---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: b21-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: 0.6304145319049836
---

<!-- 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. -->

# b21-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: 0.8091
- Wer: 0.6304

## 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: 0.0004
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.5829        | 0.76  | 100  | 2.9564          | 1.0    |
| 2.9568        | 1.52  | 200  | 3.0768          | 1.0    |
| 2.9578        | 2.29  | 300  | 3.0654          | 1.0    |
| 2.957         | 3.05  | 400  | 2.9377          | 1.0    |
| 2.9419        | 3.81  | 500  | 2.9408          | 1.0    |
| 2.9567        | 4.58  | 600  | 2.9395          | 1.0    |
| 2.9625        | 5.34  | 700  | 2.9388          | 1.0    |
| 2.9395        | 6.11  | 800  | 2.9374          | 1.0    |
| 2.9285        | 6.87  | 900  | 2.9240          | 1.0    |
| 2.9187        | 7.63  | 1000 | 2.9057          | 1.0    |
| 2.9251        | 8.4   | 1100 | 2.8985          | 1.0    |
| 2.9033        | 9.16  | 1200 | 2.8942          | 1.0    |
| 2.8877        | 9.92  | 1300 | 2.8917          | 1.0    |
| 2.8586        | 10.68 | 1400 | 2.7719          | 1.0    |
| 2.5777        | 11.45 | 1500 | 2.2424          | 1.0    |
| 1.9243        | 12.21 | 1600 | 1.7068          | 0.9772 |
| 1.4534        | 12.97 | 1700 | 1.2780          | 0.9585 |
| 1.1793        | 13.74 | 1800 | 1.1482          | 0.9360 |
| 1.0026        | 14.5  | 1900 | 1.0673          | 0.8852 |
| 0.8879        | 15.27 | 2000 | 0.9651          | 0.8433 |
| 0.7933        | 16.03 | 2100 | 0.8973          | 0.8216 |
| 0.6895        | 16.79 | 2200 | 0.8396          | 0.8034 |
| 0.6531        | 17.56 | 2300 | 0.8131          | 0.7713 |
| 0.5753        | 18.32 | 2400 | 0.8388          | 0.7531 |
| 0.5621        | 19.08 | 2500 | 0.7844          | 0.7632 |
| 0.5076        | 19.84 | 2600 | 0.7629          | 0.7485 |
| 0.4672        | 20.61 | 2700 | 0.7777          | 0.7497 |
| 0.443         | 21.37 | 2800 | 0.8001          | 0.7292 |
| 0.4129        | 22.14 | 2900 | 0.7902          | 0.7094 |
| 0.3767        | 22.9  | 3000 | 0.7569          | 0.6784 |
| 0.357         | 23.66 | 3100 | 0.7726          | 0.6903 |
| 0.3378        | 24.43 | 3200 | 0.8016          | 0.6882 |
| 0.3199        | 25.19 | 3300 | 0.7854          | 0.6677 |
| 0.3144        | 25.95 | 3400 | 0.7792          | 0.6509 |
| 0.3025        | 26.71 | 3500 | 0.8157          | 0.6695 |
| 0.2919        | 27.48 | 3600 | 0.8215          | 0.6633 |
| 0.2762        | 28.24 | 3700 | 0.8167          | 0.6500 |
| 0.2679        | 29.01 | 3800 | 0.8144          | 0.6311 |
| 0.2671        | 29.77 | 3900 | 0.8091          | 0.6304 |


### Framework versions

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