metadata

library_name: transformers
language:
  - tg
license: apache-2.0
base_model: openai/whisper-small
tags:
  - hf-asr-leaderboard
  - whisper-event
  - generated_from_trainer
datasets:
  - fleurs
metrics:
  - wer
model-index:
  - name: Whisper Tajik
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: CUSTOM
          type: fleurs
          config: tg_tj
          split: None
          args: 'config: tg, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 18.951830443159924

Whisper Tajik

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

Loss: 0.4538
Wer: 18.9518

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: 4000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0245	6.2893	1000	0.3726	20.9634
0.0043	12.5786	2000	0.4167	20.5318
0.0003	18.8679	3000	0.4431	19.2062
0.0002	25.1572	4000	0.4538	18.9518

Framework versions

Transformers 4.49.0.dev0
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.0