Upload app.py

app.py

@@ -0,0 +1,1529 @@
+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 "<table border='1'>" + "".join(
+            f"<tr style='{'font-weight: bold; font-size: 1.2em;' if i == 0 else ''}'>" + 
+            "".join(f"<td>{cell}</td>" for cell in re.split(r"\s{2,}", line.strip())) + 
+            "</tr>" 
+            for i, line in enumerate(re.findall(r"^(?!-+)(.+)$", result.stdout.strip(), re.MULTILINE))
+        ) + "</table>"
+    
+    except Exception as e:
+        return f"Error: {e}"
+
+@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 = "🎵 UVR5 UI 🎵") as app:
+    gr.Markdown("<h1> 🎵 UVR5 UI 🎵 </h1>")
+    gr.Markdown(i18n("If you liked this HF Space you can give me a ❤️"))
+    gr.Markdown(i18n("Try UVR5 UI using Colab [here](https://colab.research.google.com/github/Eddycrack864/UVR5-UI/blob/main/UVR_UI.ipynb)"))
+    with gr.Tabs():
+        with gr.TabItem("BS/Mel Roformer"):
+            with gr.Row():
+                roformer_model = gr.Dropdown(
+                    label = i18n("Select the model"),
+                    choices = list(roformer_models.keys()),
+                    value = lambda : None,
+                    interactive = True
+                )
+                roformer_output_format = gr.Dropdown(
+                    label = i18n("Select the output format"),
+                    choices = output_format,
+                    value = lambda : None,
+                    interactive = True
+                )
+            with gr.Accordion(i18n("Advanced settings"), open = False):
+                with gr.Group():
+                    with gr.Row():
+                        roformer_segment_size = gr.Slider(
+                            label = i18n("Segment size"),
+                            info = i18n("Larger consumes more resources, but may give better results"),
+                            minimum = 32,
+                            maximum = 4000,
+                            step = 32,
+                            value = 256,
+                            interactive = True
+                        )
+                        roformer_override_segment_size = gr.Checkbox(
+                            label = i18n("Override segment size"),
+                            info = i18n("Override model default segment size instead of using the model default value"),
+                            value = False,
+                            interactive = True
+                        )
+                    with gr.Row():
+                        roformer_overlap = gr.Slider(
+                            label = i18n("Overlap"),
+                            info = i18n("Amount of overlap between prediction windows"),
+                            minimum = 2,
+                            maximum = 10,
+                            step = 1,
+                            value = 8,
+                            interactive = True
+                        )
+                        roformer_batch_size = gr.Slider(
+                            label = i18n("Batch size"),
+                            info = i18n("Larger consumes more RAM but may process slightly faster"),
+                            minimum = 1,
+                            maximum = 16,
+                            step = 1,
+                            value = 1,
+                            interactive = True
+                        )
+                    with gr.Row():
+                        roformer_normalization_threshold = gr.Slider(
+                            label = i18n("Normalization threshold"),
+                            info = i18n("The threshold for audio normalization"),
+                            minimum = 0.1,
+                            maximum = 1,
+                            step = 0.1,
+                            value = 0.9,
+                            interactive = True
+                        )
+                        roformer_amplification_threshold = gr.Slider(
+                            label = i18n("Amplification threshold"),
+                            info = i18n("The threshold for audio amplification"),
+                            minimum = 0.1,
+                            maximum = 1,
+                            step = 0.1,
+                            value = 0.7,
+                            interactive = True
+                        )
+                    with gr.Row():
+                        roformer_single_stem = gr.Textbox(
+                            label = i18n("Output only single stem"),
+                            placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
+                            interactive = True
+                        )
+            with gr.Row():
+                roformer_audio = gr.Audio(
+                    label = i18n("Input audio"),
+                    type = "filepath",
+                    interactive = True
+                )
+            with gr.Accordion(i18n("Separation by link"), open = False):
+                with gr.Row():
+                    roformer_link = gr.Textbox(
+                        label = i18n("Link"),
+                        placeholder = i18n("Paste the link here"),
+                        interactive = True
+                    )
+                with gr.Row():
+                    gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
+                with gr.Row():
+                    roformer_download_button = gr.Button(
+                        i18n("Download!"),
+                        variant = "primary"
+                    )
+
+            roformer_download_button.click(download_audio, [roformer_link], [roformer_audio])
+
+            with gr.Accordion(i18n("Batch separation"), open = False):
+                with gr.Row():
+                    roformer_input_path = gr.Textbox(
