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Running
on
Zero
| 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 "<table border='1'>" + "".join( | |
| f"<tr style='{'font-weight: bold; font-size: 1.2em;' if i == 0 else ''}'>" + | |
| "".join(f"<td>{cell}</td>" for cell in re.split(r"\s{2,}", line.strip())) + | |
| "</tr>" | |
| for i, line in enumerate(re.findall(r"^(?!-+)(.+)$", result.stdout.strip(), re.MULTILINE)) | |
| ) + "</table>" | |
| 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": {} | |
| } | |
| 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 | |
| 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 | |
| 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 | |
| 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 | |
| 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) | |
| 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) | |
| 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) | |
| 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) | |
| 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) | |
| 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("<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)")) | |
| 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: | |
| [](https://www.buymeacoffee.com/uvr5) | |
| """ | |
| ) | |
| app.queue() | |
| app.launch() |