---
base_model: Aviral2412/mini_model
tags:
- generated_from_trainer
datasets:
- common_voice_1_0
metrics:
- wer
model-index:
- name: fineturning-with-pretraining
  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: 1.0010991853097115
---

<!-- 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

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: 2.3739
- Wer: 1.0011

## 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: 5e-05
- train_batch_size: 16
- 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: 500
- num_epochs: 25
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.3381        | 2.15  | 500  | 2.5389          | 1.0011 |
| 2.4622        | 4.29  | 1000 | 2.4761          | 1.0011 |
| 2.4477        | 6.44  | 1500 | 2.5567          | 1.0011 |
| 2.4325        | 8.58  | 2000 | 2.4334          | 1.0011 |
| 2.4205        | 10.73 | 2500 | 2.4067          | 1.0011 |
| 2.3995        | 12.88 | 3000 | 2.3828          | 1.0011 |
| 2.3869        | 15.02 | 3500 | 2.3752          | 1.0011 |
| 2.3857        | 17.17 | 4000 | 2.3759          | 1.0011 |
| 2.3717        | 19.31 | 4500 | 2.3684          | 1.0011 |
| 2.3625        | 21.46 | 5000 | 2.3601          | 1.0011 |
| 2.3648        | 23.61 | 5500 | 2.3739          | 1.0011 |


### Framework versions

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