---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-bengali-macro
  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-macro

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3787
- Wer: 0.88

## 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: 1
- 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: 1

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.686         | 0.02  | 500   | 2.9368          | 0.9254 |
| 1.465         | 0.03  | 1000  | 1.6714          | 0.88   |
| 1.2139        | 0.05  | 1500  | 1.6254          | 0.8292 |
| 1.1463        | 0.07  | 2000  | 1.5170          | 0.8292 |
| 1.12          | 0.08  | 2500  | 1.4973          | 0.7966 |
| 1.0766        | 0.1   | 3000  | 1.5682          | 0.8129 |
| 1.0547        | 0.12  | 3500  | 1.3838          | 0.7458 |
| 1.0163        | 0.13  | 4000  | 1.6073          | 0.8685 |
| 1.0149        | 0.15  | 4500  | 1.3993          | 0.7247 |
| 1.0125        | 0.17  | 5000  | 1.4888          | 0.7749 |
| 0.9882        | 0.18  | 5500  | 1.3766          | 0.7444 |
| 0.9736        | 0.2   | 6000  | 1.5816          | 0.8027 |
| 0.9737        | 0.22  | 6500  | 1.5761          | 0.7783 |
| 0.9445        | 0.23  | 7000  | 1.3593          | 0.7505 |
| 0.9335        | 0.25  | 7500  | 1.3453          | 0.7247 |
| 0.931         | 0.27  | 8000  | 1.4024          | 0.7397 |
| 0.9389        | 0.28  | 8500  | 1.5973          | 0.8508 |
| 0.9152        | 0.3   | 9000  | 1.4021          | 0.7193 |
| 0.9042        | 0.32  | 9500  | 1.3642          | 0.7620 |
| 0.8962        | 0.33  | 10000 | 1.4298          | 0.7383 |
| 0.8767        | 0.35  | 10500 | 1.4478          | 0.7580 |
| 0.8853        | 0.37  | 11000 | 1.3255          | 0.7302 |
| 0.8739        | 0.38  | 11500 | 1.3791          | 0.7431 |
| 0.8597        | 0.4   | 12000 | 1.5847          | 0.8325 |
| 0.8815        | 0.42  | 12500 | 1.6785          | 0.8163 |
| 0.8736        | 0.43  | 13000 | 1.6222          | 0.7871 |
| 0.8643        | 0.45  | 13500 | 1.8635          | 0.8502 |
| 0.84          | 0.46  | 14000 | 1.4343          | 0.7803 |
| 0.8323        | 0.48  | 14500 | 1.7500          | 0.8427 |
| 0.8223        | 0.5   | 15000 | 1.6916          | 0.8278 |
| 0.827         | 0.51  | 15500 | 2.6214          | 0.9085 |
| 0.8149        | 0.53  | 16000 | 1.6750          | 0.8169 |
| 0.8149        | 0.55  | 16500 | 1.7646          | 0.8142 |
| 0.8032        | 0.56  | 17000 | 2.1347          | 0.8617 |
| 0.8005        | 0.58  | 17500 | 1.7216          | 0.8122 |
| 0.7956        | 0.6   | 18000 | 2.3053          | 0.8936 |
| 0.7888        | 0.61  | 18500 | 1.7773          | 0.8359 |
| 0.7919        | 0.63  | 19000 | 2.2394          | 0.8597 |
| 0.7888        | 0.65  | 19500 | 1.5470          | 0.7403 |
| 0.7721        | 0.66  | 20000 | 1.6034          | 0.7593 |
| 0.7603        | 0.68  | 20500 | 1.6808          | 0.7803 |
| 0.751         | 0.7   | 21000 | 1.7942          | 0.8217 |
| 0.7555        | 0.71  | 21500 | 1.9897          | 0.8441 |
| 0.7583        | 0.73  | 22000 | 2.3329          | 0.8576 |
| 0.7346        | 0.75  | 22500 | 2.2255          | 0.8515 |
| 0.754         | 0.76  | 23000 | 2.2606          | 0.8861 |
| 0.7309        | 0.78  | 23500 | 2.0292          | 0.8529 |
| 0.7351        | 0.8   | 24000 | 2.4471          | 0.8942 |
| 0.7456        | 0.81  | 24500 | 2.1406          | 0.8224 |
| 0.7229        | 0.83  | 25000 | 2.4474          | 0.8888 |
| 0.7253        | 0.85  | 25500 | 2.0324          | 0.8441 |
| 0.7109        | 0.86  | 26000 | 2.2594          | 0.8671 |
| 0.7316        | 0.88  | 26500 | 2.3887          | 0.8827 |
| 0.716         | 0.9   | 27000 | 2.4739          | 0.8915 |
| 0.7264        | 0.91  | 27500 | 2.4291          | 0.8922 |
| 0.701         | 0.93  | 28000 | 2.3306          | 0.8936 |
| 0.7025        | 0.95  | 28500 | 2.3172          | 0.8834 |
| 0.6963        | 0.96  | 29000 | 2.4020          | 0.8841 |
| 0.6952        | 0.98  | 29500 | 2.4324          | 0.8895 |
| 0.6985        | 1.0   | 30000 | 2.3787          | 0.88   |


### Framework versions

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