Igor Gadelha
commited on
Commit
•
0dd283a
0
Parent(s):
Initial Commit
Browse files- .gitignore +129 -0
- README.md +102 -0
- benchmark/htdemucs4.csv +941 -0
- benchmark/htdemucs6.csv +529 -0
- benchmark/oracle4.csv +0 -0
- benchmark/oracle5.csv +0 -0
- benchmark/oracle6.csv +0 -0
- benchmark/spleeter4.csv +941 -0
- benchmark/spleeter5.csv +521 -0
- moisesdb/__init__.py +0 -0
- moisesdb/activity.py +57 -0
- moisesdb/dataset.py +87 -0
- moisesdb/defaults.py +101 -0
- moisesdb/signal.py +52 -0
- moisesdb/track.py +159 -0
- moisesdb/utils.py +40 -0
- pyproject.toml +24 -0
.gitignore
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| 1 |
+
# Byte-compiled / optimized / DLL files
|
| 2 |
+
__pycache__/
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| 3 |
+
*.py[cod]
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| 4 |
+
*$py.class
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| 5 |
+
|
| 6 |
+
# C extensions
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| 7 |
+
*.so
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| 8 |
+
|
| 9 |
+
# Distribution / packaging
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| 10 |
+
.Python
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| 11 |
+
build/
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| 12 |
+
develop-eggs/
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| 13 |
+
dist/
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| 14 |
+
downloads/
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| 15 |
+
eggs/
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| 16 |
+
.eggs/
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| 17 |
+
lib/
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| 18 |
+
lib64/
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| 19 |
+
parts/
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| 20 |
+
sdist/
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| 21 |
+
var/
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| 22 |
+
wheels/
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| 23 |
+
pip-wheel-metadata/
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| 24 |
+
share/python-wheels/
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| 25 |
+
*.egg-info/
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| 26 |
+
.installed.cfg
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| 27 |
+
*.egg
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| 28 |
+
MANIFEST
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| 29 |
+
|
| 30 |
+
# PyInstaller
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| 31 |
+
# Usually these files are written by a python script from a template
|
| 32 |
+
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
| 33 |
+
*.manifest
|
| 34 |
+
*.spec
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| 35 |
+
|
| 36 |
+
# Installer logs
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| 37 |
+
pip-log.txt
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| 38 |
+
pip-delete-this-directory.txt
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| 39 |
+
|
| 40 |
+
# Unit test / coverage reports
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| 41 |
+
htmlcov/
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| 42 |
+
.tox/
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| 43 |
+
.nox/
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| 44 |
+
.coverage
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| 45 |
+
.coverage.*
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| 46 |
+
.cache
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| 47 |
+
nosetests.xml
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| 48 |
+
coverage.xml
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| 49 |
+
*.cover
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| 50 |
+
*.py,cover
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| 51 |
+
.hypothesis/
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| 52 |
+
.pytest_cache/
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| 53 |
+
|
| 54 |
+
# Translations
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| 55 |
+
*.mo
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| 56 |
+
*.pot
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| 57 |
+
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| 58 |
+
# Django stuff:
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| 59 |
+
*.log
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| 60 |
+
local_settings.py
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| 61 |
+
db.sqlite3
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| 62 |
+
db.sqlite3-journal
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| 63 |
+
|
| 64 |
+
# Flask stuff:
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| 65 |
+
instance/
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| 66 |
+
.webassets-cache
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| 67 |
+
|
| 68 |
+
# Scrapy stuff:
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| 69 |
+
.scrapy
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| 70 |
+
|
| 71 |
+
# Sphinx documentation
|
| 72 |
+
docs/_build/
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| 73 |
+
|
| 74 |
+
# PyBuilder
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| 75 |
+
target/
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| 76 |
+
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| 77 |
+
# Jupyter Notebook
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| 78 |
+
.ipynb_checkpoints
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| 79 |
+
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| 80 |
+
# IPython
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| 81 |
+
profile_default/
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| 82 |
+
ipython_config.py
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| 83 |
+
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| 84 |
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# pyenv
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| 85 |
+
.python-version
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| 86 |
+
|
| 87 |
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# pipenv
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| 88 |
+
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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| 89 |
+
# However, in case of collaboration, if having platform-specific dependencies or dependencies
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| 90 |
+
# having no cross-platform support, pipenv may install dependencies that don't work, or not
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| 91 |
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# install all needed dependencies.
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| 92 |
+
#Pipfile.lock
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| 93 |
+
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| 94 |
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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| 95 |
+
__pypackages__/
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| 96 |
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| 97 |
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# Celery stuff
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| 98 |
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celerybeat-schedule
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| 99 |
+
celerybeat.pid
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| 100 |
+
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| 101 |
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# SageMath parsed files
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| 102 |
+
*.sage.py
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| 103 |
+
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| 104 |
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# Environments
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| 105 |
+
.env
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| 106 |
+
.venv
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| 107 |
+
env/
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| 108 |
+
venv/
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| 109 |
+
ENV/
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| 110 |
+
env.bak/
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| 111 |
+
venv.bak/
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| 112 |
+
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| 113 |
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# Spyder project settings
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| 114 |
+
.spyderproject
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| 115 |
+
.spyproject
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| 116 |
+
|
| 117 |
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# Rope project settings
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| 118 |
+
.ropeproject
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| 119 |
+
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| 120 |
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# mkdocs documentation
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| 121 |
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/site
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| 122 |
+
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| 123 |
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# mypy
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| 124 |
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.mypy_cache/
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| 125 |
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.dmypy.json
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| 126 |
+
dmypy.json
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| 127 |
+
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| 128 |
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# Pyre type checker
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| 129 |
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.pyre/
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README.md
ADDED
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# MoisesDB
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Moises Dataset for Source Separation
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# Download the data
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| 5 |
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Please download the dataset at our research [website](https://developer.moises.ai/research), extract it and configure the environment variable `MOISESDB_PATH` accordingly.
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```
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export MOISESDB_PATH=./moises-db-data
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```
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| 11 |
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| 12 |
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The directory structure should be
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| 13 |
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| 14 |
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```
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| 15 |
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moisesdb:
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| 16 |
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moisesdb_v0.1
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| 17 |
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track uuid 0
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| 18 |
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track uuid 1
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| 19 |
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.
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| 20 |
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.
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| 21 |
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.
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| 22 |
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```
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| 23 |
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| 24 |
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# Install
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| 25 |
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| 26 |
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You can install this package with
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| 27 |
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| 28 |
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```
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| 29 |
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pip install moisesdb
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| 30 |
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```
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| 31 |
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# Usage
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| 33 |
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| 34 |
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## `MoisesDB`
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| 35 |
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| 36 |
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After downloading and configuring the path for the dataset, you can create an instance of `MoisesDB` to access the tracks. You can also provide the dataset path with the `data_path` argument.
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| 37 |
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| 38 |
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```
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| 39 |
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from moisesdb.dataset import MoisesDB
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| 40 |
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| 41 |
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db = MoisesDB(
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| 42 |
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data_path='./moisesdb',
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| 43 |
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sample_rate=44100
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| 44 |
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)
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| 45 |
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```
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| 46 |
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| 47 |
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The `MoisesDB` object has iterator properties that you can use to access all files within the dataset.
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| 48 |
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| 49 |
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```
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n_songs = len(db)
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track = db[0] # Returns a MoisesDBTrack object
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| 52 |
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```
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| 53 |
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| 54 |
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## `MoisesDBTrack`
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| 55 |
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| 56 |
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The `MoisesDBTrack` object holds information about a track in the dataset, perform on-the-fly mixing for stems and multiple sources within a stem.
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| 57 |
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You can access all the stems and mixture from the `stem` and `audio` properties. The `stem` property returns a dictionary whith available stems as keys and `nd.array` on values. The `audio` property results in a `nd.array` with the mixture.
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| 59 |
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| 60 |
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```
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| 61 |
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track = db[0]
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stems = track.stems # stems = {'vocals': ..., 'bass': ..., ...}
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| 63 |
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mixture track.audio # mixture = nd.array
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```
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| 65 |
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| 66 |
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The `MoisesDBTrack` object also contains other non-audio information from the track such as:
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| 67 |
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- `track.id`
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| 68 |
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- `track.provider`
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| 69 |
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- `track.artist`
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| 70 |
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- `track.name`
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| 71 |
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- `track.genre`
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| 72 |
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- `track.sources`
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| 73 |
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- `track.bleedings`
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| 74 |
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- `track.activity`
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| 75 |
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| 76 |
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The stems and mixture are computed on-the-fly. You can create a stems-only version of the dataset using the `save_stems` method of the `MoisesDBTrack`.
