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---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-lg-CV-Fleurs_filtered-100hrs-v13
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: fleurs
type: fleurs
config: lg_ug
split: None
args: lg_ug
metrics:
- name: Wer
type: wer
value: 0.44538386783284745
---
<!-- 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-xls-r-300m-lg-CV-Fleurs_filtered-100hrs-v13
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4759
- Wer: 0.4454
- Cer: 0.0854
## 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: 0.0001
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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
- num_epochs: 70
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|
| 0.7277 | 1.0 | 7125 | 0.4018 | 0.4833 | 0.1016 |
| 0.3225 | 2.0 | 14250 | 0.3800 | 0.4945 | 0.1067 |
| 0.2726 | 3.0 | 21375 | 0.3745 | 0.4588 | 0.0919 |
| 0.2416 | 4.0 | 28500 | 0.3439 | 0.4419 | 0.0885 |
| 0.2188 | 5.0 | 35625 | 0.3353 | 0.4657 | 0.0906 |
| 0.2024 | 6.0 | 42750 | 0.3289 | 0.4563 | 0.0881 |
| 0.1888 | 7.0 | 49875 | 0.3272 | 0.4451 | 0.0863 |
| 0.1767 | 8.0 | 57000 | 0.3267 | 0.4226 | 0.0830 |
| 0.1668 | 9.0 | 64125 | 0.3354 | 0.4305 | 0.0837 |
| 0.1568 | 10.0 | 71250 | 0.3277 | 0.4297 | 0.0857 |
| 0.1483 | 11.0 | 78375 | 0.3310 | 0.4425 | 0.0857 |
| 0.1398 | 12.0 | 85500 | 0.3433 | 0.4299 | 0.0836 |
| 0.1323 | 13.0 | 92625 | 0.3448 | 0.4472 | 0.0870 |
| 0.125 | 14.0 | 99750 | 0.3585 | 0.4388 | 0.0849 |
| 0.1174 | 15.0 | 106875 | 0.3623 | 0.4250 | 0.0828 |
| 0.1121 | 16.0 | 114000 | 0.3813 | 0.4333 | 0.0843 |
| 0.1059 | 17.0 | 121125 | 0.3788 | 0.4251 | 0.0825 |
| 0.0996 | 18.0 | 128250 | 0.3882 | 0.4434 | 0.0863 |
| 0.0944 | 19.0 | 135375 | 0.4082 | 0.4444 | 0.0860 |
| 0.0889 | 20.0 | 142500 | 0.4227 | 0.4446 | 0.0848 |
| 0.0846 | 21.0 | 149625 | 0.4323 | 0.4422 | 0.0852 |
| 0.081 | 22.0 | 156750 | 0.4540 | 0.4506 | 0.0881 |
| 0.0767 | 23.0 | 163875 | 0.4759 | 0.4454 | 0.0854 |
### Framework versions
- Transformers 4.46.2
- Pytorch 2.1.0+cu118
- Datasets 3.1.0
- Tokenizers 0.20.3
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