transformers tensorboard safetensors wav2vec2-bert automatic-speech-recognition generated_from_trainer dataset:audiofolder base_model:facebook/w2v-bert-2.0 license:mit model-index endpoints_compatible region:us
---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: w2v-bert-2.0-arabic-4
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: audiofolder
      type: audiofolder
      config: default
      split: test
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.1809009009009009
---

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# w2v-bert-2.0-arabic-4

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1952
- Wer: 0.1809

## 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: 5e-05
- 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: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.5106        | 0.96  | 300  | 0.2448          | 0.2858 |
| 0.2479        | 1.92  | 600  | 0.1952          | 0.1809 |


### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.17.1
- Tokenizers 0.15.2