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| 77 |
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| 78 |
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```
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| 79 |
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track = db[0]
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| 80 |
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path = './moises-db-stems/0'
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| 81 |
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track.save_stems(path)
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| 82 |
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```
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| 83 |
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| 84 |
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# Performance Evaluation
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| 85 |
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| 86 |
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We run a few source separation algorithms as well as oracle methods to evaluate the performance of each track of the `MoisesDB`. These results are located in `csv` files at the `benchmark` folder.
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| 87 |
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| 88 |
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# Citing
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| 89 |
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| 90 |
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If you used the `MoisesDB` dataset on your research, please cite the following paper.
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| 91 |
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| 92 |
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```
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| 93 |
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@misc{pereira2023moisesdb,
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| 94 |
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title={Moisesdb: A dataset for source separation beyond 4-stems},
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| 95 |
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author={Igor Pereira and Felipe Araújo and Filip Korzeniowski and Richard Vogl},
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| 96 |
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year={2023},
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| 97 |
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eprint={2307.15913},
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| 98 |
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archivePrefix={arXiv},
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| 99 |
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primaryClass={cs.SD}
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| 100 |
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}
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| 101 |
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```
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| 102 |
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benchmark/htdemucs4.csv
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|
| 1 |
+
,track_id,stem,sdr
|
| 2 |
+
0,014f3712-293b-42af-9f29-0ed1785be792,drums,14.063470840454102
|
| 3 |
+
1,0d528a19-cb0f-4421-b250-444f9343e51c,drums,9.744112968444824
|
| 4 |
+
2,1f98fe4d-26c7-460f-9f68-33964bc4d8d3,drums,12.559717178344727
|
| 5 |
+
3,2c020edb-5947-4fa7-afea-ebc592cea683,drums,12.554443359375
|
| 6 |
+
4,3c3b5fdb-f15e-4ba4-884a-b083ce2426c6,drums,12.258882522583008
|
| 7 |
+
5,4a896cde-57c6-4646-b610-1b0b654d0349,drums,14.873862266540527
|
| 8 |
+
6,6681f493-c996-424a-9bdb-c671912ea9db,drums,9.450496673583984
|
| 9 |
+
7,73efd911-79c3-4235-a4ae-45b41d6997b9,drums,8.126161575317383
|
| 10 |
+
8,8427760a-b82e-4136-8f12-dfd53cad9bc9,drums,11.663336753845215
|
| 11 |
+
9,95378cf3-e939-42e0-b486-ebf2ca951664,drums,8.960331916809082
|
| 12 |
+
10,ad9bbefc-8762-46c9-b847-da14b10802b6,drums,11.8125581741333
|
| 13 |
+
11,bdcc429e-ed95-40d3-a1af-bad268d66b25,drums,10.619111061096191
|
| 14 |
+
12,d4262245-3143-4c05-8423-6cbdc6253042,drums,8.350297927856445
|
| 15 |
+
13,e37cdb09-e648-4e9b-bc06-d178a964161c,drums,10.71603012084961
|
| 16 |
+
14,f76e2c13-9a9a-4cac-b6dd-45b5111aac6d,drums,9.59836196899414
|
| 17 |
+
15,01c8ba69-8eee-485b-bab0-41a76f9e8892,drums,10.121007919311523
|
| 18 |
+
16,0e0d57cd-8662-4091-86d4-ed3e35d04ef6,drums,14.613385200500488
|
| 19 |
+
17,1fc37390-1769-452d-9bea-19025be4c467,drums,10.375368118286133
|
| 20 |
+
18,2d39a32d-5993-4f66-89ff-bf9dabb8e45b,drums,14.005346298217773
|
| 21 |
+
19,3c557409-3a34-43c2-9159-5421bbad5ecb,drums,10.149299621582031
|
| 22 |
+
20,4b9f86f4-23e4-458b-839e-8a63b584bea3,drums,12.895807266235352
|
| 23 |
+
21,69dff382-cfcb-457c-98e5-978450e1b76a,drums,13.688638687133789
|
| 24 |
+
22,747d5c98-665b-4470-a696-7a6cf6968ef1,drums,8.933956146240234
|
| 25 |
+
23,87a5da23-f17b-44da-accf-c04832f81a14,drums,11.465436935424805
|
| 26 |
+
24,9653a690-c28c-4e8f-962e-ff7ed18b8ee9,drums,12.96296215057373
|
| 27 |
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878,8e2d0c5c-6764-4d74-a740-391d7931ffd7,vocals,7.674003601074219
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883,f0101ed8-7c32-4744-8b2a-1b621cb4833b,vocals,8.44519329071045
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886,28748b6e-6125-42f7-998d-2ad734e39b6c,vocals,8.238324165344238
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887,378742ba-5ba8-44c2-9cfd-8a609decca57,vocals,13.040791511535645
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888,491c1ff5-1e7b-4046-8029-a82d4a8aefb4,vocals,14.72779655456543
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889,606e3380-b062-4b74-88ac-b60d6f4d0a37,vocals,12.489072799682617
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890,7180bffa-dd48-49b9-bc02-f6e3f7f165b0,vocals,7.4842095375061035
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891,7efcb55d-c0db-472f-b765-1739ad7536aa,vocals,14.113214492797852
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892,8f36f17f-c033-4c1b-a793-80dce43d507b,vocals,11.033763885498047
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893,a56d9450-3a26-485c-8ac3-24b6b54e2c1d,vocals,13.727033615112305
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894,bbf40b5a-8ef9-4aec-a6c3-8b8706eb2ba0,vocals,11.927970886230469
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895,cda46831-26d1-4dd8-ac50-6004d27d45dd,vocals,12.403908729553223
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896,dfb0e076-cb6b-4dcc-9934-c60070ff04d7,vocals,8.900306701660156
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897,f0c565c5-fc73-4da1-b979-0fac0167f671,vocals,15.407655715942383
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| 900 |
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898,07c17c22-a321-4a98-a486-ef7ee1bae50e,vocals,7.358219146728516
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900,2973adc9-6bf8-4422-9f7a-6fd0038eb565,vocals,13.932910919189453
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901,390b4fea-92be-4fe3-9576-86529f80b4ae,vocals,8.899980545043945
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902,49478a32-483f-48d2-a594-d272b44bf587,vocals,9.730234146118164
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903,63b68795-0076-476b-a917-dec9e89bf91e,vocals,10.103124618530273
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904,72c6f013-11ea-4bf7-93b2-c6ef2c117718,vocals,10.298752784729004
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905,8042b88a-6179-406b-9ec4-b45a4cdd4a71,vocals,7.901751518249512
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906,92ba4404-76ff-4523-a787-652057e06c13,vocals,9.17357349395752
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907,aa600069-0a98-45e6-94fa-4000bfe46c25,vocals,10.177884101867676
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| 910 |
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908,bc1f2967-f834-43bd-aadc-95afc897cfe7,vocals,9.26424789428711
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| 911 |
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909,cff5bcde-6a15-4c5b-b529-e1c528c46335,vocals,10.849296569824219
