metadata

library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - common_voice_17_0
metrics:
  - wer
model-index:
  - name: whisper-small-ha
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: ha
          split: None
          args: ha
        metrics:
          - name: Wer
            type: wer
            value: 73.44557107327338

whisper-small-ha

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: 1.2029
Wer: 73.4456

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: 1e-05
train_batch_size: 16
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.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_steps: 150
training_steps: 100
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.5582	0.1592	25	3.2028	102.2384
2.4066	0.3185	50	1.9236	90.0899
1.4713	0.4777	75	1.4503	79.5102
1.1217	0.6369	100	1.2029	73.4456

Framework versions

Transformers 4.49.0
Pytorch 2.5.1+cu124
Datasets 3.3.1
Tokenizers 0.21.0