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1-800-BAD-CODE/morsecode_en_quartznet_10x5
morsecode_en_quartznet_10x5 is a machine learning model from 1-800-BAD-CODE. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for nemo.
This model is a Morse Code recognition model. It was trained with the package at https://github.com/1-800-BAD-CODE/MorseCodeToolkit.
Downloads · 30 days
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From the Hugging Face model README
This model is a Morse Code recognition model. It was trained with the package at https://github.com/1-800-BAD-CODE/MorseCodeToolkit.
This model accepts as input audio signals sampled at 8khz containing Morse code. The model produces the English transcription of the Morse code signal.
For inference, only the base NeMo package needs to be installed because this is just an ASR model trained to decode Morse code signals rather than speech signals.
With NeMo is installed, this model can be used to run inference on Morse code audio files.
import nemo.collections.asr as nemo_asr
asr_model = nemo_asr.models.ASRModel.from_pretrained("1-800-BAD-CODE/morsecode_en_quartznet_10x5")
First, let's download an example Morse code audio file from Wikipedia:
wget https://upload.wikimedia.org/wikipedia/commons/0/04/Wikipedia-Morse.ogg
Then simply do:
asr_model.transcribe(['Wikipedia-Morse.ogg'])
['WELCOME TO WIKIPEDIA, THE FREE ENCYCLOPEDIA THAT ANYONE CAN EDIT.']
This model was trained on synthetic Morse code data generated by https://github.com/1-800-BAD-CODE/MorseCodeToolkit. Any Morse code generated with parameters outside of the range of the parameters used to generate the training data will not be well recognized by the model.