From 0f9d36144df56a9459be68c647107990345ac544 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Andrew=20Beveridge=20=E2=98=84=EF=B8=8F?=
 <andrew@beveridge.uk>
Date: Fri, 26 Apr 2024 10:40:35 -0400
Subject: [PATCH] Attempt to fix windows MDX ONNX loading tempfile bug in
 onnx2torch (#63)

* Attempt to fix windows MDX ONNX loading tempfile bug in onnx2torch

* Bumped version with onnx2torch windows fix
---
 .../separator/architectures/mdx_separator.py  |   9 +-
 poetry.lock                                   | 817 +++++++++---------
 pyproject.toml                                |   2 +-
 3 files changed, 439 insertions(+), 389 deletions(-)

diff --git a/audio_separator/separator/architectures/mdx_separator.py b/audio_separator/separator/architectures/mdx_separator.py
index f79ac69..9313151 100644
--- a/audio_separator/separator/architectures/mdx_separator.py
+++ b/audio_separator/separator/architectures/mdx_separator.py
@@ -1,7 +1,9 @@
 """Module for separating audio sources using MDX architecture models."""
 
 import os
+import platform
 import torch
+import onnx
 import onnxruntime as ort
 import numpy as np
 import onnx2torch
@@ -121,7 +123,12 @@ def load_model(self):
             self.model_run = lambda spek: ort_inference_session.run(None, {"input": spek.cpu().numpy()})[0]
             self.logger.debug("Model loaded successfully using ONNXruntime inferencing session.")
         else:
-            self.model_run = onnx2torch.convert(self.model_path)
+            if platform.system() == 'Windows':
+                onnx_model = onnx.load(self.model_path)
+                self.model_run = onnx2torch.convert(onnx_model)
+            else:
+                self.model_run = onnx2torch.convert(self.model_path)
+   
             self.model_run.to(self.torch_device).eval()
             self.logger.warning("Model converted from onnx to pytorch due to segment size not matching dim_t, processing may be slower.")
 
diff --git a/poetry.lock b/poetry.lock
index c4e032e..a39fb78 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -27,33 +27,33 @@ test = ["tox"]
 
 [[package]]
 name = "black"
-version = "24.3.0"
+version = "24.4.2"
 description = "The uncompromising code formatter."
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -300,63 +300,63 @@ files = [
 
 [[package]]
 name = "coverage"
-version = "7.4.4"
+version = "7.5.0"
 description = "Code coverage measurement for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "exceptiongroup-1.2.1.tar.gz", hash = "sha256:a4785e48b045528f5bfe627b6ad554ff32def154f42372786903b7abcfe1aa16"},
 ]
 
 [package.extras]
@@ -514,13 +514,13 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "filelock"
-version = "3.13.3"
+version = "3.13.4"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "filelock-3.13.3-py3-none-any.whl", hash = "sha256:5ffa845303983e7a0b7ae17636509bc97997d58afeafa72fb141a17b152284cb"},
-    {file = "filelock-3.13.3.tar.gz", hash = "sha256:a79895a25bbefdf55d1a2a0a80968f7dbb28edcd6d4234a0afb3f37ecde4b546"},
+    {file = "filelock-3.13.4-py3-none-any.whl", hash = "sha256:404e5e9253aa60ad457cae1be07c0f0ca90a63931200a47d9b6a6af84fd7b45f"},
+    {file = "filelock-3.13.4.tar.gz", hash = "sha256:d13f466618bfde72bd2c18255e269f72542c6e70e7bac83a0232d6b1cc5c8cf4"},
 ]
 
 [package.extras]
@@ -590,13 +590,13 @@ pyreadline3 = {version = "*", markers = "sys_platform == \"win32\" and python_ve
 
 [[package]]
 name = "idna"
-version = "3.6"
+version = "3.7"
 description = "Internationalized Domain Names in Applications (IDNA)"
 optional = false
 python-versions = ">=3.5"
 files = [
-    {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
-    {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
+    {file = "idna-3.7-py3-none-any.whl", hash = "sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"},
+    {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
 ]
 
 [[package]]
@@ -610,6 +610,20 @@ files = [
     {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"},
 ]
 
