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lib_v5/filelist.py
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423
lib_v5/filelist.py
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import json
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def get_vr_download_list(list):
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with open("lib_v5/filelists/download_lists/vr_download_list.txt", "r") as f:
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text=f.read().splitlines()
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list = text
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return list
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def get_mdx_download_list(list):
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with open("lib_v5/filelists/download_lists/mdx_download_list.txt", "r") as f:
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text=f.read().splitlines()
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list = text
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return list
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def get_demucs_download_list(list):
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with open("lib_v5/filelists/download_lists/demucs_download_list.txt", "r") as f:
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text=f.read().splitlines()
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list = text
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return list
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def get_mdx_demucs_en_list(list):
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with open("lib_v5/filelists/ensemble_list/mdx_demuc_en_list.txt", "r") as f:
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text=f.read().splitlines()
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list = text
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return list
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def get_vr_en_list(list):
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with open("lib_v5/filelists/ensemble_list/vr_en_list.txt", "r") as f:
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text=f.read().splitlines()
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list = text
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return list
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def get_download_links(links, downloads=''):
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f = open(f"lib_v5/filelists/download_lists/download_links.json")
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download_links = json.load(f)
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if downloads == 'Demucs v3: mdx':
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url_1 = download_links['Demucs_v3_mdx_url_1']
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url_2 = download_links['Demucs_v3_mdx_url_2']
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url_3 = download_links['Demucs_v3_mdx_url_3']
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url_4 = download_links['Demucs_v3_mdx_url_4']
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url_5 = download_links['Demucs_v3_mdx_url_5']
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links = url_1, url_2, url_3, url_4, url_5
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if downloads == 'Demucs v3: mdx_q':
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url_1 = download_links['Demucs_v3_mdx_q_url_1']
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url_2 = download_links['Demucs_v3_mdx_q_url_2']
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url_3 = download_links['Demucs_v3_mdx_q_url_3']
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url_4 = download_links['Demucs_v3_mdx_q_url_4']
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url_5 = download_links['Demucs_v3_mdx_q_url_5']
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links = url_1, url_2, url_3, url_4, url_5
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if downloads == 'Demucs v3: mdx_extra':
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url_1 = download_links['Demucs_v3_mdx_extra_url_1']
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url_2 = download_links['Demucs_v3_mdx_extra_url_1']
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url_3 = download_links['Demucs_v3_mdx_extra_url_1']
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url_4 = download_links['Demucs_v3_mdx_extra_url_1']
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url_5 = download_links['Demucs_v3_mdx_extra_url_1']
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links = url_1, url_2, url_3, url_4, url_5
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if downloads == 'Demucs v3: mdx_extra_q':
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url_1 = download_links['Demucs_v3_mdx_extra_q_url_1']
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url_2 = download_links['Demucs_v3_mdx_extra_q_url_2']
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url_3 = download_links['Demucs_v3_mdx_extra_q_url_3']
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url_4 = download_links['Demucs_v3_mdx_extra_q_url_4']
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url_5 = download_links['Demucs_v3_mdx_extra_q_url_5']
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links = url_1, url_2, url_3, url_4, url_5
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if downloads == 'Demucs v3: UVR Models':
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url_1 = download_links['Demucs_v3_UVR_url_1']
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url_2 = download_links['Demucs_v3_UVR_url_2']
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url_3 = download_links['Demucs_v3_UVR_url_3']
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url_4 = download_links['Demucs_v3_UVR_url_4']
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url_5 = download_links['Demucs_v3_UVR_url_5']
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links = url_1, url_2, url_3, url_4, url_5
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if downloads == 'Demucs v2: demucs':
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url_1 = download_links['Demucs_v2_demucs_url_1']
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links = url_1
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if downloads == 'Demucs v2: demucs_extra':
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url_1 = download_links['Demucs_v2_demucs_extra_url_1']
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links = url_1
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if downloads == 'Demucs v2: demucs48_hq':
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url_1 = download_links['Demucs_v2_demucs48_hq_url_1']
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links = url_1
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if downloads == 'Demucs v2: tasnet':
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url_1 = download_links['Demucs_v2_tasnet_url_1']
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links = url_1
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if downloads == 'Demucs v2: tasnet_extra':
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url_1 = download_links['Demucs_v2_tasnet_extra_url_1']
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links = url_1
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if downloads == 'Demucs v2: demucs_unittest':
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url_1 = download_links['Demucs_v2_demucs_unittest_url_1']
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links = url_1
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if downloads == 'Demucs v1: demucs':
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url_1 = download_links['Demucs_v1_demucs_url_1']
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links = url_1