+                        label = i18n("Input path"),
+                        placeholder = i18n("Place the input path here"),
+                        interactive = True
+                    )
+                    roformer_output_path = gr.Textbox(
+                        label = i18n("Output path"),
+                        placeholder = i18n("Place the output path here"),
+                        interactive = True
+                    )
+                with gr.Row():
+                    roformer_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
+                with gr.Row():
+                    roformer_info = gr.Textbox(
+                        label = i18n("Output information"),
+                        interactive = False
+                    )
+
+            roformer_bath_button.click(roformer_batch, [roformer_input_path, roformer_output_path, roformer_model, roformer_output_format, roformer_segment_size, roformer_override_segment_size, roformer_overlap, roformer_batch_size, roformer_normalization_threshold, roformer_amplification_threshold, roformer_single_stem], [roformer_info])
+
+            with gr.Row():
+                roformer_button = gr.Button(i18n("Separate!"), variant = "primary")
+            with gr.Row():
+                roformer_stem1 = gr.Audio(
+                    show_download_button = True,
+                    interactive = False,
+                    label = i18n("Stem 1"),
+                    type = "filepath"
+                )
+                roformer_stem2 = gr.Audio(
+                    show_download_button = True,
+                    interactive = False,
+                    label = i18n("Stem 2"),
+                    type = "filepath"
+                )
+
+            roformer_button.click(roformer_separator, [roformer_audio, roformer_model, roformer_output_format, roformer_segment_size, roformer_override_segment_size, roformer_overlap, roformer_batch_size, roformer_normalization_threshold, roformer_amplification_threshold, roformer_single_stem], [roformer_stem1, roformer_stem2])
+
+        with gr.TabItem("MDX23C"):
+            with gr.Row():
+                mdx23c_model = gr.Dropdown(
+                    label = i18n("Select the model"),
+                    choices = mdx23c_models,
+                    value = lambda : None,
+                    interactive = True
+                )
+                mdx23c_output_format = gr.Dropdown(
+                    label = i18n("Select the output format"),
+                    choices = output_format,
+                    value = lambda : None,
+                    interactive = True
+                )
+            with gr.Accordion(i18n("Advanced settings"), open = False):
+                with gr.Group():
+                    with gr.Row():
+                        mdx23c_segment_size = gr.Slider(
+                            minimum = 32,
+                            maximum = 4000,
+                            step = 32,
+                            label = i18n("Segment size"),
+                            info = i18n("Larger consumes more resources, but may give better results"),
+                            value = 256,
+                            interactive = True
+                        )
+                        mdx23c_override_segment_size = gr.Checkbox(
+                            label = i18n("Override segment size"),
+                            info = i18n("Override model default segment size instead of using the model default value"),
+                            value = False,
+                            interactive = True
+                        )
+                    with gr.Row():
+                        mdx23c_overlap = gr.Slider(
+                            minimum = 2,
+                            maximum = 50,
+                            step = 1,
+                            label = i18n("Overlap"),
+                            info = i18n("Amount of overlap between prediction windows"),
+                            value = 8,
+                            interactive = True
+                        )
+                        mdx23c_batch_size = gr.Slider(
+                            label = i18n("Batch size"),
+                            info = i18n("Larger consumes more RAM but may process slightly faster"),
+                            minimum = 1,
+                            maximum = 16,
+                            step = 1,
+                            value = 1,
+                            interactive = True
+                        )
+                    with gr.Row():
+                        mdx23c_normalization_threshold = gr.Slider(
+                            label = i18n("Normalization threshold"),
+                            info = i18n("The threshold for audio normalization"),
+                            minimum = 0.1,
+                            maximum = 1,
+                            step = 0.1,
+                            value = 0.9,
+                            interactive = True
+                        )
+                        mdx23c_amplification_threshold = gr.Slider(
+                            label = i18n("Amplification threshold"),
+                            info = i18n("The threshold for audio amplification"),
+                            minimum = 0.1,
+                            maximum = 1,
+                            step = 0.1,
+                            value = 0.7,
+                            interactive = True
+                        )
+                    with gr.Row():
+                        mdx23c_single_stem = gr.Textbox(
+                            label = i18n("Output only single stem"),
+                            placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
+                            interactive = True
