---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
datasets:
- common_voice_16_1
metrics:
- wer
model-index:
- name: wav2vec2-large-xls-r-300m-upper-sorbian-cz-frozen-2-colab
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_16_1
      type: common_voice_16_1
      config: hsb
      split: test
      args: hsb
    metrics:
    - name: Wer
      type: wer
      value: 0.4301780693533271
---

<!-- 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-upper-sorbian-cz-frozen-2-colab

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_16_1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7163
- Wer: 0.4302
- Cer: 0.1003

## 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: 60
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.6172        | 3.23  | 100  | 0.6599          | 0.6999 | 0.1787 |
| 0.4414        | 6.45  | 200  | 0.6030          | 0.6251 | 0.1524 |
| 0.289         | 9.68  | 300  | 0.5899          | 0.5670 | 0.1336 |
| 0.1953        | 12.9  | 400  | 0.6095          | 0.5457 | 0.1308 |
| 0.1388        | 16.13 | 500  | 0.6628          | 0.5159 | 0.1224 |
| 0.1187        | 19.35 | 600  | 0.7075          | 0.4932 | 0.1180 |
| 0.0994        | 22.58 | 700  | 0.7131          | 0.4780 | 0.1143 |
| 0.0816        | 25.81 | 800  | 0.6959          | 0.4752 | 0.1101 |
| 0.0727        | 29.03 | 900  | 0.7201          | 0.4644 | 0.1104 |
| 0.0637        | 32.26 | 1000 | 0.7288          | 0.4630 | 0.1080 |
| 0.0592        | 35.48 | 1100 | 0.7219          | 0.4524 | 0.1056 |
| 0.0549        | 38.71 | 1200 | 0.7204          | 0.4480 | 0.1041 |
| 0.0473        | 41.94 | 1300 | 0.7238          | 0.4470 | 0.1048 |
| 0.0412        | 45.16 | 1400 | 0.7109          | 0.4278 | 0.1011 |
| 0.0423        | 48.39 | 1500 | 0.7252          | 0.4407 | 0.1045 |
| 0.0419        | 51.61 | 1600 | 0.7193          | 0.4393 | 0.1028 |
| 0.0365        | 54.84 | 1700 | 0.7231          | 0.4318 | 0.1010 |
| 0.0347        | 58.06 | 1800 | 0.7163          | 0.4302 | 0.1003 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2