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metadata
language:
  - kz
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
  - hf-asr-leaderboard
  - generated_from_trainer
datasets:
  - ISSAI_KSC2
metrics:
  - wer
model-index:
  - name: Kammi
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: BilalS96/ISSAI_KSC2
          type: ISSAI_KSC2
          args: 'config: kzk, split: test'
        metrics:
          - name: Wer
            type: wer
            value: 0.38223702730599607

Kammi

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the BilalS96/ISSAI_KSC2 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6797
  • Wer: 0.3822

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: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
5.2923 0.4278 400 4.4181 1.0
3.3189 0.8556 800 3.6105 1.0
3.2115 1.2834 1200 3.3715 1.0
3.148 1.7112 1600 3.1163 1.0
3.0788 2.1390 2000 3.2185 1.0
2.9677 2.5668 2400 2.7724 1.0000
2.3283 2.9947 2800 1.7294 0.9985
1.6653 3.4225 3200 1.3565 0.9627
1.4308 3.8503 3600 1.1434 0.9235
1.2196 4.2781 4000 0.9823 0.8583
1.0644 4.7059 4400 0.8573 0.8191
0.9649 5.1337 4800 0.8064 0.7725
0.849 5.5615 5200 0.7391 0.7389
0.8208 5.9893 5600 0.7014 0.6868
0.6995 6.4171 6000 0.6765 0.6687
0.703 6.8449 6400 0.6347 0.6476
0.6136 7.2727 6800 0.6371 0.6226
0.5957 7.7005 7200 0.6068 0.6000
0.5616 8.1283 7600 0.5877 0.5774
0.5128 8.5561 8000 0.5878 0.5605
0.5093 8.9840 8400 0.5502 0.5469
0.4544 9.4118 8800 0.5823 0.5424
0.4622 9.8396 9200 0.5546 0.5219
0.424 10.2674 9600 0.5910 0.5247
0.4041 10.6952 10000 0.5735 0.5130
0.3956 11.1230 10400 0.5673 0.5005
0.3694 11.5508 10800 0.5336 0.4940
0.3675 11.9786 11200 0.5304 0.4886
0.338 12.4064 11600 0.6132 0.4859
0.3355 12.8342 12000 0.6146 0.4872
0.3251 13.2620 12400 0.5979 0.4753
0.309 13.6898 12800 0.5721 0.4657
0.3065 14.1176 13200 0.5849 0.4598
0.2824 14.5455 13600 0.5872 0.4644
0.2875 14.9733 14000 0.5864 0.4540
0.2663 15.4011 14400 0.5885 0.4513
0.2711 15.8289 14800 0.6090 0.4553
0.2566 16.2567 15200 0.6312 0.4532
0.2524 16.6845 15600 0.6248 0.4450
0.2528 17.1123 16000 0.6329 0.4390
0.2381 17.5401 16400 0.6040 0.4370
0.2336 17.9679 16800 0.5855 0.4327
0.2184 18.3957 17200 0.6107 0.4327
0.2253 18.8235 17600 0.6087 0.4316
0.2169 19.2513 18000 0.6169 0.4261
0.2142 19.6791 18400 0.6025 0.4321
0.2125 20.1070 18800 0.6478 0.4261
0.1994 20.5348 19200 0.6504 0.4238
0.2025 20.9626 19600 0.6580 0.4229
0.1954 21.3904 20000 0.6401 0.4170
0.1939 21.8182 20400 0.6443 0.4119
0.1865 22.2460 20800 0.6588 0.4140
0.1847 22.6738 21200 0.6463 0.4087
0.185 23.1016 21600 0.6490 0.4058
0.1796 23.5294 22000 0.6653 0.4070
0.1745 23.9572 22400 0.6452 0.4042
0.173 24.3850 22800 0.6895 0.4018
0.1653 24.8128 23200 0.6482 0.4017
0.165 25.2406 23600 0.6620 0.3962
0.1622 25.6684 24000 0.6702 0.3971
0.1565 26.0963 24400 0.6899 0.3985
0.1563 26.5241 24800 0.7042 0.3932
0.1555 26.9519 25200 0.7017 0.3931
0.1548 27.3797 25600 0.6751 0.3895
0.1543 27.8075 26000 0.6831 0.3895
0.1464 28.2353 26400 0.6765 0.3842
0.1475 28.6631 26800 0.6842 0.3858
0.144 29.0909 27200 0.6904 0.3851
0.1461 29.5187 27600 0.6821 0.3834
0.1417 29.9465 28000 0.6797 0.3822

Framework versions

  • Transformers 4.41.1
  • Pytorch 2.3.0+cu121
  • Datasets 2.19.1
  • Tokenizers 0.19.1