metadata

library_name: transformers
license: apache-2.0
base_model: openai/whisper-small
tags:
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_17_0
metrics:
  - wer
model-index:
  - name: whisper-small-ar2
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_17_0 ar
          type: mozilla-foundation/common_voice_17_0
          config: ar
          split: None
          args: ar
        metrics:
          - name: Wer
            type: wer
            value: 0.763668430335097

whisper-small-ar2

This model is a fine-tuned version of openai/whisper-small on the mozilla-foundation/common_voice_17_0 ar dataset. It achieves the following results on the evaluation set:

Loss: 1.2796
Wer: 0.7637

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: 16
seed: 42
distributed_type: multi-GPU
num_devices: 2
total_train_batch_size: 32
total_eval_batch_size: 32
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: 500
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.3226	20.8333	250	1.0813	0.7584
0.0021	41.6667	500	1.2796	0.7637

Framework versions

Transformers 4.48.0.dev0
Pytorch 2.4.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0