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import os |
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import sys |
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import subprocess |
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import re |
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import platform |
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import torch |
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import logging |
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import yt_dlp |
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import json |
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import gradio as gr |
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import spaces |
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import assets.themes.loadThemes as loadThemes |
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from audio_separator.separator import Separator |
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from assets.i18n.i18n import I18nAuto |
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from argparse import ArgumentParser |
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from assets.presence.discord_presence import RPCManager, track_presence |
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i18n = I18nAuto() |
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now_dir = os.getcwd() |
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sys.path.append(now_dir) |
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config_file = os.path.join(now_dir, "assets", "config.json") |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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use_autocast = device == "cuda" |
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if os.path.isdir("env"): |
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if platform.system() == "Windows": |
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separator_location = ".\\env\\Scripts\\audio-separator.exe" |
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elif platform.system() == "Linux": |
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separator_location = "env/bin/audio-separator" |
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else: |
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separator_location = "audio-separator" |
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roformer_models = { |
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'BS-Roformer-Viperx-1297': 'model_bs_roformer_ep_317_sdr_12.9755.ckpt', |
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'BS-Roformer-Viperx-1296': 'model_bs_roformer_ep_368_sdr_12.9628.ckpt', |
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'BS-Roformer-Viperx-1053': 'model_bs_roformer_ep_937_sdr_10.5309.ckpt', |
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'Mel-Roformer-Viperx-1143': 'model_mel_band_roformer_ep_3005_sdr_11.4360.ckpt', |
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'BS-Roformer-De-Reverb': 'deverb_bs_roformer_8_384dim_10depth.ckpt', |
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'Mel-Roformer-Crowd-Aufr33-Viperx': 'mel_band_roformer_crowd_aufr33_viperx_sdr_8.7144.ckpt', |
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'Mel-Roformer-Denoise-Aufr33': 'denoise_mel_band_roformer_aufr33_sdr_27.9959.ckpt', |
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'Mel-Roformer-Denoise-Aufr33-Aggr' : 'denoise_mel_band_roformer_aufr33_aggr_sdr_27.9768.ckpt', |
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'MelBand Roformer | Denoise-Debleed by Gabox' : 'mel_band_roformer_denoise_debleed_gabox.ckpt', |
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'Mel-Roformer-Karaoke-Aufr33-Viperx': 'mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt', |
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'MelBand Roformer | Karaoke by Gabox' : 'mel_band_roformer_karaoke_gabox.ckpt', |
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'MelBand Roformer | Vocals by Kimberley Jensen' : 'vocals_mel_band_roformer.ckpt', |
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'MelBand Roformer Kim | FT by unwa' : 'mel_band_roformer_kim_ft_unwa.ckpt', |
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'MelBand Roformer Kim | FT 2 by unwa' : 'mel_band_roformer_kim_ft2_unwa.ckpt', |
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'MelBand Roformer Kim | FT 2 Bleedless by unwa' : 'mel_band_roformer_kim_ft2_bleedless_unwa.ckpt', |
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'MelBand Roformer Kim | Inst V1 by Unwa' : 'melband_roformer_inst_v1.ckpt', |
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'MelBand Roformer Kim | Inst V1 (E) by Unwa' : 'melband_roformer_inst_v1e.ckpt', |
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'MelBand Roformer Kim | Inst V2 by Unwa' : 'melband_roformer_inst_v2.ckpt', |
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'MelBand Roformer Kim | InstVoc Duality V1 by Unwa' : 'melband_roformer_instvoc_duality_v1.ckpt', |
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'MelBand Roformer Kim | InstVoc Duality V2 by Unwa' : 'melband_roformer_instvox_duality_v2.ckpt', |
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'MelBand Roformer | Vocals by becruily' : 'mel_band_roformer_vocals_becruily.ckpt', |
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'MelBand Roformer | Instrumental by becruily' : 'mel_band_roformer_instrumental_becruily.ckpt', |
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'MelBand Roformer | Vocals Fullness by Aname' : 'mel_band_roformer_vocal_fullness_aname.ckpt', |
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'BS Roformer | Vocals by Gabox' : 'bs_roformer_vocals_gabox.ckpt', |
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'MelBand Roformer | Vocals by Gabox' : 'mel_band_roformer_vocals_gabox.ckpt', |
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'MelBand Roformer | Vocals FV1 by Gabox' : 'mel_band_roformer_vocals_fv1_gabox.ckpt', |
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'MelBand Roformer | Vocals FV2 by Gabox' : 'mel_band_roformer_vocals_fv2_gabox.ckpt', |
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'MelBand Roformer | Vocals FV3 by Gabox' : 'mel_band_roformer_vocals_fv3_gabox.ckpt', |
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'MelBand Roformer | Vocals FV4 by Gabox' : 'mel_band_roformer_vocals_fv4_gabox.ckpt', |
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'MelBand Roformer | Instrumental by Gabox' : 'mel_band_roformer_instrumental_gabox.ckpt', |
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'MelBand Roformer | Instrumental 2 by Gabox' : 'mel_band_roformer_instrumental_2_gabox.ckpt', |
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'MelBand Roformer | Instrumental 3 by Gabox' : 'mel_band_roformer_instrumental_3_gabox.ckpt', |
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'MelBand Roformer | Instrumental Bleedless V1 by Gabox' : 'mel_band_roformer_instrumental_bleedless_v1_gabox.ckpt', |
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'MelBand Roformer | Instrumental Bleedless V2 by Gabox' : 'mel_band_roformer_instrumental_bleedless_v2_gabox.ckpt', |
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'MelBand Roformer | Instrumental Fullness V1 by Gabox' : 'mel_band_roformer_instrumental_fullness_v1_gabox.ckpt', |
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'MelBand Roformer | Instrumental Fullness V2 by Gabox' : 'mel_band_roformer_instrumental_fullness_v2_gabox.ckpt', |
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'MelBand Roformer | Instrumental Fullness V3 by Gabox' : 'mel_band_roformer_instrumental_fullness_v3_gabox.ckpt', |
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'MelBand Roformer | Instrumental Fullness Noisy V4 by Gabox' : 'mel_band_roformer_instrumental_fullness_noise_v4_gabox.ckpt', |
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'MelBand Roformer | INSTV5 by Gabox' : 'mel_band_roformer_instrumental_instv5_gabox.ckpt', |
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'MelBand Roformer | INSTV5N by Gabox' : 'mel_band_roformer_instrumental_instv5n_gabox.ckpt', |
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'MelBand Roformer | INSTV6 by Gabox' : 'mel_band_roformer_instrumental_instv6_gabox.ckpt', |
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'MelBand Roformer | INSTV6N by Gabox' : 'mel_band_roformer_instrumental_instv6n_gabox.ckpt', |
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'MelBand Roformer | INSTV7 by Gabox' : 'mel_band_roformer_instrumental_instv7_gabox.ckpt', |
