import os import sys import subprocess import re import platform import torch import logging import yt_dlp import json import copy import spaces import gradio as gr import urllib.parse import assets.themes.loadThemes as loadThemes from audio_separator.separator import Separator from assets.i18n.i18n import I18nAuto from argparse import ArgumentParser from assets.presence.discord_presence import RPCManager, track_presence i18n = I18nAuto() now_dir = os.getcwd() sys.path.append(now_dir) config_file = os.path.join(now_dir, "assets", "config.json") models_file = os.path.join(now_dir, "assets", "models.json") default_settings_file = os.path.join(now_dir, "assets", "default_settings.json") custom_settings_file = os.path.join(now_dir, "assets", "custom_settings.json") device = "cuda" if torch.cuda.is_available() else "cpu" use_autocast = device == "cuda" if os.path.isdir("env"): if platform.system() == "Windows": python_location = ".\\env\\python.exe" separator_location = ".\\env\\Scripts\\audio-separator.exe" elif platform.system() == "Linux": python_location = "env/bin/python" separator_location = "env/bin/audio-separator" else: python_location = None separator_location = "audio-separator" #=========================# # Roformer Models # #=========================# roformer_models = { 'BS-Roformer-Viperx-1297': 'model_bs_roformer_ep_317_sdr_12.9755.ckpt', 'BS-Roformer-Viperx-1296': 'model_bs_roformer_ep_368_sdr_12.9628.ckpt', 'BS-Roformer-Viperx-1053': 'model_bs_roformer_ep_937_sdr_10.5309.ckpt', 'Mel-Roformer-Viperx-1143': 'model_mel_band_roformer_ep_3005_sdr_11.4360.ckpt', 'BS-Roformer-De-Reverb': 'deverb_bs_roformer_8_384dim_10depth.ckpt', 'Mel-Roformer-Crowd-Aufr33-Viperx': 'mel_band_roformer_crowd_aufr33_viperx_sdr_8.7144.ckpt', 'Mel-Roformer-Denoise-Aufr33': 'denoise_mel_band_roformer_aufr33_sdr_27.9959.ckpt', 'Mel-Roformer-Denoise-Aufr33-Aggr' : 'denoise_mel_band_roformer_aufr33_aggr_sdr_27.9768.ckpt', 'MelBand Roformer | Denoise-Debleed by Gabox' : 'mel_band_roformer_denoise_debleed_gabox.ckpt', 'Mel-Roformer-Karaoke-Aufr33-Viperx': 'mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt', 'MelBand Roformer | Karaoke by Gabox' : 'mel_band_roformer_karaoke_gabox.ckpt', 'MelBand Roformer | Karaoke by becruily' : 'mel_band_roformer_karaoke_becruily.ckpt', 'MelBand Roformer | Vocals by Kimberley Jensen' : 'vocals_mel_band_roformer.ckpt', 'MelBand Roformer Kim | FT by unwa' : 'mel_band_roformer_kim_ft_unwa.ckpt', 'MelBand Roformer Kim | FT 2 by unwa' : 'mel_band_roformer_kim_ft2_unwa.ckpt', 'MelBand Roformer Kim | FT 2 Bleedless by unwa' : 'mel_band_roformer_kim_ft2_bleedless_unwa.ckpt', 'MelBand Roformer Kim | FT 3 by unwa' : 'mel_band_roformer_kim_ft3_unwa.ckpt', 'MelBand Roformer Kim | Inst V1 by Unwa' : 'melband_roformer_inst_v1.ckpt', 'MelBand Roformer Kim | Inst V1 Plus by Unwa' : 'melband_roformer_inst_v1_plus.ckpt', 'MelBand Roformer Kim | Inst V1 (E) by Unwa' : 'melband_roformer_inst_v1e.ckpt', 'MelBand Roformer Kim | Inst V1 (E) Plus by Unwa' : 'melband_roformer_inst_v1e_plus.ckpt', 'MelBand Roformer Kim | Inst V2 by Unwa' : 'melband_roformer_inst_v2.ckpt', 'MelBand Roformer Kim | InstVoc Duality V1 by Unwa' : 'melband_roformer_instvoc_duality_v1.ckpt', 'MelBand Roformer Kim | InstVoc Duality V2 by Unwa' : 'melband_roformer_instvox_duality_v2.ckpt', 'MelBand Roformer | Vocals by becruily' : 'mel_band_roformer_vocals_becruily.ckpt', 'MelBand Roformer | Instrumental by becruily' : 'mel_band_roformer_instrumental_becruily.ckpt', 'MelBand Roformer | Vocals Fullness by Aname' : 'mel_band_roformer_vocal_fullness_aname.ckpt', 'BS Roformer | Vocals by Gabox' : 'bs_roformer_vocals_gabox.ckpt', 'MelBand Roformer | Vocals by Gabox' : 'mel_band_roformer_vocals_gabox.ckpt', 'MelBand Roformer | Vocals FV1 by Gabox' : 'mel_band_roformer_vocals_fv1_gabox.ckpt', 'MelBand Roformer | Vocals FV2 by Gabox' : 'mel_band_roformer_vocals_fv2_gabox.ckpt', 'MelBand Roformer | Vocals FV3 by Gabox' : 'mel_band_roformer_vocals_fv3_gabox.ckpt', 'MelBand Roformer | Vocals FV4 by Gabox' : 'mel_band_roformer_vocals_fv4_gabox.ckpt', 'MelBand Roformer | Instrumental by Gabox' : 'mel_band_roformer_instrumental_gabox.ckpt', 'MelBand Roformer | Instrumental 2 by Gabox' : 'mel_band_roformer_instrumental_2_gabox.ckpt', 'MelBand Roformer | Instrumental 3 by Gabox' : 'mel_band_roformer_instrumental_3_gabox.ckpt', 'MelBand Roformer | Instrumental Bleedless V1 by Gabox' : 'mel_band_roformer_instrumental_bleedless_v1_gabox.ckpt', 'MelBand