Use inference/msst_cli.py.
usage: msst_cli.py [-h] [-d] [--device {auto,cpu,cuda,mps}] [--device_ids DEVICE_IDS [DEVICE_IDS ...]] [-i INPUT_FOLDER] [-o OUTPUT_FOLDER] [--output_format {wav,flac,mp3}]
[--model_type MODEL_TYPE] [--model_path MODEL_PATH] [--config_path CONFIG_PATH] [--use_tta] [--wav_bit_depth {PCM_16,PCM_24,PCM_32,FLOAT}]
[--flac_bit_depth {PCM_16,PCM_24}] [--mp3_bit_rate {96k,128k,192k,256k,320k}]
Music Source Separation Command Line Interface
options:
-h, --help show this help message and exit
-d, --debug Enable debug logging (default: False). Example: --debug
--device {auto,cpu,cuda,mps} Device to use for inference (default: auto). Example: --device=cuda
--device_ids DEVICE_IDS [DEVICE_IDS ...] List of gpu ids, only used when device is cuda (default: 0). Example: --device_ids 0 1
Separation I/O Params:
-i INPUT_FOLDER, --input_folder INPUT_FOLDER Folder with mixtures to process. [required]
-o OUTPUT_FOLDER, --output_folder OUTPUT_FOLDER Folder to store separated files. str for single folder, dict with instrument keys for multiple folders. Example: --output_folder=results or --output_folder="{'vocals': 'results/vocals', 'instrumental': 'results/instrumental'}"
--output_format {wav,flac,mp3} Output format for separated files (default: wav). Example: --output_format=wav
Model Params:
--model_type MODEL_TYPE One of ['bs_roformer', 'mel_band_roformer', 'segm_models', 'htdemucs', 'mdx23c', 'swin_upernet', 'bandit', 'bandit_v2', 'scnet', 'scnet_unofficial', 'torchseg', 'apollo', 'bs_mamba2']. [required]
--model_path MODEL_PATH Path to model checkpoint. [required]
--config_path CONFIG_PATH Path to config file. [required]
--use_tta Flag adds test time augmentation during inference (polarity and channel inverse). While this triples the runtime, it reduces noise and slightly improves prediction quality (default: False). Example: --use_tta
Audio Params:
--wav_bit_depth {PCM_16,PCM_24,PCM_32,FLOAT} Bit depth for wav output (default: FLOAT). Example: --wav_bit_depth=PCM_32
--flac_bit_depth {PCM_16,PCM_24} Bit depth for flac output (default: PCM_24). Example: --flac_bit_depth=PCM_24
--mp3_bit_rate {96k,128k,192k,256k,320k} Bit rate for mp3 output (default: 320k). Example: --mp3_bit_rate=320kfrom inference.msst_infer import MSSeparator
from utils.logger import get_logger
separator = MSSeparator(
model_type="bs_roformer",
config_path="configs/vocal_models/model_bs_roformer_ep_368_sdr_12.9628.yaml",
model_path="pretrain/vocal_models/model_bs_roformer_ep_368_sdr_12.9628.ckpt",
device='auto',
device_ids=[0],
output_format='mp3',
use_tta=False,
store_dirs={"vocals": "results/vocals", "instrumental": "results/instrumental"},
audio_params = {"wav_bit_depth": "FLOAT", "flac_bit_depth": "PCM_24", "mp3_bit_rate": "320k"},
logger=get_logger(),
debug=True
)
# Separate a folder of mixtures.
# Returns a list of successful separated files. Examples: ["input/test01.wav", "input/test02.wav"]
results_list = separator.process_folder("input")
separator.del_cache()
# Separate from a single numpy array
# Returns a dict with separated audio arrays. Examples: {"vocals": NDArray, "instrumental": NDArray}
import numpy as np
mix = np.load("input/test02.npy") # load numpy array for example
audio_array = separator.separate(mix)
separator.del_cache()Note
str or dict can be used for output_folder. If str is used (for example, store_dirs="results"), all separated stems will be stored in the same folder. If dict is used, separated stems will be stored in different folders based on the keys. Example:
store_dirs={
"vocals": "results/vocals",
"instrumental": "results/instrumental"
}Also, the value can be list, which means the stem will be stored in multiple folders. Example:
store_dirs={
"vocals": ["results/vocals", "results/vocals2"],
"instrumental": "results/instrumental"
}The keys in the dict are the names of the stems, and the values are the paths to the folders where the stems will be stored. If you do not want to store a stem, you can omit it from the dict or use "" as its value.
