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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: ''
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8128
- Wer: 0.8392
- Cer: 0.2063
## 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: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- num_epochs: 100.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 69.215 | 0.74 | 500 | 74.9751 | 1.0 | 1.0 |
| 8.2109 | 1.48 | 1000 | 7.0617 | 1.0 | 1.0 |
| 6.4277 | 2.22 | 1500 | 6.3811 | 1.0 | 1.0 |
| 6.3513 | 2.95 | 2000 | 6.3061 | 1.0 | 1.0 |
| 6.2522 | 3.69 | 2500 | 6.2147 | 1.0 | 1.0 |
| 5.9757 | 4.43 | 3000 | 5.7906 | 1.1004 | 0.9924 |
| 5.0642 | 5.17 | 3500 | 4.2984 | 1.7729 | 0.8214 |
| 4.6346 | 5.91 | 4000 | 3.7129 | 1.8946 | 0.7728 |
| 4.267 | 6.65 | 4500 | 3.2177 | 1.7526 | 0.6922 |
| 3.9964 | 7.39 | 5000 | 2.8337 | 1.8055 | 0.6546 |
| 3.8035 | 8.12 | 5500 | 2.5726 | 2.1851 | 0.6992 |
| 3.6273 | 8.86 | 6000 | 2.3391 | 2.1029 | 0.6511 |
| 3.5248 | 9.6 | 6500 | 2.1944 | 2.3617 | 0.6859 |
| 3.3683 | 10.34 | 7000 | 1.9827 | 2.1014 | 0.6063 |
| 3.2411 | 11.08 | 7500 | 1.8610 | 1.6160 | 0.5135 |
| 3.1299 | 11.82 | 8000 | 1.7446 | 1.5948 | 0.4946 |
| 3.0574 | 12.56 | 8500 | 1.6454 | 1.1291 | 0.4051 |
| 2.985 | 13.29 | 9000 | 1.5919 | 1.0673 | 0.3893 |
| 2.9573 | 14.03 | 9500 | 1.4903 | 1.0604 | 0.3766 |
| 2.8897 | 14.77 | 10000 | 1.4614 | 1.0059 | 0.3653 |
| 2.8169 | 15.51 | 10500 | 1.3997 | 1.0030 | 0.3550 |
| 2.8155 | 16.25 | 11000 | 1.3444 | 0.9980 | 0.3441 |
| 2.7595 | 16.99 | 11500 | 1.2911 | 0.9703 | 0.3325 |
| 2.7107 | 17.72 | 12000 | 1.2462 | 0.9565 | 0.3227 |
| 2.6358 | 18.46 | 12500 | 1.2466 | 0.9955 | 0.3333 |
| 2.5801 | 19.2 | 13000 | 1.2059 | 1.0010 | 0.3226 |
| 2.5554 | 19.94 | 13500 | 1.1919 | 1.0094 | 0.3223 |
| 2.5314 | 20.68 | 14000 | 1.1703 | 0.9847 | 0.3156 |
| 2.509 | 21.42 | 14500 | 1.1733 | 0.9896 | 0.3177 |
| 2.4391 | 22.16 | 15000 | 1.1811 | 0.9723 | 0.3164 |
| 2.4631 | 22.89 | 15500 | 1.1382 | 0.9698 | 0.3059 |
| 2.4414 | 23.63 | 16000 | 1.0893 | 0.9644 | 0.2972 |
| 2.3771 | 24.37 | 16500 | 1.0930 | 0.9505 | 0.2954 |
| 2.3658 | 25.11 | 17000 | 1.0756 | 0.9609 | 0.2926 |
| 2.3215 | 25.85 | 17500 | 1.0512 | 0.9614 | 0.2890 |
| 2.3327 | 26.59 | 18000 | 1.0627 | 1.1984 | 0.3282 |
| 2.3055 | 27.33 | 18500 | 1.0582 | 0.9520 | 0.2841 |
| 2.299 | 28.06 | 19000 | 1.0356 | 0.9480 | 0.2817 |
| 2.2673 | 28.8 | 19500 | 1.0305 | 0.9367 | 0.2771 |
| 2.2166 | 29.54 | 20000 | 1.0139 | 0.9223 | 0.2702 |
| 2.2378 | 30.28 | 20500 | 1.0095 | 0.9268 | 0.2722 |
| 2.2168 | 31.02 | 21000 | 1.0001 | 0.9085 | 0.2691 |
