metadata

library_name: transformers
language:
  - ps
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_17_0
metrics:
  - wer
model-index:
  - name: Whisper Small - Hanif Rahman
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 17.0
          type: mozilla-foundation/common_voice_17_0
          config: ps
          split: test
          args: 'config: ps, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 47.980613893376415

Whisper Small - Hanif Rahman

This model is a fine-tuned version of openai/whisper-small on the Common Voice 17.0 dataset. It achieves the following results on the evaluation set:

Loss: 0.8094
Wer Ortho: 51.6855
Wer: 47.9806

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: 32
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch 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: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.6754	0.9346	100	0.6689	62.1021	58.4888
0.4477	1.8692	200	0.6215	57.3134	53.5101
0.2243	2.8037	300	0.6222	55.8883	52.0928
0.0949	3.7383	400	0.6822	54.6007	49.6989
0.0448	4.6729	500	0.7240	53.5301	49.4346
0.0201	5.6075	600	0.7355	52.7344	48.9646
0.0124	6.5421	700	0.7615	52.3944	48.6929
0.0035	7.4766	800	0.7868	51.0778	47.2243
0.002	8.4112	900	0.8025	51.6276	47.6869
0.0011	9.3458	1000	0.8094	51.6855	47.9806

Framework versions

Transformers 4.46.2
Pytorch 2.5.0+cu121
Datasets 3.1.0
Tokenizers 0.20.3