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'MelBand Roformer | De-Reverb by anvuew' : 'dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt', |
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'MelBand Roformer | De-Reverb Less Aggressive by anvuew' : 'dereverb_mel_band_roformer_less_aggressive_anvuew_sdr_18.8050.ckpt', |
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'MelBand Roformer | De-Reverb Mono by anvuew' : 'dereverb_mel_band_roformer_mono_anvuew.ckpt', |
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'MelBand Roformer | De-Reverb Big by Sucial' : 'dereverb_big_mbr_ep_362.ckpt', |
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'MelBand Roformer | De-Reverb Super Big by Sucial' : 'dereverb_super_big_mbr_ep_346.ckpt', |
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'MelBand Roformer | De-Reverb-Echo by Sucial' : 'dereverb-echo_mel_band_roformer_sdr_10.0169.ckpt', |
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'MelBand Roformer | De-Reverb-Echo V2 by Sucial' : 'dereverb-echo_mel_band_roformer_sdr_13.4843_v2.ckpt', |
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'MelBand Roformer | De-Reverb-Echo Fused by Sucial' : 'dereverb_echo_mbr_fused.ckpt', |
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'MelBand Roformer Kim | SYHFT by SYH99999' : 'MelBandRoformerSYHFT.ckpt', |
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'MelBand Roformer Kim | SYHFT V2 by SYH99999' : 'MelBandRoformerSYHFTV2.ckpt', |
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'MelBand Roformer Kim | SYHFT V2.5 by SYH99999' : 'MelBandRoformerSYHFTV2.5.ckpt', |
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'MelBand Roformer Kim | SYHFT V3 by SYH99999' : 'MelBandRoformerSYHFTV3Epsilon.ckpt', |
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'MelBand Roformer Kim | Big SYHFT V1 by SYH99999' : 'MelBandRoformerBigSYHFTV1.ckpt', |
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'MelBand Roformer Kim | Big Beta 4 FT by unwa' : 'melband_roformer_big_beta4.ckpt', |
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'MelBand Roformer Kim | Big Beta 5e FT by unwa' : 'melband_roformer_big_beta5e.ckpt', |
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'MelBand Roformer | Big Beta 6 by unwa' : 'melband_roformer_big_beta6.ckpt', |
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'BS Roformer | Chorus Male-Female by Sucial' : 'model_chorus_bs_roformer_ep_267_sdr_24.1275.ckpt', |
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'BS Roformer | Male-Female by aufr33' : 'bs_roformer_male_female_by_aufr33_sdr_7.2889.ckpt', |
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'MelBand Roformer | Aspiration by Sucial' : 'aspiration_mel_band_roformer_sdr_18.9845.ckpt', |
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'MelBand Roformer | Aspiration Less Aggressive by Sucial' : 'aspiration_mel_band_roformer_less_aggr_sdr_18.1201.ckpt', |
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'MelBand Roformer | Bleed Suppressor V1 by unwa-97chris' : 'mel_band_roformer_bleed_suppressor_v1.ckpt' |
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} |
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mdx23c_models = [ |
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'MDX23C_D1581.ckpt', |
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'MDX23C-8KFFT-InstVoc_HQ.ckpt', |
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'MDX23C-8KFFT-InstVoc_HQ_2.ckpt', |
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'MDX23C-De-Reverb-aufr33-jarredou.ckpt', |
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'MDX23C-DrumSep-aufr33-jarredou.ckpt' |
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] |
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mdxnet_models = [ |
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'UVR-MDX-NET-Inst_full_292.onnx', |
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'UVR-MDX-NET_Inst_187_beta.onnx', |
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'UVR-MDX-NET_Inst_82_beta.onnx', |
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'UVR-MDX-NET_Inst_90_beta.onnx', |
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'UVR-MDX-NET_Main_340.onnx', |
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'UVR-MDX-NET_Main_390.onnx', |
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'UVR-MDX-NET_Main_406.onnx', |
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'UVR-MDX-NET_Main_427.onnx', |
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'UVR-MDX-NET_Main_438.onnx', |
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'UVR-MDX-NET-Inst_HQ_1.onnx', |
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'UVR-MDX-NET-Inst_HQ_2.onnx', |
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'UVR-MDX-NET-Inst_HQ_3.onnx', |
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'UVR-MDX-NET-Inst_HQ_4.onnx', |
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'UVR-MDX-NET-Inst_HQ_5.onnx', |
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'UVR_MDXNET_Main.onnx', |
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'UVR-MDX-NET-Inst_Main.onnx', |
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'UVR_MDXNET_1_9703.onnx', |
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'UVR_MDXNET_2_9682.onnx', |
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'UVR_MDXNET_3_9662.onnx', |
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'UVR-MDX-NET-Inst_1.onnx', |
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'UVR-MDX-NET-Inst_2.onnx', |
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'UVR-MDX-NET-Inst_3.onnx', |
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'UVR_MDXNET_KARA.onnx', |
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'UVR_MDXNET_KARA_2.onnx', |
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'UVR_MDXNET_9482.onnx', |
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'UVR-MDX-NET-Voc_FT.onnx', |
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'Kim_Vocal_1.onnx', |
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'Kim_Vocal_2.onnx', |
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'Kim_Inst.onnx', |
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'Reverb_HQ_By_FoxJoy.onnx', |
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'UVR-MDX-NET_Crowd_HQ_1.onnx', |
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'kuielab_a_vocals.onnx', |
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'kuielab_a_other.onnx', |
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'kuielab_a_bass.onnx', |
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'kuielab_a_drums.onnx', |
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'kuielab_b_vocals.onnx', |
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'kuielab_b_other.onnx', |
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'kuielab_b_bass.onnx', |
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'kuielab_b_drums.onnx', |
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] |
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vrarch_models = [ |
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'1_HP-UVR.pth', |
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'2_HP-UVR.pth', |
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'3_HP-Vocal-UVR.pth', |
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'4_HP-Vocal-UVR.pth', |
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'5_HP-Karaoke-UVR.pth', |
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'6_HP-Karaoke-UVR.pth', |
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'7_HP2-UVR.pth', |
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'8_HP2-UVR.pth', |
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'9_HP2-UVR.pth', |
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'10_SP-UVR-2B-32000-1.pth', |
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'11_SP-UVR-2B-32000-2.pth', |
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'12_SP-UVR-3B-44100.pth', |
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'13_SP-UVR-4B-44100-1.pth', |
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'14_SP-UVR-4B-44100-2.pth', |
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'15_SP-UVR-MID-44100-1.pth', |
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'16_SP-UVR-MID-44100-2.pth', |
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'17_HP-Wind_Inst-UVR.pth', |
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'UVR-De-Echo-Aggressive.pth', |
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'UVR-De-Echo-Normal.pth', |
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'UVR-DeEcho-DeReverb.pth', |
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'UVR-De-Reverb-aufr33-jarredou.pth', |
