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wav2vec2_xls_r_300m_DigitalUmuganda_Afrivoice_Shona_50hr_v1

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wav2vec2
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metadata
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2_xls_r_300m_DigitalUmuganda_Afrivoice_Shona_50hr_v1
    results: []

wav2vec2_xls_r_300m_DigitalUmuganda_Afrivoice_Shona_50hr_v1

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

  • Loss: 0.3698
  • Wer: 0.3938
  • Cer: 0.0785

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: 5e-05
  • train_batch_size: 4
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
9.205 0.1832 200 3.6211 1.0 1.0
3.1436 0.3663 400 2.9405 1.0 1.0
2.8535 0.5495 600 2.7629 1.0 0.9086
0.9706 0.7326 800 2.2864 1.0 0.8127
0.4797 0.9158 1000 1.8810 0.9999 0.7236
0.3773 1.0989 1200 1.4982 0.9987 0.6202
0.3256 1.2821 1400 1.3982 0.9981 0.5665
0.2972 1.4652 1600 1.0015 0.9896 0.4103
0.2913 1.6484 1800 0.8134 0.9836 0.3450
0.2792 1.8315 2000 0.4965 0.8095 0.1868
0.2701 2.0147 2200 0.4560 0.8199 0.1718
0.2314 2.1978 2400 0.3556 0.6607 0.1108
0.223 2.3810 2600 0.3025 0.5262 0.0872
0.2119 2.5641 2800 0.2845 0.4968 0.0781
0.2197 2.7473 3000 0.2595 0.4313 0.0679
0.2164 2.9304 3200 0.2635 0.4804 0.0724
0.1953 3.1136 3400 0.2598 0.4284 0.0689
0.1801 3.2967 3600 0.2723 0.4893 0.0775
0.1769 3.4799 3800 0.2504 0.3996 0.0652
0.1872 3.6630 4000 0.2477 0.3981 0.0655
0.1933 3.8462 4200 0.2608 0.4524 0.0717
0.185 4.0293 4400 0.2327 0.3352 0.0572
0.162 4.2125 4600 0.2352 0.3511 0.0580
0.1597 4.3956 4800 0.2326 0.3472 0.0576
0.1648 4.5788 5000 0.2303 0.3252 0.0543
0.1615 4.7619 5200 0.2326 0.3513 0.0574
0.1551 4.9451 5400 0.2256 0.3309 0.0553
0.1375 5.1282 5600 0.2290 0.3382 0.0546
0.1414 5.3114 5800 0.2389 0.3579 0.0607
0.1309 5.4945 6000 0.2290 0.3204 0.0551
0.143 5.6777 6200 0.2281 0.3042 0.0517
0.1443 5.8608 6400 0.2301 0.3287 0.0564
0.1405 6.0440 6600 0.2367 0.3406 0.0565
0.1107 6.2271 6800 0.2332 0.3096 0.0523
0.1193 6.4103 7000 0.2387 0.3065 0.0527
0.1217 6.5934 7200 0.2413 0.3361 0.0554
0.1184 6.7766 7400 0.2351 0.3138 0.0539
0.1264 6.9597 7600 0.2368 0.3360 0.0543
0.1068 7.1429 7800 0.2402 0.3232 0.0527
0.1036 7.3260 8000 0.2483 0.3294 0.0554
0.0995 7.5092 8200 0.2401 0.3201 0.0532

Framework versions

  • Transformers 4.44.0
  • Pytorch 2.2.0+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1