---
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!).

![OpenAI results with Whisper Medium and Test dataset of Commons Voice 9.0](https://huggingface.co/pierreguillou/whisper-medium-portuguese/resolve/main/whisper_medium_portuguese_wer_commonvoice9.png)

## 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