Basic usage
Loading the tools
compIAM does not have terminal functionalities but it is to be used within Python based-projects. First, import the library to
your Python project with: import compiam
.
The integrated tools and models are organized by:
First, the following fundamental musical aspects: melody, rhythm, structure, timbre, and from v0.3.0 we have included separation as well.
Then, the tools are grouped by tasks.
You can access the several included tools by importing them from their corresponding modules:
from compiam.melody.pitch_extraction import FTANetCarnatic
from compiam.rhythm.transcription import FourWayTabla
We provide nice functionalities to explore where do the tools live:
Print out the available tasks for each category:
compiam.melody.list_tasks()
Print out the available tools for each module using:
compiam.melody.list_tools()
Print out only the tools for a particular task:
compiam.melody.pitch_extraction.list_tools()
Important
Some tools (especially the ML/DL models) require specific dependencies that are not installed by default,
because of their size or compatibility issues. If a tool is loaded and a particular dependency is missing,
an alert will be displayed, to inform the user which dependency is missing and how to proceed to install
it in the right version. See optional_requirements.txt
where the optional dependencies and
the specific versions we use in compiam
are listed.
Wrappers
compIAM also includes wrappers to easily initialize relevant datasets, corpora, and also pre-trained models for particular problems.
|
Alias function to load a mirdata Dataset class. |
|
Wrapper to load access to the Dunya corpora. |
|
Wrapper for loading pre-trained models. |
Tip
When listing available tools using the list_tools()
functions, some will appear with a “*” at the end. That is meant to
indicate that such tools have pre-trained models available, which may be loaded using the wrapper compiam.load_model()
.
Model weights are large in size and therefore, not included in the library from v0.2.1 on. We have included a .download_model()
function to all ML/DL models that require pre-trained weights, so that the user can download them on demand. This function is
automatically run when invoking the model through the compiam.load_model()
wrapper. The model weights are then stored in the
corresponding default folder ./compiam/model/
. If the model is already downloaded, the function will not download it again.
Note
From v0.3.0 on, compiam.load_model()
wrapper has an argument data_home
, in where you can specify to which folder you
want the models to be downloaded and read from.
Note
From v0.4.0 on, compiam.load_model()
wrapper has an argument version
, in where you can specify which version of the
pre-trained model you want to use. You may want to use compiam.get_model_info(<model_key>)
to print out the entire model
information in compiam/data.py and visualisse the available versions. By default, the model contributor selects a default version
to be loaded without the user having to specify it, so this argument is option. If you try to load a non-existing version, an
error will be thrown.