metadata

license: apache-2.0
base_model: arun100/whisper-base-uk-1
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - google/fleurs
metrics:
  - wer
model-index:
  - name: Whisper Base Ukrainian
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: google/fleurs uk_ua
          type: google/fleurs
          config: uk_ua
          split: test
          args: uk_ua
        metrics:
          - name: Wer
            type: wer
            value: 33.562978427279056

Whisper Base Ukrainian

This model is a fine-tuned version of arun100/whisper-base-uk-1 on the google/fleurs uk_ua dataset. It achieves the following results on the evaluation set:

Loss: 0.4710
Wer: 33.5630

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: 2.5e-07
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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
0.2683	95.0	1000	0.4710	33.5630
0.142	190.0	2000	0.4714	33.8344
0.0871	285.0	3000	0.4782	33.9596
0.0656	380.0	4000	0.4830	33.7230
0.0595	476.0	5000	0.4847	33.7161

Framework versions

Transformers 4.37.0.dev0
Pytorch 2.1.2+cu121
Datasets 2.16.2.dev0
Tokenizers 0.15.0