whisper-small-GL-EN

This model is a fine-tuned version of openai/whisper-small on juanjucm/FLEURS-SpeechT-GL-EN. The training dataset has been augmented using train split from juanjucm/OpenSLR-SpeechT-GL-EN

It achieves the following results on the evaluation set (evaluated only on juanjucm/FLEURS-SpeechT-GL-EN):

  • Loss: 1.6335
  • Wer: 67.2612
  • Bleu: 22.2158

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: 1.25e-05
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 2
  • total_train_batch_size: 32
  • total_eval_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • num_epochs: 8
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Bleu
0.6816 1.0 236 1.6335 67.2612 22.2158
0.1904 2.0 472 1.7234 69.9647 21.0583
0.2177 3.0 708 1.8764 73.2720 19.0086
0.0334 4.0 944 2.0541 72.6774 19.7679
0.0129 5.0 1180 2.1722 70.6708 19.8076
0.011 6.0 1416 2.2637 71.2653 19.7416
0.0062 7.0 1652 2.3214 70.3920 20.3474
0.0067 8.0 1888 2.3405 71.9621 20.1999

Framework versions

  • Transformers 4.45.1
  • Pytorch 2.4.1+cu121
  • Datasets 3.0.1
  • Tokenizers 0.20.0
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