From 913c4c0515a622bacc7ee1abe41d894f438c028f Mon Sep 17 00:00:00 2001 From: Andrew Beveridge Date: Fri, 7 Jul 2023 14:33:08 -0500 Subject: [PATCH] Updated readme with new params --- README.md | 6 ++++++ pyproject.toml | 2 +- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 86cabda..3760b23 100644 --- a/README.md +++ b/README.md @@ -36,6 +36,9 @@ audio-separator [audio_file] --model_name [model_name] model_name: (Optional) The name of the model to use for separation. Default: UVR_MDXNET_KARA_2 model_file_dir: (Optional) Directory to cache model files in. Default: /tmp/audio-separator-models/ output_dir: (Optional) The directory where the separated files will be saved. If not specified, outputs to current dir. + use_cuda: (Optional) Flag to use Nvidia GPU via CUDA for separation if available. Default: False + log_level: (Optional) The log level. Default: logging.DEBUG + log_formatter: (Optional) The log format. Default: '%(asctime)s - %(name)s - %(levelname)s - %(message)s' ``` Example: @@ -69,6 +72,9 @@ print(f'Secondary stem saved at {secondary_stem_path}') - model_name: (Optional) The name of the model to use for separation. Defaults to 'UVR_MDXNET_KARA_2', a very powerful model for Karaoke instrumental tracks. - model_file_dir: (Optional) Directory to cache model files in. Default: /tmp/audio-separator-models/ - output_dir: (Optional) Directory where the separated files will be saved. If not specified, outputs to current dir. +- use_cuda: (Optional) Flag to use Nvidia GPU via CUDA for separation if available. Default: False +- log_level: (Optional) The log level. Default: logging.DEBUG +- log_formatter: (Optional) The log format. Default: '%(asctime)s - %(name)s - %(levelname)s - %(message)s' ## Requirements 📋 diff --git a/pyproject.toml b/pyproject.toml index 70dcf0b..f0f9266 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "audio-separator" -version = "0.3.1" +version = "0.3.2" 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 "] license = "MIT"