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'UVR-DeNoise-Lite.pth', |
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'UVR-DeNoise.pth', |
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'UVR-BVE-4B_SN-44100-1.pth', |
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'MGM_HIGHEND_v4.pth', |
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'MGM_LOWEND_A_v4.pth', |
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'MGM_LOWEND_B_v4.pth', |
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'MGM_MAIN_v4.pth', |
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] |
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demucs_models = [ |
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'htdemucs_ft.yaml', |
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'htdemucs_6s.yaml', |
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'htdemucs.yaml', |
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'hdemucs_mmi.yaml', |
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] |
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output_format = [ |
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'wav', |
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'flac', |
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'mp3', |
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'ogg', |
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'opus', |
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'm4a', |
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'aiff', |
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'ac3' |
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] |
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found_files = [] |
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logs = [] |
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out_dir = "./outputs" |
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models_dir = "./models" |
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extensions = (".wav", ".flac", ".mp3", ".ogg", ".opus", ".m4a", ".aiff", ".ac3") |
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def load_config_presence(): |
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with open(config_file, "r", encoding="utf8") as file: |
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config = json.load(file) |
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return config["discord_presence"] |
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def initialize_presence(): |
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if load_config_presence(): |
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RPCManager.start_presence() |
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initialize_presence() |
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def download_audio(url, output_dir="ytdl"): |
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os.makedirs(output_dir, exist_ok=True) |
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ydl_opts = { |
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'format': 'bestaudio/best', |
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'postprocessors': [{ |
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'key': 'FFmpegExtractAudio', |
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'preferredcodec': 'wav', |
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'preferredquality': '32', |
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}], |
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'outtmpl': os.path.join(output_dir, '%(title)s.%(ext)s'), |
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'postprocessor_args': [ |
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'-acodec', 'pcm_f32le' |
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], |
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} |
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try: |
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with yt_dlp.YoutubeDL(ydl_opts) as ydl: |
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info = ydl.extract_info(url, download=False) |
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video_title = info['title'] |
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ydl.download([url]) |
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file_path = os.path.join(output_dir, f"{video_title}.wav") |
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if os.path.exists(file_path): |
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return os.path.abspath(file_path) |
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else: |
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raise Exception("Something went wrong") |
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except Exception as e: |
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raise Exception(f"Error extracting audio with yt-dlp: {str(e)}") |
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def leaderboard(list_filter): |
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try: |
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result = subprocess.run( |
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[separator_location, "-l", f"--list_filter={list_filter}"], |
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capture_output=True, |
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text=True, |
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) |
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if result.returncode != 0: |
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return f"Error: {result.stderr}" |
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return "<table border='1'>" + "".join( |
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f"<tr style='{'font-weight: bold; font-size: 1.2em;' if i == 0 else ''}'>" + |
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"".join(f"<td>{cell}</td>" for cell in re.split(r"\s{2,}", line.strip())) + |
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"</tr>" |
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for i, line in enumerate(re.findall(r"^(?!-+)(.+)$", result.stdout.strip(), re.MULTILINE)) |
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) + "</table>" |
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except Exception as e: |
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return f"Error: {e}" |
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@track_presence("Performing BS/Mel Roformer Separation") |
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@spaces.GPU(duration=600) |
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def roformer_separator(audio, model_key, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress(track_tqdm=True)): |
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base_name = os.path.splitext(os.path.basename(audio))[0] |
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roformer_model = roformer_models[model_key] |
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try: |
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separator = Separator( |
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log_level=logging.WARNING, |
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model_file_dir=models_dir, |
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output_dir=out_dir, |
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output_format=out_format, |
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use_autocast=use_autocast, |
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normalization_threshold=norm_thresh, |
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amplification_threshold=amp_thresh, |
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output_single_stem=single_stem, |
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mdxc_params={ |
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"segment_size": segment_size, |
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"override_model_segment_size": override_seg_size, |
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"batch_size": batch_size, |
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"overlap": overlap, |
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} |
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) |
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progress(0.2, desc="Loading model...") |
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separator.load_model(model_filename=roformer_model) |
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progress(0.7, desc="Separating audio...") |
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separation = separator.separate(audio) |
