---
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