78 lines
1.8 KiB
Markdown
78 lines
1.8 KiB
Markdown
# Audio separatio tools
|
|
|
|
Some random tools used during development.
|
|
|
|
They are intended to be invoked from the root of the nodes.
|
|
|
|
# ONNX to safetensors converter
|
|
|
|
File: onnx2safetensors.py
|
|
|
|
Used to convert MDX-Net models in ONNX format to safetensors.
|
|
Might work for other ONNX files using the same operands.
|
|
But you'll need a PyTorch class for its architecture that creates the layers
|
|
in the same order as the ONNX file.
|
|
|
|
# Batch converter
|
|
|
|
File: batch_convert.py
|
|
|
|
Used to convert all the MDX-Net files in one run
|
|
|
|
# Demix
|
|
|
|
File: demix.py
|
|
|
|
Used to run the inference on audio files performing the demixing process
|
|
|
|
# Download Model
|
|
|
|
File: download_model.py
|
|
|
|
Downloads a model listed in the DB
|
|
|
|
# Show Data Base
|
|
|
|
File: show_db.py
|
|
|
|
Prints the content of the models data base.
|
|
This is useful to adjust details in the DB, but you need to modify the code (`apply_process`)
|
|
|
|
# Show ONNX
|
|
|
|
File: show_onnx.py
|
|
|
|
Displays the ONNX input, output and layers.
|
|
Is a text representation, not as nice as [Netron](https://netron.app/), but you
|
|
can extract a lot of information from it.
|
|
|
|
It can also show details of what onnx2pytorch interprets from it.
|
|
|
|
And can do an inferece run using random data. We used it to determine some
|
|
details of the onnx2pytorch details.
|
|
|
|
# Show Class
|
|
|
|
File: show_class.py
|
|
|
|
Used to display our PyTorch class, also the state_dict keys.
|
|
It can optionally export the class structure as an ONNX file that can be loaded by
|
|
Netron, but contains too much extra names.
|
|
|
|
# Style Fixer
|
|
|
|
File: style_fixer.py
|
|
|
|
Fixes various common style errors found in Gemini 2.5 Pro code.
|
|
The script was created by Gemini ... can create the script, but can't stop making
|
|
the same errors over and over.
|
|
|
|
# UVR Hash
|
|
|
|
File: uvr_hash.py
|
|
|
|
Computes the hash used by UVR to identify a model. We use the same hash, as many
|
|
tools do.
|
|
|
|
This is the key value for aur model database.
|