---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: XLRS_FullDataset
  results: []
datasets:
- timit-asr/timit_asr
language:
- en
base_model:
- facebook/wav2vec2-base
pipeline_tag: automatic-speech-recognition
metrics:
- wer
library_name: transformers
---

<!-- 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. -->

# XLRS_FullDataset

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3057
- Wer: 0.2697

## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.5696        | 1.0   | 500   | 3.1546          | 1.0    |
| 2.491         | 2.01  | 1000  | 0.8309          | 0.7872 |
| 0.7519        | 3.01  | 1500  | 0.3648          | 0.4364 |
| 0.4704        | 4.02  | 2000  | 0.2998          | 0.3758 |
| 0.3385        | 5.02  | 2500  | 0.2639          | 0.3439 |
| 0.2837        | 6.02  | 3000  | 0.2604          | 0.3309 |
| 0.2233        | 7.03  | 3500  | 0.2734          | 0.3143 |
| 0.1997        | 8.03  | 4000  | 0.2676          | 0.3121 |
| 0.1717        | 9.04  | 4500  | 0.2489          | 0.2941 |
| 0.1558        | 10.04 | 5000  | 0.2777          | 0.2969 |
| 0.1497        | 11.04 | 5500  | 0.2693          | 0.2890 |
| 0.1326        | 12.05 | 6000  | 0.2844          | 0.2921 |
| 0.118         | 13.05 | 6500  | 0.2818          | 0.2969 |
| 0.119         | 14.06 | 7000  | 0.2798          | 0.2854 |
| 0.0991        | 15.06 | 7500  | 0.2765          | 0.2858 |
| 0.108         | 16.06 | 8000  | 0.2904          | 0.2794 |
| 0.0935        | 17.07 | 8500  | 0.2846          | 0.2773 |
| 0.0857        | 18.07 | 9000  | 0.3120          | 0.2812 |
| 0.0928        | 19.08 | 9500  | 0.3073          | 0.2820 |
| 0.0832        | 20.08 | 10000 | 0.2981          | 0.2808 |
| 0.0768        | 21.08 | 10500 | 0.3065          | 0.2807 |
| 0.0768        | 22.09 | 11000 | 0.2960          | 0.2766 |
| 0.0754        | 23.09 | 11500 | 0.3007          | 0.2783 |
| 0.063         | 24.1  | 12000 | 0.2918          | 0.2739 |
| 0.0614        | 25.1  | 12500 | 0.3144          | 0.2748 |
| 0.0628        | 26.1  | 13000 | 0.3074          | 0.2713 |
| 0.0595        | 27.11 | 13500 | 0.3103          | 0.2695 |
| 0.0616        | 28.11 | 14000 | 0.3108          | 0.2697 |
| 0.0587        | 29.12 | 14500 | 0.3057          | 0.2697 |


### Framework versions

- Transformers 4.17.0
- Pytorch 2.5.1+cu121
- Datasets 1.18.3
- Tokenizers 0.20.3