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WhisperForSpokenNER

This model is a fine-tuned version of /esat/audioslave/qmeeus/exp/whisper_slu/train/whisper-small-spoken-ner on the facebook/voxpopuli de+es+fr+nl dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0444
  • F1 Score: 0.6098
  • Label F1: 0.8369
  • Wer: 0.1086

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-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 32
  • 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: 100
  • training_steps: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss F1 Score Label F1 Wer
0.0433 0.36 200 0.0523 0.6251 0.8320 0.1043
0.0391 0.71 400 0.0504 0.6207 0.8346 0.1047
0.0381 1.07 600 0.0496 0.6142 0.8322 0.1065
0.0374 1.43 800 0.0484 0.6158 0.8360 0.1071
0.0374 1.79 1000 0.0474 0.6155 0.8370 0.1069
0.0342 2.14 1200 0.0474 0.6118 0.8362 0.1077
0.0362 2.5 1400 0.0468 0.6138 0.8375 0.1079
0.0351 2.86 1600 0.0461 0.6102 0.8361 0.1082
0.0339 3.22 1800 0.0466 0.6111 0.8388 0.1079
0.0323 3.57 2000 0.0467 0.6168 0.8419 0.1088
0.0338 3.93 2200 0.0457 0.6093 0.8426 0.1086
0.032 4.29 2400 0.0452 0.6090 0.8398 0.1085
0.0307 4.65 2600 0.0451 0.6139 0.8422 0.1086
0.0321 5.0 2800 0.0452 0.6116 0.8398 0.1083
0.0313 5.36 3000 0.0448 0.6116 0.8404 0.1092
0.0309 5.72 3200 0.0449 0.6109 0.8402 0.1083
0.0305 6.08 3400 0.0448 0.6086 0.8402 0.1083
0.0301 6.43 3600 0.0447 0.6116 0.8375 0.1081
0.03 6.79 3800 0.0446 0.6103 0.8401 0.1087
0.0302 7.15 4000 0.0445 0.6120 0.8388 0.1084
0.0294 7.51 4200 0.0442 0.6132 0.8396 0.1086
0.03 7.86 4400 0.0444 0.6112 0.8382 0.1088
0.03 8.22 4600 0.0445 0.6109 0.8371 0.1087
0.0296 8.58 4800 0.0444 0.6117 0.8378 0.1084
0.0297 8.94 5000 0.0444 0.6098 0.8369 0.1086

Framework versions

  • 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-pipeline-finetune-slu

Evaluation results