diff --git "a/app.py" "b/app.py"
--- "a/app.py"
+++ "b/app.py"
@@ -1,1529 +1,1562 @@
-import os
-import sys
-import subprocess
-import re
-import platform
-import torch
-import logging
-import yt_dlp
-import json
-import gradio as gr
-import spaces
-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")
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-use_autocast = device == "cuda"
-
-if os.path.isdir("env"):
- if platform.system() == "Windows":
- separator_location = ".\\env\\Scripts\\audio-separator.exe"
- elif platform.system() == "Linux":
- separator_location = "env/bin/audio-separator"
-else:
- 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',
- 'Mel-Roformer-Karaoke-Aufr33-Viperx': 'mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.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 | Inst V1 by Unwa' : 'melband_roformer_inst_v1.ckpt',
- 'MelBand Roformer Kim | Inst V1 (E) by Unwa' : 'melband_roformer_inst_v1e.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 | 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-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 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',
- 'BS Roformer | Chorus Male-Female by Sucial' : 'model_chorus_bs_roformer_ep_267_sdr_24.1275.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:
- result = subprocess.run(
- [separator_location, "-l", f"--list_filter={list_filter}"],
- capture_output=True,
- text=True,
- )
- if result.returncode != 0:
- return f"Error: {result.stderr}"
-
- return "" + "".join(
- f"" +
- "".join(f"| {cell} | " for cell in re.split(r"\s{2,}", line.strip())) +
- "
"
- for i, line in enumerate(re.findall(r"^(?!-+)(.+)$", result.stdout.strip(), re.MULTILINE))
- ) + "
"
-
- except Exception as e:
- return f"Error: {e}"
-
-@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)):
- base_name = os.path.splitext(os.path.basename(audio))[0]
- roformer_model = roformer_models[model_key]
- try:
- separator = Separator(
- log_level=logging.WARNING,
- model_file_dir=models_dir,
- output_dir=out_dir,
- output_format=out_format,
- use_autocast=use_autocast,
- normalization_threshold=norm_thresh,
- amplification_threshold=amp_thresh,
- output_single_stem=single_stem,
- 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)):
- base_name = os.path.splitext(os.path.basename(audio))[0]
- try:
- separator = Separator(
- log_level=logging.WARNING,
- model_file_dir=models_dir,
- output_dir=out_dir,
- output_format=out_format,
- use_autocast=use_autocast,
- normalization_threshold=norm_thresh,
- amplification_threshold=amp_thresh,
- output_single_stem=single_stem,
- 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)):
- base_name = os.path.splitext(os.path.basename(audio))[0]
- try:
- separator = Separator(
- log_level=logging.WARNING,
- model_file_dir=models_dir,
- output_dir=out_dir,
- output_format=out_format,
- use_autocast=use_autocast,
- normalization_threshold=norm_thresh,
- amplification_threshold=amp_thresh,
- output_single_stem=single_stem,
- mdx_params={
- "hop_length": hop_length,
- "segment_size": segment_size,
- "overlap": overlap,
- "batch_size": batch_size,
- "enable_denoise": denoise,
- }
- )
-
- progress(0.2, desc="Loading model...")
- separator.load_model(model_filename=model)
-
- progress(0.7, desc="Separating audio...")
- separation = separator.separate(audio)
-
- stems = [os.path.join(out_dir, file_name) for file_name in separation]
-
- if single_stem.strip():
- return stems[0], None
-
- return stems[0], stems[1]
-
- except Exception as e:
- raise RuntimeError(f"MDX-NET separation failed: {e}") from e
-
-@track_presence("Performing VR Arch Separation")
-@spaces.GPU(duration=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)):
- base_name = os.path.splitext(os.path.basename(audio))[0]
- try:
- separator = Separator(
- log_level=logging.WARNING,
- model_file_dir=models_dir,
- output_dir=out_dir,
- output_format=out_format,
- use_autocast=use_autocast,
- normalization_threshold=norm_thresh,
- amplification_threshold=amp_thresh,
- output_single_stem=single_stem,
- vr_params={
- "batch_size": batch_size,
- "window_size": window_size,
- "aggression": aggression,
- "enable_tta": tta,
- "enable_post_process": post_process,
- "post_process_threshold": post_process_threshold,
- "high_end_process": high_end_process,
- }
- )
-
- progress(0.2, desc="Loading model...")
- separator.load_model(model_filename=model)
-
- progress(0.7, desc="Separating audio...")
- separation = separator.separate(audio)
-
- stems = [os.path.join(out_dir, file_name) for file_name in separation]
-
- if single_stem.strip():
- return stems[0], None
-
- return stems[0], stems[1]
-
- except Exception as e:
- raise RuntimeError(f"VR ARCH separation failed: {e}") from e
-
-@track_presence("Performing Demucs Separation")
-@spaces.GPU(duration=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)):
- base_name = os.path.splitext(os.path.basename(audio))[0]
- try:
- separator = Separator(
- log_level=logging.WARNING,
- model_file_dir=models_dir,
- output_dir=out_dir,
- output_format=out_format,
- use_autocast=use_autocast,
- normalization_threshold=norm_thresh,
- amplification_threshold=amp_thresh,
- demucs_params={
- "batch_size": batch_size,
- "segment_size": segment_size,
- "shifts": shifts,
- "overlap": overlap,
- "segments_enabled": segments_enabled,
- }
- )
-
- progress(0.2, desc="Loading model...")
- separator.load_model(model_filename=model)
-
- progress(0.7, desc="Separating audio...")
- separation = separator.separate(audio)
-
- stems = [os.path.join(out_dir, file_name) for file_name in separation]
-
- if model == "htdemucs_6s.yaml":
- return stems[0], stems[1], stems[2], stems[3], stems[4], stems[5]
- else:
- return stems[0], stems[1], stems[2], stems[3], None, None
-
- except Exception as e:
- raise RuntimeError(f"Demucs separation failed: {e}") from e
-
-def update_stems(model):
- if model == "htdemucs_6s.yaml":
- return gr.update(visible=True)
- else:
- return gr.update(visible=False)
-
-@track_presence("Performing BS/Mel Roformer Batch Separation")
-@spaces.GPU(duration=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):
- found_files.clear()
- logs.clear()
- roformer_model = roformer_models[model_key]
-
- for audio_files in os.listdir(path_input):
- if audio_files.endswith(extensions):
- found_files.append(audio_files)
- total_files = len(found_files)
-
- if total_files == 0:
- logs.append("No valid audio files.")
- yield "\n".join(logs)
- else:
- logs.append(f"{total_files} audio files found")
- found_files.sort()
-
- for audio_files in found_files:
- file_path = os.path.join(path_input, audio_files)
- base_name = os.path.splitext(os.path.basename(file_path))[0]
- try:
- separator = Separator(
- log_level=logging.WARNING,
- model_file_dir=models_dir,
- output_dir=path_output,
- output_format=out_format,
- use_autocast=use_autocast,
- normalization_threshold=norm_thresh,
- amplification_threshold=amp_thresh,
- output_single_stem=single_stem,
- mdxc_params={
- "segment_size": segment_size,
- "override_model_segment_size": override_seg_size,
- "batch_size": batch_size,
- "overlap": overlap,
- }
- )
-
- logs.append("Loading model...")
- yield "\n".join(logs)
- separator.load_model(model_filename=roformer_model)
-
- logs.append(f"Separating file: {audio_files}")
- yield "\n".join(logs)
- separator.separate(file_path)
- logs.append(f"File: {audio_files} separated!")
- yield "\n".join(logs)
- except Exception as e:
- raise RuntimeError(f"Roformer batch separation failed: {e}") from e
-
-@track_presence("Performing MDXC Batch Separation")
-@spaces.GPU(duration=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):
- found_files.clear()
- logs.clear()
-
- for audio_files in os.listdir(path_input):
- if audio_files.endswith(extensions):
- found_files.append(audio_files)
- total_files = len(found_files)
-
- if total_files == 0:
- logs.append("No valid audio files.")
- yield "\n".join(logs)
- else:
- logs.append(f"{total_files} audio files found")
- found_files.sort()
-
- for audio_files in found_files:
- file_path = os.path.join(path_input, audio_files)
- base_name = os.path.splitext(os.path.basename(file_path))[0]
- try:
- separator = Separator(
- log_level=logging.WARNING,
- model_file_dir=models_dir,
- output_dir=path_output,
- output_format=out_format,
- use_autocast=use_autocast,
- normalization_threshold=norm_thresh,
- amplification_threshold=amp_thresh,
- output_single_stem=single_stem,
- mdxc_params={
- "segment_size": segment_size,
- "override_model_segment_size": override_seg_size,
- "batch_size": batch_size,
- "overlap": overlap,
- }
- )
-
- logs.append("Loading model...")
- yield "\n".join(logs)
- separator.load_model(model_filename=model)
-
- logs.append(f"Separating file: {audio_files}")
- yield "\n".join(logs)
- separator.separate(file_path)
- logs.append(f"File: {audio_files} separated!")
- yield "\n".join(logs)
- except Exception as e:
- raise RuntimeError(f"Roformer batch separation failed: {e}") from e
-
-@track_presence("Performing MDX-NET Batch Separation")
-@spaces.GPU(duration=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):
- found_files.clear()
- logs.clear()
-
- for audio_files in os.listdir(path_input):
- if audio_files.endswith(extensions):
- found_files.append(audio_files)
- total_files = len(found_files)
-
- if total_files == 0:
- logs.append("No valid audio files.")
- yield "\n".join(logs)
- else:
- logs.append(f"{total_files} audio files found")
- found_files.sort()
-
- for audio_files in found_files:
- file_path = os.path.join(path_input, audio_files)
- base_name = os.path.splitext(os.path.basename(file_path))[0]
- try:
- separator = Separator(
- log_level=logging.WARNING,
- model_file_dir=models_dir,
- output_dir=path_output,
- output_format=out_format,
- use_autocast=use_autocast,
- normalization_threshold=norm_thresh,
- amplification_threshold=amp_thresh,
- output_single_stem=single_stem,
- mdx_params={
- "hop_length": hop_length,
- "segment_size": segment_size,
- "overlap": overlap,
- "batch_size": batch_size,
- "enable_denoise": denoise,
- }
- )
-
- logs.append("Loading model...")
- yield "\n".join(logs)
- separator.load_model(model_filename=model)
-
- logs.append(f"Separating file: {audio_files}")
- yield "\n".join(logs)
- separator.separate(file_path)
- logs.append(f"File: {audio_files} separated!")
