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--- |
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license: apache-2.0 |
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base_model: facebook/hubert-large-ll60k |
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tags: |
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- automatic-speech-recognition |
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- macabdul9/librispeech-hubert-discrete-tokens |
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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: hubert-large-ll60k-librispeech-multi-gpu |
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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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# hubert-large-ll60k-librispeech-multi-gpu |
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This model is a fine-tuned version of [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) on the MACABDUL9/LIBRISPEECH-HUBERT-DISCRETE-TOKENS - NA dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0616 |
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- Wer: 0.0460 |
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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: 2 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- total_train_batch_size: 8 |
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- total_eval_batch_size: 32 |
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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: 4.0 |
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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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| 2.9692 | 0.03 | 100 | 2.9336 | 1.0 | |
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| 2.7936 | 0.06 | 200 | 2.8514 | 1.0 | |
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| 0.8892 | 0.08 | 300 | 0.7173 | 0.5438 | |
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| 0.4725 | 0.11 | 400 | 0.4293 | 0.3818 | |
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| 0.5347 | 0.14 | 500 | 0.3474 | 0.3284 | |
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| 0.3143 | 0.17 | 600 | 0.2966 | 0.2852 | |
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| 0.4144 | 0.2 | 700 | 0.2498 | 0.2383 | |
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| 0.516 | 0.22 | 800 | 0.2156 | 0.2174 | |
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| 0.2537 | 0.25 | 900 | 0.1810 | 0.1877 | |
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| 0.3008 | 0.28 | 1000 | 0.1699 | 0.1737 | |
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| 0.3078 | 0.31 | 1100 | 0.1485 | 0.1600 | |
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| 0.2922 | 0.34 | 1200 | 0.1444 | 0.1448 | |
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| 0.2303 | 0.36 | 1300 | 0.1319 | 0.1334 | |
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| 0.1858 | 0.39 | 1400 | 0.1286 | 0.1353 | |
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| 0.0837 | 0.42 | 1500 | 0.1162 | 0.1262 | |
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| 0.177 | 0.45 | 1600 | 0.1097 | 0.1100 | |
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| 0.3476 | 0.48 | 1700 | 0.1140 | 0.1114 | |
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| 0.2538 | 0.5 | 1800 | 0.1118 | 0.1086 | |
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| 0.1296 | 0.53 | 1900 | 0.1052 | 0.1016 | |
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| 0.3905 | 0.56 | 2000 | 0.1034 | 0.0950 | |
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| 0.2085 | 0.59 | 2100 | 0.1001 | 0.1003 | |
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| 0.3563 | 0.62 | 2200 | 0.0974 | 0.0958 | |
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| 0.1258 | 0.64 | 2300 | 0.1067 | 0.1018 | |
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| 0.0972 | 0.67 | 2400 | 0.0938 | 0.0930 | |
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| 0.2588 | 0.7 | 2500 | 0.0913 | 0.0861 | |
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| 0.2066 | 0.73 | 2600 | 0.0946 | 0.0902 | |
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| 0.1706 | 0.76 | 2700 | 0.0944 | 0.0956 | |
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| 0.1129 | 0.78 | 2800 | 0.0918 | 0.0904 | |
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| 0.1805 | 0.81 | 2900 | 0.0913 | 0.0890 | |
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| 0.2328 | 0.84 | 3000 | 0.0907 | 0.0893 | |
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| 0.2699 | 0.87 | 3100 | 0.0918 | 0.0849 | |
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| 0.1653 | 0.9 | 3200 | 0.0902 | 0.0827 | |
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| 0.2637 | 0.92 | 3300 | 0.0873 | 0.0836 | |
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| 0.2511 | 0.95 | 3400 | 0.0902 | 0.0851 | |
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| 0.2053 | 0.98 | 3500 | 0.0907 | 0.0842 | |
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| 0.0719 | 1.01 | 3600 | 0.0808 | 0.0752 | |
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| 0.0633 | 1.04 | 3700 | 0.0874 | 0.0775 | |
