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xlsr_koen_exp3

Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
./sample_speech.py
Generated from Trainer
Inference Endpoints
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xlsr_koen_exp3 / README.md
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metadata
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
  - automatic-speech-recognition
  - ./sample_speech.py
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: ko-xlsr2
    results: []

ko-xlsr2

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the ./SAMPLE_SPEECH.PY - NA dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4239
  • Cer: 0.1113
  • Wer: 0.3038

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: 0.0003
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • total_eval_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Cer Wer
1.7721 0.94 2000 1.1368 0.2903 0.6589
1.3501 1.89 4000 0.8561 0.2240 0.5451
1.2133 2.83 6000 0.7505 0.2003 0.4974
1.0981 3.77 8000 0.6768 0.1842 0.4686
1.0375 4.72 10000 0.6413 0.1707 0.4404
0.9927 5.66 12000 0.6106 0.1634 0.4246
0.9439 6.6 14000 0.5999 0.1613 0.4159
0.9059 7.55 16000 0.5740 0.1535 0.3985
0.8772 8.49 18000 0.5569 0.1478 0.3954
0.8483 9.43 20000 0.5407 0.1427 0.3784
0.81 10.37 22000 0.5283 0.1415 0.3744
0.793 11.32 24000 0.5179 0.1366 0.3663
0.7577 12.26 26000 0.5059 0.1359 0.3595
0.7379 13.2 28000 0.4969 0.1333 0.3532
0.7328 14.15 30000 0.4908 0.1308 0.3475
0.7119 15.09 32000 0.4887 0.1286 0.3478
0.7572 16.03 34000 0.5170 0.1327 0.3577
0.8198 16.98 36000 0.5839 0.1432 0.3825
0.8008 17.92 38000 0.5447 0.1376 0.3661
0.759 18.86 40000 0.4998 0.1337 0.3534
0.6907 19.81 42000 0.4710 0.1288 0.3412
0.659 20.75 44000 0.4578 0.1242 0.3325
0.6345 21.69 46000 0.4531 0.1221 0.3257
0.6242 22.64 48000 0.4498 0.1209 0.3218
0.6163 23.58 50000 0.4552 0.1194 0.3188
0.6121 24.52 52000 0.4633 0.1154 0.3137
0.6054 25.47 54000 0.4623 0.1176 0.3171
0.591 26.41 56000 0.4413 0.1146 0.3116
0.5713 27.35 58000 0.4338 0.1135 0.3093
0.5703 28.3 60000 0.4280 0.1121 0.3061
0.5576 29.24 62000 0.4248 0.1119 0.3047

Framework versions

  • Transformers 4.34.1
  • Pytorch 2.1.0+cu121
  • Datasets 2.14.5
  • Tokenizers 0.14.1