AVE/pretrain.csv
4
+ 5569d5cd514238ee2598da0e0966d40c240d562eb1a087c943a243ff948c5d4f csv/AVE/pretrain_test.csv
5
+ 62ba688780e866e1faaa47ac3ab6f64eb607cd010664958fca7daf19ea748bb8 csv/Kinetics-Sounds/fewshot.csv
6
+ 211414c583c28070d63316f444a8ba7c0e20bb2fb4bc34eb9b2184d0e8ff678f csv/Kinetics-Sounds/fewshot_test.csv
7
+ ca1583ce2130725477248e99c706ea2b129fad60791ee2fe749fb2fcca1b1efc csv/Kinetics-Sounds/pretrain.csv
8
+ a907f8a5407cfc2709588d7d23094c9712fe358f78ce2aebd1ab161e2c40736c csv/Kinetics-Sounds/pretrain_test.csv
9
+ 35d7e6d03b17e270746ecdfd96f01dad531ac6a1195436ff39a2712096d9c20e csv/VGGSound100/fewshot.csv
10
+ 4e634663a54d90aa6290de5f1be25ee95d0a29431905a04c8afabd4c4a8e8ad2 csv/VGGSound100/fewshot_test.csv
11
+ ffb47c7dfd4a83fbc5be754942610f709b9d9f97f93abe87f62f0087a65def9b csv/VGGSound100/pretrain.csv
12
+ 649eed82916c2e4c326a979c50d0050d9ed97c95668770f3590330e341253ad0 csv/VGGSound100/pretrain_test.csv
13
+ 37fb1da2c8f5e4d02f07d66a3a0e5e47dada0f94069bede04319d3a07ee04825 metadata/dataset_statistics.json
14
+ d0d4d3dd3ab5a369b5de57d8cba9ddb59c685a936899901b1cc59096cbdfbb9b metadata/generation_config.yaml
15
+ bfae0e1f32817f9d254c499b06092d50cd63da4bca2408cbe9bcc510ef3e3f1a metadata/label_maps.json
16
+ f213c9fbedf817709192c59c7419482aeb4111a7fdf72ddd00c581f4144eed70 viewer/AVE/fewshot.parquet
17
+ e4b935c04d77cf29c55f3cdefdb4baab0ea6d23091ac815f5efae894bf61e277 viewer/AVE/fewshot_test.parquet
18
+ 892d84f90d74e0cffa70d53302f15ec729789c257913f45895d654ee400d67c5 viewer/AVE/pretrain.parquet
19
+ da1252b520be56fc1a6d3114978895508953cc56c589be72d09e63d1022863cb viewer/AVE/pretrain_test.parquet
20
+ e4b05ac2f920f40324e4e30b853f00670febf8843c06f5f3eabecc2b8d4ea5b0 viewer/Kinetics-Sounds/fewshot.parquet
21
+ a5834c774b8645040f72a044051d9ab745b9fea3027f1d3642fb743e80e1c123 viewer/Kinetics-Sounds/fewshot_test.parquet
22
+ d42fccde5bee47096f59de8098debd480774434ba128818d67e0da4b191689b4 viewer/Kinetics-Sounds/pretrain.parquet
23
+ e7f46c3171a5f27b818b9c57ef1a856aeed492d9de94d61e60b210d5fcb0931b viewer/Kinetics-Sounds/pretrain_test.parquet
24
+ 23d2c3db346979bca6fa58209ea38b8a452da3b451ad31008a7e84287bdaa3e8 viewer/VGGSound100/fewshot.parquet
25
+ 431da80942f09142f79cffd299d54fb6eda892988dfc4e10bec5f86e2669b056 viewer/VGGSound100/fewshot_test.parquet
26
+ dad5278101c2e3c213fa48b5d8c63a325bceda93c069b064fb1352ff923b0f17 viewer/VGGSound100/pretrain.parquet
27
+ eaa7a1bc687286d58e378c3b3b4cc7c712bcb84d0a3d1d0ec3cf20e34e885036 viewer/VGGSound100/pretrain_test.parquet
metadata/dataset_statistics.json ADDED
@@ -0,0 +1,419 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "release_total_rows": 86754,
3
+ "datasets": {
4
+ "AVE": {
5
+ "rows": 4051,
6
+ "source_classes_defined": 16,
7
+ "source_classes_available": 16,
8
+ "target_classes": 12,
9
+ "splits": {
10
+ "pretrain": {
11
+ "rows": 2367,
12
+ "labels": [
13
+ 0,
14
+ 1,
15
+ 2,
16
+ 3,
17
+ 4,
18
+ 5,
19
+ 6,
20
+ 7,
21
+ 8,
22
+ 9,
23
+ 10,
24
+ 11,
25
+ 12,
26
+ 13,
27
+ 14,
28
+ 15
29
+ ],
30
+ "num_labels": 16
31
+ },
32
+ "pretrain_test": {
33
+ "rows": 252,
34
+ "labels": [
35
+ 0,
36
+ 1,
37
+ 2,
38
+ 3,
39
+ 4,
40
+ 5,
41
+ 6,
42
+ 7,
43
+ 8,
44
+ 9,
45
+ 10,
46
+ 11,
47
+ 12,
48
+ 13,
49
+ 14,
50
+ 15
51
+ ],
52
+ "num_labels": 16
53
+ },
54
+ "fewshot": {
55
+ "rows": 1290,
56
+ "labels": [
57
+ 16,
58
+ 17,
59
+ 18,
60
+ 19,
61
+ 20,
62
+ 21,
63
+ 22,
64
+ 23,
65
+ 24,
66
+ 25,
67
+ 26,
68
+ 27
69
+ ],
70
+ "num_labels": 12
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metadata/generation_config.yaml ADDED
@@ -0,0 +1,47 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Reconstructed from the retained mPLUG-2 generation scripts and configs.