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if downloads == 'Demucs v1: demucs_extra':
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url_1 = download_links['Demucs_v1_demucs_extra_url_1']
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links = url_1
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if downloads == 'Demucs v1: light':
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url_1 = download_links['Demucs_v1_light_url_1']
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links = url_1
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if downloads == 'Demucs v1: light_extra':
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url_1 = download_links['Demucs_v1_light_extra_url_1']
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links = url_1
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if downloads == 'Demucs v1: tasnet':
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url_1 = download_links['Demucs_v1_tasnet_url_1']
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links = url_1
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if downloads == 'Demucs v1: tasnet_extra':
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url_1 = download_links['Demucs_v1_tasnet_extra_url_1']
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links = url_1
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if downloads == 'model_repo':
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url_1 = download_links['model_repo_url_1']
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links = url_1
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if downloads == 'single_model_repo':
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url_1 = download_links['single_model_repo_url_1']
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links = url_1
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if downloads == 'exclusive':
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url_1 = download_links['exclusive_url_1']
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url_2 = download_links['exclusive_url_2']
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links = url_1, url_2, url_3
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if downloads == 'refresh':
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url_1 = download_links['refresh_url_1']
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url_2 = download_links['refresh_url_2']
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url_3 = download_links['refresh_url_3']
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links = url_1, url_2, url_3
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if downloads == 'app_patch':
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url_1 = download_links['app_patch']
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links = url_1
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return links
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def provide_model_param_hash(model_hash):
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#v5 Models
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if model_hash == '47939caf0cfe52a0e81442b85b971dfd':
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model_params_set=str('lib_v5/modelparams/4band_44100.json')
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param_name=str('4band_44100')
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elif model_hash == '4e4ecb9764c50a8c414fee6e10395bbe':
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model_params_set=str('lib_v5/modelparams/4band_v2.json')
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param_name=str('4band_v2')
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elif model_hash == 'e60a1e84803ce4efc0a6551206cc4b71':
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model_params_set=str('lib_v5/modelparams/4band_44100.json')
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param_name=str('4band_44100')
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elif model_hash == 'a82f14e75892e55e994376edbf0c8435':
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model_params_set=str('lib_v5/modelparams/4band_44100.json')
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param_name=str('4band_44100')
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elif model_hash == '6dd9eaa6f0420af9f1d403aaafa4cc06':
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model_params_set=str('lib_v5/modelparams/4band_v2_sn.json')
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param_name=str('4band_v2_sn')
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elif model_hash == '5c7bbca45a187e81abbbd351606164e5':
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model_params_set=str('lib_v5/modelparams/3band_44100_msb2.json')
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param_name=str('3band_44100_msb2')
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elif model_hash == 'd6b2cb685a058a091e5e7098192d3233':
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model_params_set=str('lib_v5/modelparams/3band_44100_msb2.json')
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param_name=str('3band_44100_msb2')
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elif model_hash == 'c1b9f38170a7c90e96f027992eb7c62b':
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model_params_set=str('lib_v5/modelparams/4band_44100.json')
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param_name=str('4band_44100')
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elif model_hash == 'c3448ec923fa0edf3d03a19e633faa53':
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model_params_set=str('lib_v5/modelparams/4band_44100.json')
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param_name=str('4band_44100')
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elif model_hash == '68aa2c8093d0080704b200d140f59e54':
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model_params_set=str('lib_v5/modelparams/3band_44100.json')
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param_name=str('3band_44100.json')
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elif model_hash == 'fdc83be5b798e4bd29fe00fe6600e147':
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model_params_set=str('lib_v5/modelparams/3band_44100_mid.json')
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param_name=str('3band_44100_mid.json')
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elif model_hash == '2ce34bc92fd57f55db16b7a4def3d745':
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model_params_set=str('lib_v5/modelparams/3band_44100_mid.json')
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param_name=str('3band_44100_mid.json')
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elif model_hash == '52fdca89576f06cf4340b74a4730ee5f':
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model_params_set=str('lib_v5/modelparams/4band_44100.json')
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param_name=str('4band_44100.json')
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elif model_hash == '41191165b05d38fc77f072fa9e8e8a30':
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model_params_set=str('lib_v5/modelparams/4band_44100.json')
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param_name=str('4band_44100.json')
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elif model_hash == '89e83b511ad474592689e562d5b1f80e':
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model_params_set=str('lib_v5/modelparams/2band_32000.json')
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param_name=str('2band_32000.json')
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elif model_hash == '0b954da81d453b716b114d6d7c95177f':
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model_params_set=str('lib_v5/modelparams/2band_32000.json')
