| # Attribution and licences |
|
|
| ## This release |
|
|
| The trained heads (`eot-heads-v32.onnx`), the decision-rule tables (`rules/`), the evaluation |
| script and manifests (`eval/`) and the model card are released by the OpenHRI project under the BSD 2-Clause |
| License (`LICENSE`), copyright Maxim Makatchev. |
|
|
| ## Encoder: Smart Turn v3.2 (Daily / Pipecat) |
|
|
| `smart-turn-v3.2-encoder-pooled.onnx` is Pipecat's `smart-turn-v3.2-gpu.onnx` |
| (https://huggingface.co/pipecat-ai/smart-turn-v3, BSD 2-Clause, Copyright (c) 2024–2025 Daily) |
| with one change: the attention-pooled 384-d vector that feeds the model's own classifier is added |
| as a second graph output (`sum_1`). Weights are unchanged, so the first output (`logits`) is |
| bit-exact with the original model. The encoder architecture is OpenAI's Whisper-tiny encoder |
| (MIT), as fine-tuned by Pipecat on `pipecat-ai/smart-turn-data-v3.2-train`. |
|
|
| ## Training data of the heads |
|
|
| - `pipecat-ai/smart-turn-data-v3.2-train` (Pipecat; the τ-padded and pre-offset variants described |
| in the model card are derived from it on the fly and are not redistributed). |
| - **AMI Meeting Corpus** (University of Edinburgh and the AMI consortium), |
| https://groups.inf.ed.ac.uk/ami/corpus/, released under the Creative Commons Attribution 4.0 |
| International licence (`AMI_LICENCE.txt`, the licence text shipped with the corpus annotations, |
| release 1.7). The per-speaker close-talking headset recordings and the NXT word alignments were |
| used; the heads were trained on the 137 Kaldi-recipe training meetings. |
|
|
| Citation: J. Carletta et al., "The AMI Meeting Corpus: A Pre-announcement", in *Machine Learning |
| for Multimodal Interaction (MLMI 2005)*, LNCS 3869, Springer, 2006, pp. 28–39. |
|
|
| ## Evaluation manifests (`eval/ami_dev.csv`, `eval/ami_test.csv`) |
|
|
| Derived from the AMI Meeting Corpus (CC BY 4.0) and redistributed under the same licence with |
| attribution to the corpus authors above. Each row is one labelled silence of one speaker: |
| `meeting`, `speaker` (NXT agent letter), `channel` (headset number in the corpus audio), `t` (the |
| speaker's VAD stop, seconds into the meeting), `label` (`complete`: another participant took the |
| floor within 3 s; `incomplete`: the speaker resumed), `gap_s` (incomplete: length of the pause), |
| `b_onset_s` (complete: the next speaker's onset after `t`), `a_turn_s` (speech in the speaker's turn |
| so far). The VAD comes from Silero run on the headset audio and gated by the transcript; the |
| labelling rules are documented in the public repository (`docs/a2_ami.md`). The dev split is the |
| Kaldi AMI recipe's dev set (18 meetings), the test split its eval set (16 meetings). Audio is not |
| redistributed: `eval/ami_eval.py` downloads the headset files it needs from the corpus mirror. |
|
|
| ## Not used |
|
|
| Nothing in this release was trained or fitted on Meta's Seamless Interaction dataset (CC BY-NC) or |
| on Meta's Casual Conversations dataset. |
|
|
