Windows: Instructions for building onnxruntime library

Signed-off-by: Tibor Vass <[email protected]>

Author: tiborvass

README.md

@@ -47,12 +47,17 @@ convolution support.
 
 ## Build from source
 
+Requirements are: `git`, `cmake`, and your OS's preferred compiler suite.
+
 Use this when cloning:
 
 ```
 git clone --recurse-submodules --shallow-submodules https://github.com/DamRsn/NeuralNote
  ```
 
+The following OS-specific build scripts have to be executed at least once before being able to use the project as a normal CMake project. The script downloads onnxruntime static library (that we created
+with [ort-builder](https://github.com/olilarkin/ort-builder)) before calling CMake.
+
 #### Mac
 
 ```
@@ -61,13 +66,29 @@ $ ./build.sh
 
 #### Windows
 
+Due to [a known issue](https://github.com/DamRsn/NeuralNote/issues/21), if you're not using Visual Studio 2022 (MSVC version: 19.35.x, check `cl` output), then you'll need to manually build onnxruntime.lib like so:
+
+1. Ensure you have Python installed; if not, download at https://www.python.org/downloads/windows/
+
+2. Execute each of the following lines in a command prompt:
+
 ```
-> build.bat
+git clone --depth 1 --recurse-submodules --shallow-submodules https://github.com/tiborvass/libonnxruntime-neuralnote ThirdParty\onnxruntime
+cd ThirdParty\onnxruntime
+python3 -m venv venv
+.\venv\Scripts\activate.bat
+pip install -r requirements.txt
+.\convert-model-to-ort.bat model.onnx
+.\build-win.bat model.required_operators_and_types.with_runtime_opt.config
+cp model.with_runtime_opt.ort ..\..\Lib\ModelData\features_model.ort
+cd ..\..
 ```
 
-The build script has to be executed at least once before being able to use the project as a normal CMake project.
-The script downloads onnxruntime static library (that we created
-with [ort-builder](https://github.com/olilarkin/ort-builder)) before calling CMake.
+Now you can get back to building NeuralNote as follows:
+
+```
+> .\build.bat
+```
 
 #### IDEs
 
@@ -86,7 +107,7 @@ But here's a description of the process we followed to create those files:
 - `features_model.onnx` was generated by converting a keras model containing only the CQT + Harmonic Stacking part of
   the full basic-pitch graph using `tf2onnx` (with manually added weights for batch normalization).
 - the `.json` files containing the weights of the basic-pitch cnn were generated from the tensorflow-js model available
-  in the [basic-pitch-tf repository](https://github.com/spotify/basic-pitch-ts), then converted to onnx with `tf2onnx`.
+  in the [basic-pitch-ts repository](https://github.com/spotify/basic-pitch-ts), then converted to onnx with `tf2onnx`.
   Finally, the weights were gathered manually to `.npy` thanks to [Netron](https://netron.app/) and finally applied to a
   split keras model created with [basic-pitch](https://github.com/spotify/basic-pitch) code.
 

build.bat

@@ -25,7 +25,7 @@ if "%extract%" == "true" (
 	del %archive%
 )
 
-wmic get cpu NumberOfLogicalProcessors | findstr /V NumberOfLogicalProcessors > ncpus
+wmic cpu get NumberOfLogicalProcessors | findstr /V NumberOfLogicalProcessors > ncpus
 set /p ncpus=<ncpus
 del ncpus