---
license: mit
base_model: Harveenchadha/vakyansh-wav2vec2-hindi-him-4200
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: hindi_beekeeping_wav2vec2
  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_beekeeping_wav2vec2

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: 0.6793
- Wer: 0.4048

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 26.5108       | 0.11  | 25   | 2.9146          | 1.1520 |
| 3.8351        | 0.22  | 50   | 2.1736          | 0.6667 |
| 2.1489        | 0.33  | 75   | 1.5539          | 0.5914 |
| 3.2097        | 0.44  | 100  | 1.9828          | 0.6840 |
| 1.5213        | 0.54  | 125  | 1.4369          | 0.5410 |
| 2.1797        | 0.65  | 150  | 1.7904          | 0.7449 |
| 1.2368        | 0.76  | 175  | 1.3543          | 0.5583 |
| 1.5755        | 0.87  | 200  | 1.5444          | 0.6712 |
| 1.1638        | 0.98  | 225  | 1.4639          | 0.6005 |
| 0.8985        | 1.09  | 250  | 1.0658          | 0.5139 |
| 0.9406        | 1.2   | 275  | 1.3181          | 0.5756 |
| 1.1215        | 1.31  | 300  | 1.0163          | 0.4680 |
| 1.3881        | 1.42  | 325  | 1.1547          | 0.5854 |
| 0.8113        | 1.53  | 350  | 0.9433          | 0.4771 |
| 0.689         | 1.63  | 375  | 1.0819          | 0.5064 |
| 0.726         | 1.74  | 400  | 1.0025          | 0.5252 |
| 0.6475        | 1.85  | 425  | 1.0670          | 0.5154 |
| 1.0345        | 1.96  | 450  | 0.9535          | 0.5019 |
| 0.8327        | 2.07  | 475  | 0.8866          | 0.4733 |
| 0.4843        | 2.18  | 500  | 0.9580          | 0.5087 |
| 0.6657        | 2.29  | 525  | 0.9019          | 0.4710 |
| 0.4843        | 2.4   | 550  | 0.8207          | 0.4665 |
| 0.6666        | 2.51  | 575  | 0.7377          | 0.4695 |
| 0.5012        | 2.61  | 600  | 0.8135          | 0.4537 |
| 0.7776        | 2.72  | 625  | 0.8131          | 0.4612 |
| 0.4538        | 2.83  | 650  | 0.8194          | 0.4590 |
| 0.5659        | 2.94  | 675  | 0.8480          | 0.4582 |
| 0.9362        | 3.05  | 700  | 0.8532          | 0.4485 |
| 0.3629        | 3.16  | 725  | 0.8447          | 0.4582 |
| 0.5201        | 3.27  | 750  | 0.7486          | 0.4409 |
| 0.3513        | 3.38  | 775  | 0.7865          | 0.4439 |
| 0.4152        | 3.49  | 800  | 0.7510          | 0.4364 |
| 0.8962        | 3.59  | 825  | 0.7758          | 0.4342 |
| 0.4558        | 3.7   | 850  | 0.7314          | 0.4296 |
| 0.3476        | 3.81  | 875  | 0.6861          | 0.4153 |
| 0.3978        | 3.92  | 900  | 0.6961          | 0.4153 |
| 0.3818        | 4.03  | 925  | 0.6960          | 0.4071 |
| 0.2847        | 4.14  | 950  | 0.7222          | 0.4048 |
| 0.3997        | 4.25  | 975  | 0.6960          | 0.4191 |
| 0.2695        | 4.36  | 1000 | 0.6894          | 0.4138 |
| 0.4229        | 4.47  | 1025 | 0.7215          | 0.4304 |
| 0.2665        | 4.58  | 1050 | 0.7096          | 0.4056 |
| 0.3158        | 4.68  | 1075 | 0.6904          | 0.4153 |
| 0.2452        | 4.79  | 1100 | 0.6777          | 0.4071 |
| 0.3034        | 4.9   | 1125 | 0.6793          | 0.4048 |


### Framework versions

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