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xls-r-300-cv17-bulgarian-adap-ru

Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Eval Results
Inference Endpoints
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xls-r-300-cv17-bulgarian-adap-ru / README.md
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metadata
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
datasets:
  - common_voice_17_0
metrics:
  - wer
model-index:
  - name: xls-r-300-cv17-bulgarian-adap-ru
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: bg
          split: validation
          args: bg
        metrics:
          - name: Wer
            type: wer
            value: 0.3023246994576965

Visualize in Weights & Biases

xls-r-300-cv17-bulgarian-adap-ru

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3977
  • Wer: 0.3023
  • Cer: 0.0722

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: 0.0003
  • train_batch_size: 16
  • eval_batch_size: 8
  • 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: 500
  • num_epochs: 30
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
3.1617 0.6579 100 3.1554 1.0 1.0
1.0032 1.3158 200 1.0726 0.8684 0.2419
0.5552 1.9737 300 0.4924 0.5297 0.1303
0.2763 2.6316 400 0.3795 0.4442 0.1043
0.2273 3.2895 500 0.3769 0.4222 0.1014
0.3216 3.9474 600 0.3611 0.3993 0.0971
0.1553 4.6053 700 0.3566 0.3927 0.0936
0.1414 5.2632 800 0.3676 0.3869 0.0923
0.1774 5.9211 900 0.3680 0.3758 0.0901
0.1256 6.5789 1000 0.3637 0.3775 0.0916
0.2416 7.2368 1100 0.3893 0.3963 0.0951
0.1213 7.8947 1200 0.3677 0.3596 0.0864
0.0911 8.5526 1300 0.3850 0.3739 0.0891
0.0859 9.2105 1400 0.3962 0.3658 0.0883
0.0998 9.8684 1500 0.3608 0.3530 0.0846
0.108 10.5263 1600 0.3932 0.3908 0.0920
0.0824 11.1842 1700 0.4147 0.3591 0.0870
0.0888 11.8421 1800 0.4040 0.3660 0.0878
0.0609 12.5 1900 0.4097 0.3542 0.0857
0.0692 13.1579 2000 0.4127 0.3639 0.0874
0.0513 13.8158 2100 0.4118 0.3560 0.0870
0.0752 14.4737 2200 0.4044 0.3591 0.0888
0.0833 15.1316 2300 0.3956 0.3374 0.0812
0.0826 15.7895 2400 0.3953 0.3356 0.0811
0.0934 16.4474 2500 0.4053 0.3394 0.0819
0.0562 17.1053 2600 0.4243 0.3534 0.0843
0.0661 17.7632 2700 0.4021 0.3340 0.0791
0.0496 18.4211 2800 0.4052 0.3387 0.0818
0.0599 19.0789 2900 0.4101 0.3385 0.0806
0.0446 19.7368 3000 0.3990 0.3362 0.0810
0.0482 20.3947 3100 0.4077 0.3274 0.0781
0.0309 21.0526 3200 0.4343 0.3397 0.0817
0.0757 21.7105 3300 0.4154 0.3252 0.0781
0.0377 22.3684 3400 0.4273 0.3206 0.0770
0.0282 23.0263 3500 0.3998 0.3159 0.0751
0.0676 23.6842 3600 0.3960 0.3111 0.0745
0.0673 24.3421 3700 0.3997 0.3100 0.0741
0.1793 25.0 3800 0.4065 0.3106 0.0738
0.0572 25.6579 3900 0.3951 0.3098 0.0739
0.0208 26.3158 4000 0.4097 0.3106 0.0740
0.0562 26.9737 4100 0.4016 0.3081 0.0734
0.0314 27.6316 4200 0.3939 0.3008 0.0715
0.0235 28.2895 4300 0.4008 0.3023 0.0720
0.0443 28.9474 4400 0.3963 0.3033 0.0724
0.027 29.6053 4500 0.3977 0.3023 0.0722

Framework versions

  • Transformers 4.42.0.dev0
  • Pytorch 2.3.1+cu121
  • Datasets 2.19.2
  • Tokenizers 0.19.1