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_Prod7
  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: 0.5032929202215237
---

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# wav2vec2-XLS-R-Fleurs-demo-google-colab-Ezra_William_Prod7

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: 1.0673
- Wer: 0.5033

## 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: 90
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.2829        | 7.79  | 300  | 2.8538          | 1.0    |
| 1.9733        | 15.58 | 600  | 0.8923          | 0.7851 |
| 0.4186        | 23.38 | 900  | 0.8297          | 0.6443 |
| 0.2077        | 31.17 | 1200 | 0.8573          | 0.6011 |
| 0.1535        | 38.96 | 1500 | 0.9490          | 0.5800 |
| 0.1163        | 46.75 | 1800 | 1.0380          | 0.5652 |
| 0.1001        | 54.55 | 2100 | 0.9354          | 0.5417 |
| 0.0845        | 62.34 | 2400 | 1.0226          | 0.5364 |
| 0.0711        | 70.13 | 2700 | 1.0799          | 0.5220 |
| 0.0588        | 77.92 | 3000 | 1.0550          | 0.5050 |
| 0.0492        | 85.71 | 3300 | 1.0673          | 0.5033 |


### Framework versions

- Transformers 4.39.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
