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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-large-xlsr-53 |
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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-xlsr-53-ft-btb-ccv-cy |
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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-xlsr-53-ft-btb-ccv-cy |
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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 an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4290 |
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- Wer: 0.3378 |
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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: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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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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- training_steps: 2600 |
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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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| No log | 0.0774 | 100 | 3.5325 | 1.0 | |
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| No log | 0.1549 | 200 | 2.9652 | 1.0 | |
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| No log | 0.2323 | 300 | 2.8521 | 1.0 | |
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| No log | 0.3097 | 400 | 1.2473 | 0.8265 | |
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| 3.7403 | 0.3871 | 500 | 0.9730 | 0.7234 | |
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| 3.7403 | 0.4646 | 600 | 0.8328 | 0.6178 | |
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| 3.7403 | 0.5420 | 700 | 0.7426 | 0.5505 | |
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| 3.7403 | 0.6194 | 800 | 0.7127 | 0.5540 | |
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| 3.7403 | 0.6969 | 900 | 0.6692 | 0.5080 | |
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| 0.7271 | 0.7743 | 1000 | 0.6376 | 0.5256 | |
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| 0.7271 | 0.8517 | 1100 | 0.6119 | 0.4706 | |
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| 0.7271 | 0.9292 | 1200 | 0.5987 | 0.4651 | |
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| 0.7271 | 1.0066 | 1300 | 0.5614 | 0.4267 | |
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| 0.7271 | 1.0840 | 1400 | 0.5463 | 0.4229 | |
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| 0.5511 | 1.1614 | 1500 | 0.5232 | 0.4079 | |
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| 0.5511 | 1.2389 | 1600 | 0.5185 | 0.4029 | |
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| 0.5511 | 1.3163 | 1700 | 0.5090 | 0.4042 | |
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| 0.5511 | 1.3937 | 1800 | 0.4785 | 0.3851 | |
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| 0.5511 | 1.4712 | 1900 | 0.4775 | 0.3803 | |
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| 0.4529 | 1.5486 | 2000 | 0.4677 | 0.3722 | |
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| 0.4529 | 1.6260 | 2100 | 0.4574 | 0.3544 | |
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| 0.4529 | 1.7034 | 2200 | 0.4473 | 0.3562 | |
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| 0.4529 | 1.7809 | 2300 | 0.4437 | 0.3470 | |
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| 0.4529 | 1.8583 | 2400 | 0.4353 | 0.3450 | |
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| 0.4149 | 1.9357 | 2500 | 0.4300 | 0.3401 | |
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| 0.4149 | 2.0132 | 2600 | 0.4290 | 0.3378 | |
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### Framework versions |
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- Transformers 4.40.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
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