- yield "\n".join(logs)
- except Exception as e:
- raise RuntimeError(f"Roformer batch separation failed: {e}") from e
-
-@track_presence("Performing VR Arch Batch Separation")
-@spaces.GPU(duration=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):
- found_files.clear()
- logs.clear()
-
- for audio_files in os.listdir(path_input):
- if audio_files.endswith(extensions):
- found_files.append(audio_files)
- total_files = len(found_files)
-
- if total_files == 0:
- logs.append("No valid audio files.")
- yield "\n".join(logs)
- else:
- logs.append(f"{total_files} audio files found")
- found_files.sort()
-
- for audio_files in found_files:
- file_path = os.path.join(path_input, audio_files)
- base_name = os.path.splitext(os.path.basename(file_path))[0]
- try:
- separator = Separator(
- log_level=logging.WARNING,
- model_file_dir=models_dir,
- output_dir=path_output,
- output_format=out_format,
- use_autocast=use_autocast,
- normalization_threshold=norm_thresh,
- amplification_threshold=amp_thresh,
- output_single_stem=single_stem,
- vr_params={
- "batch_size": batch_size,
- "window_size": window_size,
- "aggression": aggression,
- "enable_tta": tta,
- "enable_post_process": post_process,
- "post_process_threshold": post_process_threshold,
- "high_end_process": high_end_process,
- }
- )
-
- logs.append("Loading model...")
- yield "\n".join(logs)
- separator.load_model(model_filename=model)
-
- logs.append(f"Separating file: {audio_files}")
- yield "\n".join(logs)
- separator.separate(file_path)
- logs.append(f"File: {audio_files} separated!")
- yield "\n".join(logs)
- except Exception as e:
- raise RuntimeError(f"Roformer batch separation failed: {e}") from e
-
-@track_presence("Performing Demucs Batch Separation")
-@spaces.GPU(duration=60)
-def demucs_batch(path_input, path_output, model, out_format, shifts, segment_size, segments_enabled, overlap, batch_size, norm_thresh, amp_thresh):
- found_files.clear()
- logs.clear()
-
- for audio_files in os.listdir(path_input):
- if audio_files.endswith(extensions):
- found_files.append(audio_files)
- total_files = len(found_files)
-
- if total_files == 0:
- logs.append("No valid audio files.")
- yield "\n".join(logs)
- else:
- logs.append(f"{total_files} audio files found")
- found_files.sort()
-
- for audio_files in found_files:
- file_path = os.path.join(path_input, audio_files)
- try:
- separator = Separator(
- log_level=logging.WARNING,
- model_file_dir=models_dir,
- output_dir=path_output,
- output_format=out_format,
- use_autocast=use_autocast,
- normalization_threshold=norm_thresh,
- amplification_threshold=amp_thresh,
- demucs_params={
- "batch_size": batch_size,
- "segment_size": segment_size,
- "shifts": shifts,
- "overlap": overlap,
- "segments_enabled": segments_enabled,
- }
- )
-
- logs.append("Loading model...")
- yield "\n".join(logs)
- separator.load_model(model_filename=model)
-
- logs.append(f"Separating file: {audio_files}")
- yield "\n".join(logs)
- separator.separate(file_path)
- logs.append(f"File: {audio_files} separated!")
- yield "\n".join(logs)
- except Exception as e:
- raise RuntimeError(f"Roformer batch separation failed: {e}") from e
-
-with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "đ” UVR5 UI đ”") as app:
- gr.Markdown(" đ” UVR5 UI đ”
")
- gr.Markdown(i18n("If you liked this HF Space you can give me a â€ïž"))
- gr.Markdown(i18n("Try UVR5 UI using Colab [here](https://colab.research.google.com/github/Eddycrack864/UVR5-UI/blob/main/UVR_UI.ipynb)"))
- with gr.Tabs():
- with gr.TabItem("BS/Mel Roformer"):
- with gr.Row():
- roformer_model = gr.Dropdown(
- label = i18n("Select the model"),
- choices = list(roformer_models.keys()),
- value = lambda : None,
- interactive = True
- )
- roformer_output_format = gr.Dropdown(
- label = i18n("Select the output format"),
- choices = output_format,
- value = lambda : None,
- interactive = True
- )
- with gr.Accordion(i18n("Advanced settings"), open = False):
- with gr.Group():
- with gr.Row():
- roformer_segment_size = gr.Slider(
- label = i18n("Segment size"),
- info = i18n("Larger consumes more resources, but may give better results"),
- minimum = 32,
- maximum = 4000,
- step = 32,
- value = 256,
- interactive = True
- )
- roformer_override_segment_size = gr.Checkbox(
- label = i18n("Override segment size"),
- info = i18n("Override model default segment size instead of using the model default value"),
- value = False,
- interactive = True
- )
- with gr.Row():
- roformer_overlap = gr.Slider(
- label = i18n("Overlap"),
- info = i18n("Amount of overlap between prediction windows"),
- minimum = 2,
- maximum = 10,
- step = 1,
- value = 8,
- interactive = True
- )
- roformer_batch_size = gr.Slider(
- label = i18n("Batch size"),
- info = i18n("Larger consumes more RAM but may process slightly faster"),
- minimum = 1,
- maximum = 16,
- step = 1,
- value = 1,
- interactive = True
- )
- with gr.Row():
- roformer_normalization_threshold = gr.Slider(
- label = i18n("Normalization threshold"),
- info = i18n("The threshold for audio normalization"),
- minimum = 0.1,
- maximum = 1,
- step = 0.1,
- value = 0.9,
- interactive = True
- )
- roformer_amplification_threshold = gr.Slider(
- label = i18n("Amplification threshold"),
- info = i18n("The threshold for audio amplification"),
- minimum = 0.1,
- maximum = 1,
- step = 0.1,
- value = 0.7,
- interactive = True
- )
- with gr.Row():
- roformer_single_stem = gr.Textbox(
- label = i18n("Output only single stem"),
- placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
- interactive = True
- )
- with gr.Row():
- roformer_audio = gr.Audio(
- label = i18n("Input audio"),
- type = "filepath",
- interactive = True
- )
- with gr.Accordion(i18n("Separation by link"), open = False):
- with gr.Row():
- roformer_link = gr.Textbox(
- label = i18n("Link"),
- placeholder = i18n("Paste the link here"),
- interactive = True
- )
- with gr.Row():
- gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
- with gr.Row():
- roformer_download_button = gr.Button(
- i18n("Download!"),
- variant = "primary"
- )
-
- roformer_download_button.click(download_audio, [roformer_link], [roformer_audio])
-
- with gr.Accordion(i18n("Batch separation"), open = False):
- with gr.Row():
- roformer_input_path = gr.Textbox(
- label = i18n("Input path"),
- placeholder = i18n("Place the input path here"),
- interactive = True
- )
- roformer_output_path = gr.Textbox(
- label = i18n("Output path"),
- placeholder = i18n("Place the output path here"),
- interactive = True
- )
- with gr.Row():
- roformer_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
- with gr.Row():
- roformer_info = gr.Textbox(
- label = i18n("Output information"),
- interactive = False
- )
-
- roformer_bath_button.click(roformer_batch, [roformer_input_path, roformer_output_path, roformer_model, roformer_output_format, roformer_segment_size, roformer_override_segment_size, roformer_overlap, roformer_batch_size, roformer_normalization_threshold, roformer_amplification_threshold, roformer_single_stem], [roformer_info])
-
- with gr.Row():
- roformer_button = gr.Button(i18n("Separate!"), variant = "primary")
- with gr.Row():
- roformer_stem1 = gr.Audio(
- show_download_button = True,
- interactive = False,
- label = i18n("Stem 1"),
- type = "filepath"
- )
- roformer_stem2 = gr.Audio(
- show_download_button = True,
- interactive = False,
- label = i18n("Stem 2"),
- type = "filepath"
- )
-
- roformer_button.click(roformer_separator, [roformer_audio, roformer_model, roformer_output_format, roformer_segment_size, roformer_override_segment_size, roformer_overlap, roformer_batch_size, roformer_normalization_threshold, roformer_amplification_threshold, roformer_single_stem], [roformer_stem1, roformer_stem2])
-
- with gr.TabItem("MDX23C"):
- with gr.Row():
- mdx23c_model = gr.Dropdown(
- label = i18n("Select the model"),
- choices = mdx23c_models,
- value = lambda : None,
- interactive = True
- )
- mdx23c_output_format = gr.Dropdown(
- label = i18n("Select the output format"),
- choices = output_format,
- value = lambda : None,
- interactive = True
- )
- with gr.Accordion(i18n("Advanced settings"), open = False):
- with gr.Group():
- with gr.Row():
- mdx23c_segment_size = gr.Slider(
- minimum = 32,
- maximum = 4000,
- step = 32,
- label = i18n("Segment size"),
- info = i18n("Larger consumes more resources, but may give better results"),
- value = 256,
- interactive = True
- )
- mdx23c_override_segment_size = gr.Checkbox(
- label = i18n("Override segment size"),
- info = i18n("Override model default segment size instead of using the model default value"),
- value = False,
- interactive = True
- )
- with gr.Row():
- mdx23c_overlap = gr.Slider(
- minimum = 2,
- maximum = 50,
- step = 1,
- label = i18n("Overlap"),
- info = i18n("Amount of overlap between prediction windows"),
- value = 8,
- interactive = True
- )
- mdx23c_batch_size = gr.Slider(
- label = i18n("Batch size"),
- info = i18n("Larger consumes more RAM but may process slightly faster"),
- minimum = 1,
- maximum = 16,
- step = 1,
- value = 1,
- interactive = True
- )
- with gr.Row():
- mdx23c_normalization_threshold = gr.Slider(
- label = i18n("Normalization threshold"),
- info = i18n("The threshold for audio normalization"),
- minimum = 0.1,
