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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: wav2vec2-xls-r-300m-cv8-da
    results: []

wav2vec2-xls-r-300m-cv8-da

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice dataset. It achieves the following results on the evaluation set:

  • Loss: 287.3684
  • Wer: 0.2978

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: 4e-05
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 4242
  • gradient_accumulation_steps: 8
  • 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: 500
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
736.2597 5.55 300 1418.4758 1.0
577.3605 11.11 600 1267.1588 1.0
476.434 16.66 900 842.5760 1.0028
215.5165 22.22 1200 356.9064 0.6405
138.4598 27.77 1500 256.0349 0.4889
104.3224 33.33 1800 228.9147 0.4435
82.2922 38.88 2100 208.3896 0.4134
68.0471 44.44 2400 205.3142 0.3976
57.6613 49.99 2700 208.4677 0.3935
52.6747 55.55 3000 213.9339 0.3928
43.4302 61.11 3300 212.9035 0.3808
40.6623 66.66 3600 221.2281 0.3850
34.9752 72.22 3900 224.3349 0.3795
33.6799 77.77 4200 218.9585 0.3696
31.5797 83.33 4500 211.9424 0.3713
28.2278 88.88 4800 226.6527 0.3705
26.6871 94.44 5100 227.9236 0.3689
25.8799 99.99 5400 227.0796 0.3653
25.0987 105.55 5700 231.7458 0.3640
22.328 111.11 6000 236.6981 0.3644
21.8799 116.66 6300 219.9040 0.3573
20.1069 122.22 6600 230.2603 0.3592
20.8641 127.77 6900 248.2307 0.3638
18.6306 133.33 7200 239.0962 0.3583
18.2617 138.88 7500 245.4144 0.3549
17.5175 144.44 7800 258.2551 0.3617
56.1866 149.99 8100 278.0422 0.3384
28.7438 155.55 8400 271.7619 0.3107
23.462 161.11 8700 279.4823 0.3091
20.5816 166.66 9000 274.7589 0.3070
18.7921 172.22 9300 265.1793 0.3024
16.5106 177.77 9600 269.0261 0.2977
17.1239 183.33 9900 261.8192 0.2956
15.4832 188.88 10200 271.2451 0.2982
16.2146 194.44 10500 269.5397 0.2957
13.7332 199.99 10800 276.0069 0.3017
13.914 205.55 11100 278.2491 0.3023
13.7551 211.11 11400 260.8341 0.2900
11.6575 216.66 11700 276.8361 0.2906
12.4438 222.22 12000 280.0102 0.2954
13.1577 227.77 12300 268.7986 0.2949
12.2083 233.33 12600 272.5989 0.2949
11.1147 238.88 12900 287.3684 0.2978

Framework versions

  • Transformers 4.16.2
  • Pytorch 1.8.1+cu101
  • Datasets 1.18.3
  • Tokenizers 0.11.0