Roformer | Instrumental Bleedless V2 by Gabox' : 'mel_band_roformer_instrumental_bleedless_v2_gabox.ckpt', 'MelBand Roformer | Instrumental Bleedless V3 by Gabox' : 'mel_band_roformer_instrumental_bleedless_v3_gabox.ckpt', 'MelBand Roformer | Instrumental Fullness V1 by Gabox' : 'mel_band_roformer_instrumental_fullness_v1_gabox.ckpt', 'MelBand Roformer | Instrumental Fullness V2 by Gabox' : 'mel_band_roformer_instrumental_fullness_v2_gabox.ckpt', 'MelBand Roformer | Instrumental Fullness V3 by Gabox' : 'mel_band_roformer_instrumental_fullness_v3_gabox.ckpt', 'MelBand Roformer | Instrumental Fullness Noisy V4 by Gabox' : 'mel_band_roformer_instrumental_fullness_noise_v4_gabox.ckpt', 'MelBand Roformer | INSTV5 by Gabox' : 'mel_band_roformer_instrumental_instv5_gabox.ckpt', 'MelBand Roformer | INSTV5N by Gabox' : 'mel_band_roformer_instrumental_instv5n_gabox.ckpt', 'MelBand Roformer | INSTV6 by Gabox' : 'mel_band_roformer_instrumental_instv6_gabox.ckpt', 'MelBand Roformer | INSTV6N by Gabox' : 'mel_band_roformer_instrumental_instv6n_gabox.ckpt', 'MelBand Roformer | INSTV7 by Gabox' : 'mel_band_roformer_instrumental_instv7_gabox.ckpt', 'MelBand Roformer | INSTV7N by Gabox' : 'mel_band_roformer_instrumental_instv7n_gabox.ckpt', 'MelBand Roformer | INSTV8 by Gabox' : 'mel_band_roformer_instrumental_instv8_gabox.ckpt', 'MelBand Roformer | INSTV8N by Gabox' : 'mel_band_roformer_instrumental_instv8n_gabox.ckpt', 'MelBand Roformer | FVX by Gabox' : 'mel_band_roformer_instrumental_fvx_gabox.ckpt', 'MelBand Roformer | De-Reverb by anvuew' : 'dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt', 'MelBand Roformer | De-Reverb Less Aggressive by anvuew' : 'dereverb_mel_band_roformer_less_aggressive_anvuew_sdr_18.8050.ckpt', 'MelBand Roformer | De-Reverb Mono by anvuew' : 'dereverb_mel_band_roformer_mono_anvuew.ckpt', 'MelBand Roformer | De-Reverb Big by Sucial' : 'dereverb_big_mbr_ep_362.ckpt', 'MelBand Roformer | De-Reverb Super Big by Sucial' : 'dereverb_super_big_mbr_ep_346.ckpt', 'MelBand Roformer | De-Reverb-Echo by Sucial' : 'dereverb-echo_mel_band_roformer_sdr_10.0169.ckpt', 'MelBand Roformer | De-Reverb-Echo V2 by Sucial' : 'dereverb-echo_mel_band_roformer_sdr_13.4843_v2.ckpt', 'MelBand Roformer | De-Reverb-Echo Fused by Sucial' : 'dereverb_echo_mbr_fused.ckpt', 'MelBand Roformer Kim | SYHFT by SYH99999' : 'MelBandRoformerSYHFT.ckpt', 'MelBand Roformer Kim | SYHFT V2 by SYH99999' : 'MelBandRoformerSYHFTV2.ckpt', 'MelBand Roformer Kim | SYHFT V2.5 by SYH99999' : 'MelBandRoformerSYHFTV2.5.ckpt', 'MelBand Roformer Kim | SYHFT V3 by SYH99999' : 'MelBandRoformerSYHFTV3Epsilon.ckpt', 'MelBand Roformer Kim | Big SYHFT V1 by SYH99999' : 'MelBandRoformerBigSYHFTV1.ckpt', 'MelBand Roformer Kim | Big Beta 4 FT by unwa' : 'melband_roformer_big_beta4.ckpt', 'MelBand Roformer Kim | Big Beta 5e FT by unwa' : 'melband_roformer_big_beta5e.ckpt', 'MelBand Roformer | Big Beta 6 by unwa' : 'melband_roformer_big_beta6.ckpt', 'MelBand Roformer | Big Beta 6X by unwa' : 'melband_roformer_big_beta6x.ckpt', 'BS Roformer | Chorus Male-Female by Sucial' : 'model_chorus_bs_roformer_ep_267_sdr_24.1275.ckpt', 'BS Roformer | Male-Female by aufr33' : 'bs_roformer_male_female_by_aufr33_sdr_7.2889.ckpt', 'MelBand Roformer | Aspiration by Sucial' : 'aspiration_mel_band_roformer_sdr_18.9845.ckpt', 'MelBand Roformer | Aspiration Less Aggressive by Sucial' : 'aspiration_mel_band_roformer_less_aggr_sdr_18.1201.ckpt', 'MelBand Roformer | Bleed Suppressor V1 by unwa-97chris' : 'mel_band_roformer_bleed_suppressor_v1.ckpt' } #=========================# # MDX23C Models # #=========================# mdx23c_models = [ 'MDX23C_D1581.ckpt', 'MDX23C-8KFFT-InstVoc_HQ.ckpt', 'MDX23C-8KFFT-InstVoc_HQ_2.ckpt', 'MDX23C-De-Reverb-aufr33-jarredou.ckpt', 'MDX23C-DrumSep-aufr33-jarredou.ckpt' ] #=========================# # MDXN-NET Models # #=========================# mdxnet_models = [ 'UVR-MDX-NET-Inst_full_292.onnx', 'UVR-MDX-NET_Inst_187_beta.onnx', 'UVR-MDX-NET_Inst_82_beta.onnx', 'UVR-MDX-NET_Inst_90_beta.onnx', 'UVR-MDX-NET_Main_340.onnx', 'UVR-MDX-NET_Main_390.onnx', 'UVR-MDX-NET_Main_406.onnx', 'UVR-MDX-NET_Main_427.onnx', 'UVR-MDX-NET_Main_438.onnx', 'UVR-MDX-NET-Inst_HQ_1.onnx', 'UVR-MDX-NET-Inst_HQ_2.onnx', 'UVR-MDX-NET-Inst_HQ_3.onnx', 'UVR-MDX-NET-Inst_HQ_4.onnx', 'UVR-MDX-NET-Inst_HQ_5.onnx', 'UVR_MDXNET_Main.onnx', 'UVR-MDX-NET-Inst_Main.onnx', 'UVR_MDXNET_1_9703.onnx', 'UVR_MDXNET_2_9682.onnx', 'UVR_MDXNET_3_9662.onnx', 'UVR-MDX-NET-Inst_1.onnx', 