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---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: b25-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: 0.24149976711690732
---
<!-- 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. -->
# b25-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: 0.3303
- Wer: 0.2415
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.1605 | 3.05 | 400 | 2.9535 | 1.0 |
| 2.9451 | 6.11 | 800 | 2.9092 | 1.0 |
| 1.7795 | 9.16 | 1200 | 0.4982 | 0.4951 |
| 0.4094 | 12.21 | 1600 | 0.3883 | 0.3575 |
| 0.2374 | 15.27 | 2000 | 0.3151 | 0.2876 |
| 0.1674 | 18.32 | 2400 | 0.3284 | 0.2783 |
| 0.1385 | 21.37 | 2800 | 0.3408 | 0.2641 |
| 0.1133 | 24.43 | 3200 | 0.3355 | 0.2538 |
| 0.1015 | 27.48 | 3600 | 0.3303 | 0.2415 |
### Framework versions
- Transformers 4.26.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
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