---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_13_0
metrics:
- wer
model-index:
- name: b19-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: 0.29902189101071264
---

<!-- 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. -->

# b19-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: 0.3604
- Wer: 0.2990

## 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: 50
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 11.864        | 0.38  | 50   | 4.3744          | 1.0    |
| 3.6659        | 0.76  | 100  | 3.0489          | 1.0    |
| 2.9989        | 1.14  | 150  | 2.9634          | 1.0    |
| 2.9763        | 1.52  | 200  | 3.0247          | 1.0    |
| 2.954         | 1.9   | 250  | 2.9476          | 1.0    |
| 2.9795        | 2.29  | 300  | 3.0039          | 1.0    |
| 2.9296        | 2.67  | 350  | 2.9468          | 1.0    |
| 2.9904        | 3.05  | 400  | 2.9507          | 1.0    |
| 2.9245        | 3.43  | 450  | 2.9430          | 1.0    |
| 2.9721        | 3.81  | 500  | 2.9444          | 1.0    |
| 2.949         | 4.2   | 550  | 2.9392          | 1.0    |
| 2.9766        | 4.58  | 600  | 2.9383          | 1.0    |
| 2.9396        | 4.96  | 650  | 2.9393          | 1.0    |
| 2.9899        | 5.34  | 700  | 2.9373          | 1.0    |
| 2.9325        | 5.72  | 750  | 2.9331          | 1.0    |
| 2.9492        | 6.11  | 800  | 2.9287          | 1.0    |
| 2.9493        | 6.49  | 850  | 2.9379          | 1.0    |
| 2.9095        | 6.87  | 900  | 2.9258          | 1.0    |
| 2.9572        | 7.25  | 950  | 2.9377          | 1.0    |
| 2.9053        | 7.63  | 1000 | 2.9177          | 1.0    |
| 3.0002        | 8.02  | 1050 | 2.9122          | 1.0    |
| 2.8892        | 8.4   | 1100 | 2.8918          | 1.0    |
| 2.8927        | 8.78  | 1150 | 2.8707          | 1.0    |
| 2.8945        | 9.16  | 1200 | 2.8234          | 0.9991 |
| 2.7721        | 9.54  | 1250 | 2.4910          | 1.0    |
| 2.283         | 9.92  | 1300 | 1.5046          | 0.9991 |
| 1.6257        | 10.3  | 1350 | 1.0799          | 0.9998 |
| 1.2815        | 10.68 | 1400 | 0.8832          | 0.8158 |
| 1.1252        | 11.07 | 1450 | 0.7531          | 0.7189 |
| 0.9593        | 11.45 | 1500 | 0.6509          | 0.6309 |
| 0.8695        | 11.83 | 1550 | 0.6119          | 0.5836 |
| 0.8199        | 12.21 | 1600 | 0.5643          | 0.5680 |
| 0.7481        | 12.59 | 1650 | 0.5298          | 0.5340 |
| 0.7029        | 12.97 | 1700 | 0.5342          | 0.5254 |
| 0.6472        | 13.36 | 1750 | 0.5047          | 0.4856 |
| 0.6388        | 13.74 | 1800 | 0.4892          | 0.4844 |
| 0.5849        | 14.12 | 1850 | 0.4585          | 0.4548 |
| 0.5642        | 14.5  | 1900 | 0.4496          | 0.4497 |
| 0.546         | 14.88 | 1950 | 0.4274          | 0.4218 |
| 0.5345        | 15.27 | 2000 | 0.4218          | 0.4176 |
| 0.4925        | 15.65 | 2050 | 0.3989          | 0.3996 |
| 0.4794        | 16.03 | 2100 | 0.3887          | 0.3936 |
| 0.4528        | 16.41 | 2150 | 0.4039          | 0.3922 |
| 0.4542        | 16.79 | 2200 | 0.4135          | 0.3882 |
| 0.4408        | 17.17 | 2250 | 0.4018          | 0.3826 |
| 0.4144        | 17.56 | 2300 | 0.4072          | 0.3701 |
| 0.4048        | 17.94 | 2350 | 0.4026          | 0.3759 |
| 0.3751        | 18.32 | 2400 | 0.3774          | 0.3570 |
| 0.393         | 18.7  | 2450 | 0.3799          | 0.3654 |
| 0.3763        | 19.08 | 2500 | 0.3820          | 0.3484 |
| 0.3657        | 19.46 | 2550 | 0.3839          | 0.3500 |
| 0.3483        | 19.84 | 2600 | 0.3967          | 0.3505 |
| 0.3601        | 20.23 | 2650 | 0.3854          | 0.3524 |
| 0.3432        | 20.61 | 2700 | 0.3770          | 0.3428 |
| 0.3382        | 20.99 | 2750 | 0.3797          | 0.3337 |
| 0.3277        | 21.37 | 2800 | 0.3922          | 0.3414 |
| 0.3192        | 21.75 | 2850 | 0.3838          | 0.3479 |
| 0.3087        | 22.14 | 2900 | 0.3766          | 0.3337 |
| 0.3165        | 22.52 | 2950 | 0.3712          | 0.3242 |
| 0.3197        | 22.9  | 3000 | 0.3640          | 0.3207 |
| 0.317         | 23.28 | 3050 | 0.3630          | 0.3232 |
| 0.3123        | 23.66 | 3100 | 0.3777          | 0.3302 |
| 0.2804        | 24.05 | 3150 | 0.3640          | 0.3156 |
| 0.2768        | 24.43 | 3200 | 0.3598          | 0.3034 |
| 0.3038        | 24.81 | 3250 | 0.3627          | 0.3088 |
| 0.2716        | 25.19 | 3300 | 0.3571          | 0.3093 |
| 0.2911        | 25.57 | 3350 | 0.3619          | 0.3102 |
| 0.2903        | 25.95 | 3400 | 0.3697          | 0.3167 |
| 0.291         | 26.33 | 3450 | 0.3579          | 0.3093 |
| 0.2721        | 26.71 | 3500 | 0.3726          | 0.3128 |
| 0.2589        | 27.1  | 3550 | 0.3603          | 0.3020 |
| 0.2745        | 27.48 | 3600 | 0.3704          | 0.3074 |
| 0.2757        | 27.86 | 3650 | 0.3605          | 0.3018 |
| 0.2677        | 28.24 | 3700 | 0.3572          | 0.3034 |
| 0.2613        | 28.62 | 3750 | 0.3614          | 0.3014 |
| 0.2758        | 29.01 | 3800 | 0.3637          | 0.2979 |
| 0.2652        | 29.39 | 3850 | 0.3615          | 0.3000 |
| 0.2736        | 29.77 | 3900 | 0.3604          | 0.2990 |


### Framework versions

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