onewhere

Acknowledgements

Bundled soundtracks (CC0 1.0)

The app bundles 16 soundtracks offered under CC0 1.0 (https://creativecommons.org/publicdomain/zero/1.0/) on their source pages. Attribution is optional; we thank the creators. Converted to stereo 48 kHz AAC and loudness-normalized (AtlasStory/AtlasAssets/Audio/LICENSES.md).

Bundled soundtracks (Pixabay Content License)

The following tracks are licensed under the Pixabay Content License (https://pixabay.com/service/license-summary/). They are embedded in onewhere's story videos, not distributed as standalone audio. Attribution is not required, but we thank the creators. The links lead to each title's search page on Pixabay; the original files and SHA-256 hashes are recorded in AtlasStory/AtlasAssets/Audio/catalog.json.

SigLIP 2 (google/siglip2-base-patch16-224)

SigLIP 2 (google/siglip2-base-patch16-224) — Copyright Google LLC. Licensed under the Apache License, Version 2.0 (https://www.apache.org/licenses/LICENSE-2.0); a copy is included below. onewhere includes the model's image encoder converted to Core ML and compressed to 6-bit palettized weights, text embeddings computed from it for fixed labels, and, as the optional "precise search" download, its text encoder and tokenizer vocabulary. Tschannen et al., "SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features", arXiv:2502.14786.

Changes made (Apache-2.0 §4(b)): the vision tower was traced with its output L2-normalised and its attention-pooling head written out by hand, converted to a Core ML ML Program, its weights palettized to 6 bits, and compiled (Scripts/siglip2/convert_vision.py, Scripts/siglip2/package_for_app.py). Text embeddings for fixed English prompts and a search lexicon were computed from the text tower (Scripts/siglip2/embed_labels.py). A phrase table of 14,162 bilingual phrases was also computed from the text tower, projected to 384 dimensions with PCA and quantised to int8 (Scripts/siglip2/build_phrase_table.py). For precise search, an optional On-Demand Resources download not in the app itself, the text tower was traced with its output L2-normalised, its token embedding table cut to the 207,096 rows of Gemma's SentencePiece vocabulary that Latin, Han and CJK-punctuation text and single characters can reach (the other pieces are kept in the tokenizer's vocabulary file, marked unused, so the encoder segments text as SentencePiece does), converted to a Core ML ML Program, its weights quantised to int8 in blocks of 32, and compiled (Scripts/siglip2/convert_text_tower_for_app.py); the tokenizer was re-implemented in Swift from that vocabulary file. The upstream repository carries no NOTICE file. No trademark licence is granted, and nothing in onewhere is endorsed by Google.

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