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---
language:
- pt
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Sussurrar
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: pt
split: test
args: pt
metrics:
- name: Wer
type: wer
value: 26.260504201680675
---
<!-- 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. -->
# Sussurrar
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4367
- Wer: 26.2605
## Model description
The model is fine-tuned for ASR in Portuguese. We decided to train in Portuguese because it is a very common language, yet does not have many resources in terms of NLP.
## Intended uses & limitations
The model is used for Automatic Speach Recognition. It is fine-tuned in the Portuguese language.
## Training and evaluation data
Trained and evaluated on the Common Voice 11 Portuguese data.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- 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
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.4076 | 0.1 | 200 | 0.5182 | 32.4930 |
| 0.3462 | 0.2 | 400 | 0.4912 | 29.0266 |
| 0.3283 | 0.3 | 600 | 0.4671 | 27.0308 |
| 0.3579 | 0.4 | 800 | 0.4662 | 26.6457 |
| 0.2766 | 0.5 | 1000 | 0.4639 | 26.7157 |
| 0.2147 | 1.03 | 1200 | 0.4470 | 26.7857 |
| 0.1877 | 1.13 | 1400 | 0.4382 | 26.4006 |
| 0.192 | 1.23 | 1600 | 0.4430 | 26.3655 |
| 0.1894 | 1.33 | 1800 | 0.4349 | 26.4006 |
| 0.1725 | 1.43 | 2000 | 0.4367 | 26.2605 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu116
- Datasets 2.8.1.dev0
- Tokenizers 0.13.2
|