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| 0.063 | 1.07 | 3800 | 0.0855 | 0.0757 | |
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| 0.0653 | 1.09 | 3900 | 0.0916 | 0.0766 | |
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| 0.0381 | 1.12 | 4000 | 0.0864 | 0.0756 | |
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| 0.0457 | 1.15 | 4100 | 0.0850 | 0.0761 | |
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| 0.0399 | 1.18 | 4200 | 0.0842 | 0.0842 | |
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| 0.0403 | 1.21 | 4300 | 0.0834 | 0.0754 | |
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| 0.0462 | 1.23 | 4400 | 0.0833 | 0.0753 | |
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| 0.0312 | 1.26 | 4500 | 0.0815 | 0.0731 | |
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| 0.0432 | 1.29 | 4600 | 0.0812 | 0.0748 | |
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| 0.032 | 1.32 | 4700 | 0.0783 | 0.0683 | |
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| 0.0502 | 1.35 | 4800 | 0.0786 | 0.0699 | |
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| 0.0743 | 1.37 | 4900 | 0.0839 | 0.0678 | |
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| 0.0229 | 1.4 | 5000 | 0.0771 | 0.0664 | |
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| 0.0672 | 1.43 | 5100 | 0.0782 | 0.0662 | |
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| 0.0758 | 1.46 | 5200 | 0.0808 | 0.0696 | |
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| 0.0309 | 1.49 | 5300 | 0.0783 | 0.0710 | |
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| 0.043 | 1.51 | 5400 | 0.0748 | 0.0682 | |
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| 0.076 | 1.54 | 5500 | 0.0778 | 0.0685 | |
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| 0.041 | 1.57 | 5600 | 0.0792 | 0.0726 | |
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| 0.0784 | 1.6 | 5700 | 0.0763 | 0.0639 | |
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| 0.097 | 1.63 | 5800 | 0.0751 | 0.0668 | |
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| 0.0915 | 1.65 | 5900 | 0.0748 | 0.0661 | |
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| 0.0511 | 1.68 | 6000 | 0.0767 | 0.0641 | |
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| 0.0254 | 1.71 | 6100 | 0.0754 | 0.0656 | |
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| 0.024 | 1.74 | 6200 | 0.0732 | 0.0657 | |
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| 0.041 | 1.77 | 6300 | 0.0713 | 0.0673 | |
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| 0.034 | 1.79 | 6400 | 0.0686 | 0.0647 | |
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| 0.0604 | 1.82 | 6500 | 0.0733 | 0.0658 | |
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| 0.0456 | 1.85 | 6600 | 0.0743 | 0.0658 | |
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| 0.0431 | 1.88 | 6700 | 0.0760 | 0.0637 | |
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| 0.1149 | 1.91 | 6800 | 0.0757 | 0.0620 | |
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| 0.0431 | 1.93 | 6900 | 0.0727 | 0.0618 | |
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| 0.0425 | 1.96 | 7000 | 0.0717 | 0.0619 | |
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| 0.0452 | 1.99 | 7100 | 0.0708 | 0.0637 | |
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| 0.0172 | 2.02 | 7200 | 0.0740 | 0.0620 | |
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| 0.0413 | 2.05 | 7300 | 0.0695 | 0.0628 | |
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| 0.018 | 2.07 | 7400 | 0.0749 | 0.0626 | |
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| 0.0303 | 2.1 | 7500 | 0.0723 | 0.0625 | |
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| 0.0196 | 2.13 | 7600 | 0.0741 | 0.0629 | |
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| 0.015 | 2.16 | 7700 | 0.0699 | 0.0583 | |
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| 0.0145 | 2.19 | 7800 | 0.0735 | 0.0602 | |
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| 0.0287 | 2.21 | 7900 | 0.0726 | 0.0619 | |
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| 0.0735 | 2.24 | 8000 | 0.0705 | 0.0576 | |
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| 0.0161 | 2.27 | 8100 | 0.0715 | 0.0563 | |
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| 0.0549 | 2.3 | 8200 | 0.0692 | 0.0563 | |
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| 0.0227 | 2.33 | 8300 | 0.0715 | 0.0545 | |
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| 0.0156 | 2.35 | 8400 | 0.0702 | 0.0536 | |
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| 0.045 | 2.38 | 8500 | 0.0672 | 0.0533 | |
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| 0.0286 | 2.41 | 8600 | 0.0670 | 0.0537 | |
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| 0.0176 | 2.44 | 8700 | 0.0693 | 0.0554 | |
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| 0.0128 | 2.47 | 8800 | 0.0696 | 0.0549 | |
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| 0.0279 | 2.49 | 8900 | 0.0685 | 0.0541 | |
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| 0.0328 | 2.52 | 9000 | 0.0674 | 0.0574 | |
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| 0.0416 | 2.55 | 9100 | 0.0674 | 0.0581 | |
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| 0.0364 | 2.58 | 9200 | 0.0642 | 0.0540 | |