2
+ # This documents the settings used for the released semantic prompts; it is
3
+ # not a guarantee of bit-for-bit regeneration because the original processed
4
+ # frame snapshot and filesystem ordering are not redistributed.
5
+ caption_model:
6
+ name: mPLUG-2
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+ upstream_repository: https://github.com/X-PLUG/mPLUG-2
8
+ task: video_captioning
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+ checkpoint: mPLUG2_MSVD_Caption.pth
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+ checkpoint_description: MSVD-finetuned video-captioning checkpoint
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+ visual_encoder: ViT-L-14
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+ text_encoder: bert-large-uncased
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+
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+ visual_input:
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+ modality: RGB_frames_only
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+ frames_per_clip: 16
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+ sampling: uniformly_spaced_over_available_stored_frame_list
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+ frame_filename_order_explicitly_sorted: false
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+ resolution: 224
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+ normalization:
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+ mean: [0.48145466, 0.4578275, 0.40821073]
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+ std: [0.26862954, 0.26130258, 0.27577711]
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+
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+ decoding:
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+ method: deterministic_beam_search
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+ beam_size: 5
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+ minimum_length: 4
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+ maximum_length: 20
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+ length_penalty_alpha: 0.6
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+ random_seed: 42
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+
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+ postprocessing:
33
+ configured_prompt: "a video of"
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+ note: >-
35
+ A leading "a video of" phrase was removed from decoded output when it was
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+ present. The semantic_prompt column contains the resulting caption.
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+
38
+ retained_generation_entry_points:
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+ AVE:
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+ script: video_caption_mplug2_AVE.py
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+ config: configs_video/VideoCaption_AVE.yaml
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+ Kinetics-Sounds:
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+ script: video_caption_mplug2_VGGsound.py
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+ config: configs_video/VideoCaption_Ksound.yaml
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+ VGGSound100:
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+ script: visualencoder_mplug2_VGGSound.py
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+ config: configs_video/VideoCaption_VGGsound.yaml
metadata/label_maps.json ADDED
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+ {
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+ "class_name": "Bark",
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+ },
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+ {
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+ "label": 3,
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+ "class_name": "Fixed-wing aircraft airplane",
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+ "split_role": "source",
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+ "available": true
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+ "available": true
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+ "label": 5,
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+ "split_role": "source",
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+ {
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+ "label": 7,
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+ "class_name": "Violin fiddle",
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+ "available": true
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+ },
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+ {
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+ "class_name": "Flute",
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+ "split_role": "source",
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+ },
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+ {
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+ "label": 10,
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+ "split_role": "source",
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+ {
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+ {
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+ "label": 21,
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+ {
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+ "label": 23,
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+ {
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+ {
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+ "available": true
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+ },
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+ "source_class_name": "playing guitar",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 13,
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+ "source_class_name": "playing harmonica",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 15,
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+ "source_class_name": "playing organ",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 16,
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+ "source_class_name": "playing piano",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 17,
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+ "source_class_name": "playing saxophone",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 18,
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+ "source_class_name": "playing trombone",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "source_class_name": "playing violin",
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "available": true
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+ },
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+ {
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+ "available": true
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+ },
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+ {
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+ "available": true
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+ },
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+ {
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "label": 26,
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "source_class_name": "tap dancing",
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "label": 29,
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+ "source_class_name": "tapping guitar",
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "label": 30,
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+ "source_class_name": "tapping pen",
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+ "available": true
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+ },
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+ {
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+ "label": 31,
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+ "source_class_name": "tickling",
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+ "split_role": "target",
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+ "available": true
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+ }
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+ ],
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+ "VGGSound100": [
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+ {
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+ "label": 0,
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+ "source_class_name": "playing theremin",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 1,
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+ "available": true
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+ },
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+ {
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+ "split_role": "source",
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+ "available": true
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+ },
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+ "available": true
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+ },
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+ },
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+ "available": true
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+ },
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+ "available": true
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+ },
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+ },
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+ },
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+ {
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+ },
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+ },
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+ "available": false,
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+ "note": "No obtainable media was available in the release snapshot."