'UVR-MDX-NET-Inst_2.onnx', 'UVR-MDX-NET-Inst_3.onnx', 'UVR_MDXNET_KARA.onnx', 'UVR_MDXNET_KARA_2.onnx', 'UVR_MDXNET_9482.onnx', 'UVR-MDX-NET-Voc_FT.onnx', 'Kim_Vocal_1.onnx', 'Kim_Vocal_2.onnx', 'Kim_Inst.onnx', 'Reverb_HQ_By_FoxJoy.onnx', 'UVR-MDX-NET_Crowd_HQ_1.onnx', 'kuielab_a_vocals.onnx', 'kuielab_a_other.onnx', 'kuielab_a_bass.onnx', 'kuielab_a_drums.onnx', 'kuielab_b_vocals.onnx', 'kuielab_b_other.onnx', 'kuielab_b_bass.onnx', 'kuielab_b_drums.onnx', ] #========================# # VR-ARCH Models # #========================# vrarch_models = [ '1_HP-UVR.pth', '2_HP-UVR.pth', '3_HP-Vocal-UVR.pth', '4_HP-Vocal-UVR.pth', '5_HP-Karaoke-UVR.pth', '6_HP-Karaoke-UVR.pth', '7_HP2-UVR.pth', '8_HP2-UVR.pth', '9_HP2-UVR.pth', '10_SP-UVR-2B-32000-1.pth', '11_SP-UVR-2B-32000-2.pth', '12_SP-UVR-3B-44100.pth', '13_SP-UVR-4B-44100-1.pth', '14_SP-UVR-4B-44100-2.pth', '15_SP-UVR-MID-44100-1.pth', '16_SP-UVR-MID-44100-2.pth', '17_HP-Wind_Inst-UVR.pth', 'UVR-De-Echo-Aggressive.pth', 'UVR-De-Echo-Normal.pth', 'UVR-DeEcho-DeReverb.pth', 'UVR-De-Reverb-aufr33-jarredou.pth', 'UVR-DeNoise-Lite.pth', 'UVR-DeNoise.pth', 'UVR-BVE-4B_SN-44100-1.pth', 'MGM_HIGHEND_v4.pth', 'MGM_LOWEND_A_v4.pth', 'MGM_LOWEND_B_v4.pth', 'MGM_MAIN_v4.pth', ] #=======================# # DEMUCS Models # #=======================# demucs_models = [ 'htdemucs_ft.yaml', 'htdemucs_6s.yaml', 'htdemucs.yaml', 'hdemucs_mmi.yaml', ] output_format = [ 'wav', 'flac', 'mp3', 'ogg', 'opus', 'm4a', 'aiff', 'ac3' ] found_files = [] logs = [] out_dir = "./outputs" models_dir = "./models" extensions = (".wav", ".flac", ".mp3", ".ogg", ".opus", ".m4a", ".aiff", ".ac3") def load_config_presence(): with open(config_file, "r", encoding="utf8") as file: config = json.load(file) return config["discord_presence"] def initialize_presence(): if load_config_presence(): RPCManager.start_presence() initialize_presence() def download_audio(url, output_dir="ytdl"): os.makedirs(output_dir, exist_ok=True) ydl_opts = { 'format': 'bestaudio/best', 'postprocessors': [{ 'key': 'FFmpegExtractAudio', 'preferredcodec': 'wav', 'preferredquality': '32', }], 'outtmpl': os.path.join(output_dir, '%(title)s.%(ext)s'), 'postprocessor_args': [ '-acodec', 'pcm_f32le' ], } try: with yt_dlp.YoutubeDL(ydl_opts) as ydl: info = ydl.extract_info(url, download=False) video_title = info['title'] ydl.download([url]) file_path = os.path.join(output_dir, f"{video_title}.wav") if os.path.exists(file_path): return os.path.abspath(file_path) else: raise Exception("Something went wrong") except Exception as e: raise Exception(f"Error extracting audio with yt-dlp: {str(e)}") def leaderboard(list_filter): try: if python_location: command = [python_location, separator_location, "-l", f"--list_filter={list_filter}"] else: command = [separator_location, "-l", f"--list_filter={list_filter}"] result = subprocess.run( command, capture_output=True, text=True, ) if result.returncode != 0: return f"Error: {result.stderr}" return "" + "".join( f"" + "".join(f"" for cell in re.split(r"\s{2,}", line.strip())) + "" for i, line in enumerate(re.findall(r"^(?!-+)(.+)$", result.stdout.strip(), re.MULTILINE)) ) + "
{cell}
" except Exception as e: return f"Error: {e}" def get_language_settings(): with open(config_file, "r", encoding="utf8") as file: config = json.load(file) if config["lang"]["override"] == False: return "Language automatically detected by system" else: return config["lang"]["selected_lang"] def save_lang_settings(selected_language): with open(config_file, "r", encoding="utf8") as file: config = json.load(file) if selected_language == "Language automatically detected by system": config["lang"]["override"] = False else: config["lang"]["override"] = True config["lang"]["selected_lang"] = selected_language gr.Info(i18n("Language have been saved. Restart UVR5 UI to apply the changes")) with open(config_file, "w", encoding="utf8") as file: json.dump(config, file, indent=2) def alternative_model_downloader(method, key, output_dir="models", progress=gr.Progress()): logs.clear() with open(models_file, 'r', encoding='utf-8') as file: model_data = json.load(file) if key not in model_data: return f"Model '{key}' cannot be found." total_files = len(model_data[key]) progress(0, desc="Starting downloads...") for i, url in enumerate(model_data[key]): filename = os.path.basename(urllib.parse.urlparse(url).path) full_name = os.path.join(output_dir, filename) if os.path.exists(full_name): logs.append(f"{filename} already exists.") continue