---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- voxpopuli
metrics:
- wer
model-index:
- name: wav2vec2-classic-300m-norwegian-colab-hung
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: voxpopuli
      type: voxpopuli
      config: fi
      split: test
      args: fi
    metrics:
    - name: Wer
      type: wer
      value: 1.7882131661442007
---

<!-- 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-hung

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8820
- Wer: 1.7882

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.7686        | 2.57  | 400  | 2.9953          | 1.0    |
| 2.5005        | 5.14  | 800  | 2.2739          | 1.9808 |
| 1.6554        | 7.72  | 1200 | 2.4720          | 1.6708 |
| 1.1995        | 10.29 | 1600 | 2.2613          | 1.2480 |
| 0.8972        | 12.86 | 2000 | 2.7599          | 1.8873 |
| 0.6962        | 15.43 | 2400 | 3.2783          | 1.9560 |
| 0.5554        | 18.01 | 2800 | 3.2272          | 1.7544 |
| 0.4234        | 20.58 | 3200 | 3.0755          | 1.5645 |
| 0.3341        | 23.15 | 3600 | 3.5022          | 1.7442 |
| 0.2832        | 25.72 | 4000 | 3.7905          | 1.8324 |
| 0.2293        | 28.3  | 4400 | 3.8820          | 1.7882 |


### Framework versions

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