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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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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-large-xls-r-300m-lg |
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results: [] |
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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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# wav2vec2-large-xls-r-300m-lg |
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This model is a fine-tuned version of [Alvin-Nahabwe/wav2vec2-large-xls-r-300m-gn](https://huggingface.co/Alvin-Nahabwe/wav2vec2-large-xls-r-300m-gn) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2283 |
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- Wer: 0.1569 |
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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: 0.0003 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 128 |
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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: 500 |
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- num_epochs: 22 |
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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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| 0.2883 | 1.39 | 400 | 0.2180 | 0.2128 | |
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| 0.2603 | 2.78 | 800 | 0.2108 | 0.2082 | |
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| 0.2341 | 4.17 | 1200 | 0.2127 | 0.2085 | |
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| 0.2194 | 5.56 | 1600 | 0.2004 | 0.2050 | |
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| 0.1846 | 6.95 | 2000 | 0.1961 | 0.1898 | |
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| 0.1627 | 8.34 | 2400 | 0.1919 | 0.1779 | |
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| 0.1464 | 9.75 | 2800 | 0.1867 | 0.1677 | |
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| 0.1273 | 11.14 | 3200 | 0.1949 | 0.1710 | |
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| 0.1153 | 12.53 | 3600 | 0.1965 | 0.1639 | |
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| 0.1006 | 13.93 | 4000 | 0.1983 | 0.1603 | |
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| 0.1056 | 15.32 | 4400 | 0.2159 | 0.1686 | |
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| 0.1011 | 16.71 | 4800 | 0.2104 | 0.1663 | |
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| 0.0895 | 18.1 | 5200 | 0.2211 | 0.1634 | |
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| 0.0818 | 19.49 | 5600 | 0.2234 | 0.1610 | |
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| 0.0778 | 20.88 | 6000 | 0.2283 | 0.1569 | |
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### Framework versions |
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- Transformers 4.29.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.13.3 |
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