---
base_model: cpierse/wav2vec2-large-xlsr-53-esperanto
datasets:
- audiofolder
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: TrainEsperanto
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: None
      args: default
    metrics:
    - type: wer
      value: 0.1883670612192949
      name: Wer
---

<!-- 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. -->

# TrainEsperanto

This model is a fine-tuned version of [cpierse/wav2vec2-large-xlsr-53-esperanto](https://huggingface.co/cpierse/wav2vec2-large-xlsr-53-esperanto) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0591
- Wer: 0.1884

## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 5.9902        | 2.6596  | 500  | 8.6294          | 1.0309 |
| 3.3           | 5.3191  | 1000 | 2.9688          | 1.0    |
| 2.8744        | 7.9787  | 1500 | 2.4117          | 1.0    |
| 0.7214        | 10.6383 | 2000 | 0.1825          | 0.2954 |
| 0.1552        | 13.2979 | 2500 | 0.0689          | 0.1971 |
| 0.1038        | 15.9574 | 3000 | 0.0621          | 0.1932 |
| 0.092         | 18.6170 | 3500 | 0.0624          | 0.1900 |
| 0.0877        | 21.2766 | 4000 | 0.0615          | 0.1926 |
| 0.082         | 23.9362 | 4500 | 0.0609          | 0.1899 |
| 0.0779        | 26.5957 | 5000 | 0.0591          | 0.1887 |
| 0.077         | 29.2553 | 5500 | 0.0591          | 0.1884 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.1
- Datasets 2.19.1
- Tokenizers 0.20.1