This repository contains a minimal Hugging Face Transformers checkpoint for the manuscript Compositional Domain Adaptation for Automatic Speech Recognition with Headwise Selective Attention Merging.

Model Details

Model type: Whisper sequence-to-sequence ASR model
Base model: openai/whisper-small
Release group: Whisper Small baselines
Checkpoint kind: Single-source supervised fine-tuned checkpoint
Manuscript role: Oracle target-domain upper bound
Source artifact: 01_baselines_small/whisper_small_train_spon_bk1

Method Context

This is a single-source fine-tuned target-domain oracle/upper-bound checkpoint used to contextualize the model-merging results.

Training/adaptation context: OGI spontaneous child speech target-domain/oracle split.

The broader manuscript studies whether speech foundation model adaptations for different distribution shifts, such as acoustic condition, speaking style, speaker population, and dialect, can be recombined for low-resource and intersectional ASR without direct joint-supervision data.

Intended Use

Use this checkpoint to reproduce or extend the paper's ASR model-merging experiments. It is intended for research on child ASR, compositional domain adaptation, robustness, cross-corpus transfer, dialectal variation, and scaling behavior across Whisper model sizes.

How To Load

python

from transformers import WhisperForConditionalGeneration, WhisperProcessor

model_id = "balaji1312/whisper_small_sft_ogi_spon_0_2_oracle"
processor = WhisperProcessor.from_pretrained(model_id)
model = WhisperForConditionalGeneration.from_pretrained(model_id)

For local use before upload:

python

from pathlib import Path
from transformers import WhisperForConditionalGeneration, WhisperProcessor

model_dir = Path("final_release_models") / "01_baselines_small" / "whisper_small_sft_ogi_spon_0_2_oracle"
processor = WhisperProcessor.from_pretrained(model_dir)
model = WhisperForConditionalGeneration.from_pretrained(model_dir)

Release Files

This model card was generated for the curated release tree. The model-loading payload consists of:

config.json, generation_config.json, preprocessor_config.json, tokenizer_config.json, tokenizer.json, vocab.json, merges.txt, normalizer.json, special_tokens_map.json, added_tokens.json, model.safetensors

Training state, optimizer state, decode logs, hypotheses, references, and intermediate experiment outputs were intentionally omitted.

Limitations

The checkpoint is released for research reproducibility. Results outside the paper's child ASR, robustness, cross-corpus, dialectal, and scaling-law settings are not characterized here. Reproducing WER numbers requires the manuscript evaluation pipeline and authorized access to the relevant speech corpora; no evaluation audio or transcripts are redistributed in this model folder.

Citation

If you use this checkpoint, please cite the manuscript:

bibtex

@article{shankara2026compositional,
  title = {Compositional Domain Adaptation for Automatic Speech Recognition with Headwise Selective Attention Merging},
  author = {Shankara, Natarajan Balaji and Wang, Zilai and Eren, Eray and Alwan, Abeer},
  year = {2026},
  note = {Manuscript submitted to Computer Speech & Language}
}