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910,e1108928-9776-434c-bc57-c32dfdb7839c,vocals,5.7580461502075195
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911,f40ffd10-4e8b-41e6-bd8a-971929ca9138,vocals,8.287281036376953
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912,07fb2df2-91d6-458d-9230-9638b4edac08,vocals,7.490575790405273
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913,1e957e76-d7a2-4b84-bc4a-afa87979ff57,vocals,16.138784408569336
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914,2a3fd99d-c86f-4275-9321-ad7146364503,vocals,8.725335121154785
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915,3a047d1a-f56d-4bf4-9910-f4f77206e53d,vocals,8.26213264465332
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916,4999a0bf-a753-4e0e-85b1-690259dabf96,vocals,7.887294769287109
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917,64549dc5-4104-4f8c-bce1-560d44e94562,vocals,7.258859157562256
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918,731893c6-67b9-42f0-aea6-d1f70c2c9870,vocals,6.971168041229248
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| 921 |
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919,810d7318-4c9e-4160-b147-bbdb65d41861,vocals,7.500638961791992
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| 922 |
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920,93dda0e8-dd76-49e4-b08f-54a82387cdf6,vocals,8.612542152404785
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921,aaf0fc7b-d7f5-412c-b3b9-313a7c483666,vocals,14.986802101135254
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| 924 |
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922,bc964128-da16-4e4c-af95-4d1211e78c70,vocals,11.34657096862793
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| 925 |
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923,d028d7c2-45c1-4846-b7df-4964238fd460,vocals,8.035419464111328
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924,e2ccbc17-44bf-431a-af2b-4cf2fbd19a72,vocals,8.57163143157959
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925,f4b735de-14b1-4091-a9ba-c8b30c0740a7,vocals,7.408098220825195
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926,0a589d65-50a3-4999-8f16-b5b6199bceee,vocals,10.217694282531738
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927,1ee489e8-55ad-4d4a-8277-24051b85c02d,vocals,8.601893424987793
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928,2b4e8304-c92d-4347-b09e-cfb9b3e29bf2,vocals,7.0170745849609375
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| 931 |
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929,3a6023c3-4cd8-46c0-a376-d353743648a9,vocals,7.718344688415527
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| 932 |
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930,49ed7cef-8ffb-4833-bc96-6df7b8ff5b43,vocals,10.298752784729004
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| 933 |
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931,664cc931-4e00-441e-9a78-e7a292515cea,vocals,8.506699562072754
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| 934 |
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932,737356b2-ce9c-448b-877b-e42b3ed94563,vocals,11.587211608886719
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933,825f697d-5fcd-4429-ba33-23163c726ca7,vocals,10.816327095031738
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934,94fafb2a-9f4e-4a01-bee9-998008f95f41,vocals,12.403600692749023
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| 937 |
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935,ad6c3742-e517-42eb-9dec-e74ed05387cc,vocals,12.733434677124023
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| 938 |
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936,bd065894-3521-415f-b39f-5a6980f024fb,vocals,9.291380882263184
|
| 939 |
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937,d2401d3d-967c-46be-b9a0-3da571105158,vocals,5.800188064575195
|
| 940 |
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938,e2e4ce50-cd0d-4144-809d-4cf8c8e4912e,vocals,6.461667537689209
|
| 941 |
+
939,f4d79498-4ba0-4813-9b9e-986374242573,vocals,12.661442756652832
|
benchmark/htdemucs6.csv
ADDED
|
@@ -0,0 +1,529 @@
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|
| 1 |
+
,track_id,stem,sdr
|
| 2 |
+
0,02ee37da-eea3-42b4-83bf-ab7f243afa13,drums,12.892800331115723
|
| 3 |
+
1,33b1d4e9-96d7-4b18-9ffc-199c579aad78,drums,9.327590942382812
|
| 4 |
+
2,53808b95-cfe9-461d-a113-ffadf32817a1,drums,11.488829612731934
|
| 5 |
+
3,89c515c9-5e93-4cb4-9806-20432d2d074d,drums,11.785785675048828
|
| 6 |
+
4,bb45cf1a-4c58-4fe3-88ea-97ef27527507,drums,14.71016788482666
|
| 7 |
+
5,e37cdb09-e648-4e9b-bc06-d178a964161c,drums,10.649117469787598
|
| 8 |
+
6,07fb2df2-91d6-458d-9230-9638b4edac08,drums,12.522161483764648
|
| 9 |
+
7,35983cdd-3903-46cb-b184-f96274ced57b,drums,13.087230682373047
|
| 10 |
+
8,553048ce-7afd-4e0e-b4cb-4896620287a1,drums,13.539713859558105
|
| 11 |
+
9,8a307c5d-9d65-4f1c-a024-5eaeff1faa34,drums,9.219365119934082
|
| 12 |
+
10,bb6d72a2-63ba-405e-a2c1-c7442b4190bb,drums,13.989423751831055
|
| 13 |
+
11,e4de8632-6f69-4c63-8081-f4c2b77b40df,drums,7.294001579284668
|
| 14 |
+
12,0e0d57cd-8662-4091-86d4-ed3e35d04ef6,drums,14.300178527832031
|
| 15 |
+
13,390b4fea-92be-4fe3-9576-86529f80b4ae,drums,12.164161682128906
|
| 16 |
+
14,5a6df5c7-a58a-479e-bdfb-c5946c221933,drums,11.664939880371094
|
| 17 |
+
15,8ce11544-9a6f-4f1e-ac2f-fc10343f15c8,drums,8.324136734008789
|
| 18 |
+
16,bc1f2967-f834-43bd-aadc-95afc897cfe7,drums,9.397150039672852
|
| 19 |
+
17,f0101ed8-7c32-4744-8b2a-1b621cb4833b,drums,9.534517288208008
|
| 20 |
+
18,0f5fb60c-51d4-4618-871d-650c9e927b79,drums,12.417516708374023
|
| 21 |
+
19,3a047d1a-f56d-4bf4-9910-f4f77206e53d,drums,9.510025024414062
|
| 22 |
+
20,5f04798d-c7be-4b8a-90bd-1fcd9946e875,drums,6.788541793823242
|
| 23 |
+
21,8e2d0c5c-6764-4d74-a740-391d7931ffd7,drums,7.8941850662231445
|
| 24 |
+
22,bc964128-da16-4e4c-af95-4d1211e78c70,drums,10.487995147705078
|
| 25 |
+
23,f4d79498-4ba0-4813-9b9e-986374242573,drums,5.3844451904296875
|
| 26 |
+
24,13f233aa-a2e5-4683-8533-2f1e344b55b4,drums,5.256643772125244
|
| 27 |
+
25,3a6023c3-4cd8-46c0-a376-d353743648a9,drums,11.055954933166504
|
| 28 |
+
26,6031b120-f6e2-4999-96ba-a1e31be68ea8,drums,8.539691925048828
|
| 29 |
+
27,8f36f17f-c033-4c1b-a793-80dce43d507b,drums,10.764142036437988
|
| 30 |
+
28,bdcc429e-ed95-40d3-a1af-bad268d66b25,drums,10.383797645568848
|
| 31 |
+
29,f97e0ccd-e2a1-4da9-b8b8-c58e11adc4d9,drums,9.244477272033691
|
| 32 |
+
30,174a115f-3688-45dc-8c39-9d05f21758e1,drums,11.480527877807617
|
| 33 |
+
31,3e389000-8fdc-4b63-b8b8-ab044273790d,drums,15.13459300994873
|
| 34 |
+
32,63b68795-0076-476b-a917-dec9e89bf91e,drums,12.100645065307617
|
| 35 |
+
33,92ba4404-76ff-4523-a787-652057e06c13,drums,10.289382934570312
|
| 36 |
+
34,c228818e-eabe-434b-9d60-2fb84a6c5b2a,drums,10.351015090942383
|
| 37 |
+
35,fc3c9e48-e2ac-4088-af65-68404baa7f12,drums,11.459803581237793
|
| 38 |
+
36,1afe1b3b-3e2e-48d3-b859-f50e222cbaf4,drums,8.837386131286621
|
| 39 |
+
37,3e656eec-84d4-4a45-b410-d3817d849f92,drums,7.812609672546387
|
| 40 |
+
38,6b168ae6-9d8a-4dc2-9d27-898e6871bf8b,drums,12.848987579345703
|
| 41 |
+
39,93dda0e8-dd76-49e4-b08f-54a82387cdf6,drums,7.342761039733887
|
| 42 |
+
40,c70471f9-9c4a-41c9-b8f8-20ac38847a8e,drums,13.091559410095215
|
| 43 |
+
41,fcd1937f-2b21-4a78-889a-7b7e63e0ebdd,drums,15.023350715637207
|
| 44 |
+
42,1ee489e8-55ad-4d4a-8277-24051b85c02d,drums,10.107339859008789
|
| 45 |
+
43,3f5233cb-57fa-4772-b389-f295a6f416ae,drums,7.402430057525635
|
| 46 |
+
44,6c70d5e0-5972-444a-86f8-a558dbb92d92,drums,12.499223709106445
|
| 47 |
+
45,97b07e0e-274e-4212-a66b-44210a48724d,drums,8.563185691833496
|
| 48 |
+
46,cff5bcde-6a15-4c5b-b529-e1c528c46335,drums,14.61428451538086
|
| 49 |
+
47,fd6e4b4a-f33a-4f3c-aa6e-7c65fd5dc0bc,drums,13.846840858459473
|
| 50 |
+
48,1fc37390-1769-452d-9bea-19025be4c467,drums,10.328987121582031
|
| 51 |
+
49,4380ad97-2620-4419-a011-ddfa29a87f54,drums,14.619502067565918
|
| 52 |
+
50,6cd44645-ed19-4ecc-a57c-58d400005b29,drums,16.795316696166992
|
| 53 |
+
51,9ac2612b-e25f-4d27-8d43-b957e7e5a74b,drums,12.504977226257324
|
| 54 |
+
52,d2401d3d-967c-46be-b9a0-3da571105158,drums,7.298438549041748
|
| 55 |
+
53,212bb137-fd01-465e-80f3-a890fb0ebcdd,drums,10.89592170715332
|
| 56 |
+
