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--- |
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license: apache-2.0 |
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tags: |
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- automatic-speech-recognition |
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- experiments/data/atcosim_corpus/train |
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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: 0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl |
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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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# 0.0ld_0.05ad_0.05attd_0.0fpd_0.03mtp_10mtl_0.0mfp_10mfl |
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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 EXPERIMENTS/DATA/ATCOSIM_CORPUS/TRAIN - NA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0988 |
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- Wer: 0.0736 |
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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: 0.0005 |
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- train_batch_size: 24 |
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- eval_batch_size: 24 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 96 |
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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: 20000 |
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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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| 1.9105 | 6.41 | 500 | 0.1622 | 0.1531 | |
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| 0.1119 | 12.82 | 1000 | 0.0971 | 0.0936 | |
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| 0.0614 | 19.23 | 1500 | 0.1002 | 0.0983 | |
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| 0.044 | 25.64 | 2000 | 0.1011 | 0.0929 | |
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| 0.0366 | 32.05 | 2500 | 0.0932 | 0.0828 | |
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| 0.0315 | 38.46 | 3000 | 0.0926 | 0.0880 | |
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| 0.0297 | 44.87 | 3500 | 0.0972 | 0.0882 | |
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| 0.0216 | 51.28 | 4000 | 0.0911 | 0.0774 | |
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| 0.0211 | 57.69 | 4500 | 0.0982 | 0.0891 | |
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| 0.0187 | 64.1 | 5000 | 0.1009 | 0.0863 | |
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| 0.02 | 70.51 | 5500 | 0.0953 | 0.0852 | |
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| 0.0163 | 76.92 | 6000 | 0.1028 | 0.0804 | |
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| 0.0128 | 83.33 | 6500 | 0.0930 | 0.0856 | |
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| 0.0127 | 89.74 | 7000 | 0.0892 | 0.0676 | |
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| 0.0116 | 96.15 | 7500 | 0.0857 | 0.0753 | |
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| 0.0139 | 102.56 | 8000 | 0.1078 | 0.0481 | |
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| 0.0107 | 108.97 | 8500 | 0.0955 | 0.0683 | |
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| 0.0096 | 115.38 | 9000 | 0.0846 | 0.0697 | |
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| 0.0089 | 121.79 | 9500 | 0.0854 | 0.0675 | |
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| 0.0084 | 128.21 | 10000 | 0.0875 | 0.0779 | |
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| 0.0074 | 134.62 | 10500 | 0.0840 | 0.0770 | |
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| 0.0061 | 141.03 | 11000 | 0.0903 | 0.0754 | |
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| 0.0076 | 147.44 | 11500 | 0.0872 | 0.0769 | |
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| 0.0069 | 153.85 | 12000 | 0.0891 | 0.0772 | |
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| 0.0061 | 160.26 | 12500 | 0.0971 | 0.0774 | |
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| 0.0049 | 166.67 | 13000 | 0.0984 | 0.0726 | |
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| 0.0045 | 173.08 | 13500 | 0.0952 | 0.0765 | |
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| 0.0039 | 179.49 | 14000 | 0.1015 | 0.0762 | |
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| 0.0031 | 185.9 | 14500 | 0.0937 | 0.0712 | |
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| 0.0032 | 192.31 | 15000 | 0.0982 | 0.0635 | |
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| 0.0028 | 198.72 | 15500 | 0.0981 | 0.0743 | |
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| 0.0024 | 205.13 | 16000 | 0.1019 | 0.0712 | |
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| 0.0024 | 211.54 | 16500 | 0.0957 | 0.0732 | |
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| 0.002 | 217.95 | 17000 | 0.0941 | 0.0732 | |
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| 0.0015 | 224.36 | 17500 | 0.1009 | 0.0717 | |
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| 0.0017 | 230.77 | 18000 | 0.0955 | 0.0730 | |
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| 0.0013 | 237.18 | 18500 | 0.0989 | 0.0732 | |
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| 0.0013 | 243.59 | 19000 | 0.0967 | 0.0738 | |
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| 0.0011 | 250.0 | 19500 | 0.0980 | 0.0734 | |
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| 0.0008 | 256.41 | 20000 | 0.0988 | 0.0736 | |
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### Framework versions |
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- Transformers 4.24.0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.6.1 |
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- Tokenizers 0.13.2 |
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