xls-r-1B-te / README.md
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metadata
language:
  - te
license: apache-2.0
tags:
  - automatic-speech-recognition
  - openslr_SLR66
  - generated_from_trainer
  - robust-speech-event
datasets:
  - openslr
  - SLR66
metrics:
  - wer
model-index:
  - name: xls-r-1B-te
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: openslr
          name: Open SLR
          args: SLR66
        metrics:
          - type: wer
            value: None
            name: Test WER
          - type: cer
            value: None
            name: Test CER

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the OPENSLR_SLR66 - NA dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3119
  • Wer: 0.2613

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: 2e-05
  • train_batch_size: 16
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 2
  • 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: 150.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
2.9038 4.8 500 3.0125 1.0
1.3777 9.61 1000 0.8681 0.8753
1.1436 14.42 1500 0.6256 0.7961
1.0997 19.23 2000 0.5244 0.6875
1.0363 24.04 2500 0.4585 0.6276
0.7996 28.84 3000 0.4072 0.5295
0.825 33.65 3500 0.3590 0.5222
0.8018 38.46 4000 0.3678 0.4671
0.7545 43.27 4500 0.3474 0.3962
0.7375 48.08 5000 0.3224 0.3869
0.6198 52.88 5500 0.3233 0.3630
0.6608 57.69 6000 0.3029 0.3308
0.645 62.5 6500 0.3195 0.3722
0.5249 67.31 7000 0.3004 0.3202
0.4875 72.11 7500 0.2826 0.2992
0.5171 76.92 8000 0.2962 0.2976
0.4974 81.73 8500 0.2990 0.2933
0.4387 86.54 9000 0.2834 0.2755
0.4511 91.34 9500 0.2886 0.2787
0.4112 96.15 10000 0.3093 0.2976
0.4064 100.96 10500 0.3123 0.2863
0.4047 105.77 11000 0.2968 0.2719
0.3519 110.57 11500 0.3106 0.2832
0.3719 115.38 12000 0.3030 0.2737
0.3669 120.19 12500 0.2964 0.2714
0.3386 125.0 13000 0.3101 0.2714
0.3137 129.8 13500 0.3063 0.2710
0.3008 134.61 14000 0.3082 0.2617
0.301 139.42 14500 0.3121 0.2628
0.3291 144.23 15000 0.3105 0.2612
0.3133 149.04 15500 0.3114 0.2624

Framework versions

  • Transformers 4.16.0.dev0
  • Pytorch 1.10.1+cu102
  • Datasets 1.17.1.dev0
  • Tokenizers 0.11.0