---
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: w2v2-bert-Wolof-10-hours-Google-Fleurs-dataset
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fleurs
      type: fleurs
      config: wo_sn
      split: None
      args: wo_sn
    metrics:
    - name: Wer
      type: wer
      value: 0.39968350853396634
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# w2v2-bert-Wolof-10-hours-Google-Fleurs-dataset

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1192
- Wer: 0.3997
- Cer: 0.1251

## 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 31

### Training results

| Training Loss | Epoch | Step | Cer    | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:------:|:---------------:|:------:|
| 1.403         | 5.23  | 400  | 0.1672 | 0.6614          | 0.4857 |
| 0.4459        | 10.46 | 800  | 0.1432 | 0.6289          | 0.4476 |
| 0.2611        | 15.69 | 1200 | 0.1402 | 0.6713          | 0.4298 |
| 0.1019        | 21.01 | 1600 | 0.8813 | 0.4052          | 0.1288 |
| 0.0291        | 26.24 | 2000 | 1.1192 | 0.3997          | 0.1251 |


### Framework versions

- Transformers 4.38.1
- Pytorch 2.1.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2