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---
license: mit
base_model: facebook/w2v-bert-2.0
tags:
- generated_from_trainer
- asr
- w2v-bert-2.0
datasets:
- common_voice_16_1
metrics:
- wer
- cer
- bertscore
model-index:
- name: w2v-bert-2.0-pt_pt_v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_16_1
type: common_voice_16_1
config: pt
split: validation
args: pt
metrics:
- name: Wer
type: wer
value: 0.08315087821729188
language:
- pt
---
<!-- 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-pt_pt_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_1 Portuguese subset using 1XRTX 3090.
It achieves the following results on the test set:
- Wer: 0.10491320595991134
- Cer: 0.032070871626631914
- Bert Score: 0.9619712047981167
- Sentence Similarity: 0.93867844
## 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_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Bert Score |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:----------:|
| 1.2735 | 1.0 | 678 | 0.2292 | 0.1589 | 0.0415 | 0.9498 |
| 0.1715 | 2.0 | 1356 | 0.1762 | 0.1283 | 0.0344 | 0.9599 |
| 0.1158 | 3.0 | 2034 | 0.1539 | 0.1100 | 0.0298 | 0.9646 |
| 0.0821 | 4.0 | 2712 | 0.1362 | 0.0949 | 0.0258 | 0.9703 |
| 0.0605 | 5.0 | 3390 | 0.1349 | 0.0860 | 0.0236 | 0.9728 |
| 0.0475 | 6.0 | 4068 | 0.1395 | 0.0871 | 0.0239 | 0.9728 |
| 0.0355 | 7.0 | 4746 | 0.1487 | 0.0837 | 0.0230 | 0.9739 |
| 0.0309 | 8.0 | 5424 | 0.1452 | 0.0873 | 0.0240 | 0.9728 |
| 0.0308 | 9.0 | 6102 | 0.1390 | 0.0843 | 0.0228 | 0.9735 |
| 0.0239 | 10.0 | 6780 | 0.1282 | 0.0832 | 0.0224 | 0.9739 |
### Evaluation results
| Test Wer | Test Cer | Test Bert Score | Runtime | Samples per second |
|:------------------:|:-------------------:|:-----------------:|:-------:|:---------------------:|
| 0.09146400542583083| 0.02643665913309742 | 0.9702128323433327| 266.8185| 35.282 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.0
- Datasets 2.18.0
- Tokenizers 0.15.2 |