whisper_sanlish_mnx_lr5e6

shoibo/whisper_sanlish_mnx_lr5e6

whisper_sanlish_mnx_lr5e6 is a automatic speech recognition model from shoibo. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Automatic Speech Recognition
Library: transformers
License: apache-2.0
Model type: whisper
Access: Public
Created: Aug 26, 2026
Updated: Aug 26, 2026
SHA: bd44b62a

Base models

openai/whisper-small

Model card

From the Hugging Face model README

whisper_sanlish_mnx_lr5e6

This model is a fine-tuned version of openai/whisper-small on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8534
Wer: 43.8951
Cer: 10.2362

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-06
train_batch_size: 8
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 200
num_epochs: 25
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	CER	Validation Loss	WER
16.1536	1.0	94	40.3500	4.5547	104.4400
8.0157	2.0	188	24.6719	1.7042	86.4783
5.1169	3.0	282	19.1951	1.3434	72.5530
4.0548	4.0	376	16.5179	1.2305	64.9849
3.5518	5.0	470	15.2406	1.1282	60.4440
3.0430	6.0	564	14.3482	1.0582	56.1049
2.7010	7.0	658	13.3158	1.0206	52.2704
2.5312	8.0	752	12.8784	1.0134	51.1604
2.2781	9.0	846	12.3360	0.9902	49.9495
2.1107	10.0	940	12.0560	0.9559	49.0414
2.0311	11.0	1034	11.8285	0.9351	48.8396
1.9949	12.0	1128	11.6360	0.9369	47.2250
1.8562	13.0	1222	11.6185	0.9258	48.0323
1.7036	14.0	1316	11.0586	0.9070	46.0141
1.7850	15.0	1410	11.0061	0.9228	45.3078
1.7146	16.0	1504	10.8661	0.8976	45.1060
1.6118	17.0	1598	10.6737	0.8963	44.6014
1.5558	18.0	1692	10.5162	0.8756	44.5005
1.5527	19.0	1786	10.4812	0.8594	44.0969
1.5170	20.0	1880	10.3587	0.8570	44.0969
1.4383	21.0	1974	10.1662	0.8623	43.4914
1.4371	22.0	2068	10.1312	0.8496	43.1887
1.4151	23.0	2162	10.2187	0.8519	43.8951
1.3676	24.0	2256	10.2887	0.8538	43.9960
1.4520	25.0	2350	10.2362	0.8534	43.8951

Framework versions

Transformers 5.15.0
Pytorch 2.11.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

Training configuration

Setting	Value
Base model	`openai/whisper-small`
Target transcription scheme	Sanlish (Roman-script Santali)
Learning rate	5e-06
Planned epochs	25
Warmup steps	200
Batch size (train / eval)	8 / 4 (grad accumulation 2)
Precision	fp16
Save/eval strategy	per epoch
Hub checkpoint strategy	`checkpoint` (full resumable state pushed every epoch)
Early stopping	patience=5 on eval WER
Train / val / test split	1489 / 192 / 194

Final test results

Metric	Score
Test WER	41.16%
Test CER	9.52%

Task: Automatic Speech Recognition
Library: transformers
Type: whisper
License: apache-2.0
Created: Aug 26, 2026
Updated: Aug 26, 2026