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param_name=str('2band_32000.json')
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#v4 Models
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elif model_hash == '6a00461c51c2920fd68937d4609ed6c8':
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model_params_set=str('lib_v5/modelparams/1band_sr16000_hl512.json')
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param_name=str('1band_sr16000_hl512')
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elif model_hash == '0ab504864d20f1bd378fe9c81ef37140':
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model_params_set=str('lib_v5/modelparams/1band_sr32000_hl512.json')
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param_name=str('1band_sr32000_hl512')
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elif model_hash == '7dd21065bf91c10f7fccb57d7d83b07f':
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model_params_set=str('lib_v5/modelparams/1band_sr32000_hl512.json')
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param_name=str('1band_sr32000_hl512')
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elif model_hash == '80ab74d65e515caa3622728d2de07d23':
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model_params_set=str('lib_v5/modelparams/1band_sr32000_hl512.json')
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param_name=str('1band_sr32000_hl512')
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elif model_hash == 'edc115e7fc523245062200c00caa847f':
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model_params_set=str('lib_v5/modelparams/1band_sr33075_hl384.json')
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param_name=str('1band_sr33075_hl384')
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elif model_hash == '28063e9f6ab5b341c5f6d3c67f2045b7':
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model_params_set=str('lib_v5/modelparams/1band_sr33075_hl384.json')
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param_name=str('1band_sr33075_hl384')
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elif model_hash == 'b58090534c52cbc3e9b5104bad666ef2':
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model_params_set=str('lib_v5/modelparams/1band_sr44100_hl512.json')
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param_name=str('1band_sr44100_hl512')
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elif model_hash == '0cdab9947f1b0928705f518f3c78ea8f':
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model_params_set=str('lib_v5/modelparams/1band_sr44100_hl512.json')
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param_name=str('1band_sr44100_hl512')
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elif model_hash == 'ae702fed0238afb5346db8356fe25f13':
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model_params_set=str('lib_v5/modelparams/1band_sr44100_hl1024.json')
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param_name=str('1band_sr44100_hl1024')
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else:
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try:
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with open(f"lib_v5/filelists/model_cache/vr_param_cache/{model_hash}.txt", "r") as f:
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name = f.read()
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model_params_set=str(f'lib_v5/modelparams/{name}')
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param_name=str(name)
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('using text of hash worked')
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except:
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model_params_set=str('Not Found Using Hash')
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param_name=str('Not Found Using Hash')
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model_params = model_params_set, param_name
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return model_params
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def provide_model_param_name(ModelName):
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#1 Band
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if '1band_sr16000_hl512' in ModelName:
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model_params_set=str('lib_v5/modelparams/1band_sr16000_hl512.json')
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param_name=str('1band_sr16000_hl512')
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elif '1band_sr32000_hl512' in ModelName:
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model_params_set=str('lib_v5/modelparams/1band_sr32000_hl512.json')
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param_name=str('1band_sr32000_hl512')
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elif '1band_sr33075_hl384' in ModelName:
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model_params_set=str('lib_v5/modelparams/1band_sr33075_hl384.json')
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param_name=str('1band_sr33075_hl384')
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elif '1band_sr44100_hl256' in ModelName:
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model_params_set=str('lib_v5/modelparams/1band_sr44100_hl256.json')
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param_name=str('1band_sr44100_hl256')
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elif '1band_sr44100_hl512' in ModelName:
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model_params_set=str('lib_v5/modelparams/1band_sr44100_hl512.json')
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param_name=str('1band_sr44100_hl512')
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elif '1band_sr44100_hl1024' in ModelName:
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model_params_set=str('lib_v5/modelparams/1band_sr44100_hl1024.json')
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param_name=str('1band_sr44100_hl1024')
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#2 Band
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elif '2band_44100_lofi' in ModelName:
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model_params_set=str('lib_v5/modelparams/2band_44100_lofi.json')
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param_name=str('2band_44100_lofi')
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#3 Band
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elif '3band_44100_mid' in ModelName:
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model_params_set=str('lib_v5/modelparams/3band_44100_mid.json')
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param_name=str('3band_44100_mid')
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elif '3band_44100_msb2' in ModelName:
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model_params_set=str('lib_v5/modelparams/3band_44100_msb2.json')
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param_name=str('3band_44100_msb2')
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#4 Band
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elif '4band_44100_msb' in ModelName:
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model_params_set=str('lib_v5/modelparams/4band_44100_msb.json')
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param_name=str('4band_44100_msb')
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elif '4band_44100_msb2' in ModelName:
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model_params_set=str('lib_v5/modelparams/4band_44100_msb2.json')
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param_name=str('4band_44100_msb2')
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elif '4band_44100_reverse' in ModelName:
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model_params_set=str('lib_v5/modelparams/4band_44100_reverse.json')
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param_name=str('4band_44100_reverse')
