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wav2vec2-xls-r-1B-german

Automatic Speech Recognition
Transformers
PyTorch
German
wav2vec2
mozilla-foundation/common_voice_8_0
Generated from Trainer
robust-speech-event
hf-asr-leaderboard
Eval Results
Inference Endpoints
Model card Files Community
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: ''
    results: []

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the common_voice dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1355
  • Wer: 0.1531

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: 7.5e-05
  • train_batch_size: 64
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 2000
  • num_epochs: 3.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
1.0826 0.07 1000 0.4637 0.4654
1.118 0.15 2000 0.2595 0.2687
1.1268 0.22 3000 0.2635 0.2661
1.0919 0.29 4000 0.2417 0.2566
1.1013 0.37 5000 0.2414 0.2567
1.0898 0.44 6000 0.2546 0.2731
1.0808 0.51 7000 0.2399 0.2535
1.0719 0.59 8000 0.2353 0.2528
1.0446 0.66 9000 0.2427 0.2545
1.0347 0.73 10000 0.2266 0.2402
1.0457 0.81 11000 0.2290 0.2448
1.0124 0.88 12000 0.2295 0.2448
1.025 0.95 13000 0.2138 0.2345
1.0107 1.03 14000 0.2108 0.2294
0.9758 1.1 15000 0.2019 0.2204
0.9547 1.17 16000 0.2000 0.2178
0.986 1.25 17000 0.2018 0.2200
0.9588 1.32 18000 0.1992 0.2138
0.9413 1.39 19000 0.1898 0.2049
0.9339 1.47 20000 0.1874 0.2056
0.9268 1.54 21000 0.1797 0.1976
0.9194 1.61 22000 0.1743 0.1905
0.8987 1.69 23000 0.1738 0.1932
0.8884 1.76 24000 0.1703 0.1873
0.8939 1.83 25000 0.1633 0.1831
0.8629 1.91 26000 0.1549 0.1750
0.8607 1.98 27000 0.1550 0.1738
0.8316 2.05 28000 0.1512 0.1709
0.8321 2.13 29000 0.1481 0.1657
0.825 2.2 30000 0.1446 0.1627
0.8115 2.27 31000 0.1396 0.1583
0.7959 2.35 32000 0.1389 0.1569
0.7835 2.42 33000 0.1362 0.1545
0.7959 2.49 34000 0.1355 0.1531

Framework versions

  • Transformers 4.17.0.dev0
  • Pytorch 1.10.2+cu102
  • Datasets 1.18.2.dev0
  • Tokenizers 0.11.0