# Attribution and license scope

oruk-loop-153M is a derivative of **Qwen3-ASR-1.7B** ([Qwen/Qwen3-ASR-1.7B-hf](https://huggingface.co/Qwen/Qwen3-ASR-1.7B-hf)),
© Alibaba Cloud / the Qwen team, distributed under the Apache License 2.0 (full text in [LICENSE-QWEN](LICENSE-QWEN)).
The following come from that model:

- the architecture and configuration (`config.json`, apart from the `oruk_loop` block and `auto_map`);
- the initial values of every looped weight: each shared weight started as the mean of the teacher's layers, and
  each per-depth adapter as a truncated SVD of that layer's difference from the mean;
- the modules kept unchanged: the convolutional audio front-end, the encoder output LayerNorm, the audio projector
  and the final decoder RMSNorm;
- the token table's exact rows and the initial values of its factorised rows;
- `tokenizer.json`, `tokenizer_config.json`, `chat_template.jinja`, `processor_config.json` and the generation settings.

Oruk AI changed the following: each transformer stack was replaced by one shared looped block with per-depth
adapters; the vocabulary was trimmed and factorised; the model was distilled from Qwen3-ASR-1.7B, weight-averaged and
MWER fine-tuned; and the inference code and evaluation were written.

## Licenses in this repository

- `model.safetensors` (the oruk-loop-153M weights): **PolyForm Small Business 1.0.0 or PolyForm Noncommercial 1.0.0**,
  at the user's option; other commercial use needs a commercial license from Oruk AI. See
  [LICENSE-WEIGHTS.md](LICENSE-WEIGHTS.md). Keep its Required Notice and the Qwen3-ASR attribution above when
  redistributing.
- `modeling_oruk_loop.py`: **MIT**, see [LICENSE](LICENSE).
- Unmodified Qwen3-ASR files (tokenizer, chat template, processor configuration): Apache-2.0, see [LICENSE-QWEN](LICENSE-QWEN).
- `results/` and `media/`: CC BY 4.0, attributed to Oruk AI.

## Training data credits

Training audio came from the training splits of the following corpora. Their copyrights and terms remain with
their owners.

- LibriSpeech (Panayotov et al.; OpenSLR 12): CC BY 4.0.
- SPGISpeech (Kensho Technologies): Kensho Terms of Usage, which permit academic research and internal use.
- GigaSpeech (SpeechColab): access for non-commercial research and educational purposes. Its card notes that
  speech recognition models trained on it may be eligible for commercial licensing.
- VoxPopuli (Meta AI): CC0 1.0.
- TED-LIUM release 3 (LIUM): CC BY-NC-ND 3.0.
- AMI Meeting Corpus (University of Edinburgh et al.): CC BY 4.0.
- Earnings-22 (Rev.com), train split of `sanchit-gandhi/earnings22_split`: CC BY-SA 4.0.
- YODAS3 English (ESPnet / CMU WAVLab): CC BY 3.0, used in an early warm-up stage.
- FLEURS en_us train (Google): CC BY 4.0, used in an early warm-up stage.

Several of these terms restrict commercial use of the data itself. Whether and how they bear on models trained on
the data is for each user to assess for their own use. No endorsement by Alibaba Cloud, the Qwen team or the
dataset providers is implied.