+[[package]]
+name = "intel-openmp"
+version = "2021.4.0"
+description = "Intel OpenMP* Runtime Library"
+optional = false
+python-versions = "*"
+files = [
+    {file = "intel_openmp-2021.4.0-py2.py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.whl", hash = "sha256:41c01e266a7fdb631a7609191709322da2bbf24b252ba763f125dd651bcc7675"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:3b921236a38384e2016f0f3d65af6732cf2c12918087128a9163225451e776f2"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:e2240ab8d01472fed04f3544a878cda5da16c26232b7ea1b59132dbfb48b186e"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-win32.whl", hash = "sha256:6e863d8fd3d7e8ef389d52cf97a50fe2afe1a19247e8c0d168ce021546f96fc9"},
+    {file = "intel_openmp-2021.4.0-py2.py3-none-win_amd64.whl", hash = "sha256:eef4c8bcc8acefd7f5cd3b9384dbf73d59e2c99fc56545712ded913f43c4a94f"},
+]
+
 [[package]]
 name = "jinja2"
 version = "3.1.3"
@@ -629,13 +643,13 @@ i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "joblib"
-version = "1.3.2"
+version = "1.4.0"
 description = "Lightweight pipelining with Python functions"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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+    {file = "joblib-1.4.0-py3-none-any.whl", hash = "sha256:42942470d4062537be4d54c83511186da1fc14ba354961a2114da91efa9a4ed7"},
+    {file = "joblib-1.4.0.tar.gz", hash = "sha256:1eb0dc091919cd384490de890cb5dfd538410a6d4b3b54eef09fb8c50b409b1c"},
 ]
 
 [[package]]
@@ -656,17 +670,21 @@ dev = ["coverage", "flake8", "mypy", "onnxruntime", "pdoc3", "resampy (==0.2.2)"
 
 [[package]]
 name = "lazy-loader"
-version = "0.3"
-description = "lazy_loader"
+version = "0.4"
+description = "Makes it easy to load subpackages and functions on demand."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "lazy_loader-0.3-py3-none-any.whl", hash = "sha256:1e9e76ee8631e264c62ce10006718e80b2cfc74340d17d1031e0f84af7478554"},
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+    {file = "lazy_loader-0.4-py3-none-any.whl", hash = "sha256:342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc"},
+    {file = "lazy_loader-0.4.tar.gz", hash = "sha256:47c75182589b91a4e1a85a136c074285a5ad4d9f39c63e0d7fb76391c4574cd1"},
 ]
 
+[package.dependencies]
+packaging = "*"
+
 [package.extras]
-lint = ["pre-commit (>=3.3)"]
+dev = ["changelist (==0.5)"]
+lint = ["pre-commit (==3.7.0)"]
 test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
 
 [[package]]
@@ -799,6 +817,24 @@ files = [
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 ]
 
+[[package]]
+name = "mkl"
+version = "2021.4.0"
+description = "Intel® oneAPI Math Kernel Library"
+optional = false
+python-versions = "*"
+files = [
+    {file = "mkl-2021.4.0-py2.py3-none-macosx_10_15_x86_64.macosx_11_0_x86_64.whl", hash = "sha256:67460f5cd7e30e405b54d70d1ed3ca78118370b65f7327d495e9c8847705e2fb"},
+    {file = "mkl-2021.4.0-py2.py3-none-manylinux1_i686.whl", hash = "sha256:636d07d90e68ccc9630c654d47ce9fdeb036bb46e2b193b3a9ac8cfea683cce5"},
+    {file = "mkl-2021.4.0-py2.py3-none-manylinux1_x86_64.whl", hash = "sha256:398dbf2b0d12acaf54117a5210e8f191827f373d362d796091d161f610c1ebfb"},
+    {file = "mkl-2021.4.0-py2.py3-none-win32.whl", hash = "sha256:439c640b269a5668134e3dcbcea4350459c4a8bc46469669b2d67e07e3d330e8"},
+    {file = "mkl-2021.4.0-py2.py3-none-win_amd64.whl", hash = "sha256:ceef3cafce4c009dd25f65d7ad0d833a0fbadc3d8903991ec92351fe5de1e718"},
+]
+
+[package.dependencies]
+intel-openmp = "==2021.*"
+tbb = "==2021.*"
+
 [[package]]
 name = "ml-collections"
 version = "0.1.1"
@@ -1117,23 +1153,24 @@ nvidia-nvjitlink-cu12 = "*"
 
 [[package]]
 name = "nvidia-nccl-cu12"
-version = "2.19.3"
+version = "2.20.5"
 description = "NVIDIA Collective Communication Library (NCCL) Runtime"
 optional = false
 python-versions = ">=3"
 files = [
-    {file = "nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl", hash = "sha256:a9734707a2c96443331c1e48c717024aa6678a0e2a4cb66b2c364d18cee6b48d"},
+    {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1fc150d5c3250b170b29410ba682384b14581db722b2531b0d8d33c595f33d01"},
+    {file = "nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl", hash = "sha256:057f6bf9685f75215d0c53bf3ac4a10b3e6578351de307abad9e18a99182af56"},
 ]
 
 [[package]]
 name = "nvidia-nvjitlink-cu12"
-version = "12.4.99"
+version = "12.4.127"
 description = "Nvidia JIT LTO Library"
 optional = false
 python-versions = ">=3"
 files = [
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+    {file = "nvidia_nvjitlink_cu12-12.4.127-py3-none-manylinux2014_x86_64.whl", hash = "sha256:06b3b9b25bf3f8af351d664978ca26a16d2c5127dbd53c0497e28d1fb9611d57"},
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 ]
 
 [[package]]
@@ -1196,13 +1233,13 @@ reference = ["Pillow", "google-re2"]
 
