---
license: apache-2.0
base_model: jmaczan/wav2vec2-large-xls-r-300m-dysarthria-big-dataset
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-dysarthria-big-dataset
  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. -->

# wav2vec2-large-xls-r-300m-dysarthria-big-dataset

This model is a fine-tuned version of [jmaczan/wav2vec2-large-xls-r-300m-dysarthria-big-dataset](https://huggingface.co/jmaczan/wav2vec2-large-xls-r-300m-dysarthria-big-dataset) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0864
- Wer: 0.182

## 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
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer   |
|:-------------:|:-----:|:----:|:---------------:|:-----:|
| 1.419         | 3.2   | 200  | 0.7599          | 0.668 |
| 0.7759        | 6.4   | 400  | 0.4966          | 0.618 |
| 0.5808        | 9.6   | 600  | 0.3352          | 0.508 |
| 0.3652        | 12.8  | 800  | 0.2214          | 0.386 |
| 0.2347        | 16.0  | 1000 | 0.1566          | 0.246 |
| 0.1738        | 19.2  | 1200 | 0.1340          | 0.23  |
| 0.1076        | 22.4  | 1400 | 0.1244          | 0.242 |
| 0.077         | 25.6  | 1600 | 0.0948          | 0.184 |
| 0.0566        | 28.8  | 1800 | 0.0864          | 0.182 |


### Framework versions

- Transformers 4.43.2
- Pytorch 2.2.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1