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---
language: pt
license: apache-2.0
tags:
- generated_from_trainer
- whisper-event
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: openai/whisper-medium
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: mozilla-foundation/common_voice_11_0
type: mozilla-foundation/common_voice_11_0
config: pt
split: test
args: pt
metrics:
- name: Wer
type: wer
value: 6.598745817992301
---
This model is a conversion to ggml from [pierreguillou/whisper-medium-portuguese](https://huggingface.co/pierreguillou/whisper-medium-portuguese) .
The conversion was done at 2023-09-11 with the official script convert-h5-to-ggml.py from whisper.cpp. No special parameters were used.
# Original Card - Portuguese Medium Whisper
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2628
- Wer: 6.5987
## Blog post
All information about this model in this blog post: [Speech-to-Text & IA | Transcreva qualquer áudio para o português com o Whisper (OpenAI)... sem nenhum custo!](https://medium.com/@pierre_guillou/speech-to-text-ia-transcreva-qualquer-%C3%A1udio-para-o-portugu%C3%AAs-com-o-whisper-openai-sem-ad0c17384681).
## New SOTA
The Normalized WER in the [OpenAI Whisper article](https://cdn.openai.com/papers/whisper.pdf) with the [Common Voice 9.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_9_0) test dataset is 8.1.
As this test dataset is similar to the [Common Voice 11.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) test dataset used to evaluate our model (WER and WER Norm), it means that **our Portuguese Medium Whisper is better than the [Medium Whisper](https://huggingface.co/openai/whisper-medium) model at transcribing audios Portuguese in text** (and even better than the [Whisper Large](https://huggingface.co/openai/whisper-large) that has a WER Norm of 7.1!).
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 9e-06
- train_batch_size: 32
- eval_batch_size: 16
- 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: 6000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0333 | 2.07 | 1500 | 0.2073 | 6.9770 |
| 0.0061 | 5.05 | 3000 | 0.2628 | 6.5987 |
| 0.0007 | 8.03 | 4500 | 0.2960 | 6.6979 |
| 0.0004 | 11.0 | 6000 | 0.3212 | 6.6794 |
### Framework versions
- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2
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