cdli-whisper-en-ug-ke-sunbird-encoder-a40 – AI Model by KasuleTrevor | AlphaNeural AI

You can deploy this model and start earning money today!

KasuleTrevor/
cdli-whisper-en-ug-ke-sunbird-encoder-a40

like

0

automatic-speech-recognition

generated_from_trainer

cdli/ugandan_english_nonstandard_speech_v1.0

cdli/kenyan_english_nonstandard_speech_v1.0

Sunbird/asr-whisper-large-v3-salt

endpoints_compatible

Views

No views yet

cdli-whisper-en-ug-ke-sunbird-encoder-a40

This model is a fine-tuned version of Sunbird/asr-whisper-large-v3-salt on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.8885
Wer: 0.2945
Cer: 0.2021
test_cer = 0.1224
test_loss = 0.6973
test_runtime = 0:23:36.28
test_samples_per_second = 1.369
test_steps_per_second = 0.342
test_wer = 0.1977

Ugandan English

utterance_avg_wer = 0.238785
utterance_avg_cer = 0.144570
wer = 0.255675
cer = 0.151887

Kenyan English

utterance_avg_wer = 0.191583
utterance_avg_cer = 0.114696
wer = 0.197774
cer = 0.119329

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: 2e-05
train_batch_size: 4
eval_batch_size: 4
seed: 42
gradient_accumulation_steps: 4
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: 150
training_steps: 2500

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
0.8367	0.4248	250	0.9348	0.3188	0.2204
0.7745	0.8496	500	0.9045	0.3089	0.2130
0.6983	1.2736	750	0.9017	0.3057	0.2104
0.6503	1.6984	1000	0.8888	0.2970	0.2038
0.6562	2.1223	1250	0.8883	0.3022	0.2087
0.6649	2.5472	1500	0.8885	0.2978	0.2055
0.7165	2.9720	1750	0.8887	0.2976	0.2051
0.6558	3.3959	2000	0.8885	0.2958	0.2031
0.691	3.8207	2250	0.8895	0.2948	0.2019
0.5922	4.2447	2500	0.8885	0.2945	0.2021

Framework versions

Transformers 4.52.0
Pytorch 2.7.1+cu118
Datasets 3.6.0
Tokenizers 0.21.4