- maximum = 1,
- step = 0.1,
- value = 0.9,
- interactive = True
- )
- mdx23c_amplification_threshold = gr.Slider(
- label = i18n("Amplification threshold"),
- info = i18n("The threshold for audio amplification"),
- minimum = 0.1,
- maximum = 1,
- step = 0.1,
- value = 0.7,
- interactive = True
- )
- with gr.Row():
- mdx23c_single_stem = gr.Textbox(
- label = i18n("Output only single stem"),
- placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
- interactive = True
- )
- with gr.Row():
- mdx23c_audio = gr.Audio(
- label = i18n("Input audio"),
- type = "filepath",
- interactive = True
- )
- with gr.Accordion(i18n("Separation by link"), open = False):
- with gr.Row():
- mdx23c_link = gr.Textbox(
- label = i18n("Link"),
- placeholder = i18n("Paste the link here"),
- interactive = True
- )
- with gr.Row():
- gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
- with gr.Row():
- mdx23c_download_button = gr.Button(
- i18n("Download!"),
- variant = "primary"
- )
-
- mdx23c_download_button.click(download_audio, [mdx23c_link], [mdx23c_audio])
-
- with gr.Accordion(i18n("Batch separation"), open = False):
- with gr.Row():
- mdx23c_input_path = gr.Textbox(
- label = i18n("Input path"),
- placeholder = i18n("Place the input path here"),
- interactive = True
- )
- mdx23c_output_path = gr.Textbox(
- label = i18n("Output path"),
- placeholder = i18n("Place the output path here"),
- interactive = True
- )
- with gr.Row():
- mdx23c_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
- with gr.Row():
- mdx23c_info = gr.Textbox(
- label = i18n("Output information"),
- interactive = False
- )
-
- mdx23c_bath_button.click(mdx23c_batch, [mdx23c_input_path, mdx23c_output_path, mdx23c_model, mdx23c_output_format, mdx23c_segment_size, mdx23c_override_segment_size, mdx23c_overlap, mdx23c_batch_size, mdx23c_normalization_threshold, mdx23c_amplification_threshold, mdx23c_single_stem], [mdx23c_info])
-
- with gr.Row():
- mdx23c_button = gr.Button(i18n("Separate!"), variant = "primary")
- with gr.Row():
- mdx23c_stem1 = gr.Audio(
- show_download_button = True,
- interactive = False,
- label = i18n("Stem 1"),
- type = "filepath"
- )
- mdx23c_stem2 = gr.Audio(
- show_download_button = True,
- interactive = False,
- label = i18n("Stem 2"),
- type = "filepath"
- )
-
- mdx23c_button.click(mdxc_separator, [mdx23c_audio, mdx23c_model, mdx23c_output_format, mdx23c_segment_size, mdx23c_override_segment_size, mdx23c_overlap, mdx23c_batch_size, mdx23c_normalization_threshold, mdx23c_amplification_threshold, mdx23c_single_stem], [mdx23c_stem1, mdx23c_stem2])
-
- with gr.TabItem("MDX-NET"):
- with gr.Row():
- mdxnet_model = gr.Dropdown(
- label = i18n("Select the model"),
- choices = mdxnet_models,
- value = lambda : None,
- interactive = True
- )
- mdxnet_output_format = gr.Dropdown(
- label = i18n("Select the output format"),
- choices = output_format,
- value = lambda : None,
- interactive = True
- )
- with gr.Accordion(i18n("Advanced settings"), open = False):
- with gr.Group():
- with gr.Row():
- mdxnet_hop_length = gr.Slider(
- label = i18n("Hop length"),
- info = i18n("Usually called stride in neural networks; only change if you know what you're doing"),
- minimum = 32,
- maximum = 2048,
- step = 32,
- value = 1024,
- interactive = True
- )
- mdxnet_segment_size = gr.Slider(
- minimum = 32,
- maximum = 4000,
- step = 32,
- label = i18n("Segment size"),
- info = i18n("Larger consumes more resources, but may give better results"),
- value = 256,
- interactive = True
- )
- mdxnet_denoise = gr.Checkbox(
- label = i18n("Denoise"),
- info = i18n("Enable denoising during separation"),
- value = True,
- interactive = True
- )
- with gr.Row():
- mdxnet_overlap = gr.Slider(
- label = i18n("Overlap"),
- info = i18n("Amount of overlap between prediction windows"),
- minimum = 0.001,
- maximum = 0.999,
- step = 0.001,
- value = 0.25,
- interactive = True
- )
- mdxnet_batch_size = gr.Slider(
- label = i18n("Batch size"),
- info = i18n("Larger consumes more RAM but may process slightly faster"),
- minimum = 1,
- maximum = 16,
- step = 1,
- value = 1,
- interactive = True
- )
- with gr.Row():
- mdxnet_normalization_threshold = gr.Slider(
- label = i18n("Normalization threshold"),
- info = i18n("The threshold for audio normalization"),
- minimum = 0.1,
- maximum = 1,
- step = 0.1,
- value = 0.9,
- interactive = True
- )
- mdxnet_amplification_threshold = gr.Slider(
- label = i18n("Amplification threshold"),
- info = i18n("The threshold for audio amplification"),
- minimum = 0.1,
- maximum = 1,
- step = 0.1,
- value = 0.7,
- interactive = True
- )
- with gr.Row():
- mdxnet_single_stem = gr.Textbox(
- label = i18n("Output only single stem"),
- placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
- interactive = True
- )
- with gr.Row():
- mdxnet_audio = gr.Audio(
- label = i18n("Input audio"),
- type = "filepath",
- interactive = True
- )
- with gr.Accordion(i18n("Separation by link"), open = False):
- with gr.Row():
- mdxnet_link = gr.Textbox(
- label = i18n("Link"),
- placeholder = i18n("Paste the link here"),
- interactive = True
- )
- with gr.Row():
- gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
- with gr.Row():
- mdxnet_download_button = gr.Button(
- i18n("Download!"),
- variant = "primary"
- )
-
- mdxnet_download_button.click(download_audio, [mdxnet_link], [mdxnet_audio])
-
- with gr.Accordion(i18n("Batch separation"), open = False):
- with gr.Row():
- mdxnet_input_path = gr.Textbox(
- label = i18n("Input path"),
- placeholder = i18n("Place the input path here"),
- interactive = True
- )
- mdxnet_output_path = gr.Textbox(
- label = i18n("Output path"),
- placeholder = i18n("Place the output path here"),
- interactive = True
- )
- with gr.Row():
- mdxnet_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
- with gr.Row():
- mdxnet_info = gr.Textbox(
- label = i18n("Output information"),
- interactive = False
- )
-
- mdxnet_bath_button.click(mdxnet_batch, [mdxnet_input_path, mdxnet_output_path, mdxnet_model, mdxnet_output_format, mdxnet_hop_length, mdxnet_segment_size, mdxnet_denoise, mdxnet_overlap, mdxnet_batch_size, mdxnet_normalization_threshold, mdxnet_amplification_threshold, mdxnet_single_stem], [mdxnet_info])
-
- with gr.Row():
- mdxnet_button = gr.Button(i18n("Separate!"), variant = "primary")
- with gr.Row():
- mdxnet_stem1 = gr.Audio(
- show_download_button = True,
- interactive = False,
- label = i18n("Stem 1"),
- type = "filepath"
- )
- mdxnet_stem2 = gr.Audio(
- show_download_button = True,
- interactive = False,
- label = i18n("Stem 2"),
- type = "filepath"
- )
-
- mdxnet_button.click(mdxnet_separator, [mdxnet_audio, mdxnet_model, mdxnet_output_format, mdxnet_hop_length, mdxnet_segment_size, mdxnet_denoise, mdxnet_overlap, mdxnet_batch_size, mdxnet_normalization_threshold, mdxnet_amplification_threshold, mdxnet_single_stem], [mdxnet_stem1, mdxnet_stem2])
-
- with gr.TabItem("VR ARCH"):
- with gr.Row():
- vrarch_model = gr.Dropdown(
- label = i18n("Select the model"),
- choices = vrarch_models,
- value = lambda : None,
- interactive = True
- )
- vrarch_output_format = gr.Dropdown(
- label = i18n("Select the output format"),
- choices = output_format,
- value = lambda : None,
- interactive = True
- )
- with gr.Accordion(i18n("Advanced settings"), open = False):
- with gr.Group():
- with gr.Row():
- vrarch_window_size = gr.Slider(
- label = i18n("Window size"),
- info = i18n("Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality"),
- minimum=320,
- maximum=1024,
- step=32,
- value = 512,
- interactive = True
- )
- vrarch_agression = gr.Slider(
- minimum = 1,
- maximum = 50,
- step = 1,
- label = i18n("Agression"),
- info = i18n("Intensity of primary stem extraction"),
- value = 5,
- interactive = True
- )
- vrarch_tta = gr.Checkbox(
- label = i18n("TTA"),
- info = i18n("Enable Test-Time-Augmentation; slow but improves quality"),
- value = True,
- visible = True,
- interactive = True
- )
- with gr.Row():
- vrarch_post_process = gr.Checkbox(
- label = i18n("Post process"),
- info = i18n("Identify leftover artifacts within vocal output; may improve separation for some songs"),
- value = False,
- visible = True,
- interactive = True
- )
- vrarch_post_process_threshold = gr.Slider(
- label = i18n("Post process threshold"),
- info = i18n("Threshold for post-processing"),
- minimum = 0.1,
- maximum = 0.3,
- step = 0.1,
- value = 0.2,
- interactive = True
- )
- with gr.Row():
- vrarch_high_end_process = gr.Checkbox(
- label = i18n("High end process"),
- info = i18n("Mirror the missing frequency range of the output"),
- value = False,
- visible = True,
- interactive = True,
- )
- vrarch_batch_size = gr.Slider(
- label = i18n("Batch size"),
- info = i18n("Larger consumes more RAM but may process slightly faster"),
- minimum = 1,
- maximum = 16,
- step = 1,
- value = 1,
- interactive = True
- )
- with gr.Row():
- vrarch_normalization_threshold = gr.Slider(
- label = i18n("Normalization threshold"),
- info = i18n("The threshold for audio normalization"),
- minimum = 0.1,
- maximum = 1,
- step = 0.1,
- value = 0.9,
- interactive = True
- )
- vrarch_amplification_threshold = gr.Slider(
- label = i18n("Amplification threshold"),
- info = i18n("The threshold for audio amplification"),
