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---
language:
- pt
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Whisper small using Common Voice 16 (pt)
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Mozilla Common Voices - 16.0 - Portuguese
type: mozilla-foundation/common_voice_16_0
config: pt
split: test
args: pt
metrics:
- name: Wer
type: wer
value: 16.035875888817067
---
<!-- 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. -->
# Whisper small using Common Voice 16 (pt)
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Mozilla Common Voices - 16.0 - Portuguese dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2220
- Wer: 16.0359
- Wer Normalized: 10.3867
## 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-06
- 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
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Wer Normalized |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:--------------:|
| 0.2484 | 0.26 | 500 | 0.2712 | 19.2259 | 13.0929 |
| 0.2184 | 0.52 | 1000 | 0.2464 | 17.8895 | 11.9404 |
| 0.236 | 0.77 | 1500 | 0.2339 | 17.1348 | 11.3016 |
| 0.1401 | 1.03 | 2000 | 0.2285 | 16.7001 | 11.0432 |
| 0.1206 | 1.29 | 2500 | 0.2251 | 16.3235 | 10.6467 |
| 0.1199 | 1.55 | 3000 | 0.2236 | 16.1732 | 10.5424 |
| 0.1231 | 1.81 | 3500 | 0.2197 | 16.1587 | 10.5038 |
| 0.0935 | 2.06 | 4000 | 0.2220 | 16.0359 | 10.3867 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
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