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YAML Metadata Error: "language[0]" must only contain lowercase characters
YAML Metadata Error: "language[0]" with value "zh-TW" is not valid. It must be an ISO 639-1, 639-2 or 639-3 code (two/three letters), or a special value like "code", "multilingual". If you want to use BCP-47 identifiers, you can specify them in language_bcp47.
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This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the COMMON_VOICE - ZH-TW dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1786
  • Wer: 0.8594
  • Cer: 0.2964

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: 7.5e-05
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • 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: 100.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
64.6189 2.51 500 63.8077 1.0 1.0
8.0561 5.03 1000 6.8014 1.0 1.0
6.0427 7.54 1500 6.0745 1.0 1.0
5.9357 10.05 2000 5.8682 1.0 1.0
5.0489 12.56 2500 4.4032 0.9990 0.7750
4.6184 15.08 3000 3.8383 0.9983 0.6768
4.365 17.59 3500 3.4633 0.9959 0.6299
4.1026 20.1 4000 3.0732 0.9902 0.5814
3.8655 22.61 4500 2.7638 0.9868 0.5465
3.6991 25.13 5000 2.4759 0.9811 0.5088
3.4894 27.64 5500 2.2937 0.9746 0.4852
3.3983 30.15 6000 2.1684 0.9733 0.4674
3.2736 32.66 6500 2.0372 0.9659 0.4458
3.1884 35.18 7000 1.9267 0.9648 0.4329
3.1248 37.69 7500 1.8408 0.9591 0.4217
3.0381 40.2 8000 1.7531 0.9503 0.4074
2.9515 42.71 8500 1.6880 0.9459 0.3967
2.8704 45.23 9000 1.6264 0.9378 0.3884
2.8128 47.74 9500 1.5621 0.9341 0.3782
2.7386 50.25 10000 1.5011 0.9243 0.3664
2.6646 52.76 10500 1.4608 0.9192 0.3575
2.6072 55.28 11000 1.4251 0.9148 0.3501
2.569 57.79 11500 1.3837 0.9060 0.3462
2.5091 60.3 12000 1.3589 0.9070 0.3392
2.4588 62.81 12500 1.3261 0.8966 0.3284
2.4083 65.33 13000 1.3052 0.8982 0.3265
2.3787 67.84 13500 1.2997 0.8908 0.3243
2.3457 70.35 14000 1.2778 0.8898 0.3187
2.3099 72.86 14500 1.2661 0.8830 0.3172
2.2559 75.38 15000 1.2475 0.8851 0.3143
2.2264 77.89 15500 1.2319 0.8739 0.3085
2.196 80.4 16000 1.2218 0.8722 0.3049
2.1613 82.91 16500 1.2093 0.8719 0.3051
2.1455 85.43 17000 1.2055 0.8624 0.3005
2.1193 87.94 17500 1.1975 0.8600 0.2982
2.0911 90.45 18000 1.1960 0.8648 0.3003
2.0884 92.96 18500 1.1871 0.8638 0.2971
2.0766 95.48 19000 1.1814 0.8617 0.2967
2.0735 97.99 19500 1.1801 0.8621 0.2969

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0
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Dataset used to train StevenLimcorn/wav2vec2-xls-r-300m-zh-TW

Evaluation results

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