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ajikadev/
wavlm-base-ug-demo

Name: wavlm-base-ug-demo
Availability: InStock

transformers

safetensors

wavlm

automatic-speech-recognition

ajikadev/uganda-court-speech

generated_from_trainer

microsoft/wavlm-base

finetune

endpoints_compatible

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wavlm-base-ug-demo

This model is a fine-tuned version of microsoft/wavlm-base on the AJIKADEV/UGANDA-COURT-SPEECH - NA dataset. It achieves the following results on the evaluation set:

Loss: 0.1244
Wer: 0.1379

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: 0.0003
train_batch_size: 8
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
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: 500.0
training_steps: 10000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.6536	0.8536	1000	0.4550	0.4195
0.3254	1.7068	2000	0.2806	0.2955
0.2162	2.5600	3000	0.2283	0.2417
0.1657	3.4131	4000	0.1805	0.2044
0.1344	4.2663	5000	0.1623	0.1951
0.1038	5.1195	6000	0.1489	0.1737
0.0833	5.9731	7000	0.1348	0.1626
0.0637	6.8263	8000	0.1310	0.1496
0.05	7.6795	9000	0.1272	0.1424
0.0409	8.5327	10000	0.1244	0.1379

Framework versions

Transformers 5.0.0.dev0
Pytorch 2.9.1+cu128
Datasets 3.6.0
Tokenizers 0.22.1

ajikadev/wavlm-base-ug-demo

wavlm-base-ug-demo

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

ajikadev/
wavlm-base-ug-demo