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---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: result_data-4
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: uk
split: test
args: uk
metrics:
- name: Wer
type: wer
value: 0.39399943390885933
---
<!-- 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. -->
# result_data-4
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2488
- Wer: 0.3940
- Cer: 0.1776
## 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: 4.355619094803853e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 32
- 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: 102
- num_epochs: 7.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|
| 0.9556 | 0.9099 | 1000 | 0.7514 | 0.7659 | 0.2976 |
| 0.4941 | 1.8198 | 2000 | 0.3987 | 0.5471 | 0.2174 |
| 0.3694 | 2.7298 | 3000 | 0.3282 | 0.4874 | 0.2005 |
| 0.3199 | 3.6397 | 4000 | 0.2846 | 0.4506 | 0.1901 |
| 0.2805 | 4.5496 | 5000 | 0.2716 | 0.4254 | 0.1855 |
| 0.2572 | 5.4595 | 6000 | 0.2622 | 0.4084 | 0.1810 |
| 0.2389 | 6.3694 | 7000 | 0.2483 | 0.4002 | 0.1791 |
### Framework versions
- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0
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