---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
model-index:
- name: w2v-bert-2.0-bangala-gpu-CV16.0_v2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_16_0
      type: common_voice_16_0
      config: bn
      split: test
      args: bn
    metrics:
    - name: Wer
      type: wer
      value: 0.4811011116993118
---

<!-- 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-2.0-bangala-gpu-CV16.0_v2

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the common_voice_16_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4490
- Wer: 0.4811

## 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: 4.42184e-05
- 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.5221        | 0.31  | 300  | 0.5900          | 0.6271 |
| 1.2024        | 0.63  | 600  | 0.4088          | 0.4071 |
| 0.9149        | 0.94  | 900  | 0.3200          | 0.3270 |
| 0.8124        | 1.26  | 1200 | 0.2965          | 0.3080 |
| 0.7028        | 1.57  | 1500 | 0.2759          | 0.2884 |
| 0.6301        | 1.89  | 1800 | 0.2435          | 0.2671 |
| 0.6147        | 2.2   | 2100 | 0.2335          | 0.2477 |
| 0.6304        | 2.52  | 2400 | 0.2248          | 0.2458 |
| 0.5921        | 2.83  | 2700 | 0.2326          | 0.2441 |
| 0.495         | 3.15  | 3000 | 0.2180          | 0.2378 |
| 0.4987        | 3.46  | 3300 | 0.2139          | 0.2227 |
| 0.5669        | 3.78  | 3600 | 0.2097          | 0.2236 |
| 0.5904        | 4.09  | 3900 | 0.2038          | 0.2178 |
| 0.6016        | 4.41  | 4200 | 0.2091          | 0.2131 |
| 0.5325        | 4.72  | 4500 | 0.2064          | 0.2147 |
| 0.5271        | 5.04  | 4800 | 0.2002          | 0.2159 |
| 0.5229        | 5.35  | 5100 | 0.2069          | 0.2209 |
| 0.5843        | 5.67  | 5400 | 0.2090          | 0.2202 |
| 0.5477        | 5.98  | 5700 | 0.2085          | 0.2175 |
| 0.508         | 6.3   | 6000 | 0.2046          | 0.2158 |
| 0.5226        | 6.61  | 6300 | 0.2515          | 0.3250 |
| 0.7576        | 6.93  | 6600 | 0.2343          | 0.2364 |
| 1.0089        | 7.24  | 6900 | 0.2731          | 0.2713 |
| 0.9462        | 7.56  | 7200 | 0.2588          | 0.2648 |
| 0.8648        | 7.87  | 7500 | 0.2916          | 0.3393 |
| 1.1282        | 8.19  | 7800 | 0.3830          | 0.4583 |
| 1.3279        | 8.5   | 8100 | 0.3910          | 0.4117 |
| 1.2722        | 8.82  | 8400 | 0.4424          | 0.3442 |
| 1.2886        | 9.13  | 8700 | 0.4421          | 0.4011 |
| 1.3274        | 9.45  | 9000 | 0.4483          | 0.4769 |
| 1.3235        | 9.76  | 9300 | 0.4490          | 0.4811 |


### Framework versions

- Transformers 4.39.3
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0