---
license: mit
base_model: TheAIchemist13/hindi_wav2vec2
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: hindi_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_wav2vec2

This model is a fine-tuned version of [TheAIchemist13/hindi_wav2vec2](https://huggingface.co/TheAIchemist13/hindi_wav2vec2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0003
- Wer: 0.5333

## 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: 500

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| No log        | 7.14   | 25   | 1.2980          | 1.4167 |
| No log        | 14.29  | 50   | 0.7841          | 1.2833 |
| No log        | 21.43  | 75   | 0.5604          | 1.05   |
| No log        | 28.57  | 100  | 0.5288          | 1.05   |
| No log        | 35.71  | 125  | 0.2642          | 0.8833 |
| No log        | 42.86  | 150  | 0.3233          | 1.0167 |
| No log        | 50.0   | 175  | 0.5526          | 1.0667 |
| No log        | 57.14  | 200  | 0.1759          | 0.8167 |
| No log        | 64.29  | 225  | 0.1275          | 0.6833 |
| No log        | 71.43  | 250  | 0.1004          | 0.7    |
| No log        | 78.57  | 275  | 0.1294          | 0.75   |
| No log        | 85.71  | 300  | 0.1928          | 0.8333 |
| No log        | 92.86  | 325  | 0.1206          | 0.7167 |
| No log        | 100.0  | 350  | 0.1060          | 0.7    |
| No log        | 107.14 | 375  | 0.0676          | 0.65   |
| No log        | 114.29 | 400  | 0.1803          | 0.8667 |
| No log        | 121.43 | 425  | 0.0502          | 0.6333 |
| No log        | 128.57 | 450  | 0.0978          | 0.6833 |
| No log        | 135.71 | 475  | 0.0817          | 0.6167 |
| 0.553         | 142.86 | 500  | 0.0695          | 0.6667 |
| 0.553         | 150.0  | 525  | 0.2449          | 0.8333 |
| 0.553         | 157.14 | 550  | 0.0407          | 0.6    |
| 0.553         | 164.29 | 575  | 0.0713          | 0.65   |
| 0.553         | 171.43 | 600  | 0.0317          | 0.6333 |
| 0.553         | 178.57 | 625  | 0.0383          | 0.6833 |
| 0.553         | 185.71 | 650  | 0.0217          | 0.6    |
| 0.553         | 192.86 | 675  | 0.0087          | 0.5667 |
| 0.553         | 200.0  | 700  | 0.0270          | 0.6167 |
| 0.553         | 207.14 | 725  | 0.1069          | 0.7    |
| 0.553         | 214.29 | 750  | 0.0118          | 0.5833 |
| 0.553         | 221.43 | 775  | 0.0089          | 0.6    |
| 0.553         | 228.57 | 800  | 0.0072          | 0.5667 |
| 0.553         | 235.71 | 825  | 0.0510          | 0.5833 |
| 0.553         | 242.86 | 850  | 0.0187          | 0.5833 |
| 0.553         | 250.0  | 875  | 0.0199          | 0.5833 |
| 0.553         | 257.14 | 900  | 0.0105          | 0.5833 |
| 0.553         | 264.29 | 925  | 0.0082          | 0.5833 |
| 0.553         | 271.43 | 950  | 0.0177          | 0.5833 |
| 0.553         | 278.57 | 975  | 0.0032          | 0.55   |
| 0.103         | 285.71 | 1000 | 0.0036          | 0.55   |
| 0.103         | 292.86 | 1025 | 0.0028          | 0.5333 |
| 0.103         | 300.0  | 1050 | 0.0040          | 0.5667 |
| 0.103         | 307.14 | 1075 | 0.0416          | 0.5667 |
| 0.103         | 314.29 | 1100 | 0.0055          | 0.5667 |
| 0.103         | 321.43 | 1125 | 0.0026          | 0.55   |
| 0.103         | 328.57 | 1150 | 0.0029          | 0.55   |
| 0.103         | 335.71 | 1175 | 0.0010          | 0.5333 |
| 0.103         | 342.86 | 1200 | 0.0036          | 0.55   |
| 0.103         | 350.0  | 1225 | 0.0013          | 0.55   |
| 0.103         | 357.14 | 1250 | 0.0010          | 0.5333 |
| 0.103         | 364.29 | 1275 | 0.0013          | 0.5333 |
| 0.103         | 371.43 | 1300 | 0.0007          | 0.5333 |
| 0.103         | 378.57 | 1325 | 0.0006          | 0.5333 |
| 0.103         | 385.71 | 1350 | 0.0005          | 0.5333 |
| 0.103         | 392.86 | 1375 | 0.0004          | 0.5333 |
| 0.103         | 400.0  | 1400 | 0.0004          | 0.5333 |
| 0.103         | 407.14 | 1425 | 0.0004          | 0.5333 |
| 0.103         | 414.29 | 1450 | 0.0003          | 0.5333 |
| 0.103         | 421.43 | 1475 | 0.0003          | 0.5333 |
| 0.0142        | 428.57 | 1500 | 0.0003          | 0.5333 |


### Framework versions

- Transformers 4.34.0
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.14.0