54,43ca388d-3e62-4df2-b72d-abf407a7aa5a,drums,7.923990249633789
|
| 57 |
+
55,6ce087b4-e571-4472-9be2-04b5340311c6,drums,9.533978462219238
|
| 58 |
+
56,9efa702f-c6b1-40bb-93ef-095b34a5b775,drums,10.539542198181152
|
| 59 |
+
57,d4fe2408-c123-4739-93bb-22f558ae99d7,drums,12.344213485717773
|
| 60 |
+
58,24f8a652-0168-4702-8725-42f9924e6729,drums,14.077018737792969
|
| 61 |
+
59,444fcc6d-1ad1-4584-b3e5-bf3f4f613c8f,drums,12.729986190795898
|
| 62 |
+
60,72c6f013-11ea-4bf7-93b2-c6ef2c117718,drums,12.740063667297363
|
| 63 |
+
61,9f581867-9f63-4ec4-8a43-e62c3c4230a6,drums,11.979190826416016
|
| 64 |
+
62,d624037a-1a76-4dd9-9e60-4ba380748a0b,drums,12.15692138671875
|
| 65 |
+
63,25516a0f-3197-47e4-8fbf-de9500fc68d2,drums,11.201095581054688
|
| 66 |
+
64,491c1ff5-1e7b-4046-8029-a82d4a8aefb4,drums,10.58218765258789
|
| 67 |
+
65,7a9f3169-7bdf-483c-9634-8e5b097d50df,drums,12.842942237854004
|
| 68 |
+
66,a0b9a4e4-51f5-4c98-a090-0317fb891056,drums,12.764814376831055
|
| 69 |
+
67,d7d28204-a8ac-4c2b-bb3c-c941f4a00b85,drums,12.056589126586914
|
| 70 |
+
68,2d39a32d-5993-4f66-89ff-bf9dabb8e45b,drums,13.742973327636719
|
| 71 |
+
69,49ed7cef-8ffb-4833-bc96-6df7b8ff5b43,drums,12.740063667297363
|
| 72 |
+
70,7bfa233c-24ed-4c7a-9096-11e3aa00c55d,drums,12.221175193786621
|
| 73 |
+
71,a199697c-3cdc-47c7-a9c7-c1b07dd6c9dd,drums,9.066368103027344
|
| 74 |
+
72,d890ff35-300d-49f2-8054-49ab47262987,drums,5.771133899688721
|
| 75 |
+
73,2dc237cd-1637-46f0-8f58-ca68dc6f6031,drums,7.818794250488281
|
| 76 |
+
74,4a896cde-57c6-4646-b610-1b0b654d0349,drums,14.77495002746582
|
| 77 |
+
75,7e74ce11-1603-440b-8794-b6e665b917e1,drums,10.76779556274414
|
| 78 |
+
76,aa600069-0a98-45e6-94fa-4000bfe46c25,drums,12.765446662902832
|
| 79 |
+
77,dbb07bdc-2706-4e67-8b59-43f98cf1608a,drums,12.562414169311523
|
| 80 |
+
78,30cfc60a-5a57-4000-a05e-65006c8f6f74,drums,7.958719730377197
|
| 81 |
+
79,4cbd6c36-87a2-4d50-86e3-52d39b98fad3,drums,8.523983001708984
|
| 82 |
+
80,87a5da23-f17b-44da-accf-c04832f81a14,drums,11.654163360595703
|
| 83 |
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81,ad6c3742-e517-42eb-9dec-e74ed05387cc,drums,14.46706771850586
|
| 84 |
+
82,e1108928-9776-434c-bc57-c32dfdb7839c,drums,10.575292587280273
|
| 85 |
+
83,312bec8d-1c61-43e0-924a-1fb87ddc3e41,drums,9.781785011291504
|
| 86 |
+
84,524ab371-f6c6-4ff7-b896-e83750c8bef7,drums,11.350852966308594
|
| 87 |
+
85,8804c154-6294-481a-ad63-bc61162cae2f,drums,11.8714599609375
|
| 88 |
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86,adb5e3fd-d039-42e4-b09b-fd0784fa0c82,drums,8.344938278198242
|
| 89 |
+
87,e2ccbc17-44bf-431a-af2b-4cf2fbd19a72,drums,10.623619079589844
|
| 90 |
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88,02ee37da-eea3-42b4-83bf-ab7f243afa13,bass,9.800694465637207
|
| 91 |
+
89,33b1d4e9-96d7-4b18-9ffc-199c579aad78,bass,7.715909004211426
|
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518,30cfc60a-5a57-4000-a05e-65006c8f6f74,piano,2.613452672958374
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519,4cbd6c36-87a2-4d50-86e3-52d39b98fad3,piano,1.6241055727005005
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520,87a5da23-f17b-44da-accf-c04832f81a14,piano,3.099369525909424
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524,524ab371-f6c6-4ff7-b896-e83750c8bef7,piano,0.008274586871266365
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|
benchmark/oracle4.csv
ADDED
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benchmark/oracle5.csv
ADDED
|
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benchmark/oracle6.csv
ADDED
|
The diff for this file is too large to render.
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|
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benchmark/spleeter4.csv
ADDED
|
@@ -0,0 +1,941 @@
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| 1 |
+
,track_id,stem,sdr
|
| 2 |
+
0,014f3712-293b-42af-9f29-0ed1785be792,vocals,7.603781223297119
|
| 3 |
+
1,0d528a19-cb0f-4421-b250-444f9343e51c,vocals,7.158985614776611
|
| 4 |
+
2,1f98fe4d-26c7-460f-9f68-33964bc4d8d3,vocals,4.723952293395996
|
| 5 |
+
3,2c020edb-5947-4fa7-afea-ebc592cea683,vocals,6.434378147125244
|
| 6 |
+
4,3c3b5fdb-f15e-4ba4-884a-b083ce2426c6,vocals,7.270874500274658
|
| 7 |
+
5,4a896cde-57c6-4646-b610-1b0b654d0349,vocals,8.955146789550781
|
| 8 |
+
6,6681f493-c996-424a-9bdb-c671912ea9db,vocals,5.6858296394348145
|
| 9 |
+
7,73efd911-79c3-4235-a4ae-45b41d6997b9,vocals,8.384944915771484
|
| 10 |
+
8,8427760a-b82e-4136-8f12-dfd53cad9bc9,vocals,11.391905784606934
|
| 11 |
+
9,95378cf3-e939-42e0-b486-ebf2ca951664,vocals,9.805368423461914
|
| 12 |
+
10,ad9bbefc-8762-46c9-b847-da14b10802b6,vocals,6.524868011474609
|
| 13 |
+
11,bdcc429e-ed95-40d3-a1af-bad268d66b25,vocals,5.7113142013549805
|
| 14 |
+
12,d4262245-3143-4c05-8423-6cbdc6253042,vocals,1.2411561012268066
|
| 15 |
+
13,e37cdb09-e648-4e9b-bc06-d178a964161c,vocals,7.061549186706543
|
| 16 |
+
14,f76e2c13-9a9a-4cac-b6dd-45b5111aac6d,vocals,7.500033855438232
|
| 17 |
+
15,01c8ba69-8eee-485b-bab0-41a76f9e8892,vocals,7.817267417907715
|
| 18 |
+
16,0e0d57cd-8662-4091-86d4-ed3e35d04ef6,vocals,7.640987396240234
|
| 19 |
+
17,1fc37390-1769-452d-9bea-19025be4c467,vocals,6.421692371368408
|
| 20 |
+
18,2d39a32d-5993-4f66-89ff-bf9dabb8e45b,vocals,6.137048721313477
|
| 21 |
+
19,3c557409-3a34-43c2-9159-5421bbad5ecb,vocals,6.742732524871826
|
| 22 |
+
20,4b9f86f4-23e4-458b-839e-8a63b584bea3,vocals,5.438480854034424
|
| 23 |
+
21,69dff382-cfcb-457c-98e5-978450e1b76a,vocals,11.946136474609375
|
| 24 |
+
22,747d5c98-665b-4470-a696-7a6cf6968ef1,vocals,5.306399345397949
|
| 25 |
+
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| 941 |
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|
benchmark/spleeter5.csv
ADDED
|
@@ -0,0 +1,521 @@
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| 1 |
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| 2 |
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447,7efcb55d-c0db-472f-b765-1739ad7536aa,other,2.4693551063537598
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448,a0b9a4e4-51f5-4c98-a090-0317fb891056,other,1.4485079050064087
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449,cff5bcde-6a15-4c5b-b529-e1c528c46335,other,2.1197052001953125
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450,f97e0ccd-e2a1-4da9-b8b8-c58e11adc4d9,other,2.063385009765625
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451,0f5fb60c-51d4-4618-871d-650c9e927b79,other,1.748044490814209
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452,33b1d4e9-96d7-4b18-9ffc-199c579aad78,other,-3.0816781520843506
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453,53808b95-cfe9-461d-a113-ffadf32817a1,other,2.7708933353424072
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454,810d7318-4c9e-4160-b147-bbdb65d41861,other,0.9126865863800049
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455,a199697c-3cdc-47c7-a9c7-c1b07dd6c9dd,other,1.0056098699569702
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456,d2401d3d-967c-46be-b9a0-3da571105158,other,3.6451170444488525
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457,fc3c9e48-e2ac-4088-af65-68404baa7f12,other,3.1255853176116943
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458,13f233aa-a2e5-4683-8533-2f1e344b55b4,other,2.892490863800049
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459,35983cdd-3903-46cb-b184-f96274ced57b,other,3.015090227127075
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460,553048ce-7afd-4e0e-b4cb-4896620287a1,other,3.311314821243286
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461,87a5da23-f17b-44da-accf-c04832f81a14,other,4.860238075256348
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463,d4fe2408-c123-4739-93bb-22f558ae99d7,other,2.3946855068206787
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464,fcd1937f-2b21-4a78-889a-7b7e63e0ebdd,other,2.487236261367798
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465,174a115f-3688-45dc-8c39-9d05f21758e1,other,3.6124749183654785
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466,390b4fea-92be-4fe3-9576-86529f80b4ae,other,2.356322765350342
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467,5a6df5c7-a58a-479e-bdfb-c5946c221933,other,1.1632115840911865
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468,8804c154-6294-481a-ad63-bc61162cae2f,other,2.7776777744293213