- minimum = 0.1,
- maximum = 1,
- step = 0.1,
- value = 0.7,
- interactive = True
- )
- with gr.Row():
- vrarch_single_stem = gr.Textbox(
- label = i18n("Output only single stem"),
- placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
- interactive = True
- )
- with gr.Row():
- vrarch_audio = gr.Audio(
- label = i18n("Input audio"),
- type = "filepath",
- interactive = True
- )
- with gr.Accordion(i18n("Separation by link"), open = False):
- with gr.Row():
- vrarch_link = gr.Textbox(
- label = i18n("Link"),
- placeholder = i18n("Paste the link here"),
- interactive = True
- )
- with gr.Row():
- gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
- with gr.Row():
- vrarch_download_button = gr.Button(
- i18n("Download!"),
- variant = "primary"
- )
-
- vrarch_download_button.click(download_audio, [vrarch_link], [vrarch_audio])
-
- with gr.Accordion(i18n("Batch separation"), open = False):
- with gr.Row():
- vrarch_input_path = gr.Textbox(
- label = i18n("Input path"),
- placeholder = i18n("Place the input path here"),
- interactive = True
- )
- vrarch_output_path = gr.Textbox(
- label = i18n("Output path"),
- placeholder = i18n("Place the output path here"),
- interactive = True
- )
- with gr.Row():
- vrarch_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
- with gr.Row():
- vrarch_info = gr.Textbox(
- label = i18n("Output information"),
- interactive = False
- )
-
- vrarch_bath_button.click(vrarch_batch, [vrarch_input_path, vrarch_output_path, vrarch_model, vrarch_output_format, vrarch_window_size, vrarch_agression, vrarch_tta, vrarch_post_process, vrarch_post_process_threshold, vrarch_high_end_process, vrarch_batch_size, vrarch_normalization_threshold, vrarch_amplification_threshold, vrarch_single_stem], [vrarch_info])
-
- with gr.Row():
- vrarch_button = gr.Button(i18n("Separate!"), variant = "primary")
- with gr.Row():
- vrarch_stem1 = gr.Audio(
- show_download_button = True,
- interactive = False,
- type = "filepath",
- label = i18n("Stem 1")
- )
- vrarch_stem2 = gr.Audio(
- show_download_button = True,
- interactive = False,
- type = "filepath",
- label = i18n("Stem 2")
- )
-
- vrarch_button.click(vrarch_separator, [vrarch_audio, vrarch_model, vrarch_output_format, vrarch_window_size, vrarch_agression, vrarch_tta, vrarch_post_process, vrarch_post_process_threshold, vrarch_high_end_process, vrarch_batch_size, vrarch_normalization_threshold, vrarch_amplification_threshold, vrarch_single_stem], [vrarch_stem1, vrarch_stem2])
-
- with gr.TabItem("Demucs"):
- with gr.Row():
- demucs_model = gr.Dropdown(
- label = i18n("Select the model"),
- choices = demucs_models,
- value = lambda : None,
- interactive = True
- )
- demucs_output_format = gr.Dropdown(
- label = i18n("Select the output format"),
- choices = output_format,
- value = lambda : None,
- interactive = True
- )
- with gr.Accordion(i18n("Advanced settings"), open = False):
- with gr.Group():
- with gr.Row():
- demucs_shifts = gr.Slider(
- label = i18n("Shifts"),
- info = i18n("Number of predictions with random shifts, higher = slower but better quality"),
- minimum = 1,
- maximum = 20,
- step = 1,
- value = 2,
- interactive = True
- )
- demucs_segment_size = gr.Slider(
- label = i18n("Segment size"),
- info = i18n("Size of segments into which the audio is split. Higher = slower but better quality"),
- minimum = 1,
- maximum = 100,
- step = 1,
- value = 40,
- interactive = True
- )
- demucs_segments_enabled = gr.Checkbox(
- label = i18n("Segment-wise processing"),
- info = i18n("Enable segment-wise processing"),
- value = True,
- interactive = True
- )
- with gr.Row():
- demucs_overlap = gr.Slider(
- label = i18n("Overlap"),
- info = i18n("Overlap between prediction windows. Higher = slower but better quality"),
- minimum=0.001,
- maximum=0.999,
- step=0.001,
- value = 0.25,
- interactive = True
- )
- demucs_batch_size = gr.Slider(
- label = i18n("Batch size"),
- info = i18n("Larger consumes more RAM but may process slightly faster"),
- minimum = 1,
- maximum = 16,
- step = 1,
- value = 1,
- interactive = True
- )
- with gr.Row():
- demucs_normalization_threshold = gr.Slider(
- label = i18n("Normalization threshold"),
- info = i18n("The threshold for audio normalization"),
- minimum = 0.1,
- maximum = 1,
- step = 0.1,
- value = 0.9,
- interactive = True
- )
- demucs_amplification_threshold = gr.Slider(
- label = i18n("Amplification threshold"),
- info = i18n("The threshold for audio amplification"),
- minimum = 0.1,
- maximum = 1,
- step = 0.1,
- value = 0.7,
- interactive = True
- )
- with gr.Row():
- demucs_audio = gr.Audio(
- label = i18n("Input audio"),
- type = "filepath",
- interactive = True
- )
- with gr.Accordion(i18n("Separation by link"), open = False):
- with gr.Row():
- demucs_link = gr.Textbox(
- label = i18n("Link"),
- placeholder = i18n("Paste the link here"),
- interactive = True
- )
- with gr.Row():
- gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
- with gr.Row():
- demucs_download_button = gr.Button(
- i18n("Download!"),
- variant = "primary"
- )
-
- demucs_download_button.click(download_audio, [demucs_link], [demucs_audio])
-
- with gr.Accordion(i18n("Batch separation"), open = False):
- with gr.Row():
- demucs_input_path = gr.Textbox(
- label = i18n("Input path"),
- placeholder = i18n("Place the input path here"),
- interactive = True
- )
- demucs_output_path = gr.Textbox(
- label = i18n("Output path"),
- placeholder = i18n("Place the output path here"),
- interactive = True
- )
- with gr.Row():
- demucs_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
- with gr.Row():
- demucs_info = gr.Textbox(
- label = i18n("Output information"),
- interactive = False
- )
-
- demucs_bath_button.click(demucs_batch, [demucs_input_path, demucs_output_path, demucs_model, demucs_output_format, demucs_shifts, demucs_segment_size, demucs_segments_enabled, demucs_overlap, demucs_batch_size, demucs_normalization_threshold, demucs_amplification_threshold], [demucs_info])
-
- with gr.Row():
- demucs_button = gr.Button(i18n("Separate!"), variant = "primary")
- with gr.Row():
- demucs_stem1 = gr.Audio(
- show_download_button = True,
- interactive = False,
- type = "filepath",
- label = i18n("Stem 1")
- )
- demucs_stem2 = gr.Audio(
- show_download_button = True,
- interactive = False,
- type = "filepath",
- label = i18n("Stem 2")
- )
- with gr.Row():
- demucs_stem3 = gr.Audio(
- show_download_button = True,
- interactive = False,
- type = "filepath",
- label = i18n("Stem 3")
- )
- demucs_stem4 = gr.Audio(
- show_download_button = True,
- interactive = False,
- type = "filepath",
- label = i18n("Stem 4")
- )
- with gr.Row(visible=False) as stem6:
- demucs_stem5 = gr.Audio(
- show_download_button = True,
- interactive = False,
- type = "filepath",
- label = i18n("Stem 5")
- )
- demucs_stem6 = gr.Audio(
- show_download_button = True,
- interactive = False,
- type = "filepath",
- label = i18n("Stem 6")
- )
-
- demucs_model.change(update_stems, inputs=[demucs_model], outputs=stem6)
-
- demucs_button.click(demucs_separator, [demucs_audio, demucs_model, demucs_output_format, demucs_shifts, demucs_segment_size, demucs_segments_enabled, demucs_overlap, demucs_batch_size, demucs_normalization_threshold, demucs_amplification_threshold], [demucs_stem1, demucs_stem2, demucs_stem3, demucs_stem4, demucs_stem5, demucs_stem6])
-
- with gr.TabItem(i18n("Leaderboard")):
- with gr.Group():
- with gr.Row(equal_height=True):
- list_filter = gr.Dropdown(
- label = i18n("List filter"),
- info = i18n("Filter and sort the model list by stem"),
- choices = ["vocals", "instrumental", "reverb", "echo", "noise", "crowd", "dry", "aspiration", "male", "woodwinds", "kick", "drums", "bass", "guitar", "piano", "other"],
- value = lambda : None
- )
- list_button = gr.Button(i18n("Show list!"), variant = "primary")
- output_list = gr.HTML(label = i18n("Leaderboard"))
-
- list_button.click(leaderboard, inputs=list_filter, outputs=output_list)
-
- with gr.TabItem(i18n("Themes")):
- themes_select = gr.Dropdown(
- label = i18n("Theme"),
- info = i18n("Select the theme you want to use. (Requires restarting the App)"),
- choices = loadThemes.get_list(),
- value = loadThemes.read_json(),
- visible = True
- )
- dummy_output = gr.Textbox(visible = False)
-
- themes_select.change(
- fn = loadThemes.select_theme,
- inputs = themes_select,
- outputs = [dummy_output]
- )
-
- with gr.TabItem(i18n("Credits")):
- gr.Markdown(
- """
- UVR5 UI created by **[Eddycrack 864](https://github.com/Eddycrack864).** Join **[AI HUB](https://discord.gg/aihub)** community.
- * python-audio-separator by [beveradb](https://github.com/beveradb).
- * Special thanks to [Ilaria](https://github.com/TheStingerX) for hosting this space and help.
- * Thanks to [Mikus](https://github.com/cappuch) for the help with the code.
- * Thanks to [Nick088](https://huggingface.co/Nick088) for the help to fix roformers.
- * Thanks to [yt_dlp](https://github.com/yt-dlp/yt-dlp) devs.
- * Separation by link source code and improvements by [Blane187](https://huggingface.co/Blane187).
- * Thanks to [ArisDev](https://github.com/aris-py) for porting UVR5 UI to Kaggle and improvements.
- * Thanks to [Bebra777228](https://github.com/Bebra777228)'s code for guiding me to improve my code.
