kid-whisper-tiny-en-myst-ours

Fine-tuned openai/whisper-tiny.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-tiny.en (39M 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 your preprocess_myst.py / thesis Chapter 2 if published] for full filter specification.
• Training framework: HuggingFace Seq2SeqTrainer
• Precision: fp16 mixed-precision training

Training Hyperparameters

Evaluation

Training-time dev WER (early-stopping metric only): 13.14% 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.

Reportable baseline WER: 14.53% 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 four numbers were measured under the identical evaluation protocol above, making this the first case in this research where the tiny-scale result is directly comparable to the Small/Medium-scale checkpoints rather than relying on separately-reported, differently-measured numbers.

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 on a different MyST filtering/split scheme)

This checkpoint was trained specifically to extend a compression study (quantization + pruning) to tiny-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 tiny.en checkpoint with a different, undocumented data version.

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-tiny.en license.