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update model card README.md
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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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datasets:
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- common_voice_13_0
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metrics:
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- wer
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model-index:
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- name: b21-wav2vec2-large-xls-r-romansh-colab
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results:
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- task:
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name: Automatic Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: common_voice_13_0
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type: common_voice_13_0
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config: rm-vallader
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split: test
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args: rm-vallader
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metrics:
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- name: Wer
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type: wer
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value: 0.6304145319049836
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# b21-wav2vec2-large-xls-r-romansh-colab
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_13_0 dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.8091
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- Wer: 0.6304
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0004
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- train_batch_size: 4
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 2
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- total_train_batch_size: 8
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_steps: 100
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- num_epochs: 30
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Wer |
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|:-------------:|:-----:|:----:|:---------------:|:------:|
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| 6.5829 | 0.76 | 100 | 2.9564 | 1.0 |
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| 2.9568 | 1.52 | 200 | 3.0768 | 1.0 |
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| 2.9578 | 2.29 | 300 | 3.0654 | 1.0 |
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| 2.957 | 3.05 | 400 | 2.9377 | 1.0 |
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| 2.9419 | 3.81 | 500 | 2.9408 | 1.0 |
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| 2.9567 | 4.58 | 600 | 2.9395 | 1.0 |
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| 2.9625 | 5.34 | 700 | 2.9388 | 1.0 |
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| 2.9395 | 6.11 | 800 | 2.9374 | 1.0 |
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| 2.9285 | 6.87 | 900 | 2.9240 | 1.0 |
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| 2.9187 | 7.63 | 1000 | 2.9057 | 1.0 |
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| 2.9251 | 8.4 | 1100 | 2.8985 | 1.0 |
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| 2.9033 | 9.16 | 1200 | 2.8942 | 1.0 |
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| 2.8877 | 9.92 | 1300 | 2.8917 | 1.0 |
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| 2.8586 | 10.68 | 1400 | 2.7719 | 1.0 |
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| 2.5777 | 11.45 | 1500 | 2.2424 | 1.0 |
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| 1.9243 | 12.21 | 1600 | 1.7068 | 0.9772 |
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| 1.4534 | 12.97 | 1700 | 1.2780 | 0.9585 |
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| 1.1793 | 13.74 | 1800 | 1.1482 | 0.9360 |
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| 1.0026 | 14.5 | 1900 | 1.0673 | 0.8852 |
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| 0.8879 | 15.27 | 2000 | 0.9651 | 0.8433 |
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| 0.7933 | 16.03 | 2100 | 0.8973 | 0.8216 |
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| 0.6895 | 16.79 | 2200 | 0.8396 | 0.8034 |
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| 0.6531 | 17.56 | 2300 | 0.8131 | 0.7713 |
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| 0.5753 | 18.32 | 2400 | 0.8388 | 0.7531 |
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| 0.5621 | 19.08 | 2500 | 0.7844 | 0.7632 |
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| 0.5076 | 19.84 | 2600 | 0.7629 | 0.7485 |
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| 0.4672 | 20.61 | 2700 | 0.7777 | 0.7497 |
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| 0.443 | 21.37 | 2800 | 0.8001 | 0.7292 |
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| 0.4129 | 22.14 | 2900 | 0.7902 | 0.7094 |
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| 0.3767 | 22.9 | 3000 | 0.7569 | 0.6784 |
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| 0.357 | 23.66 | 3100 | 0.7726 | 0.6903 |
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| 0.3378 | 24.43 | 3200 | 0.8016 | 0.6882 |
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| 0.3199 | 25.19 | 3300 | 0.7854 | 0.6677 |
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| 0.3144 | 25.95 | 3400 | 0.7792 | 0.6509 |
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| 0.3025 | 26.71 | 3500 | 0.8157 | 0.6695 |
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| 0.2919 | 27.48 | 3600 | 0.8215 | 0.6633 |
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| 0.2762 | 28.24 | 3700 | 0.8167 | 0.6500 |
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| 0.2679 | 29.01 | 3800 | 0.8144 | 0.6311 |
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| 0.2671 | 29.77 | 3900 | 0.8091 | 0.6304 |
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### Framework versions
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- Transformers 4.26.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.14.4
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- Tokenizers 0.13.3
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