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hindi_wav2vec2_optimized
This model is a fine-tuned version of Harveenchadha/vakyansh-wav2vec2-hindi-him-4200 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4854
- Wer: 0.3632
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 2.1601 | 0.11 | 25 | 1.5548 | 0.4892 |
| 2.8628 | 0.22 | 50 | 1.4072 | 0.6004 |
| 1.5585 | 0.33 | 75 | 1.3488 | 0.5654 |
| 2.8588 | 0.44 | 100 | 1.5733 | 0.5500 |
| 1.3384 | 0.54 | 125 | 1.2629 | 0.5381 |
| 1.7817 | 0.65 | 150 | 1.4159 | 0.5626 |
| 1.124 | 0.76 | 175 | 1.1186 | 0.5003 |
| 1.502 | 0.87 | 200 | 1.3352 | 0.5738 |
| 1.0237 | 0.98 | 225 | 1.2497 | 0.6165 |
| 0.9385 | 1.09 | 250 | 0.9858 | 0.5073 |
| 1.5313 | 1.2 | 275 | 1.1366 | 0.5619 |
| 0.9124 | 1.31 | 300 | 0.9704 | 0.4787 |
| 0.649 | 1.42 | 325 | 1.1915 | 0.5458 |
| 0.838 | 1.53 | 350 | 1.5229 | 0.5836 |
| 0.6835 | 1.63 | 375 | 1.0692 | 0.5052 |
| 0.823 | 1.74 | 400 | 0.9683 | 0.4640 |
| 0.968 | 1.85 | 425 | 0.9629 | 0.4836 |
| 0.8596 | 1.96 | 450 | 0.8242 | 0.4682 |
| 0.6729 | 2.07 | 475 | 0.7999 | 0.4346 |
| 0.6426 | 2.18 | 500 | 0.9678 | 0.4885 |
| 0.7213 | 2.29 | 525 | 1.1779 | 0.5353 |
| 0.4131 | 2.4 | 550 | 0.7007 | 0.4738 |
| 0.4188 | 2.51 | 575 | 0.6085 | 0.4199 |
| 0.3784 | 2.61 | 600 | 0.6526 | 0.4542 |
| 0.4181 | 2.72 | 625 | 0.6716 | 0.4157 |
| 0.3194 | 2.83 | 650 | 0.6058 | 0.4185 |
| 0.3553 | 2.94 | 675 | 0.6023 | 0.4276 |
| 0.3242 | 3.05 | 700 | 0.5864 | 0.4178 |
| 0.2655 | 3.16 | 725 | 0.5705 | 0.3989 |
| 0.3149 | 3.27 | 750 | 0.5275 | 0.3961 |
| 0.2454 | 3.38 | 775 | 0.5401 | 0.3968 |
| 0.2792 | 3.49 | 800 | 0.5303 | 0.3919 |
| 0.2488 | 3.59 | 825 | 0.5459 | 0.4010 |
| 0.3013 | 3.7 | 850 | 0.5180 | 0.3856 |
| 0.2306 | 3.81 | 875 | 0.5179 | 0.3814 |
| 0.2578 | 3.92 | 900 | 0.5168 | 0.3765 |
| 0.2344 | 4.03 | 925 | 0.5212 | 0.3737 |
| 0.2002 | 4.14 | 950 | 0.5026 | 0.3674 |
| 0.2443 | 4.25 | 975 | 0.5045 | 0.3716 |
| 0.1857 | 4.36 | 1000 | 0.5083 | 0.3751 |
| 0.2158 | 4.47 | 1025 | 0.4941 | 0.3681 |
| 0.1922 | 4.58 | 1050 | 0.4996 | 0.3723 |
| 0.217 | 4.68 | 1075 | 0.4943 | 0.3695 |
| 0.1888 | 4.79 | 1100 | 0.4849 | 0.3653 |
| 0.1971 | 4.9 | 1125 | 0.4854 | 0.3632 |
Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
