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whisper-large-v3-pt-1000h

This model is a fine-tuned version of openai/whisper-large-v3 on the fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba default dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5576
  • Wer: 0.1113

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: 5e-06
  • 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
  • lr_scheduler_warmup_steps: 10000
  • training_steps: 82000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.2717 0.39 10000 0.4143 0.1341
0.2646 0.79 20000 0.4141 0.1284
0.2244 1.18 30000 0.5361 0.1253
0.2056 1.57 40000 0.4714 0.1223
0.2034 1.97 50000 0.4937 0.1195
0.1717 2.36 60000 0.5127 0.1178
0.1692 2.75 70000 0.6040 0.1146
0.121 3.15 80000 0.5361 0.1130

Framework versions

  • Transformers 4.39.0.dev0
  • Pytorch 2.2.1
  • Datasets 2.18.1.dev0
  • Tokenizers 0.15.2
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Evaluation results

  • Wer on fsicoli/cv17-fleurs-coraa-mls-ted-alcaim-cf-cdc-lapsbm-lapsmail-sydney-lingualibre-voxforge-tatoeba default
    self-reported
    0.111