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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: b32-wav2vec2-large-xls-r-romansh-colab
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_13_0
type: common_voice_13_0
config: rm-vallader
split: test
args: rm-vallader
metrics:
- name: Wer
type: wer
value: 0.4585468095016302
---
<!-- 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. -->
# b32-wav2vec2-large-xls-r-romansh-colab
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_13_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4636
- Wer: 0.4585
## 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: 3e-05
- 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: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 8.8775 | 3.05 | 400 | 3.2335 | 1.0 |
| 3.0144 | 6.11 | 800 | 2.9346 | 1.0 |
| 2.919 | 9.16 | 1200 | 2.8833 | 0.9988 |
| 1.8698 | 12.21 | 1600 | 0.8435 | 0.6490 |
| 0.6704 | 15.27 | 2000 | 0.5729 | 0.5249 |
| 0.448 | 18.32 | 2400 | 0.4981 | 0.4823 |
| 0.3501 | 21.37 | 2800 | 0.4763 | 0.4662 |
| 0.2999 | 24.43 | 3200 | 0.4610 | 0.4567 |
| 0.2773 | 27.48 | 3600 | 0.4636 | 0.4585 |
### Framework versions
- Transformers 4.26.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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