metadata

language:
  - ms
license: apache-2.0
base_model: openai/whisper-medium
tags:
  - generated_from_trainer
datasets:
  - clt013/malay-speech-1.6-million-rows-dataset
metrics:
  - wer
model-index:
  - name: Whisper Medium FT Malay - CLT013
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: Malay Speech 1.6 million
          type: clt013/malay-speech-1.6-million-rows-dataset
          config: default
          split: train
          args: default
        metrics:
          - name: Wer
            type: wer
            value: 39.65666891696403

Whisper Medium FT Malay - CLT013

This model is a fine-tuned version of openai/whisper-medium on the Malay Speech 1.6 million dataset. It achieves the following results on the evaluation set:

Loss: 0.7057
Wer: 39.6567

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: 50
training_steps: 1000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
1.0434	0.1	100	0.9250	53.3417
0.8131	0.2	200	0.8394	46.5908
0.7852	0.3	300	0.8033	45.1635
0.7643	0.4	400	0.7769	53.5732
0.7424	0.5	500	0.7582	46.6969
0.7406	0.6	600	0.7451	39.6760
0.7913	0.7	700	0.7288	39.3866
0.7452	0.8	800	0.7164	37.9979
0.718	0.9	900	0.7099	38.7694
0.7328	1.0	1000	0.7057	39.6567

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1