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+ },
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+ {
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+ },
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+ "available": true
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+ },
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+ },
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+ "available": true
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+ },
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+ "available": true
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+ },
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+ {
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "available": true
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+ },
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+ "available": true
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+ },
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+ {
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 25,
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 26,
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+ "source_class_name": "playing tuning fork",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 27,
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+ "class_name": "playing violin, fiddle",
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+ "source_class_name": "playing violin, fiddle",
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+ "available": true
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+ },
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+ {
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+ "label": 28,
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+ "class_name": "singing choir",
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+ "available": true
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+ },
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+ "source_class_name": "playing timbales",
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+ "available": true
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+ },
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+ {
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+ "label": 30,
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+ "available": true
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+ },
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+ {
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+ "label": 31,
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+ "available": true
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+ "available": true
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+ },
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 34,
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+ "source_class_name": "bull bellowing",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 35,
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+ "class_name": "lawn mowing",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 36,
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+ "source_class_name": "playing bugle",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 37,
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+ "class_name": "mouse squeaking",
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+ "source_class_name": "mouse squeaking",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 38,
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+ "class_name": "child singing",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 39,
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+ "class_name": "playing tympani",
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+ "source_class_name": "playing tympani",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 40,
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+ "source_class_name": "hair dryer drying",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 41,
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+ "source_class_name": "basketball bounce",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 42,
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+ "class_name": "driving snowmobile",
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+ "source_class_name": "driving snowmobile",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 43,
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+ "class_name": "train whistling",
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+ "source_class_name": "train whistling",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 44,
738
+ "class_name": "thunder",
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+ "source_class_name": "thunder",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 45,
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+ "source_class_name": "dog bow-wow",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 46,
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+ "class_name": "ocean burbling",
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+ "source_class_name": "ocean burbling",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 47,
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+ "class_name": "cuckoo bird calling",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 48,
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+ "class_name": "sheep bleating",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 49,
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+ "source_class_name": "splashing water",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 50,
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+ "class_name": "air conditioning noise",
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+ "source_class_name": "air conditioning noise",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 52,
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+ "class_name": "eagle screaming",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "class_name": "air horn",
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+ "source_class_name": "air horn",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 54,
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+ "class_name": "playing bass guitar",
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+ "source_class_name": "playing bass guitar",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 55,
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+ "class_name": "sloshing water",
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+ "source_class_name": "sloshing water",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 56,
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+ "class_name": "tap dancing",
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+ "source_class_name": "tap dancing",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "class_name": "running electric fan",
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+ "source_class_name": "running electric fan",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "class_name": "playing ukulele",
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+ "source_class_name": "playing ukulele",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 59,
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+ "class_name": "playing guiro",
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+ "source_class_name": "playing guiro",
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+ "split_role": "source",
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+ "available": true
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+ },
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+ {
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+ "label": 60,
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "label": 61,
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "label": 62,
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+ "class_name": "people whispering",
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+ "source_class_name": "people whispering",
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "label": 63,
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+ "source_class_name": "people finger snapping",
