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xls-r-300-cv17-polish

Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Eval Results
Inference Endpoints
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xls-r-300-cv17-polish / 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-polish
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: pl
          split: validation
          args: pl
        metrics:
          - name: Wer
            type: wer
            value: 0.2788608461984298

Visualize in Weights & Biases

xls-r-300-cv17-polish

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: 0.3950
  • Wer: 0.2789
  • Cer: 0.0606

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: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
4.0552 1.6 100 4.2577 1.0 1.0
3.2887 3.2 200 3.2578 1.0 1.0
3.1481 4.8 300 3.1634 1.0 1.0
0.742 6.4 400 0.6905 0.6579 0.1603
0.4458 8.0 500 0.4687 0.4969 0.1169
0.2013 9.6 600 0.4327 0.4055 0.0929
0.2196 11.2 700 0.4180 0.4006 0.0903
0.1264 12.8 800 0.4360 0.3943 0.0898
0.1678 14.4 900 0.4157 0.3635 0.0818
0.1306 16.0 1000 0.3980 0.3667 0.0814
0.0471 17.6 1100 0.4206 0.3630 0.0828
0.1018 19.2 1200 0.3908 0.3522 0.0796
0.0637 20.8 1300 0.4277 0.3517 0.0785
0.1134 22.4 1400 0.4209 0.3373 0.0750
0.0709 24.0 1500 0.4255 0.3387 0.0766
0.046 25.6 1600 0.4301 0.3352 0.0746
0.065 27.2 1700 0.4087 0.3278 0.0724
0.0625 28.8 1800 0.4203 0.3454 0.0761
0.0344 30.4 1900 0.4317 0.3203 0.0714
0.0667 32.0 2000 0.4319 0.3258 0.0725
0.0305 33.6 2100 0.4260 0.3216 0.0716
0.04 35.2 2200 0.4172 0.3175 0.0697
0.0454 36.8 2300 0.4182 0.2996 0.0658
0.0273 38.4 2400 0.3966 0.2970 0.0654
0.0463 40.0 2500 0.4111 0.2926 0.0644
0.0321 41.6 2600 0.4094 0.2893 0.0633
0.0197 43.2 2700 0.3953 0.2846 0.0622
0.0306 44.8 2800 0.3980 0.2817 0.0613
0.0459 46.4 2900 0.3937 0.2807 0.0613
0.006 48.0 3000 0.3953 0.2780 0.0604
0.0329 49.6 3100 0.3950 0.2789 0.0606

Framework versions

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