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xls-r-uyghur-cv7

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Safetensors
Uyghur
wav2vec2
mozilla-foundation/common_voice_7_0
Generated from Trainer
robust-speech-event
hf-asr-leaderboard
Eval Results
Inference Endpoints
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xls-r-uyghur-cv7 / README.md
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metadata
language:
  - ug
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: xls-r-uyghur-cv7
    results: []

xls-r-uyghur-cv7

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - UG dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1772
  • Wer: 0.2589

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: 0.0001
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • 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: 100.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.3043 2.73 500 3.2415 1.0
3.0482 5.46 1000 2.9591 1.0
1.4767 8.2 1500 0.4779 0.5777
1.3152 10.93 2000 0.3697 0.4938
1.2246 13.66 2500 0.3084 0.4459
1.1781 16.39 3000 0.2842 0.4154
1.1351 19.13 3500 0.2615 0.3929
1.1052 21.86 4000 0.2462 0.3747
1.0711 24.59 4500 0.2366 0.3652
1.035 27.32 5000 0.2268 0.3557
1.0277 30.05 5500 0.2243 0.3450
1.002 32.79 6000 0.2204 0.3389
0.9837 35.52 6500 0.2156 0.3349
0.9773 38.25 7000 0.2127 0.3289
0.9807 40.98 7500 0.2142 0.3274
0.9582 43.72 8000 0.2004 0.3142
0.9548 46.45 8500 0.2022 0.3050
0.9251 49.18 9000 0.2019 0.3035
0.9103 51.91 9500 0.1964 0.3021
0.915 54.64 10000 0.1970 0.3032
0.8962 57.38 10500 0.2007 0.3046
0.8729 60.11 11000 0.1967 0.2942
0.8744 62.84 11500 0.1952 0.2885
0.874 65.57 12000 0.1894 0.2895
0.8457 68.31 12500 0.1895 0.2828
0.8519 71.04 13000 0.1912 0.2875
0.8301 73.77 13500 0.1878 0.2760
0.8226 76.5 14000 0.1808 0.2701
0.8071 79.23 14500 0.1849 0.2741
0.7999 81.97 15000 0.1808 0.2717
0.7947 84.7 15500 0.1821 0.2716
0.7783 87.43 16000 0.1824 0.2661
0.7729 90.16 16500 0.1773 0.2639
0.7759 92.9 17000 0.1767 0.2629
0.7713 95.63 17500 0.1780 0.2621
0.7628 98.36 18000 0.1773 0.2594

Framework versions

  • Transformers 4.16.0.dev0
  • Pytorch 1.10.1+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0