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xls-r-300-cv17-upper-sorbian-adap-pl

Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Eval Results
Inference Endpoints
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xls-r-300-cv17-upper-sorbian-adap-pl / 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-upper-sorbian-adap-pl
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: hsb
          split: validation
          args: hsb
        metrics:
          - name: Wer
            type: wer
            value: 0.7246835443037974

Visualize in Weights & Biases

xls-r-300-cv17-upper-sorbian-adap-pl

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: 1.0564
  • Wer: 0.7247
  • Cer: 0.1754

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: 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: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
3.5302 3.9216 100 3.5256 1.0 1.0
3.2181 7.8431 200 3.2314 1.0 1.0
1.5479 11.7647 300 1.6991 0.9797 0.3943
0.3971 15.6863 400 0.9388 0.8582 0.2274
0.2782 19.6078 500 0.9310 0.8291 0.2203
0.1388 23.5294 600 0.9292 0.8 0.2045
0.1438 27.4510 700 0.9533 0.8006 0.2011
0.0815 31.3725 800 0.9446 0.7816 0.1975
0.0873 35.2941 900 0.9855 0.7728 0.1913
0.1213 39.2157 1000 0.9705 0.7652 0.1955
0.0589 43.1373 1100 0.9832 0.7614 0.1876
0.0865 47.0588 1200 1.0001 0.7582 0.1875
0.0762 50.9804 1300 1.0280 0.7538 0.1854
0.0564 54.9020 1400 0.9799 0.7468 0.1820
0.0607 58.8235 1500 1.0192 0.7443 0.1793
0.0729 62.7451 1600 1.0057 0.7424 0.1762
0.0518 66.6667 1700 1.0240 0.7437 0.1765
0.059 70.5882 1800 1.0379 0.7278 0.1759
0.031 74.5098 1900 1.0444 0.7152 0.1718
0.051 78.4314 2000 1.0530 0.7335 0.1773
0.0539 82.3529 2100 1.0402 0.7241 0.1773
0.0399 86.2745 2200 1.0495 0.7177 0.1744
0.06 90.1961 2300 1.0674 0.7222 0.1764
0.0459 94.1176 2400 1.0576 0.7222 0.1747
0.0614 98.0392 2500 1.0564 0.7247 0.1754

Framework versions

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