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elif 'tmodelparam' in ModelName:
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model_params_set=str('lib_v5/modelparams/tmodelparam.json')
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param_name=str('User Model Param Set')
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else:
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model_params_set=str('Not Found Using Name')
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param_name=str('Not Found Using Name')
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model_params = model_params_set, param_name
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return model_params
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def provide_mdx_model_param_name(modelhash):
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with open("lib_v5/filelists/hashes/mdx_original_hashes.txt", "r") as f:
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mdx_original=f.read()
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with open("lib_v5/filelists/hashes/mdx_new_hashes.txt", "r") as f:
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mdx_new=f.read()
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with open("lib_v5/filelists/hashes/mdx_new_inst_hashes.txt", "r") as f:
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mdx_new_inst=f.read()
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if modelhash in mdx_original:
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MDX_modeltype = 'mdx_original'
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elif modelhash in mdx_new:
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MDX_modeltype = 'mdx_new'
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elif modelhash in mdx_new_inst:
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MDX_modeltype = 'mdx_new_inst'
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else:
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MDX_modeltype = 'None'
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if MDX_modeltype == 'mdx_original':
|
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modeltype = 'v'
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noise_pro = 'MDX-NET_Noise_Profile_14_kHz'
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stemset_n = '(Vocals)'
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compensate = 1.03597672895
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source_val = 3
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n_fft_scale_set=6144
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dim_f_set=2048
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elif MDX_modeltype == 'mdx_new':
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modeltype = 'v'
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noise_pro = 'MDX-NET_Noise_Profile_17_kHz'
|
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stemset_n = '(Vocals)'
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compensate = 1.08
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source_val = 3
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||||
n_fft_scale_set=7680
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dim_f_set=3072
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elif MDX_modeltype == 'mdx_new_inst':
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modeltype = 'v'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_17_kHz'
|
||||
stemset_n = '(Instrumental)'
|
||||
compensate = 1.08
|
||||
source_val = 3
|
||||
n_fft_scale_set=7680
|
||||
dim_f_set=3072
|
||||
elif modelhash == '6f7eefc2e6b9d819ba88dc0578056ca5':
|
||||
modeltype = 'o'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
|
||||
stemset_n = '(Other)'
|
||||
compensate = 1.03597672895
|
||||
source_val = 2
|
||||
n_fft_scale_set=8192
|
||||
dim_f_set=2048
|
||||
elif modelhash == '72a27258a69b2381b60523a50982e9f1':
|
||||
modeltype = 'd'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
|
||||
stemset_n = '(Drums)'
|
||||
compensate = 1.03597672895
|
||||
source_val = 1
|
||||
n_fft_scale_set=4096
|
||||
dim_f_set=2048
|
||||
elif modelhash == '7051d7315c04285e94a97edcac3f2f76':
|
||||
modeltype = 'b'
|
||||
noise_pro = 'MDX-NET_Noise_Profile_Full_Band'
|
||||
stemset_n = '(Bass)'
|
||||
compensate = 1.03597672895
|
||||
source_val = 0
|
||||
n_fft_scale_set=16384
|
||||
dim_f_set=2048
|
||||
else:
|
||||
try:
|
||||
f = open(f"lib_v5/filelists/model_cache/mdx_model_cache/{modelhash}.json")
|
||||
mdx_model_de = json.load(f)
|
||||
modeltype = mdx_model_de["modeltype"]
|
||||
noise_pro = mdx_model_de["noise_pro"]
|
||||
stemset_n = mdx_model_de["stemset_n"]
|
||||
compensate = mdx_model_de["compensate"]
|
||||
source_val = mdx_model_de["source_val"]
|
||||
n_fft_scale_set = mdx_model_de["n_fft_scale_set"]
|
||||
dim_f_set = mdx_model_de["dim_f_set"]
|
||||
except:
|
||||
modeltype = 'Not Set'
|
||||
noise_pro = 'Not Set'
|
||||
stemset_n = 'Not Set'
|
||||
compensate = 'Not Set'
|
||||
source_val = 'Not Set'
|
||||
n_fft_scale_set='Not Set'
|
||||
dim_f_set='Not Set'
|
||||
|
||||
|
||||
model_params = modeltype, noise_pro, stemset_n, compensate, source_val, n_fft_scale_set, dim_f_set
|
||||
|
||||
return model_params
|
||||
1
lib_v5/filelists/download_codes/user_code.txt
Normal file
1
lib_v5/filelists/download_codes/user_code.txt
Normal file
@ -0,0 +1 @@
|
||||
Developer
|
||||
1
lib_v5/filelists/download_codes/user_code_download.txt
Normal file
1
lib_v5/filelists/download_codes/user_code_download.txt
Normal file
@ -0,0 +1 @@
|
||||
46702515b083df4d
|
||||
19
lib_v5/filelists/download_lists/demucs_download_list.txt
Normal file
19
lib_v5/filelists/download_lists/demucs_download_list.txt
Normal file
@ -0,0 +1,19 @@
|
||||
No Model Selected
|
||||
No Model Selected
|
||||
Demucs v3: UVR Models
|
||||
Demucs v3: mdx
|
||||
Demucs v3: mdx_q
|
||||
Demucs v3: mdx_extra
|
||||
Demucs v3: mdx_extra_q
|
||||
Demucs v2: demucs
|
||||
Demucs v2: demucs_extra
|
||||
Demucs v2: demucs48_hq
|
||||
Demucs v2: tasnet
|
||||
Demucs v2: tasnet_extra
|
||||
Demucs v2: demucs_unittest
|
||||
Demucs v1: demucs
|
||||
Demucs v1: demucs_extra
|
||||
Demucs v1: light
|
||||
Demucs v1: light_extra
|
||||
Demucs v1: tasnet
|
||||
Demucs v1: tasnet_extra
|
||||
42
lib_v5/filelists/download_lists/download_links.json
Normal file
42
lib_v5/filelists/download_lists/download_links.json
Normal file
@ -0,0 +1,42 @@
|
||||
{
|
||||
"Demucs_v3_mdx_url_1": "https://dl.fbaipublicfiles.com/demucs/mdx_final/0d19c1c6-0f06f20e.th",
|
||||
"Demucs_v3_mdx_url_2": "https://dl.fbaipublicfiles.com/demucs/mdx_final/7ecf8ec1-70f50cc9.th",
|
||||
"Demucs_v3_mdx_url_3": "https://dl.fbaipublicfiles.com/demucs/mdx_final/c511e2ab-fe698775.th",
|
||||
"Demucs_v3_mdx_url_4": "https://dl.fbaipublicfiles.com/demucs/mdx_final/7d865c68-3d5dd56b.th",
|
||||
"Demucs_v3_mdx_url_5": "https://raw.githubusercontent.com/facebookresearch/demucs/main/demucs/remote/mdx.yaml",
|
||||
"Demucs_v3_mdx_q_url_1": "https://dl.fbaipublicfiles.com/demucs/mdx_final/6b9c2ca1-3fd82607.th",
|
||||
"Demucs_v3_mdx_q_url_2": "https://dl.fbaipublicfiles.com/demucs/mdx_final/b72baf4e-8778635e.th",
|
||||
"Demucs_v3_mdx_q_url_3": "https://dl.fbaipublicfiles.com/demucs/mdx_final/42e558d4-196e0e1b.th",
|
||||
"Demucs_v3_mdx_q_url_4": "https://dl.fbaipublicfiles.com/demucs/mdx_final/305bc58f-18378783.th",
|
||||
"Demucs_v3_mdx_q_url_5": "https://raw.githubusercontent.com/facebookresearch/demucs/main/demucs/remote/mdx_q.yaml",
|
||||
"Demucs_v3_mdx_extra_url_1": "https://dl.fbaipublicfiles.com/demucs/mdx_final/e51eebcc-c1b80bdd.th",
|
||||
"Demucs_v3_mdx_extra_url_2": "https://dl.fbaipublicfiles.com/demucs/mdx_final/a1d90b5c-ae9d2452.th",
|
||||
"Demucs_v3_mdx_extra_url_3": "https://dl.fbaipublicfiles.com/demucs/mdx_final/5d2d6c55-db83574e.th",
|
||||
"Demucs_v3_mdx_extra_url_4": "https://dl.fbaipublicfiles.com/demucs/mdx_final/cfa93e08-61801ae1.th",