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469,ad6c3742-e517-42eb-9dec-e74ed05387cc,other,2.612576961517334
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470,d624037a-1a76-4dd9-9e60-4ba380748a0b,other,2.3015048503875732
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471,fd6e4b4a-f33a-4f3c-aa6e-7c65fd5dc0bc,other,-0.1387609839439392
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472,1a32e987-cae8-458f-9c26-d9a6abf5348d,other,1.5030536651611328
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473,3a047d1a-f56d-4bf4-9910-f4f77206e53d,other,1.4600242376327515
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474,5f04798d-c7be-4b8a-90bd-1fcd9946e875,other,3.826005220413208
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475,89c515c9-5e93-4cb4-9806-20432d2d074d,other,2.162888765335083
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476,adb5e3fd-d039-42e4-b09b-fd0784fa0c82,other,1.0860135555267334
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477,d7d28204-a8ac-4c2b-bb3c-c941f4a00b85,other,2.552596092224121
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478,1ade22ad-99bc-4954-b2b9-fb9e8fa06d41,other,-0.4554942846298218
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479,3a6023c3-4cd8-46c0-a376-d353743648a9,other,2.248575210571289
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480,6031b120-f6e2-4999-96ba-a1e31be68ea8,other,1.888193130493164
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481,8a307c5d-9d65-4f1c-a024-5eaeff1faa34,other,-2.0222373008728027
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482,aefc1609-976b-423e-8516-f7d588d64ff7,other,1.9788320064544678
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483,d890ff35-300d-49f2-8054-49ab47262987,other,-0.0639171451330185
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484,1afe1b3b-3e2e-48d3-b859-f50e222cbaf4,other,3.0537068843841553
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485,3e389000-8fdc-4b63-b8b8-ab044273790d,other,1.6755774021148682
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486,63b68795-0076-476b-a917-dec9e89bf91e,other,0.12410110980272293
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487,8b83ba75-5e35-48c4-b42f-5419da2e6301,other,1.3786499500274658
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488,b207da3d-4baf-485a-98e1-657602479b3a,other,1.0633527040481567
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489,dbb07bdc-2706-4e67-8b59-43f98cf1608a,other,3.817150354385376
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490,1ee489e8-55ad-4d4a-8277-24051b85c02d,other,2.467855930328369
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491,3e656eec-84d4-4a45-b410-d3817d849f92,other,3.7140896320343018
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492,6b168ae6-9d8a-4dc2-9d27-898e6871bf8b,other,4.750701427459717
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493,8ce11544-9a6f-4f1e-ac2f-fc10343f15c8,other,2.3443360328674316
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494,bb45cf1a-4c58-4fe3-88ea-97ef27527507,other,-3.0395736694335938
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495,de22c812-b5c3-4c7d-92aa-f62faf8b63f7,other,0.4712969660758972
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496,1fc37390-1769-452d-9bea-19025be4c467,other,3.410939931869507
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497,3f5233cb-57fa-4772-b389-f295a6f416ae,other,3.2390406131744385
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498,6c70d5e0-5972-444a-86f8-a558dbb92d92,other,1.503043293952942
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499,8e2d0c5c-6764-4d74-a740-391d7931ffd7,other,2.136528253555298
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500,bb6d72a2-63ba-405e-a2c1-c7442b4190bb,other,2.309187412261963
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501,dfb0e076-cb6b-4dcc-9934-c60070ff04d7,other,2.598883628845215
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502,212bb137-fd01-465e-80f3-a890fb0ebcdd,other,1.3422077894210815
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503,4380ad97-2620-4419-a011-ddfa29a87f54,other,-0.7181473970413208
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504,6cd44645-ed19-4ecc-a57c-58d400005b29,other,0.28171855211257935
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505,8f36f17f-c033-4c1b-a793-80dce43d507b,other,4.527069568634033
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506,bc1f2967-f834-43bd-aadc-95afc897cfe7,other,2.8212151527404785
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507,e1108928-9776-434c-bc57-c32dfdb7839c,other,4.213688850402832
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508,24f8a652-0168-4702-8725-42f9924e6729,other,2.051417589187622
|
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509,43ca388d-3e62-4df2-b72d-abf407a7aa5a,other,2.1885454654693604
|
| 512 |
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510,6ce087b4-e571-4472-9be2-04b5340311c6,other,1.7064417600631714
|
| 513 |
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511,92ba4404-76ff-4523-a787-652057e06c13,other,2.0003347396850586
|
| 514 |
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512,bc964128-da16-4e4c-af95-4d1211e78c70,other,1.9810682535171509
|
| 515 |
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513,e2ccbc17-44bf-431a-af2b-4cf2fbd19a72,other,2.587129831314087
|
| 516 |
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514,25516a0f-3197-47e4-8fbf-de9500fc68d2,other,-0.2689034938812256
|
| 517 |
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515,444fcc6d-1ad1-4584-b3e5-bf3f4f613c8f,other,0.5341020822525024
|
| 518 |
+
516,72c6f013-11ea-4bf7-93b2-c6ef2c117718,other,1.3083405494689941
|
| 519 |
+
517,93dda0e8-dd76-49e4-b08f-54a82387cdf6,other,5.867372035980225
|
| 520 |
+
518,bdcc429e-ed95-40d3-a1af-bad268d66b25,other,-1.2444431781768799
|
| 521 |
+
519,e2e4ce50-cd0d-4144-809d-4cf8c8e4912e,other,0.6553434729576111
|
moisesdb/__init__.py
ADDED
|
File without changes
|
moisesdb/activity.py
ADDED
|
@@ -0,0 +1,57 @@
|
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|
|
|
|
|
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|
|
|
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|
|
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|
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|
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|
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|
|
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|
|
|
|
|
|
|
|
|
| 1 |
+
import numpy as np
|
| 2 |
+
|
| 3 |
+
from moisesdb.signal import (
|
| 4 |
+
frame_signal,
|
| 5 |
+
hwr,
|
| 6 |
+
pad_along_axis,
|
| 7 |
+
triangular_window,
|
| 8 |
+
unframe_signal,
|
| 9 |
+
)
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
def track_energy(x, frame_length, hop_length, win):
|
| 13 |
+
x = pad_along_axis(x, frame_length - hop_length, axis=-1)
|
| 14 |
+
xmat = frame_signal(x, frame_length=frame_length, hop_length=hop_length)
|
| 15 |
+
xmat = hwr(xmat) ** 0.5
|
| 16 |
+
return np.mean((xmat.transpose(0, 1, 3, 2) * win), axis=-1)
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
def compute_activity_signal(
|
| 20 |
+
x,
|
| 21 |
+
frame_length=4096,
|
| 22 |
+
hop_length=2048,
|
| 23 |
+
win=triangular_window,
|
| 24 |
+
var_lambda=20.0,
|
| 25 |
+
theta=0.15,
|
| 26 |
+
):
|
| 27 |
+
"""
|
| 28 |
+
x: nd.array with shape [stem, channels, samples]
|
| 29 |
+
return: nd.array with the same shape as x containing the activity signal
|
| 30 |
+
"""
|
| 31 |
+
e = track_energy(x, frame_length, hop_length, win(frame_length))
|
| 32 |
+
e = e / e.max(axis=-1, keepdims=True)[0]
|
| 33 |
+
c = 1.0 - (1.0 / (1.0 + np.exp(np.inner(var_lambda, (e - theta)))))
|
| 34 |
+
c = to_original_size(
|
| 35 |
+
c, size=x.shape[-1], frame_length=frame_length, hop_length=hop_length
|
| 36 |
+
)
|
| 37 |
+
return c
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
def to_original_size(act_signal, size, frame_length=4096, hop_length=2048):
|
| 41 |
+
*shape, nwin = act_signal.shape
|
| 42 |
+
hop_mat = np.ones((*shape, nwin, frame_length)) * act_signal[..., None]
|
| 43 |
+
hop_mat = unframe_signal(hop_mat.transpose(-2, 0, 1, 3), hop_length)
|
| 44 |
+
return hop_mat[..., :size]
|
| 45 |
+
|
| 46 |
+
|
| 47 |
+
def filter_from_mask(x, mask):
|
| 48 |
+
"""
|
| 49 |
+
x: nd.array with the activity signal
|
| 50 |
+
mask: nd.array of type bool
|
| 51 |
+
ex.
