ASR/hyperparameters.md

Hyperparameters — Whisper ATC Fine-tune

Model

Key	Value
Base model	openai/whisper-large-v3
Architecture	Whisper Large v3
d_model	1280
Encoder layers	32
Decoder layers	32
Encoder attention heads	20
Decoder attention heads	20
Mel bins	128

Training

Key	Value
Optimizer	AdamW (bitsandbytes 8-bit)
Learning rate	1e-05
LR scheduler	Linear
Warmup ratio	0.05
Adam β₁ / β₂ / ε	0.9 / 0.999 / 1e-8
Weight decay	0.01
Per-device train batch size	1
Per-device eval batch size	8
Gradient accumulation steps	16
Effective batch size	16
Gradient checkpointing	Yes (use_reentrant=False)
Mixed precision	fp16
Max grad norm	1.0
Max epochs (configured)	25
Early stop patience	5 epochs
Label smoothing	0.0
Freeze encoder	No
Seed	42

Augmentation

• Gaussian noise (p=0.4, amplitude 0.001–0.015)
• Time stretch (p=0.3, rate 0.9–1.1)
• Random silence padding (p=0.5, 0–0.7s each end)
• BandPassFilter (p=0.75, 300–3400 Hz, VHF radio simulation)
• Clip (p=0.2, ±0.8)
• Mp3Compression (p=0.3, 32–64 kbps)
• SpecAugment: FrequencyMasking(freq_mask_param=27) + TimeMasking(time_mask_param=100, p=0.05)

Early stopping

Key	Value
Metric	WER (lower is better)
Stopped at	Step 6919 / Epoch 11
Patience	5 epochs

Results

Epoch	Eval loss	WER
1.0	0.0496	3.46%
2.0	0.0288	1.84%
3.0	0.0239	0.82%
4.0	0.0245	1.55%
5.0	0.0195	0.92%
6.0	0.0231	0.66% ← best
7.0	0.0199	0.70%
8.0	0.0211	2.62%
9.0	0.0191	0.72%
10.0	0.0186	4.43%
11.0	0.0172	0.69%

Best checkpoint: training/output_run8/checkpoint-3774 (epoch 6, WER 0.66%)

Output

Key	Value
Best HF checkpoint	training/output_run8/best/
CTranslate2 model	training/saved_models/ct2_run8/
Quantization	float16
Inference backend	faster-whisper