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stems = [os.path.join(out_dir, file_name) for file_name in separation] |
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if single_stem.strip(): |
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return stems[0], None |
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return stems[0], stems[1] |
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except Exception as e: |
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raise RuntimeError(f"Roformer separation failed: {e}") from e |
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@track_presence("Performing MDXC Separationn") |
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@spaces.GPU(duration=600) |
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def mdxc_separator(audio, model, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress(track_tqdm=True)): |
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base_name = os.path.splitext(os.path.basename(audio))[0] |
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try: |
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separator = Separator( |
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log_level=logging.WARNING, |
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model_file_dir=models_dir, |
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output_dir=out_dir, |
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output_format=out_format, |
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use_autocast=use_autocast, |
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normalization_threshold=norm_thresh, |
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amplification_threshold=amp_thresh, |
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output_single_stem=single_stem, |
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mdxc_params={ |
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"segment_size": segment_size, |
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"override_model_segment_size": override_seg_size, |
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"batch_size": batch_size, |
|
|
"overlap": overlap, |
|
|
} |
|
|
) |
|
|
|
|
|
progress(0.2, desc="Loading model...") |
|
|
separator.load_model(model_filename=model) |
|
|
|
|
|
progress(0.7, desc="Separating audio...") |
|
|
separation = separator.separate(audio) |
|
|
|
|
|
stems = [os.path.join(out_dir, file_name) for file_name in separation] |
|
|
|
|
|
if single_stem.strip(): |
|
|
return stems[0], None |
|
|
|
|
|
return stems[0], stems[1] |
|
|
|
|
|
except Exception as e: |
|
|
raise RuntimeError(f"MDX23C separation failed: {e}") from e |
|
|
|
|
|
@track_presence("Performing MDX-NET Separation") |
|
|
@spaces.GPU(duration=600) |
|
|
def mdxnet_separator(audio, model, out_format, hop_length, segment_size, denoise, overlap, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress(track_tqdm=True)): |
|
|
base_name = os.path.splitext(os.path.basename(audio))[0] |
|
|
try: |
|
|
separator = Separator( |
|
|
log_level=logging.WARNING, |
|
|
model_file_dir=models_dir, |
|
|
output_dir=out_dir, |
|
|
output_format=out_format, |
|
|
use_autocast=use_autocast, |
|
|
normalization_threshold=norm_thresh, |
|
|
amplification_threshold=amp_thresh, |
|
|
output_single_stem=single_stem, |
|
|
mdx_params={ |
|
|
"hop_length": hop_length, |
|
|
"segment_size": segment_size, |
|
|
"overlap": overlap, |
|
|
"batch_size": batch_size, |
|
|
"enable_denoise": denoise, |
|
|
} |
|
|
) |
|
|
|
|
|
progress(0.2, desc="Loading model...") |
|
|
separator.load_model(model_filename=model) |
|
|
|
|
|
progress(0.7, desc="Separating audio...") |
|
|
separation = separator.separate(audio) |
|
|
|
|
|
stems = [os.path.join(out_dir, file_name) for file_name in separation] |
|
|
|
|
|
if single_stem.strip(): |
|
|
return stems[0], None |
|
|
|
|
|
return stems[0], stems[1] |
|
|
|
|
|
except Exception as e: |
|
|
raise RuntimeError(f"MDX-NET separation failed: {e}") from e |
|
|
|
|
|
@track_presence("Performing VR Arch Separation") |
|
|
@spaces.GPU(duration=600) |
|
|
def vrarch_separator(audio, model, out_format, window_size, aggression, tta, post_process, post_process_threshold, high_end_process, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress(track_tqdm=True)): |
|
|
base_name = os.path.splitext(os.path.basename(audio))[0] |
|
|
try: |
|
|
separator = Separator( |
|
|
log_level=logging.WARNING, |
|
|
model_file_dir=models_dir, |
|
|
output_dir=out_dir, |
|
|
output_format=out_format, |
|
|
use_autocast=use_autocast, |
|
|
normalization_threshold=norm_thresh, |
|
|
amplification_threshold=amp_thresh, |
|
|
output_single_stem=single_stem, |
|
|
vr_params={ |
|
|
"batch_size": batch_size, |
|
|
"window_size": window_size, |
|
|
"aggression": aggression, |
|
|
"enable_tta": tta, |
|
|
"enable_post_process": post_process, |
|
|
"post_process_threshold": post_process_threshold, |
|
|
"high_end_process": high_end_process, |
|
|
} |
|
|
) |
|
|
|
|
|
progress(0.2, desc="Loading model...") |
|
|
separator.load_model(model_filename=model) |
|
|
|
|
|
progress(0.7, desc="Separating audio...") |
|
|
separation = separator.separate(audio) |
|
|
|
|
|
stems = [os.path.join(out_dir, file_name) for file_name in separation] |
|
|
|
|
|
if single_stem.strip(): |
|
|
return stems[0], None |
|
|
|
|
|
return stems[0], stems[1] |
|
|
|
|
|
except Exception as e: |
|
|
raise RuntimeError(f"VR ARCH separation failed: {e}") from e |
|
|
|
|
|
@track_presence("Performing Demucs Separation") |
|
|
@spaces.GPU(duration=600) |
|
|
def demucs_separator(audio, model, out_format, shifts, segment_size, segments_enabled, overlap, batch_size, norm_thresh, amp_thresh, progress=gr.Progress(track_tqdm=True)): |
|
|
base_name = os.path.splitext(os.path.basename(audio))[0] |
|
|
try: |
|
|
separator = Separator( |
|
|
log_level=logging.WARNING, |
|
|
model_file_dir=models_dir, |
|
|
output_dir=out_dir, |
|
|
output_format=out_format, |
|
|
use_autocast=use_autocast, |
|
|
normalization_threshold=norm_thresh, |
|
|
amplification_threshold=amp_thresh, |
|
|
demucs_params={ |
|
|
"batch_size": batch_size, |
|
|
"segment_size": segment_size, |
|
|
"shifts": shifts, |
|
|
"overlap": overlap, |
|
|
"segments_enabled": segments_enabled, |
|
|
} |
|
|
) |
|
|
|
|
|
progress(0.2, desc="Loading model...") |
|
|
separator.load_model(model_filename=model) |
|
|
|
|
|
progress(0.7, desc="Separating audio...") |
|
|
separation = separator.separate(audio) |
|
|
|
|
|
stems = [os.path.join(out_dir, file_name) for file_name in separation] |
|
|
|
|
|
if model == "htdemucs_6s.yaml": |
|
|
return stems[0], stems[1], stems[2], stems[3], stems[4], stems[5] |
|
|
else: |
|
|
return stems[0], stems[1], stems[2], stems[3], None, None |
|
|
|
|
|
except Exception as e: |
|
|
raise RuntimeError(f"Demucs separation failed: {e}") from e |
|
|
|
|
|
def update_stems(model): |
|
|
if model == "htdemucs_6s.yaml": |
|
|
return gr.update(visible=True) |
|
|
else: |
|
|
return gr.update(visible=False) |
|
|
|
|
|
@track_presence("Performing BS/Mel Roformer Batch Separation") |
|
|
@spaces.GPU(duration=600) |
|
|
def roformer_batch(path_input, path_output, model_key, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh, single_stem): |
|
|
found_files.clear() |
|
|
logs.clear() |
|
|
roformer_model = roformer_models[model_key] |
|
|
|
|
|
for audio_files in os.listdir(path_input): |
|
|
if audio_files.endswith(extensions): |
|
|
found_files.append(audio_files) |
|
|
total_files = len(found_files) |
|
|
|
|
|
if total_files == 0: |
|
|
logs.append("No valid audio files.") |
|
|
yield "\n".join(logs) |
|
|
else: |
|
|
logs.append(f"{total_files} audio files found") |
|
|
found_files.sort() |
|
|
|
|
|
for audio_files in found_files: |
|
|
file_path = os.path.join(path_input, audio_files) |
|
|
base_name = os.path.splitext(os.path.basename(file_path))[0] |
|
|
try: |
|
|
separator = Separator( |
|
|
log_level=logging.WARNING, |
|
|
model_file_dir=models_dir, |
|
|
output_dir=path_output, |
|
|
output_format=out_format, |
|
|
use_autocast=use_autocast, |
|
|
normalization_threshold=norm_thresh, |
|
|
amplification_threshold=amp_thresh, |
|
|
output_single_stem=single_stem, |
|
|
mdxc_params={ |
|
|
"segment_size": segment_size, |
|
|
"override_model_segment_size": override_seg_size, |
|
|
"batch_size": batch_size, |
|
|
"overlap": overlap, |
|
|
} |
|
|
) |
|
|
|
|
|
logs.append("Loading model...") |
|
|
yield "\n".join(logs) |
|
|
separator.load_model(model_filename=roformer_model) |
|
|
|
|
|
logs.append(f"Separating file: {audio_files}") |
|
|
yield "\n".join(logs) |
|
|
separator.separate(file_path) |
|
|
logs.append(f"File: {audio_files} separated!") |
|
|
yield "\n".join(logs) |
|
|
except Exception as e: |
|
|