|
| 52 |
+
mask = activity > 0.25
|
| 53 |
+
audio_non_silent = filter_from_mask(audio, mask)
|
| 54 |
+
"""
|
| 55 |
+
for d in range(mask.ndim - 1):
|
| 56 |
+
mask = mask.prod(0).astype(bool)
|
| 57 |
+
return x[..., mask]
|
moisesdb/dataset.py
ADDED
|
@@ -0,0 +1,87 @@
|
|
|
|
|
|
|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
from concurrent.futures import ThreadPoolExecutor
|
| 3 |
+
|
| 4 |
+
from tqdm import tqdm
|
| 5 |
+
|
| 6 |
+
from moisesdb.defaults import default_data_path, default_sample_rate
|
| 7 |
+
from moisesdb.track import MoisesDBTrack
|
| 8 |
+
from moisesdb.utils import get_fs
|
| 9 |
+
|
| 10 |
+
|
| 11 |
+
class MoisesDB:
|
| 12 |
+
def __init__(
|
| 13 |
+
self, data_path=default_data_path, sample_rate=default_sample_rate, quiet=False
|
| 14 |
+
):
|
| 15 |
+
self.data_path = data_path
|
| 16 |
+
self.sample_rate = sample_rate
|
| 17 |
+
self.quiet = quiet
|
| 18 |
+
self.providers_list = self.get_providers(data_path)
|
| 19 |
+
self.providers_tracks = self.get_tracks_list(self.providers_list)
|
| 20 |
+
self.tracks = self.build_tracks(self.providers_tracks)
|
| 21 |
+
self.flatten_map = self.build_flatten_map(self.tracks)
|
| 22 |
+
|
| 23 |
+
def get_providers(self, data_path):
|
| 24 |
+
fs = get_fs(self.data_path)
|
| 25 |
+
return [os.path.basename(p) for p in fs.ls(self.data_path)]
|
| 26 |
+
|
| 27 |
+
def get_providers_tracks(self, provider):
|
| 28 |
+
fs = get_fs(self.data_path)
|
| 29 |
+
providers_tracks = [
|
| 30 |
+
os.path.basename(p) for p in fs.ls(os.path.join(self.data_path, provider))
|
| 31 |
+
]
|
| 32 |
+
futures = []
|
| 33 |
+
with ThreadPoolExecutor() as executor:
|
| 34 |
+
futures.extend(
|
| 35 |
+
executor.submit(
|
| 36 |
+
fs.exists,
|
| 37 |
+
os.path.join(self.data_path, provider, t, "data.json"),
|
| 38 |
+
)
|
| 39 |
+
for t in providers_tracks
|
| 40 |
+
)
|
| 41 |
+
track_exists = [f.result() for f in futures]
|
| 42 |
+
return [t for t, te in zip(providers_tracks, track_exists) if te]
|
| 43 |
+
|
| 44 |
+
def get_tracks_list(self, providers_list):
|
| 45 |
+
futures = []
|
| 46 |
+
with ThreadPoolExecutor() as executor:
|
| 47 |
+
futures.extend(
|
| 48 |
+
executor.submit(self.get_providers_tracks, provider=p)
|
| 49 |
+
for p in providers_list
|
| 50 |
+
)
|
| 51 |
+
return {p: f.result() for p, f in zip(providers_list, futures)}
|
| 52 |
+
|
| 53 |
+
def build_tracks(self, providers_tracks):
|
| 54 |
+
tracks = {}
|
| 55 |
+
with ThreadPoolExecutor() as executor:
|
| 56 |
+
for provider, tracks_list in providers_tracks.items():
|
| 57 |
+
futures = [
|
| 58 |
+
executor.submit(
|
| 59 |
+
MoisesDBTrack,
|
| 60 |
+
provider=provider,
|
| 61 |
+
track_id=t,
|
| 62 |
+
data_path=self.data_path,
|
| 63 |
+
sample_rate=self.sample_rate,
|
| 64 |
+
)
|
| 65 |
+
for t in tracks_list
|
| 66 |
+
]
|
| 67 |
+
itt = tqdm(
|
| 68 |
+
futures,
|
| 69 |
+
total=len(futures),
|
| 70 |
+
desc=f"Loading tracks info from provider {provider}",
|
| 71 |
+
disable=self.quiet,
|
| 72 |
+
)
|
| 73 |
+
tracks[provider] = [future.result() for future in itt]
|
| 74 |
+
return tracks
|
| 75 |
+
|
| 76 |
+
def build_flatten_map(self, tracks):
|
| 77 |
+
flatten_map = []
|
| 78 |
+
for provider, tracks_list in tracks.items():
|
| 79 |
+
for track in tracks_list:
|
| 80 |
+
flatten_map.append(track)
|
| 81 |
+
return flatten_map
|
| 82 |
+
|
| 83 |
+
def __len__(self):
|
| 84 |
+
return len(self.flatten_map)
|
| 85 |
+
|
| 86 |
+
def __getitem__(self, idx):
|
| 87 |
+
return self.flatten_map[idx]
|
moisesdb/defaults.py
ADDED
|
@@ -0,0 +1,101 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import os
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
default_sample_rate = 44100
|
| 5 |
+
default_data_path = os.environ.get("MOISESDB_PATH", '.')