progress((i + 0.1) / total_files, desc=f"Starting download of {filename} ({i+1}/{total_files})") if method == 'wget': cmd = ['wget', '--progress=bar:force', '-O', full_name, url] elif method == 'curl': cmd = ['curl', '-L', '-#', '-o', full_name, url] try: process = subprocess.Popen( cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True, bufsize=1 ) for line in process.stderr: if method == 'wget' and '%' in line: try: percent = int(line.strip().split('%')[0].split()[-1]) file_progress = percent / 100.0 total_progress = (i + file_progress) / total_files progress(total_progress, desc=f"File {i+1}/{total_files}: {filename} ({percent}%)") except (ValueError, IndexError): pass elif method == 'curl' and '##' in line: try: hash_count = line.count('#') file_progress = min(hash_count / 50.0, 1.0) total_progress = (i + file_progress) / total_files percent = int(file_progress * 100) progress(total_progress, desc=f"File {i+1}/{total_files}: {filename} ({percent}%)") except Exception: pass process.wait() if process.returncode != 0: logs.append(f"Error downloading {filename}") else: logs.append(f"{filename} downloaded successfully!") progress((i + 1) / total_files, desc=f"File {i+1}/{total_files} completed") except Exception as e: logs.append(f"Error running download command: {str(e)}") progress(1.0, desc="Download process completed") return "\n".join(logs) def read_main_config(): try: with open(config_file, "r", encoding="utf8") as f: return json.load(f) except Exception as e: print(f"Error reading main config file '{config_file}': {e}") gr.Warning(i18n("Error reading main config file")) def write_main_config(data): try: with open(config_file, "w", encoding="utf8") as f: json.dump(data, f, indent=2) except Exception as e: print(f"Error writing to main config file '{config_file}': {e}") gr.Warning(i18n("Error writing to main config file")) def load_settings_from_file(filepath): try: with open(filepath, 'r', encoding='utf-8') as f: return json.load(f) except Exception as e: print(f"Error reading settings file '{filepath}': {e}") gr.Warning(i18n("Error reading settings file")) return None def get_initial_settings(): main_config = read_main_config() load_custom = main_config.get('load_custom_settings', False) settings_to_load = {} default_settings = load_settings_from_file(default_settings_file) if load_custom: print("Attempting to load custom settings...") custom_settings = load_settings_from_file(custom_settings_file) if custom_settings: settings_to_load = copy.deepcopy(default_settings) for section, params in custom_settings.items(): if section in settings_to_load: for key, value in params.items(): settings_to_load[section][key] = value else: settings_to_load[section] = params print("Custom settings loaded successfully.") else: print("Custom settings file not found or invalid. Falling back to default settings.") settings_to_load = default_settings else: print("Loading default settings...") settings_to_load = default_settings return settings_to_load initial_settings = get_initial_settings() def get_all_components(components_dict): all_comps = [] for section in components_dict.values(): all_comps.extend(section.values()) return all_comps def save_current_settings(*values): global components try: current_config_data = {} value_index = 0 for section_name, section_comps in components.items(): current_config_data[section_name] = {} for comp_name in section_comps.keys(): current_config_data[section_name][comp_name] = values[value_index] value_index += 1 with open(custom_settings_file, 'w', encoding='utf-8') as f: json.dump(current_config_data, f, indent=4) main_config = read_main_config() main_config['load_custom_settings'] = True write_main_config(main_config) gr.Info(i18n("Current settings saved successfully! They will be loaded next time")) except Exception as e: print(f"Error saving settings: {e}") gr.Warning(i18n("Error saving settings")) def reset_settings_to_default(): global components, default_settings_file updates = [] all_comps_flat = get_all_components(components) try: default_settings = load_settings_from_file(default_settings_file) for section_name, section_comps in components.items(): for comp_name, comp_instance in section_comps.items(): default_value = default_settings.get(section_name, {}).get(comp_name, None) if isinstance(comp_instance, gr.Dropdown) and hasattr(comp_instance, 'choices') and default_value is not None: if default_value not in comp_instance.choices: print(f"Warning: Default value '{default_value}' for '{comp_name}' not in choices {comp_instance.choices}. Setting to None.") default_value = None updates.append(gr.update(value=default_value)) main_config = read_main_config() main_config['load_custom_settings'] = False write_main_config(main_config) gr.Info(i18n("Settings reset to default. Default settings will be loaded next time")) return updates except Exception as e: print(f"Error resetting settings: {e}") gr.Warning(i18n("Error resetting settings")) return [gr.update() for _ in all_comps_flat] components = { "Roformer": {}, "MDX23C": {}, "MDX-NET": {}, "VR Arch": {}, "Demucs": {} } @track_presence("Performing BS/Mel Roformer Separation") @spaces.GPU(duration=60) 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)): roformer_model = roformer_models[model_key] model_path = os.path.join(models_dir, roformer_model) try: if not os.path.exists(model_path): gr.Info(f"This is the first time the {model_key} model is being used. The separation will take a little longer because the model needs to be downloaded.") 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, mdxc_params={ "segment_size": segment_size, "override_model_segment_size": override_seg_size, "batch_size": batch_size, "overlap": overlap, } ) progress(0.2, desc="Loading model...") separator.load_model(model_filename=roformer_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"Roformer separation failed: {e}") from e @track_presence("Performing MDXC Separationn") @spaces.GPU(duration=60) 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)): model_path = os.path.join(models_dir, model) try: if not os.path.exists(model_path): gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.") 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, mdxc_params={ "segment_size": segment_size, "override_model_segment_size": override_seg_size, "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=60) 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)): model_path = os.path.join(models_dir, model) try: if not os.path.exists(model_path): gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.") 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=60) 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)): model_path = os.path.join(models_dir, model) try: if not os.path.exists(model_path): gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.") 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=60) 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)): model_path = os.path.join(models_dir, model) try: if not os.path.exists(model_path): gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.") 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=60) 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, progress=gr.Progress()): found_files.clear() logs.clear() roformer_model = roformer_models[model_key] model_path = os.path.join(models_dir, roformer_model) if not os.path.exists(model_path): gr.Info(f"This is the first time the {model_key} model is being used. The separation will take a little longer because the model needs to be downloaded.") 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.") return "\n".join(logs) else: logs.append(f"{total_files} audio files found") found_files.sort() progress(0, desc="Starting processing...") for i, audio_files in enumerate(found_files): progress((i / total_files), desc=f"Processing file {i+1}/{total_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, 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...") separator.load_model(model_filename=roformer_model) logs.append(f"Separating file: {audio_files}") separator.separate(file_path) logs.append(f"File: {audio_files} separated!") except Exception as e: raise RuntimeError(f"BS/Mel Roformer batch separation failed: {e}") from e progress(1.0, desc="Processing complete") return "\n".join(logs) @track_presence("Performing MDXC Batch Separation") @spaces.GPU(duration=60) def mdx23c_batch(path_input, path_output, model, out_format, segment_size, override_seg_size, overlap, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress()): found_files.clear() logs.clear() model_path = os.path.join(models_dir, model) if not os.path.exists(model_path): gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.") 