transformers tensorboard safetensors wav2vec2 automatic-speech-recognition generated_from_trainer dataset:xtreme_s 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:
- xtreme_s
metrics:
- wer
model-index:
- name: wav2vec2-XLS-R-Fleurs-demo-google-colab-Ezra_William_Prod5
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: xtreme_s
      type: xtreme_s
      config: fleurs.id_id
      split: test
      args: fleurs.id_id
    metrics:
    - name: Wer
      type: wer
      value: 0.5365963179164795
---

<!-- 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-XLS-R-Fleurs-demo-google-colab-Ezra_William_Prod5

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the xtreme_s dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9790
- Wer: 0.5366

## 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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 600
- num_epochs: 60
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.9955        | 9.23  | 300  | 2.8534          | 1.0    |
| 1.7522        | 18.46 | 600  | 0.7939          | 0.7079 |
| 0.3374        | 27.69 | 900  | 0.8635          | 0.6423 |
| 0.1617        | 36.92 | 1200 | 0.9916          | 0.5929 |
| 0.1102        | 46.15 | 1500 | 0.9796          | 0.5648 |
| 0.0815        | 55.38 | 1800 | 0.9790          | 0.5366 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
