metadata

license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - audiofolder
metrics:
  - wer
model-index:
  - name: whisper-kaggle-be3
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: audiofolder
          type: audiofolder
          config: default
          split: train
          args: default
        metrics:
          - name: Wer
            type: wer
            value: 45.60938682816049

whisper-kaggle-be3

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

Loss: 0.3213
Wer: 45.6094

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 4000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0749	3.15	1000	0.1886	50.9273
0.0117	6.31	2000	0.2440	47.2937
0.0014	9.46	3000	0.2978	45.8933
0.0003	12.62	4000	0.3213	45.6094

Framework versions

Transformers 4.33.0.dev0
Pytorch 2.0.0
Datasets 2.14.4.dev0
Tokenizers 0.13.3