metadata

base_model: openai/whisper-base
datasets:
  - fleurs
language:
  - nl
library_name: transformers
license: apache-2.0
metrics:
  - wer
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
model-index:
  - name: Whisper Base Dutch Punctuation 5k - Chee Li
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Google Fleurs
          type: fleurs
          config: nl_nl
          split: None
          args: 'config: nl split: test'
        metrics:
          - type: wer
            value: 54.440154440154444
            name: Wer

Whisper Base Dutch Punctuation 5k - Chee Li

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

Loss: 0.8128
Wer: 54.4402

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 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: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0633	5.1546	1000	0.6299	49.1433
0.0055	10.3093	2000	0.7255	43.7379
0.0025	15.4639	3000	0.7736	45.1014
0.0017	20.6186	4000	0.8005	50.4585
0.0014	25.7732	5000	0.8128	54.4402

Framework versions

Transformers 4.46.2
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.20.3