---
base_model: Aviral2412/mini_model
tags:
- generated_from_trainer
datasets:
- common_voice_1_0
metrics:
- wer
model-index:
- name: fineturning-with-pretraining-2
  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: 1.0046553730764256
---

<!-- 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-2

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: 2.6209
- Wer: 1.0047

## 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: 0.0009
- 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: 35
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4746        | 4.29  | 500  | 2.5056          | 1.0013 |
| 2.4704        | 8.58  | 1000 | 2.4840          | 1.0013 |
| 2.4346        | 12.88 | 1500 | 2.4060          | 1.0013 |
| 2.3825        | 17.17 | 2000 | 2.4998          | 1.0014 |
| 2.2596        | 21.46 | 2500 | 2.6122          | 1.0019 |
| 2.1902        | 25.75 | 3000 | 2.6619          | 1.0027 |
| 2.1675        | 30.04 | 3500 | 2.6117          | 1.0048 |
| 2.143         | 34.33 | 4000 | 2.6209          | 1.0047 |


### Framework versions

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