---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: w2v-bert-hindi_voip
  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. -->

# w2v-bert-hindi_voip

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1299
- Wer: 0.0737

## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 60000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 0.9895        | 0.1439 | 2000  | 0.8051          | 0.4624 |
| 0.8084        | 0.2878 | 4000  | 0.6416          | 0.3877 |
| 0.6948        | 0.4317 | 6000  | 0.5950          | 0.3660 |
| 0.6599        | 0.5756 | 8000  | 0.5228          | 0.3330 |
| 0.564         | 0.7195 | 10000 | 0.4980          | 0.3001 |
| 0.5112        | 0.8634 | 12000 | 0.4413          | 0.2928 |
| 0.4827        | 1.0073 | 14000 | 0.3999          | 0.2646 |
| 0.4198        | 1.1512 | 16000 | 0.3734          | 0.2438 |
| 0.3831        | 1.2951 | 18000 | 0.3779          | 0.2365 |
| 0.3699        | 1.4390 | 20000 | 0.3223          | 0.2144 |
| 0.34          | 1.5828 | 22000 | 0.3074          | 0.1973 |
| 0.3157        | 1.7267 | 24000 | 0.2809          | 0.1922 |
| 0.3041        | 1.8706 | 26000 | 0.2538          | 0.1751 |
| 0.2642        | 2.0145 | 28000 | 0.2620          | 0.1687 |
| 0.2296        | 2.1584 | 30000 | 0.2355          | 0.1521 |
| 0.2099        | 2.3023 | 32000 | 0.2279          | 0.1454 |
| 0.2037        | 2.4462 | 34000 | 0.2235          | 0.1405 |
| 0.1905        | 2.5901 | 36000 | 0.1988          | 0.1244 |
| 0.1876        | 2.7340 | 38000 | 0.1919          | 0.1227 |
| 0.1739        | 2.8779 | 40000 | 0.1865          | 0.1153 |
| 0.1387        | 3.0218 | 42000 | 0.1831          | 0.1077 |
| 0.1261        | 3.1657 | 44000 | 0.1760          | 0.1007 |
| 0.1146        | 3.3096 | 46000 | 0.1597          | 0.0945 |
| 0.1142        | 3.4535 | 48000 | 0.1602          | 0.0936 |
| 0.0998        | 3.5974 | 50000 | 0.1472          | 0.0861 |
| 0.102         | 3.7413 | 52000 | 0.1413          | 0.0823 |
| 0.0985        | 3.8852 | 54000 | 0.1376          | 0.0783 |
| 0.08          | 4.0291 | 56000 | 0.1338          | 0.0765 |
| 0.072         | 4.1730 | 58000 | 0.1319          | 0.0743 |
| 0.0677        | 4.3169 | 60000 | 0.1299          | 0.0737 |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1