---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: xlsr-big-kbnnn
  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. -->

# xlsr-big-kbnnn

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
- Wer: 0.0555

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 132
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 2.1412        | 1.3652  | 200   | 0.9308          | 0.8207 |
| 0.6085        | 2.7304  | 400   | 0.1016          | 0.1634 |
| 0.1952        | 4.0956  | 600   | 0.0382          | 0.0936 |
| 0.0912        | 5.4608  | 800   | 0.0131          | 0.0637 |
| 0.0745        | 6.8259  | 1000  | 0.0106          | 0.0599 |
| 0.0537        | 8.1911  | 1200  | 0.0123          | 0.0605 |
| 0.0503        | 9.5563  | 1400  | 0.0087          | 0.0585 |
| 0.0457        | 10.9215 | 1600  | 0.0088          | 0.0569 |
| 0.0356        | 12.2867 | 1800  | 0.0102          | 0.0579 |
| 0.0328        | 13.6519 | 2000  | 0.0060          | 0.0589 |
| 0.0313        | 15.0171 | 2200  | 0.0032          | 0.0561 |
| 0.0313        | 16.3823 | 2400  | 0.0036          | 0.0581 |
| 0.0256        | 17.7474 | 2600  | 0.0047          | 0.0563 |
| 0.0234        | 19.1126 | 2800  | 0.0034          | 0.0516 |
| 0.0186        | 20.4778 | 3000  | 0.0029          | 0.0541 |
| 0.0205        | 21.8430 | 3200  | 0.0009          | 0.0527 |
| 0.0161        | 23.2082 | 3400  | 0.0045          | 0.0531 |
| 0.019         | 24.5734 | 3600  | 0.0021          | 0.0533 |
| 0.0162        | 25.9386 | 3800  | 0.0014          | 0.0518 |
| 0.0151        | 27.3038 | 4000  | 0.0046          | 0.0573 |
| 0.0162        | 28.6689 | 4200  | 0.0178          | 0.0555 |
| 0.0202        | 30.0341 | 4400  | 0.0040          | 0.0561 |
| 0.0204        | 31.3993 | 4600  | 0.0005          | 0.0529 |
| 0.018         | 32.7645 | 4800  | 0.0010          | 0.0561 |
| 0.0108        | 34.1297 | 5000  | 0.0001          | 0.0559 |
| 0.0124        | 35.4949 | 5200  | 0.0007          | 0.0625 |
| 0.011         | 36.8601 | 5400  | 0.0004          | 0.0523 |
| 0.0119        | 38.2253 | 5600  | 0.0019          | 0.0675 |
| 0.0138        | 39.5904 | 5800  | 0.0003          | 0.0585 |
| 0.0114        | 40.9556 | 6000  | 0.0011          | 0.0533 |
| 0.0094        | 42.3208 | 6200  | 0.0006          | 0.0543 |
| 0.0097        | 43.6860 | 6400  | 0.0002          | 0.0539 |
| 0.0079        | 45.0512 | 6600  | 0.0003          | 0.0523 |
| 0.0107        | 46.4164 | 6800  | 0.0001          | 0.0575 |
| 0.0077        | 47.7816 | 7000  | 0.0016          | 0.0527 |
| 0.008         | 49.1468 | 7200  | 0.0044          | 0.0603 |
| 0.0087        | 50.5119 | 7400  | 0.0001          | 0.0525 |
| 0.008         | 51.8771 | 7600  | 0.0001          | 0.0514 |
| 0.0064        | 53.2423 | 7800  | 0.0002          | 0.0579 |
| 0.0057        | 54.6075 | 8000  | 0.0003          | 0.0525 |
| 0.0061        | 55.9727 | 8200  | 0.0027          | 0.0547 |
| 0.0082        | 57.3379 | 8400  | 0.0004          | 0.0557 |
| 0.0065        | 58.7031 | 8600  | 0.0002          | 0.0555 |
| 0.0074        | 60.0683 | 8800  | 0.0000          | 0.0591 |
| 0.0066        | 61.4334 | 9000  | 0.0000          | 0.0543 |
| 0.0056        | 62.7986 | 9200  | 0.0000          | 0.0531 |
| 0.0043        | 64.1638 | 9400  | 0.0003          | 0.0549 |
| 0.0035        | 65.5290 | 9600  | 0.0000          | 0.0569 |
| 0.0049        | 66.8942 | 9800  | 0.0012          | 0.0603 |
| 0.0045        | 68.2594 | 10000 | 0.0001          | 0.0543 |
| 0.0037        | 69.6246 | 10200 | 0.0000          | 0.0537 |
| 0.0061        | 70.9898 | 10400 | 0.0000          | 0.0571 |
| 0.0041        | 72.3549 | 10600 | 0.0006          | 0.0569 |
| 0.0031        | 73.7201 | 10800 | 0.0000          | 0.0583 |
| 0.0042        | 75.0853 | 11000 | 0.0000          | 0.0561 |
| 0.0031        | 76.4505 | 11200 | 0.0000          | 0.0551 |
| 0.0027        | 77.8157 | 11400 | 0.0000          | 0.0567 |
| 0.0029        | 79.1809 | 11600 | 0.0000          | 0.0537 |
| 0.0026        | 80.5461 | 11800 | 0.0000          | 0.0539 |
| 0.001         | 81.9113 | 12000 | 0.0000          | 0.0529 |
| 0.0044        | 83.2765 | 12200 | 0.0000          | 0.0520 |
| 0.0016        | 84.6416 | 12400 | 0.0000          | 0.0559 |
| 0.0022        | 86.0068 | 12600 | 0.0000          | 0.0553 |
| 0.0018        | 87.3720 | 12800 | 0.0000          | 0.0557 |
| 0.0016        | 88.7372 | 13000 | 0.0000          | 0.0555 |
| 0.0012        | 90.1024 | 13200 | 0.0000          | 0.0561 |
| 0.001         | 91.4676 | 13400 | 0.0000          | 0.0575 |
| 0.0011        | 92.8328 | 13600 | 0.0000          | 0.0563 |
| 0.0008        | 94.1980 | 13800 | 0.0000          | 0.0569 |
| 0.001         | 95.5631 | 14000 | 0.0000          | 0.0561 |
| 0.0007        | 96.9283 | 14200 | 0.0000          | 0.0559 |
| 0.0009        | 98.2935 | 14400 | 0.0000          | 0.0553 |
| 0.0009        | 99.6587 | 14600 | 0.0000          | 0.0555 |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.1.2
- Datasets 2.20.0
- Tokenizers 0.19.1