---
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- fleurs
metrics:
- wer
model-index:
- name: w2v-bert-2.0-lg-CV-Fleurs-filtered-100hrs-v11
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: fleurs
      type: fleurs
      config: lg_ug
      split: test
      args: lg_ug
    metrics:
    - name: Wer
      type: wer
      value: 0.42011661807580175
---

<!-- 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-lg-CV-Fleurs-filtered-100hrs-v11

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4163
- Wer: 0.4201
- Cer: 0.0816

## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use 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_ratio: 0.1
- num_epochs: 70
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step   | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|
| 0.8755        | 1.0   | 7125   | 0.3901          | 0.4546 | 0.0912 |
| 0.1904        | 2.0   | 14250  | 0.3507          | 0.4646 | 0.0896 |
| 0.1641        | 3.0   | 21375  | 0.3312          | 0.4389 | 0.0854 |
| 0.1511        | 4.0   | 28500  | 0.3229          | 0.4311 | 0.0802 |
| 0.145         | 5.0   | 35625  | 0.3518          | 0.4273 | 0.0820 |
| 0.1406        | 6.0   | 42750  | 0.3275          | 0.4302 | 0.0812 |
| 0.1379        | 7.0   | 49875  | 0.3627          | 0.5317 | 0.0944 |
| 0.1316        | 8.0   | 57000  | 0.3234          | 0.4175 | 0.0817 |
| 0.1182        | 9.0   | 64125  | 0.3534          | 0.4317 | 0.0819 |
| 0.1082        | 10.0  | 71250  | 0.3230          | 0.4053 | 0.0777 |
| 0.0976        | 11.0  | 78375  | 0.3261          | 0.4194 | 0.0791 |
| 0.0884        | 12.0  | 85500  | 0.3183          | 0.4119 | 0.0798 |
| 0.0803        | 13.0  | 92625  | 0.3695          | 0.4170 | 0.0791 |
| 0.072         | 14.0  | 99750  | 0.3596          | 0.4102 | 0.0799 |
| 0.0637        | 15.0  | 106875 | 0.3625          | 0.4137 | 0.0803 |
| 0.0554        | 16.0  | 114000 | 0.3958          | 0.4336 | 0.0822 |
| 0.0481        | 17.0  | 121125 | 0.3820          | 0.4128 | 0.0796 |
| 0.0422        | 18.0  | 128250 | 0.4239          | 0.4134 | 0.0807 |
| 0.0357        | 19.0  | 135375 | 0.3978          | 0.4436 | 0.0839 |
| 0.031         | 20.0  | 142500 | 0.4163          | 0.4201 | 0.0816 |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.1.0+cu118
- Datasets 3.1.0
- Tokenizers 0.20.3