+                        )
+            with gr.Row():
+                mdx23c_audio = gr.Audio(
+                    label = i18n("Input audio"),
+                    type = "filepath",
+                    interactive = True
+                )
+            with gr.Accordion(i18n("Separation by link"), open = False):
+                with gr.Row():
+                    mdx23c_link = gr.Textbox(
+                        label = i18n("Link"),
+                        placeholder = i18n("Paste the link here"),
+                        interactive = True
+                    )
+                with gr.Row():
+                    gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
+                with gr.Row():
+                    mdx23c_download_button = gr.Button(
+                        i18n("Download!"),
+                        variant = "primary"
+                    )
+
+            mdx23c_download_button.click(download_audio, [mdx23c_link], [mdx23c_audio])
+
+            with gr.Accordion(i18n("Batch separation"), open = False):
+                with gr.Row():
+                    mdx23c_input_path = gr.Textbox(
+                        label = i18n("Input path"),
+                        placeholder = i18n("Place the input path here"),
+                        interactive = True
+                    )
+                    mdx23c_output_path = gr.Textbox(
+                        label = i18n("Output path"),
+                        placeholder = i18n("Place the output path here"),
+                        interactive = True
+                    )
+                with gr.Row():
+                    mdx23c_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
+                with gr.Row():
+                    mdx23c_info = gr.Textbox(
+                        label = i18n("Output information"),
+                        interactive = False
+                    )
+
+            mdx23c_bath_button.click(mdx23c_batch, [mdx23c_input_path, mdx23c_output_path, mdx23c_model, mdx23c_output_format, mdx23c_segment_size, mdx23c_override_segment_size, mdx23c_overlap, mdx23c_batch_size, mdx23c_normalization_threshold, mdx23c_amplification_threshold, mdx23c_single_stem], [mdx23c_info])
+
+            with gr.Row():
+                mdx23c_button = gr.Button(i18n("Separate!"), variant = "primary")
+            with gr.Row():
+                mdx23c_stem1 = gr.Audio(
+                    show_download_button = True,
+                    interactive = False,
+                    label = i18n("Stem 1"),
+                    type = "filepath"
+                )
+                mdx23c_stem2 = gr.Audio(
+                    show_download_button = True,
+                    interactive = False,
+                    label = i18n("Stem 2"),
+                    type = "filepath"
+                )
+
+            mdx23c_button.click(mdxc_separator, [mdx23c_audio, mdx23c_model, mdx23c_output_format, mdx23c_segment_size, mdx23c_override_segment_size, mdx23c_overlap, mdx23c_batch_size, mdx23c_normalization_threshold, mdx23c_amplification_threshold, mdx23c_single_stem], [mdx23c_stem1, mdx23c_stem2])
+                
+        with gr.TabItem("MDX-NET"):
+            with gr.Row():
+                mdxnet_model = gr.Dropdown(
+                    label = i18n("Select the model"),
+                    choices = mdxnet_models,
+                    value = lambda : None,
+                    interactive = True
+                )
+                mdxnet_output_format = gr.Dropdown(
+                    label = i18n("Select the output format"),
+                    choices = output_format,
+                    value = lambda : None,
+                    interactive = True
+                )
+            with gr.Accordion(i18n("Advanced settings"), open = False):
+                with gr.Group():
+                    with gr.Row():
+                        mdxnet_hop_length = gr.Slider(
+                            label = i18n("Hop length"),
+                            info = i18n("Usually called stride in neural networks; only change if you know what you're doing"),
+                            minimum = 32,
+                            maximum = 2048,
+                            step = 32,
+                            value = 1024,
+                            interactive = True
+                        )
+                        mdxnet_segment_size = gr.Slider(
+                            minimum = 32,
+                            maximum = 4000,
+                            step = 32,
+                            label = i18n("Segment size"),
+                            info = i18n("Larger consumes more resources, but may give better results"),
+                            value = 256,
+                            interactive = True
+                        )
+                        mdxnet_denoise = gr.Checkbox(
+                            label = i18n("Denoise"),
+                            info = i18n("Enable denoising during separation"),
+                            value = True,
+                            interactive = True
+                        )
+                    with gr.Row():
+                        mdxnet_overlap = gr.Slider(
+                            label = i18n("Overlap"),
+                            info = i18n("Amount of overlap between prediction windows"),
+                            minimum = 0.001,
+                            maximum = 0.999,
+                            step = 0.001,
+                            value = 0.25,
+                            interactive = True
+                        )
+                        mdxnet_batch_size = gr.Slider(
+                            label = i18n("Batch size"),
+                            info = i18n("Larger consumes more RAM but may process slightly faster"),