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "label": 64,
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+ "source_class_name": "car engine idling",
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "label": 65,
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+ "class_name": "bathroom ventilation fan running",
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+ "source_class_name": "bathroom ventilation fan running",
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "label": 66,
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+ "class_name": "police car (siren)",
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+ "source_class_name": "police car (siren)",
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "label": 67,
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+ "class_name": "roller coaster running",
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+ "source_class_name": "roller coaster running",
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+ "split_role": "target",
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+ "available": true
903
+ },
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+ {
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+ "label": 68,
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+ "class_name": "playing french horn",
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+ "source_class_name": "playing french horn",
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "label": 69,
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+ "class_name": "swimming",
914
+ "source_class_name": "swimming",
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
919
+ "label": 70,
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+ "source_class_name": "lighting firecrackers",
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "label": 71,
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+ "class_name": "playing electric guitar",
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+ "source_class_name": "playing electric guitar",
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "label": 72,
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+ "class_name": "playing castanets",
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+ "source_class_name": "playing castanets",
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+ "split_role": "target",
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+ "available": true
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+ },
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+ {
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+ "label": 73,
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+ "class_name": "people babbling",
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+ "source_class_name": "people babbling",
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+ "split_role": "target",
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+ "available": true
945
+ },
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+ {
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+ "label": 74,
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+ "class_name": "arc welding",
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+ "source_class_name": "arc welding",
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+ "split_role": "target",
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+ "available": true
952
+ },
953
+ {
954
+ "label": 75,
955
+ "class_name": "wood thrush calling",
956
+ "source_class_name": "wood thrush calling",
957
+ "split_role": "target",
958
+ "available": true
959
+ },
960
+ {
961
+ "label": 76,
962
+ "class_name": "wind rustling leaves",
963
+ "source_class_name": "wind rustling leaves",
964
+ "split_role": "target",
965
+ "available": true
966
+ },
967
+ {
968
+ "label": 77,
969
+ "class_name": "playing darts",
970
+ "source_class_name": "playing darts",
971
+ "split_role": "target",
972
+ "available": true
973
+ },
974
+ {
975
+ "label": 78,
976
+ "class_name": "planing timber",
977
+ "source_class_name": "planing timber",
978
+ "split_role": "target",
979
+ "available": true
980
+ },
981
+ {
982
+ "label": 79,
983
+ "class_name": "crow cawing",
984
+ "source_class_name": "crow cawing",
985
+ "split_role": "target",
986
+ "available": true
987
+ },
988
+ {
989
+ "label": 80,
990
+ "class_name": "shot football",
991
+ "source_class_name": "shot football",
992
+ "split_role": "target",
993
+ "available": true
994
+ },
995
+ {
996
+ "label": 81,
997
+ "class_name": "writing on blackboard with chalk",
998
+ "source_class_name": "writing on blackboard with chalk",
999
+ "split_role": "target",
1000
+ "available": true
1001
+ },
1002
+ {
1003
+ "label": 82,
1004
+ "class_name": "people slapping",
1005
+ "source_class_name": "people slapping",
1006
+ "split_role": "target",
1007
+ "available": true
1008
+ },
1009
+ {
1010
+ "label": 83,
1011
+ "class_name": "using sewing machines",
1012
+ "source_class_name": "using sewing machines",
1013
+ "split_role": "target",
1014
+ "available": true
1015
+ },
1016
+ {
1017
+ "label": 84,
1018
+ "class_name": "raining",
1019
+ "source_class_name": "raining",
1020
+ "split_role": "target",
1021
+ "available": true
1022
+ },
1023
+ {
1024
+ "label": 85,
1025
+ "class_name": "dog howling",
1026
+ "source_class_name": "dog howling",
1027
+ "split_role": "target",
1028
+ "available": true
1029
+ },
1030
+ {
1031
+ "label": 86,
1032
+ "class_name": "playing cello",
1033
+ "source_class_name": "playing cello",
1034
+ "split_role": "target",
1035
+ "available": true
1036
+ },
1037
+ {
1038
+ "label": 87,
1039
+ "class_name": "playing trumpet",
1040
+ "source_class_name": "playing trumpet",
1041
+ "split_role": "target",
1042
+ "available": true
1043
+ },
1044
+ {
1045
+ "label": 88,
1046
+ "class_name": "fox barking",
1047
+ "source_class_name": "fox barking",
1048
+ "split_role": "target",
1049
+ "available": true
1050
+ },
1051
+ {
1052
+ "label": 89,
1053
+ "class_name": "bowling impact",
1054
+ "source_class_name": "bowling impact",
1055
+ "split_role": "target",
1056
+ "available": true
1057
+ },
1058
+ {
1059
+ "label": 90,
1060
+ "class_name": "people crowd",
1061
+ "source_class_name": "people crowd",
1062
+ "split_role": "target",
1063
+ "available": true
1064
+ },
1065
+ {
1066
+ "label": 91,
1067
+ "class_name": "pumping water",
1068
+ "source_class_name": "pumping water",
1069
+ "split_role": "target",
1070
+ "available": true
1071
+ },
1072
+ {
1073
+ "label": 92,
1074
+ "class_name": "ice cracking",
1075
+ "source_class_name": "ice cracking",
1076
+ "split_role": "target",
1077
+ "available": true
1078
+ },
1079
+ {
1080
+ "label": 93,
1081
+ "class_name": "baby crying",
1082
+ "source_class_name": "baby crying",
1083
+ "split_role": "target",
1084
+ "available": true
1085
+ },
1086
+ {
1087
+ "label": 94,
1088
+ "class_name": "playing bass drum",
1089
+ "source_class_name": "playing bass drum",
1090
+ "split_role": "target",
1091
+ "available": true
1092
+ },
1093
+ {
1094
+ "label": 95,
1095
+ "class_name": "playing bongo",
1096
+ "source_class_name": "playing bongo",
1097
+ "split_role": "target",
1098
+ "available": true
1099
+ },
1100
+ {
1101
+ "label": 96,
1102
+ "class_name": "tornado roaring",
1103
+ "source_class_name": "tornado roaring",
1104
+ "split_role": "target",
1105
+ "available": true
1106
+ },
1107
+ {
1108
+ "label": 97,
1109
+ "class_name": "playing steel guitar, slide guitar",
1110
+ "source_class_name": "playing steel guitar, slide guitar",
1111
+ "split_role": "target",
1112
+ "available": true
1113
+ },
1114
+ {
1115
+ "label": 98,
1116
+ "class_name": "playing squash",
1117
+ "source_class_name": "playing squash",
1118
+ "split_role": "target",
1119
+ "available": true
1120
+ },
1121
+ {
1122
+ "label": 99,
1123
+ "class_name": "typing on typewriter",
1124
+ "source_class_name": "typing on typewriter",
1125
+ "split_role": "target",
1126
+ "available": true
1127
+ }
1128
+ ]
1129
+ }
requirements-build.txt ADDED
@@ -0,0 +1 @@
 
 
1
+ pyarrow>=14,<22
scripts/build_release.py ADDED
@@ -0,0 +1,432 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ """Validate the public CSV files and build Viewer-friendly Parquet mirrors.
