metadata

language:
  - eu
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_16_0
metrics:
  - wer
model-index:
  - name: Whisper Medium Basque
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_16_0 eu
          type: mozilla-foundation/common_voice_16_0
          config: eu
          split: test
          args: eu
        metrics:
          - name: Wer
            type: wer
            value: 9.188591686749389

Whisper Medium Basque

This model is a fine-tuned version of openai/whisper-medium on the mozilla-foundation/common_voice_16_0 eu dataset. It achieves the following results on the evaluation set:

Loss: 0.1503
Wer: 9.1886

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: 4
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: 8000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.4647	0.06	500	0.4529	34.2140
0.3163	0.12	1000	0.3516	26.0232
0.3232	0.19	1500	0.2996	21.1825
0.266	0.25	2000	0.2686	18.5126
0.2383	0.31	2500	0.2489	16.9412
0.1916	0.38	3000	0.2233	15.2831
0.2009	0.44	3500	0.2134	14.1419
0.2014	0.5	4000	0.2015	13.6579
0.1964	0.56	4500	0.1853	12.0198
0.1758	0.62	5000	0.1796	11.4651
0.2067	0.69	5500	0.1679	10.7989
0.213	0.75	6000	0.1618	10.3139
0.1272	1.03	6500	0.1551	9.8687
0.0744	1.09	7000	0.1534	9.5172
0.0726	1.16	7500	0.1518	9.3240
0.0627	1.22	8000	0.1503	9.1886

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.1+cu117
Datasets 2.8.1.dev0
Tokenizers 0.13.2