---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: b29-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: 0.231951560316721
---

<!-- 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. -->

# b29-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: 0.2967
- Wer: 0.2320

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.3337        | 3.05  | 400  | 2.9529          | 1.0    |
| 2.9274        | 6.11  | 800  | 2.8462          | 0.9995 |
| 1.0082        | 9.16  | 1200 | 0.3782          | 0.3628 |
| 0.2754        | 12.21 | 1600 | 0.3225          | 0.2857 |
| 0.168         | 15.27 | 2000 | 0.3102          | 0.2748 |
| 0.1198        | 18.32 | 2400 | 0.3077          | 0.2513 |
| 0.1053        | 21.37 | 2800 | 0.3086          | 0.2531 |
| 0.0829        | 24.43 | 3200 | 0.2985          | 0.2396 |
| 0.0726        | 27.48 | 3600 | 0.2967          | 0.2320 |


### Framework versions

- Transformers 4.26.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3