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

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the NATBED - BEM dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7026
- Wer: 0.7512

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 30.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 5.0296        | 0.2503 | 100  | 2.8071          | 1.0    |
| 1.5792        | 0.5006 | 200  | 1.1184          | 0.9645 |
| 1.1422        | 0.7509 | 300  | 1.0389          | 0.9606 |
| 0.9883        | 1.0013 | 400  | 1.0494          | 0.9989 |
| 0.8999        | 1.2516 | 500  | 0.8692          | 0.8683 |
| 0.9135        | 1.5019 | 600  | 0.8564          | 0.8430 |
| 0.8898        | 1.7522 | 700  | 0.8451          | 0.8522 |
| 0.9089        | 2.0025 | 800  | 0.8857          | 0.8485 |
| 0.8292        | 2.2528 | 900  | 0.8662          | 0.8580 |
| 0.7921        | 2.5031 | 1000 | 0.7964          | 0.7969 |
| 0.7983        | 2.7534 | 1100 | 0.7896          | 0.7951 |
| 0.7946        | 3.0038 | 1200 | 0.7667          | 0.7947 |
| 0.7488        | 3.2541 | 1300 | 0.8180          | 0.8495 |
| 0.7428        | 3.5044 | 1400 | 0.7548          | 0.7688 |
| 0.7256        | 3.7547 | 1500 | 0.7258          | 0.7596 |
| 0.741         | 4.0050 | 1600 | 0.7665          | 0.7718 |
| 0.6775        | 4.2553 | 1700 | 0.7922          | 0.7775 |
| 0.6795        | 4.5056 | 1800 | 0.7026          | 0.7512 |
| 0.683         | 4.7559 | 1900 | 0.7051          | 0.7225 |
| 0.6838        | 5.0063 | 2000 | 0.7196          | 0.7503 |
| 0.6005        | 5.2566 | 2100 | 0.7032          | 0.7424 |


### Framework versions

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