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wav2vec_asr_swbd

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
Generated from Trainer
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wav2vec_asr_swbd / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
model-index:
  - name: wav2vec_asr_swbd
    results: []

wav2vec_asr_swbd

This model is a fine-tuned version of facebook/wav2vec2-large-robust-ft-swbd-300h on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3052
  • Wer: 0.5302

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: 0.0004
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 20
  • total_train_batch_size: 80
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 20
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.5445 0.29 500 0.9114 0.6197
0.9397 0.58 1000 0.5057 0.5902
0.8557 0.86 1500 0.4465 0.6264
0.7716 1.15 2000 0.4182 0.5594
0.7659 1.44 2500 0.4111 0.7048
0.7406 1.73 3000 0.3927 0.5944
0.6857 2.02 3500 0.3852 0.7118
0.7113 2.31 4000 0.3775 0.5608
0.6804 2.59 4500 0.3885 0.5759
0.6654 2.88 5000 0.3703 0.7226
0.6569 3.17 5500 0.3688 0.5972
0.6335 3.46 6000 0.3661 0.7278
0.6309 3.75 6500 0.3579 0.6324
0.6231 4.03 7000 0.3620 0.5770
0.6171 4.32 7500 0.3640 0.5772
0.6191 4.61 8000 0.3553 0.6075
0.6142 4.9 8500 0.3543 0.6126
0.5905 5.19 9000 0.3601 0.6319
0.5846 5.48 9500 0.3429 0.7343
0.5874 5.76 10000 0.3429 0.5962
0.5768 6.05 10500 0.3381 0.7410
0.5783 6.34 11000 0.3391 0.5823
0.5835 6.63 11500 0.3447 0.5821
0.5817 6.92 12000 0.3314 0.6890
0.5459 7.2 12500 0.3363 0.5727
0.5575 7.49 13000 0.3363 0.7387
0.5505 7.78 13500 0.3368 0.5685
0.55 8.07 14000 0.3330 0.5587
0.5523 8.36 14500 0.3338 0.5484
0.5116 8.65 15000 0.3350 0.4351
0.5263 8.93 15500 0.3254 0.6235
0.5265 9.22 16000 0.3297 0.6207
0.5265 9.51 16500 0.3279 0.6143
0.5172 9.8 17000 0.3260 0.5800
0.5028 10.09 17500 0.3259 0.5774
0.5062 10.37 18000 0.3259 0.5552
0.5112 10.66 18500 0.3201 0.6625
0.5149 10.95 19000 0.3184 0.6865
0.4939 11.24 19500 0.3152 0.6116
0.5065 11.53 20000 0.3172 0.5246
0.5129 11.82 20500 0.3129 0.5908
0.4909 12.1 21000 0.3152 0.6075
0.4865 12.39 21500 0.3160 0.5037
0.4805 12.68 22000 0.3139 0.5458
0.4691 12.97 22500 0.3225 0.5815
0.4534 13.26 23000 0.3168 0.5614
0.4661 13.54 23500 0.3135 0.6053
0.4636 13.83 24000 0.3120 0.5142
0.4554 14.12 24500 0.3127 0.5552
0.4602 14.41 25000 0.3117 0.5562
0.4521 14.7 25500 0.3106 0.4995
0.4369 14.99 26000 0.3100 0.5663
0.4249 15.27 26500 0.3110 0.5262
0.4321 15.56 27000 0.3106 0.5183
0.4293 15.85 27500 0.3091 0.5311
0.4537 16.14 28000 0.3134 0.4986
0.4258 16.43 28500 0.3138 0.4487
0.4347 16.71 29000 0.3091 0.5011
0.4615 17.0 29500 0.3068 0.5616
0.4163 17.29 30000 0.3115 0.5426
0.4074 17.58 30500 0.3079 0.5341
0.4121 17.87 31000 0.3047 0.5619
0.4219 18.16 31500 0.3085 0.5051
0.4049 18.44 32000 0.3084 0.5116
0.4119 18.73 32500 0.3071 0.5028
0.4129 19.02 33000 0.3064 0.5030
0.4143 19.31 33500 0.3040 0.5086
0.4013 19.6 34000 0.3057 0.5271
0.4162 19.88 34500 0.3052 0.5302

Framework versions

  • Transformers 4.18.0
  • Pytorch 1.11.0+cu113
  • Datasets 1.18.4
  • Tokenizers 0.11.6