tqdm progress bar

args.py

@@ -173,7 +173,7 @@ mdx23c_8kfft_instvoc_hq_process_data = {
     'set_progress_bar': lambda step, inference_iterations=0: print(
         f"iteration {inference_iterations} of step #{step}"),
     'write_to_console': lambda progress_text, base_text='': print(
-        f"{progress_text} {base_text}"),
+        f"{progress_text} {base_text} @ MDX23C Model: MDX23C-InstVoc HQ"),
     'process_iteration': lambda iteration: iteration + 1,
     'cached_source_callback': cached_source_callback,
     'cached_model_source_holder': cached_model_source_holder,
@@ -301,7 +301,7 @@ uvr_mdx_net_voc_ft_process_data = {
     'set_progress_bar': lambda step, inference_iterations=0: print(
         f"iteration {inference_iterations} of step #{step}"),
     'write_to_console': lambda progress_text, base_text='': print(
-        f"{progress_text} {base_text}"),
+        f"{progress_text} {base_text} @ MDX-Net Model: UVR-MDX-NET Voc FT"),
     'process_iteration': lambda iteration: iteration + 1,
     'cached_source_callback': cached_source_callback,
     'cached_model_source_holder': cached_model_source_holder,
@@ -425,7 +425,7 @@ htdemucs_ft_process_data = {
     'set_progress_bar': lambda step, inference_iterations=0: print(
         f"iteration {inference_iterations} of step #{step}"),
     'write_to_console': lambda progress_text, base_text='': print(
-        f"{progress_text} {base_text}"),
+        f"{progress_text} {base_text} @ Demucs v4: htdemucs_ft"),
     'process_iteration': lambda iteration: iteration + 1,
     'cached_source_callback': cached_source_callback,
     'cached_model_source_holder': cached_model_source_holder,

inst.py

@@ -5,6 +5,8 @@ from datetime import datetime
 from pathlib import Path
 from time import sleep
 
+from tqdm import tqdm
+
 from args import mdx23c_8kfft_instvoc_hq_process_data, htdemucs_ft_process_data, uvr_mdx_net_voc_ft_process_data
 from download import download_model, get_model_file
 from gui_data.constants import VR_ARCH_TYPE, MDX_ARCH_TYPE, DEMUCS_ARCH_TYPE, ENSEMBLE_MODE, TIME_STRETCH, \
@@ -36,11 +38,25 @@ def run_ensemble_models(audio_path, export_path, format=WAV, clean=True):
     vocals_export_paths = []
 
     for process_data in process_datas:
+        progress_bar = tqdm(total=100, desc=process_data["model_name"], unit="%")
+
+        def set_progress_bar(step, inference_iterations=0):
+            # print(step, inference_iterations, round(inference_iterations * 100, 2))
+            if inference_iterations > 0:
+                progress_bar.update(round(inference_iterations * 100, 2) - progress_bar.n)
+
+        def write_to_console(progress_text, base_text=''):
+            text = f"{progress_text} {base_text}"
+            if text.strip():
+                return f'{text} @ process_data["model_name"]'
+
         current_model = process_data['model_data']
         audio_file_base = Path(audio_path).stem + '_' + current_model.model_basename
         process_data['export_path'] = temp_export_path
         process_data['audio_file_base'] = audio_file_base
         process_data['audio_file'] = audio_path
+        process_data['set_progress_bar'] = set_progress_bar
+        process_data['write_to_console'] = write_to_console
 
         if current_model.process_method == VR_ARCH_TYPE:
             seperator = SeperateVR(current_model, process_data)
@@ -76,6 +92,8 @@ def run_ensemble_models(audio_path, export_path, format=WAV, clean=True):
 
 
 def ensemble(stem_outputs, stem_save_path, format=WAV):
+    stem_save_path = str(stem_save_path)
+    stem_outputs = [str(s) for s in stem_outputs]
     algorithm = 'Average'
     is_normalization = True
     spec_utils.ensemble_inputs(stem_outputs, algorithm, is_normalization, 'PCM_16', stem_save_path, is_wave=True)

requirements.txt

@@ -42,5 +42,6 @@ PySoundFile==0.9.0.post1; sys_platform == 'darwin'
 numpy==1.23.5
 addict
 matplotlib
-sklearn==0.0.post12
+#sklearn==0.0.post12
+scikit-learn
 click