library_name, license, base_model, tags, metrics, model-index, datasets

library_name	license	base_model	tags	metrics	model-index	datasets
transformers	mit	Sunbird/asr-whisper-large-v3-salt	generated_from_trainer	wer	name results cdli-whisper-ml-eng-lug-full-a40-5e-5	cdli/ugandan_luganda_nonstandard_speech_v1.0 cdli/ugandan_english_nonstandard_speech_v1.0

cdli-whisper-ml-eng-lug-full-a40-5e-5

This is a multilingual model and is a fine-tuned version of Sunbird/asr-whisper-large-v3-salt on the Ugandan CDLI Atypical speech datasets. It achieves the following results on the evaluation set:

• Loss: 1.2283
• Wer: 0.4137
• Cer: 0.2271

On the test set with repetition penalty of 1.3 and no_repeat_ngram_size of 2 it obtains:

• test_cer = 0.1268
• test_loss = 0.8137
• test_runtime = 0:22:24.23
• test_samples_per_second = 1.518
• test_steps_per_second = 0.379
• test_wer = 0.2851

English

• Overall WER (normalized): 0.224
• Overall CER (normalized): 0.135
• Avg WER (normalized): 0.214
• Avg CER (normalized): 0.133

Luganda

• Overall WER (normalized): 0.414
• Overall CER (normalized): 0.146
• Avg WER (normalized): 0.354
• Avg CER (normalized): 0.12

Model description

The training was resumed from epoch 7.2255 and the Wer reported after that is a bit dirty (CER instead of WER)

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-05
• train_batch_size: 4
• eval_batch_size: 4
• seed: 42
• gradient_accumulation_steps: 8
• total_train_batch_size: 32
• 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: 150
• training_steps: 4000

Training results

Training Loss	Epoch	Step	Cer	Validation Loss	Wer
0.6253	0.7228	250	0.2722	0.8156	0.4660
0.4188	1.4452	500	0.2362	0.8119	0.4247
0.2709	2.1677	750	0.2352	0.8229	0.4206
0.2571	2.8905	1000	0.2261	0.8141	0.4153
0.1581	3.6129	1250	0.2292	0.9097	0.4167
0.083	4.3354	1500	0.2271	0.9749	0.4177
0.0593	5.0578	1750	0.2266	1.0613	0.4107
0.0518	5.7806	2000	0.2235	1.0547	0.4108
0.0382	6.5031	2250	0.2249	1.1098	0.4095
0.0356	7.2255	2500	0.2238	1.1149	0.4087
0.0408	7.9483	2750	1.1168	0.4139	0.2261
0.0368	8.6737	3000	1.1499	0.4172	0.2279
0.0271	9.3961	3250	1.2052	0.4132	0.2271
0.0237	10.1185	3500	1.2107	0.4114	0.2263
0.0212	10.8413	3750	1.2275	0.4111	0.2250
0.0221	11.5638	4000	1.2283	0.4137	0.2271

Framework versions

• Transformers 4.52.0
• Pytorch 2.7.1+cu118
• Datasets 3.6.0
• Tokenizers 0.21.4