|
| 6 |
+
|
| 7 |
+
|
| 8 |
+
taxonomy = {
|
| 9 |
+
"vocals": [
|
| 10 |
+
"lead male singer",
|
| 11 |
+
"lead female singer",
|
| 12 |
+
"human choir",
|
| 13 |
+
"background vocals",
|
| 14 |
+
"other (vocoder, beatboxing etc)",
|
| 15 |
+
],
|
| 16 |
+
"bass": [
|
| 17 |
+
"bass guitar",
|
| 18 |
+
"bass synthesizer (moog etc)",
|
| 19 |
+
"contrabass/double bass (bass of instrings)",
|
| 20 |
+
"tuba (bass of brass)",
|
| 21 |
+
"bassoon (bass of woodwind)",
|
| 22 |
+
],
|
| 23 |
+
"drums": [
|
| 24 |
+
"snare drum",
|
| 25 |
+
"toms",
|
| 26 |
+
"kick drum",
|
| 27 |
+
"cymbals",
|
| 28 |
+
"overheads",
|
| 29 |
+
"full acoustic drumkit",
|
| 30 |
+
"drum machine",
|
| 31 |
+
],
|
| 32 |
+
"other": [
|
| 33 |
+
"fx/processed sound, scratches, gun shots, explosions etc",
|
| 34 |
+
"click track",
|
| 35 |
+
],
|
| 36 |
+
"guitar": [
|
| 37 |
+
"clean electric guitar",
|
| 38 |
+
"distorted electric guitar",
|
| 39 |
+
"lap steel guitar or slide guitar",
|
| 40 |
+
"acoustic guitar",
|
| 41 |
+
],
|
| 42 |
+
"other plucked": ["banjo, mandolin, ukulele, harp etc"],
|
| 43 |
+
"percussion": [
|
| 44 |
+
"a-tonal percussion (claps, shakers, congas, cowbell etc)",
|
| 45 |
+
"pitched percussion (mallets, glockenspiel, ...)",
|
| 46 |
+
],
|
| 47 |
+
"piano": [
|
| 48 |
+
"grand piano",
|
| 49 |
+
"electric piano (rhodes, wurlitzer, piano sound alike)",
|
| 50 |
+
],
|
| 51 |
+
"other keys": [
|
| 52 |
+
"organ, electric organ",
|
| 53 |
+
"synth pad",
|
| 54 |
+
"synth lead",
|
| 55 |
+
"other sounds (hapischord, melotron etc)",
|
| 56 |
+
],
|
| 57 |
+
"bowed strings": [
|
| 58 |
+
"violin (solo)",
|
| 59 |
+
"viola (solo)",
|
| 60 |
+
"cello (solo)",
|
| 61 |
+
"violin section",
|
| 62 |
+
"viola section",
|
| 63 |
+
"cello section",
|
| 64 |
+
"string section",
|
| 65 |
+
"other strings",
|
| 66 |
+
],
|
| 67 |
+
"wind": [
|
| 68 |
+
"brass (trumpet, trombone, french horn, brass etc)",
|
| 69 |
+
"flutes (piccolo, bamboo flute, panpipes, flutes etc)",
|
| 70 |
+
"reeds (saxophone, clarinets, oboe, english horn, bagpipe)",
|
| 71 |
+
"other wind",
|
| 72 |
+
],
|
| 73 |
+
}
|
| 74 |
+
|
| 75 |
+
all_stems = [t.replace(" ", "_") for t in taxonomy]
|
| 76 |
+
|
| 77 |
+
mix_4_stems = {
|
| 78 |
+
"vocals": ["vocals"],
|
| 79 |
+
"bass": ["bass"],
|
| 80 |
+
"drums": ["drums"],
|
| 81 |
+
"other": [s for s in all_stems if s not in ["vocals", "bass", "drums"]],
|
| 82 |
+
}
|
| 83 |
+
|
| 84 |
+
mix_5_stems = {
|
| 85 |
+
"vocals": ["vocals"],
|
| 86 |
+
"bass": ["bass"],
|
| 87 |
+
"guitar": ["guitar"],
|
| 88 |
+
"drums": ["drums"],
|
| 89 |
+
"other": [s for s in all_stems if s not in ["vocals", "bass", "drums", "guitar"]],
|
| 90 |
+
}
|
| 91 |
+
|
| 92 |
+
mix_6_stems = {
|
| 93 |
+
"vocals": ["vocals"],
|
| 94 |
+
"bass": ["bass"],
|
| 95 |
+
"guitar": ["guitar"],
|
| 96 |
+
"piano": ["piano"],
|
| 97 |
+
"drums": ["drums"],
|
| 98 |
+
"other": [
|
| 99 |
+
s for s in all_stems if s not in ["vocals", "bass", "drums", "guitar", "piano"]
|
| 100 |
+
],
|
| 101 |
+
}
|
moisesdb/signal.py
ADDED
|
@@ -0,0 +1,52 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import numpy as np
|
| 2 |
+
|
| 3 |
+
|
| 4 |
+
def pad_along_axis(array, target_length, axis=0):
|
| 5 |
+
pad_size = target_length - array.shape[axis]
|
| 6 |
+
if pad_size <= 0:
|
| 7 |
+
return array
|
| 8 |
+
npad = [(0, 0)] * array.ndim
|
| 9 |
+
npad[axis] = (0, pad_size)
|
| 10 |
+
return np.pad(array, pad_width=npad, mode="constant", constant_values=0)
|
| 11 |
+
|
| 12 |
+
|
| 13 |
+
def hwr(x, floor=1e-7):
|
| 14 |
+
return (x + np.abs(x)) / 2
|
| 15 |
+
|
| 16 |
+
|
| 17 |
+
def compute_nbwin(size, frame_length, hop_length):
|
| 18 |
+
return np.ceil((size - (frame_length - hop_length)) / hop_length).astype(int)
|
| 19 |
+
|
| 20 |
+
|
| 21 |
+
def compute_length(nbwin, frame_length, hop_length):
|
| 22 |
+
return hop_length * nbwin + (frame_length - hop_length)
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
def triangular_window(length, power=1.0):
|
| 26 |
+
window = np.concatenate(
|
| 27 |
+
[np.arange(1, length // 2 + 1), np.arange(length - length // 2, 0, -1)]
|
| 28 |
+
)
|
| 29 |
+
return (window / window.max()) ** power
|
| 30 |
+
|
| 31 |
+
|
| 32 |
+
def frame_signal(x, frame_length, hop_length):
|
| 33 |
+
n_frames = compute_nbwin(x.shape[-1], frame_length, hop_length)
|
| 34 |
+
x = pad_along_axis(x, n_frames * hop_length, axis=-1)
|
| 35 |
+
strides = np.array(x.strides)
|
| 36 |
+
shape = list(x.shape)[:-1] + [frame_length, n_frames]
|
| 37 |
+
strides = list(x.strides) + [hop_length * strides[-1]]
|
| 38 |
+
return np.lib.stride_tricks.as_strided(x, shape, strides)
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
def unframe_signal(framed, hop_length, window_fn=triangular_window):
|
| 42 |
+
nbwin, nbsamples = framed.shape[0], framed.shape[-1]
|
| 43 |
+
signal_length = compute_length(nbwin, nbsamples, hop_length)
|
| 44 |
+
win = window_fn(nbsamples)
|
| 45 |
+
framed = framed * win
|
| 46 |
+
offsets = range(0, signal_length - hop_length, hop_length)
|
| 47 |
+
reconstructed = np.zeros((*framed.shape[1:-1], signal_length))
|
| 48 |
+
weights = np.zeros((*framed.shape[1:-1], signal_length))
|
| 49 |
+
for ofidx, offset in enumerate(offsets):
|
| 50 |
+
reconstructed[..., offset : offset + nbsamples] += framed[ofidx]
|
| 51 |
+
weights[..., offset : offset + nbsamples] += win
|
| 52 |
+
return reconstructed / weights
|
moisesdb/track.py
ADDED
|
@@ -0,0 +1,159 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
| 1 |
+
import logging
|
| 2 |
+
import os
|
| 3 |
+
|
| 4 |
+
import librosa
|
| 5 |
+
import numpy as np
|
| 6 |
+
|
| 7 |
+
from moisesdb.activity import compute_activity_signal
|
| 8 |
+
from moisesdb.defaults import all_stems, default_data_path
|
| 9 |
+
from moisesdb.utils import load_audio, load_json, save_audio
|
| 10 |
+
|
| 11 |
+
logger = logging.getLogger(__name__)
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
class MoisesDBTrack:
|
| 15 |
+
def __init__(
|
| 16 |
+
self, provider, track_id, data_path=default_data_path, sample_rate=44100
|
| 17 |
+
):
|
| 18 |
+
self.data_path = data_path
|
| 19 |
+
self.path = os.path.join(self.data_path, provider, track_id)
|
| 20 |
+
self.json_data = load_json(os.path.join(self.path, "data.json"))
|
| 21 |
+
|
| 22 |
+
self.sr = sample_rate
|
| 23 |
+
self.provider = provider
|
| 24 |
+
self.id = track_id
|
| 25 |
+
self.artist = self.json_data.get("artist", "")
|
| 26 |
+
self.name = self.json_data.get("song", "untitled")
|
| 27 |
+
self.genre = self.json_data.get("genre", "undefined")
|
| 28 |
+
self.sources = self._parse_sources(self.json_data.get("stems", {}))
|
| 29 |
+
self.bleedings = self._parse_bleeding(self.json_data.get("stems", {}))
|
| 30 |
+
|
| 31 |
+
def _parse_bleeding(self, stems):
|
| 32 |
+
bleeds = {s.get("stemName"): {} for s in stems}
|
| 33 |
+
for stem in stems:
|
| 34 |
+
unique_tracks = list({t["trackType"] for t in stem.get("tracks")})
|
| 35 |
+
parsed_tracks = {t: [] for t in unique_tracks}
|
| 36 |
+
stem_name = stem.get("stemName")
|
| 37 |
+
for track in stem.get("tracks", []):
|
| 38 |
+
parsed_tracks[track["trackType"]].append(track.get("has_bleed", False))
|
| 39 |
+
|
| 40 |
+
if bleeds[stem_name]:
|
| 41 |
+
for track_type, tracks in bleeds.items():
|
| 42 |
+
bleeds[stem_name].setdefault(track_type, []).extend(tracks)
|
| 43 |
+
else:
|
| 44 |
+
bleeds[stem_name].update(parsed_tracks)
|
| 45 |
+
return bleeds
|
| 46 |
+
|
| 47 |
+
def _parse_sources(self, stems):
|
| 48 |
+
parsed_stems = {s.get("stemName"): {} for s in stems}
|
| 49 |
+
for stem in stems:
|
| 50 |
+
unique_tracks = list({t["trackType"] for t in stem.get("tracks")})
|
| 51 |
+
parsed_tracks = {t: [] for t in unique_tracks}
|
| 52 |
+
stem_name = stem.get("stemName")
|
| 53 |
+
for track in stem.get("tracks", []):
|
| 54 |
+
file_path = (
|
| 55 |
+
os.path.join(
|
| 56 |
+
self.data_path,
|
| 57 |
+
self.provider,
|
| 58 |
+
self.id,
|
| 59 |
+
stem_name,
|
| 60 |
+
track.get("id"),
|
| 61 |
+
)
|
| 62 |
+
+ "."
