---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: w2v-bert-2.0-tigre-colab-CV17.0-v2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: common_voice_17_0
      type: common_voice_17_0
      config: tig
      split: test
      args: tig
    metrics:
    - name: Wer
      type: wer
      value: 0.43169398907103823
---

<!-- 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-tigre-colab-CV17.0-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_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6193
- Wer: 0.4317

## 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: 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_ratio: 0.15
- training_steps: 2000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch    | Step | Validation Loss | Wer    |
|:-------------:|:--------:|:----:|:---------------:|:------:|
| 6.7881        | 13.7931  | 200  | 0.9737          | 0.6175 |
| 0.1599        | 27.5862  | 400  | 1.2407          | 0.5310 |
| 0.0256        | 41.3793  | 600  | 1.3566          | 0.4781 |
| 0.0036        | 55.1724  | 800  | 1.5251          | 0.4554 |
| 0.0058        | 68.9655  | 1000 | 1.4813          | 0.4699 |
| 0.0023        | 82.7586  | 1200 | 1.5533          | 0.4435 |
| 0.0001        | 96.5517  | 1400 | 1.5861          | 0.4372 |
| 0.0001        | 110.3448 | 1600 | 1.6056          | 0.4362 |
| 0.0001        | 124.1379 | 1800 | 1.6159          | 0.4326 |
| 0.0001        | 137.9310 | 2000 | 1.6193          | 0.4317 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1