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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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model-index: |
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- name: Fine_Tuned_XLSR_English |
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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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# Fine_Tuned_XLSR_English |
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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 [timit_asr](https://huggingface.co/datasets/timit_asr) dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4033 |
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- Wer: 0.3163 |
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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: 1000 |
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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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| 4.3757 | 1.0 | 500 | 3.1570 | 1.0 | |
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| 2.4891 | 2.01 | 1000 | 0.9252 | 0.8430 | |
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| 0.8725 | 3.01 | 1500 | 0.4581 | 0.4931 | |
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| 0.544 | 4.02 | 2000 | 0.3757 | 0.4328 | |
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| 0.4043 | 5.02 | 2500 | 0.3621 | 0.4087 | |
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| 0.3376 | 6.02 | 3000 | 0.3682 | 0.3931 | |
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| 0.2937 | 7.03 | 3500 | 0.3541 | 0.3743 | |
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| 0.2573 | 8.03 | 4000 | 0.3565 | 0.3593 | |
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| 0.2257 | 9.04 | 4500 | 0.3634 | 0.3654 | |
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| 0.215 | 10.04 | 5000 | 0.3695 | 0.3537 | |
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| 0.1879 | 11.04 | 5500 | 0.3690 | 0.3486 | |
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| 0.1599 | 12.05 | 6000 | 0.3743 | 0.3490 | |
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| 0.1499 | 13.05 | 6500 | 0.4108 | 0.3424 | |
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| 0.147 | 14.06 | 7000 | 0.4048 | 0.3400 | |
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| 0.1355 | 15.06 | 7500 | 0.3988 | 0.3357 | |
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| 0.1278 | 16.06 | 8000 | 0.3672 | 0.3384 | |
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| 0.1189 | 17.07 | 8500 | 0.4011 | 0.3340 | |
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| 0.1089 | 18.07 | 9000 | 0.3948 | 0.3300 | |
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| 0.1039 | 19.08 | 9500 | 0.4062 | 0.3317 | |
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| 0.0971 | 20.08 | 10000 | 0.4041 | 0.3252 | |
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| 0.0902 | 21.08 | 10500 | 0.4112 | 0.3301 | |
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| 0.0883 | 22.09 | 11000 | 0.4154 | 0.3292 | |
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| 0.0864 | 23.09 | 11500 | 0.3746 | 0.3189 | |
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| 0.0746 | 24.1 | 12000 | 0.3991 | 0.3230 | |
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| 0.0711 | 25.1 | 12500 | 0.3916 | 0.3200 | |
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| 0.0712 | 26.1 | 13000 | 0.4024 | 0.3193 | |
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| 0.0663 | 27.11 | 13500 | 0.3976 | 0.3184 | |
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| 0.0626 | 28.11 | 14000 | 0.4046 | 0.3168 | |
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| 0.0641 | 29.12 | 14500 | 0.4033 | 0.3163 | |
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### Framework versions |
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- Transformers 4.17.0 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 1.18.3 |
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- Tokenizers 0.12.1 |
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