You don't need to transpose the numpy array. For example: audio.shape=(2, 838928) is fine.
Use inference/vr_cli.py
usage: vr_cli.py [-h] [-d] [--use_cpu] [-i INPUT_FOLDER] [-o OUTPUT_FOLDER] [--output_format {wav,flac,mp3}] [-m MODEL_PATH] [--invert_spect] [--batch_size BATCH_SIZE]
[--window_size WINDOW_SIZE] [--aggression AGGRESSION] [--enable_tta] [--high_end_process] [--enable_post_process]
[--post_process_threshold POST_PROCESS_THRESHOLD] [--wav_bit_depth {PCM_16,PCM_24,PCM_32,FLOAT}] [--flac_bit_depth {PCM_16,PCM_24}]
[--mp3_bit_rate {96k,128k,192k,256k,320k}]
Vocal Remover Command Line Interface
options:
-h, --help show this help message and exit
-d, --debug Enable debug logging (default: False). Example: --debug
--use_cpu Use CPU instead of GPU for inference (default: False). Example: --use_cpu
Separation I/O Params:
-i INPUT_FOLDER, --input_folder INPUT_FOLDER Folder with mixtures to process. [required]
-o OUTPUT_FOLDER, --output_folder OUTPUT_FOLDER Folder to store separated files. str for single folder, dict with instrument keys for multiple folders. Example: --output_folder=results or --output_folder="{'vocals': 'results/vocals', 'instrumental': 'results/instrumental'}"
--output_format {wav,flac,mp3} Output format for separated files (default: wav). Example: --output_format=wav
Common Separation Parameters:
-m MODEL_PATH, --model_path MODEL_PATH Path to model checkpoint. [required]
--invert_spect Invert secondary stem using spectogram (default: False). Example: --invert_spect
VR Architecture Parameters:
--batch_size BATCH_SIZE Number of batches to process at a time. higher = more RAM, slightly faster processing (default: 2). Example: --batch_size=16
--window_size WINDOW_SIZE Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality. (default: 512). Example: --window_size=320
--aggression AGGRESSION Intensity of primary stem extraction, -100 - 100. typically 5 for vocals & instrumentals (default: 5). Example: --aggression=2
--enable_tta Enable Test-Time-Augmentation; slow but improves quality (default: False). Example: --enable_tta
--high_end_process Mirror the missing frequency range of the output (default: False). Example: --high_end_process
--enable_post_process Identify leftover artifacts within vocal output; may improve separation for some songs (default: False). Example: --enable_post_process
--post_process_threshold POST_PROCESS_THRESHOLD Threshold for post_process feature: 0.1-0.3 (default: 0.2). Example: --post_process_threshold=0.1
Audio Params:
--wav_bit_depth {PCM_16,PCM_24,PCM_32,FLOAT} Bit depth for wav output (default: FLOAT). Example: --wav_bit_depth=PCM_32
--flac_bit_depth {PCM_16,PCM_24} Bit depth for flac output (default: PCM_24). Example: --flac_bit_depth=PCM_24
--mp3_bit_rate {96k,128k,192k,256k,320k} Bit rate for mp3 output (default: 320k). Example: --mp3_bit_rate=320kfrom modules.vocal_remover.separator import Separator
from utils.logger import get_logger
separator = Separator(
logger=get_logger(),
debug=True,
model_file="pretrain/VR_Models/1_HP-UVR.pth",
output_dir={"Vocals": "results/Vocals", "Instrumental": "results/instrumental"},
output_format="mp3",
invert_using_spec=False,
use_cpu=False,
vr_params={"batch_size": 2, "window_size": 512, "aggression": 5, "enable_tta": False, "enable_post_process": False, "post_process_threshold": 0.2, "high_end_process": False},
audio_params = {"wav_bit_depth": "FLOAT", "flac_bit_depth": "PCM_24", "mp3_bit_rate": "320k"},
)
# Separate a folder of mixtures.
# Returns a list of successful separated files. Examples: ["input/test01.wav", "input/test02.wav"]
results_list = separator.process_folder("input")
separator.del_cache()
# Separate from a single numpy array
# Returns a dict with separated audio arrays. Examples: {"vocals": NDArray, "instrumental": NDArray}
import numpy as np
mix = np.load("input/test02.npy") # load numpy array for example
audio_array = separator.separate(mix)
separator.del_cache()