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 🗣:

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

Framework versions

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