{
"song_id": [[start_time, end_time, pitch], ...]
...
}
For example:
{
"1": [[0.0, 0.5, 60], ...],
...
"123": [[0.0, 0.5, 58], ...]
}
This script is used to run efficientnet (TODO: run RNN with pretrained model).
It does svs first (using spleeter), and then run efficientnet. Finally, it generates a midi file.
python do_everything.py $input_wav_file $output_path
input_wav_file: Path to the input file.output_path: Output midi path.
The default model is efficientnet-b3 and the model path is "sing_voice_transcription/efficientnet/b4_e_6600"
If you want to specify the model path, then you can add another argument:
python do_everything.py $input_wav_file $output_path -mp $model_path
model_path: Path to the model file.
This script is used to calculate the evaluation measures.
python evaluate.py $gt_file $predicted_file
gt_file: Ground truth file with .json format.predicted_file: Predicted file with .json format.
This script will read from a data directory and generate custom dataset class instance into a binary file.
python generate_dataset.py $data_dir $output_dir --for-rnn
data_dir: Directory of the songs.output_dir: Path to the directory to save the dataset.pkl.--for-rnn: Flag for generating dataset for RNN training.