---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: xls-r-300-cv17-upper-sorbian-adap-pl
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: hsb
      split: validation
      args: hsb
    metrics:
    - name: Wer
      type: wer
      value: 0.7246835443037974
---

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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/badr-nlp/xlsr-continual-finetuning-polish/runs/kcnysagl)
# xls-r-300-cv17-upper-sorbian-adap-pl

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0564
- Wer: 0.7247
- Cer: 0.1754

## 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: 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: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 3.5302        | 3.9216  | 100  | 3.5256          | 1.0    | 1.0    |
| 3.2181        | 7.8431  | 200  | 3.2314          | 1.0    | 1.0    |
| 1.5479        | 11.7647 | 300  | 1.6991          | 0.9797 | 0.3943 |
| 0.3971        | 15.6863 | 400  | 0.9388          | 0.8582 | 0.2274 |
| 0.2782        | 19.6078 | 500  | 0.9310          | 0.8291 | 0.2203 |
| 0.1388        | 23.5294 | 600  | 0.9292          | 0.8    | 0.2045 |
| 0.1438        | 27.4510 | 700  | 0.9533          | 0.8006 | 0.2011 |
| 0.0815        | 31.3725 | 800  | 0.9446          | 0.7816 | 0.1975 |
| 0.0873        | 35.2941 | 900  | 0.9855          | 0.7728 | 0.1913 |
| 0.1213        | 39.2157 | 1000 | 0.9705          | 0.7652 | 0.1955 |
| 0.0589        | 43.1373 | 1100 | 0.9832          | 0.7614 | 0.1876 |
| 0.0865        | 47.0588 | 1200 | 1.0001          | 0.7582 | 0.1875 |
| 0.0762        | 50.9804 | 1300 | 1.0280          | 0.7538 | 0.1854 |
| 0.0564        | 54.9020 | 1400 | 0.9799          | 0.7468 | 0.1820 |
| 0.0607        | 58.8235 | 1500 | 1.0192          | 0.7443 | 0.1793 |
| 0.0729        | 62.7451 | 1600 | 1.0057          | 0.7424 | 0.1762 |
| 0.0518        | 66.6667 | 1700 | 1.0240          | 0.7437 | 0.1765 |
| 0.059         | 70.5882 | 1800 | 1.0379          | 0.7278 | 0.1759 |
| 0.031         | 74.5098 | 1900 | 1.0444          | 0.7152 | 0.1718 |
| 0.051         | 78.4314 | 2000 | 1.0530          | 0.7335 | 0.1773 |
| 0.0539        | 82.3529 | 2100 | 1.0402          | 0.7241 | 0.1773 |
| 0.0399        | 86.2745 | 2200 | 1.0495          | 0.7177 | 0.1744 |
| 0.06          | 90.1961 | 2300 | 1.0674          | 0.7222 | 0.1764 |
| 0.0459        | 94.1176 | 2400 | 1.0576          | 0.7222 | 0.1747 |
| 0.0614        | 98.0392 | 2500 | 1.0564          | 0.7247 | 0.1754 |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1