---
base_model: Umong/wav2vec2-xls-r-300m-bengali
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-bengali
  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. -->

# wav2vec2-xls-r-300m-bengali

This model is a fine-tuned version of [Umong/wav2vec2-xls-r-300m-bengali](https://huggingface.co/Umong/wav2vec2-xls-r-300m-bengali) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1636
- Wer: 0.0883

## 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: 500
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 5.3076        | 0.16  | 400   | 1.5883          | 0.9394 |
| 0.8841        | 0.33  | 800   | 0.5188          | 0.5337 |
| 0.5896        | 0.49  | 1200  | 0.4029          | 0.4340 |
| 0.4964        | 0.66  | 1600  | 0.3429          | 0.3766 |
| 0.4553        | 0.82  | 2000  | 0.3196          | 0.3642 |
| 0.4222        | 0.99  | 2400  | 0.3004          | 0.3436 |
| 0.3709        | 1.15  | 2800  | 0.2812          | 0.3225 |
| 0.352         | 1.32  | 3200  | 0.2753          | 0.3124 |
| 0.3283        | 1.48  | 3600  | 0.2616          | 0.2979 |
| 0.3235        | 1.65  | 4000  | 0.2573          | 0.2944 |
| 0.3129        | 1.81  | 4400  | 0.2458          | 0.2809 |
| 0.306         | 1.98  | 4800  | 0.2344          | 0.2771 |
| 0.2701        | 2.14  | 5200  | 0.2318          | 0.2661 |
| 0.2653        | 2.31  | 5600  | 0.2253          | 0.2629 |
| 0.2626        | 2.47  | 6000  | 0.2186          | 0.2542 |
| 0.2541        | 2.63  | 6400  | 0.2074          | 0.2474 |
| 0.2235        | 2.8   | 6800  | 0.2102          | 0.2442 |
| 0.2185        | 2.96  | 7200  | 0.2019          | 0.2327 |
| 0.2061        | 3.13  | 7600  | 0.1994          | 0.2308 |
| 0.2011        | 3.29  | 8000  | 0.1942          | 0.2260 |
| 0.1986        | 3.46  | 8400  | 0.1867          | 0.2187 |
| 0.197         | 3.62  | 8800  | 0.1825          | 0.2177 |
| 0.1931        | 3.79  | 9200  | 0.1856          | 0.2153 |
| 0.1879        | 3.95  | 9600  | 0.1777          | 0.2088 |
| 0.1599        | 4.12  | 10000 | 0.1781          | 0.0968 |
| 0.153         | 4.28  | 10400 | 0.1738          | 0.0944 |
| 0.1475        | 4.45  | 10800 | 0.1713          | 0.0905 |
| 0.1448        | 4.61  | 11200 | 0.1683          | 0.0907 |
| 0.1445        | 4.78  | 11600 | 0.1649          | 0.0897 |
| 0.1423        | 4.94  | 12000 | 0.1636          | 0.0883 |


### Framework versions

- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.14.5
- Tokenizers 0.13.3