- * Thanks to Nick088, MrM0dZ, Ryouko-Yamanda65777, lucinamari, perariroswe, Enes, LĂ©o 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()
+import os
+import sys
+import subprocess
+import re
+import platform
+import torch
+import logging
+import yt_dlp
+import json
+import gradio as gr
+import spaces
+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")
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+use_autocast = device == "cuda"
+
+if os.path.isdir("env"):
+ if platform.system() == "Windows":
+ separator_location = ".\\env\\Scripts\\audio-separator.exe"
+ elif platform.system() == "Linux":
+ separator_location = "env/bin/audio-separator"
+else:
+ 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 | 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 | Inst V1 by Unwa' : 'melband_roformer_inst_v1.ckpt',
+ 'MelBand Roformer Kim | Inst V1 (E) by Unwa' : 'melband_roformer_inst_v1e.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 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 | 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',
+ '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:
+ result = subprocess.run(
+ [separator_location, "-l", f"--list_filter={list_filter}"],
+ capture_output=True,
+ text=True,
+ )
+ if result.returncode != 0:
+ return f"Error: {result.stderr}"
+
+ return "" + "".join(
+ f"" +
+ "".join(f"| {cell} | " for cell in re.split(r"\s{2,}", line.strip())) +
+ "
"
+ for i, line in enumerate(re.findall(r"^(?!-+)(.+)$", result.stdout.strip(), re.MULTILINE))
+ ) + "
"
+
+ except Exception as e:
+ return f"Error: {e}"
+
+@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)):
+ base_name = os.path.splitext(os.path.basename(audio))[0]
+ roformer_model = roformer_models[model_key]
+ try:
+ separator = Separator(
+ log_level=logging.WARNING,
+ model_file_dir=models_dir,
+ output_dir=out_dir,
+ output_format=out_format,
+ use_autocast=use_autocast,
+ normalization_threshold=norm_thresh,
+ amplification_threshold=amp_thresh,
+ output_single_stem=single_stem,
+ 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)):
+ base_name = os.path.splitext(os.path.basename(audio))[0]
+ try:
+ separator = Separator(
+ log_level=logging.WARNING,
+ model_file_dir=models_dir,
+ output_dir=out_dir,
+ output_format=out_format,
+ use_autocast=use_autocast,
+ normalization_threshold=norm_thresh,
+ amplification_threshold=amp_thresh,
+ output_single_stem=single_stem,
+ 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)):
+ base_name = os.path.splitext(os.path.basename(audio))[0]
+ try:
+ separator = Separator(
+ log_level=logging.WARNING,
+ model_file_dir=models_dir,
+ output_dir=out_dir,
+ output_format=out_format,
+ use_autocast=use_autocast,
+ normalization_threshold=norm_thresh,
+ amplification_threshold=amp_thresh,
+ output_single_stem=single_stem,
+ mdx_params={
+ "hop_length": hop_length,
+ "segment_size": segment_size,
+ "overlap": overlap,
+ "batch_size": batch_size,
+ "enable_denoise": denoise,
+ }
+ )
+
+ progress(0.2, desc="Loading model...")
+ separator.load_model(model_filename=model)
+
+ progress(0.7, desc="Separating audio...")
+ separation = separator.separate(audio)
+
+ stems = [os.path.join(out_dir, file_name) for file_name in separation]
+
+ if single_stem.strip():
+ return stems[0], None
+
+ return stems[0], stems[1]
+
+ except Exception as e:
+ raise RuntimeError(f"MDX-NET separation failed: {e}") from e
+
+@track_presence("Performing VR Arch Separation")
+@spaces.GPU(duration=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)):
+ base_name = os.path.splitext(os.path.basename(audio))[0]
+ try:
+ separator = Separator(
+ log_level=logging.WARNING,
+ model_file_dir=models_dir,
+ output_dir=out_dir,
+ output_format=out_format,
+ use_autocast=use_autocast,
+ normalization_threshold=norm_thresh,
+ amplification_threshold=amp_thresh,
+ output_single_stem=single_stem,
+ vr_params={
+ "batch_size": batch_size,
+ "window_size": window_size,
+ "aggression": aggression,
+ "enable_tta": tta,
+ "enable_post_process": post_process,
+ "post_process_threshold": post_process_threshold,
+ "high_end_process": high_end_process,
+ }
+ )
+
+ progress(0.2, desc="Loading model...")
+ separator.load_model(model_filename=model)
+
+ progress(0.7, desc="Separating audio...")
+ separation = separator.separate(audio)
+
+ stems = [os.path.join(out_dir, file_name) for file_name in separation]
+
+ if single_stem.strip():
+ return stems[0], None
+
+ return stems[0], stems[1]
+
+ except Exception as e:
+ raise RuntimeError(f"VR ARCH separation failed: {e}") from e
+
+@track_presence("Performing Demucs Separation")
+@spaces.GPU(duration=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)):
+ base_name = os.path.splitext(os.path.basename(audio))[0]
+ try:
+ separator = Separator(
+ log_level=logging.WARNING,
+ model_file_dir=models_dir,
+ output_dir=out_dir,
+ output_format=out_format,
+ use_autocast=use_autocast,
+ normalization_threshold=norm_thresh,
+ amplification_threshold=amp_thresh,
+ demucs_params={
+ "batch_size": batch_size,
+ "segment_size": segment_size,
+ "shifts": shifts,
+ "overlap": overlap,
+ "segments_enabled": segments_enabled,
+ }
+ )
+
+ progress(0.2, desc="Loading model...")
+ separator.load_model(model_filename=model)
+
+ progress(0.7, desc="Separating audio...")
+ separation = separator.separate(audio)
+
+ stems = [os.path.join(out_dir, file_name) for file_name in separation]
+
+ if model == "htdemucs_6s.yaml":
+ return stems[0], stems[1], stems[2], stems[3], stems[4], stems[5]
+ else:
+ return stems[0], stems[1], stems[2], stems[3], None, None
+
+ except Exception as e:
+ raise RuntimeError(f"Demucs separation failed: {e}") from e
+
+def update_stems(model):
+ if model == "htdemucs_6s.yaml":
+ return gr.update(visible=True)
+ else:
+ return gr.update(visible=False)
+
+@track_presence("Performing BS/Mel Roformer Batch Separation")
+@spaces.GPU(duration=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):
+ found_files.clear()
+ logs.clear()
+ roformer_model = roformer_models[model_key]
+
+ for audio_files in os.listdir(path_input):
+ if audio_files.endswith(extensions):
+ found_files.append(audio_files)
+ total_files = len(found_files)
+
+ if total_files == 0:
+ logs.append("No valid audio files.")
+ yield "\n".join(logs)
+ else:
+ logs.append(f"{total_files} audio files found")
+ found_files.sort()
+
+ for audio_files in found_files:
+ file_path = os.path.join(path_input, audio_files)
+ base_name = os.path.splitext(os.path.basename(file_path))[0]
+ try:
+ separator = Separator(
+ log_level=logging.WARNING,
+ model_file_dir=models_dir,
+ output_dir=path_output,
+ output_format=out_format,
+ use_autocast=use_autocast,
+ normalization_threshold=norm_thresh,
+ amplification_threshold=amp_thresh,
+ output_single_stem=single_stem,
+ mdxc_params={
+ "segment_size": segment_size,
+ "override_model_segment_size": override_seg_size,
+ "batch_size": batch_size,
+ "overlap": overlap,
+ }
+ )
+
+ logs.append("Loading model...")
+ yield "\n".join(logs)
+ separator.load_model(model_filename=roformer_model)
+
+ logs.append(f"Separating file: {audio_files}")
+ yield "\n".join(logs)
+ separator.separate(file_path)
+ logs.append(f"File: {audio_files} separated!")
+ yield "\n".join(logs)
+ except Exception as e:
+ raise RuntimeError(f"Roformer batch separation failed: {e}") from e
+
+@track_presence("Performing MDXC Batch Separation")
+@spaces.GPU(duration=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):
+ found_files.clear()
+ logs.clear()
+
+ for audio_files in os.listdir(path_input):
+ if audio_files.endswith(extensions):
+ found_files.append(audio_files)
+ total_files = len(found_files)
+
+ if total_files == 0:
+ logs.append("No valid audio files.")
+ yield "\n".join(logs)
+ else:
+ logs.append(f"{total_files} audio files found")
+ found_files.sort()
+
+ for audio_files in found_files:
+ file_path = os.path.join(path_input, audio_files)
+ base_name = os.path.splitext(os.path.basename(file_path))[0]
+ try:
+ separator = Separator(
+ log_level=logging.WARNING,
+ model_file_dir=models_dir,
+ output_dir=path_output,
+ output_format=out_format,
+ use_autocast=use_autocast,
+ normalization_threshold=norm_thresh,
+ amplification_threshold=amp_thresh,
+ output_single_stem=single_stem,
+ mdxc_params={
+ "segment_size": segment_size,
+ "override_model_segment_size": override_seg_size,
+ "batch_size": batch_size,
+ "overlap": overlap,
+ }
+ )
+
+ logs.append("Loading model...")
+ yield "\n".join(logs)
+ separator.load_model(model_filename=model)
+
+ logs.append(f"Separating file: {audio_files}")
+ yield "\n".join(logs)
+ separator.separate(file_path)
+ logs.append(f"File: {audio_files} separated!")
+ yield "\n".join(logs)
+ except Exception as e:
+ raise RuntimeError(f"Roformer batch separation failed: {e}") from e
+
+@track_presence("Performing MDX-NET Batch Separation")
+@spaces.GPU(duration=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):
+ found_files.clear()
+ logs.clear()
+
+ for audio_files in os.listdir(path_input):
+ if audio_files.endswith(extensions):
+ found_files.append(audio_files)
+ total_files = len(found_files)
+
+ if total_files == 0:
+ logs.append("No valid audio files.")
+ yield "\n".join(logs)
+ else:
+ logs.append(f"{total_files} audio files found")
+ found_files.sort()
+
+ for audio_files in found_files:
+ file_path = os.path.join(path_input, audio_files)
+ base_name = os.path.splitext(os.path.basename(file_path))[0]
+ try:
+ separator = Separator(
+ log_level=logging.WARNING,
+ model_file_dir=models_dir,
+ output_dir=path_output,
+ output_format=out_format,
+ use_autocast=use_autocast,
+ normalization_threshold=norm_thresh,
+ amplification_threshold=amp_thresh,
+ output_single_stem=single_stem,
+ mdx_params={
+ "hop_length": hop_length,
+ "segment_size": segment_size,
+ "overlap": overlap,
+ "batch_size": batch_size,
+ "enable_denoise": denoise,
+ }
+ )
+
+ logs.append("Loading model...")
+ yield "\n".join(logs)
+ separator.load_model(model_filename=model)
+
+ logs.append(f"Separating file: {audio_files}")
+ yield "\n".join(logs)
+ separator.separate(file_path)
+ logs.append(f"File: {audio_files} separated!")
+ yield "\n".join(logs)
+ except Exception as e:
+ raise RuntimeError(f"Roformer batch separation failed: {e}") from e
+
+@track_presence("Performing VR Arch Batch Separation")
+@spaces.GPU(duration=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):
+ found_files.clear()
+ logs.clear()
+
+ for audio_files in os.listdir(path_input):
+ if audio_files.endswith(extensions):
+ found_files.append(audio_files)
+ total_files = len(found_files)
+
+ if total_files == 0:
+ logs.append("No valid audio files.")
+ yield "\n".join(logs)
+ else:
+ logs.append(f"{total_files} audio files found")
+ found_files.sort()
+
+ for audio_files in found_files:
+ file_path = os.path.join(path_input, audio_files)
+ base_name = os.path.splitext(os.path.basename(file_path))[0]
+ try:
+ separator = Separator(
+ log_level=logging.WARNING,
+ model_file_dir=models_dir,
+ output_dir=path_output,
+ output_format=out_format,
+ use_autocast=use_autocast,
+ normalization_threshold=norm_thresh,
+ amplification_threshold=amp_thresh,
+ output_single_stem=single_stem,
+ vr_params={
+ "batch_size": batch_size,
+ "window_size": window_size,
+ "aggression": aggression,
+ "enable_tta": tta,
+ "enable_post_process": post_process,
+ "post_process_threshold": post_process_threshold,
+ "high_end_process": high_end_process,
+ }
+ )
+
+ logs.append("Loading model...")
