---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-cv_vi
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_11_0
      type: common_voice_11_0
      config: vi
      split: test
      args: vi
    metrics:
    - name: Wer
      type: wer
      value: 0.663156740155753
---

<!-- 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-cv_vi

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_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3858
- Wer: 0.6632

## 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: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 500

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 14.1667       | 9.2    | 200   | 4.5633          | 1.0    |
| 3.6334        | 18.39  | 400   | 3.4332          | 1.0    |
| 1.938         | 27.59  | 600   | 1.2434          | 0.7082 |
| 0.3082        | 36.78  | 800   | 1.2288          | 0.6534 |
| 0.1766        | 45.98  | 1000  | 1.2915          | 0.6500 |
| 0.1287        | 55.17  | 1200  | 1.3452          | 0.6269 |
| 0.1043        | 64.37  | 1400  | 1.4746          | 0.6395 |
| 0.0834        | 73.56  | 1600  | 1.4731          | 0.6347 |
| 0.0837        | 82.76  | 1800  | 1.5893          | 0.6493 |
| 0.0711        | 91.95  | 2000  | 1.6205          | 0.6522 |
| 0.0672        | 101.15 | 2200  | 1.5513          | 0.6503 |
| 0.0745        | 110.34 | 2400  | 1.6509          | 0.6774 |
| 0.07          | 119.54 | 2600  | 1.6779          | 0.6543 |
| 0.0492        | 128.74 | 2800  | 1.7616          | 0.6611 |
| 0.0473        | 137.93 | 3000  | 1.7885          | 0.6634 |
| 0.0535        | 147.13 | 3200  | 1.8877          | 0.6806 |
| 0.0468        | 156.32 | 3400  | 1.7766          | 0.6671 |
| 0.0386        | 165.52 | 3600  | 1.7956          | 0.6494 |
| 0.0418        | 174.71 | 3800  | 1.9402          | 0.6851 |
| 0.0426        | 183.91 | 4000  | 1.9777          | 0.6927 |
| 0.0395        | 193.1  | 4200  | 1.8733          | 0.6689 |
| 0.0392        | 202.3  | 4400  | 1.8994          | 0.6774 |
| 0.0377        | 211.49 | 4600  | 1.9983          | 0.6889 |
| 0.0354        | 220.69 | 4800  | 1.8858          | 0.6645 |
| 0.0315        | 229.89 | 5000  | 1.9716          | 0.6805 |
| 0.0312        | 239.08 | 5200  | 2.0422          | 0.6825 |
| 0.0292        | 248.28 | 5400  | 2.0780          | 0.7019 |
| 0.0283        | 257.47 | 5600  | 1.9102          | 0.6743 |
| 0.025         | 266.67 | 5800  | 1.9745          | 0.6756 |
| 0.0246        | 275.86 | 6000  | 2.1289          | 0.6918 |
| 0.0234        | 285.06 | 6200  | 2.1775          | 0.7068 |
| 0.0219        | 294.25 | 6400  | 2.1755          | 0.6935 |
| 0.0182        | 303.45 | 6600  | 2.1602          | 0.6764 |
| 0.0174        | 312.64 | 6800  | 2.1359          | 0.6596 |
| 0.0157        | 321.84 | 7000  | 2.1958          | 0.6797 |
| 0.0147        | 331.03 | 7200  | 2.1460          | 0.6657 |
| 0.0135        | 340.23 | 7400  | 2.2716          | 0.6719 |
| 0.0124        | 349.43 | 7600  | 2.3556          | 0.6762 |
| 0.0109        | 358.62 | 7800  | 2.2520          | 0.6632 |
| 0.0115        | 367.82 | 8000  | 2.3112          | 0.6802 |
| 0.0108        | 377.01 | 8200  | 2.2925          | 0.6659 |
| 0.0106        | 386.21 | 8400  | 2.2950          | 0.6726 |
| 0.0088        | 395.4  | 8600  | 2.3078          | 0.6735 |
| 0.0084        | 404.6  | 8800  | 2.3538          | 0.6723 |
| 0.0079        | 413.79 | 9000  | 2.3212          | 0.6615 |
| 0.0074        | 422.99 | 9200  | 2.3908          | 0.6774 |
| 0.0094        | 432.18 | 9400  | 2.3164          | 0.6779 |
| 0.0077        | 441.38 | 9600  | 2.3105          | 0.6649 |
| 0.0066        | 450.57 | 9800  | 2.3599          | 0.6742 |
| 0.007         | 459.77 | 10000 | 2.3675          | 0.6709 |
| 0.0056        | 468.97 | 10200 | 2.3964          | 0.6677 |
| 0.0049        | 478.16 | 10400 | 2.3858          | 0.6632 |


### Framework versions

- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3