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whisper-medium-portuguese-ggml-model

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metadata
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 .

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!).

OpenAI results with Whisper Medium and Test dataset of Commons Voice 9.0

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