---
library_name: transformers
pipeline_tag: automatic-speech-recognition
language:
- en
base_model: openai/whisper-small
tags:
- automatic-speech-recognition
- whisper
- child-speech
- model-merging
- compositional-domain-adaptation
- 02_core_merges_small
- supervised-fine-tuning
---

# Whisper Small SFT: OGI Spon 6-10

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:** Core Whisper Small merges
- **Checkpoint kind:** Single-source supervised fine-tuned checkpoint
- **Manuscript role:** Older spontaneous-speech source model
- **Source artifact:** `02_core_merges_small/whisper_small_train_spon_bk3`

## Method Context

This is a single-source fine-tuned checkpoint. In the manuscript it serves as a source model, baseline, or task-vector endpoint for studying how distribution-shift factors can be recombined.

Training/adaptation context: OGI spontaneous child speech adaptation for the older 6-10 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_6_10"
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") / "02_core_merges_small" / "whisper_small_sft_ogi_spon_6_10"
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}
}
```