This model is a conversion to ggml from 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 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!.

New SOTA

The Normalized WER in the OpenAI Whisper article with the Common Voice 9.0 test dataset is 8.1.

As this test dataset is similar to the 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 model at transcribing audios Portuguese in text (and even better than the 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

Gustrd
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whisper-medium-portuguese-ggml-model