| 2.1766 | 31.76 | 21500 | 0.9884 | 0.9050 | 0.2640 |
| 2.1715 | 32.5 | 22000 | 0.9730 | 0.9505 | 0.2719 |
| 2.1104 | 33.23 | 22500 | 0.9752 | 0.9362 | 0.2656 |
| 2.1158 | 33.97 | 23000 | 0.9720 | 0.9263 | 0.2624 |
| 2.0718 | 34.71 | 23500 | 0.9573 | 1.0005 | 0.2759 |
| 2.0824 | 35.45 | 24000 | 0.9609 | 0.9525 | 0.2643 |
| 2.0591 | 36.19 | 24500 | 0.9662 | 0.9570 | 0.2667 |
| 2.0768 | 36.93 | 25000 | 0.9528 | 0.9574 | 0.2646 |
| 2.0893 | 37.67 | 25500 | 0.9810 | 0.9169 | 0.2612 |
| 2.0282 | 38.4 | 26000 | 0.9556 | 0.8877 | 0.2528 |
| 1.997 | 39.14 | 26500 | 0.9523 | 0.8723 | 0.2501 |
| 2.0209 | 39.88 | 27000 | 0.9542 | 0.8773 | 0.2503 |
| 1.987 | 40.62 | 27500 | 0.9427 | 0.8867 | 0.2500 |
| 1.9663 | 41.36 | 28000 | 0.9546 | 0.9065 | 0.2546 |
| 1.9945 | 42.1 | 28500 | 0.9431 | 0.9119 | 0.2536 |
| 1.9604 | 42.84 | 29000 | 0.9367 | 0.9030 | 0.2490 |
| 1.933 | 43.57 | 29500 | 0.9071 | 0.8916 | 0.2432 |
| 1.9227 | 44.31 | 30000 | 0.9048 | 0.8882 | 0.2428 |
| 1.8784 | 45.05 | 30500 | 0.9106 | 0.8991 | 0.2437 |
| 1.8844 | 45.79 | 31000 | 0.8996 | 0.8758 | 0.2379 |
| 1.8776 | 46.53 | 31500 | 0.9028 | 0.8798 | 0.2395 |
| 1.8372 | 47.27 | 32000 | 0.9047 | 0.8778 | 0.2379 |
| 1.832 | 48.01 | 32500 | 0.9016 | 0.8941 | 0.2393 |
| 1.8154 | 48.74 | 33000 | 0.8915 | 0.8916 | 0.2372 |
| 1.8072 | 49.48 | 33500 | 0.8781 | 0.8872 | 0.2365 |
| 1.7489 | 50.22 | 34000 | 0.8738 | 0.8956 | 0.2340 |
| 1.7928 | 50.96 | 34500 | 0.8684 | 0.8872 | 0.2323 |
| 1.7748 | 51.7 | 35000 | 0.8723 | 0.8718 | 0.2321 |
| 1.7355 | 52.44 | 35500 | 0.8760 | 0.8842 | 0.2331 |
| 1.7167 | 53.18 | 36000 | 0.8746 | 0.8817 | 0.2324 |
| 1.7479 | 53.91 | 36500 | 0.8762 | 0.8753 | 0.2281 |
| 1.7428 | 54.65 | 37000 | 0.8733 | 0.8699 | 0.2277 |
| 1.7058 | 55.39 | 37500 | 0.8816 | 0.8649 | 0.2263 |
| 1.7045 | 56.13 | 38000 | 0.8733 | 0.8689 | 0.2297 |
| 1.709 | 56.87 | 38500 | 0.8648 | 0.8654 | 0.2232 |
| 1.6799 | 57.61 | 39000 | 0.8717 | 0.8580 | 0.2244 |
| 1.664 | 58.35 | 39500 | 0.8653 | 0.8723 | 0.2259 |
| 1.6488 | 59.08 | 40000 | 0.8637 | 0.8803 | 0.2271 |
| 1.6298 | 59.82 | 40500 | 0.8553 | 0.8768 | 0.2253 |
| 1.6185 | 60.56 | 41000 | 0.8512 | 0.8718 | 0.2240 |
| 1.574 | 61.3 | 41500 | 0.8579 | 0.8773 | 0.2251 |
| 1.6192 | 62.04 | 42000 | 0.8499 | 0.8743 | 0.2242 |
| 1.6275 | 62.78 | 42500 | 0.8419 | 0.8758 | 0.2216 |
| 1.5697 | 63.52 | 43000 | 0.8446 | 0.8699 | 0.2222 |
| 1.5384 | 64.25 | 43500 | 0.8462 | 0.8580 | 0.2200 |
| 1.5115 | 64.99 | 44000 | 0.8467 | 0.8674 | 0.2214 |
| 1.5547 | 65.73 | 44500 | 0.8505 | 0.8669 | 0.2204 |
| 1.5597 | 66.47 | 45000 | 0.8421 | 0.8684 | 0.2192 |
