---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-1b
tags:
- automatic-speech-recognition
- BembaSpeech
- generated_from_trainer
metrics:
- wer
model-index:
- name: xls-r-1b-bem-sv-male
  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. -->

# xls-r-1b-bem-sv-male

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the BEMBASPEECH - BEM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1473
- Wer: 0.6229

## 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.9808        | 0.5206 | 500  | 0.2580          | 0.8362 |
| 0.3877        | 1.0411 | 1000 | 0.2041          | 0.7790 |
| 0.298         | 1.5617 | 1500 | 0.1843          | 0.7619 |
| 0.2433        | 2.0822 | 2000 | 0.1621          | 0.7010 |
| 0.188         | 2.6028 | 2500 | 0.1653          | 0.6838 |
| 0.1521        | 3.1234 | 3000 | 0.1491          | 0.6590 |
| 0.1316        | 3.6439 | 3500 | 0.1473          | 0.6229 |
| 0.0925        | 4.1645 | 4000 | 0.1552          | 0.6448 |
| 0.0767        | 4.6851 | 4500 | 0.1523          | 0.6229 |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1