metadata

base_model: openai/whisper-small
datasets:
  - fleurs
language:
  - tr
license: apache-2.0
metrics:
  - wer
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
model-index:
  - name: Whisper Small - Chee Li
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: Google Fleurs
          type: fleurs
          config: tr_tr
          split: None
          args: 'config: tr split: test'
        metrics:
          - type: wer
            value: 25.933416303671436
            name: Wer

Whisper Small - Chee Li

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

Loss: 0.2891
Wer: 25.9334

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
0.0097	5.5866	1000	0.2532	24.5100
0.0011	11.1732	2000	0.2725	21.6164
0.0006	16.7598	3000	0.2845	24.3311
0.0005	22.3464	4000	0.2891	25.9334

Framework versions

Transformers 4.44.0
Pytorch 2.3.1+cu121
Datasets 2.21.0
Tokenizers 0.19.1