lokeshkumar79/kid-whisper-base-en-myst-ours

kid-whisper-base-en-myst-ours

Fine-tuned openai/whisper-base.en on the MyST children's speech corpus, for research into compression (quantization + pruning) of children's ASR models at small scale.

Model Details

• —Base model: openai/whisper-base.en (74M parameters)
• —Fine-tuning data: MyST corpus, filtered train/dev splits (57,687 train utterances / 9,017 dev utterances)
• —Filtering pipeline: custom preprocessing (missing-transcript removal, reference-ASR quality filtering, non-speech-tag handling, short-utterance removal, duration capping for train/dev). See [link to preprocess_myst.py / thesis Chapter 2 after published] for full filter specification.
• —Training framework: HuggingFace Seq2SeqTrainer
• —Precision: fp16 mixed-precision training

Training Hyperparameters

Note on batch configuration. The companion tiny.en model was trained with per-device batch 16 × gradient accumulation 4. At base.en scale that configuration exhausts the 8GB VRAM of the training GPU (RTX 4060), so the micro-batch was halved and accumulation doubled. The effective batch size is 64 in both cases, keeping the optimization setup equivalent and the two models directly comparable.

Evaluation

Training-time dev WER (early-stopping metric only): 10.65% Measured on individual (non-concatenated) data/filtered/dev utterances, used solely for checkpoint selection during training. This is not the model's reportable evaluation number.

Training ran the full 8000 steps. Dev WER reached its minimum at step 4500 and did not improve across the eight subsequent evaluations, confirming the plateau rather than stopping early on it:

Reportable baseline WER: 11.91% Measured via HuggingFace ASR pipeline with chunk_length_s=30, beam search (beam=5), batch=4, against the full 3,972-chunk held-out MyST concatenated test set (data/concatenated/test) — the identical protocol used to evaluate every other checkpoint in this research.

All five numbers were measured under the identical evaluation protocol above, giving a clean, monotonically-improving capacity scaling curve (39M → 74M → 244M → 769M) under a single consistent methodology — with no discontinuity between the self-trained checkpoints (tiny-en, base-en) and the externally-trained ones (small-en, small-multilingual, medium-en).

Relationship to Other KID-Whisper Checkpoints

This checkpoint is independently trained and is not derived from or affiliated with:

• —aadel4/kid-whisper-small-en-myst / aadel4/kid-whisper-medium-en-myst (Attia et al., fine-tuned Small/Medium Whisper on MyST)
• —SatwikDutta/kid-whisper-tiny-en-myst (Dutta et al., fine-tuned tiny.en — a different model size — on a different MyST filtering/split scheme)

No publicly available, ungated, MyST-fine-tuned base.en checkpoint with a documented filtering scheme matching this study's pipeline was available at time of training. This checkpoint was therefore trained specifically to extend a compression study (quantization + pruning) to base-scale Whisper using the same data filtering and evaluation pipeline already used for Small/Medium-scale checkpoints in that study, avoiding a training-data confound that would have resulted from mixing in an externally-trained checkpoint with a different, undocumented data version.

It is the direct counterpart of kid-whisper-tiny-en-myst-ours, trained on identical data with an identical protocol and effective batch size; the two differ only in base model scale.

Intended Use

Research use for studying model compression (post-training quantization, magnitude/importance-based pruning) on children's speech ASR at small model scale. Not evaluated or intended for production deployment.

License

MIT, matching the base openai/whisper-base.en license.