---
base_model: wav2vec2-pretrained-base-gumbelVQ
tags:
- automatic-speech-recognition
- timit_asr
- generated_from_trainer
datasets:
- timit_asr
metrics:
- wer
model-index:
- name: wav2vec2-base-gumbelVQ-timit-fine-tuned
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: TIMIT_ASR - NA
      type: timit_asr
      config: clean
      split: test
      args: 'Config: na, Training split: train, Eval split: test'
    metrics:
    - name: Wer
      type: wer
      value: 0.4901798635517883
---

<!-- 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-gumbelVQ-timit-fine-tuned

This model is a fine-tuned version of [wav2vec2-pretrained-base-gumbelVQ](https://huggingface.co/wav2vec2-pretrained-base-gumbelVQ) on the TIMIT_ASR - NA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7549
- Wer: 0.4902

## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 20.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.4628        | 10.0  | 1450 | 0.6779          | 0.5171 |
| 0.3036        | 20.0  | 2900 | 0.7549          | 0.4902 |


### Framework versions

- Transformers 4.36.2
- Pytorch 2.3.0.dev20231229+cu118
- Datasets 2.16.0
- Tokenizers 0.15.0