transformers tensorboard safetensors wav2vec2 automatic-speech-recognition generated_from_trainer dataset:common_voice_15_0 base_model:facebook/wav2vec2-large-xlsr-53 license:apache-2.0 model-index endpoints_compatible region:us
---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
datasets:
- common_voice_15_0
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-53-br
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_15_0
      type: common_voice_15_0
      config: br
      split: None
      args: br
    metrics:
    - name: Wer
      type: wer
      value: 54.71511888739345
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# wav2vec2-large-xlsr-53-br

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the common_voice_15_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7879
- Wer: 54.7151
- Cer: 19.2493

## 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: 6e-05
- 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_steps: 300
- num_epochs: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     | Cer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| 6.3257        | 2.18  | 500  | 3.0700          | 100.0   | 99.0871 |
| 2.2071        | 4.36  | 1000 | 1.1541          | 80.0449 | 29.4230 |
| 1.0019        | 6.54  | 1500 | 0.8986          | 69.2059 | 24.3938 |
| 0.7796        | 8.71  | 2000 | 0.8015          | 63.3737 | 22.1296 |
| 0.6677        | 10.89 | 2500 | 0.8014          | 61.4984 | 21.4568 |
| 0.5937        | 13.07 | 3000 | 0.7623          | 58.9323 | 20.4929 |
| 0.5454        | 15.25 | 3500 | 0.7975          | 57.8466 | 20.2585 |
| 0.5075        | 17.43 | 4000 | 0.7831          | 56.7250 | 19.7879 |
| 0.4837        | 19.61 | 4500 | 0.7902          | 55.9623 | 19.5101 |
| 0.4529        | 21.79 | 5000 | 0.7851          | 54.9753 | 19.0924 |
| 0.4381        | 23.97 | 5500 | 0.7865          | 55.1727 | 19.3211 |
| 0.4208        | 26.14 | 6000 | 0.8168          | 55.1817 | 19.3967 |
| 0.4197        | 28.32 | 6500 | 0.7879          | 54.7151 | 19.2493 |


### Framework versions

- Transformers 4.39.1
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2
