---
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: wav2vec2-xslr-tr-testv2
  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. -->

# wav2vec2-xslr-tr-testv2

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: 0.2789
- Wer: 0.4783

## 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: 5.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| No log        | 0.1375 | 100  | 2.9646          | 1.0    |
| No log        | 0.2749 | 200  | 0.9689          | 0.9848 |
| No log        | 0.4124 | 300  | 0.8561          | 0.9170 |
| No log        | 0.5498 | 400  | 0.7970          | 0.912  |
| 1.9898        | 0.6873 | 500  | 0.8464          | 0.9258 |
| 1.9898        | 0.8247 | 600  | 0.7358          | 0.8872 |
| 1.9898        | 0.9622 | 700  | 0.6374          | 0.8608 |
| 1.9898        | 1.0997 | 800  | 0.5180          | 0.7297 |
| 1.9898        | 1.2371 | 900  | 0.4852          | 0.7212 |
| 0.663         | 1.3746 | 1000 | 0.4840          | 0.7278 |
| 0.663         | 1.5120 | 1100 | 0.4626          | 0.7135 |
| 0.663         | 1.6495 | 1200 | 0.4493          | 0.676  |
| 0.663         | 1.7869 | 1300 | 0.4419          | 0.6813 |
| 0.663         | 1.9244 | 1400 | 0.4306          | 0.6749 |
| 0.5455        | 2.0619 | 1500 | 0.4329          | 0.6846 |
| 0.5455        | 2.1993 | 1600 | 0.4227          | 0.6685 |
| 0.5455        | 2.3368 | 1700 | 0.4097          | 0.6472 |
| 0.5455        | 2.4742 | 1800 | 0.4035          | 0.6343 |
| 0.5455        | 2.6117 | 1900 | 0.4041          | 0.6304 |
| 0.433         | 2.7491 | 2000 | 0.3962          | 0.6542 |
| 0.433         | 2.8866 | 2100 | 0.3601          | 0.6041 |
| 0.433         | 3.0241 | 2200 | 0.3473          | 0.5864 |
| 0.433         | 3.1615 | 2300 | 0.3456          | 0.5723 |
| 0.433         | 3.2990 | 2400 | 0.3380          | 0.5617 |
| 0.3509        | 3.4364 | 2500 | 0.3267          | 0.5563 |
| 0.3509        | 3.5739 | 2600 | 0.3208          | 0.5570 |
| 0.3509        | 3.7113 | 2700 | 0.3124          | 0.5397 |
| 0.3509        | 3.8488 | 2800 | 0.3038          | 0.5272 |
| 0.3509        | 3.9863 | 2900 | 0.2994          | 0.5254 |
| 0.2871        | 4.1237 | 3000 | 0.3073          | 0.5247 |
| 0.2871        | 4.2612 | 3100 | 0.3009          | 0.5122 |
| 0.2871        | 4.3986 | 3200 | 0.2975          | 0.4953 |
| 0.2871        | 4.5361 | 3300 | 0.2898          | 0.4938 |
| 0.2871        | 4.6735 | 3400 | 0.2835          | 0.4902 |
| 0.2198        | 4.8110 | 3500 | 0.2804          | 0.4802 |
| 0.2198        | 4.9485 | 3600 | 0.2789          | 0.4783 |


### Framework versions

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