| 1.505 | 67.21 | 45500 | 0.8485 | 0.8619 | 0.2187 |
| 1.5101 | 67.95 | 46000 | 0.8489 | 0.8649 | 0.2204 |
| 1.5199 | 68.69 | 46500 | 0.8407 | 0.8619 | 0.2180 |
| 1.5207 | 69.42 | 47000 | 0.8379 | 0.8496 | 0.2163 |
| 1.478 | 70.16 | 47500 | 0.8357 | 0.8595 | 0.2163 |
| 1.4817 | 70.9 | 48000 | 0.8346 | 0.8496 | 0.2151 |
| 1.4827 | 71.64 | 48500 | 0.8362 | 0.8624 | 0.2169 |
| 1.4513 | 72.38 | 49000 | 0.8355 | 0.8451 | 0.2137 |
| 1.4988 | 73.12 | 49500 | 0.8325 | 0.8624 | 0.2161 |
| 1.4267 | 73.85 | 50000 | 0.8396 | 0.8481 | 0.2157 |
| 1.4421 | 74.59 | 50500 | 0.8355 | 0.8491 | 0.2122 |
| 1.4311 | 75.33 | 51000 | 0.8358 | 0.8476 | 0.2118 |
| 1.4174 | 76.07 | 51500 | 0.8289 | 0.8451 | 0.2101 |
| 1.4349 | 76.81 | 52000 | 0.8372 | 0.8580 | 0.2140 |
| 1.3959 | 77.55 | 52500 | 0.8325 | 0.8436 | 0.2116 |
| 1.4087 | 78.29 | 53000 | 0.8351 | 0.8446 | 0.2105 |
| 1.415 | 79.03 | 53500 | 0.8363 | 0.8476 | 0.2123 |
| 1.4122 | 79.76 | 54000 | 0.8310 | 0.8481 | 0.2112 |
| 1.3969 | 80.5 | 54500 | 0.8239 | 0.8446 | 0.2095 |
| 1.361 | 81.24 | 55000 | 0.8282 | 0.8427 | 0.2091 |
| 1.3611 | 81.98 | 55500 | 0.8282 | 0.8407 | 0.2092 |
| 1.3677 | 82.72 | 56000 | 0.8235 | 0.8436 | 0.2084 |
| 1.3361 | 83.46 | 56500 | 0.8231 | 0.8377 | 0.2069 |
| 1.3779 | 84.19 | 57000 | 0.8206 | 0.8436 | 0.2070 |
| 1.3727 | 84.93 | 57500 | 0.8204 | 0.8392 | 0.2065 |
| 1.3317 | 85.67 | 58000 | 0.8207 | 0.8436 | 0.2065 |
| 1.3332 | 86.41 | 58500 | 0.8186 | 0.8357 | 0.2055 |
| 1.3299 | 87.15 | 59000 | 0.8193 | 0.8417 | 0.2075 |
| 1.3129 | 87.89 | 59500 | 0.8183 | 0.8431 | 0.2065 |
| 1.3352 | 88.63 | 60000 | 0.8151 | 0.8471 | 0.2062 |
| 1.3026 | 89.36 | 60500 | 0.8125 | 0.8486 | 0.2067 |
| 1.3468 | 90.1 | 61000 | 0.8124 | 0.8407 | 0.2058 |
| 1.3028 | 90.84 | 61500 | 0.8122 | 0.8461 | 0.2051 |
| 1.2884 | 91.58 | 62000 | 0.8086 | 0.8427 | 0.2048 |
| 1.3005 | 92.32 | 62500 | 0.8110 | 0.8387 | 0.2055 |
| 1.2996 | 93.06 | 63000 | 0.8126 | 0.8328 | 0.2057 |
| 1.2707 | 93.8 | 63500 | 0.8098 | 0.8402 | 0.2047 |
| 1.3026 | 94.53 | 64000 | 0.8097 | 0.8402 | 0.2050 |
| 1.2546 | 95.27 | 64500 | 0.8111 | 0.8402 | 0.2055 |
| 1.2426 | 96.01 | 65000 | 0.8088 | 0.8372 | 0.2059 |
| 1.2869 | 96.75 | 65500 | 0.8093 | 0.8397 | 0.2048 |
| 1.2782 | 97.49 | 66000 | 0.8099 | 0.8412 | 0.2049 |
| 1.2457 | 98.23 | 66500 | 0.8134 | 0.8412 | 0.2062 |
| 1.2967 | 98.97 | 67000 | 0.8115 | 0.8382 | 0.2055 |
| 1.2817 | 99.7 | 67500 | 0.8128 | 0.8392 | 0.2063 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3.dev0
- Tokenizers 0.11.0
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