---
library_name: transformers
language:
- lg
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- Grain
metrics:
- wer
model-index:
- name: w
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Grain
      type: Grain
    metrics:
    - name: Wer
      type: wer
      value: 0.029878515924263983
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# w

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the Grain dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0469
- Wer: 0.0299
- Cer: 0.0077

## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 0.2995        | 1.0   | 1164  | 0.1521          | 0.1390 | 0.0283 |
| 0.1049        | 2.0   | 2328  | 0.0931          | 0.0946 | 0.0189 |
| 0.0719        | 3.0   | 3492  | 0.0861          | 0.0902 | 0.0183 |
| 0.0546        | 4.0   | 4656  | 0.0788          | 0.0704 | 0.0166 |
| 0.0447        | 5.0   | 5820  | 0.0609          | 0.0627 | 0.0135 |
| 0.0374        | 6.0   | 6984  | 0.0744          | 0.0618 | 0.0141 |
| 0.0338        | 7.0   | 8148  | 0.0673          | 0.0535 | 0.0137 |
| 0.029         | 8.0   | 9312  | 0.0770          | 0.0540 | 0.0128 |
| 0.0278        | 9.0   | 10476 | 0.0565          | 0.0482 | 0.0116 |
| 0.0227        | 10.0  | 11640 | 0.0516          | 0.0500 | 0.0115 |
| 0.0211        | 11.0  | 12804 | 0.0457          | 0.0392 | 0.0096 |
| 0.0207        | 12.0  | 13968 | 0.0527          | 0.0452 | 0.0098 |
| 0.0179        | 13.0  | 15132 | 0.0463          | 0.0370 | 0.0089 |
| 0.017         | 14.0  | 16296 | 0.0530          | 0.0452 | 0.0109 |
| 0.0167        | 15.0  | 17460 | 0.0447          | 0.0360 | 0.0091 |
| 0.0141        | 16.0  | 18624 | 0.0529          | 0.0434 | 0.0104 |
| 0.015         | 17.0  | 19788 | 0.0410          | 0.0387 | 0.0090 |
| 0.0141        | 18.0  | 20952 | 0.0480          | 0.0416 | 0.0102 |
| 0.0136        | 19.0  | 22116 | 0.0472          | 0.0368 | 0.0087 |
| 0.0125        | 20.0  | 23280 | 0.0428          | 0.0380 | 0.0091 |
| 0.0117        | 21.0  | 24444 | 0.0375          | 0.0328 | 0.0081 |
| 0.0113        | 22.0  | 25608 | 0.0392          | 0.0312 | 0.0083 |
| 0.0093        | 23.0  | 26772 | 0.0554          | 0.0394 | 0.0102 |
| 0.0111        | 24.0  | 27936 | 0.0624          | 0.0452 | 0.0108 |
| 0.0107        | 25.0  | 29100 | 0.0390          | 0.0346 | 0.0076 |
| 0.0082        | 26.0  | 30264 | 0.0505          | 0.0426 | 0.0101 |
| 0.0087        | 27.0  | 31428 | 0.0430          | 0.0320 | 0.0081 |
| 0.0086        | 28.0  | 32592 | 0.0541          | 0.0398 | 0.0101 |
| 0.0079        | 29.0  | 33756 | 0.0404          | 0.0304 | 0.0070 |
| 0.0084        | 30.0  | 34920 | 0.0416          | 0.0315 | 0.0075 |
| 0.0084        | 31.0  | 36084 | 0.0495          | 0.0366 | 0.0092 |
| 0.0075        | 32.0  | 37248 | 0.0469          | 0.0299 | 0.0077 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.1.0+cu118
- Datasets 3.0.1
- Tokenizers 0.20.1