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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_8_0 |
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model-index: |
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- name: xlsr_ur_training |
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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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# xlsr_ur_training |
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This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_8_0 dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 1.2610 |
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- Wer: 0.7325 |
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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: 0.0001 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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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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| 14.5044 | 1.69 | 100 | 3.9173 | 1.0 | |
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| 3.3645 | 3.39 | 200 | 3.2475 | 1.0 | |
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| 3.2318 | 5.08 | 300 | 3.2143 | 1.0 | |
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| 3.1887 | 6.78 | 400 | 3.1672 | 1.0 | |
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| 3.1233 | 8.47 | 500 | 3.0927 | 1.0 | |
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| 3.0938 | 10.17 | 600 | 3.0836 | 0.9970 | |
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| 3.0706 | 11.86 | 700 | 3.0319 | 0.9996 | |
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| 2.9622 | 13.56 | 800 | 2.7973 | 0.9985 | |
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| 2.6267 | 15.25 | 900 | 2.2553 | 0.9974 | |
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| 1.9748 | 16.95 | 1000 | 1.6858 | 0.9170 | |
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| 1.4739 | 18.64 | 1100 | 1.4620 | 0.8125 | |
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| 1.2102 | 20.34 | 1200 | 1.3890 | 0.7779 | |
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| 1.036 | 22.03 | 1300 | 1.3347 | 0.7672 | |
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| 0.9462 | 23.73 | 1400 | 1.2970 | 0.7476 | |
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| 0.8725 | 25.42 | 1500 | 1.2792 | 0.7461 | |
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| 0.8374 | 27.12 | 1600 | 1.2574 | 0.7384 | |
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| 0.7976 | 28.81 | 1700 | 1.2610 | 0.7325 | |
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### Framework versions |
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- Transformers 4.21.0 |
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- Pytorch 1.11.0+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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