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xls-r-uzbek-cv8

Automatic Speech Recognition
Transformers
TensorBoard
Safetensors
Uzbek
wav2vec2
mozilla-foundation/common_voice_8_0
Generated from Trainer
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metadata
language:
  - uz
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_8_0
  - generated_from_trainer
datasets:
  - common_voice_8_0
metrics:
  - wer
model-index:
  - name: xls-r-uzbek-cv8
    results: []

xls-r-uzbek-cv8

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - UZ dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2924
  • Wer: 0.3780
  • Cer: 0.0760

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: 3e-05
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 10.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
3.1444 0.4055 500 3.1200 1.0 1.0
2.9488 0.8110 1000 2.9562 1.0 0.9807
1.4553 1.2165 1500 0.7868 0.7034 0.1644
1.1495 1.6221 2000 0.5598 0.6076 0.1337
1.041 2.0276 2500 0.4650 0.5537 0.1174
0.9524 2.4331 3000 0.4204 0.5098 0.1061
0.902 2.8386 3500 0.3919 0.4984 0.1026
0.8505 3.2441 4000 0.3688 0.4678 0.0965
0.8353 3.6496 4500 0.3491 0.4488 0.0915
0.8015 4.0552 5000 0.3410 0.4356 0.0896
0.7771 4.4607 5500 0.3367 0.4330 0.0883
0.7894 4.8662 6000 0.3274 0.4201 0.0858
0.7624 5.2717 6500 0.3266 0.4115 0.0835
0.7522 5.6772 7000 0.3172 0.4072 0.0825
0.7545 6.0827 7500 0.3096 0.4034 0.0817
0.7412 6.4882 8000 0.3062 0.4014 0.0810
0.7405 6.8938 8500 0.3057 0.3933 0.0796
0.703 7.2993 9000 0.2966 0.3894 0.0784
0.7091 7.7048 9500 0.3000 0.3895 0.0784
0.7117 8.1103 10000 0.2988 0.3881 0.0781
0.6871 8.5158 10500 0.2939 0.3832 0.0771
0.6942 8.9213 11000 0.2950 0.3816 0.0766
0.6919 9.3268 11500 0.2910 0.3781 0.0760
0.6756 9.7324 12000 0.2927 0.3785 0.0760

Framework versions

  • Transformers 4.40.2
  • Pytorch 2.3.0+cu118
  • Datasets 2.19.1
  • Tokenizers 0.19.1