|
||||
"Demucs_v3_mdx_extra_url_5": "https://raw.githubusercontent.com/facebookresearch/demucs/main/demucs/remote/mdx_extra.yaml",
|
||||
"Demucs_v3_mdx_extra_q_url_1": "https://dl.fbaipublicfiles.com/demucs/mdx_final/83fc094f-4a16d450.th",
|
||||
"Demucs_v3_mdx_extra_q_url_2": "https://dl.fbaipublicfiles.com/demucs/mdx_final/464b36d7-e5a9386e.th",
|
||||
"Demucs_v3_mdx_extra_q_url_3": "https://dl.fbaipublicfiles.com/demucs/mdx_final/14fc6a69-a89dd0ee.th",
|
||||
"Demucs_v3_mdx_extra_q_url_4": "https://dl.fbaipublicfiles.com/demucs/mdx_final/7fd6ef75-a905dd85.th",
|
||||
"Demucs_v3_mdx_extra_q_url_5": "https://raw.githubusercontent.com/facebookresearch/demucs/main/demucs/remote/mdx_extra_q.yaml",
|
||||
"Demucs_v3_UVR_url_1": "https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/ebf34a2d.th",
|
||||
"Demucs_v3_UVR_url_2": "https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/ebf34a2db.th",
|
||||
"Demucs_v3_UVR_url_3": "https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/UVR_Demucs_Model_1.yaml",
|
||||
"Demucs_v3_UVR_url_4": "https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/UVR_Demucs_Model_2.yaml",
|
||||
"Demucs_v3_UVR_url_5": "https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/UVR_Demucs_Model_Bag.yaml",
|
||||
"Demucs_v2_demucs_url_1": "https://dl.fbaipublicfiles.com/demucs/v3.0/demucs-e07c671f.th",
|
||||
"Demucs_v2_demucs_extra_url_1": "https://dl.fbaipublicfiles.com/demucs/v3.0/demucs_extra-3646af93.th",
|
||||
"Demucs_v2_demucs48_hq_url_1": "https://dl.fbaipublicfiles.com/demucs/v3.0/demucs48_hq-28a1282c.th",
|
||||
"Demucs_v2_tasnet_url_1": "https://dl.fbaipublicfiles.com/demucs/v3.0/tasnet-beb46fac.th",
|
||||
"Demucs_v2_tasnet_extra_url_1": "https://dl.fbaipublicfiles.com/demucs/v3.0/tasnet_extra-df3777b2.th",
|
||||
"Demucs_v2_demucs_unittest_url_1": "https://dl.fbaipublicfiles.com/demucs/v3.0/demucs_unittest-09ebc15f.th",
|
||||
"Demucs_v1_demucs_url_1": "https://dl.fbaipublicfiles.com/demucs/v2.0/demucs.th",
|
||||
"Demucs_v1_demucs_extra_url_1": "https://dl.fbaipublicfiles.com/demucs/v2.0/demucs_extra.th",
|
||||
"Demucs_v1_light_url_1": "https://dl.fbaipublicfiles.com/demucs/v2.0/light.th",
|
||||
"Demucs_v1_light_extra_url_1": "https://dl.fbaipublicfiles.com/demucs/v2.0/light_extra.th",
|
||||
"Demucs_v1_tasnet_url_1": "https://dl.fbaipublicfiles.com/demucs/v2.0/tasnet.th",
|
||||
"Demucs_v1_tasnet_extra_url_1": "https://dl.fbaipublicfiles.com/demucs/v2.0/tasnet_extra.th",
|
||||
"model_repo_url_1": "https://github.com/TRvlvr/model_repo/releases/download/model_pack_repo/",
|
||||
"single_model_repo_url_1": "https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/",
|
||||
"app_patch": "https://github.com/TRvlvr/model_repo/releases/download/uvr_update_patches/"
|
||||
}
|
||||
13
lib_v5/filelists/download_lists/mdx_download_list.txt
Normal file
13
lib_v5/filelists/download_lists/mdx_download_list.txt
Normal file
@ -0,0 +1,13 @@
|
||||
No Model Selected
|
||||
No Model Selected
|
||||
MDX-Net Model: UVR_MDXNET_Main
|
||||
MDX-Net Model: UVR_MDXNET_1_9703
|
||||
MDX-Net Model: UVR_MDXNET_2_9682
|
||||
MDX-Net Model: UVR_MDXNET_3_9662
|
||||
MDX-Net Model: UVR_MDXNET_9482
|
||||
MDX-Net Model: UVR_MDXNET_KARA
|
||||
Developer Pack: voc_model_epochs_434-439
|
||||
Developer Pack: inst_model_epochs_10-17
|
||||
Developer Pack: inst_model_epochs_26-40
|
||||
Developer Pack: inst_model_epochs_51-57
|
||||
Developer Pack: inst_model_epochs_58-64
|
||||
25
lib_v5/filelists/download_lists/vr_download_list.txt
Normal file
25
lib_v5/filelists/download_lists/vr_download_list.txt
Normal file
@ -0,0 +1,25 @@
|
||||
No Model Selected
|
||||
No Model Selected
|
||||
VR Arch Model Pack v5: SP Models
|
||||
VR Arch Model Pack v5: HP2 Models
|
||||
VR Arch Model Pack v4: Main Models
|
||||
VR Arch Single Model v5: 1_HP-UVR
|
||||
VR Arch Single Model v5: 2_HP-UVR
|
||||
VR Arch Single Model v5: 3_HP-Vocal-UVR
|
||||
VR Arch Single Model v5: 4_HP-Vocal-UVR
|
||||
VR Arch Single Model v5: 5_HP-Karaoke-UVR
|
||||
VR Arch Single Model v5: 6_HP-Karaoke-UVR
|
||||
VR Arch Single Model v5: 7_HP2-UVR
|
||||
VR Arch Single Model v5: 8_HP2-UVR
|
||||
VR Arch Single Model v5: 9_HP2-UVR
|
||||
VR Arch Single Model v5: 10_SP-UVR-2B-32000-1
|
||||
VR Arch Single Model v5: 11_SP-UVR-2B-32000-2
|
||||
VR Arch Single Model v5: 12_SP-UVR-3B-44100
|
||||
VR Arch Single Model v5: 13_SP-UVR-4B-44100-1
|
||||
VR Arch Single Model v5: 14_SP-UVR-4B-44100-2
|
||||
VR Arch Single Model v5: 15_SP-UVR-MID-44100-1
|
||||
VR Arch Single Model v5: 16_SP-UVR-MID-44100-2
|
||||
VR Arch Single Model v4: MGM_HIGHEND_v4
|
||||
VR Arch Single Model v4: MGM_LOWEND_A_v4
|
||||
VR Arch Single Model v4: MGM_LOWEND_B_v4
|
||||
VR Arch Single Model v4: MGM_MAIN_v4
|
||||
16
lib_v5/filelists/ensemble_list/mdx_demuc_en_list.txt
Normal file
16
lib_v5/filelists/ensemble_list/mdx_demuc_en_list.txt
Normal file
@ -0,0 +1,16 @@
|
||||
No Model
|
||||
No Model
|
||||
MDX-Net: UVR-MDX-NET 1
|
||||
MDX-Net: UVR-MDX-NET 2
|
||||
MDX-Net: UVR-MDX-NET 3
|
||||
MDX-Net: UVR_MDXNET_9482
|
||||
MDX-Net: UVR-MDX-NET Karaoke
|
||||
MDX-Net: bass
|
||||
Demucs: UVR_Demucs_Model_1
|
||||
Demucs: UVR_Demucs_Model_2
|
||||
Demucs: UVR_Demucs_Model_Bag
|
||||
Demucs: Demucs_unittest v2
|
||||
Demucs: mdx_extra
|
||||
Demucs: mdx_q
|
||||
Demucs: Tasnet v1
|
||||
Demucs: Tasnet_extra v1
|
||||
13
lib_v5/filelists/ensemble_list/vr_en_list.txt
Normal file
13
lib_v5/filelists/ensemble_list/vr_en_list.txt
Normal file
@ -0,0 +1,13 @@
|
||||
No Model
|
||||
No Model
|
||||
1_HP-UVR
|
||||
2_HP-UVR
|
||||
6_HP-Karaoke-UVR
|
||||
11_SP-UVR-2B-32000-2
|
||||
13_SP-UVR-4B-44100-1
|
||||
MGM_HIGHEND_v4 (1)
|
||||
MGM_HIGHEND_v4
|
||||
MGM_LOWEND_A_v4
|
||||
MGM_LOWEND_B_v4
|
||||
MGM_MAIN_v4
|
||||
WIP-Piano-4band-129605kb
|
||||
266
lib_v5/filelists/hashes/mdx_new_hashes.txt
Normal file
266
lib_v5/filelists/hashes/mdx_new_hashes.txt
Normal file
@ -0,0 +1,266 @@
|
||||
0374836ab954ccd5946b29668c36bfdd
|
||||
e494001f6e38f3ff9f8db07073dbcc38
|
||||
1284cfb6ca5cbe68778a392bf92fe448
|
||||
927f5993571090980f7042a5087ad7d5
|
||||
8a7b42b609383c9bd11c093e357c8d01
|
||||
d7b9b49bf75c78afad0e88786bd729ae
|
||||
8737fb18a216d62ba2fec7948da59721
|
||||
432308a1f31d144ba117dc5f87e97809
|
||||
39eedabe4309ecbcda1df37a5cdd6adf
|
||||
fbee5264485e2c4b4003db897dea3528
|
||||
443ca4b02bf26bc42d9ef114aa25adc8
|
||||
8d700d4589ba30b876114823af40bcea
|
||||
52be0e4c18ddfba376081a7edaca5f56
|
||||
f0fd48dc0422a992e08e18f9cf318678
|
||||
af21b8645202d0b6d93cb383284ad0e6
|
||||
c5f39697c5dd372c4fb9656f01c814cc
|
||||
3f2bd7da922c4b45ac8923cccce41455
|
||||
8c467b5fbce83858b67436e28efa31c3
|
||||
16418df565fbaddd8e91ead6693a7418
|
||||
06f4c4b9e7cc2c3503bdcb9cfad4298b
|
||||
3a28db13d74c27f4ef255e5acb2f66b1
|
||||
2c7e0a31f0aa7947865b66a8ccfdf08f
|
||||
ef878de3d28e6ef376ad35527d86e4dc
|
||||
52b4c815669b7a6c1898453f5c42a617
|
||||
63c28c788f40af6d49ad8013600149bf
|
||||
787c1fa7256250695b14d2c62d57070c
|
||||
3eac6076f597da01788946563cc2756d
|
||||
d3efcccba9417bd0567845958d55bed5
|
||||
5d4b6d50847807833b82009be6519364
|
||||
cd988a4722f06c7fc9798750da9a6216
|
||||
5c0953d6f2d6a6ac3c738c0ba04fb518
|
||||
c488149f2cd37c710da574f3b784e6e3
|
||||
e46ef7b671b13a9a6bada197d8ccc0cf
|
||||
a57f5884b67bc0be16759045d9734b7f
|
||||
9b60b21391809845bb4435bc7096aabf
|
||||
5a4897d7a3afcae86f6948ba9275a49e
|
||||
d521fcb9b6cb6818710bf1609d1a8182
|
||||
3c6420e4e3646d7906d6d1b86d0410bc
|
||||
a85c8174b2c91e780a67b391650ae264
|
||||
b5a906653199f49f0e0215e0b22901b4
|
||||
16de23223764249902734d64f2a0223f
|
||||
2957853f7346c5039fc2f4d1b0f3c65c
|
||||
a8fb404acc3631cfbb8aacf20f39d65d
|
||||
f1e62e9e52477dff0c1b92c218bcd7f0
|
||||
2067c316fe7a097040f6da35451bde94
|
||||
b6653ac3158e60561f05933ebe4d6477
|
||||
274f631b444ce3ec6fbb71778ca58ae5
|
||||
5dc5f89788c7385a99787a15e78cc852
|
||||
740e38a99d835f113ffc396e144fbfa8
|
||||
b21de390242db154b1e6d45b9c44a912
|
||||
