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import os
import shutil
import sys
from datetime import datetime
from pathlib import Path
from time import sleep
import requests
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
from gui_data.constants import VR_ARCH_TYPE, MDX_ARCH_TYPE, DEMUCS_ARCH_TYPE, WAV
from lib_v5 import spec_utils
from separate import SeperateDemucs, SeperateMDX, SeperateMDXC, SeperateVR, save_format # Model-related
def run_ensemble_models(audio_path, export_path, format=WAV, clean=True):
vocals_final_path = Path(export_path) / f"{Path(audio_path).stem}.vocal.{format.lower()}"
instrumental_final_path = Path(export_path) / f"{Path(audio_path).stem}.inst.{format.lower()}"
if os.path.isfile(instrumental_final_path) and os.path.isfile(vocals_final_path):
return instrumental_final_path, vocals_final_path
start = datetime.now()
process_datas = [mdx23c_8kfft_instvoc_hq_process_data, uvr_mdx_net_voc_ft_process_data,
htdemucs_ft_process_data]
# download models
for process_data in process_datas:
download_model(process_data['model_name'])
# create folder
os.makedirs(export_path, exist_ok=True)
temp_export_path = os.path.join(export_path, 'uvr5_' + datetime.now().strftime("%Y-%m-%d_%H%M%S"))
os.makedirs(temp_export_path, exist_ok=True)
print(f'temp_export_path', temp_export_path)
instrumental_export_paths = []
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)
elif current_model.process_method == MDX_ARCH_TYPE:
seperator = SeperateMDXC(current_model, process_data) if current_model.is_mdx_c else SeperateMDX(
current_model, process_data)
elif current_model.process_method == DEMUCS_ARCH_TYPE:
seperator = SeperateDemucs(current_model, process_data, vocal_stem_path=(audio_path, audio_file_base))
else:
raise Exception(f'model not found')
seperator.seperate()
instrumental_path = Path(temp_export_path) / f"{audio_file_base}_(Instrumental).{format.lower()}"
vocals_path = Path(temp_export_path) / f"{audio_file_base}_(Vocals).{format.lower()}"
instrumental_export_paths.append(str(instrumental_path))
vocals_export_paths.append(str(vocals_path))
# merge each model outputs
ensemble(vocals_export_paths, vocals_final_path)
ensemble(instrumental_export_paths, instrumental_final_path)
print(f'instrumental_final_path', instrumental_final_path)
print(f'vocals_final_path', vocals_final_path)
print(f'Finished in {datetime.now() - start}')
if clean:
sleep(10)
shutil.rmtree(temp_export_path, ignore_errors=True)
return instrumental_final_path, vocals_final_path
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)
save_format(stem_save_path, format, '320k')
def run_uvr_job(song_id, platform='netease'):
audio_dir = os.getcwd()
audio_file = f'{song_id}.m4a' if platform == 'youtube' else f'{song_id}.mp3'
audio_path = os.path.join(audio_dir, audio_file)
if not os.path.isfile(audio_path):
url = f"http://or.luotao.net/api/download_song?song_id={song_id}&platform={platform}"
r = requests.get(url, allow_redirects=True)
open(audio_path, 'wb').write(r.content)
instrumental_path, vocals_path = run_ensemble_models(audio_file, audio_dir)
return instrumental_path, vocals_path
# /Users/taoluo/Downloads/test/kimk_audio_MDX23C-8KFFT-InstVoc_HQ_(Instrumental).WAV
#
if __name__ == '__main__':
audio_file = '/Users/taoluo/Downloads/assets/audio/kimk_audio.mp3'
audio_file = sys.argv[1]
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, vocals_path = run_uvr_job(song_id, platform)
print('instrumental_path: ', instrumental_path)
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