---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice
metrics:
- wer
model-index:
- name: Model_G_Wav2Vec2_Version3
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice
      type: common_voice
      config: id
      split: test
      args: id
    metrics:
    - name: Wer
      type: wer
      value: 0.3604325936975573
---

<!-- 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. -->

# Model_G_Wav2Vec2_Version3

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: 0.4581
- Wer: 0.3604
- Cer: 0.0922

## 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    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 3.8299        | 5.97  | 400  | 0.7799          | 0.6921 | 0.1952 |
| 0.3573        | 11.94 | 800  | 0.4949          | 0.4799 | 0.1235 |
| 0.1567        | 17.91 | 1200 | 0.4528          | 0.4228 | 0.1092 |
| 0.1011        | 23.88 | 1600 | 0.4695          | 0.3877 | 0.0993 |
| 0.0707        | 29.85 | 2000 | 0.4581          | 0.3604 | 0.0922 |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 1.18.3
- Tokenizers 0.13.3