Improved hardware acceleration readme steps

README.md

@@ -20,42 +20,51 @@ The primary stem typically contains the instrumental part of the audio, while th
 
 You'll need `ffmpeg` and `libsndfile`, which should be easy to install on most platforms, e.g.:
 
-- Debian/Ubuntu: `apt-get update; apt-get install -y libsndfile1-dev ffmpeg`
-- macOS: `brew update; brew install libsndfile ffmpeg`
+### 🐧 Debian/Ubuntu
+`apt-get update; apt-get install -y libsndfile1-dev ffmpeg`
+
+###  macOS
+`brew update; brew install libsndfile ffmpeg`
 
 You can then install Audio Separator using pip, use one of the following to install the correct onnxruntime dependency for your device:
 
-### Nvidia GPU with CUDA acceleration:
+### 🎮 Nvidia GPU with CUDA acceleration
 
 `pip install audio-separator[gpu]`
 
-### Apple Silicon, macOS Sonoma+ with CoreML acceleration: 
+- 💬 If successfully configured, you should see this log message when running audio-separator:
+ `ONNXruntime has CUDAExecutionProvider available, enabling acceleration`
+
+###  Apple Silicon, macOS Sonoma+ with CoreML acceleration
 
 `pip install audio-separator[silicon]`
 
-### No hardware acceleration, CPU only:
+- 💬 If successfully configured, you should see this log message when running audio-separator:
+ `ONNXruntime has CoreMLExecutionProvider available, enabling acceleration`
+
+### 🐢 No hardware acceleration, CPU only:
 
 `pip install audio-separator[cpu]`
 
 
-### GPU / CUDA specific steps 
+## GPU / CUDA specific installation steps
 
 In theory, all you should need to do to get `audio-separator` working with a GPU is install it with the `[gpu]` extra as above.
 
-However, sometimes getting both PyTorch and ONNX Runtime working with CUDA support can be a bit tricky so sometimes it may not be that easy.
+However, sometimes getting both PyTorch and ONNX Runtime working with CUDA support can be a bit tricky so it may not work that easily.
 
-You may need to reinstall them directly, allowing pip to calculate the right versions for your platform:
+You may need to reinstall both packages directly, allowing pip to calculate the right versions for your platform:
 
 - `pip uninstall torch onnxruntime`
 - `pip cache purge`
-- `pip install torch torchvision torchaudio`
-- `pip install onnxruntime-gpu`
+- `pip install --force-reinstall torch torchvision torchaudio`
+- `pip install --force-reinstall onnxruntime-gpu`
 
 Depending on your hardware, you may get better performance with the optimum version of onnxruntime:
-- `pip install "optimum[onnxruntime-gpu]"`
+- `pip install --force-reinstall "optimum[onnxruntime-gpu]"`
 
 Depending on your CUDA version and hardware, you may need to install torch from the `cu118` index instead:
-- `pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118`
+- `pip install --force-reinstall torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118`
 
 > Note: if anyone knows how to make this cleaner so we can support both different platform-specific dependencies for hardware acceleration without a separate installation process for each, please let me know or raise a PR!
 

audio_separator/separator.py

@@ -117,7 +117,7 @@ def __init__(
             self.device = torch.device("cuda")
 
             if ort_device == "GPU" and "CUDAExecutionProvider" in ort_providers:
-                self.logger.info("ONNXruntime device is GPU with CUDAExecutionProvider available, enabling acceleration")
+                self.logger.info("ONNXruntime has CUDAExecutionProvider available, enabling acceleration")
                 self.onnx_execution_provider = ["CUDAExecutionProvider"]
                 hardware_acceleration_enabled = True
             else:

audio_separator/utils/cli.py

@@ -2,7 +2,6 @@
 import argparse
 import logging
 import pkg_resources
-from audio_separator import Separator
 
 
 def main():
@@ -83,6 +82,9 @@ def main():
 
     logger.info(f"Separator beginning with input file: {args.audio_file}")
 
+    # Deliberately import here to avoid loading heave dependencies when just running --help
+    from audio_separator import Separator
+
     separator = Separator(
         args.audio_file,
         log_formatter=log_formatter,

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "audio-separator"
-version = "0.9.1"
+version = "0.9.2"
 description = "Easy to use vocal separation on CLI or as a python package, using the amazing MDX-Net models from UVR trained by @Anjok07"
 authors = ["Andrew Beveridge <[email protected]>"]
 license = "MIT"