3
+
4
+ Run from anywhere with Python 3.9+ and pyarrow installed:
5
+
6
+ python scripts/build_release.py
7
+
8
+ The script never rewrites the CSV files. It validates their schema and split
9
+ integrity, writes Parquet mirrors, and refreshes release metadata/checksums.
10
+ """
11
+
12
+ from __future__ import annotations
13
+
14
+ import csv
15
+ import hashlib
16
+ import json
17
+ from collections import defaultdict
18
+ from pathlib import Path
19
+ from typing import Any
20
+
21
+ import pyarrow as pa
22
+ import pyarrow.parquet as pq
23
+
24
+
25
+ ROOT = Path(__file__).resolve().parents[1]
26
+ CSV_ROOT = ROOT / "csv"
27
+ VIEWER_ROOT = ROOT / "viewer"
28
+ METADATA_ROOT = ROOT / "metadata"
29
+ SPLITS = ("pretrain", "pretrain_test", "fewshot", "fewshot_test")
30
+
31
+ DATASETS: dict[str, dict[str, Any]] = {
32
+ "AVE": {
33
+ "columns": 3,
34
+ "expected_rows": {
35
+ "pretrain": 2367,
36
+ "pretrain_test": 252,
37
+ "fewshot": 1290,
38
+ "fewshot_test": 142,
39
+ },
40
+ "source_labels": set(range(16)),
41
+ "target_labels": set(range(16, 28)),
42
+ },
43
+ "Kinetics-Sounds": {
44
+ "columns": 4,
45
+ "expected_rows": {
46
+ "pretrain": 13252,
47
+ "pretrain_test": 1627,
48
+ "fewshot": 7012,
49
+ "fewshot_test": 1017,
50
+ },
51
+ "source_labels": set(range(19)),
52
+ "target_labels": set(range(19, 32)),
53
+ },
54
+ "VGGSound100": {
55
+ "columns": 4,
56
+ "expected_rows": {
57
+ "pretrain": 31081,
58
+ "pretrain_test": 2920,
59
+ "fewshot": 23823,
60
+ "fewshot_test": 1971,
61
+ },
62
+ "source_labels": set(range(60)),
63
+ "target_labels": set(range(60, 100)),
64
+ "known_unavailable_labels": {14},
65
+ },
66
+ }
67
+
68
+ # Recovered from the category mapping used by the original VGGSound100 data
69
+ # preparation code. The label-14 typo is retained in source_class_name below,
70
+ # while its normalized public name is "subway, metro".
71
+ VGGSOUND100_SOURCE_NAMES = [
72
+ "playing theremin",
73
+ "donkey, ass braying",
74
+ "playing electronic organ",
75
+ "zebra braying",
76
+ "people eating noodle",
77
+ "airplane flyby",
78
+ "playing double bass",
79
+ "cat growling",
80
+ "footsteps on snow",
81
+ "playing tennis",
82
+ "black capped chickadee calling",
83
+ "bouncing on trampoline",
84
+ "playing steelpan",
85
+ "waterfall burbling",
86
+ "subway, metr",
87
+ "people clapping",
88
+ "chipmunk chirping",
89
+ "chopping food",
90
+ "people shuffling",
91
+ "elk bugling",
92
+ "alarm clock ringing",
93
+ "people booing",
94
+ "canary calling",
95
+ "chopping wood",
96
+ "people humming",
97
+ "lathe spinning",
98
+ "playing tuning fork",
99
+ "playing violin, fiddle",
100
+ "singing choir",
101
+ "playing timbales",
102
+ "children shouting",
103
+ "chicken crowing",
104
+ "car passing by",
105
+ "driving motorcycle",
106
+ "bull bellowing",
107
+ "lawn mowing",
108
+ "playing bugle",
109
+ "mouse squeaking",
110
+ "child singing",
111
+ "playing tympani",
112
+ "hair dryer drying",
113
+ "basketball bounce",
114
+ "driving snowmobile",
115
+ "train whistling",
116
+ "thunder",
117
+ "dog bow-wow",
118
+ "ocean burbling",
119
+ "cuckoo bird calling",
120
+ "sheep bleating",
121
+ "splashing water",
122
+ "air conditioning noise",
123
+ "cattle mooing",
124
+ "eagle screaming",
125
+ "air horn",
126
+ "playing bass guitar",
127
+ "sloshing water",
128
+ "tap dancing",
129
+ "running electric fan",
130
+ "playing ukulele",
131
+ "playing guiro",
132
+ "playing shofar",
133
+ "people sniggering",
134
+ "people whispering",
135
+ "people finger snapping",
136
+ "car engine idling",
137
+ "bathroom ventilation fan running",
138
+ "police car (siren)",
139
+ "roller coaster running",
140
+ "playing french horn",
141
+ "swimming",
142
+ "lighting firecrackers",
143
+ "playing electric guitar",
144
+ "playing castanets",
145
+ "people babbling",
146
+ "arc welding",
147
+ "wood thrush calling",
148
+ "wind rustling leaves",
149
+ "playing darts",
150
+ "planing timber",
151
+ "crow cawing",
152
+ "shot football",
153
+ "writing on blackboard with chalk",
154
+ "people slapping",
155
+ "using sewing machines",
156
+ "raining",
157
+ "dog howling",
158
+ "playing cello",
159
+ "playing trumpet",
160
+ "fox barking",
161
+ "bowling impact",
162
+ "people crowd",
163
+ "pumping water",
164
+ "ice cracking",
165
+ "baby crying",
166
+ "playing bass drum",
167
+ "playing bongo",
168
+ "tornado roaring",
169
+ "playing steel guitar, slide guitar",
170
+ "playing squash",
171
+ "typing on typewriter",
172
+ ]
173
+
174
+
175
+ def ave_source_name(clip_id: str) -> str:
176
+ """Extract the AVE category suffix after the 11-character video ID."""
