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: th_th
          split: None
          args: 'config: nl split: test'
        metrics:
          - type: wer
            value: 196.69912134884825
            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: 0.6304
Wer: 196.6991

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
0.1341	5.2356	1000	0.4705	244.7875
0.0167	10.4712	2000	0.5314	231.2040
0.0025	15.7068	3000	0.5875	214.1297
0.0013	20.9424	4000	0.6187	199.7863
0.001	26.1780	5000	0.6304	196.6991

Framework versions

Transformers 4.46.2
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.20.3