---
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-hu-colab
  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: 0.5363311494937463
---

<!-- 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-hu-colab

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: 0.5578
- Wer: 0.5363

## 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: 500
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.9484        | 4.97  | 400  | 3.2466          | 1.0    |
| 1.5016        | 9.94  | 800  | 0.6870          | 0.8148 |
| 0.2838        | 14.91 | 1200 | 0.5523          | 0.6087 |
| 0.1566        | 19.88 | 1600 | 0.5592          | 0.5801 |
| 0.105         | 24.84 | 2000 | 0.5685          | 0.5655 |
| 0.0761        | 29.81 | 2400 | 0.5578          | 0.5363 |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1