Reproduce from scratch¶
To recreate the ground-truth in our format you have to convert the annotations
using the scirpt generate_ground_truth.py.
N.B. You should have ``wget`` installed in your system, otherwise SMD dataset can’t be downloaded.
You can run the script with python 3. You can also skip the already
existing datasets by using the --blacklist and --whitelist argument. If
you do this, their ground truth will not be added to the final archive, thus,
remember to backup the previous one and to merge the archives.
Generate misaligned data¶
If you want, you can generate misaligned data using the --train and
--misalign options of generate_ground_truth.py. It will run
alignment_stats.py, which collects data about the datasets with real
non-aligned scores and saves stats in _alignment_stats.pkl file in the ASMD
module directory. Then, it runs generate_ground_truth.py using the collected
statistics: it will generate misaligned data by using the same deviation
distribution of the available non-aligned data.
Note that misaligned data should be annotated as 2 in the ground_truth
value of the dataset groups description (see ASMD: Audio-Score Meta Dataset ), otherwise no
misaligned value will be added to the misaligned field. Moreover, the
dataset group data should have precise_alignment or broad_alignment filled
by the annotation conversion step, otherwise errors can raise during the
misalignment procedure.
For more info, see python -m asmd.generate_ground_truth -h.
A usual pipeline is:
- Generate music score data and other ground-truth except artificial one:
python -m asmd.generate_ground_truth --normal - Train a statistical model (can skip this):
python -m asmd.generate_ground_truth --train - Generate misalignment using the trained model (trains it if not available):
python -m asmd.generate_ground_truth --misalign