177
+
178
+ if len(clip_id) < 13 or clip_id[11] != "_":
179
+ raise ValueError(f"Unexpected AVE clip_id format: {clip_id!r}")
180
+ return clip_id[12:]
181
+
182
+
183
+ def read_split(dataset: str, split: str) -> list[dict[str, Any]]:
184
+ path = CSV_ROOT / dataset / f"{split}.csv"
185
+ expected_columns = DATASETS[dataset]["columns"]
186
+ records: list[dict[str, Any]] = []
187
+ seen_ids: set[str] = set()
188
+
189
+ with path.open("r", encoding="utf-8-sig", newline="") as handle:
190
+ for line_number, row in enumerate(csv.reader(handle), start=1):
191
+ if len(row) != expected_columns:
192
+ raise ValueError(
193
+ f"{path}:{line_number}: expected {expected_columns} columns, "
194
+ f"found {len(row)}"
195
+ )
196
+ if any(value == "" for value in row):
197
+ raise ValueError(f"{path}:{line_number}: blank field")
198
+
199
+ clip_id, label_text, semantic_prompt = row[:3]
200
+ try:
201
+ label = int(label_text)
202
+ except ValueError as exc:
203
+ raise ValueError(
204
+ f"{path}:{line_number}: invalid integer label {label_text!r}"
205
+ ) from exc
206
+
207
+ if clip_id in seen_ids:
208
+ raise ValueError(f"{path}:{line_number}: duplicate clip_id {clip_id!r}")
209
+ seen_ids.add(clip_id)
210
+
211
+ if dataset == "AVE":
212
+ source_name = ave_source_name(clip_id)
213
+ class_name = source_name.replace("_", " ")
214
+ else:
215
+ source_name = row[3]
216
+ class_name = source_name
217
+
218
+ records.append(
219
+ {
220
+ "clip_id": clip_id,
221
+ "label": label,
222
+ "semantic_prompt": semantic_prompt,
223
+ "class_name": class_name,
224
+ "source_class_name": source_name,
225
+ }
226
+ )
227
+
228
+ expected_rows = DATASETS[dataset]["expected_rows"][split]
229
+ if len(records) != expected_rows:
230
+ raise ValueError(f"{path}: expected {expected_rows} rows, found {len(records)}")
231
+ return records
232
+
233
+
234
+ def validate_labels(
235
+ dataset: str, records_by_split: dict[str, list[dict[str, Any]]]
236
+ ) -> dict[int, str]:
237
+ label_to_names: dict[int, set[str]] = defaultdict(set)
238
+ for records in records_by_split.values():
239
+ for record in records:
240
+ label_to_names[record["label"]].add(record["source_class_name"])
241
+
242
+ inconsistent = {
243
+ label: sorted(names) for label, names in label_to_names.items() if len(names) != 1
244
+ }
245
+ if inconsistent:
246
+ raise ValueError(f"{dataset}: labels map to multiple class names: {inconsistent}")
247
+
248
+ observed = set(label_to_names)
249
+ expected = DATASETS[dataset]["source_labels"] | DATASETS[dataset]["target_labels"]
250
+ unavailable = DATASETS[dataset].get("known_unavailable_labels", set())
251
+ if observed != expected - unavailable:
252
+ raise ValueError(
253
+ f"{dataset}: unexpected label coverage; missing={sorted(expected - observed)}, "
254
+ f"extra={sorted(observed - expected)}"
255
+ )
256
+
257
+ return {label: next(iter(names)) for label, names in label_to_names.items()}
258
+
259
+
260
+ def validate_partition_integrity(
261
+ dataset: str, records_by_split: dict[str, list[dict[str, Any]]]
262
+ ) -> None:
263
+ expected_source = DATASETS[dataset]["source_labels"]
264
+ expected_target = DATASETS[dataset]["target_labels"]
265
+ unavailable = DATASETS[dataset].get("known_unavailable_labels", set())
266
+
267
+ for split in ("pretrain", "pretrain_test"):
268