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| 0.0223 | 2.61 | 9300 | 0.0686 | 0.0544 | |
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| 0.0162 | 2.63 | 9400 | 0.0680 | 0.0521 | |
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| 0.0392 | 2.66 | 9500 | 0.0646 | 0.0512 | |
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| 0.027 | 2.69 | 9600 | 0.0663 | 0.0544 | |
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| 0.0192 | 2.72 | 9700 | 0.0654 | 0.0529 | |
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| 0.0339 | 2.75 | 9800 | 0.0654 | 0.0526 | |
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| 0.0269 | 2.77 | 9900 | 0.0651 | 0.0545 | |
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| 0.0263 | 2.8 | 10000 | 0.0636 | 0.0521 | |
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| 0.0444 | 2.83 | 10100 | 0.0650 | 0.0520 | |
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| 0.0322 | 2.86 | 10200 | 0.0622 | 0.0513 | |
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| 0.0171 | 2.89 | 10300 | 0.0641 | 0.0516 | |
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| 0.0121 | 2.91 | 10400 | 0.0633 | 0.0515 | |
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| 0.0347 | 2.94 | 10500 | 0.0610 | 0.0518 | |
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| 0.0432 | 2.97 | 10600 | 0.0605 | 0.0512 | |
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| 0.0187 | 3.0 | 10700 | 0.0623 | 0.0501 | |
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| 0.0147 | 3.03 | 10800 | 0.0652 | 0.0486 | |
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| 0.0112 | 3.05 | 10900 | 0.0648 | 0.05 | |
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| 0.0146 | 3.08 | 11000 | 0.0638 | 0.0514 | |
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| 0.0064 | 3.11 | 11100 | 0.0665 | 0.0486 | |
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| 0.0223 | 3.14 | 11200 | 0.0654 | 0.0487 | |
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| 0.0261 | 3.17 | 11300 | 0.0655 | 0.0488 | |
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| 0.0144 | 3.2 | 11400 | 0.0638 | 0.0488 | |
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| 0.0195 | 3.22 | 11500 | 0.0655 | 0.0491 | |
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| 0.0091 | 3.25 | 11600 | 0.0648 | 0.0492 | |
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| 0.0171 | 3.28 | 11700 | 0.0633 | 0.0483 | |
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| 0.0538 | 3.31 | 11800 | 0.0654 | 0.0488 | |
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| 0.0047 | 3.34 | 11900 | 0.0625 | 0.0490 | |
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| 0.015 | 3.36 | 12000 | 0.0634 | 0.0476 | |
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| 0.0323 | 3.39 | 12100 | 0.0655 | 0.0494 | |
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| 0.0173 | 3.42 | 12200 | 0.0637 | 0.0495 | |
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| 0.0188 | 3.45 | 12300 | 0.0638 | 0.0479 | |
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| 0.004 | 3.48 | 12400 | 0.0637 | 0.0459 | |
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| 0.022 | 3.5 | 12500 | 0.0637 | 0.0467 | |
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| 0.0139 | 3.53 | 12600 | 0.0638 | 0.0466 | |
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| 0.024 | 3.56 | 12700 | 0.0633 | 0.0467 | |
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| 0.0047 | 3.59 | 12800 | 0.0650 | 0.0468 | |
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| 0.0099 | 3.62 | 12900 | 0.0636 | 0.0476 | |
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| 0.0418 | 3.64 | 13000 | 0.0631 | 0.0473 | |
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| 0.0298 | 3.67 | 13100 | 0.0620 | 0.0476 | |
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| 0.0382 | 3.7 | 13200 | 0.0617 | 0.0468 | |
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| 0.0281 | 3.73 | 13300 | 0.0619 | 0.0464 | |
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| 0.0256 | 3.76 | 13400 | 0.0621 | 0.0463 | |
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| 0.0024 | 3.78 | 13500 | 0.0612 | 0.0463 | |
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| 0.0107 | 3.81 | 13600 | 0.0611 | 0.0456 | |
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| 0.0084 | 3.84 | 13700 | 0.0613 | 0.0456 | |
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| 0.0039 | 3.87 | 13800 | 0.0614 | 0.0463 | |
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| 0.0032 | 3.9 | 13900 | 0.0611 | 0.0461 | |
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| 0.0043 | 3.92 | 14000 | 0.0614 | 0.0458 | |
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| 0.0073 | 3.95 | 14100 | 0.0615 | 0.0460 | |
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| 0.0145 | 3.98 | 14200 | 0.0616 | 0.0460 | |
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### Framework versions |
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- Transformers 4.39.0.dev0 |
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- Pytorch 2.2.1 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.1 |
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