metadata

library_name: transformers
license: apache-2.0
base_model: openai/whisper-base
tags:
  - generated_from_trainer
datasets:
  - fleurs
metrics:
  - wer
model-index:
  - name: whisper-base-khmer
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: fleurs
          type: fleurs
          config: km_kh
          split: test
          args: km_kh
        metrics:
          - name: Wer
            type: wer
            value: 0.9567538446468802

whisper-base-khmer

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

Loss: 0.6861
Wer: 0.9568

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: 64
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 128
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.1913	1.0	158	1.1945	1.0348
0.8548	2.0	316	0.8276	0.9761
0.6434	3.0	474	0.6861	0.9568

Framework versions

Transformers 4.48.3
Pytorch 2.5.1+cu124
Datasets 3.3.1
Tokenizers 0.21.0