+                            minimum = 1,
+                            maximum = 16,
+                            step = 1,
+                            value = 1,
+                            interactive = True
+                        )
+                    with gr.Row():
+                        mdxnet_normalization_threshold = gr.Slider(
+                            label = i18n("Normalization threshold"),
+                            info = i18n("The threshold for audio normalization"),
+                            minimum = 0.1,
+                            maximum = 1,
+                            step = 0.1,
+                            value = 0.9,
+                            interactive = True
+                        )
+                        mdxnet_amplification_threshold = gr.Slider(
+                            label = i18n("Amplification threshold"),
+                            info = i18n("The threshold for audio amplification"),
+                            minimum = 0.1,
+                            maximum = 1,
+                            step = 0.1,
+                            value = 0.7,
+                            interactive = True
+                        )
+                    with gr.Row():
+                        mdxnet_single_stem = gr.Textbox(
+                            label = i18n("Output only single stem"),
+                            placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
+                            interactive = True
+                        )
+            with gr.Row():
+                mdxnet_audio = gr.Audio(
+                    label = i18n("Input audio"),
+                    type = "filepath",
+                    interactive = True
+                )
+            with gr.Accordion(i18n("Separation by link"), open = False):
+                with gr.Row():
+                    mdxnet_link = gr.Textbox(
+                        label = i18n("Link"),
+                        placeholder = i18n("Paste the link here"),
+                        interactive = True
+                    )
+                with gr.Row():
+                    gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
+                with gr.Row():
+                    mdxnet_download_button = gr.Button(
+                        i18n("Download!"),
+                        variant = "primary"
+                    )
+                
+            mdxnet_download_button.click(download_audio, [mdxnet_link], [mdxnet_audio])
+
+            with gr.Accordion(i18n("Batch separation"), open = False):
+                with gr.Row():
+                    mdxnet_input_path = gr.Textbox(
+                        label = i18n("Input path"),
+                        placeholder = i18n("Place the input path here"),
+                        interactive = True
+                    )
+                    mdxnet_output_path = gr.Textbox(
+                        label = i18n("Output path"),
+                        placeholder = i18n("Place the output path here"),
+                        interactive = True
+                    )
+                with gr.Row():
+                    mdxnet_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
+                with gr.Row():
+                    mdxnet_info = gr.Textbox(
+                        label = i18n("Output information"),
+                        interactive = False
+                    )
+
+            mdxnet_bath_button.click(mdxnet_batch, [mdxnet_input_path, mdxnet_output_path, mdxnet_model, mdxnet_output_format, mdxnet_hop_length, mdxnet_segment_size, mdxnet_denoise, mdxnet_overlap, mdxnet_batch_size, mdxnet_normalization_threshold, mdxnet_amplification_threshold, mdxnet_single_stem], [mdxnet_info])
+
+            with gr.Row():
+                mdxnet_button = gr.Button(i18n("Separate!"), variant = "primary")
+            with gr.Row():
+                mdxnet_stem1 = gr.Audio(
+                    show_download_button = True,
+                    interactive = False,
+                    label = i18n("Stem 1"),
+                    type = "filepath"
+                )
+                mdxnet_stem2 = gr.Audio(
+                    show_download_button = True,
+                    interactive = False,
+                    label = i18n("Stem 2"),
+                    type = "filepath"
+                )
+
+            mdxnet_button.click(mdxnet_separator, [mdxnet_audio, mdxnet_model, mdxnet_output_format, mdxnet_hop_length, mdxnet_segment_size, mdxnet_denoise, mdxnet_overlap, mdxnet_batch_size, mdxnet_normalization_threshold, mdxnet_amplification_threshold, mdxnet_single_stem], [mdxnet_stem1, mdxnet_stem2])
+
+        with gr.TabItem("VR ARCH"):
+            with gr.Row():
+                vrarch_model = gr.Dropdown(
+                    label = i18n("Select the model"),
+                    choices = vrarch_models,
+                    value = lambda : None,
+                    interactive = True
+                )
+                vrarch_output_format = gr.Dropdown(
+                    label = i18n("Select the output format"),
+                    choices = output_format,
+                    value = lambda : None,
+                    interactive = True
+                )
+            with gr.Accordion(i18n("Advanced settings"), open = False):
+                with gr.Group():
+                    with gr.Row():
+                        vrarch_window_size = gr.Slider(
+                            label = i18n("Window size"),
+                            info = i18n("Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality"),
+                            minimum=320,
+                            maximum=1024,
+                            step=32,
+                            value = 512,
+                            interactive = True