raise RuntimeError(f"Roformer batch separation failed: {e}") from e |
|
|
|
|
|
@track_presence("Performing MDXC Batch Separation") |
|
|
@spaces.GPU(duration=600) |
|
|
def mdx23c_batch(path_input, path_output, model, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh, single_stem): |
|
|
found_files.clear() |
|
|
logs.clear() |
|
|
|
|
|
for audio_files in os.listdir(path_input): |
|
|
if audio_files.endswith(extensions): |
|
|
found_files.append(audio_files) |
|
|
total_files = len(found_files) |
|
|
|
|
|
if total_files == 0: |
|
|
logs.append("No valid audio files.") |
|
|
yield "\n".join(logs) |
|
|
else: |
|
|
logs.append(f"{total_files} audio files found") |
|
|
found_files.sort() |
|
|
|
|
|
for audio_files in found_files: |
|
|
file_path = os.path.join(path_input, audio_files) |
|
|
base_name = os.path.splitext(os.path.basename(file_path))[0] |
|
|
try: |
|
|
separator = Separator( |
|
|
log_level=logging.WARNING, |
|
|
model_file_dir=models_dir, |
|
|
output_dir=path_output, |
|
|
output_format=out_format, |
|
|
use_autocast=use_autocast, |
|
|
normalization_threshold=norm_thresh, |
|
|
amplification_threshold=amp_thresh, |
|
|
output_single_stem=single_stem, |
|
|
mdxc_params={ |
|
|
"segment_size": segment_size, |
|
|
"override_model_segment_size": override_seg_size, |
|
|
"batch_size": batch_size, |
|
|
"overlap": overlap, |
|
|
} |
|
|
) |
|
|
|
|
|
logs.append("Loading model...") |
|
|
yield "\n".join(logs) |
|
|
separator.load_model(model_filename=model) |
|
|
|
|
|
logs.append(f"Separating file: {audio_files}") |
|
|
yield "\n".join(logs) |
|
|
separator.separate(file_path) |
|
|
logs.append(f"File: {audio_files} separated!") |
|
|
yield "\n".join(logs) |
|
|
except Exception as e: |
|
|
raise RuntimeError(f"Roformer batch separation failed: {e}") from e |
|
|
|
|
|
@track_presence("Performing MDX-NET Batch Separation") |
|
|
@spaces.GPU(duration=600) |
|
|
def mdxnet_batch(path_input, path_output, model, out_format, hop_length, segment_size, denoise, overlap, batch_size, norm_thresh, amp_thresh, single_stem): |
|
|
found_files.clear() |
|
|
logs.clear() |
|
|
|
|
|
for audio_files in os.listdir(path_input): |
|
|
if audio_files.endswith(extensions): |
|
|
found_files.append(audio_files) |
|
|
total_files = len(found_files) |
|
|
|
|
|
if total_files == 0: |
|
|
logs.append("No valid audio files.") |
|
|
yield "\n".join(logs) |
|
|
else: |
|
|
logs.append(f"{total_files} audio files found") |
|
|
found_files.sort() |
|
|
|
|
|
for audio_files in found_files: |
|
|
file_path = os.path.join(path_input, audio_files) |
|
|
base_name = os.path.splitext(os.path.basename(file_path))[0] |
|
|
try: |
|
|
separator = Separator( |
|
|
log_level=logging.WARNING, |
|
|
model_file_dir=models_dir, |
|
|
output_dir=path_output, |
|
|
output_format=out_format, |
|
|
use_autocast=use_autocast, |
|
|
normalization_threshold=norm_thresh, |
|
|
amplification_threshold=amp_thresh, |
|
|
output_single_stem=single_stem, |
|
|
mdx_params={ |
|
|
"hop_length": hop_length, |
|
|
"segment_size": segment_size, |
|
|
"overlap": overlap, |
|
|
"batch_size": batch_size, |
|
|
"enable_denoise": denoise, |
|
|
} |
|
|
) |
|
|
|
|
|
logs.append("Loading model...") |
|
|
yield "\n".join(logs) |
|
|
separator.load_model(model_filename=model) |
|
|
|
|
|
logs.append(f"Separating file: {audio_files}") |
|
|
yield "\n".join(logs) |
|
|
separator.separate(file_path) |
|
|
logs.append(f"File: {audio_files} separated!") |
|
|
yield "\n".join(logs) |
|
|
except Exception as e: |
|
|
raise RuntimeError(f"Roformer batch separation failed: {e}") from e |
|
|
|
|
|
@track_presence("Performing VR Arch Batch Separation") |
|
|
@spaces.GPU(duration=600) |
|
|
def vrarch_batch(path_input, path_output, model, out_format, window_size, aggression, tta, post_process, post_process_threshold, high_end_process, batch_size, norm_thresh, amp_thresh, single_stem): |
|
|
found_files.clear() |
|
|
logs.clear() |
|
|
|
|
|
for audio_files in os.listdir(path_input): |
|
|
if audio_files.endswith(extensions): |
|
|
found_files.append(audio_files) |
|
|
total_files = len(found_files) |
|
|
|
|
|
if total_files == 0: |
|
|
logs.append("No valid audio files.") |
|
|
yield "\n".join(logs) |
|
|
else: |
|
|
logs.append(f"{total_files} audio files found") |
|
|
found_files.sort() |
|
|
|
|
|
for audio_files in found_files: |
|
|
file_path = os.path.join(path_input, audio_files) |
|
|
base_name = os.path.splitext(os.path.basename(file_path))[0] |
|
|
try: |
|
|
separator = Separator( |
|
|
log_level=logging.WARNING, |
|
|
model_file_dir=models_dir, |
|
|
output_dir=path_output, |
|
|
output_format=out_format, |
|
|
use_autocast=use_autocast, |
|
|
normalization_threshold=norm_thresh, |
|
|
amplification_threshold=amp_thresh, |
|
|
output_single_stem=single_stem, |
|
|
vr_params={ |
|
|
"batch_size": batch_size, |
|
|
"window_size": window_size, |
|
|
"aggression": aggression, |
|
|
"enable_tta": tta, |
|
|
"enable_post_process": post_process, |
|
|
"post_process_threshold": post_process_threshold, |
|
|
"high_end_process": high_end_process, |
|
|
} |
|
|
) |
|
|
|
|
|
logs.append("Loading model...") |
|
|
yield "\n".join(logs) |
|
|
separator.load_model(model_filename=model) |
|
|
|
|
|
logs.append(f"Separating file: {audio_files}") |
|
|
yield "\n".join(logs) |
|
|
separator.separate(file_path) |
|
|
logs.append(f"File: {audio_files} separated!") |
|
|
yield "\n".join(logs) |
|
|
except Exception as e: |
|
|
raise RuntimeError(f"Roformer batch separation failed: {e}") from e |
|
|
|
|
|
@track_presence("Performing Demucs Batch Separation") |
|
|
@spaces.GPU(duration=600) |
|
|
def demucs_batch(path_input, path_output, model, out_format, shifts, segment_size, segments_enabled, overlap, batch_size, norm_thresh, amp_thresh): |
|
|
found_files.clear() |
|
|
logs.clear() |
|
|
|
|
|
for audio_files in os.listdir(path_input): |
|
|
if audio_files.endswith(extensions): |
|
|
found_files.append(audio_files) |
|
|
total_files = len(found_files) |
|
|
|
|
|
if total_files == 0: |
|
|
logs.append("No valid audio files.") |
|
|
yield "\n".join(logs) |
|
|
else: |
|
|
logs.append(f"{total_files} audio files found") |
|
|
found_files.sort() |
|
|
|
|
|
for audio_files in found_files: |
|
|
file_path = os.path.join(path_input, audio_files) |
|
|
try: |
|
|
separator = Separator( |
|
|
log_level=logging.WARNING, |
|
|
model_file_dir=models_dir, |
|
|
output_dir=path_output, |
|
|
output_format=out_format, |
|
|
use_autocast=use_autocast, |
|
|
normalization_threshold=norm_thresh, |
|
|
amplification_threshold=amp_thresh, |
|
|
demucs_params={ |
|
|
"batch_size": batch_size, |
|
|
"segment_size": segment_size, |
|
|
"shifts": shifts, |
|
|
"overlap": overlap, |
|
|
"segments_enabled": segments_enabled, |
|
|
} |
|
|
) |
|
|
|
|
|
logs.append("Loading model...") |
|
|
yield "\n".join(logs) |
|
|
separator.load_model(model_filename=model) |
|
|
|
|
|
logs.append(f"Separating file: {audio_files}") |
|
|
yield "\n".join(logs) |
|
|
separator.separate(file_path) |
|
|
logs.append(f"File: {audio_files} separated!") |
|
|
yield "\n".join(logs) |
|
|
except Exception as e: |
|
|
raise RuntimeError(f"Roformer batch separation failed: {e}") from e |
|
|
|
|
|
with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 UVR5 UI 🎵") as app: |
|
|
gr.Markdown("<h1> 🎵 UVR5 UI 🎵 </h1>") |
|
|
gr.Markdown(i18n("If you liked this HF Space you can give me a ❤️")) |
|
|
gr.Markdown(i18n("Try UVR5 UI using Colab [here](https://colab.research.google.com/github/Eddycrack864/UVR5-UI/blob/main/UVR_UI.ipynb)")) |
|
|
with gr.Tabs(): |
|
|
with gr.TabItem("BS/Mel Roformer"): |
|
|
with gr.Row(): |
|
|
roformer_model = gr.Dropdown( |
|
|
label = i18n("Select the model"), |
|
|
choices = list(roformer_models.keys()), |
|
|
value = lambda : None, |
|
|
interactive = True |
|
|
) |
|
|
roformer_output_format = gr.Dropdown( |
|
|
label = i18n("Select the output format"), |
|
|
choices = output_format, |
|
|
value = lambda : None, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Accordion(i18n("Advanced settings"), open = False): |
|
|
with gr.Group(): |
|
|
with gr.Row(): |
|
|
roformer_segment_size = gr.Slider( |
|
|
label = i18n("Segment size"), |
|
|
info = i18n("Larger consumes more resources, but may give better results"), |
|
|
minimum = 32, |
|
|
maximum = 4000, |
|
|
step = 32, |
|
|
value = 256, |
|
|
interactive = True |
|
|
) |
|
|
roformer_override_segment_size = gr.Checkbox( |
|
|
label = i18n("Override segment size"), |
|
|
info = i18n("Override model default segment size instead of using the model default value"), |
|
|
value = False, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
roformer_overlap = gr.Slider( |
|
|
label = i18n("Overlap"), |
|
|
