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--- |
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language: |
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- pt |
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license: apache-2.0 |
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tags: |
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- whisper-event |
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- generated_from_trainer |
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datasets: |
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- mozilla-foundation/common_voice_11_0 |
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metrics: |
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- wer |
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model-index: |
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- name: Whisper Medium Portuguese |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: mozilla-foundation/common_voice_11_0 pt |
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type: mozilla-foundation/common_voice_11_0 |
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config: pt |
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split: test |
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args: pt |
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metrics: |
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- name: Wer |
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type: wer |
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value: 6.5785713084850626 |
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--- |
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# Whisper Medium Portuguese 🇧🇷🇵🇹 |
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Bem-vindo ao whisper medium para transcrição em português 👋🏻 |
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If you are looking to **quickly**, and **reliably**, transcribe Portuguese audio to text, you are in the right place! |
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With a state-of-the-art [Word Error Rate](https://huggingface.co/spaces/evaluate-metric/wer) (WER) of just **6.579** in Common Voice 11, this model offers an **x2** precision increase compared to prior state-of-the-art [wav2vec2](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) models. Compared to the original [whisper-medium](https://huggingface.co/openai/whisper-medium) model it delivers an **x1.2** improvement 🚀. |
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This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the [mozilla-foundation/common_voice_11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) dataset. |
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The following table displays a **comparison** between the results of our model and those achieved by the most downloaded models in the hub for [Portuguese Automatic Speech Recognition](https://huggingface.co/models?language=pt&pipeline_tag=automatic-speech-recognition&sort=downloads) 🗣: |
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| Model | WER | Parameters | |
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|--------------------------------------------------|:--------:|:------------:| |
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| [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) | 8.100 | 769M | |
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| [jlondonobo/whisper-medium-pt](https://huggingface.co/jlondonobo/whisper-medium-pt) | **6.579** 🤗 | 769M | |
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| [jonatasgrosman/wav2vec2-large-xlsr-53-portuguese](https://huggingface.co/jonatasgrosman/wav2vec2-large-xlsr-53-portuguese) | 11.310 | 317M | |
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| [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) | 20.080 | 317M | |
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### Training hyperparameters |
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We used the following hyperparameters for training: |
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- `learning_rate`: 1e-05 |
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- `train_batch_size`: 32 |
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- `eval_batch_size`: 16 |
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- `seed`: 42 |
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- `optimizer`: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_warmup_steps`: 500 |
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- `training_steps`: 5000 |
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- `mixed_precision_training`: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:----:|:---------------:|:------:| |
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| 0.0698 | 1.09 | 1000 | 0.1876 | 7.189 | |
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| 0.0218 | 3.07 | 2000 | 0.2254 | 7.110 | |
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| 0.0053 | 5.06 | 3000 | 0.2711 | 6.969 | |
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| 0.0017 | 7.04 | 4000 | 0.3030 | 6.686 | |
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| 0.0005 | 9.02 | 5000 | 0.3205 | **6.579** 🤗 | |
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### Framework versions |
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- Transformers 4.26.0.dev0 |
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- Pytorch 1.13.0+cu117 |
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- Datasets 2.7.1.dev0 |
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- Tokenizers 0.13.2 |
