---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: b22-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: 0.4990684676292501
---

<!-- 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. -->

# b22-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: 0.7362
- Wer: 0.4991

## 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: 0.0003
- 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: 100
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.8045        | 0.76  | 100  | 2.9604          | 1.0    |
| 2.9578        | 1.52  | 200  | 3.0626          | 1.0    |
| 2.9565        | 2.29  | 300  | 3.0432          | 1.0    |
| 2.9533        | 3.05  | 400  | 2.9304          | 1.0    |
| 2.9263        | 3.81  | 500  | 2.9134          | 1.0    |
| 2.9174        | 4.58  | 600  | 2.9022          | 1.0    |
| 2.9282        | 5.34  | 700  | 2.8967          | 1.0    |
| 2.8973        | 6.11  | 800  | 2.8477          | 1.0    |
| 2.6047        | 6.87  | 900  | 2.0269          | 1.0    |
| 1.6468        | 7.63  | 1000 | 1.0780          | 0.9029 |
| 1.1006        | 8.4   | 1100 | 0.8305          | 0.8319 |
| 0.8708        | 9.16  | 1200 | 0.7704          | 0.8055 |
| 0.7708        | 9.92  | 1300 | 0.6815          | 0.7385 |
| 0.6608        | 10.68 | 1400 | 0.6738          | 0.7212 |
| 0.6014        | 11.45 | 1500 | 0.6535          | 0.6940 |
| 0.5419        | 12.21 | 1600 | 0.6608          | 0.6639 |
| 0.4961        | 12.97 | 1700 | 0.6568          | 0.6372 |
| 0.4462        | 13.74 | 1800 | 0.6557          | 0.6362 |
| 0.4169        | 14.5  | 1900 | 0.6487          | 0.5985 |
| 0.3951        | 15.27 | 2000 | 0.7126          | 0.6376 |
| 0.3643        | 16.03 | 2100 | 0.6539          | 0.5859 |
| 0.3243        | 16.79 | 2200 | 0.6803          | 0.5946 |
| 0.3243        | 17.56 | 2300 | 0.6619          | 0.5745 |
| 0.2869        | 18.32 | 2400 | 0.6826          | 0.5592 |
| 0.2895        | 19.08 | 2500 | 0.6980          | 0.5524 |
| 0.2612        | 19.84 | 2600 | 0.6599          | 0.5445 |
| 0.2492        | 20.61 | 2700 | 0.6533          | 0.5394 |
| 0.2485        | 21.37 | 2800 | 0.7103          | 0.5494 |
| 0.2352        | 22.14 | 2900 | 0.7339          | 0.5501 |
| 0.2136        | 22.9  | 3000 | 0.7154          | 0.5470 |
| 0.2079        | 23.66 | 3100 | 0.7360          | 0.5389 |
| 0.2011        | 24.43 | 3200 | 0.7481          | 0.5263 |
| 0.1925        | 25.19 | 3300 | 0.7409          | 0.5186 |
| 0.193         | 25.95 | 3400 | 0.7334          | 0.5091 |
| 0.1874        | 26.71 | 3500 | 0.7493          | 0.5075 |
| 0.1802        | 27.48 | 3600 | 0.7362          | 0.5102 |
| 0.1736        | 28.24 | 3700 | 0.7427          | 0.5033 |
| 0.1725        | 29.01 | 3800 | 0.7404          | 0.5033 |
| 0.1684        | 29.77 | 3900 | 0.7362          | 0.4991 |


### Framework versions

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