Hugging Face's logo

NbAiLab
/
wav2vec2-xls-r-1b-npsc-bokmaal

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
wav2vec2
Eval Results
Inference Endpoints
Model card Files Metrics Community
wav2vec2-xls-r-1b-npsc-bokmaal / README.md
versae's picture
versae
update model card README.md
582537b
preview code
|
raw
history blame
No virus
4.29 kB
metadata
license: apache-2.0
tags:
  - automatic-speech-recognition
  - NbAiLab/NPSC
  - generated_from_trainer
model-index:
  - name: wav2vec2-xls-r-1b-npsc
    results: []

wav2vec2-xls-r-1b-npsc

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1598
  • Wer: 0.0966

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
0.8361 0.32 500 0.6304 0.4970
0.5703 0.64 1000 0.3195 0.2775
0.5451 0.97 1500 0.2700 0.2246
0.47 1.29 2000 0.2564 0.2329
0.4063 1.61 2500 0.2459 0.2099
0.374 1.93 3000 0.2175 0.1894
0.3297 2.26 3500 0.2036 0.1755
0.3145 2.58 4000 0.1957 0.1757
0.3989 2.9 4500 0.1923 0.1723
0.271 3.22 5000 0.1889 0.1649
0.2758 3.55 5500 0.1768 0.1588
0.2683 3.87 6000 0.1720 0.1534
0.2341 4.19 6500 0.1689 0.1471
0.2316 4.51 7000 0.1706 0.1405
0.2383 4.84 7500 0.1637 0.1426
0.2148 5.16 8000 0.1584 0.1347
0.2085 5.48 8500 0.1601 0.1387
0.2944 5.8 9000 0.1566 0.1294
0.1944 6.13 9500 0.1494 0.1271
0.1853 6.45 10000 0.1561 0.1247
0.235 6.77 10500 0.1461 0.1215
0.2286 7.09 11000 0.1447 0.1167
0.1781 7.41 11500 0.1502 0.1199
0.1714 7.74 12000 0.1425 0.1179
0.1725 8.06 12500 0.1427 0.1173
0.143 8.38 13000 0.1448 0.1142
0.154 8.7 13500 0.1392 0.1104
0.1447 9.03 14000 0.1404 0.1094
0.1471 9.35 14500 0.1404 0.1088
0.1479 9.67 15000 0.1414 0.1133
0.1607 9.99 15500 0.1458 0.1171
0.166 10.32 16000 0.1652 0.1264
0.188 10.64 16500 0.1713 0.1322
0.1461 10.96 17000 0.1423 0.1111
0.1289 11.28 17500 0.1388 0.1097
0.1273 11.61 18000 0.1438 0.1074
0.1317 11.93 18500 0.1312 0.1066
0.1448 12.25 19000 0.1446 0.1042
0.1424 12.57 19500 0.1386 0.1015
0.1392 12.89 20000 0.1379 0.1005
0.1408 13.22 20500 0.1408 0.0992
0.1239 13.54 21000 0.1338 0.0968
0.1244 13.86 21500 0.1335 0.0957
0.1254 14.18 22000 0.1382 0.0950
0.1597 14.51 22500 0.1544 0.0970
0.1566 14.83 23000 0.1589 0.0963

Framework versions

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