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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: ssw_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: ssw |
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split: test |
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args: ssw |
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metrics: |
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- name: Wer |
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type: wer |
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value: 42.14876033057851 |
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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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# ssw_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: 0.4301 |
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- Wer: 42.1488 |
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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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| 22.1208 | 0.8333 | 10 | 25.1031 | 100.5510 | |
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| 12.838 | 1.6667 | 20 | 10.4898 | 100.0 | |
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| 4.2236 | 2.5 | 30 | 3.9356 | 100.0 | |
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| 3.4491 | 3.3333 | 40 | 3.4590 | 100.0 | |
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| 3.2593 | 4.1667 | 50 | 3.3211 | 100.0 | |
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| 3.1611 | 5.0 | 60 | 3.1737 | 100.0 | |
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| 3.1157 | 5.8333 | 70 | 3.1089 | 100.0 | |
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| 3.0472 | 6.6667 | 80 | 3.0868 | 100.0 | |
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| 3.0291 | 7.5 | 90 | 3.0445 | 100.0 | |
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| 2.9996 | 8.3333 | 100 | 3.0058 | 100.0 | |
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| 2.9187 | 9.1667 | 110 | 2.9600 | 100.0 | |
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| 2.7708 | 10.0 | 120 | 2.7274 | 100.0 | |
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| 2.5396 | 10.8333 | 130 | 2.4602 | 100.0 | |
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| 2.0911 | 11.6667 | 140 | 1.8863 | 100.0 | |
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| 1.4477 | 12.5 | 150 | 1.2924 | 95.8678 | |
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| 1.042 | 13.3333 | 160 | 0.9620 | 80.1653 | |
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| 0.8089 | 14.1667 | 170 | 0.7520 | 67.4931 | |
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| 0.6621 | 15.0 | 180 | 0.6530 | 53.7190 | |
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| 0.5476 | 15.8333 | 190 | 0.5838 | 50.6887 | |
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| 0.4866 | 16.6667 | 200 | 0.5662 | 50.4132 | |
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| 0.4296 | 17.5 | 210 | 0.5303 | 49.5868 | |
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| 0.3977 | 18.3333 | 220 | 0.5121 | 47.9339 | |
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| 0.392 | 19.1667 | 230 | 0.4895 | 47.3829 | |
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| 0.346 | 20.0 | 240 | 0.4825 | 44.3526 | |
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| 0.3226 | 20.8333 | 250 | 0.4628 | 45.1791 | |
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| 0.3145 | 21.6667 | 260 | 0.4662 | 45.1791 | |
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| 0.2948 | 22.5 | 270 | 0.4492 | 41.8733 | |
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| 0.2857 | 23.3333 | 280 | 0.4484 | 43.2507 | |
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| 0.2571 | 24.1667 | 290 | 0.4511 | 43.2507 | |
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| 0.2706 | 25.0 | 300 | 0.4382 | 41.8733 | |
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| 0.2404 | 25.8333 | 310 | 0.4528 | 42.1488 | |
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| 0.2498 | 26.6667 | 320 | 0.4428 | 41.5978 | |
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| 0.2381 | 27.5 | 330 | 0.4377 | 40.2204 | |
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| 0.2142 | 28.3333 | 340 | 0.4300 | 41.0468 | |
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| 0.2236 | 29.1667 | 350 | 0.4305 | 42.1488 | |
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| 0.2249 | 30.0 | 360 | 0.4253 | 41.0468 | |
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| 0.209 | 30.8333 | 370 | 0.4272 | 42.9752 | |
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| 0.2071 | 31.6667 | 380 | 0.4363 | 43.8017 | |
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| 0.2209 | 32.5 | 390 | 0.4328 | 44.6281 | |
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| 0.2012 | 33.3333 | 400 | 0.4351 | 44.0771 | |
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| 0.1895 | 34.1667 | 410 | 0.4362 | 43.8017 | |
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| 0.1921 | 35.0 | 420 | 0.4383 | 45.1791 | |
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| 0.1805 | 35.8333 | 430 | 0.4381 | 45.1791 | |
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| 0.1963 | 36.6667 | 440 | 0.4331 | 41.3223 | |
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| 0.1829 | 37.5 | 450 | 0.4301 | 41.5978 | |
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| 0.1927 | 38.3333 | 460 | 0.4290 | 41.8733 | |
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| 0.1779 | 39.1667 | 470 | 0.4289 | 42.4242 | |
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| 0.1892 | 40.0 | 480 | 0.4302 | 42.1488 | |
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| 0.2025 | 40.8333 | 490 | 0.4300 | 42.4242 | |
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| 0.2105 | 41.6667 | 500 | 0.4301 | 42.1488 | |
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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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