+ yield "\n".join(logs)
+ separator.load_model(model_filename=model)
+
+ logs.append(f"Separating file: {audio_files}")
+ yield "\n".join(logs)
+ separator.separate(file_path)
+ logs.append(f"File: {audio_files} separated!")
+ yield "\n".join(logs)
+ except Exception as e:
+ raise RuntimeError(f"Roformer batch separation failed: {e}") from e
+
+@track_presence("Performing Demucs Batch Separation")
+@spaces.GPU(duration=60)
+def demucs_batch(path_input, path_output, model, out_format, shifts, segment_size, segments_enabled, overlap, batch_size, norm_thresh, amp_thresh):
+ found_files.clear()
+ logs.clear()
+
+ for audio_files in os.listdir(path_input):
+ if audio_files.endswith(extensions):
+ found_files.append(audio_files)
+ total_files = len(found_files)
+
+ if total_files == 0:
+ logs.append("No valid audio files.")
+ yield "\n".join(logs)
+ else:
+ logs.append(f"{total_files} audio files found")
+ found_files.sort()
+
+ for audio_files in found_files:
+ file_path = os.path.join(path_input, audio_files)
+ try:
+ separator = Separator(
+ log_level=logging.WARNING,
+ model_file_dir=models_dir,
+ output_dir=path_output,
+ output_format=out_format,
+ use_autocast=use_autocast,
+ normalization_threshold=norm_thresh,
+ amplification_threshold=amp_thresh,
+ demucs_params={
+ "batch_size": batch_size,
+ "segment_size": segment_size,
+ "shifts": shifts,
+ "overlap": overlap,
+ "segments_enabled": segments_enabled,
+ }
+ )
+
+ logs.append("Loading model...")
+ yield "\n".join(logs)
+ separator.load_model(model_filename=model)
+
+ logs.append(f"Separating file: {audio_files}")
+ yield "\n".join(logs)
+ separator.separate(file_path)
+ logs.append(f"File: {audio_files} separated!")
+ yield "\n".join(logs)
+ except Exception as e:
+ raise RuntimeError(f"Roformer batch separation failed: {e}") from e
+
+with gr.Blocks(theme = loadThemes.load_json() or "NoCrypt/miku", title = "đ” UVR5 UI đ”") as app:
+ gr.Markdown(" đ” UVR5 UI đ”
")
+ gr.Markdown(i18n("If you liked this HF Space you can give me a â€ïž"))
+ gr.Markdown(i18n("Try UVR5 UI using Colab [here](https://colab.research.google.com/github/Eddycrack864/UVR5-UI/blob/main/UVR_UI.ipynb)"))
+ with gr.Tabs():
+ with gr.TabItem("BS/Mel Roformer"):
+ with gr.Row():
+ roformer_model = gr.Dropdown(
+ label = i18n("Select the model"),
+ choices = list(roformer_models.keys()),
+ value = lambda : None,
+ interactive = True
+ )
+ roformer_output_format = gr.Dropdown(
+ label = i18n("Select the output format"),
+ choices = output_format,
+ value = lambda : None,
+ interactive = True
+ )
+ with gr.Accordion(i18n("Advanced settings"), open = False):
+ with gr.Group():
+ with gr.Row():
+ roformer_segment_size = gr.Slider(
+ label = i18n("Segment size"),
+ info = i18n("Larger consumes more resources, but may give better results"),
+ minimum = 32,
+ maximum = 4000,
+ step = 32,
+ value = 256,
+ interactive = True
+ )
+ roformer_override_segment_size = gr.Checkbox(
+ label = i18n("Override segment size"),
+ info = i18n("Override model default segment size instead of using the model default value"),
+ value = False,
+ interactive = True
+ )
+ with gr.Row():
+ roformer_overlap = gr.Slider(
+ label = i18n("Overlap"),
+ info = i18n("Amount of overlap between prediction windows"),
+ minimum = 2,
+ maximum = 10,
+ step = 1,
+ value = 8,
+ interactive = True
+ )
+ roformer_batch_size = gr.Slider(
+ label = i18n("Batch size"),
+ info = i18n("Larger consumes more RAM but may process slightly faster"),
+ minimum = 1,
+ maximum = 16,
+ step = 1,
+ value = 1,
+ interactive = True
+ )
+ with gr.Row():
+ roformer_normalization_threshold = gr.Slider(
+ label = i18n("Normalization threshold"),
+ info = i18n("The threshold for audio normalization"),
+ minimum = 0.1,
+ maximum = 1,
+ step = 0.1,
+ value = 0.9,
+ interactive = True
+ )
+ roformer_amplification_threshold = gr.Slider(
+ label = i18n("Amplification threshold"),
+ info = i18n("The threshold for audio amplification"),
+ minimum = 0.1,
+ maximum = 1,
+ step = 0.1,
+ value = 0.7,
+ interactive = True
+ )
+ with gr.Row():
+ roformer_single_stem = gr.Textbox(
+ label = i18n("Output only single stem"),
+ placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
+ interactive = True
+ )
+ with gr.Row():
+ roformer_audio = gr.Audio(
+ label = i18n("Input audio"),
+ type = "filepath",
+ interactive = True
+ )
+ with gr.Accordion(i18n("Separation by link"), open = False):
+ with gr.Row():
+ roformer_link = gr.Textbox(
+ label = i18n("Link"),
+ placeholder = i18n("Paste the link here"),
+ interactive = True
+ )
+ with gr.Row():
+ gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
+ with gr.Row():
+ roformer_download_button = gr.Button(
+ i18n("Download!"),
+ variant = "primary"
+ )
+
+ roformer_download_button.click(download_audio, [roformer_link], [roformer_audio])
+
+ with gr.Accordion(i18n("Batch separation"), open = False):
+ with gr.Row():
+ roformer_input_path = gr.Textbox(
+ label = i18n("Input path"),
+ placeholder = i18n("Place the input path here"),
+ interactive = True
+ )
+ roformer_output_path = gr.Textbox(
+ label = i18n("Output path"),
+ placeholder = i18n("Place the output path here"),
+ interactive = True
+ )
+ with gr.Row():
+ roformer_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
+ with gr.Row():
+ roformer_info = gr.Textbox(
+ label = i18n("Output information"),
+ interactive = False
+ )
+
+ roformer_bath_button.click(roformer_batch, [roformer_input_path, roformer_output_path, roformer_model, roformer_output_format, roformer_segment_size, roformer_override_segment_size, roformer_overlap, roformer_batch_size, roformer_normalization_threshold, roformer_amplification_threshold, roformer_single_stem], [roformer_info])
+
+ with gr.Row():
+ roformer_button = gr.Button(i18n("Separate!"), variant = "primary")
+ with gr.Row():
+ roformer_stem1 = gr.Audio(
+ show_download_button = True,
+ interactive = False,
+ label = i18n("Stem 1"),
+ type = "filepath"
+ )
+ roformer_stem2 = gr.Audio(
+ show_download_button = True,
+ interactive = False,
+ label = i18n("Stem 2"),
+ type = "filepath"
+ )
+
+ roformer_button.click(roformer_separator, [roformer_audio, roformer_model, roformer_output_format, roformer_segment_size, roformer_override_segment_size, roformer_overlap, roformer_batch_size, roformer_normalization_threshold, roformer_amplification_threshold, roformer_single_stem], [roformer_stem1, roformer_stem2])
+
+ with gr.TabItem("MDX23C"):
+ with gr.Row():
+ mdx23c_model = gr.Dropdown(
+ label = i18n("Select the model"),
+ choices = mdx23c_models,
+ value = lambda : None,
+ interactive = True
+ )
+ mdx23c_output_format = gr.Dropdown(
+ label = i18n("Select the output format"),
+ choices = output_format,
+ value = lambda : None,
+ interactive = True
+ )
+ with gr.Accordion(i18n("Advanced settings"), open = False):
+ with gr.Group():
+ with gr.Row():
+ mdx23c_segment_size = gr.Slider(
+ minimum = 32,
+ maximum = 4000,
+ step = 32,
+ label = i18n("Segment size"),
+ info = i18n("Larger consumes more resources, but may give better results"),
+ value = 256,
+ interactive = True
+ )
+ mdx23c_override_segment_size = gr.Checkbox(
+ label = i18n("Override segment size"),
+ info = i18n("Override model default segment size instead of using the model default value"),
+ value = False,
+ interactive = True
+ )
+ with gr.Row():
+ mdx23c_overlap = gr.Slider(
+ minimum = 2,
+ maximum = 50,
+ step = 1,
+ label = i18n("Overlap"),
+ info = i18n("Amount of overlap between prediction windows"),
+ value = 8,
+ interactive = True
+ )
+ mdx23c_batch_size = gr.Slider(
+ label = i18n("Batch size"),
+ info = i18n("Larger consumes more RAM but may process slightly faster"),
+ minimum = 1,
+ maximum = 16,
+ step = 1,
+ value = 1,
+ interactive = True
+ )
+ with gr.Row():
+ mdx23c_normalization_threshold = gr.Slider(
+ label = i18n("Normalization threshold"),
+ info = i18n("The threshold for audio normalization"),
+ minimum = 0.1,
+ maximum = 1,
+ step = 0.1,
+ value = 0.9,
+ interactive = True
+ )
+ mdx23c_amplification_threshold = gr.Slider(
+ label = i18n("Amplification threshold"),
+ info = i18n("The threshold for audio amplification"),
+ minimum = 0.1,
+ maximum = 1,
+ step = 0.1,
+ value = 0.7,
+ interactive = True
+ )
+ with gr.Row():
+ mdx23c_single_stem = gr.Textbox(
+ label = i18n("Output only single stem"),
+ placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
+ interactive = True
+ )
+ with gr.Row():
+ mdx23c_audio = gr.Audio(
+ label = i18n("Input audio"),
+ type = "filepath",
+ interactive = True
+ )
+ with gr.Accordion(i18n("Separation by link"), open = False):
+ with gr.Row():
+ mdx23c_link = gr.Textbox(
+ label = i18n("Link"),
+ placeholder = i18n("Paste the link here"),
+ interactive = True
+ )
+ with gr.Row():
+ gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
+ with gr.Row():
+ mdx23c_download_button = gr.Button(
