transformers tensorboard safetensors wav2vec2 automatic-speech-recognition generated_from_trainer dataset:nb_samtale base_model:Max200293/wav2vec2-large-xls-r-300m-norwegian-colab license:apache-2.0 model-index endpoints_compatible region:us
---
license: apache-2.0
base_model: Max200293/wav2vec2-large-xls-r-300m-norwegian-colab
tags:
- generated_from_trainer
datasets:
- nb_samtale
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-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: 0.8874701041793344
---

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# wav2vec2-large-xls-r-300m-norwegian-colab

This model is a fine-tuned version of [Max200293/wav2vec2-large-xls-r-300m-norwegian-colab](https://huggingface.co/Max200293/wav2vec2-large-xls-r-300m-norwegian-colab) on the nb_samtale dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4992
- Wer: 0.8875

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5741        | 1.45  | 400  | 2.4902          | 0.8743 |
| 0.7795        | 2.89  | 800  | 2.3048          | 0.9138 |
| 0.6192        | 4.34  | 1200 | 2.4992          | 0.8875 |


### Framework versions

- Transformers 4.35.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
