---
base_model: facebook/wav2vec2-xls-r-300m
datasets:
- fleurs
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-xlsr-fula-google-fleurs-5-hours
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: fleurs
      type: fleurs
      config: ff_sn
      split: None
      args: ff_sn
    metrics:
    - type: wer
      value: 0.646049896049896
      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. -->

# wav2vec2-xlsr-fula-google-fleurs-5-hours

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1949
- Wer: 0.6460
- Cer: 0.2359

## 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: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 7.1138        | 10.96 | 200  | 2.9561          | 1.0    | 1.0    |
| 2.8708        | 21.92 | 400  | 2.0221          | 1.0    | 0.6369 |
| 1.0031        | 32.88 | 600  | 0.9750          | 0.6509 | 0.2222 |
| 0.4471        | 43.84 | 800  | 1.1949          | 0.6460 | 0.2359 |


### Framework versions

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