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This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the NBAILAB/NPSC - 16K_MP3 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1957
  • Wer: 0.1697

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: 16
  • eval_batch_size: 16
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • num_epochs: 20.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
4.4527 0.28 250 4.0144 1.0
3.1828 0.56 500 3.1369 1.0
2.9927 0.85 750 3.0183 1.0
2.9591 1.13 1000 2.9991 1.0
2.8989 1.41 1250 2.9000 1.0000
2.4286 1.69 1500 1.7688 0.9550
1.6765 1.98 1750 0.6842 0.4855
1.4521 2.26 2000 0.5096 0.3736
1.3589 2.54 2250 0.4479 0.3335
1.3136 2.82 2500 0.4056 0.3123
1.2856 3.11 2750 0.3870 0.2987
1.2283 3.39 3000 0.3646 0.2828
1.2053 3.67 3250 0.3499 0.2748
1.2087 3.95 3500 0.3345 0.2603
1.2002 4.24 3750 0.3320 0.2523
1.1383 4.52 4000 0.3117 0.2439
1.1364 4.8 4250 0.3198 0.2383
1.158 5.08 4500 0.3071 0.2342
1.108 5.37 4750 0.3011 0.2314
1.1025 5.65 5000 0.2875 0.2289
1.0697 5.93 5250 0.2926 0.2256
1.0904 6.21 5500 0.2695 0.2245
1.0802 6.5 5750 0.2602 0.2189
1.0882 6.78 6000 0.2603 0.2168
1.0881 7.06 6250 0.2540 0.2293
1.0378 7.34 6500 0.2614 0.2193
1.0397 7.63 6750 0.2707 0.2104
1.0296 7.91 7000 0.2483 0.2119
1.0249 8.19 7250 0.2483 0.2047
1.013 8.47 7500 0.2487 0.2042
1.0064 8.76 7750 0.2456 0.2016
1.0668 9.04 8000 0.2397 0.1995
1.0129 9.32 8250 0.2374 0.1994
1.0164 9.6 8500 0.2206 0.1992
0.975 9.89 8750 0.2247 0.1973
0.9849 10.17 9000 0.2325 0.1953
0.9826 10.45 9250 0.2301 0.1934
0.9835 10.73 9500 0.2192 0.1942
0.9676 11.02 9750 0.2266 0.1913
0.9627 11.3 10000 0.2193 0.1921
0.976 11.58 10250 0.2309 0.1882
0.969 11.86 10500 0.2268 0.1886
0.9611 12.15 10750 0.2322 0.1863
0.9397 12.43 11000 0.2197 0.1844
0.9601 12.71 11250 0.2211 0.1871
0.9718 12.99 11500 0.2079 0.1898
0.9347 13.28 11750 0.2054 0.1843
0.9377 13.56 12000 0.2031 0.1842
0.934 13.84 12250 0.2059 0.1806
0.9295 14.12 12500 0.2122 0.1861
0.935 14.41 12750 0.2072 0.1787
0.9021 14.69 13000 0.2105 0.1781
0.9193 14.97 13250 0.2035 0.1786
0.9214 15.25 13500 0.2035 0.1766
0.9048 15.54 13750 0.1964 0.1758
0.9006 15.82 14000 0.1984 0.1757
0.9027 16.1 14250 0.2022 0.1743
0.9083 16.38 14500 0.1969 0.1744
0.9761 16.67 14750 0.1963 0.1728
0.9311 16.95 15000 0.1960 0.1737
0.886 17.23 15250 0.1929 0.1726
0.8969 17.51 15500 0.1928 0.1734
0.9084 17.8 15750 0.1937 0.1713
0.8795 18.08 16000 0.1978 0.1709
0.8883 18.36 16250 0.1956 0.1703
0.8901 18.64 16500 0.1933 0.1705
0.8922 18.93 16750 0.1962 0.1711
0.8765 19.21 17000 0.1962 0.1711
0.8992 19.49 17250 0.1965 0.1703
0.8778 19.77 17500 0.1957 0.1699

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.0+cu113
  • Datasets 1.18.1
  • Tokenizers 0.11.0
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Evaluation results

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