pytorch wav2vec2 automatic-speech-recognition transformers generated_from_trainer license:apache-2.0 model-index
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xls-r-300m-slowenian-with-lm
  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-large-xls-r-300m-slowenian-with-lm

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3935
- Wer: 0.3480

## 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: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 7.9937        | 2.5   | 100  | 3.1565          | 1.0    |
| 3.0466        | 5.0   | 200  | 3.0009          | 0.9992 |
| 2.9708        | 7.5   | 300  | 2.9494          | 0.9992 |
| 2.0519        | 10.0  | 400  | 0.8874          | 0.7290 |
| 0.5773        | 12.5  | 500  | 0.5258          | 0.5037 |
| 0.3427        | 15.0  | 600  | 0.4767          | 0.4649 |
| 0.2612        | 17.5  | 700  | 0.4549          | 0.4209 |
| 0.212         | 20.0  | 800  | 0.4294          | 0.3860 |
| 0.1748        | 22.5  | 900  | 0.4085          | 0.3769 |
| 0.1587        | 25.0  | 1000 | 0.4017          | 0.3673 |
| 0.1435        | 27.5  | 1100 | 0.3927          | 0.3538 |
| 0.1314        | 30.0  | 1200 | 0.3935          | 0.3480 |


### Framework versions

- Transformers 4.17.0
- Pytorch 1.9.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