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.") return "\n".join(logs) else: logs.append(f"{total_files} audio files found") found_files.sort() progress(0, desc="Starting processing...") for i, audio_files in enumerate(found_files): progress((i / total_files), desc=f"Processing file {i+1}/{total_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, 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...") separator.load_model(model_filename=model) logs.append(f"Separating file: {audio_files}") separator.separate(file_path) logs.append(f"File: {audio_files} separated!") except Exception as e: raise RuntimeError(f"MDXC batch separation failed: {e}") from e progress(1.0, desc="Processing complete") return "\n".join(logs) @track_presence("Performing MDX-NET Batch Separation") @spaces.GPU(duration=60) def mdxnet_batch(path_input, path_output, model, out_format, hop_length, segment_size, denoise, overlap, batch_size, norm_thresh, amp_thresh, single_stem, progress=gr.Progress()): found_files.clear() logs.clear() model_path = os.path.join(models_dir, model) if not os.path.exists(model_path): gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.") 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.") return "\n".join(logs) else: logs.append(f"{total_files} audio files found") found_files.sort() progress(0, desc="Starting processing...") for i, audio_files in enumerate(found_files): progress((i / total_files), desc=f"Processing file {i+1}/{total_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, 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...") separator.load_model(model_filename=model) logs.append(f"Separating file: {audio_files}") separator.separate(file_path) logs.append(f"File: {audio_files} separated!") except Exception as e: raise RuntimeError(f"MDX-NET batch separation failed: {e}") from e progress(1.0, desc="Processing complete") return "\n".join(logs) @track_presence("Performing VR Arch Batch Separation") @spaces.GPU(duration=60) 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, progress=gr.Progress()): found_files.clear() logs.clear() model_path = os.path.join(models_dir, model) if not os.path.exists(model_path): gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.") 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.") return "\n".join(logs) else: logs.append(f"{total_files} audio files found") found_files.sort() progress(0, desc="Starting processing...") for i, audio_files in enumerate(found_files): progress((i / total_files), desc=f"Processing file {i+1}/{total_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, 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...") separator.load_model(model_filename=model) logs.append(f"Separating file: {audio_files}") separator.separate(file_path) logs.append(f"File: {audio_files} separated!") except Exception as e: raise RuntimeError(f"VR Arch batch separation failed: {e}") from e progress(1.0, desc="Processing complete") return "\n".join(logs) @track_presence("Performing Demucs Batch Separation") @spaces.GPU(duration=60) def demucs_batch(path_input, path_output, model, out_format, shifts, segment_size, segments_enabled, overlap, batch_size, norm_thresh, amp_thresh, progress=gr.Progress()): found_files.clear() logs.clear() model_path = os.path.join(models_dir, model) if not os.path.exists(model_path): gr.Info(f"This is the first time the {model} model is being used. The separation will take a little longer because the model needs to be downloaded.") 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.") return "\n".join(logs) else: logs.append(f"{total_files} audio files found") found_files.sort() progress(0, desc="Starting processing...") for i, audio_files in enumerate(found_files): progress((i / total_files), desc=f"Processing file {i+1}/{total_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...") separator.load_model(model_filename=model) logs.append(f"Separating file: {audio_files}") separator.separate(file_path) logs.append(f"File: {audio_files} separated!") except Exception as e: raise RuntimeError(f"Demucs batch separation failed: {e}") from e progress(1.0, desc="Processing complete") return "\n".join(logs) with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "🎵 UVR5 UI 🎵") as app: gr.Markdown("

🎵 UVR5 UI 🎵

