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--- |
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license: apache-2.0 |
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base_model: Akashpb13/Swahili_xlsr |
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tags: |
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- generated_from_trainer |
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datasets: |
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- ml-superb-subset |
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metrics: |
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- wer |
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model-index: |
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- name: xty_finetune |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: ml-superb-subset |
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type: ml-superb-subset |
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config: xty |
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split: test |
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args: xty |
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metrics: |
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- name: Wer |
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type: wer |
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value: 89.12633563796355 |
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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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# xty_finetune |
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This model is a fine-tuned version of [Akashpb13/Swahili_xlsr](https://huggingface.co/Akashpb13/Swahili_xlsr) on the ml-superb-subset dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.0874 |
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- Wer: 89.1263 |
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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: 9.6e-05 |
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- train_batch_size: 32 |
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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: 64 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 25 |
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- training_steps: 500 |
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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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| 8.5823 | 1.0526 | 10 | 7.7265 | 100.0629 | |
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| 5.0617 | 2.1053 | 20 | 4.2231 | 100.0 | |
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| 3.6512 | 3.1579 | 30 | 3.5631 | 100.0 | |
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| 3.3211 | 4.2105 | 40 | 3.3414 | 100.0 | |
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| 3.2144 | 5.2632 | 50 | 3.2086 | 100.0 | |
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| 3.128 | 6.3158 | 60 | 3.1724 | 100.0 | |
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| 3.0999 | 7.3684 | 70 | 3.1261 | 100.0 | |
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| 3.0585 | 8.4211 | 80 | 3.1200 | 100.0 | |
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| 3.0318 | 9.4737 | 90 | 3.1001 | 100.0 | |
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| 3.0166 | 10.5263 | 100 | 3.0985 | 100.0 | |
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| 3.0147 | 11.5789 | 110 | 3.0971 | 100.0 | |
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| 3.0028 | 12.6316 | 120 | 3.0775 | 100.0 | |
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| 2.991 | 13.6842 | 130 | 3.0619 | 100.0 | |
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| 2.9692 | 14.7368 | 140 | 3.0477 | 100.0 | |
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| 2.9355 | 15.7895 | 150 | 3.0081 | 100.0 | |
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| 2.8754 | 16.8421 | 160 | 2.9190 | 100.0 | |
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| 2.7087 | 17.8947 | 170 | 2.7367 | 100.0 | |
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| 2.4346 | 18.9474 | 180 | 2.5043 | 108.5481 | |
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| 2.3184 | 20.0 | 190 | 2.3709 | 103.8969 | |
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| 2.0887 | 21.0526 | 200 | 2.2196 | 103.3941 | |
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| 1.9198 | 22.1053 | 210 | 2.1078 | 104.7140 | |
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| 1.6995 | 23.1579 | 220 | 2.0556 | 98.4287 | |
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| 1.6576 | 24.2105 | 230 | 2.0081 | 100.6914 | |
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| 1.4855 | 25.2632 | 240 | 1.9958 | 98.0515 | |
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| 1.3788 | 26.3158 | 250 | 1.9729 | 94.8460 | |
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| 1.3202 | 27.3684 | 260 | 1.9618 | 98.6172 | |
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| 1.2237 | 28.4211 | 270 | 1.9662 | 93.6518 | |
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| 1.1389 | 29.4737 | 280 | 1.9882 | 92.7090 | |
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| 1.0597 | 30.5263 | 290 | 1.9655 | 92.3947 | |
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| 1.0203 | 31.5789 | 300 | 1.9616 | 90.1948 | |
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| 0.9778 | 32.6316 | 310 | 1.9585 | 90.8234 | |
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| 0.9553 | 33.6842 | 320 | 1.9875 | 90.5091 | |
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| 0.895 | 34.7368 | 330 | 1.9913 | 91.3891 | |
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| 0.9021 | 35.7895 | 340 | 1.9906 | 90.2577 | |
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| 0.8105 | 36.8421 | 350 | 2.0182 | 89.4406 | |
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| 0.8052 | 37.8947 | 360 | 2.0227 | 89.5663 | |
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| 0.7484 | 38.9474 | 370 | 2.0539 | 89.0006 | |
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| 0.7886 | 40.0 | 380 | 2.0616 | 90.6977 | |
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| 0.7348 | 41.0526 | 390 | 2.0590 | 89.1892 | |
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| 0.7079 | 42.1053 | 400 | 2.0790 | 89.8806 | |
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| 0.7215 | 43.1579 | 410 | 2.0701 | 89.3149 | |
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| 0.6997 | 44.2105 | 420 | 2.0832 | 89.3777 | |
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| 0.721 | 45.2632 | 430 | 2.0798 | 89.3149 | |
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| 0.6609 | 46.3158 | 440 | 2.0834 | 88.4349 | |
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| 0.6562 | 47.3684 | 450 | 2.0892 | 89.0006 | |
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| 0.6418 | 48.4211 | 460 | 2.0878 | 89.3777 | |
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| 0.677 | 49.4737 | 470 | 2.0874 | 89.2520 | |
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| 0.6821 | 50.5263 | 480 | 2.0874 | 89.1263 | |
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| 0.6798 | 51.5789 | 490 | 2.0875 | 89.0635 | |
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| 0.7188 | 52.6316 | 500 | 2.0874 | 89.1263 | |
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### Framework versions |
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- Transformers 4.41.1 |
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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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