metadata

base_model: openai/whisper-base
datasets:
  - fleurs
language:
  - en
library_name: transformers
license: apache-2.0
metrics:
  - wer
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
model-index:
  - name: Whisper Base English Punctuation 5k - Chee Li
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Google Fleurs
          type: fleurs
          config: en_us
          split: None
          args: 'config: en split: test'
        metrics:
          - type: wer
            value: 19.829988851727983
            name: Wer

Whisper Base English 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: 0.6360
Wer: 19.8300

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
0.0204	5.3191	1000	0.4849	18.1368
0.0018	10.6383	2000	0.5678	18.4225
0.0009	15.9574	3000	0.6035	19.2795
0.0006	21.2766	4000	0.6268	19.6210
0.0005	26.5957	5000	0.6360	19.8300

Framework versions

Transformers 4.46.2
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.20.3