") 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)")) all_configurable_inputs = [] 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 = initial_settings.get("Roformer", {}).get("model", None), interactive = True ) roformer_output_format = gr.Dropdown( label = i18n("Select the output format"), choices = output_format, value = initial_settings.get("Roformer", {}).get("output_format", 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 = initial_settings.get("Roformer", {}).get("segment_size", 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 = initial_settings.get("Roformer", {}).get("override_segment_size", 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 = initial_settings.get("Roformer", {}).get("overlap", 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 = initial_settings.get("Roformer", {}).get("batch_size", 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 = initial_settings.get("Roformer", {}).get("normalization_threshold", 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 = initial_settings.get("Roformer", {}).get("amplification_threshold", 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"), value = initial_settings.get("Roformer", {}).get("single_stem", ""), interactive = True ) components["Roformer"] = { "model": roformer_model, "output_format": roformer_output_format, "segment_size": roformer_segment_size, "override_segment_size": roformer_override_segment_size, "overlap": roformer_overlap, "batch_size": roformer_batch_size, "normalization_threshold": roformer_normalization_threshold, "amplification_threshold": roformer_amplification_threshold, "single_stem": roformer_single_stem } all_configurable_inputs.extend(components["Roformer"].values()) 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 = initial_settings.get("MDX23C", {}).get("model", None), interactive = True ) mdx23c_output_format = gr.Dropdown( label = i18n("Select the output format"), choices = output_format, value = initial_settings.get("MDX23C", {}).get("output_format", 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 = initial_settings.get("MDX23C", {}).get("segment_size", 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 = initial_settings.get("MDX23C", {}).get("override_segment_size", 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 = initial_settings.get("MDX23C", {}).get("overlap", 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 = initial_settings.get("MDX23C", {}).get("batch_size", 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 = initial_settings.get("MDX23C", {}).get("normalization_threshold", 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 = initial_settings.get("MDX23C", {}).get("amplification_threshold", 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"), value = initial_settings.get("MDX23C", {}).get("single_stem", ""), interactive = True ) components["MDX23C"] = { "model": mdx23c_model, "output_format": mdx23c_output_format, "segment_size": mdx23c_segment_size, "override_segment_size": mdx23c_override_segment_size, "overlap": mdx23c_overlap, "batch_size": mdx23c_batch_size, "normalization_threshold": mdx23c_normalization_threshold, "amplification_threshold": mdx23c_amplification_threshold, "single_stem": mdx23c_single_stem } all_configurable_inputs.extend(components["MDX23C"].values()) 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 = initial_settings.get("MDX-NET", {}).get("model", None), interactive = True ) mdxnet_output_format = gr.Dropdown( label = i18n("Select the output format"), choices = output_format, value = initial_settings.get("MDX-NET", {}).get("output_format", 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 = initial_settings.get("MDX-NET", {}).get("hop_length", 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 = initial_settings.get("MDX-NET", {}).get("segment_size", 256), interactive = True ) mdxnet_denoise = gr.Checkbox( label = i18n("Denoise"), info = i18n("Enable denoising during separation"), value = initial_settings.get("MDX-NET", {}).get("denoise", 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 = initial_settings.get("MDX-NET", {}).get("overlap", 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 = initial_settings.get("MDX-NET", {}).get("batch_size", 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 = initial_settings.get("MDX-NET", {}).get("normalization_threshold", 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 = initial_settings.get("MDX-NET", {}).get("amplification_threshold", 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"), value = initial_settings.get("MDX-NET", {}).get("single_stem", ""), interactive = True ) components["MDX-NET"] = { "model": mdxnet_model, "output_format": mdxnet_output_format, "hop_length": mdxnet_hop_length, "segment_size": mdxnet_segment_size, "denoise": mdxnet_denoise, "overlap": mdxnet_overlap, "batch_size": mdxnet_batch_size, "normalization_threshold": mdxnet_normalization_threshold, "amplification_threshold": mdxnet_amplification_threshold, "single_stem": mdxnet_single_stem } all_configurable_inputs.extend(components["MDX-NET"].values()) 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 = initial_settings.get("VR Arch", {}).get("model", None), interactive = True ) vrarch_output_format = gr.Dropdown( label = i18n("Select the output format"), choices = output_format, value = initial_settings.get("VR Arch", {}).get("output_format", 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 = initial_settings.get("VR Arch", {}).get("window_size", 512), interactive = True ) vrarch_agression = gr.Slider( minimum = 1, maximum = 50, step = 1, label = i18n("Agression"), info = i18n("Intensity of primary stem extraction"), value = initial_settings.get("VR Arch", {}).get("aggression", 5), interactive = True ) vrarch_tta = gr.Checkbox( label = i18n("TTA"), info = i18n("Enable Test-Time-Augmentation; slow but improves quality"), value = initial_settings.get("VR Arch", {}).get("tta", 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 = initial_settings.get("VR Arch", {}).get("post_process", 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 = initial_settings.get("VR Arch", {}).get("post_process_threshold", 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 = initial_settings.get("VR Arch", {}).get("high_end_process", 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 = initial_settings.get("VR Arch", {}).get("batch_size", 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 = initial_settings.get("VR Arch", {}).get("normalization_threshold", 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 = initial_settings.get("VR Arch", {}).get("amplification_threshold", 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"), value = initial_settings.get("VR Arch", {}).get("single_stem", ""), interactive = True ) components["VR Arch"] = { "model": vrarch_model, "output_format": vrarch_output_format, "window_size": vrarch_window_size, "aggression": vrarch_agression, "tta": vrarch_tta, "post_process": vrarch_post_process, "post_process_threshold": vrarch_post_process_threshold, "high_end_process": vrarch_high_end_process, "batch_size": vrarch_batch_size, "normalization_threshold": vrarch_normalization_threshold, "amplification_threshold": vrarch_amplification_threshold, "single_stem": vrarch_single_stem } all_configurable_inputs.extend(components["VR Arch"].values()) 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 = initial_settings.get("Demucs", {}).get("model", None), interactive = True ) demucs_output_format = gr.Dropdown( label = i18n("Select the output format"), choices = output_format, value = initial_settings.get("Demucs", {}).get("output_format", 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 = initial_settings.get("Demucs", {}).get("shifts", 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 = initial_settings.get("Demucs", {}).get("segment_size", 40), interactive = True ) demucs_segments_enabled = gr.Checkbox( label = i18n("Segment-wise processing"), info = i18n("Enable segment-wise processing"), value = initial_settings.get("Demucs", {}).get("segments_enabled", 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 = initial_settings.get("Demucs", {}).get("overlap", 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 = initial_settings.get("Demucs", {}).get("batch_size", 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 = initial_settings.get("Demucs", {}).get("normalization_threshold", 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 = initial_settings.get("Demucs", {}).get("amplification_threshold", 0.7), interactive = True ) components["Demucs"] = { "model": demucs_model, "output_format": demucs_output_format, "shifts": demucs_shifts, "segment_size": demucs_segment_size, "segments_enabled": demucs_segments_enabled, "overlap": demucs_overlap, "batch_size": demucs_batch_size, "normalization_threshold": demucs_normalization_threshold, "amplification_threshold": demucs_amplification_threshold } all_configurable_inputs.extend(components["Demucs"].values()) 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(), interactive = True ) themes_select.change( fn = loadThemes.select_theme, inputs = themes_select, outputs = [] ) 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 [NeoDev](https://github.com/TheNeodev). * 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: [!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/uvr5) """ ) app.queue() app.launch()