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ASR-test

Automatic Speech Recognition
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Safetensors
Vietnamese
wav2vec2
Generated from Trainer
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metadata
language:
  - vi
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
  - generated_from_trainer
datasets:
  - ducha07/audio_HTV_thoisu
metrics:
  - wer
model-index:
  - name: ASR-test
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: HTV news
          type: ducha07/audio_HTV_thoisu
        metrics:
          - name: Wer
            type: wer
            value: 0.2796665364074508

ASR-test-1

This model is a fine-tuned version of facebook/mms-1b-all on the HTV news dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6593
  • Wer: 0.2797

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: 0.001
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
4.8562 0.92 100 0.8316 0.4500
1.0777 1.83 200 0.6898 0.3899
0.98 2.75 300 0.6811 0.3740
0.8967 3.67 400 0.6332 0.3565
0.8965 4.59 500 0.6038 0.3517
0.8396 5.5 600 0.6040 0.3479
0.8137 6.42 700 0.5929 0.3408
0.8304 7.34 800 0.5911 0.3513
0.7894 8.26 900 0.6078 0.3357
0.7412 9.17 1000 0.6214 0.3230
0.7653 10.09 1100 0.5869 0.3444
0.7437 11.01 1200 0.5906 0.3213
0.7083 11.93 1300 0.5952 0.3139
0.7168 12.84 1400 0.5721 0.3267
0.7008 13.76 1500 0.5895 0.3177
0.6825 14.68 1600 0.5909 0.3098
0.6989 15.6 1700 0.5979 0.3673
0.6717 16.51 1800 0.5863 0.3077
0.6496 17.43 1900 0.5798 0.3043
0.6609 18.35 2000 0.5787 0.3555
0.628 19.27 2100 0.5889 0.3133
0.6322 20.18 2200 0.5913 0.3077
0.634 21.1 2300 0.5769 0.3193
0.6172 22.02 2400 0.5731 0.3005
0.6043 22.94 2500 0.5820 0.3075
0.6051 23.85 2600 0.5831 0.3435
0.5865 24.77 2700 0.5790 0.3029
0.5806 25.69 2800 0.5945 0.3053
0.5901 26.61 2900 0.5780 0.3126
0.5769 27.52 3000 0.5732 0.2963
0.5539 28.44 3100 0.5837 0.2950
0.5799 29.36 3200 0.5835 0.3178
0.5518 30.28 3300 0.5941 0.2943
0.549 31.19 3400 0.5960 0.2979
0.5612 32.11 3500 0.5747 0.3167
0.5411 33.03 3600 0.5855 0.2978
0.536 33.94 3700 0.5720 0.2944
0.5329 34.86 3800 0.5998 0.3186
0.5185 35.78 3900 0.5936 0.2884
0.5186 36.7 4000 0.5773 0.2901
0.5027 37.61 4100 0.5969 0.3264
0.52 38.53 4200 0.6184 0.2939
0.4992 39.45 4300 0.5887 0.2943
0.5064 40.37 4400 0.5814 0.2966
0.4928 41.28 4500 0.6128 0.2902
0.508 42.2 4600 0.5943 0.2923
0.4887 43.12 4700 0.6100 0.3039
0.4872 44.04 4800 0.6044 0.2875
0.4711 44.95 4900 0.5961 0.2974
0.4813 45.87 5000 0.6022 0.2945
0.4818 46.79 5100 0.6199 0.2898
0.4492 47.71 5200 0.6161 0.2943
0.4715 48.62 5300 0.6038 0.2838
0.4601 49.54 5400 0.6223 0.2829
0.4432 50.46 5500 0.6058 0.2965
0.4419 51.38 5600 0.6134 0.2917
0.4564 52.29 5700 0.6124 0.2857
0.4349 53.21 5800 0.6229 0.2877
0.4358 54.13 5900 0.6095 0.2898
0.4432 55.05 6000 0.6365 0.2881
0.4277 55.96 6100 0.6169 0.2870
0.4397 56.88 6200 0.6174 0.2849
0.4245 57.8 6300 0.6340 0.2858
0.4203 58.72 6400 0.6321 0.2909
0.4112 59.63 6500 0.6243 0.2866
0.4244 60.55 6600 0.6318 0.2775
0.4119 61.47 6700 0.6215 0.2798
0.403 62.39 6800 0.6213 0.2829
0.4158 63.3 6900 0.6451 0.2795
0.3997 64.22 7000 0.6317 0.2854
0.4006 65.14 7100 0.6329 0.2846
0.4051 66.06 7200 0.6318 0.2834
0.3953 66.97 7300 0.6442 0.2855
0.4119 67.89 7400 0.6345 0.2893
0.3976 68.81 7500 0.6361 0.2798
0.3965 69.72 7600 0.6355 0.2853
0.3957 70.64 7700 0.6457 0.2814
0.3837 71.56 7800 0.6396 0.2855
0.3893 72.48 7900 0.6424 0.2842
0.3816 73.39 8000 0.6496 0.2778
0.3855 74.31 8100 0.6427 0.2881
0.3767 75.23 8200 0.6394 0.2858
0.3747 76.15 8300 0.6513 0.2844
0.3829 77.06 8400 0.6602 0.2775
0.3721 77.98 8500 0.6427 0.2825
0.3708 78.9 8600 0.6507 0.2847
0.3767 79.82 8700 0.6518 0.2816
0.3655 80.73 8800 0.6597 0.2802
0.3614 81.65 8900 0.6542 0.2781
0.3629 82.57 9000 0.6520 0.2782
0.3621 83.49 9100 0.6501 0.2797
0.3616 84.4 9200 0.6528 0.2777
0.3519 85.32 9300 0.6549 0.2798
0.3572 86.24 9400 0.6541 0.2789
0.3585 87.16 9500 0.6497 0.2778
0.3531 88.07 9600 0.6523 0.2781
0.3586 88.99 9700 0.6578 0.2789
0.3463 89.91 9800 0.6565 0.2816
0.3508 90.83 9900 0.6559 0.2797
0.3513 91.74 10000 0.6611 0.2794
0.3425 92.66 10100 0.6538 0.2804
0.3596 93.58 10200 0.6639 0.2808
0.3632 94.5 10300 0.6561 0.2789
0.348 95.41 10400 0.6556 0.2786
0.3514 96.33 10500 0.6575 0.2791
0.3499 97.25 10600 0.6573 0.2795
0.3353 98.17 10700 0.6589 0.2797
0.3468 99.08 10800 0.6589 0.2799
0.3571 100.0 10900 0.6593 0.2797

Framework versions

  • Transformers 4.37.0.dev0
  • Pytorch 2.1.0+cu121
  • Datasets 2.16.1
  • Tokenizers 0.15.0