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sulaimank/
whisper-small-luganda-radio-mh

Name: whisper-small-luganda-radio-mh
Availability: InStock

transformers

safetensors

whisper

automatic-speech-recognition

generated_from_trainer

sulaimank/whisper-small-swahili-400hr

finetune

apache-2.0

endpoints_compatible

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whisper-small-luganda-radio-mh

This model is a fine-tuned version of sulaimank/whisper-small-swahili-400hr on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.1962
Wer: 0.5717
Cer: 0.2111

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-05
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_ratio: 0.1
num_epochs: 20
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer	Cer
3.9846	1.0	1234	1.4073	0.7990	0.2925
1.1773	2.0	2468	0.9149	0.6521	0.2332
0.7181	3.0	3702	0.7786	0.5410	0.1875
0.4213	4.0	4936	0.8041	0.5606	0.1972
0.2192	5.0	6170	0.9955	0.5560	0.1933
0.1088	6.0	7404	1.1962	0.5717	0.2111

Framework versions

Transformers 4.57.1
Pytorch 2.9.1+cu128
Datasets 3.6.0
Tokenizers 0.22.1

sulaimank/whisper-small-luganda-radio-mh

whisper-small-luganda-radio-mh

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

sulaimank/
whisper-small-luganda-radio-mh