---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: wav2vec2-xlsr-53-espeak-cv-ft-bak3-ntsema-colab
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: train
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.161594963273872
---

<!-- 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-xlsr-53-espeak-cv-ft-bak3-ntsema-colab

This model is a fine-tuned version of [ntsema/wav2vec2-xlsr-53-espeak-cv-ft-bak-ntsema-colab](https://huggingface.co/ntsema/wav2vec2-xlsr-53-espeak-cv-ft-bak-ntsema-colab) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1249
- Wer: 0.1616

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- 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: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.042         | 7.99  | 400  | 0.1121          | 0.1401 |
| 0.521         | 15.99 | 800  | 0.1623          | 0.2046 |
| 0.332         | 23.99 | 1200 | 0.1249          | 0.1616 |


### Framework versions

- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.2