---
base_model: Aviral2412/mini_model
tags:
- generated_from_trainer
datasets:
- common_voice_1_0
metrics:
- wer
model-index:
- name: fineturning-with-pretraining-3
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_1_0
      type: common_voice_1_0
      config: en
      split: validation
      args: en
    metrics:
    - name: Wer
      type: wer
      value: 1.0000323289796973
---

<!-- 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. -->

# fineturning-with-pretraining-3

This model is a fine-tuned version of [Aviral2412/mini_model](https://huggingface.co/Aviral2412/mini_model) on the common_voice_1_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9160
- Wer: 1.0000

## 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: 8
- 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: 30
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 6.0042        | 4.27  | 500  | 3.1928          | 1.0000 |
| 2.9673        | 8.55  | 1000 | 3.0856          | 1.0000 |
| 2.9929        | 12.82 | 1500 | 3.0173          | 1.0000 |
| 2.9458        | 17.09 | 2000 | 2.9282          | 1.0000 |
| 2.9084        | 21.37 | 2500 | 2.9734          | 1.0000 |
| 2.8651        | 25.64 | 3000 | 2.9234          | 1.0000 |
| 2.8307        | 29.91 | 3500 | 2.9160          | 1.0000 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2