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
| Parameter | Value |
|---|---|
| Learning rate | 1e-5 |
| Per-device batch size | 16 |
| Gradient accumulation | 4 (effective batch 64) |
| Warmup steps | 500 |
| Early stopping | patience=5, metric=WER on dev |
| Best checkpoint | step 5000 |
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.
| Model | Parameters | FP16 Baseline WER |
|---|---|---|
| kid-whisper-tiny-en-myst-ours (this model) | 39M | 14.53% |
| kid-whisper-small-en-myst (Attia et al.) | 244M | 9.16% |
| kid-whisper-medium-en-myst (Attia et al.) | 769M | 8.94% |
| kid-whisper-small-myst, multilingual (Attia et al.) | 244M | 9.91% |
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.