---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- nb_samtale
metrics:
- wer
model-index:
- name: wav2vec2-classic-300m-norwegian-colab
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: nb_samtale
      type: nb_samtale
      config: annotations
      split: test
      args: annotations
    metrics:
    - name: Wer
      type: wer
      value: 0.7528477035956058
---

<!-- 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. -->

# wav2vec2-classic-300m-norwegian-colab

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co./facebook/wav2vec2-xls-r-300m) on the nb_samtale dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2190
- Wer: 0.7528

## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.8141        | 2.57  | 400  | 3.0571          | 1.0    |
| 3.0777        | 5.14  | 800  | 2.9987          | 1.0    |
| 2.7311        | 7.72  | 1200 | 2.5705          | 0.9829 |
| 2.1302        | 10.29 | 1600 | 1.8399          | 0.9225 |
| 1.6827        | 12.86 | 2000 | 1.6372          | 0.8559 |
| 1.312         | 15.43 | 2400 | 1.8908          | 0.9172 |
| 0.9979        | 18.01 | 2800 | 1.7908          | 0.7890 |
| 0.7456        | 20.58 | 3200 | 1.8110          | 0.7720 |
| 0.592         | 23.15 | 3600 | 2.0024          | 0.7686 |
| 0.4946        | 25.72 | 4000 | 2.1173          | 0.7702 |
| 0.4093        | 28.3  | 4400 | 2.2190          | 0.7528 |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0