 [[package]]
 name = "onnx2torch"
-version = "1.5.13"
+version = "1.5.14"
 description = "ONNX to PyTorch converter"
 optional = false
 python-versions = ">=3.6"
 files = [
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+    {file = "onnx2torch-1.5.14.tar.gz", hash = "sha256:f7185ffe3030410c34d579769bfcefcdce3e23a902e0f8b5472d3d0aae794740"},
 ]
 
 [package.dependencies]
@@ -1216,36 +1253,36 @@ dev = ["Pillow", "black", "googledrivedownloader", "isort", "onnxruntime", "pre-
 
 [[package]]
 name = "onnxruntime"
-version = "1.17.1"
+version = "1.17.3"
 description = "ONNX Runtime is a runtime accelerator for Machine Learning models"
 optional = true
 python-versions = "*"
 files = [
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 [[package]]
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+version = "2.22"
 description = "C parser in Python"
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 [[package]]
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 scipy = ">=1.6.0"
 threadpoolctl = ">=2.0.0"
 
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 [[package]]
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 [package.dependencies]
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+numpy = ">=1.22.4,<2.3"
 
 [package.extras]
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 [[package]]
 name = "six"
@@ -1828,6 +1867,19 @@ files = [
 [package.dependencies]
 mpmath = ">=0.19"
 
+[[package]]
+name = "tbb"
+version = "2021.12.0"
+description = "Intel® oneAPI Threading Building Blocks (oneTBB)"
+optional = false
+python-versions = "*"
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+
 [[package]]
 name = "threadpoolctl"
 version = "3.4.0"
@@ -1852,42 +1904,38 @@ files = [
 
 [[package]]
 name = "torch"
-version = "2.2.2"
+version = "2.3.0"
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 python-versions = ">=3.8.0"
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 ]
 
 [package.dependencies]
 filelock = "*"
 fsspec = "*"
 jinja2 = "*"
+mkl = {version = ">=2021.1.1,<=2021.4.0", markers = "platform_system == \"Windows\""}
 networkx = "*"
 nvidia-cublas-cu12 = {version = "12.1.3.1", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 nvidia-cuda-cupti-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
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-nvidia-nccl-cu12 = {version = "2.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
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 nvidia-nvtx-cu12 = {version = "12.1.105", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\""}
 sympy = "*"
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+triton = {version = "2.3.0", markers = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.12\""}
 typing-extensions = ">=4.8.0"
 
 [package.extras]
@@ -1910,42 +1958,37 @@ optree = ["optree (>=0.9.1)"]
 
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 name = "torchvision"
-version = "0.17.2"
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 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.dependencies]
 numpy = "*"
 pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
-torch = "2.2.2"
+torch = "2.3.0"
 
 [package.extras]
 scipy = ["scipy"]
@@ -1972,17 +2015,17 @@ telegram = ["requests"]
 
 [[package]]
 name = "triton"
-version = "2.2.0"
+version = "2.3.0"
 description = "A language and compiler for custom Deep Learning operations"
 optional = false
 python-versions = "*"
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+    {file = "triton-2.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5ce4b8ff70c48e47274c66f269cce8861cf1dc347ceeb7a67414ca151b1822d8"},
+    {file = "triton-2.3.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3c3d9607f85103afdb279938fc1dd2a66e4f5999a58eb48a346bd42738f986dd"},
+    {file = "triton-2.3.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:218d742e67480d9581bafb73ed598416cc8a56f6316152e5562ee65e33de01c0"},
+    {file = "triton-2.3.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:381ec6b3dac06922d3e4099cfc943ef032893b25415de295e82b1a82b0359d2c"},
+    {file = "triton-2.3.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:038e06a09c06a164fef9c48de3af1e13a63dc1ba3c792871e61a8e79720ea440"},
+    {file = "triton-2.3.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6d8f636e0341ac348899a47a057c3daea99ea7db31528a225a3ba4ded28ccc65"},
 ]
 
 [package.dependencies]
@@ -1995,13 +2038,13 @@ tutorials = ["matplotlib", "pandas", "tabulate", "torch"]
 
 [[package]]
 name = "typing-extensions"
-version = "4.10.0"
+version = "4.11.0"
 description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.10.0-py3-none-any.whl", hash = "sha256:69b1a937c3a517342112fb4c6df7e72fc39a38e7891a5730ed4985b5214b5475"},
-    {file = "typing_extensions-4.10.0.tar.gz", hash = "sha256:b0abd7c89e8fb96f98db18d86106ff1d90ab692004eb746cf6eda2682f91b3cb"},
+    {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
+    {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
 ]
 
 [[package]]
diff --git a/pyproject.toml b/pyproject.toml
index eb21ebe..2051e9f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "audio-separator"
-version = "0.16.4"
+version = "0.16.5"
 description = "Easy to use audio stem separation, using various models from UVR trained primarily by @Anjok07"
 authors = ["Andrew Beveridge <andrew@beveridge.uk>"]
 license = "MIT"