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---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53-spanish
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-gn
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: gn
split: test
args: gn
metrics:
- name: Wer
type: wer
value: 0.3430613460393091
---
<!-- 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-large-xls-r-300m-gn
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53-spanish](https://huggingface.co/facebook/wav2vec2-large-xlsr-53-spanish) on the common_voice_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3713
- Wer: 0.3431
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.7177 | 3.62 | 400 | 0.3649 | 0.5816 |
| 0.2738 | 7.24 | 800 | 0.4029 | 0.5024 |
| 0.1768 | 10.86 | 1200 | 0.3779 | 0.4285 |
| 0.1128 | 14.48 | 1600 | 0.3929 | 0.4205 |
| 0.0842 | 18.1 | 2000 | 0.3683 | 0.3916 |
| 0.0616 | 21.72 | 2400 | 0.3943 | 0.3675 |
| 0.0461 | 25.34 | 2800 | 0.4127 | 0.3571 |
| 0.0368 | 28.96 | 3200 | 0.3713 | 0.3431 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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