metadata

license: apache-2.0
base_model: openai/whisper-tiny
tags:
  - generated_from_trainer
datasets:
  - PolyAI/minds14
metrics:
  - wer
model-index:
  - name: whisper-tiny-en-US
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: PolyAI/minds14
          type: PolyAI/minds14
          config: en-AU
          split: train
          args: en-AU
        metrics:
          - name: Wer
            type: wer
            value: 0.20146619603584034

whisper-tiny-en-US

This model is a fine-tuned version of openai/whisper-tiny on the PolyAI/minds14 dataset. It achieves the following results on the evaluation set:

Loss: 0.6756
Wer Ortho: 0.2044
Wer: 0.2015

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: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: constant_with_warmup
lr_scheduler_warmup_steps: 50
training_steps: 4000

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Ortho	Wer
0.0007	17.86	500	0.5138	0.1941	0.1920
0.0002	35.71	1000	0.5565	0.1958	0.1936
0.0001	53.57	1500	0.5851	0.1981	0.1958
0.0001	71.43	2000	0.6081	0.2030	0.1998
0.0	89.29	2500	0.6273	0.2038	0.2009
0.0	107.14	3000	0.6441	0.2021	0.1996
0.0	125.0	3500	0.6602	0.2035	0.2007
0.0	142.86	4000	0.6756	0.2044	0.2015

Framework versions

Transformers 4.31.0.dev0
Pytorch 1.12.1+cu116
Datasets 2.4.0
Tokenizers 0.12.1