metadata

library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - asierhv/composite_corpus_eu_v2.1
language:
  - eu
metrics:
  - wer
model-index:
  - name: Whisper Small Basque
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Common Voice 17.0
          type: mozilla-foundation/common_voice_17_0
          config: eu
          split: test
          args:
            language: eu
        metrics:
          - name: Test WER
            type: wer
            value: 8.33
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: asierhv/composite_corpus_eu_v2.1
          type: asierhv/composite_corpus_eu_v2.1
        metrics:
          - name: Wer
            type: wer
            value: 10.886229784051602

Whisper Small Basque

This model is a fine-tuned version of openai/whisper-small on the asierhv/composite_corpus_eu_v2.1 dataset. It achieves the following results on the evaluation set:

Loss: 0.1836
Wer: 10.8862
Wer on Mozilla Common Voice, test split: 8.33

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: 32
eval_batch_size: 16
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: 8000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.3998	0.125	1000	0.3651	21.5014
0.1975	0.25	2000	0.2918	15.8736
0.1433	0.375	3000	0.2721	13.9011
0.1925	0.5	4000	0.2565	12.7372
0.0818	0.625	5000	0.2563	11.9426
0.1038	0.75	6000	0.2390	11.0732
0.1282	0.875	7000	0.2344	11.3910
0.0959	1.0	8000	0.1836	10.8862

Framework versions

Transformers 4.49.0.dev0
Pytorch 2.6.0+cu124
Datasets 3.2.1.dev0
Tokenizers 0.21.0