metadata

base_model: openai/whisper-large-v3
datasets:
  - fleurs
language:
  - pl
license: apache-2.0
metrics:
  - wer
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
model-index:
  - name: Whisper Large V3 pl preprocessed - Chee Li
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Google Fleurs
          type: fleurs
          config: pl_pl
          split: None
          args: 'config: pl split: test'
        metrics:
          - type: wer
            value: 332.944005496393
            name: Wer

Whisper Large V3 pl preprocessed - Chee Li

This model is a fine-tuned version of openai/whisper-large-v3 on the Google Fleurs dataset. It achieves the following results on the evaluation set:

Loss: 0.1369
Wer: 332.9440

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 500
training_steps: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.006	5.0251	1000	0.1174	340.0275
0.0002	10.0503	2000	0.1296	200.6596
0.0001	15.0754	3000	0.1343	331.7760
0.0001	20.1005	4000	0.1369	332.9440

Framework versions

Transformers 4.44.0
Pytorch 2.3.1+cu121
Datasets 2.21.0
Tokenizers 0.19.1