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import os
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import shutil
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import sys
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from datetime import datetime
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from pathlib import Path
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from time import sleep
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from args import mdx23c_8kfft_instvoc_hq_process_data, htdemucs_ft_process_data, uvr_mdx_net_voc_ft_process_data
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from download import download_model, get_model_file
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from gui_data.constants import VR_ARCH_TYPE, MDX_ARCH_TYPE, DEMUCS_ARCH_TYPE, ENSEMBLE_MODE, TIME_STRETCH, \
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MANUAL_ENSEMBLE, MATCH_INPUTS, ALIGN_INPUTS, ALL_STEMS, DEFAULT, VOCAL_STEM, MP3_BIT_RATES, WAV, DEMUCS_2_SOURCE, \
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DEMUCS_2_SOURCE_MAPPER, INST_STEM, CKPT, ONNX, MDX_POP_NFFT, secondary_stem, PRIMARY_STEM, SECONDARY_STEM
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from lib_v5 import spec_utils
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from separate import (
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SeperateDemucs, SeperateMDX, SeperateMDXC, SeperateVR,
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save_format, clear_gpu_cache,
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cuda_available, mps_available,
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)
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def run_ensemble_models(audio_path, export_path, format=WAV, clean=True):
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start = datetime.now()
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process_datas = [mdx23c_8kfft_instvoc_hq_process_data, uvr_mdx_net_voc_ft_process_data,
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htdemucs_ft_process_data]
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for process_data in process_datas:
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download_model(process_data['model_name'])
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os.makedirs(export_path, exist_ok=True)
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temp_export_path = os.path.join(export_path, 'uvr5_' + datetime.now().strftime("%Y-%m-%d_%H%M%S"))
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os.makedirs(temp_export_path, exist_ok=True)
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print(f'temp_export_path', temp_export_path)
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instrumental_export_paths = []
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vocals_export_paths = []
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for process_data in process_datas:
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current_model = process_data['model_data']
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audio_file_base = Path(audio_path).stem + '_' + current_model.model_basename
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process_data['export_path'] = temp_export_path
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process_data['audio_file_base'] = audio_file_base
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process_data['audio_file'] = audio_path
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if current_model.process_method == VR_ARCH_TYPE:
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seperator = SeperateVR(current_model, process_data)
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elif current_model.process_method == MDX_ARCH_TYPE:
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seperator = SeperateMDXC(current_model, process_data) if current_model.is_mdx_c else SeperateMDX(
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current_model, process_data)
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elif current_model.process_method == DEMUCS_ARCH_TYPE:
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seperator = SeperateDemucs(current_model, process_data, vocal_stem_path=(audio_path, audio_file_base))
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else:
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raise Exception(f'model not found')
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seperator.seperate()
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instrumental_path = Path(temp_export_path) / f"{audio_file_base}_(Instrumental).{format.lower()}"
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vocals_path = Path(temp_export_path) / f"{audio_file_base}_(Vocals).{format.lower()}"
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instrumental_export_paths.append(str(instrumental_path))
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vocals_export_paths.append(str(vocals_path))
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vocals_final_path = Path(export_path) / f"{Path(audio_path).stem}.vocal.{format.lower()}"
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instrumental_final_path = Path(export_path) / f"{Path(audio_path).stem}.instrumental.{format.lower()}"
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ensemble(vocals_export_paths, vocals_final_path)
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ensemble(instrumental_export_paths, instrumental_final_path)
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print(f'instrumental_final_path', instrumental_final_path)
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print(f'vocals_final_path', vocals_final_path)
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print(f'Finished in {datetime.now() - start}')
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if clean:
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sleep(10)
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shutil.rmtree(temp_export_path, ignore_errors=True)
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return instrumental_final_path, vocals_final_path
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def ensemble(stem_outputs, stem_save_path, format=WAV):
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algorithm = 'Average'
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is_normalization = True
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spec_utils.ensemble_inputs(stem_outputs, algorithm, is_normalization, 'PCM_16', stem_save_path, is_wave=True)
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save_format(stem_save_path, format, '320k')
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if __name__ == '__main__':
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audio_file = '/Users/taoluo/Downloads/assets/audio/kimk_audio.mp3'
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audio_file = sys.argv[1]
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if not os.path.isfile(audio_file):
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raise FileNotFoundError(f"File {audio_file} not exist.")
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output_dir = os.path.dirname(audio_file)
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print(output_dir)
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run_ensemble_models(audio_file, output_dir)
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