Update README.md

README.md

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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!).
+
+![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
+
+
+