info = i18n("Amount of overlap between prediction windows"), |
|
|
minimum = 2, |
|
|
maximum = 10, |
|
|
step = 1, |
|
|
value = 8, |
|
|
interactive = True |
|
|
) |
|
|
roformer_batch_size = gr.Slider( |
|
|
label = i18n("Batch size"), |
|
|
info = i18n("Larger consumes more RAM but may process slightly faster"), |
|
|
minimum = 1, |
|
|
maximum = 16, |
|
|
step = 1, |
|
|
value = 1, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
roformer_normalization_threshold = gr.Slider( |
|
|
label = i18n("Normalization threshold"), |
|
|
info = i18n("The threshold for audio normalization"), |
|
|
minimum = 0.1, |
|
|
maximum = 1, |
|
|
step = 0.1, |
|
|
value = 0.9, |
|
|
interactive = True |
|
|
) |
|
|
roformer_amplification_threshold = gr.Slider( |
|
|
label = i18n("Amplification threshold"), |
|
|
info = i18n("The threshold for audio amplification"), |
|
|
minimum = 0.1, |
|
|
maximum = 1, |
|
|
step = 0.1, |
|
|
value = 0.7, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
roformer_single_stem = gr.Textbox( |
|
|
label = i18n("Output only single stem"), |
|
|
placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"), |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
roformer_audio = gr.Audio( |
|
|
label = i18n("Input audio"), |
|
|
type = "filepath", |
|
|
interactive = True |
|
|
) |
|
|
with gr.Accordion(i18n("Separation by link"), open = False): |
|
|
with gr.Row(): |
|
|
roformer_link = gr.Textbox( |
|
|
label = i18n("Link"), |
|
|
placeholder = i18n("Paste the link here"), |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)")) |
|
|
with gr.Row(): |
|
|
roformer_download_button = gr.Button( |
|
|
i18n("Download!"), |
|
|
variant = "primary" |
|
|
) |
|
|
|
|
|
roformer_download_button.click(download_audio, [roformer_link], [roformer_audio]) |
|
|
|
|
|
with gr.Accordion(i18n("Batch separation"), open = False): |
|
|
with gr.Row(): |
|
|
roformer_input_path = gr.Textbox( |
|
|
label = i18n("Input path"), |
|
|
placeholder = i18n("Place the input path here"), |
|
|
interactive = True |
|
|
) |
|
|
roformer_output_path = gr.Textbox( |
|
|
label = i18n("Output path"), |
|
|
placeholder = i18n("Place the output path here"), |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
roformer_bath_button = gr.Button(i18n("Separate!"), variant = "primary") |
|
|
with gr.Row(): |
|
|
roformer_info = gr.Textbox( |
|
|
label = i18n("Output information"), |
|
|
interactive = False |
|
|
) |
|
|
|
|
|
roformer_bath_button.click(roformer_batch, [roformer_input_path, roformer_output_path, roformer_model, roformer_output_format, roformer_segment_size, roformer_override_segment_size, roformer_overlap, roformer_batch_size, roformer_normalization_threshold, roformer_amplification_threshold, roformer_single_stem], [roformer_info]) |
|
|
|
|
|
with gr.Row(): |
|
|
roformer_button = gr.Button(i18n("Separate!"), variant = "primary") |
|
|
with gr.Row(): |
|
|
roformer_stem1 = gr.Audio( |
|
|
show_download_button = True, |
|
|
interactive = False, |
|
|
label = i18n("Stem 1"), |
|
|
type = "filepath" |
|
|
) |
|
|
roformer_stem2 = gr.Audio( |
|
|
show_download_button = True, |
|
|
interactive = False, |
|
|
label = i18n("Stem 2"), |
|
|
type = "filepath" |
|
|
) |
|
|
|
|
|
roformer_button.click(roformer_separator, [roformer_audio, roformer_model, roformer_output_format, roformer_segment_size, roformer_override_segment_size, roformer_overlap, roformer_batch_size, roformer_normalization_threshold, roformer_amplification_threshold, roformer_single_stem], [roformer_stem1, roformer_stem2]) |
|
|
|
|
|
with gr.TabItem("MDX23C"): |
|
|
with gr.Row(): |
|
|
mdx23c_model = gr.Dropdown( |
|
|
label = i18n("Select the model"), |
|
|
choices = mdx23c_models, |
|
|
value = lambda : None, |
|
|
interactive = True |
|
|
) |
|
|
mdx23c_output_format = gr.Dropdown( |
|
|
label = i18n("Select the output format"), |
|
|
choices = output_format, |
|
|
value = lambda : None, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Accordion(i18n("Advanced settings"), open = False): |
|
|
with gr.Group(): |
|
|
with gr.Row(): |
|
|
mdx23c_segment_size = gr.Slider( |
|
|
minimum = 32, |
|
|
maximum = 4000, |
|
|
step = 32, |
|
|
label = i18n("Segment size"), |
|
|
info = i18n("Larger consumes more resources, but may give better results"), |
|
|
value = 256, |
|
|
interactive = True |
|
|
) |
|
|
mdx23c_override_segment_size = gr.Checkbox( |
|
|
label = i18n("Override segment size"), |
|
|
info = i18n("Override model default segment size instead of using the model default value"), |
|
|
value = False, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
mdx23c_overlap = gr.Slider( |
|
|
minimum = 2, |
|
|
maximum = 50, |
|
|
step = 1, |
|
|
label = i18n("Overlap"), |
|
|
info = i18n("Amount of overlap between prediction windows"), |
|
|
value = 8, |
|
|
interactive = True |
|
|
) |
|
|
mdx23c_batch_size = gr.Slider( |
|
|
label = i18n("Batch size"), |
|
|
info = i18n("Larger consumes more RAM but may process slightly faster"), |
|
|
minimum = 1, |
|
|
maximum = 16, |
|
|
step = 1, |
|
|
value = 1, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
mdx23c_normalization_threshold = gr.Slider( |
|
|
label = i18n("Normalization threshold"), |
|
|
info = i18n("The threshold for audio normalization"), |
|
|
minimum = 0.1, |
|
|
maximum = 1, |
|
|
step = 0.1, |
|
|
value = 0.9, |
|
|
interactive = True |
|
|
) |
|
|
mdx23c_amplification_threshold = gr.Slider( |
|
|
label = i18n("Amplification threshold"), |
|
|
info = i18n("The threshold for audio amplification"), |
|
|
minimum = 0.1, |
|
|
maximum = 1, |
|
|
step = 0.1, |
|
|
value = 0.7, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
mdx23c_single_stem = gr.Textbox( |
|
|
label = i18n("Output only single stem"), |
|
|
placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"), |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
mdx23c_audio = gr.Audio( |
|
|
label = i18n("Input audio"), |
|
|
type = "filepath", |
|
|
interactive = True |
|
|
) |
|
|
with gr.Accordion(i18n("Separation by link"), open = False): |
|
|
with gr.Row(): |
|
|
mdx23c_link = gr.Textbox( |
|
|
label = i18n("Link"), |
|
|
placeholder = i18n("Paste the link here"), |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)")) |
|
|
with gr.Row(): |
|
|
mdx23c_download_button = gr.Button( |
|
|
i18n("Download!"), |
|
|
variant = "primary" |
|
|
) |
|
|
|
|
|
mdx23c_download_button.click(download_audio, [mdx23c_link], [mdx23c_audio]) |
|
|
|
|
|
with gr.Accordion(i18n("Batch separation"), open = False): |
|
|
with gr.Row(): |
|
|
mdx23c_input_path = gr.Textbox( |
|
|
label = i18n("Input path"), |
|
|
placeholder = i18n("Place the input path here"), |
|
|
interactive = True |
|
|
) |
|
|
mdx23c_output_path = gr.Textbox( |
|
|
label = i18n("Output path"), |
|
|
placeholder = i18n("Place the output path here"), |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
mdx23c_bath_button = gr.Button(i18n("Separate!"), variant = "primary") |
|
|
with gr.Row(): |
|
|
mdx23c_info = gr.Textbox( |
|
|
label = i18n("Output information"), |
|
|
interactive = False |
|
|
) |
|
|
|
|
|
mdx23c_bath_button.click(mdx23c_batch, [mdx23c_input_path, mdx23c_output_path, mdx23c_model, mdx23c_output_format, mdx23c_segment_size, mdx23c_override_segment_size, mdx23c_overlap, mdx23c_batch_size, mdx23c_normalization_threshold, mdx23c_amplification_threshold, mdx23c_single_stem], [mdx23c_info]) |
|
|
|
|
|
with gr.Row(): |
|
|
mdx23c_button = gr.Button(i18n("Separate!"), variant = "primary") |
|
|
with gr.Row(): |
|
|
mdx23c_stem1 = gr.Audio( |
|
|
show_download_button = True, |
|
|
interactive = False, |
|
|
label = i18n("Stem 1"), |
|
|
type = "filepath" |
|
|
) |
|
|
mdx23c_stem2 = gr.Audio( |
|
|
show_download_button = True, |
|
|
interactive = False, |
|
|
label = i18n("Stem 2"), |
|
|
type = "filepath" |
|
|
) |
|
|
|
|
|
mdx23c_button.click(mdxc_separator, [mdx23c_audio, mdx23c_model, mdx23c_output_format, mdx23c_segment_size, mdx23c_override_segment_size, mdx23c_overlap, mdx23c_batch_size, mdx23c_normalization_threshold, mdx23c_amplification_threshold, mdx23c_single_stem], [mdx23c_stem1, mdx23c_stem2]) |
|
|
|
|
|
with gr.TabItem("MDX-NET"): |
|
|
with gr.Row(): |
|
|
mdxnet_model = gr.Dropdown( |
|
|
label = i18n("Select the model"), |
|
|
choices = mdxnet_models, |
|
|
value = lambda : None, |
|
|
interactive = True |
|
|
) |
|
|
mdxnet_output_format = gr.Dropdown( |
|
|
label = i18n("Select the output format"), |
|
|
choices = output_format, |
|
|
value = lambda : None, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Accordion(i18n("Advanced settings"), open = False): |
|
|
with gr.Group(): |
|
|
with gr.Row(): |
|
|
mdxnet_hop_length = gr.Slider( |
|
|
label = i18n("Hop length"), |
|
|
info = i18n("Usually called stride in neural networks; only change if you know what you're doing"), |
