---
license: mit
base_model: Harveenchadha/vakyansh-wav2vec2-hindi-him-4200
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: hindi_wav2vec2_optimized
  results: []
---

<!-- 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. -->

# hindi_wav2vec2_optimized

This model is a fine-tuned version of [Harveenchadha/vakyansh-wav2vec2-hindi-him-4200](https://huggingface.co/Harveenchadha/vakyansh-wav2vec2-hindi-him-4200) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4854
- Wer: 0.3632

## 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: 0.0003
- 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: 100
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.1601        | 0.11  | 25   | 1.5548          | 0.4892 |
| 2.8628        | 0.22  | 50   | 1.4072          | 0.6004 |
| 1.5585        | 0.33  | 75   | 1.3488          | 0.5654 |
| 2.8588        | 0.44  | 100  | 1.5733          | 0.5500 |
| 1.3384        | 0.54  | 125  | 1.2629          | 0.5381 |
| 1.7817        | 0.65  | 150  | 1.4159          | 0.5626 |
| 1.124         | 0.76  | 175  | 1.1186          | 0.5003 |
| 1.502         | 0.87  | 200  | 1.3352          | 0.5738 |
| 1.0237        | 0.98  | 225  | 1.2497          | 0.6165 |
| 0.9385        | 1.09  | 250  | 0.9858          | 0.5073 |
| 1.5313        | 1.2   | 275  | 1.1366          | 0.5619 |
| 0.9124        | 1.31  | 300  | 0.9704          | 0.4787 |
| 0.649         | 1.42  | 325  | 1.1915          | 0.5458 |
| 0.838         | 1.53  | 350  | 1.5229          | 0.5836 |
| 0.6835        | 1.63  | 375  | 1.0692          | 0.5052 |
| 0.823         | 1.74  | 400  | 0.9683          | 0.4640 |
| 0.968         | 1.85  | 425  | 0.9629          | 0.4836 |
| 0.8596        | 1.96  | 450  | 0.8242          | 0.4682 |
| 0.6729        | 2.07  | 475  | 0.7999          | 0.4346 |
| 0.6426        | 2.18  | 500  | 0.9678          | 0.4885 |
| 0.7213        | 2.29  | 525  | 1.1779          | 0.5353 |
| 0.4131        | 2.4   | 550  | 0.7007          | 0.4738 |
| 0.4188        | 2.51  | 575  | 0.6085          | 0.4199 |
| 0.3784        | 2.61  | 600  | 0.6526          | 0.4542 |
| 0.4181        | 2.72  | 625  | 0.6716          | 0.4157 |
| 0.3194        | 2.83  | 650  | 0.6058          | 0.4185 |
| 0.3553        | 2.94  | 675  | 0.6023          | 0.4276 |
| 0.3242        | 3.05  | 700  | 0.5864          | 0.4178 |
| 0.2655        | 3.16  | 725  | 0.5705          | 0.3989 |
| 0.3149        | 3.27  | 750  | 0.5275          | 0.3961 |
| 0.2454        | 3.38  | 775  | 0.5401          | 0.3968 |
| 0.2792        | 3.49  | 800  | 0.5303          | 0.3919 |
| 0.2488        | 3.59  | 825  | 0.5459          | 0.4010 |
| 0.3013        | 3.7   | 850  | 0.5180          | 0.3856 |
| 0.2306        | 3.81  | 875  | 0.5179          | 0.3814 |
| 0.2578        | 3.92  | 900  | 0.5168          | 0.3765 |
| 0.2344        | 4.03  | 925  | 0.5212          | 0.3737 |
| 0.2002        | 4.14  | 950  | 0.5026          | 0.3674 |
| 0.2443        | 4.25  | 975  | 0.5045          | 0.3716 |
| 0.1857        | 4.36  | 1000 | 0.5083          | 0.3751 |
| 0.2158        | 4.47  | 1025 | 0.4941          | 0.3681 |
| 0.1922        | 4.58  | 1050 | 0.4996          | 0.3723 |
| 0.217         | 4.68  | 1075 | 0.4943          | 0.3695 |
| 0.1888        | 4.79  | 1100 | 0.4849          | 0.3653 |
| 0.1971        | 4.9   | 1125 | 0.4854          | 0.3632 |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1