+                        )
+                        vrarch_agression = gr.Slider(
+                            minimum = 1,
+                            maximum = 50,
+                            step = 1,
+                            label = i18n("Agression"),
+                            info = i18n("Intensity of primary stem extraction"),
+                            value = 5,
+                            interactive = True
+                        )
+                        vrarch_tta = gr.Checkbox(
+                            label = i18n("TTA"),
+                            info = i18n("Enable Test-Time-Augmentation; slow but improves quality"),
+                            value = True,
+                            visible = True,
+                            interactive = True
+                        )
+                    with gr.Row():
+                        vrarch_post_process = gr.Checkbox(
+                            label = i18n("Post process"),
+                            info = i18n("Identify leftover artifacts within vocal output; may improve separation for some songs"),
+                            value = False,
+                            visible = True,
+                            interactive = True
+                        )
+                        vrarch_post_process_threshold = gr.Slider(
+                            label = i18n("Post process threshold"),
+                            info = i18n("Threshold for post-processing"),
+                            minimum = 0.1,
+                            maximum = 0.3,
+                            step = 0.1,
+                            value = 0.2,
+                            interactive = True
+                        )
+                    with gr.Row():
+                        vrarch_high_end_process = gr.Checkbox(
+                            label = i18n("High end process"),
+                            info = i18n("Mirror the missing frequency range of the output"),
+                            value = False,
+                            visible = True,
+                            interactive = True,
+                        )
+                        vrarch_batch_size = gr.Slider(
+                            label = i18n("Batch size"),
+                            info = i18n("Larger consumes more RAM but may process slightly faster"),
+                            minimum = 1,
+                            maximum = 16,
+                            step = 1,
+                            value = 1,
+                            interactive = True
+                        )
+                    with gr.Row():
+                        vrarch_normalization_threshold = gr.Slider(
+                            label = i18n("Normalization threshold"),
+                            info = i18n("The threshold for audio normalization"),
+                            minimum = 0.1,
+                            maximum = 1,
+                            step = 0.1,
+                            value = 0.9,
+                            interactive = True
+                        )
+                        vrarch_amplification_threshold = gr.Slider(
+                            label = i18n("Amplification threshold"),
+                            info = i18n("The threshold for audio amplification"),
+                            minimum = 0.1,
+                            maximum = 1,
+                            step = 0.1,
+                            value = 0.7,
+                            interactive = True
+                        )
+                    with gr.Row():
+                        vrarch_single_stem = gr.Textbox(
+                            label = i18n("Output only single stem"),
+                            placeholder = i18n("Write the stem you want, check the stems of each model on Leaderboard. e.g. Instrumental"),
+                            interactive = True
+                        )
+            with gr.Row():
+                vrarch_audio = gr.Audio(
+                    label = i18n("Input audio"),
+                    type = "filepath",
+                    interactive = True
+                )
+            with gr.Accordion(i18n("Separation by link"), open = False):
+                with gr.Row():
+                    vrarch_link = gr.Textbox(
+                        label = i18n("Link"),
+                        placeholder = i18n("Paste the link here"),
+                        interactive = True
+                    )
+                with gr.Row():
+                    gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
+                with gr.Row():
+                    vrarch_download_button = gr.Button(
+                        i18n("Download!"),
+                        variant = "primary"
+                )
+
+            vrarch_download_button.click(download_audio, [vrarch_link], [vrarch_audio])
+            
+            with gr.Accordion(i18n("Batch separation"), open = False):
+                with gr.Row():
+                    vrarch_input_path = gr.Textbox(
+                        label = i18n("Input path"),
+                        placeholder = i18n("Place the input path here"),
+                        interactive = True
+                    )
+                    vrarch_output_path = gr.Textbox(
+                        label = i18n("Output path"),
+                        placeholder = i18n("Place the output path here"),
+                        interactive = True
+                    )
+                with gr.Row():
+                    vrarch_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
+                with gr.Row():
+                    vrarch_info = gr.Textbox(
+                        label = i18n("Output information"),
+                        interactive = False
+                    )