|
|
minimum = 32, |
|
|
maximum = 2048, |
|
|
step = 32, |
|
|
value = 1024, |
|
|
interactive = True |
|
|
) |
|
|
mdxnet_segment_size = gr.Slider( |
|
|
minimum = 32, |
|
|
maximum = 4000, |
|
|
step = 32, |
|
|
label = i18n("Segment size"), |
|
|
info = i18n("Larger consumes more resources, but may give better results"), |
|
|
value = 256, |
|
|
interactive = True |
|
|
) |
|
|
mdxnet_denoise = gr.Checkbox( |
|
|
label = i18n("Denoise"), |
|
|
info = i18n("Enable denoising during separation"), |
|
|
value = True, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
mdxnet_overlap = gr.Slider( |
|
|
label = i18n("Overlap"), |
|
|
info = i18n("Amount of overlap between prediction windows"), |
|
|
minimum = 0.001, |
|
|
maximum = 0.999, |
|
|
step = 0.001, |
|
|
value = 0.25, |
|
|
interactive = True |
|
|
) |
|
|
mdxnet_batch_size = gr.Slider( |
|
|
label = i18n("Batch size"), |
|
|
info = i18n("Larger consumes more RAM but may process slightly faster"), |
|
|
minimum = 1, |
|
|
maximum = 16, |
|
|
step = 1, |
|
|
value = 1, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
mdxnet_normalization_threshold = gr.Slider( |
|
|
label = i18n("Normalization threshold"), |
|
|
info = i18n("The threshold for audio normalization"), |
|
|
minimum = 0.1, |
|
|
maximum = 1, |
|
|
step = 0.1, |
|
|
value = 0.9, |
|
|
interactive = True |
|
|
) |
|
|
mdxnet_amplification_threshold = gr.Slider( |
|
|
label = i18n("Amplification threshold"), |
|
|
info = i18n("The threshold for audio amplification"), |
|
|
minimum = 0.1, |
|
|
maximum = 1, |
|
|
step = 0.1, |
|
|
value = 0.7, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
mdxnet_single_stem = gr.Textbox( |
|
|
label = i18n("Output only single stem"), |
|
|
placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"), |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
mdxnet_audio = gr.Audio( |
|
|
label = i18n("Input audio"), |
|
|
type = "filepath", |
|
|
interactive = True |
|
|
) |
|
|
with gr.Accordion(i18n("Separation by link"), open = False): |
|
|
with gr.Row(): |
|
|
mdxnet_link = gr.Textbox( |
|
|
label = i18n("Link"), |
|
|
placeholder = i18n("Paste the link here"), |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)")) |
|
|
with gr.Row(): |
|
|
mdxnet_download_button = gr.Button( |
|
|
i18n("Download!"), |
|
|
variant = "primary" |
|
|
) |
|
|
|
|
|
mdxnet_download_button.click(download_audio, [mdxnet_link], [mdxnet_audio]) |
|
|
|
|
|
with gr.Accordion(i18n("Batch separation"), open = False): |
|
|
with gr.Row(): |
|
|
mdxnet_input_path = gr.Textbox( |
|
|
label = i18n("Input path"), |
|
|
placeholder = i18n("Place the input path here"), |
|
|
interactive = True |
|
|
) |
|
|
mdxnet_output_path = gr.Textbox( |
|
|
label = i18n("Output path"), |
|
|
placeholder = i18n("Place the output path here"), |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
mdxnet_bath_button = gr.Button(i18n("Separate!"), variant = "primary") |
|
|
with gr.Row(): |
|
|
mdxnet_info = gr.Textbox( |
|
|
label = i18n("Output information"), |
|
|
interactive = False |
|
|
) |
|
|
|
|
|
mdxnet_bath_button.click(mdxnet_batch, [mdxnet_input_path, mdxnet_output_path, mdxnet_model, mdxnet_output_format, mdxnet_hop_length, mdxnet_segment_size, mdxnet_denoise, mdxnet_overlap, mdxnet_batch_size, mdxnet_normalization_threshold, mdxnet_amplification_threshold, mdxnet_single_stem], [mdxnet_info]) |
|
|
|
|
|
with gr.Row(): |
|
|
mdxnet_button = gr.Button(i18n("Separate!"), variant = "primary") |
|
|
with gr.Row(): |
|
|
mdxnet_stem1 = gr.Audio( |
|
|
show_download_button = True, |
|
|
interactive = False, |
|
|
label = i18n("Stem 1"), |
|
|
type = "filepath" |
|
|
) |
|
|
mdxnet_stem2 = gr.Audio( |
|
|
show_download_button = True, |
|
|
interactive = False, |
|
|
label = i18n("Stem 2"), |
|
|
type = "filepath" |
|
|
) |
|
|
|
|
|
mdxnet_button.click(mdxnet_separator, [mdxnet_audio, mdxnet_model, mdxnet_output_format, mdxnet_hop_length, mdxnet_segment_size, mdxnet_denoise, mdxnet_overlap, mdxnet_batch_size, mdxnet_normalization_threshold, mdxnet_amplification_threshold, mdxnet_single_stem], [mdxnet_stem1, mdxnet_stem2]) |
|
|
|
|
|
with gr.TabItem("VR ARCH"): |
|
|
with gr.Row(): |
|
|
vrarch_model = gr.Dropdown( |
|
|
label = i18n("Select the model"), |
|
|
choices = vrarch_models, |
|
|
value = lambda : None, |
|
|
interactive = True |
|
|
) |
|
|
vrarch_output_format = gr.Dropdown( |
|
|
label = i18n("Select the output format"), |
|
|
choices = output_format, |
|
|
value = lambda : None, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Accordion(i18n("Advanced settings"), open = False): |
|
|
with gr.Group(): |
|
|
with gr.Row(): |
|
|
vrarch_window_size = gr.Slider( |
|
|
label = i18n("Window size"), |
|
|
info = i18n("Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality"), |
|
|
minimum=320, |
|
|
maximum=1024, |
|
|
step=32, |
|
|
value = 512, |
|
|
interactive = True |
|
|
) |
|
|
vrarch_agression = gr.Slider( |
|
|
minimum = 1, |
|
|
maximum = 50, |
|
|
step = 1, |
|
|
label = i18n("Agression"), |
|
|
info = i18n("Intensity of primary stem extraction"), |
|
|
value = 5, |
|
|
interactive = True |
|
|
) |
|
|
vrarch_tta = gr.Checkbox( |
|
|
label = i18n("TTA"), |
|
|
info = i18n("Enable Test-Time-Augmentation; slow but improves quality"), |
|
|
value = True, |
|
|
visible = True, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
vrarch_post_process = gr.Checkbox( |
|
|
label = i18n("Post process"), |
|
|
info = i18n("Identify leftover artifacts within vocal output; may improve separation for some songs"), |
|
|
value = False, |
|
|
visible = True, |
|
|
interactive = True |
|
|
) |
|
|
vrarch_post_process_threshold = gr.Slider( |
|
|
label = i18n("Post process threshold"), |
|
|
info = i18n("Threshold for post-processing"), |
|
|
minimum = 0.1, |
|
|
maximum = 0.3, |
|
|
step = 0.1, |
|
|
value = 0.2, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
vrarch_high_end_process = gr.Checkbox( |
|
|
label = i18n("High end process"), |
|
|
info = i18n("Mirror the missing frequency range of the output"), |
|
|
value = False, |
|
|
visible = True, |
|
|
interactive = True, |
|
|
) |
|
|
vrarch_batch_size = gr.Slider( |
|
|
label = i18n("Batch size"), |
|
|
info = i18n("Larger consumes more RAM but may process slightly faster"), |
|
|
minimum = 1, |
|
|
maximum = 16, |
|
|
step = 1, |
|
|
value = 1, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
vrarch_normalization_threshold = gr.Slider( |
|
|
label = i18n("Normalization threshold"), |
|
|
info = i18n("The threshold for audio normalization"), |
|
|
minimum = 0.1, |
|
|
maximum = 1, |
|
|
step = 0.1, |
|
|
value = 0.9, |
|
|
interactive = True |
|
|
) |
|
|
vrarch_amplification_threshold = gr.Slider( |
|
|
label = i18n("Amplification threshold"), |
|
|
info = i18n("The threshold for audio amplification"), |
|
|
minimum = 0.1, |
|
|
maximum = 1, |
|
|
step = 0.1, |
|
|
value = 0.7, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
vrarch_single_stem = gr.Textbox( |
|
|
label = i18n("Output only single stem"), |
|
|
placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"), |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
vrarch_audio = gr.Audio( |
|
|
label = i18n("Input audio"), |
|
|
type = "filepath", |
|
|
interactive = True |
|
|
) |
|
|
with gr.Accordion(i18n("Separation by link"), open = False): |
|
|
with gr.Row(): |
|
|
vrarch_link = gr.Textbox( |
|
|
label = i18n("Link"), |
|
|
placeholder = i18n("Paste the link here"), |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)")) |
|
|
with gr.Row(): |
|
|
vrarch_download_button = gr.Button( |
|
|
i18n("Download!"), |
|
|
variant = "primary" |
|
|
) |
|
|
|
|
|
vrarch_download_button.click(download_audio, [vrarch_link], [vrarch_audio]) |
|
|
|
|
|
with gr.Accordion(i18n("Batch separation"), open = False): |
|
|
with gr.Row(): |
|
|
vrarch_input_path = gr.Textbox( |
|
|
label = i18n("Input path"), |
|
|
placeholder = i18n("Place the input path here"), |
|
|
interactive = True |
|
|
) |
|
|
vrarch_output_path = gr.Textbox( |
|
|
label = i18n("Output path"), |
|
|
placeholder = i18n("Place the output path here"), |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
vrarch_bath_button = gr.Button(i18n("Separate!"), variant = "primary") |
|
|
with gr.Row(): |
|
|
vrarch_info = gr.Textbox( |
|
|
label = i18n("Output information"), |
|
|
interactive = False |
|
|
) |
|
|
|
|
|
vrarch_bath_button.click(vrarch_batch, [vrarch_input_path, vrarch_output_path, vrarch_model, vrarch_output_format, vrarch_window_size, vrarch_agression, vrarch_tta, vrarch_post_process, vrarch_post_process_threshold, vrarch_high_end_process, vrarch_batch_size, vrarch_normalization_threshold, vrarch_amplification_threshold, vrarch_single_stem], [vrarch_info]) |
