metadata

language:
  - ms
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - google/fleurs
model-index:
  - name: Whisper Medium MS - FLEURS
    results:
      - task:
          type: automatic-speech-recognition
          name: Automatic Speech Recognition
        dataset:
          name: google/fleurs
          type: google/fleurs
          config: ms_my
          split: test
        metrics:
          - type: wer
            value: 11.75
            name: WER
          - type: cer
            value: 3.49
            name: CER

Whisper Medium MS - FLEURS

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

eval_loss: 0.2941
eval_wer: 10.2
eval_runtime: 954.9
eval_samples_per_second: 0.784
eval_steps_per_second: 0.049
epoch: 53.2
step: 5000

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: 32
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 1
total_train_batch_size: 32
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

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.0+cu117
Datasets 2.7.1.dev0
Tokenizers 0.13.2