Swahili_100hrv2 / README.md
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metadata
base_model: facebook/wav2vec2-large
license: apache-2.0
metrics:
  - wer
tags:
  - generated_from_trainer
model-index:
  - name: wav2vec2-large-sw-cv-100hr-v2
    results: []

wav2vec2-large-sw-cv-100hr-v2

This model is a fine-tuned version of facebook/wav2vec2-large on an unknown dataset. It achieves the following results on the evaluation set:

  • Loss: 0.6293
  • Model Preparation Time: 0.004
  • Wer: 0.3558
  • Cer: 0.1353

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: 0.0007
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.6
  • num_epochs: 120
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Model Preparation Time Wer Cer
3.5004 1.0 1040 0.7862 0.004 0.6822 0.1909
0.4704 2.0 2080 0.5245 0.004 0.4970 0.1335
0.3202 3.0 3120 0.4288 0.004 0.3956 0.1066
0.2551 4.0 4160 0.3800 0.004 0.3644 0.0962
0.2213 5.0 5200 0.3481 0.004 0.3358 0.0911
0.2005 6.0 6240 0.3241 0.004 0.3101 0.0839
0.1911 7.0 7280 0.3535 0.004 0.3283 0.0924
0.1854 8.0 8320 0.3161 0.004 0.3002 0.0843
0.1819 9.0 9360 0.3181 0.004 0.3233 0.0964
0.1788 10.0 10400 0.3099 0.004 0.3086 0.0832
0.1796 11.0 11440 0.3245 0.004 0.3011 0.0827
0.1805 12.0 12480 0.3068 0.004 0.2967 0.0845
0.1791 13.0 13520 0.3163 0.004 0.3076 0.0864
0.1812 14.0 14560 0.3336 0.004 0.3104 0.0867
0.1844 15.0 15600 0.3257 0.004 0.3117 0.0864
0.1884 16.0 16640 0.3440 0.004 0.3136 0.0886
0.1979 17.0 17680 0.3444 0.004 0.3172 0.0899
0.2058 18.0 18720 0.3286 0.004 0.3456 0.1092
0.2114 19.0 19760 0.3603 0.004 0.3358 0.0969
0.2229 20.0 20800 0.3658 0.004 0.3301 0.0954
0.2275 21.0 21840 0.3849 0.004 0.3729 0.1197
0.2351 22.0 22880 0.3753 0.004 0.3488 0.1011

Framework versions

  • Transformers 4.44.0
  • Pytorch 2.1.0+cu118
  • Datasets 2.20.0
  • Tokenizers 0.19.1