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--- |
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library_name: transformers |
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license: cc-by-nc-4.0 |
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base_model: facebook/mms-1b-all |
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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: bambara-mms-10-hours-oza75bambara-asr-hf |
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/asr-africa-research-team/ASR%20Africa/runs/g15oyt2j) |
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# bambara-mms-10-hours-oza75bambara-asr-hf |
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This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.1148 |
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- Wer: 0.5199 |
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- Cer: 0.2465 |
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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: 8 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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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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- num_epochs: 50 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | |
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|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:| |
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| 1.5938 | 0.8313 | 500 | 1.2774 | 0.8294 | 0.3861 | |
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| 1.5142 | 1.6625 | 1000 | 1.2540 | 0.7729 | 0.3702 | |
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| 1.3973 | 2.4938 | 1500 | 1.2161 | 0.7125 | 0.3521 | |
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| 1.326 | 3.3250 | 2000 | 1.1719 | 0.7260 | 0.3426 | |
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| 1.2698 | 4.1563 | 2500 | 1.1710 | 0.6738 | 0.3350 | |
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| 1.218 | 4.9875 | 3000 | 1.0692 | 0.6507 | 0.3147 | |
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| 1.1672 | 5.8188 | 3500 | 1.0691 | 0.6408 | 0.3071 | |
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| 1.1307 | 6.6500 | 4000 | 1.0518 | 0.6405 | 0.3035 | |
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| 1.079 | 7.4813 | 4500 | 1.1091 | 0.6304 | 0.2972 | |
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| 1.0474 | 8.3126 | 5000 | 1.0662 | 0.6240 | 0.2994 | |
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| 1.0169 | 9.1438 | 5500 | 1.0669 | 0.6173 | 0.2937 | |
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| 0.9753 | 9.9751 | 6000 | 1.0039 | 0.6329 | 0.3027 | |
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| 0.9303 | 10.8063 | 6500 | 0.9909 | 0.6101 | 0.2863 | |
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| 0.8867 | 11.6376 | 7000 | 1.0058 | 0.5929 | 0.2888 | |
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| 0.8537 | 12.4688 | 7500 | 1.0321 | 0.6015 | 0.2859 | |
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| 0.8268 | 13.3001 | 8000 | 1.0427 | 0.5960 | 0.2808 | |
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| 0.7908 | 14.1313 | 8500 | 1.0816 | 0.5847 | 0.2791 | |
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| 0.7625 | 14.9626 | 9000 | 1.0817 | 0.5839 | 0.2748 | |
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| 0.7254 | 15.7938 | 9500 | 1.1185 | 0.5764 | 0.2768 | |
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| 0.6817 | 16.6251 | 10000 | 1.1043 | 0.5658 | 0.2749 | |
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| 0.6602 | 17.4564 | 10500 | 1.1736 | 0.5640 | 0.2708 | |
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| 0.6244 | 18.2876 | 11000 | 1.1894 | 0.5725 | 0.2721 | |
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| 0.6073 | 19.1189 | 11500 | 1.2478 | 0.5647 | 0.2737 | |
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| 0.5752 | 19.9501 | 12000 | 1.1698 | 0.5671 | 0.2770 | |
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| 0.5457 | 20.7814 | 12500 | 1.1864 | 0.5587 | 0.2697 | |
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| 0.5231 | 21.6126 | 13000 | 1.1907 | 0.5592 | 0.2693 | |
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| 0.5018 | 22.4439 | 13500 | 1.1874 | 0.5675 | 0.2707 | |
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| 0.4756 | 23.2751 | 14000 | 1.2167 | 0.5626 | 0.2664 | |
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| 0.458 | 24.1064 | 14500 | 1.2149 | 0.5592 | 0.2712 | |
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| 0.4301 | 24.9377 | 15000 | 1.3165 | 0.5499 | 0.2660 | |
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| 0.4165 | 25.7689 | 15500 | 1.2436 | 0.5689 | 0.2713 | |
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| 0.3897 | 26.6002 | 16000 | 1.3646 | 0.5470 | 0.2624 | |
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| 0.3743 | 27.4314 | 16500 | 1.4319 | 0.5477 | 0.2623 | |
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| 0.355 | 28.2627 | 17000 | 1.4645 | 0.5580 | 0.2683 | |
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| 0.3414 | 29.0939 | 17500 | 1.5373 | 0.5508 | 0.2590 | |
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| 0.3251 | 29.9252 | 18000 | 1.4997 | 0.5473 | 0.2648 | |
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| 0.3049 | 30.7564 | 18500 | 1.5231 | 0.5494 | 0.2617 | |
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| 0.2924 | 31.5877 | 19000 | 1.6212 | 0.5490 | 0.2610 | |
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| 0.2807 | 32.4190 | 19500 | 1.5959 | 0.5571 | 0.2661 | |
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| 0.2669 | 33.2502 | 20000 | 1.6300 | 0.5478 | 0.2628 | |
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| 0.2575 | 34.0815 | 20500 | 1.7042 | 0.5447 | 0.2616 | |
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| 0.2426 | 34.9127 | 21000 | 1.6750 | 0.5430 | 0.2607 | |
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| 0.2301 | 35.7440 | 21500 | 1.7449 | 0.5421 | 0.2596 | |
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| 0.2202 | 36.5752 | 22000 | 1.6587 | 0.5418 | 0.2584 | |
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| 0.213 | 37.4065 | 22500 | 1.7982 | 0.5361 | 0.2546 | |
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| 0.2092 | 38.2377 | 23000 | 1.7748 | 0.5271 | 0.2537 | |
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| 0.1956 | 39.0690 | 23500 | 1.8427 | 0.5310 | 0.2562 | |
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| 0.1869 | 39.9002 | 24000 | 1.7940 | 0.5275 | 0.2541 | |
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| 0.1799 | 40.7315 | 24500 | 1.7794 | 0.5275 | 0.2520 | |
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| 0.1727 | 41.5628 | 25000 | 1.9008 | 0.5374 | 0.2540 | |
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| 0.1681 | 42.3940 | 25500 | 1.9119 | 0.5297 | 0.2522 | |
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| 0.1596 | 43.2253 | 26000 | 1.9836 | 0.5258 | 0.2472 | |
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| 0.1569 | 44.0565 | 26500 | 1.9823 | 0.5195 | 0.2472 | |
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| 0.1516 | 44.8878 | 27000 | 1.9638 | 0.5179 | 0.2483 | |
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| 0.1482 | 45.7190 | 27500 | 2.0763 | 0.5146 | 0.2468 | |
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| 0.1379 | 46.5503 | 28000 | 2.0760 | 0.5234 | 0.2483 | |
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| 0.1407 | 47.3815 | 28500 | 2.0269 | 0.5220 | 0.2482 | |
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| 0.1331 | 48.2128 | 29000 | 2.0818 | 0.5221 | 0.2481 | |
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| 0.1328 | 49.0441 | 29500 | 2.0947 | 0.5205 | 0.2467 | |
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| 0.1308 | 49.8753 | 30000 | 2.1148 | 0.5199 | 0.2465 | |
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### Framework versions |
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- Transformers 4.45.1 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.17.0 |
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- Tokenizers 0.20.3 |
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