---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-1b
tags:
- automatic-speech-recognition
- genbed
- generated_from_trainer
metrics:
- wer
model-index:
- name: xls-r-1b-bem-genbed-f-model
  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-genbed-f-model

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the GENBED - BEM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3137
- Wer: 0.5529

## 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: 4
- eval_batch_size: 8
- 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: 30.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| No log        | 0.2740 | 100  | 3.0378          | 1.0    |
| No log        | 0.5479 | 200  | 0.8302          | 0.9818 |
| No log        | 0.8219 | 300  | 0.6783          | 0.9103 |
| No log        | 1.0959 | 400  | 0.5512          | 0.8721 |
| 1.8782        | 1.3699 | 500  | 0.5296          | 0.8568 |
| 1.8782        | 1.6438 | 600  | 0.4413          | 0.7333 |
| 1.8782        | 1.9178 | 700  | 0.4747          | 0.7614 |
| 1.8782        | 2.1918 | 800  | 0.3884          | 0.6667 |
| 1.8782        | 2.4658 | 900  | 0.3577          | 0.6355 |
| 0.5114        | 2.7397 | 1000 | 0.3585          | 0.6321 |
| 0.5114        | 3.0137 | 1100 | 0.3641          | 0.6607 |
| 0.5114        | 3.2877 | 1200 | 0.3813          | 0.7282 |
| 0.5114        | 3.5616 | 1300 | 0.3829          | 0.7086 |
| 0.5114        | 3.8356 | 1400 | 0.3682          | 0.6413 |
| 0.3931        | 4.1096 | 1500 | 0.3527          | 0.6221 |
| 0.3931        | 4.3836 | 1600 | 0.3481          | 0.6297 |
| 0.3931        | 4.6575 | 1700 | 0.3541          | 0.6193 |
| 0.3931        | 4.9315 | 1800 | 0.3355          | 0.6242 |
| 0.3931        | 5.2055 | 1900 | 0.3339          | 0.5801 |
| 0.3293        | 5.4795 | 2000 | 0.3137          | 0.5529 |
| 0.3293        | 5.7534 | 2100 | 0.3132          | 0.5822 |
| 0.3293        | 6.0274 | 2200 | 0.3145          | 0.5676 |
| 0.3293        | 6.3014 | 2300 | 0.3283          | 0.5961 |
| 0.3293        | 6.5753 | 2400 | 0.3247          | 0.5988 |


### Framework versions

- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0