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WhisperForSpokenNER-end2end

This model is a fine-tuned version of openai/whisper-small on the facebook/voxpopuli de+es+fr+nl dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3381
  • Wer: 0.3889

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: 4
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 500
  • training_steps: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
2.3436 0.36 200 1.8791 0.8871
1.1682 0.71 400 1.0307 0.5048
0.7321 1.07 600 0.6300 0.3665
0.4564 1.43 800 0.4381 0.3515
0.4095 1.79 1000 0.4027 0.3330
0.3813 2.14 1200 0.3847 0.3360
0.3667 2.5 1400 0.3734 0.3392
0.3583 2.86 1600 0.3649 0.3490
0.3454 3.22 1800 0.3588 0.3572
0.3422 3.57 2000 0.3537 0.3705
0.3371 3.93 2200 0.3503 0.3811
0.3291 4.29 2400 0.3475 0.3678
0.324 4.65 2600 0.3451 0.3670
0.3262 5.0 2800 0.3431 0.3710
0.3168 5.36 3000 0.3419 0.3847
0.3178 5.72 3200 0.3406 0.3833
0.3136 6.08 3400 0.3400 0.3853
0.3092 6.43 3600 0.3393 0.3896
0.3106 6.79 3800 0.3389 0.3900
0.3057 7.15 4000 0.3388 0.3803
0.3087 7.51 4200 0.3383 0.3941
0.308 7.86 4400 0.3382 0.3874
0.3036 8.22 4600 0.3381 0.3896
0.3087 8.58 4800 0.3380 0.3910
0.3079 8.94 5000 0.3381 0.3889

Framework versions

  • PEFT 0.7.1.dev0
  • Transformers 4.37.0.dev0
  • Pytorch 2.1.0
  • Datasets 2.14.6
  • Tokenizers 0.14.1
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Dataset used to train qmeeus/whisper-small-multilingual-spoken-ner-end2end-lora

Evaluation results