8c6751c184707c356534a8d4481e2987
|
||||
057a0878236e70dfdf8e09fc8e3d9126
|
||||
0a15368a0d7b00eb1f49276c316c280f
|
||||
ae35d179e0395e53867f2b7f32de337e
|
||||
87a9c56e36d2cb159ff76929b0edfd3f
|
||||
c488149f2cd37c710da574f3b784e6e3
|
||||
18508ba57b14327516f4f958d7cb5bdd
|
||||
d2b8a00978bcf61eeb3642ffb2653431
|
||||
c1cbaeea910a925be045c4dad52e0f95
|
||||
72d739c4fa1f0c606f357cc6b4b313b0
|
||||
44d6519e1efc6c68d53f82361e382987
|
||||
7b63504b3dd9ed7ef5c6ef9fbbb6d857
|
||||
f0fb53226a97e2d1ed8af46308d0b238
|
||||
27065f326f173ee54c38e349a84caed8
|
||||
71f2b8871d21ce1ae9b3c16042d3b511
|
||||
870a12746e7b7648cc8449c0ea5f8be8
|
||||
93bcd06e20b2ae5388da783bf68c7224
|
||||
09973b74f4dabb01ecdfed706743f45e
|
||||
34c8c8870ed8cac0fbc1dc9d1ae90dec
|
||||
e3078ba5c104e5f4ec7ff17b23f4bc48
|
||||
7a84d9cc7d2a064be2328d5f183e21f8
|
||||
49efdbcfcf32bde3b0cdec517fc14f7d
|
||||
389c7c5b890a73fec8753a675dd12ac0
|
||||
34ec0124d7d734babf76c79ab94c1f5c
|
||||
2ebfd30a6f0dcae400bdadf27c138d72
|
||||
a6520bca33f5d9d47249c9dc72769869
|
||||
fb95e55c41d7b4609da78f7e189d57c7
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
d11dd98a13e6931844d099162e944175
|
||||
db59cbd3343a3e1c4077e97273947c07
|
||||
c488149f2cd37c710da574f3b784e6e3
|
||||
684895a24ed48e27092604e5ca55147b
|
||||
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|
||||
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|
||||
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|
||||
2fd39f65bbdfb44ae24952fc55ec828f
|
||||
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|
||||
49df79ad1f22a1cc51d3fe6581c04368
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
4ccf7af16a1e132915a53b5c1d0489b4
|
||||
9d6dc0eeb89233b2fc2e4cb6b0c03c8d
|
||||
9c174a9728ba905330d53404350e5030
|
||||
b1e90f473873c0de27feadd42f784cde
|
||||
d96712e0a516217a4b15f21da3e09dee
|
||||
9c64543bc5757fd9bd385ad1977d98f6
|
||||
6f3c09ff2b603f88f9cb59643bf60d27
|
||||
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|
||||
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|
||||
8700c86a88cc28873a3fa38dddeb657d
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
2398160da9da6e05ed4cf41c313ab359
|
||||
83640e4eebfa904a335fcce7308f76ee
|
||||
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|
||||
9ea57e40b2a085a34432903153b6f553
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
38e807c4d51ea851182e89b2a26823c9
|
||||
7b0e3b866a0ad9aedac142b017a46d23
|
||||
82f53c138d9ae0442f42357538c2623a
|
||||
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|
||||
221c01f90c00e98ceccef860b33ecb76
|
||||
1d39503ae9334f6eee559bdd70f8c24f
|
||||
f4b9da2652eb0dce90a9ea3335cd36c0
|
||||
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|
||||
41e7fab08927a26a7afd6a8830bc2d8c
|
||||
539e6c09143a295cea430c7459bbff33
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
83ddc19beea20010318bce4722d890d5
|
||||
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|
||||
202c811375f61cd6d5840a40cf2cb4ba
|
||||
7b3460f9e8dd28202111ec582e965d55
|
||||
dd087f5adcd94116225963397ac25889
|
||||
5e5865e8d2f48d93066c866aa31c5983
|
||||
ff2d856e56b5768688a1d1de00c143dc
|
||||
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|
||||
fb793952993781dbee22336bc6296987
|
||||
5107088c9476c07bdd32af1b9d67108e
|
||||
969bfa927f27ca1c98c00d3ae589f623
|
||||
37969f2b2add6199fb00780899ce8c0d
|
||||
122742f2e218d003cc22d108df9c668a
|
||||
c72c77ff2373bc0e0d9beebf35c0dc07
|
||||
43eefd064d333877c59ee7362d044e0c
|
||||
56
lib_v5/filelists/hashes/mdx_new_inst_hashes.txt
Normal file
56
lib_v5/filelists/hashes/mdx_new_inst_hashes.txt
Normal file
@ -0,0 +1,56 @@
|
||||
c488149f2cd37c710da574f3b784e6e3
|
||||
2223f985aa47e99a0348f987ea2a3db6
|
||||
3fc1fa83cebb144dca7d379a3767ac77
|
||||
dfe5e1f42c42dcc262f2a2e2bc4ca7aa
|
||||
69108243963e4ed3fe22fdb37b812fe8
|
||||
ec3d6dc24c75e06a72fe1a2bd970b7b0
|
||||
aee23fcb90ef135bbd036f6e1c1ad9f9
|
||||
4561b9240c9acaf13eee2bd0186b4df4
|
||||
da0f43e81eb60b0bdacedd2de4758ee9
|
||||
5ee77a84e40bbd66a9d2b08b6a4c8496
|
||||
8b0526d02937c08adc95dfd57652c915
|
||||
08ff1d3a743eb2377c96b3de793990a0
|
||||
af292ce84aa6125ab75020f31e183f5b
|
||||
7f56ee6b9ee402802c403f348fea58cb
|
||||
038bb0d0a9f3c89b5671189384cfdf91
|
||||
db79054b3ae6c30f655e8ff12d64caa7
|
||||
b2e16d43ef559782a32874ffdace8b82
|
||||
c488149f2cd37c710da574f3b784e6e3
|
||||
b71bb782cad9b8c6ea0a72c6ae69e8b4
|
||||
9d478026519f140e14e9c1bb32fcc522
|
||||
e81c4b46ec685b3ce227f426f884cfe0
|
||||
e9edcfacbbbbc513734082e1c1f7f6ad
|
||||
8d0bb54171a0996f2610be0fdc5743a8
|
||||
fbe5c4eecc1f3ad4b38364d77a774013
|
||||
66209e099302542c627c29bbb6976c59
|
||||
ee7b7dcda4e4353730cf89afc881c8cb
|
||||
0d09ab08de2cf0efe123144589345a33
|
||||
be2cff51fc6fcae172d30d3f1c142ee6
|
||||
b53e4cc77f9c47cbc6184cc7158f65ca
|
||||
c38f6a06977b9bc287c83f1e80ee0d9f
|
||||
0e05e776801b7016714015043e97373b
|
||||
fd39ea4282a92d3cb5f8d502e4c44ffa
|
||||
c937b7662b6f5cd92ff82fe595f160e7
|
||||
c0e0e9c44f815c1d0c0737343d702923
|
||||
c488149f2cd37c710da574f3b784e6e3
|
||||
823ef02eadf24f652609959e3a35206e
|
||||
674e8851d69bef3e4e1724d21247a8dc
|
||||
d3543c7b5d214515a7bd55d57b2413a0
|
||||
eb1179fb56fbcab660d9b69f9ac9cbe7
|
||||
0f7f0b21feb6b1b6e843f6047a3af13a
|
||||
b6f8de9a8316d5bd330a84eb22078ef4
|
||||
6dd6104d3ba4b3e9f47fba7d6c84dd9c
|
||||
1579df63864bf6ec11f2484cb2cfca7e
|
||||
3d2af588b96dc0e84be66804684e7c56
|
||||
6e566b37b3cec881ec93cfe415a710ec
|
||||
455c272b691c1001aa9b9cad5dfedd20
|
||||
ec892e0ea6f973d8c15645e621ee8fe1
|
||||
4a0b13b03e4db47191f387c2ced82f73
|
||||
e5d6e895bcbe4ca62ca9cc8808d8da6e
|
||||
4a0b13b03e4db47191f387c2ced82f73
|
||||
e5d6e895bcbe4ca62ca9cc8808d8da6e
|
||||
9df9f3bf4c7151fc36fb822d7728f433
|
||||
4475c1d3f79482cb2082f1bd13899e1b
|
||||
3d3523c8e0ab0a355748f38449331918
|
||||
24bb2808feae6efb2aaae9db3778886c
|
||||
947e6dd9e4aea2811eb3fb26d4bde615
|
||||
5
lib_v5/filelists/hashes/mdx_original_hashes.txt
Normal file
5
lib_v5/filelists/hashes/mdx_original_hashes.txt
Normal file
@ -0,0 +1,5 @@
|
||||
1bbcb39d8a4be721d9322e62f13de1c1
|
||||
94422d1d6eb7019eff97dbef2daba979
|
||||
d3b87173f484864674ee2a21cd7b35f2
|
||||
053f663b23c70c6c1f52938fb480f5b8
|
||||
76929c1b5b9b804f89f4ebb78712c668
|
||||
119
lib_v5/layers_129605KB.py
Normal file
119
lib_v5/layers_129605KB.py
Normal file
@ -0,0 +1,119 @@
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from lib_v5 import spec_utils
|
||||
|
||||
|
||||
class Conv2DBNActiv(nn.Module):
|
||||
|
||||
def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
|
||||
super(Conv2DBNActiv, self).__init__()
|
||||
self.conv = nn.Sequential(
|
||||
nn.Conv2d(
|
||||
nin, nout,
|
||||
kernel_size=ksize,
|
||||
stride=stride,
|
||||
padding=pad,
|
||||
dilation=dilation,
|
||||
bias=False),
|
||||
nn.BatchNorm2d(nout),
|
||||
activ()
|
||||
)
|
||||
|
||||
def __call__(self, x):
|
||||
return self.conv(x)
|
||||
|
||||
|
||||
class SeperableConv2DBNActiv(nn.Module):
|
||||
|
||||
def __init__(self, nin, nout, ksize=3, stride=1, pad=1, dilation=1, activ=nn.ReLU):
|
||||
super(SeperableConv2DBNActiv, self).__init__()
|
||||
self.conv = nn.Sequential(
|
||||
nn.Conv2d(
|
||||
nin, nin,
|
||||
kernel_size=ksize,
|
||||
stride=stride,
|
||||
padding=pad,
|
||||
dilation=dilation,
|
||||
groups=nin,
|
||||
bias=False),
|
||||
nn.Conv2d(
|
||||
nin, nout,
|
||||
kernel_size=1,
|
||||
bias=False),
|
||||
nn.BatchNorm2d(nout),
|
||||
activ()
|
||||
)
|
||||
|
||||
def __call__(self, x):
|
||||
return self.conv(x)
|
||||
|
||||
|
||||
class Encoder(nn.Module):
|
||||
|
||||
def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.LeakyReLU):
|
||||
super(Encoder, self).__init__()
|
||||
self.conv1 = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
|
||||
self.conv2 = Conv2DBNActiv(nout, nout, ksize, stride, pad, activ=activ)
|
||||
|
||||
def __call__(self, x):
|
||||
skip = self.conv1(x)
|
||||
h = self.conv2(skip)
|
||||
|
||||
return h, skip
|
||||
|
||||
|
||||
class Decoder(nn.Module):
