metadata

library_name: transformers
language:
  - id
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - kneth90/test_data_set_2
metrics:
  - wer
model-index:
  - name: Whisper Small ID - Kenn
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Test Dataset 2
          type: kneth90/test_data_set_2
          args: 'config: hi, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 63.92405063291139

Whisper Small ID - Kenn

This model is a fine-tuned version of openai/whisper-small on the Test Dataset 2 dataset. It achieves the following results on the evaluation set:

Loss: 1.6740
Wer: 63.9241

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: 500
training_steps: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0008	41.6667	1000	1.5316	64.9789
0.0001	83.3333	2000	1.6316	64.3460
0.0	125.0	3000	1.6618	64.5570
0.0	166.6667	4000	1.6740	63.9241

Framework versions

Transformers 4.47.1
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0