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wav2vec2-large-voxrex-npsc

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
NbAiLab/NPSC
Generated from Trainer
robust-speech-event
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wav2vec2-large-voxrex-npsc / README.md
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metadata
license: cc0-1.0
tags:
  - automatic-speech-recognition
  - NbAiLab/NPSC
  - generated_from_trainer
  - robust-speech-event
datasets:
  - NbAiLab/NPSC
base_model: KBLab/wav2vec2-large-voxrex
model-index:
  - name: wav2vec2-large-voxrex-npsc
    results: []

wav2vec2-large-voxrex-npsc

This model is a fine-tuned version of KBLab/wav2vec2-large-voxrex on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set:

  • Loss: nan
  • Wer: 1.0

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: 0.0001
  • train_batch_size: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • num_epochs: 15.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
2.9728 0.32 500 2.9449 1.0
2.5099 0.64 1000 1.8492 0.9910
0.7872 0.97 1500 0.4467 0.3774
0.5993 1.29 2000 0.3181 0.2819
0.5134 1.61 2500 0.2638 0.2401
0.4544 1.93 3000 0.2287 0.2091
0.4085 2.26 3500 0.2153 0.1918
0.3921 2.58 4000 0.2004 0.1804
0.4613 2.9 4500 0.1905 0.1732
0.3402 3.22 5000 0.1778 0.1659
0.3258 3.55 5500 0.1732 0.1571
0.3044 3.87 6000 0.1677 0.1497
0.2914 4.19 6500 0.1597 0.1420
0.278 4.51 7000 0.1574 0.1386
0.2858 4.84 7500 0.1552 0.1300
0.2585 5.16 8000 0.1523 0.1276
0.2827 5.48 8500 0.1448 0.1265
0.3365 5.8 9000 0.1411 0.1232
0.2488 6.13 9500 0.1456 0.1195
0.2406 6.45 10000 0.1414 0.1194
0.2488 6.77 10500 0.1393 0.1173
0.3084 7.09 11000 0.1379 0.1164
0.2365 7.41 11500 0.1387 0.1165
0.2217 7.74 12000 0.1381 0.1132
0.2381 8.06 12500 0.1360 0.1126
0.2329 8.38 13000 0.1357 0.1124
0.2103 8.7 13500 0.1335 0.1087
0.2366 9.03 14000 0.1388 0.1105
0.2289 9.35 14500 0.1383 0.1098
0.2486 9.67 15000 0.1386 0.1087
0.2772 9.99 15500 0.1598 0.1093
0.2728 10.32 16000 0.1814 0.1110
0.3437 10.64 16500 0.2505 0.1124
0.431 10.96 17000 0.2828 0.1143
0.3929 11.28 17500 0.2977 0.1149
0.4396 11.61 18000 0.3198 0.1170
0.59 11.93 18500 0.4158 0.1315
0.7813 12.25 19000 0.6123 0.2208
0.9345 12.57 19500 0.6815 0.2885
0.998 12.89 20000 0.7587 0.1991
1.0493 13.22 20500 0.7583 0.1996
1.438 13.54 21000 nan 1.0
0.0 13.86 21500 nan 1.0
0.0 14.18 22000 nan 1.0
0.0 14.51 22500 nan 1.0
0.0 14.83 23000 nan 1.0

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu113
  • Datasets 1.18.3.dev0
  • Tokenizers 0.11.0