omnizart vocal-contour

Lists the available options of each sub-command.

transcribe

omnizart vocal-contour transcribe

Transcribe a single audio and output as a WAV file.

This will output a WAV file with the same name as the given audio, except the extension will be replaced with ‘.wav’.

Example Usage

$ omnizart vocal-contour transcribe

example.wav

–model-path path/to/model

–output example.mid

omnizart vocal-contour transcribe [OPTIONS] INPUT_AUDIO

Options

-m, --model-path <model_path>

Path to the pre-trained model or the supported transcription mode.

-o, --output <output>

Path to output the prediction file (could be MIDI, CSV, …, etc.)

Default

./

Arguments

INPUT_AUDIO

Required argument

generate-feature

omnizart vocal_contour generate-feature

Extract the feature of the whole dataset for training.

The command will try to infer the dataset type from the given dataset path.

* MIR-1K

* MedleyDB

omnizart vocal_contour generate-feature [OPTIONS]

Options

-d, --dataset-path <dataset_path>

Required Path to the downloaded dataset

-o, --output-path <output_path>

Path for saving the extracted feature. Default to the folder under the dataset.

-n, --num-threads <num_threads>

Number of threads used for parallel feature extraction.

Default

4

-h, --hop-size <hop_size>

Hop size in seconds with respect to sampling rate.

Default

0.02

-s, --sampling-rate <sampling_rate>

Adjust input sampling rate to this value.

Default

16000

train-model

omnizart vocal_contour train-model

Train a new model or continue to train on a pre-trained model

omnizart vocal_contour train-model [OPTIONS]

Options

-d, --feature-path <feature_path>

Required Path to the folder of extracted feature

-m, --model-name <model_name>

Name for the output model (can be a path)

-i, --input-model <input_model>

If given, the training will continue to fine-tune the pre-trained model.

-e, --epochs <epochs>

Number of training epochs

-s, --steps <steps>

Number of training steps of each epoch

-vs, --val-steps <val_steps>

Number of validation steps of each epoch

-b, --batch-size <batch_size>

Batch size of each training step

-vb, --val-batch-size <val_batch_size>

Batch size of each validation step

--early-stop <early_stop>

Stop the training if validation accuracy does not improve over the given number of epochs.