---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-gn-pt
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_13_0
      type: common_voice_13_0
      config: gn
      split: test
      args: gn
    metrics:
    - name: Wer
      type: wer
      value: 0.5431804645622395
---

<!-- 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-gn-pt

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_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6822
- Wer: 0.5432

## 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: 35
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.1972        | 0.79  | 400   | 1.9288          | 1.0045 |
| 0.9928        | 1.58  | 800   | 0.8247          | 0.9452 |
| 0.6075        | 2.36  | 1200  | 0.7675          | 0.8451 |
| 0.4724        | 3.15  | 1600  | 0.5485          | 0.7111 |
| 0.3879        | 3.94  | 2000  | 0.5885          | 0.7433 |
| 0.3152        | 4.73  | 2400  | 0.7606          | 0.7695 |
| 0.2872        | 5.52  | 2800  | 0.5723          | 0.6608 |
| 0.258         | 6.31  | 3200  | 0.5971          | 0.6820 |
| 0.2317        | 7.09  | 3600  | 0.5845          | 0.6471 |
| 0.2137        | 7.88  | 4000  | 0.7690          | 0.7198 |
| 0.193         | 8.67  | 4400  | 0.6219          | 0.6614 |
| 0.1795        | 9.46  | 4800  | 0.6203          | 0.6703 |
| 0.1768        | 10.25 | 5200  | 0.5645          | 0.6164 |
| 0.1637        | 11.03 | 5600  | 0.5804          | 0.6412 |
| 0.1573        | 11.82 | 6000  | 0.5914          | 0.5896 |
| 0.1467        | 12.61 | 6400  | 0.6517          | 0.6200 |
| 0.141         | 13.4  | 6800  | 0.6376          | 0.6310 |
| 0.135         | 14.19 | 7200  | 0.6343          | 0.6042 |
| 0.1279        | 14.98 | 7600  | 0.6680          | 0.6325 |
| 0.1222        | 15.76 | 8000  | 0.7109          | 0.6617 |
| 0.1169        | 16.55 | 8400  | 0.7067          | 0.6361 |
| 0.114         | 17.34 | 8800  | 0.7143          | 0.6144 |
| 0.1085        | 18.13 | 9200  | 0.6871          | 0.6081 |
| 0.0996        | 18.92 | 9600  | 0.8332          | 0.6569 |
| 0.0952        | 19.7  | 10000 | 0.7076          | 0.5992 |
| 0.0929        | 20.49 | 10400 | 0.6946          | 0.6078 |
| 0.0871        | 21.28 | 10800 | 0.6197          | 0.5822 |
| 0.0823        | 22.07 | 11200 | 0.6969          | 0.5876 |
| 0.0776        | 22.86 | 11600 | 0.6285          | 0.5619 |
| 0.0758        | 23.65 | 12000 | 0.7098          | 0.6010 |
| 0.0728        | 24.43 | 12400 | 0.6618          | 0.5905 |
| 0.0664        | 25.22 | 12800 | 0.6484          | 0.5536 |
| 0.0656        | 26.01 | 13200 | 0.6417          | 0.5593 |
| 0.0603        | 26.8  | 13600 | 0.7287          | 0.5813 |
| 0.0571        | 27.59 | 14000 | 0.6727          | 0.5700 |
| 0.0559        | 28.37 | 14400 | 0.6775          | 0.5631 |
| 0.0555        | 29.16 | 14800 | 0.7849          | 0.5968 |
| 0.0506        | 29.95 | 15200 | 0.8266          | 0.6185 |
| 0.0485        | 30.74 | 15600 | 0.7347          | 0.5747 |
| 0.0461        | 31.53 | 16000 | 0.6836          | 0.5432 |
| 0.0423        | 32.32 | 16400 | 0.6913          | 0.5396 |
| 0.0407        | 33.1  | 16800 | 0.6655          | 0.5328 |
| 0.04          | 33.89 | 17200 | 0.6873          | 0.5399 |
| 0.0396        | 34.68 | 17600 | 0.6822          | 0.5432 |


### Framework versions

- Transformers 4.35.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1