|
|
|
|
|
with gr.Row(): |
|
|
vrarch_button = gr.Button(i18n("Separate!"), variant = "primary") |
|
|
with gr.Row(): |
|
|
vrarch_stem1 = gr.Audio( |
|
|
show_download_button = True, |
|
|
interactive = False, |
|
|
type = "filepath", |
|
|
label = i18n("Stem 1") |
|
|
) |
|
|
vrarch_stem2 = gr.Audio( |
|
|
show_download_button = True, |
|
|
interactive = False, |
|
|
type = "filepath", |
|
|
label = i18n("Stem 2") |
|
|
) |
|
|
|
|
|
vrarch_button.click(vrarch_separator, [vrarch_audio, vrarch_model, vrarch_output_format, vrarch_window_size, vrarch_agression, vrarch_tta, vrarch_post_process, vrarch_post_process_threshold, vrarch_high_end_process, vrarch_batch_size, vrarch_normalization_threshold, vrarch_amplification_threshold, vrarch_single_stem], [vrarch_stem1, vrarch_stem2]) |
|
|
|
|
|
with gr.TabItem("Demucs"): |
|
|
with gr.Row(): |
|
|
demucs_model = gr.Dropdown( |
|
|
label = i18n("Select the model"), |
|
|
choices = demucs_models, |
|
|
value = lambda : None, |
|
|
interactive = True |
|
|
) |
|
|
demucs_output_format = gr.Dropdown( |
|
|
label = i18n("Select the output format"), |
|
|
choices = output_format, |
|
|
value = lambda : None, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Accordion(i18n("Advanced settings"), open = False): |
|
|
with gr.Group(): |
|
|
with gr.Row(): |
|
|
demucs_shifts = gr.Slider( |
|
|
label = i18n("Shifts"), |
|
|
info = i18n("Number of predictions with random shifts, higher = slower but better quality"), |
|
|
minimum = 1, |
|
|
maximum = 20, |
|
|
step = 1, |
|
|
value = 2, |
|
|
interactive = True |
|
|
) |
|
|
demucs_segment_size = gr.Slider( |
|
|
label = i18n("Segment size"), |
|
|
info = i18n("Size of segments into which the audio is split. Higher = slower but better quality"), |
|
|
minimum = 1, |
|
|
maximum = 100, |
|
|
step = 1, |
|
|
value = 40, |
|
|
interactive = True |
|
|
) |
|
|
demucs_segments_enabled = gr.Checkbox( |
|
|
label = i18n("Segment-wise processing"), |
|
|
info = i18n("Enable segment-wise processing"), |
|
|
value = True, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
demucs_overlap = gr.Slider( |
|
|
label = i18n("Overlap"), |
|
|
info = i18n("Overlap between prediction windows. Higher = slower but better quality"), |
|
|
minimum=0.001, |
|
|
maximum=0.999, |
|
|
step=0.001, |
|
|
value = 0.25, |
|
|
interactive = True |
|
|
) |
|
|
demucs_batch_size = gr.Slider( |
|
|
label = i18n("Batch size"), |
|
|
info = i18n("Larger consumes more RAM but may process slightly faster"), |
|
|
minimum = 1, |
|
|
maximum = 16, |
|
|
step = 1, |
|
|
value = 1, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
demucs_normalization_threshold = gr.Slider( |
|
|
label = i18n("Normalization threshold"), |
|
|
info = i18n("The threshold for audio normalization"), |
|
|
minimum = 0.1, |
|
|
maximum = 1, |
|
|
step = 0.1, |
|
|
value = 0.9, |
|
|
interactive = True |
|
|
) |
|
|
demucs_amplification_threshold = gr.Slider( |
|
|
label = i18n("Amplification threshold"), |
|
|
info = i18n("The threshold for audio amplification"), |
|
|
minimum = 0.1, |
|
|
maximum = 1, |
|
|
step = 0.1, |
|
|
value = 0.7, |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
demucs_audio = gr.Audio( |
|
|
label = i18n("Input audio"), |
|
|
type = "filepath", |
|
|
interactive = True |
|
|
) |
|
|
with gr.Accordion(i18n("Separation by link"), open = False): |
|
|
with gr.Row(): |
|
|
demucs_link = gr.Textbox( |
|
|
label = i18n("Link"), |
|
|
placeholder = i18n("Paste the link here"), |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)")) |
|
|
with gr.Row(): |
|
|
demucs_download_button = gr.Button( |
|
|
i18n("Download!"), |
|
|
variant = "primary" |
|
|
) |
|
|
|
|
|
demucs_download_button.click(download_audio, [demucs_link], [demucs_audio]) |
|
|
|
|
|
with gr.Accordion(i18n("Batch separation"), open = False): |
|
|
with gr.Row(): |
|
|
demucs_input_path = gr.Textbox( |
|
|
label = i18n("Input path"), |
|
|
placeholder = i18n("Place the input path here"), |
|
|
interactive = True |
|
|
) |
|
|
demucs_output_path = gr.Textbox( |
|
|
label = i18n("Output path"), |
|
|
placeholder = i18n("Place the output path here"), |
|
|
interactive = True |
|
|
) |
|
|
with gr.Row(): |
|
|
demucs_bath_button = gr.Button(i18n("Separate!"), variant = "primary") |
|
|
with gr.Row(): |
|
|
demucs_info = gr.Textbox( |
|
|
label = i18n("Output information"), |
|
|
interactive = False |
|
|
) |
|
|
|
|
|
demucs_bath_button.click(demucs_batch, [demucs_input_path, demucs_output_path, demucs_model, demucs_output_format, demucs_shifts, demucs_segment_size, demucs_segments_enabled, demucs_overlap, demucs_batch_size, demucs_normalization_threshold, demucs_amplification_threshold], [demucs_info]) |
|
|
|
|
|
with gr.Row(): |
|
|
demucs_button = gr.Button(i18n("Separate!"), variant = "primary") |
|
|
with gr.Row(): |
|
|
demucs_stem1 = gr.Audio( |
|
|
show_download_button = True, |
|
|
interactive = False, |
|
|
type = "filepath", |
|
|
label = i18n("Stem 1") |
|
|
) |
|
|
demucs_stem2 = gr.Audio( |
|
|
show_download_button = True, |
|
|
interactive = False, |
|
|
type = "filepath", |
|
|
label = i18n("Stem 2") |
|
|
) |
|
|
with gr.Row(): |
|
|
demucs_stem3 = gr.Audio( |
|
|
show_download_button = True, |
|
|
interactive = False, |
|
|
type = "filepath", |
|
|
label = i18n("Stem 3") |
|
|
) |
|
|
demucs_stem4 = gr.Audio( |
|
|
show_download_button = True, |
|
|
interactive = False, |
|
|
type = "filepath", |
|
|
label = i18n("Stem 4") |
|
|
) |
|
|
with gr.Row(visible=False) as stem6: |
|
|
demucs_stem5 = gr.Audio( |
|
|
show_download_button = True, |
|
|
interactive = False, |
|
|
type = "filepath", |
|
|
label = i18n("Stem 5") |
|
|
) |
|
|
demucs_stem6 = gr.Audio( |
|
|
show_download_button = True, |
|
|
interactive = False, |
|
|
type = "filepath", |
|
|
label = i18n("Stem 6") |
|
|
) |
|
|
|
|
|
demucs_model.change(update_stems, inputs=[demucs_model], outputs=stem6) |
|
|
|
|
|
demucs_button.click(demucs_separator, [demucs_audio, demucs_model, demucs_output_format, demucs_shifts, demucs_segment_size, demucs_segments_enabled, demucs_overlap, demucs_batch_size, demucs_normalization_threshold, demucs_amplification_threshold], [demucs_stem1, demucs_stem2, demucs_stem3, demucs_stem4, demucs_stem5, demucs_stem6]) |
|
|
|
|
|
with gr.TabItem(i18n("Leaderboard")): |
|
|
with gr.Group(): |
|
|
with gr.Row(equal_height=True): |
|
|
list_filter = gr.Dropdown( |
|
|
label = i18n("List filter"), |
|
|
info = i18n("Filter and sort the model list by stem"), |
|
|
choices = ["vocals", "instrumental", "reverb", "echo", "noise", "crowd", "dry", "aspiration", "male", "woodwinds", "kick", "drums", "bass", "guitar", "piano", "other"], |
|
|
value = lambda : None |
|
|
) |
|
|
list_button = gr.Button(i18n("Show list!"), variant = "primary") |
|
|
output_list = gr.HTML(label = i18n("Leaderboard")) |
|
|
|
|
|
list_button.click(leaderboard, inputs=list_filter, outputs=output_list) |
|
|
|
|
|
with gr.TabItem(i18n("Themes")): |
|
|
themes_select = gr.Dropdown( |
|
|
label = i18n("Theme"), |
|
|
info = i18n("Select the theme you want to use. (Requires restarting the App)"), |
|
|
choices = loadThemes.get_list(), |
|
|
value = loadThemes.read_json(), |
|
|
visible = True |
|
|
) |
|
|
dummy_output = gr.Textbox(visible = False) |
|
|
|
|
|
themes_select.change( |
|
|
fn = loadThemes.select_theme, |
|
|
inputs = themes_select, |
|
|
outputs = [dummy_output] |
|
|
) |
|
|
|
|
|
with gr.TabItem(i18n("Credits")): |
|
|
gr.Markdown( |
|
|
""" |
|
|
UVR5 UI created by **[Eddycrack 864](https://github.com/Eddycrack864).** Join **[AI HUB](https://discord.gg/aihub)** community. |
|
|
* python-audio-separator by [beveradb](https://github.com/beveradb). |
|
|
* Special thanks to [Ilaria](https://github.com/TheStingerX) for hosting this space and help. |
|
|
* Thanks to [Mikus](https://github.com/cappuch) for the help with the code. |
|
|
* Thanks to [Nick088](https://huggingface.co/Nick088) for the help to fix roformers. |
|
|
* Thanks to [yt_dlp](https://github.com/yt-dlp/yt-dlp) devs. |
|
|
* Separation by link source code and improvements by [Blane187](https://huggingface.co/Blane187). |
|
|
* Thanks to [ArisDev](https://github.com/aris-py) for porting UVR5 UI to Kaggle and improvements. |
|
|
* Thanks to [Bebra777228](https://github.com/Bebra777228)'s code for guiding me to improve my code. |
|
|
* Thanks to Nick088, MrM0dZ, Ryouko-Yamanda65777, lucinamari, perariroswe, Enes, Léo and the_undead0 for helping translate UVR5 UI. |
|
|
* Thanks to vadigr123 for creating the images for the Discord Rich Presence. |
|
|
|
|
|
You can donate to the original UVR5 project [here](https://www.buymeacoffee.com/uvr5) |
|
|
""" |
|
|
) |
|
|
|
|
|
app.queue() |
|
|
app.launch() |