+ labels = {record["label"] for record in records_by_split[split]}
269
+ if labels != expected_source - unavailable:
270
+ raise ValueError(f"{dataset}/{split}: source label set mismatch")
271
+ for split in ("fewshot", "fewshot_test"):
272
+ labels = {record["label"] for record in records_by_split[split]}
273
+ if labels != expected_target:
274
+ raise ValueError(f"{dataset}/{split}: target label set mismatch")
275
+
276
+ split_ids = {
277
+ split: {record["clip_id"] for record in records}
278
+ for split, records in records_by_split.items()
279
+ }
280
+ for index, left in enumerate(SPLITS):
281
+ for right in SPLITS[index + 1 :]:
282
+ overlap = split_ids[left] & split_ids[right]
283
+ if overlap:
284
+ examples = sorted(overlap)[:5]
285
+ raise ValueError(
286
+ f"{dataset}: clip leakage between {left} and {right}: {examples}"
287
+ )
288
+
289
+
290
+ def write_parquet(dataset: str, split: str, records: list[dict[str, Any]]) -> None:
291
+ output_dir = VIEWER_ROOT / dataset
292
+ output_dir.mkdir(parents=True, exist_ok=True)
293
+ table = pa.table(
294
+ {
295
+ "clip_id": pa.array([record["clip_id"] for record in records], pa.string()),
296
+ "label": pa.array([record["label"] for record in records], pa.int64()),
297
+ "semantic_prompt": pa.array(
298
+ [record["semantic_prompt"] for record in records], pa.string()
299
+ ),
300
+ "class_name": pa.array(
301
+ [record["class_name"] for record in records], pa.string()
302
+ ),
303
+ }
304
+ )
305
+ output_path = output_dir / f"{split}.parquet"
306
+ pq.write_table(
307
+ table,
308
+ output_path,
309
+ compression="zstd",
310
+ use_dictionary=["label", "class_name"],
311
+ write_page_index=True,
312
+ )
313
+
314
+ # Read the artifact back and compare every exported cell. This catches
315
+ # schema coercion or truncation before a release is staged.
316
+ restored = pq.read_table(output_path).to_pydict()
317
+ expected = {
318
+ "clip_id": [record["clip_id"] for record in records],
319
+ "label": [record["label"] for record in records],
320
+ "semantic_prompt": [record["semantic_prompt"] for record in records],
321
+ "class_name": [record["class_name"] for record in records],
322
+ }
323
+ if restored != expected:
324
+ raise ValueError(f"{dataset}/{split}: Parquet round-trip mismatch")
325
+
326
+
327
+ def build_label_map(
328
+ dataset: str, observed_names: dict[int, str]
329
+ ) -> list[dict[str, Any]]:
330
+ all_labels = sorted(
331
+ DATASETS[dataset]["source_labels"] | DATASETS[dataset]["target_labels"]
332
+ )
333
+ unavailable = DATASETS[dataset].get("known_unavailable_labels", set())
334
+ entries = []
335
+ for label in all_labels:
336
+ if dataset == "VGGSound100":
337
+ source_name = VGGSOUND100_SOURCE_NAMES[label]
338
+ class_name = "subway, metro" if label == 14 else source_name
339
+ else:
340
+ source_name = observed_names[label]
341
+ class_name = source_name.replace("_", " ") if dataset == "AVE" else source_name
342
+
343
+ entry: dict[str, Any] = {
344
+ "label": label,
345
+ "class_name": class_name,
346
+ "source_class_name": source_name,
347
+ "split_role": (
348
+ "source" if label in DATASETS[dataset]["source_labels"] else "target"
349
+ ),
350
+ "available": label not in unavailable,
351
+ }
352
+ if label in unavailable:
353
+ entry["note"] = "No obtainable media was available in the release snapshot."