+ i18n("Download!"),
+ variant = "primary"
+ )
+
+ mdx23c_download_button.click(download_audio, [mdx23c_link], [mdx23c_audio])
+
+ with gr.Accordion(i18n("Batch separation"), open = False):
+ with gr.Row():
+ mdx23c_input_path = gr.Textbox(
+ label = i18n("Input path"),
+ placeholder = i18n("Place the input path here"),
+ interactive = True
+ )
+ mdx23c_output_path = gr.Textbox(
+ label = i18n("Output path"),
+ placeholder = i18n("Place the output path here"),
+ interactive = True
+ )
+ with gr.Row():
+ mdx23c_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
+ with gr.Row():
+ mdx23c_info = gr.Textbox(
+ label = i18n("Output information"),
+ interactive = False
+ )
+
+ mdx23c_bath_button.click(mdx23c_batch, [mdx23c_input_path, mdx23c_output_path, mdx23c_model, mdx23c_output_format, mdx23c_segment_size, mdx23c_override_segment_size, mdx23c_overlap, mdx23c_batch_size, mdx23c_normalization_threshold, mdx23c_amplification_threshold, mdx23c_single_stem], [mdx23c_info])
+
+ with gr.Row():
+ mdx23c_button = gr.Button(i18n("Separate!"), variant = "primary")
+ with gr.Row():
+ mdx23c_stem1 = gr.Audio(
+ show_download_button = True,
+ interactive = False,
+ label = i18n("Stem 1"),
+ type = "filepath"
+ )
+ mdx23c_stem2 = gr.Audio(
+ show_download_button = True,
+ interactive = False,
+ label = i18n("Stem 2"),
+ type = "filepath"
+ )
+
+ mdx23c_button.click(mdxc_separator, [mdx23c_audio, mdx23c_model, mdx23c_output_format, mdx23c_segment_size, mdx23c_override_segment_size, mdx23c_overlap, mdx23c_batch_size, mdx23c_normalization_threshold, mdx23c_amplification_threshold, mdx23c_single_stem], [mdx23c_stem1, mdx23c_stem2])
+
+ with gr.TabItem("MDX-NET"):
+ with gr.Row():
+ mdxnet_model = gr.Dropdown(
+ label = i18n("Select the model"),
+ choices = mdxnet_models,
+ value = lambda : None,
+ interactive = True
+ )
+ mdxnet_output_format = gr.Dropdown(
+ label = i18n("Select the output format"),
+ choices = output_format,
+ value = lambda : None,
+ interactive = True
+ )
+ with gr.Accordion(i18n("Advanced settings"), open = False):
+ with gr.Group():
+ with gr.Row():
+ mdxnet_hop_length = gr.Slider(
+ label = i18n("Hop length"),
+ info = i18n("Usually called stride in neural networks; only change if you know what you're doing"),
+ minimum = 32,
+ maximum = 2048,
+ step = 32,
+ value = 1024,
+ interactive = True
+ )
+ mdxnet_segment_size = gr.Slider(
+ minimum = 32,
+ maximum = 4000,
+ step = 32,
+ label = i18n("Segment size"),
+ info = i18n("Larger consumes more resources, but may give better results"),
+ value = 256,
+ interactive = True
+ )
+ mdxnet_denoise = gr.Checkbox(
+ label = i18n("Denoise"),
+ info = i18n("Enable denoising during separation"),
+ value = True,
+ interactive = True
+ )
+ with gr.Row():
+ mdxnet_overlap = gr.Slider(
+ label = i18n("Overlap"),
+ info = i18n("Amount of overlap between prediction windows"),
+ minimum = 0.001,
+ maximum = 0.999,
+ step = 0.001,
+ value = 0.25,
+ interactive = True
+ )
+ mdxnet_batch_size = gr.Slider(
+ label = i18n("Batch size"),
+ info = i18n("Larger consumes more RAM but may process slightly faster"),
+ minimum = 1,
+ maximum = 16,
+ step = 1,
+ value = 1,
+ interactive = True
+ )
+ with gr.Row():
+ mdxnet_normalization_threshold = gr.Slider(
+ label = i18n("Normalization threshold"),
+ info = i18n("The threshold for audio normalization"),
+ minimum = 0.1,
+ maximum = 1,
+ step = 0.1,
+ value = 0.9,
+ interactive = True
+ )
+ mdxnet_amplification_threshold = gr.Slider(
+ label = i18n("Amplification threshold"),
+ info = i18n("The threshold for audio amplification"),
+ minimum = 0.1,
+ maximum = 1,
+ step = 0.1,
+ value = 0.7,
+ interactive = True
+ )
+ with gr.Row():
+ mdxnet_single_stem = gr.Textbox(
+ label = i18n("Output only single stem"),
+ placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
+ interactive = True
+ )
+ with gr.Row():
+ mdxnet_audio = gr.Audio(
+ label = i18n("Input audio"),
+ type = "filepath",
+ interactive = True
+ )
+ with gr.Accordion(i18n("Separation by link"), open = False):
+ with gr.Row():
+ mdxnet_link = gr.Textbox(
+ label = i18n("Link"),
+ placeholder = i18n("Paste the link here"),
+ interactive = True
+ )
+ with gr.Row():
+ gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
+ with gr.Row():
+ mdxnet_download_button = gr.Button(
+ i18n("Download!"),
+ variant = "primary"
+ )
+
+ mdxnet_download_button.click(download_audio, [mdxnet_link], [mdxnet_audio])
+
+ with gr.Accordion(i18n("Batch separation"), open = False):
+ with gr.Row():
+ mdxnet_input_path = gr.Textbox(
+ label = i18n("Input path"),
+ placeholder = i18n("Place the input path here"),
+ interactive = True
+ )
+ mdxnet_output_path = gr.Textbox(
+ label = i18n("Output path"),
+ placeholder = i18n("Place the output path here"),
+ interactive = True
+ )
+ with gr.Row():
+ mdxnet_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
+ with gr.Row():
+ mdxnet_info = gr.Textbox(
+ label = i18n("Output information"),
+ interactive = False
+ )
+
+ mdxnet_bath_button.click(mdxnet_batch, [mdxnet_input_path, mdxnet_output_path, mdxnet_model, mdxnet_output_format, mdxnet_hop_length, mdxnet_segment_size, mdxnet_denoise, mdxnet_overlap, mdxnet_batch_size, mdxnet_normalization_threshold, mdxnet_amplification_threshold, mdxnet_single_stem], [mdxnet_info])
+
+ with gr.Row():
+ mdxnet_button = gr.Button(i18n("Separate!"), variant = "primary")
+ with gr.Row():
+ mdxnet_stem1 = gr.Audio(
+ show_download_button = True,
+ interactive = False,
+ label = i18n("Stem 1"),
+ type = "filepath"
+ )
+ mdxnet_stem2 = gr.Audio(
+ show_download_button = True,
+ interactive = False,
+ label = i18n("Stem 2"),
+ type = "filepath"
+ )
+
+ mdxnet_button.click(mdxnet_separator, [mdxnet_audio, mdxnet_model, mdxnet_output_format, mdxnet_hop_length, mdxnet_segment_size, mdxnet_denoise, mdxnet_overlap, mdxnet_batch_size, mdxnet_normalization_threshold, mdxnet_amplification_threshold, mdxnet_single_stem], [mdxnet_stem1, mdxnet_stem2])
+
+ with gr.TabItem("VR ARCH"):
+ with gr.Row():
+ vrarch_model = gr.Dropdown(
+ label = i18n("Select the model"),
+ choices = vrarch_models,
+ value = lambda : None,
+ interactive = True
+ )
+ vrarch_output_format = gr.Dropdown(
+ label = i18n("Select the output format"),
+ choices = output_format,
+ value = lambda : None,
+ interactive = True
+ )
+ with gr.Accordion(i18n("Advanced settings"), open = False):
+ with gr.Group():
+ with gr.Row():
+ vrarch_window_size = gr.Slider(
+ label = i18n("Window size"),
+ info = i18n("Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality"),
+ minimum=320,
+ maximum=1024,
+ step=32,
+ value = 512,
+ interactive = True
+ )
+ vrarch_agression = gr.Slider(
+ minimum = 1,
+ maximum = 50,
+ step = 1,
+ label = i18n("Agression"),
+ info = i18n("Intensity of primary stem extraction"),
+ value = 5,
+ interactive = True
+ )
+ vrarch_tta = gr.Checkbox(
+ label = i18n("TTA"),
+ info = i18n("Enable Test-Time-Augmentation; slow but improves quality"),
+ value = True,
+ visible = True,
+ interactive = True
+ )
+ with gr.Row():
+ vrarch_post_process = gr.Checkbox(
+ label = i18n("Post process"),
+ info = i18n("Identify leftover artifacts within vocal output; may improve separation for some songs"),
+ value = False,
+ visible = True,
+ interactive = True
+ )
+ vrarch_post_process_threshold = gr.Slider(
+ label = i18n("Post process threshold"),
+ info = i18n("Threshold for post-processing"),
+ minimum = 0.1,
+ maximum = 0.3,
+ step = 0.1,
+ value = 0.2,
+ interactive = True
+ )
+ with gr.Row():
+ vrarch_high_end_process = gr.Checkbox(
+ label = i18n("High end process"),
+ info = i18n("Mirror the missing frequency range of the output"),
+ value = False,
+ visible = True,
+ interactive = True,
+ )
+ vrarch_batch_size = gr.Slider(
+ label = i18n("Batch size"),
+ info = i18n("Larger consumes more RAM but may process slightly faster"),
+ minimum = 1,
+ maximum = 16,
+ step = 1,
+ value = 1,
+ interactive = True
+ )
+ with gr.Row():
+ vrarch_normalization_threshold = gr.Slider(
+ label = i18n("Normalization threshold"),
+ info = i18n("The threshold for audio normalization"),
+ minimum = 0.1,
+ maximum = 1,
+ step = 0.1,
+ value = 0.9,
+ interactive = True
+ )
+ vrarch_amplification_threshold = gr.Slider(
+ label = i18n("Amplification threshold"),
+ info = i18n("The threshold for audio amplification"),
+ minimum = 0.1,
+ maximum = 1,
+ step = 0.1,
+ value = 0.7,
+ interactive = True
+ )
+ with gr.Row():
+ vrarch_single_stem = gr.Textbox(
+ label = i18n("Output only single stem"),
+ placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
+ interactive = True
+ )
+ with gr.Row():
+ vrarch_audio = gr.Audio(
+ label = i18n("Input audio"),
+ type = "filepath",
+ interactive = True
+ )
+ with gr.Accordion(i18n("Separation by link"), open = False):
+ with gr.Row():
+ vrarch_link = gr.Textbox(
+ label = i18n("Link"),
+ placeholder = i18n("Paste the link here"),
