---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
base_model: facebook/wav2vec2-base-960h
model-index:
- name: wav2vec2-base-fleurs-329-colab-a100-2
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: fleurs
      type: fleurs
      config: en_us
      split: test
      args: en_us
    metrics:
    - type: wer
      value: 0.9917617237008872
      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-base-fleurs-329-colab-a100-2

This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5431
- Wer: 0.9918

## 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 500
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 8.5696        | 2.45  | 200  | 5.0490          | 1.0    |
| 4.0587        | 4.91  | 400  | 3.3808          | 1.0    |
| 3.0272        | 7.36  | 600  | 2.7368          | 0.9954 |
| 2.6659        | 9.82  | 800  | 2.5431          | 0.9918 |


### Framework versions

- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2