|
||||
|
||||
def __init__(self, nin, nout, ksize=3, stride=1, pad=1, activ=nn.ReLU, dropout=False):
|
||||
super(Decoder, self).__init__()
|
||||
self.conv = Conv2DBNActiv(nin, nout, ksize, 1, pad, activ=activ)
|
||||
self.dropout = nn.Dropout2d(0.1) if dropout else None
|
||||
|
||||
def __call__(self, x, skip=None):
|
||||
x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True)
|
||||
if skip is not None:
|
||||
skip = spec_utils.crop_center(skip, x)
|
||||
x = torch.cat([x, skip], dim=1)
|
||||
h = self.conv(x)
|
||||
|
||||
if self.dropout is not None:
|
||||
h = self.dropout(h)
|
||||
|
||||
return h
|
||||
|
||||
|
||||
class ASPPModule(nn.Module):
|
||||
|
||||
def __init__(self, nin, nout, dilations=(4, 8, 16, 32), activ=nn.ReLU):
|
||||
super(ASPPModule, self).__init__()
|
||||
self.conv1 = nn.Sequential(
|
||||
nn.AdaptiveAvgPool2d((1, None)),
|
||||
Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
|
||||
)
|
||||
self.conv2 = Conv2DBNActiv(nin, nin, 1, 1, 0, activ=activ)
|
||||
self.conv3 = SeperableConv2DBNActiv(
|
||||
nin, nin, 3, 1, dilations[0], dilations[0], activ=activ)
|
||||
self.conv4 = SeperableConv2DBNActiv(
|
||||
nin, nin, 3, 1, dilations[1], dilations[1], activ=activ)
|
||||
self.conv5 = SeperableConv2DBNActiv(
|
||||
nin, nin, 3, 1, dilations[2], dilations[2], activ=activ)
|
||||
self.conv6 = SeperableConv2DBNActiv(
|
||||
nin, nin, 3, 1, dilations[2], dilations[2], activ=activ)
|
||||
self.bottleneck = nn.Sequential(
|
||||
Conv2DBNActiv(nin * 6, nout, 1, 1, 0, activ=activ),
|
||||
nn.Dropout2d(0.1)
|
||||
)
|
||||
|
||||
def forward(self, x):
|
||||
_, _, h, w = x.size()
|
||||
feat1 = F.interpolate(self.conv1(x), size=(h, w), mode='bilinear', align_corners=True)
|
||||
feat2 = self.conv2(x)
|
||||
feat3 = self.conv3(x)
|
||||
feat4 = self.conv4(x)
|
||||
feat5 = self.conv5(x)
|
||||
feat6 = self.conv6(x)
|
||||
out = torch.cat((feat1, feat2, feat3, feat4, feat5, feat6), dim=1)
|
||||
bottle = self.bottleneck(out)
|
||||
return bottle
|
||||
1
lib_v5/modelparams/Auto
Normal file
1
lib_v5/modelparams/Auto
Normal file
@ -0,0 +1 @@
|
||||
Auto
|
||||
166
lib_v5/modelparamset.py
Normal file
166
lib_v5/modelparamset.py
Normal file
@ -0,0 +1,166 @@
|
||||
def provide_model_param_hash(model_hash):
|
||||
#v5 Models
|
||||
if model_hash == '47939caf0cfe52a0e81442b85b971dfd':
|
||||
model_params_set=str('lib_v5/modelparams/4band_44100.json')
|
||||
param_name=str('4band_44100')
|
||||
elif model_hash == '4e4ecb9764c50a8c414fee6e10395bbe':
|
||||
model_params_set=str('lib_v5/modelparams/4band_v2.json')
|
||||
param_name=str('4band_v2')
|
||||
elif model_hash == 'e60a1e84803ce4efc0a6551206cc4b71':
|
||||
model_params_set=str('lib_v5/modelparams/4band_44100.json')
|
||||
param_name=str('4band_44100')
|
||||
elif model_hash == 'a82f14e75892e55e994376edbf0c8435':
|
||||
model_params_set=str('lib_v5/modelparams/4band_44100.json')
|
||||
param_name=str('4band_44100')
|
||||
elif model_hash == '6dd9eaa6f0420af9f1d403aaafa4cc06':
|
||||
model_params_set=str('lib_v5/modelparams/4band_v2_sn.json')
|
||||
param_name=str('4band_v2_sn')
|
||||
elif model_hash == '5c7bbca45a187e81abbbd351606164e5':
|
||||
model_params_set=str('lib_v5/modelparams/3band_44100_msb2.json')
|
||||
param_name=str('3band_44100_msb2')
|
||||
elif model_hash == 'd6b2cb685a058a091e5e7098192d3233':
|
||||
model_params_set=str('lib_v5/modelparams/3band_44100_msb2.json')
|
||||
param_name=str('3band_44100_msb2')
|
||||
elif model_hash == 'c1b9f38170a7c90e96f027992eb7c62b':
|
||||
model_params_set=str('lib_v5/modelparams/4band_44100.json')
|
||||
param_name=str('4band_44100')
|
||||
elif model_hash == 'c3448ec923fa0edf3d03a19e633faa53':
|
||||
model_params_set=str('lib_v5/modelparams/4band_44100.json')
|
||||
param_name=str('4band_44100')
|
||||
elif model_hash == '68aa2c8093d0080704b200d140f59e54':
|
||||
model_params_set=str('lib_v5/modelparams/3band_44100.json')
|
||||
param_name=str('3band_44100.json')
|
||||
elif model_hash == 'fdc83be5b798e4bd29fe00fe6600e147':
|
||||
model_params_set=str('lib_v5/modelparams/3band_44100_mid.json')
|
||||
param_name=str('3band_44100_mid.json')
|
||||
elif model_hash == '2ce34bc92fd57f55db16b7a4def3d745':
|
||||
model_params_set=str('lib_v5/modelparams/3band_44100_mid.json')
|
||||
param_name=str('3band_44100_mid.json')
|
||||
elif model_hash == '52fdca89576f06cf4340b74a4730ee5f':
|
||||
model_params_set=str('lib_v5/modelparams/4band_44100.json')
|
||||
param_name=str('4band_44100.json')
|
||||
elif model_hash == '41191165b05d38fc77f072fa9e8e8a30':
|
||||
model_params_set=str('lib_v5/modelparams/4band_44100.json')
|
||||
param_name=str('4band_44100.json')
|
||||
elif model_hash == '89e83b511ad474592689e562d5b1f80e':
|
||||
model_params_set=str('lib_v5/modelparams/2band_32000.json')
|
||||
param_name=str('2band_32000.json')
|
||||
elif model_hash == '0b954da81d453b716b114d6d7c95177f':
|
||||
model_params_set=str('lib_v5/modelparams/2band_32000.json')
|
||||
param_name=str('2band_32000.json')
|
||||
|
||||
#v4 Models
|
||||
|
||||
elif model_hash == '6a00461c51c2920fd68937d4609ed6c8':
|
||||
model_params_set=str('lib_v5/modelparams/1band_sr16000_hl512.json')
|
||||
param_name=str('1band_sr16000_hl512')
|
||||
elif model_hash == '0ab504864d20f1bd378fe9c81ef37140':
|
||||
model_params_set=str('lib_v5/modelparams/1band_sr32000_hl512.json')
|
||||
param_name=str('1band_sr32000_hl512')
|
||||
elif model_hash == '7dd21065bf91c10f7fccb57d7d83b07f':
|
||||
model_params_set=str('lib_v5/modelparams/1band_sr32000_hl512.json')
|
||||
param_name=str('1band_sr32000_hl512')
|
||||
elif model_hash == '80ab74d65e515caa3622728d2de07d23':
|
||||
model_params_set=str('lib_v5/modelparams/1band_sr32000_hl512.json')
|
||||
param_name=str('1band_sr32000_hl512')
|
||||
elif model_hash == 'edc115e7fc523245062200c00caa847f':
|
||||
model_params_set=str('lib_v5/modelparams/1band_sr33075_hl384.json')
|
||||
param_name=str('1band_sr33075_hl384')
|
||||
elif model_hash == '28063e9f6ab5b341c5f6d3c67f2045b7':
|
||||
model_params_set=str('lib_v5/modelparams/1band_sr33075_hl384.json')
|
||||
param_name=str('1band_sr33075_hl384')
|
||||
elif model_hash == 'b58090534c52cbc3e9b5104bad666ef2':
|
||||
model_params_set=str('lib_v5/modelparams/1band_sr44100_hl512.json')
|
||||
param_name=str('1band_sr44100_hl512')
|
||||
elif model_hash == '0cdab9947f1b0928705f518f3c78ea8f':
|
||||
model_params_set=str('lib_v5/modelparams/1band_sr44100_hl512.json')
|
||||
param_name=str('1band_sr44100_hl512')
|
||||
elif model_hash == 'ae702fed0238afb5346db8356fe25f13':
|
||||
model_params_set=str('lib_v5/modelparams/1band_sr44100_hl1024.json')
|
||||
param_name=str('1band_sr44100_hl1024')
|
||||
else:
|
||||
model_params_set=str('Not Found Using Hash')
|
||||
param_name=str('Not Found Using Hash')
|
||||
|
||||
model_params = model_params_set, param_name
|
||||
|
||||
return model_params
|
||||
|
||||
def provide_model_param_name(ModelName):
|
||||
#1 Band
|
||||
if '1band_sr16000_hl512' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/1band_sr16000_hl512.json')
|
||||
param_name=str('1band_sr16000_hl512')
|
||||
elif '1band_sr32000_hl512' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/1band_sr32000_hl512.json')
|
||||
param_name=str('1band_sr32000_hl512')
|
||||
elif '1band_sr33075_hl384' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/1band_sr33075_hl384.json')
|
||||
param_name=str('1band_sr33075_hl384')
|
||||
elif '1band_sr44100_hl256' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/1band_sr44100_hl256.json')
|
||||
param_name=str('1band_sr44100_hl256')
|
||||
elif '1band_sr44100_hl512' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/1band_sr44100_hl512.json')
|
||||
param_name=str('1band_sr44100_hl512')
|
||||
elif '1band_sr44100_hl1024' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/1band_sr44100_hl1024.json')
|
||||
param_name=str('1band_sr44100_hl1024')
|
||||
|
||||
#2 Band
|
||||