+ interactive = True
+ )
+ with gr.Row():
+ gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
+ with gr.Row():
+ vrarch_download_button = gr.Button(
+ i18n("Download!"),
+ variant = "primary"
+ )
+
+ vrarch_download_button.click(download_audio, [vrarch_link], [vrarch_audio])
+
+ with gr.Accordion(i18n("Batch separation"), open = False):
+ with gr.Row():
+ vrarch_input_path = gr.Textbox(
+ label = i18n("Input path"),
+ placeholder = i18n("Place the input path here"),
+ interactive = True
+ )
+ vrarch_output_path = gr.Textbox(
+ label = i18n("Output path"),
+ placeholder = i18n("Place the output path here"),
+ interactive = True
+ )
+ with gr.Row():
+ vrarch_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
+ with gr.Row():
+ vrarch_info = gr.Textbox(
+ label = i18n("Output information"),
+ interactive = False
+ )
+
+ vrarch_bath_button.click(vrarch_batch, [vrarch_input_path, vrarch_output_path, vrarch_model, vrarch_output_format, vrarch_window_size, vrarch_agression, vrarch_tta, vrarch_post_process, vrarch_post_process_threshold, vrarch_high_end_process, vrarch_batch_size, vrarch_normalization_threshold, vrarch_amplification_threshold, vrarch_single_stem], [vrarch_info])
+
+ with gr.Row():
+ vrarch_button = gr.Button(i18n("Separate!"), variant = "primary")
+ with gr.Row():
+ vrarch_stem1 = gr.Audio(
+ show_download_button = True,
+ interactive = False,
+ type = "filepath",
+ label = i18n("Stem 1")
+ )
+ vrarch_stem2 = gr.Audio(
+ show_download_button = True,
+ interactive = False,
+ type = "filepath",
+ label = i18n("Stem 2")
+ )
+
+ vrarch_button.click(vrarch_separator, [vrarch_audio, vrarch_model, vrarch_output_format, vrarch_window_size, vrarch_agression, vrarch_tta, vrarch_post_process, vrarch_post_process_threshold, vrarch_high_end_process, vrarch_batch_size, vrarch_normalization_threshold, vrarch_amplification_threshold, vrarch_single_stem], [vrarch_stem1, vrarch_stem2])
+
+ with gr.TabItem("Demucs"):
+ with gr.Row():
+ demucs_model = gr.Dropdown(
+ label = i18n("Select the model"),
+ choices = demucs_models,
+ value = lambda : None,
+ interactive = True
+ )
+ demucs_output_format = gr.Dropdown(
+ label = i18n("Select the output format"),
+ choices = output_format,
+ value = lambda : None,
+ interactive = True
+ )
+ with gr.Accordion(i18n("Advanced settings"), open = False):
+ with gr.Group():
+ with gr.Row():
+ demucs_shifts = gr.Slider(
+ label = i18n("Shifts"),
+ info = i18n("Number of predictions with random shifts, higher = slower but better quality"),
+ minimum = 1,
+ maximum = 20,
+ step = 1,
+ value = 2,
+ interactive = True
+ )
+ demucs_segment_size = gr.Slider(
+ label = i18n("Segment size"),
+ info = i18n("Size of segments into which the audio is split. Higher = slower but better quality"),
+ minimum = 1,
+ maximum = 100,
+ step = 1,
+ value = 40,
+ interactive = True
+ )
+ demucs_segments_enabled = gr.Checkbox(
+ label = i18n("Segment-wise processing"),
+ info = i18n("Enable segment-wise processing"),
+ value = True,
+ interactive = True
+ )
+ with gr.Row():
+ demucs_overlap = gr.Slider(
+ label = i18n("Overlap"),
+ info = i18n("Overlap between prediction windows. Higher = slower but better quality"),
+ minimum=0.001,
+ maximum=0.999,
+ step=0.001,
+ value = 0.25,
+ interactive = True
+ )
+ demucs_batch_size = gr.Slider(
+ label = i18n("Batch size"),
+ info = i18n("Larger consumes more RAM but may process slightly faster"),
+ minimum = 1,
+ maximum = 16,
+ step = 1,
+ value = 1,
+ interactive = True
+ )
+ with gr.Row():
+ demucs_normalization_threshold = gr.Slider(
+ label = i18n("Normalization threshold"),
+ info = i18n("The threshold for audio normalization"),
+ minimum = 0.1,
+ maximum = 1,
+ step = 0.1,
+ value = 0.9,
+ interactive = True
+ )
+ demucs_amplification_threshold = gr.Slider(
+ label = i18n("Amplification threshold"),
+ info = i18n("The threshold for audio amplification"),
+ minimum = 0.1,
+ maximum = 1,
+ step = 0.1,
+ value = 0.7,
+ interactive = True
+ )
+ with gr.Row():
+ demucs_audio = gr.Audio(
+ label = i18n("Input audio"),
+ type = "filepath",
+ interactive = True
+ )
+ with gr.Accordion(i18n("Separation by link"), open = False):
+ with gr.Row():
+ demucs_link = gr.Textbox(
+ label = i18n("Link"),
+ placeholder = i18n("Paste the link here"),
+ interactive = True
+ )
+ with gr.Row():
+ gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
+ with gr.Row():
+ demucs_download_button = gr.Button(
+ i18n("Download!"),
+ variant = "primary"
+ )
+
+ demucs_download_button.click(download_audio, [demucs_link], [demucs_audio])
+
+ with gr.Accordion(i18n("Batch separation"), open = False):
+ with gr.Row():
+ demucs_input_path = gr.Textbox(
+ label = i18n("Input path"),
+ placeholder = i18n("Place the input path here"),
+ interactive = True
+ )
+ demucs_output_path = gr.Textbox(
+ label = i18n("Output path"),
+ placeholder = i18n("Place the output path here"),
+ interactive = True
+ )
+ with gr.Row():
+ demucs_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
+ with gr.Row():
+ demucs_info = gr.Textbox(
+ label = i18n("Output information"),
+ interactive = False
+ )
+
+ demucs_bath_button.click(demucs_batch, [demucs_input_path, demucs_output_path, demucs_model, demucs_output_format, demucs_shifts, demucs_segment_size, demucs_segments_enabled, demucs_overlap, demucs_batch_size, demucs_normalization_threshold, demucs_amplification_threshold], [demucs_info])
+
+ with gr.Row():
+ demucs_button = gr.Button(i18n("Separate!"), variant = "primary")
+ with gr.Row():
+ demucs_stem1 = gr.Audio(
+ show_download_button = True,
+ interactive = False,
+ type = "filepath",
+ label = i18n("Stem 1")
+ )
+ demucs_stem2 = gr.Audio(
+ show_download_button = True,
+ interactive = False,
+ type = "filepath",
+ label = i18n("Stem 2")
+ )
+ with gr.Row():
+ demucs_stem3 = gr.Audio(
+ show_download_button = True,
+ interactive = False,
+ type = "filepath",
+ label = i18n("Stem 3")
+ )
+ demucs_stem4 = gr.Audio(
+ show_download_button = True,
+ interactive = False,
+ type = "filepath",
+ label = i18n("Stem 4")
+ )
+ with gr.Row(visible=False) as stem6:
+ demucs_stem5 = gr.Audio(
+ show_download_button = True,
+ interactive = False,
+ type = "filepath",
+ label = i18n("Stem 5")
+ )
+ demucs_stem6 = gr.Audio(
+ show_download_button = True,
+ interactive = False,
+ type = "filepath",
+ label = i18n("Stem 6")
+ )
+
+ demucs_model.change(update_stems, inputs=[demucs_model], outputs=stem6)
+
+ demucs_button.click(demucs_separator, [demucs_audio, demucs_model, demucs_output_format, demucs_shifts, demucs_segment_size, demucs_segments_enabled, demucs_overlap, demucs_batch_size, demucs_normalization_threshold, demucs_amplification_threshold], [demucs_stem1, demucs_stem2, demucs_stem3, demucs_stem4, demucs_stem5, demucs_stem6])
+
+ with gr.TabItem(i18n("Leaderboard")):
+ with gr.Group():
+ with gr.Row(equal_height=True):
+ list_filter = gr.Dropdown(
+ label = i18n("List filter"),
+ info = i18n("Filter and sort the model list by stem"),
+ choices = ["vocals", "instrumental", "reverb", "echo", "noise", "crowd", "dry", "aspiration", "male", "woodwinds", "kick", "drums", "bass", "guitar", "piano", "other"],
+ value = lambda : None
+ )
+ list_button = gr.Button(i18n("Show list!"), variant = "primary")
+ output_list = gr.HTML(label = i18n("Leaderboard"))
+
+ list_button.click(leaderboard, inputs=list_filter, outputs=output_list)
+
+ with gr.TabItem(i18n("Themes")):
+ themes_select = gr.Dropdown(
+ label = i18n("Theme"),
+ info = i18n("Select the theme you want to use. (Requires restarting the App)"),
+ choices = loadThemes.get_list(),
+ value = loadThemes.read_json(),
+ visible = True
+ )
+ dummy_output = gr.Textbox(visible = False)
+
+ themes_select.change(
+ fn = loadThemes.select_theme,
+ inputs = themes_select,
+ outputs = [dummy_output]
+ )
+
+ with gr.TabItem(i18n("Credits")):
+ gr.Markdown(
+ """
+ UVR5 UI created by **[Eddycrack 864](https://github.com/Eddycrack864).** Join **[AI HUB](https://discord.gg/aihub)** community.
+ * python-audio-separator by [beveradb](https://github.com/beveradb).
+ * Special thanks to [Ilaria](https://github.com/TheStingerX) for hosting this space and help.
+ * Thanks to [Mikus](https://github.com/cappuch) for the help with the code.
+ * Thanks to [Nick088](https://huggingface.co/Nick088) for the help to fix roformers.
+ * Thanks to [yt_dlp](https://github.com/yt-dlp/yt-dlp) devs.
+ * Separation by link source code and improvements by [Blane187](https://huggingface.co/Blane187).
+ * Thanks to [ArisDev](https://github.com/aris-py) for porting UVR5 UI to Kaggle and improvements.
+ * Thanks to [Bebra777228](https://github.com/Bebra777228)'s code for guiding me to improve my code.
+ * Thanks to Nick088, MrM0dZ, Ryouko-Yamanda65777, lucinamari, perariroswe, Enes, LĂ©o 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()
\ No newline at end of file