354
+ entries.append(entry)
355
+ return entries
356
+
357
+
358
+ def sha256(path: Path) -> str:
359
+ digest = hashlib.sha256()
360
+ with path.open("rb") as handle:
361
+ for chunk in iter(lambda: handle.read(1024 * 1024), b""):
362
+ digest.update(chunk)
363
+ return digest.hexdigest()
364
+
365
+
366
+ def write_checksums() -> None:
367
+ included = []
368
+ for directory in (CSV_ROOT, VIEWER_ROOT, METADATA_ROOT):
369
+ included.extend(path for path in directory.rglob("*") if path.is_file())
370
+ checksum_path = METADATA_ROOT / "checksums.sha256"
371
+ included = [path for path in included if path != checksum_path]
372
+ lines = [f"{sha256(path)} {path.relative_to(ROOT).as_posix()}" for path in sorted(included)]
373
+ checksum_path.write_text("\n".join(lines) + "\n", encoding="utf-8", newline="\n")
374
+
375
+
376
+ def main() -> None:
377
+ if len(VGGSOUND100_SOURCE_NAMES) != 100:
378
+ raise ValueError("VGGSound100 label map must contain exactly 100 entries")
379
+
380
+ METADATA_ROOT.mkdir(parents=True, exist_ok=True)
381
+ all_label_maps: dict[str, list[dict[str, Any]]] = {}
382
+ statistics: dict[str, Any] = {"release_total_rows": 0, "datasets": {}}
383
+
384
+ for dataset, specification in DATASETS.items():
385
+ records_by_split = {split: read_split(dataset, split) for split in SPLITS}
386
+ validate_partition_integrity(dataset, records_by_split)
387
+ observed_names = validate_labels(dataset, records_by_split)
388
+ all_label_maps[dataset] = build_label_map(dataset, observed_names)
389
+
390
+ split_statistics: dict[str, Any] = {}
391
+ dataset_total = 0
392
+ for split, records in records_by_split.items():
393
+ write_parquet(dataset, split, records)
394
+ row_count = len(records)
395
+ dataset_total += row_count
396
+ split_statistics[split] = {
397
+ "rows": row_count,
398
+ "labels": sorted({record["label"] for record in records}),
399
+ "num_labels": len({record["label"] for record in records}),
400
+ }
401
+
402
+ statistics["datasets"][dataset] = {
403
+ "rows": dataset_total,
404
+ "source_classes_defined": len(specification["source_labels"]),
405
+ "source_classes_available": len(
406
+ specification["source_labels"]
407
+ - specification.get("known_unavailable_labels", set())
408
+ ),
409
+ "target_classes": len(specification["target_labels"]),
410
+ "splits": split_statistics,
411
+ }
412
+ statistics["release_total_rows"] += dataset_total
413
+
414
+ (METADATA_ROOT / "label_maps.json").write_text(
415
+ json.dumps(all_label_maps, indent=2, ensure_ascii=False) + "\n",
416
+ encoding="utf-8",
417
+ newline="\n",
418
+ )
419
+ (METADATA_ROOT / "dataset_statistics.json").write_text(
420
+ json.dumps(statistics, indent=2, ensure_ascii=False) + "\n",
421
+ encoding="utf-8",
422
+ newline="\n",
423
+ )
424
+ write_checksums()
425
+ print(
426
+ f"Validated and built {statistics['release_total_rows']:,} rows "
427
+ f"across {len(DATASETS)} datasets."
428
+ )
429
+
430
+
431
+ if __name__ == "__main__":
432
+ main()
scripts/verify_release.py ADDED
@@ -0,0 +1,43 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3
2
+ """Verify every SHA-256 digest listed in metadata/checksums.sha256."""
3
+
4
+ from __future__ import annotations
5
+
6
+ import hashlib
7
+ from pathlib import Path
8
+
9
+
10
+ ROOT = Path(__file__).resolve().parents[1]
11
+ CHECKSUMS = ROOT / "metadata" / "checksums.sha256"
12
+
13
+
14
+ def sha256(path: Path) -> str:
15
+ digest = hashlib.sha256()
16
+ with path.open("rb") as handle:
17
+ for chunk in iter(lambda: handle.read(1024 * 1024), b""):
18
+ digest.update(chunk)
19
+ return digest.hexdigest()
20
+
21
+
22
+ def main() -> None:
23
+ verified = 0
24
+ for line_number, line in enumerate(
25
+ CHECKSUMS.read_text(encoding="utf-8").splitlines(), start=1
26
+ ):
27
+ if not line.strip():
28
+ continue
29
+ expected, relative_path = line.split(" ", maxsplit=1)
30
+ path = ROOT / Path(relative_path)
31
+ if not path.is_file():
32
+ raise FileNotFoundError(f"Missing file listed at line {line_number}: {path}")
33
+ actual = sha256(path)
34
+ if actual != expected:
35
+ raise ValueError(
36
+ f"Checksum mismatch for {relative_path}: expected {expected}, got {actual}"
37
+ )
38
+ verified += 1
39
+ print(f"Verified {verified} release files.")
40
+
41
+
42
+ if __name__ == "__main__":
43
+ main()
viewer/AVE/fewshot.parquet ADDED
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+ size 22899
viewer/AVE/fewshot_test.parquet ADDED
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viewer/AVE/pretrain.parquet ADDED
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viewer/AVE/pretrain_test.parquet ADDED
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viewer/Kinetics-Sounds/fewshot.parquet ADDED
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viewer/Kinetics-Sounds/fewshot_test.parquet ADDED
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viewer/Kinetics-Sounds/pretrain.parquet ADDED
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viewer/Kinetics-Sounds/pretrain_test.parquet ADDED
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viewer/VGGSound100/fewshot.parquet ADDED
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viewer/VGGSound100/fewshot_test.parquet ADDED
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viewer/VGGSound100/pretrain.parquet ADDED
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viewer/VGGSound100/pretrain_test.parquet ADDED
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