elif '2band_44100_lofi' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/2band_44100_lofi.json')
|
||||
param_name=str('2band_44100_lofi')
|
||||
elif '2band_32000' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/2band_32000.json')
|
||||
param_name=str('2band_32000')
|
||||
elif '2band_48000' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/2band_48000.json')
|
||||
param_name=str('2band_48000')
|
||||
|
||||
#3 Band
|
||||
elif '3band_44100' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/3band_44100.json')
|
||||
param_name=str('3band_44100')
|
||||
elif '3band_44100_mid' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/3band_44100_mid.json')
|
||||
param_name=str('3band_44100_mid')
|
||||
elif '3band_44100_msb2' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/3band_44100_msb2.json')
|
||||
param_name=str('3band_44100_msb2')
|
||||
|
||||
#4 Band
|
||||
elif '4band_44100' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/4band_44100.json')
|
||||
param_name=str('4band_44100')
|
||||
elif '4band_44100_mid' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/4band_44100_mid.json')
|
||||
param_name=str('4band_44100_mid')
|
||||
elif '4band_44100_msb' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/4band_44100_msb.json')
|
||||
param_name=str('4band_44100_msb')
|
||||
elif '4band_44100_msb2' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/4band_44100_msb2.json')
|
||||
param_name=str('4band_44100_msb2')
|
||||
elif '4band_44100_reverse' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/4band_44100_reverse.json')
|
||||
param_name=str('4band_44100_reverse')
|
||||
elif '4band_44100_sw' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/4band_44100_sw.json')
|
||||
param_name=str('4band_44100_sw')
|
||||
elif '4band_v2' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/4band_v2.json')
|
||||
param_name=str('4band_v2')
|
||||
elif '4band_v2_sn' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/4band_v2_sn.json')
|
||||
param_name=str('4band_v2_sn')
|
||||
elif 'tmodelparam' in ModelName:
|
||||
model_params_set=str('lib_v5/modelparams/tmodelparam.json')
|
||||
param_name=str('User Model Param Set')
|
||||
else:
|
||||
model_params_set=str('Not Found Using Name')
|
||||
param_name=str('Not Found Using Name')
|
||||
|
||||
model_params = model_params_set, param_name
|
||||
|
||||
return model_params
|
||||
116
lib_v5/nets_129605KB.py
Normal file
116
lib_v5/nets_129605KB.py
Normal file
@ -0,0 +1,116 @@
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
from lib_v5 import layers_129605KB as layers
|
||||
|
||||
|
||||
class BaseASPPNet(nn.Module):
|
||||
|
||||
def __init__(self, nin, ch, dilations=(4, 8, 16, 32)):
|
||||
super(BaseASPPNet, self).__init__()
|
||||
self.enc1 = layers.Encoder(nin, ch, 3, 2, 1)
|
||||
self.enc2 = layers.Encoder(ch, ch * 2, 3, 2, 1)
|
||||
self.enc3 = layers.Encoder(ch * 2, ch * 4, 3, 2, 1)
|
||||
self.enc4 = layers.Encoder(ch * 4, ch * 8, 3, 2, 1)
|
||||
self.enc5 = layers.Encoder(ch * 8, ch * 16, 3, 2, 1)
|
||||
|
||||
self.aspp = layers.ASPPModule(ch * 16, ch * 32, dilations)
|
||||
|
||||
self.dec5 = layers.Decoder(ch * (16 + 32), ch * 16, 3, 1, 1)
|
||||
self.dec4 = layers.Decoder(ch * (8 + 16), ch * 8, 3, 1, 1)
|
||||
self.dec3 = layers.Decoder(ch * (4 + 8), ch * 4, 3, 1, 1)
|
||||
self.dec2 = layers.Decoder(ch * (2 + 4), ch * 2, 3, 1, 1)
|
||||
self.dec1 = layers.Decoder(ch * (1 + 2), ch, 3, 1, 1)
|
||||
|
||||
def __call__(self, x):
|
||||
h, e1 = self.enc1(x)
|
||||
h, e2 = self.enc2(h)
|
||||
h, e3 = self.enc3(h)
|
||||
h, e4 = self.enc4(h)
|
||||
h, e5 = self.enc5(h)
|
||||
|
||||
h = self.aspp(h)
|
||||
|
||||
h = self.dec5(h, e5)
|
||||
h = self.dec4(h, e4)
|
||||
h = self.dec3(h, e3)
|
||||
h = self.dec2(h, e2)
|
||||
h = self.dec1(h, e1)
|
||||
|
||||
return h
|
||||
|
||||
|
||||
class CascadedASPPNet(nn.Module):
|
||||
|
||||
def __init__(self, n_fft):
|
||||
super(CascadedASPPNet, self).__init__()
|
||||
self.stg1_low_band_net = BaseASPPNet(2, 16)
|
||||
self.stg1_high_band_net = BaseASPPNet(2, 16)
|
||||
|
||||
self.stg2_bridge = layers.Conv2DBNActiv(18, 8, 1, 1, 0)
|
||||
self.stg2_full_band_net = BaseASPPNet(8, 16)
|
||||
|
||||
self.stg3_bridge = layers.Conv2DBNActiv(34, 16, 1, 1, 0)
|
||||
self.stg3_full_band_net = BaseASPPNet(16, 32)
|
||||
|
||||
self.out = nn.Conv2d(32, 2, 1, bias=False)
|
||||
self.aux1_out = nn.Conv2d(16, 2, 1, bias=False)
|
||||
self.aux2_out = nn.Conv2d(16, 2, 1, bias=False)
|
||||
|
||||
self.max_bin = n_fft // 2
|
||||
self.output_bin = n_fft // 2 + 1
|
||||
|
||||
self.offset = 128
|
||||
|
||||
def forward(self, x, aggressiveness=None):
|
||||
mix = x.detach()
|
||||
x = x.clone()
|
||||
|
||||
x = x[:, :, :self.max_bin]
|
||||
|
||||
bandw = x.size()[2] // 2
|
||||
aux1 = torch.cat([
|
||||
self.stg1_low_band_net(x[:, :, :bandw]),
|
||||
self.stg1_high_band_net(x[:, :, bandw:])
|
||||
], dim=2)
|
||||
|
||||
h = torch.cat([x, aux1], dim=1)
|
||||
aux2 = self.stg2_full_band_net(self.stg2_bridge(h))
|
||||
|
||||
h = torch.cat([x, aux1, aux2], dim=1)
|
||||
h = self.stg3_full_band_net(self.stg3_bridge(h))
|
||||
|
||||
mask = torch.sigmoid(self.out(h))
|
||||
mask = F.pad(
|
||||
input=mask,
|
||||
pad=(0, 0, 0, self.output_bin - mask.size()[2]),
|
||||
mode='replicate')
|
||||
|
||||
if self.training:
|
||||
aux1 = torch.sigmoid(self.aux1_out(aux1))
|
||||
aux1 = F.pad(
|
||||
input=aux1,
|
||||
pad=(0, 0, 0, self.output_bin - aux1.size()[2]),
|
||||
mode='replicate')
|
||||
aux2 = torch.sigmoid(self.aux2_out(aux2))
|
||||
aux2 = F.pad(
|
||||
input=aux2,
|
||||
pad=(0, 0, 0, self.output_bin - aux2.size()[2]),
|
||||
mode='replicate')
|
||||
return mask * mix, aux1 * mix, aux2 * mix
|
||||
else:
|
||||
if aggressiveness:
|
||||
mask[:, :, :aggressiveness['split_bin']] = torch.pow(mask[:, :, :aggressiveness['split_bin']], 1 + aggressiveness['value'] / 3)
|
||||
mask[:, :, aggressiveness['split_bin']:] = torch.pow(mask[:, :, aggressiveness['split_bin']:], 1 + aggressiveness['value'])
|
||||
|
||||
return mask * mix
|
||||
|
||||
def predict(self, x_mag, aggressiveness=None):
|
||||
h = self.forward(x_mag, aggressiveness)
|
||||
|
||||
if self.offset > 0:
|
||||
h = h[:, :, :, self.offset:-self.offset]
|
||||
assert h.size()[3] > 0
|
||||
|
||||
return h
|
||||
@ -82,10 +82,10 @@ def normalize(wave_res):
|
||||
"""Save output music files"""
|
||||
maxv = np.abs(wave_res).max()
|
||||
if maxv > 1.0:
|
||||
print(f"Input above threshold for clipping. The result was normalized. Max:{maxv}")
|
||||
print(f"\nNormalization Set On: Input above threshold for clipping. The result was normalized. Max:{maxv}\n")
|
||||
wave_res /= maxv
|
||||
else:
|
||||
print(f"Input not above threshold for clipping. Max:{maxv}")
|
||||
print(f"\nNormalization Set On: Input not above threshold for clipping. Max:{maxv}\n")
|
||||
|
||||
return wave_res
|
||||
|
||||
@ -93,9 +93,9 @@ def nonormalize(wave_res):
|
||||
"""Save output music files"""
|
||||
maxv = np.abs(wave_res).max()
|
||||
if maxv > 1.0:
|
||||
print(f"Input above threshold for clipping. The result was not normalized. Max:{maxv}")
|
||||
print(f"\nNormalization Set Off: Input above threshold for clipping. The result was not normalized. Max:{maxv}\n")
|
||||
else:
|
||||
print(f"Input not above threshold for clipping. Max:{maxv}")
|
||||
print(f"\nNormalization Set Off: Input not above threshold for clipping. Max:{maxv}\n")
|
||||
|
||||
return wave_res
|
||||
|
||||
@ -369,7 +369,7 @@ def cmb_spectrogram_to_wave_d(spec_m, mp, extra_bins_h=None, extra_bins=None, de
|
||||
wave2 = np.add(wave, spectrogram_to_wave(spec_s, bp['hl'], mp.param['mid_side'], mp.param['mid_side_b2'], mp.param['reverse']))
|
||||
wave = librosa.resample(wave2, bp['sr'], sr, res_type="sinc_fastest")
|
||||
|
||||
print(demucs)
|
||||
#print(demucs)
|
||||
|
||||
if demucs == True:
|
||||
wave = librosa.resample(wave, bp['sr'], 44100, res_type="sinc_fastest")
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user