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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-br-d10
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. -->
# wav2vec2-large-xls-r-300m-br-d10
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: 1.1382
- Wer: 0.4895
## 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: 0.0004
- 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: 800
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 13.611 | 0.68 | 100 | 5.8492 | 1.0 |
| 3.8176 | 1.35 | 200 | 3.2181 | 1.0 |
| 3.0457 | 2.03 | 300 | 3.0902 | 1.0 |
| 2.2632 | 2.7 | 400 | 1.4882 | 0.9426 |
| 1.1965 | 3.38 | 500 | 1.1396 | 0.7950 |
| 0.984 | 4.05 | 600 | 1.0216 | 0.7583 |
| 0.8036 | 4.73 | 700 | 1.0258 | 0.7202 |
| 0.7061 | 5.41 | 800 | 0.9710 | 0.6820 |
| 0.689 | 6.08 | 900 | 0.9731 | 0.6488 |
| 0.6063 | 6.76 | 1000 | 0.9442 | 0.6569 |
| 0.5215 | 7.43 | 1100 | 1.0221 | 0.6671 |
| 0.4965 | 8.11 | 1200 | 0.9266 | 0.6181 |
| 0.4321 | 8.78 | 1300 | 0.9050 | 0.5991 |
| 0.3762 | 9.46 | 1400 | 0.9801 | 0.6134 |
| 0.3747 | 10.14 | 1500 | 0.9210 | 0.5747 |
| 0.3554 | 10.81 | 1600 | 0.9720 | 0.6051 |
| 0.3148 | 11.49 | 1700 | 0.9672 | 0.6099 |
| 0.3176 | 12.16 | 1800 | 1.0120 | 0.5966 |
| 0.2915 | 12.84 | 1900 | 0.9490 | 0.5653 |
| 0.2696 | 13.51 | 2000 | 0.9394 | 0.5819 |
| 0.2569 | 14.19 | 2100 | 1.0197 | 0.5667 |
| 0.2395 | 14.86 | 2200 | 0.9771 | 0.5608 |
| 0.2367 | 15.54 | 2300 | 1.0516 | 0.5678 |
| 0.2153 | 16.22 | 2400 | 1.0097 | 0.5679 |
| 0.2092 | 16.89 | 2500 | 1.0143 | 0.5430 |
| 0.2046 | 17.57 | 2600 | 1.0884 | 0.5631 |
| 0.1937 | 18.24 | 2700 | 1.0113 | 0.5648 |
| 0.1752 | 18.92 | 2800 | 1.0056 | 0.5470 |
| 0.164 | 19.59 | 2900 | 1.0340 | 0.5508 |
| 0.1723 | 20.27 | 3000 | 1.0743 | 0.5615 |
| 0.1535 | 20.95 | 3100 | 1.0495 | 0.5465 |
| 0.1432 | 21.62 | 3200 | 1.0390 | 0.5333 |
| 0.1561 | 22.3 | 3300 | 1.0798 | 0.5590 |
| 0.1384 | 22.97 | 3400 | 1.1716 | 0.5449 |
| 0.1359 | 23.65 | 3500 | 1.1154 | 0.5420 |
| 0.1356 | 24.32 | 3600 | 1.0883 | 0.5387 |
| 0.1355 | 25.0 | 3700 | 1.1114 | 0.5504 |
| 0.1158 | 25.68 | 3800 | 1.1171 | 0.5388 |
| 0.1166 | 26.35 | 3900 | 1.1335 | 0.5403 |
| 0.1165 | 27.03 | 4000 | 1.1374 | 0.5248 |
| 0.1064 | 27.7 | 4100 | 1.0336 | 0.5298 |
| 0.0987 | 28.38 | 4200 | 1.0407 | 0.5216 |
| 0.104 | 29.05 | 4300 | 1.1012 | 0.5350 |
| 0.0894 | 29.73 | 4400 | 1.1016 | 0.5310 |
| 0.0912 | 30.41 | 4500 | 1.1383 | 0.5302 |
| 0.0972 | 31.08 | 4600 | 1.0851 | 0.5214 |
| 0.0832 | 31.76 | 4700 | 1.1705 | 0.5311 |
| 0.0859 | 32.43 | 4800 | 1.0750 | 0.5192 |
| 0.0811 | 33.11 | 4900 | 1.0900 | 0.5180 |
| 0.0825 | 33.78 | 5000 | 1.1271 | 0.5196 |
| 0.07 | 34.46 | 5100 | 1.1289 | 0.5141 |
| 0.0689 | 35.14 | 5200 | 1.0960 | 0.5101 |
| 0.068 | 35.81 | 5300 | 1.1377 | 0.5050 |
| 0.0776 | 36.49 | 5400 | 1.0880 | 0.5194 |
| 0.0642 | 37.16 | 5500 | 1.1027 | 0.5076 |
| 0.0607 | 37.84 | 5600 | 1.1293 | 0.5119 |
| 0.0607 | 38.51 | 5700 | 1.1229 | 0.5103 |
| 0.0545 | 39.19 | 5800 | 1.1168 | 0.5103 |
| 0.0562 | 39.86 | 5900 | 1.1206 | 0.5073 |
| 0.0484 | 40.54 | 6000 | 1.1710 | 0.5019 |
| 0.0499 | 41.22 | 6100 | 1.1511 | 0.5100 |
| 0.0455 | 41.89 | 6200 | 1.1488 | 0.5009 |
| 0.0475 | 42.57 | 6300 | 1.1196 | 0.4944 |
| 0.0413 | 43.24 | 6400 | 1.1654 | 0.4996 |
| 0.0389 | 43.92 | 6500 | 1.0961 | 0.4930 |
| 0.0428 | 44.59 | 6600 | 1.0955 | 0.4938 |
| 0.039 | 45.27 | 6700 | 1.1323 | 0.4955 |
| 0.0352 | 45.95 | 6800 | 1.1040 | 0.4930 |
| 0.0334 | 46.62 | 6900 | 1.1382 | 0.4942 |
| 0.0338 | 47.3 | 7000 | 1.1264 | 0.4911 |
| 0.0307 | 47.97 | 7100 | 1.1216 | 0.4881 |
| 0.0286 | 48.65 | 7200 | 1.1459 | 0.4894 |
| 0.0348 | 49.32 | 7300 | 1.1419 | 0.4906 |
| 0.0329 | 50.0 | 7400 | 1.1382 | 0.4895 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
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