---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: 5sents_XLS-R_2_e-4
  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. -->

# 5sents_XLS-R_2_e-4

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3629
- Wer: 0.2063

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 400
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 70.1025       | 99.89   | 200  | 23.5652         | 1.0    |
| 17.8988       | 199.89  | 400  | 10.4265         | 1.0    |
| 7.3246        | 299.89  | 600  | 4.0969          | 1.0    |
| 3.5815        | 399.89  | 800  | 2.4899          | 1.0    |
| 1.4553        | 499.89  | 1000 | 1.3636          | 0.7354 |
| 0.3355        | 599.89  | 1200 | 1.4502          | 0.3651 |
| 0.1232        | 699.89  | 1400 | 0.8715          | 0.3280 |
| 0.0615        | 799.89  | 1600 | 0.9018          | 0.3968 |
| 0.0372        | 899.89  | 1800 | 1.7271          | 0.4339 |
| 0.0247        | 999.89  | 2000 | 0.6459          | 0.2751 |
| 0.0166        | 1099.89 | 2200 | 0.4516          | 0.2540 |
| 0.0216        | 1199.89 | 2400 | 0.6955          | 0.2487 |
| 0.0093        | 1299.89 | 2600 | 1.1281          | 0.2646 |
| 0.0084        | 1399.89 | 2800 | 0.6150          | 0.1376 |
| 0.0076        | 1499.89 | 3000 | 1.1476          | 0.2646 |
| 0.0125        | 1599.89 | 3200 | 1.0682          | 0.2487 |
| 0.0096        | 1699.89 | 3400 | 0.8676          | 0.2487 |
| 0.0121        | 1799.89 | 3600 | 2.8241          | 0.2963 |
| 0.0107        | 1899.89 | 3800 | 0.3758          | 0.2381 |
| 0.0107        | 1999.89 | 4000 | 0.8708          | 0.2381 |
| 0.0051        | 2099.89 | 4200 | 0.8423          | 0.2804 |
| 0.0081        | 2199.89 | 4400 | 0.9489          | 0.2698 |
| 0.0044        | 2299.89 | 4600 | 0.8984          | 0.2857 |
| 0.0026        | 2399.89 | 4800 | 0.5836          | 0.2328 |
| 0.0169        | 2499.89 | 5000 | 0.9432          | 0.2434 |
| 0.0055        | 2599.89 | 5200 | 0.4225          | 0.2381 |
| 0.0033        | 2699.89 | 5400 | 1.1866          | 0.1693 |
| 0.0019        | 2799.89 | 5600 | 0.6218          | 0.1746 |
| 0.002         | 2899.89 | 5800 | 0.3831          | 0.1799 |
| 0.0026        | 2999.89 | 6000 | 0.6229          | 0.1323 |


### Framework versions

- Transformers 4.26.1
- Pytorch 2.0.1+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3