Model_G_2 / README.md
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metadata
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
  - generated_from_trainer
datasets:
  - common_voice
metrics:
  - wer
model-index:
  - name: Model_G_2
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice
          type: common_voice
          config: id
          split: test
          args: id
        metrics:
          - name: Wer
            type: wer
            value: 0.9852694387469699

Model_G_2

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

  • Loss: 0.0359
  • Wer: 0.9853
  • Cer: 0.7143

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: 16
  • eval_batch_size: 8
  • 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: 500
  • num_epochs: 30

Training results

Training Loss Epoch Step Validation Loss Wer Cer
3.8996 0.81 400 0.7268 1.0008 0.7672
0.5216 1.61 800 0.2765 1.0171 0.7602
0.3112 2.42 1200 0.1712 0.9965 0.7335
0.2343 3.23 1600 0.1169 0.9984 0.7262
0.1911 4.03 2000 0.0970 0.9970 0.7447
0.1625 4.84 2400 0.0834 0.9941 0.7245
0.1471 5.65 2800 0.0771 0.9936 0.7239
0.1301 6.45 3200 0.0645 0.9940 0.7330
0.1241 7.26 3600 0.0621 0.9912 0.7208
0.1128 8.06 4000 0.0672 0.9892 0.7188
0.1035 8.87 4400 0.0531 0.9895 0.7332
0.0993 9.68 4800 0.0541 0.9912 0.7374
0.0917 10.48 5200 0.0516 0.9883 0.7276
0.0879 11.29 5600 0.0507 0.9841 0.7246
0.0836 12.1 6000 0.0490 0.9858 0.7335
0.0767 12.9 6400 0.0464 0.9844 0.7231
0.0744 13.71 6800 0.0458 0.9855 0.7170
0.0695 14.52 7200 0.0506 0.9893 0.7145
0.0676 15.32 7600 0.0443 0.9892 0.7151
0.0621 16.13 8000 0.0457 0.9831 0.7188
0.0593 16.94 8400 0.0437 0.9905 0.7251
0.0558 17.74 8800 0.0419 0.9881 0.7160
0.0539 18.55 9200 0.0403 0.9897 0.7128
0.0509 19.35 9600 0.0435 0.9853 0.7195
0.0482 20.16 10000 0.0451 0.9863 0.7170
0.0452 20.97 10400 0.0397 0.9874 0.7128
0.0438 21.77 10800 0.0378 0.9874 0.7108
0.0419 22.58 11200 0.0394 0.9881 0.7096
0.0389 23.39 11600 0.0412 0.9874 0.7105
0.0377 24.19 12000 0.0388 0.9847 0.7180
0.0362 25.0 12400 0.0365 0.9848 0.7149
0.0336 25.81 12800 0.0363 0.9840 0.7144
0.0315 26.61 13200 0.0366 0.9855 0.7138
0.031 27.42 13600 0.0381 0.9864 0.7171
0.0303 28.23 14000 0.0363 0.9857 0.7145
0.0276 29.03 14400 0.0365 0.9854 0.7136
0.0282 29.84 14800 0.0359 0.9853 0.7143

Framework versions

  • Transformers 4.31.0
  • Pytorch 2.0.1+cu117
  • Datasets 1.18.3
  • Tokenizers 0.13.3