|
| 63 |
+
+ track.get("extension")
|
| 64 |
+
)
|
| 65 |
+
track_type = track["trackType"]
|
| 66 |
+
parsed_tracks[track_type].append(file_path)
|
| 67 |
+
|
| 68 |
+
if parsed_stems[stem_name]:
|
| 69 |
+
for track_type, tracks in parsed_tracks.items():
|
| 70 |
+
parsed_stems[stem_name].setdefault(track_type, []).extend(tracks)
|
| 71 |
+
else:
|
| 72 |
+
parsed_stems[stem_name].update(parsed_tracks)
|
| 73 |
+
|
| 74 |
+
return parsed_stems
|
| 75 |
+
|
| 76 |
+
def stem_sources(self, stem):
|
| 77 |
+
sources = self.sources.get(stem, {})
|
| 78 |
+
sources_audio = {}
|
| 79 |
+
for source, paths in sources.items():
|
| 80 |
+
source_audios = [load_audio(p) for p in paths]
|
| 81 |
+
sample_rates = [s[1] for s in source_audios]
|
| 82 |
+
min_len = min(s[0].shape[-1] for s in source_audios)
|
| 83 |
+
sources_audio[source] = [
|
| 84 |
+
{"audio": s[0][..., :min_len], "sr": sr}
|
| 85 |
+
for s, sr in zip(source_audios, sample_rates)
|
| 86 |
+
]
|
| 87 |
+
return sources_audio
|
| 88 |
+
|
| 89 |
+
def stem_sources_mixture(self, stem):
|
| 90 |
+
stem_sources = self.stem_sources(stem=stem)
|
| 91 |
+
sources_mixture = {}
|
| 92 |
+
for source, sources_data in stem_sources.items():
|
| 93 |
+
# Resample
|
| 94 |
+
for source_data in sources_data:
|
| 95 |
+
source_data["audio"] = librosa.resample(
|
| 96 |
+
source_data["audio"], orig_sr=source_data["sr"], target_sr=self.sr
|
| 97 |
+
)
|
| 98 |
+
# Mix
|
| 99 |
+
sources_mixture[source] = trim_and_mix([s["audio"] for s in sources_data])
|
| 100 |
+
return sources_mixture
|
| 101 |
+
|
| 102 |
+
def stem_mixture(self, stem):
|
| 103 |
+
sources_mixture = self.stem_sources_mixture(stem=stem)
|
| 104 |
+
if all_sources := list(sources_mixture.values()):
|
| 105 |
+
return trim_and_mix(all_sources)
|
| 106 |
+
else:
|
| 107 |
+
return None
|
| 108 |
+
|
| 109 |
+
def save_stems(self, path):
|
| 110 |
+
for stem, audio in self.stems.items():
|
| 111 |
+
if audio is not None:
|
| 112 |
+
filepath = os.path.join(path, f"{stem}.wav")
|
| 113 |
+
logger.info(f"Saving {filepath}")
|
| 114 |
+
save_audio(filepath, audio, sr=self.sr)
|
| 115 |
+
|
| 116 |
+
def mix_stems(self, mix_map):
|
| 117 |
+
stems = {}
|
| 118 |
+
for stem, stem_sources in mix_map.items():
|
| 119 |
+
stems_to_mix = [self.stem_mixture(s) for s in stem_sources]
|
| 120 |
+
stems[stem] = trim_and_mix(stems_to_mix)
|
| 121 |
+
return stems
|
| 122 |
+
|
| 123 |
+
@property
|
| 124 |
+
def stems(self):
|
| 125 |
+
stems = {}
|
| 126 |
+
for stem in all_stems:
|
| 127 |
+
mix = self.stem_mixture(stem=stem)
|
| 128 |
+
if mix is not None:
|
| 129 |
+
stems[stem] = mix
|
| 130 |
+
return stems
|
| 131 |
+
|
| 132 |
+
@property
|
| 133 |
+
def audio(self):
|
| 134 |
+
stems = self.stems
|
| 135 |
+
stems = {k: v for k, v in stems.items() if v is not None}
|
| 136 |
+
return pad_and_mix(stems.values())
|
| 137 |
+
|
| 138 |
+
@property
|
| 139 |
+
def activity(self):
|
| 140 |
+
return {
|
| 141 |
+
stem: compute_activity_signal(audio[None, ...])[0]
|
| 142 |
+
for stem, audio in self.stems.items()
|
| 143 |
+
}
|
| 144 |
+
|
| 145 |
+
|
| 146 |
+
def pad_to_len(source, length):
|
| 147 |
+
return np.pad(source, ((0, 0), (0, length))) if length > 0 else source
|
| 148 |
+
|
| 149 |
+
|
| 150 |
+
def pad_and_mix(sources):
|
| 151 |
+
max_len = max(s.shape[-1] for s in sources)
|
| 152 |
+
pad_len = [max_len - s.shape[-1] for s in sources]
|
| 153 |
+
padded_mixtures = [pad_to_len(s, p) for s, p in zip(sources, pad_len)]
|
| 154 |
+
return np.stack(padded_mixtures).sum(0)
|
| 155 |
+
|
| 156 |
+
|
| 157 |
+
def trim_and_mix(sources):
|
| 158 |
+
min_len = min(s.shape[-1] for s in sources)
|
| 159 |
+
return np.stack([s[..., :min_len] for s in sources]).sum(0)
|
moisesdb/utils.py
ADDED
|
@@ -0,0 +1,40 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import json
|
| 2 |
+
from io import BytesIO
|
| 3 |
+
|
| 4 |
+
import fsspec
|
| 5 |
+
import librosa
|
| 6 |
+
import soundfile as sf
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
def load_json(path):
|
| 10 |
+
with fsspec.open(path, "r") as f:
|
| 11 |
+
return json.loads(f.read(-1))
|
| 12 |
+
|
| 13 |
+
|
| 14 |
+
def load_audio(path, fsspec_kwargs=None, **kwargs):
|
| 15 |
+
if fsspec_kwargs is None:
|
| 16 |
+
fsspec_kwargs = {}
|
| 17 |
+
with fsspec.open(path, "rb", **fsspec_kwargs) as f:
|
| 18 |
+
samples = list(librosa.load(f, sr=None, mono=False, **kwargs))
|
| 19 |
+
if samples[0].ndim < 2:
|
| 20 |
+
samples[0] = samples[0][None, ...].repeat(2, axis=0)
|
| 21 |
+
return samples
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
def save_audio(path, audio, **kwargs):
|
| 25 |
+
wav_io = BytesIO()
|
| 26 |
+
sf.write(
|
| 27 |
+
wav_io,
|
| 28 |
+
audio.T,
|
| 29 |
+
samplerate=kwargs.pop("sr", 44100),
|
| 30 |
+
subtype=kwargs.pop("subtype", "PCM_24"),
|
| 31 |
+
format=kwargs.pop("format", "wav"),
|
| 32 |
+
**kwargs
|
| 33 |
+
)
|
| 34 |
+
with fsspec.open(path, mode="wb") as f:
|
| 35 |
+
f.write(wav_io.getbuffer())
|
| 36 |
+
|
| 37 |
+
|
| 38 |
+
def get_fs(path):
|
| 39 |
+
fs, _ = fsspec.core.url_to_fs(str(path))
|
| 40 |
+
return fs
|
pyproject.toml
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
[tool.poetry]
|
| 2 |
+
name = "moises-db"
|
| 3 |
+
version = "0.0.3"
|
| 4 |
+
description = "moises-db"
|
| 5 |
+
authors = ["Igor Pereira <[email protected]>", "Vinícius Marques <[email protected]>"]
|
| 6 |
+
readme = "README.md"
|
| 7 |
+
homepage = "https://github.com/moises-ai/moises-db"
|
| 8 |
+
license = "MIT"
|
| 9 |
+
packages = [
|
| 10 |
+
{ include = "moisesdb" }
|
| 11 |
+
]
|
| 12 |
+
|
| 13 |
+
[tool.poetry.dependencies]
|
| 14 |
+
python = "^3.8"
|
| 15 |
+
scipy = ">=1.9.3"
|
| 16 |
+
librosa = ">=0.9.2"
|
| 17 |
+
pandas = ">=1.5.1"
|
| 18 |
+
tqdm = ">=4.64.1"
|
| 19 |
+
fsspec = ">=2022.8.2"
|
| 20 |
+
gcsfs = ">=2022.8.2"
|
| 21 |
+
|
| 22 |
+
[build-system]
|
| 23 |
+
requires = ["poetry_core>=1.0.0"]
|
| 24 |
+
build-backend = "poetry.core.masonry.api"
|