metadata

language:
  - pt
license: apache-2.0
tags:
  - whisper-event
  - generated_from_trainer
datasets:
  - mozilla-foundation/common_voice_11_0
metrics:
  - wer
model-index:
  - name: Whisper Medium Portuguese
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: mozilla-foundation/common_voice_11_0 pt
          type: mozilla-foundation/common_voice_11_0
          config: pt
          split: test
          args: pt
        metrics:
          - name: Wer
            type: wer
            value: 6.5785713084850626

Whisper Medium Portuguese 🇧🇷🇵🇹

Bem-vindo ao whisper medium para transcrição em português 👋🏻

If you are looking to quickly, and reliably, transcribe Portuguese audio to text, you are in the right place!

With a state-of-the-art Word Error Rate (WER) of just 6.579 in Common Voice 11, this model offers an x2 precision increase compared to prior state-of-the-art wav2vec2 models. Compared to the original whisper-medium model it delivers an x1.2 improvement 🚀.

This model is a fine-tuned version of openai/whisper-medium on the mozilla-foundation/common_voice_11 dataset.

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 🗣:

Model	WER	Parameters
openai/whisper-medium	8.100	769M
jlondonobo/whisper-medium-pt	6.579 🤗	769M
jonatasgrosman/wav2vec2-large-xlsr-53-portuguese	11.310	317M
Edresson/wav2vec2-large-xlsr-coraa-portuguese	20.080	317M

How to use

You can use this model directly with a pipeline. This is especially useful for short audio. For long-form transcriptions please use the code in the Long-form transcription section.

pip install git+https://github.com/huggingface/transformers --force-reinstall
pip install torch

>>> from transformers import pipeline
>>> import torch

>>> device = 0 if torch.cuda.is_available() else "cpu"

# Load the pipeline
>>> transcribe = pipeline(
...     task="automatic-speech-recognition",
...     model="jlondonobo/whisper-medium-pt",
...     chunk_length_s=30,
...     device=device,
... )

# Force model to transcribe in Portuguese
>>> transcribe.model.config.forced_decoder_ids = transcribe.tokenizer.get_decoder_prompt_ids(language="pt", task="transcribe")

# Transcribe your audio file
>>> transcribe("audio.m4a")["text"]
'Eu falo português.'

Long-form transcription

To improve the performance of long-form transcription you can convert the HF model into a whisper model, and use the original paper's matching algorithm. To do this, you must install whisper and a set of tools developed by @bayartsogt.

pip install git+https://github.com/openai/whisper.git
pip install git+https://github.com/bayartsogt-ya/whisper-multiple-hf-datasets

Then convert the HuggingFace model and transcribe:

>>> import torch
>>> import whisper
>>> from multiple_datasets.hub_default_utils import convert_hf_whisper

>>> device = "cuda" if torch.cuda.is_available() else "cpu"

# Write HF model to local whisper model
>>> convert_hf_whisper("jlondonobo/whisper-medium-pt", "local_whisper_model.pt")

# Load the whisper model
>>> model = whisper.load_model("local_whisper_model.pt", device=device)

# Transcribe arbitrarily long audio
>>> model.transcribe("long_audio.m4a", language="pt")["text"]
'Olá eu sou o José. Tenho 23 anos e trabalho...'

Training hyperparameters

We used the following hyperparameters for training:

learning_rate: 1e-05
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: 5000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0698	1.09	1000	0.1876	7.189
0.0218	3.07	2000	0.2254	7.110
0.0053	5.06	3000	0.2711	6.969
0.0017	7.04	4000	0.3030	6.686
0.0005	9.02	5000	0.3205	6.579 🤗

Framework versions

Transformers 4.26.0.dev0
Pytorch 1.13.0+cu117
Datasets 2.7.1.dev0
Tokenizers 0.13.2