inst.py with platform downloader

inst.py

@@ -122,9 +122,16 @@ def uvr_job(song_id, platform='netease'):
 if __name__ == '__main__':
     audio_file = '/Users/taoluo/Downloads/assets/audio/kimk_audio.mp3'
     audio_file = sys.argv[1]
-    if not os.path.isfile(audio_file):
-        raise FileNotFoundError(f"File {audio_file} not exist.")
-
-    output_dir = os.path.dirname(audio_file)
-    print(output_dir)
-    run_ensemble_models(audio_file, output_dir)
+    platform = sys.argv[2] if len(sys.argv) > 2 else 'netease'
+
+    # exist file
+    if os.path.isfile(audio_file):
+        output_dir = os.path.dirname(audio_file)
+        instrumental_path, vocals_path = run_ensemble_models(audio_file, output_dir)
+        print('instrumental_path: ', instrumental_path)
+        sys.exit(0)
+
+    # download from platform
+    song_id = sys.argv[1]
+    instrumental_path = uvr_job(song_id, platform)
+    print('instrumental_path: ', instrumental_path)

separate.py

@@ -1,38 +1,40 @@
 from __future__ import annotations
+
+import gc
+import gzip
+import math
+import os
+import warnings
+from pathlib import Path
 from typing import TYPE_CHECKING
+
+import audioread
+import librosa
+import numpy as np
+import onnxruntime as ort
+import pydub
+import soundfile as sf
+import torch
+# import random
+from onnx import load
+from onnx2pytorch import ConvertModel
+from scipy import signal
+
+import lib_v5.mdxnet as MdxnetSet
 from demucs.apply import apply_model, demucs_segments
 from demucs.hdemucs import HDemucs
 from demucs.model_v2 import auto_load_demucs_model_v2
 from demucs.pretrained import get_model as _gm
 from demucs.utils import apply_model_v1
 from demucs.utils import apply_model_v2
-from lib_v5.tfc_tdf_v3 import TFC_TDF_net, STFT
+from gui_data.constants import *
+from gui_data.error_handling import *
 from lib_v5 import spec_utils
+from lib_v5.tfc_tdf_v3 import TFC_TDF_net, STFT
 from lib_v5.vr_network import nets
 from lib_v5.vr_network import nets_new
 from lib_v5.vr_network.model_param_init import ModelParameters
-from pathlib import Path
-from gui_data.constants import *
-from gui_data.error_handling import *
-from scipy import signal
-import audioread
-import gzip
-import librosa
-import math
-import numpy as np
-import onnxruntime as ort
-import os
-import torch
-import warnings
-import pydub
-import soundfile as sf
-import lib_v5.mdxnet as MdxnetSet
-import math
-#import random
-from onnx import load
-from onnx2pytorch import ConvertModel
-import gc
- 
+
 if TYPE_CHECKING:
     from UVR import ModelData