---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-scandinavian-E4-100h-30-epochs-20250201_v2.2
  results: []
---

<!-- 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-xls-r-300m-scandinavian-E4-100h-30-epochs-20250201_v2.2

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co./facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5180
- Wer: 33.7460
- Cer: 9.2540

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5000
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer     | Cer     |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|:-------:|
| 3.3709        | 0.9153  | 1000  | 3.2919          | 100.0   | 99.9998 |
| 2.9006        | 1.8302  | 2000  | 2.8985          | 100.0   | 99.9998 |
| 1.0856        | 2.7451  | 3000  | 0.7243          | 59.2040 | 17.1565 |
| 0.6157        | 3.6600  | 4000  | 0.3552          | 34.2313 | 9.4613  |
| 0.4596        | 4.5748  | 5000  | 0.2829          | 27.9991 | 7.6917  |
| 0.4002        | 5.4897  | 6000  | 0.2588          | 26.1989 | 7.0489  |
| 0.8133        | 6.4046  | 7000  | 0.5522          | 30.9377 | 8.5844  |
| 1.244         | 7.3195  | 8000  | 1.0083          | 71.3211 | 25.5473 |
| 0.9065        | 8.2343  | 9000  | 0.6510          | 35.2864 | 9.6846  |
| 0.7912        | 9.1492  | 10000 | 0.5297          | 33.3216 | 9.1039  |
| 0.7898        | 10.0641 | 11000 | 0.5132          | 33.3569 | 9.1523  |
| 0.7686        | 10.9794 | 12000 | 0.5126          | 33.5706 | 9.1892  |
| 0.7419        | 11.8943 | 13000 | 0.5180          | 33.7460 | 9.2549  |
| 0.7229        | 12.8092 | 14000 | 0.5180          | 33.7438 | 9.2557  |
| 0.7368        | 13.7240 | 15000 | 0.5180          | 33.7481 | 9.2555  |
| 0.7861        | 14.6389 | 16000 | 0.5180          | 33.7385 | 9.2536  |
| 0.7975        | 15.5538 | 17000 | 0.5180          | 33.7406 | 9.2549  |
| 0.8011        | 16.4686 | 18000 | 0.5180          | 33.7481 | 9.2546  |
| 0.7773        | 17.3835 | 19000 | 0.5180          | 33.7460 | 9.2531  |
| 0.7719        | 18.2984 | 20000 | 0.5180          | 33.7449 | 9.2546  |
| 0.7456        | 19.2133 | 21000 | 0.5180          | 33.7449 | 9.2542  |
| 0.7024        | 20.1281 | 22000 | 0.5180          | 33.7449 | 9.2535  |
| 0.7654        | 21.0430 | 23000 | 0.5180          | 33.7449 | 9.2546  |
| 0.8013        | 21.9584 | 24000 | 0.5180          | 33.7460 | 9.2570  |
| 0.823         | 22.8732 | 25000 | 0.5180          | 33.7449 | 9.2551  |
| 0.8084        | 23.7881 | 26000 | 0.5180          | 33.7438 | 9.2536  |
| 0.8533        | 24.7030 | 27000 | 0.5180          | 33.7417 | 9.2533  |
| 0.7409        | 25.6178 | 28000 | 0.5180          | 33.7438 | 9.2535  |
| 0.7502        | 26.5327 | 29000 | 0.5180          | 33.7502 | 9.2548  |
| 0.7424        | 27.4476 | 30000 | 0.5180          | 33.7492 | 9.2559  |
| 0.7426        | 28.3625 | 31000 | 0.5180          | 33.7428 | 9.2538  |
| 0.7798        | 29.2773 | 32000 | 0.5180          | 33.7460 | 9.2540  |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0