+
+            vrarch_bath_button.click(vrarch_batch, [vrarch_input_path, vrarch_output_path, vrarch_model, vrarch_output_format, vrarch_window_size, vrarch_agression, vrarch_tta, vrarch_post_process, vrarch_post_process_threshold, vrarch_high_end_process, vrarch_batch_size, vrarch_normalization_threshold, vrarch_amplification_threshold, vrarch_single_stem], [vrarch_info])
+
+            with gr.Row():
+                vrarch_button = gr.Button(i18n("Separate!"), variant = "primary")
+            with gr.Row():
+                vrarch_stem1 = gr.Audio(
+                    show_download_button = True,
+                    interactive = False,
+                    type = "filepath",
+                    label = i18n("Stem 1")
+                )
+                vrarch_stem2 = gr.Audio(
+                    show_download_button = True,
+                    interactive = False,
+                    type = "filepath",
+                    label = i18n("Stem 2")
+                )
+
+            vrarch_button.click(vrarch_separator, [vrarch_audio, vrarch_model, vrarch_output_format, vrarch_window_size, vrarch_agression, vrarch_tta, vrarch_post_process, vrarch_post_process_threshold, vrarch_high_end_process, vrarch_batch_size, vrarch_normalization_threshold, vrarch_amplification_threshold, vrarch_single_stem], [vrarch_stem1, vrarch_stem2])
+
+        with gr.TabItem("Demucs"):
+            with gr.Row():
+                demucs_model = gr.Dropdown(
+                    label = i18n("Select the model"),
+                    choices = demucs_models,
+                    value = lambda : None,
+                    interactive = True
+                )
+                demucs_output_format = gr.Dropdown(
+                    label = i18n("Select the output format"),
+                    choices = output_format,
+                    value = lambda : None,
+                    interactive = True
+                )
+            with gr.Accordion(i18n("Advanced settings"), open = False):
+                with gr.Group():
+                    with gr.Row():
+                        demucs_shifts = gr.Slider(
+                            label = i18n("Shifts"),
+                            info = i18n("Number of predictions with random shifts, higher = slower but better quality"),
+                            minimum = 1,
+                            maximum = 20,
+                            step = 1,
+                            value = 2,
+                            interactive = True
+                        )
+                        demucs_segment_size = gr.Slider(
+                            label = i18n("Segment size"),
+                            info = i18n("Size of segments into which the audio is split. Higher = slower but better quality"),
+                            minimum = 1,
+                            maximum = 100,
+                            step = 1,
+                            value = 40,
+                            interactive = True
+                        )
+                        demucs_segments_enabled = gr.Checkbox(
+                            label = i18n("Segment-wise processing"),
+                            info = i18n("Enable segment-wise processing"),
+                            value = True,
+                            interactive = True
+                        )
+                    with gr.Row():
+                        demucs_overlap = gr.Slider(
+                            label = i18n("Overlap"),
+                            info = i18n("Overlap between prediction windows. Higher = slower but better quality"),
+                            minimum=0.001,
+                            maximum=0.999,
+                            step=0.001,
+                            value = 0.25,
+                            interactive = True
+                        )
+                        demucs_batch_size = gr.Slider(
+                            label = i18n("Batch size"),
+                            info = i18n("Larger consumes more RAM but may process slightly faster"),
+                            minimum = 1,
+                            maximum = 16,
+                            step = 1,
+                            value = 1,
+                            interactive = True
+                        )
+                    with gr.Row():
+                        demucs_normalization_threshold = gr.Slider(
+                            label = i18n("Normalization threshold"),
+                            info = i18n("The threshold for audio normalization"),
+                            minimum = 0.1,
+                            maximum = 1,
+                            step = 0.1,
+                            value = 0.9,
+                            interactive = True
+                        )
+                        demucs_amplification_threshold = gr.Slider(
+                            label = i18n("Amplification threshold"),
+                            info = i18n("The threshold for audio amplification"),
+                            minimum = 0.1,
+                            maximum = 1,
+                            step = 0.1,
+                            value = 0.7,
+                            interactive = True
+                        )
+            with gr.Row():
+                demucs_audio = gr.Audio(
+                    label = i18n("Input audio"),
+                    type = "filepath",
+                    interactive = True
+                )
+            with gr.Accordion(i18n("Separation by link"), open = False):
+                with gr.Row():
+                    demucs_link = gr.Textbox(
+                        label = i18n("Link"),
+                        placeholder = i18n("Paste the link here"),
+                        interactive = True
+                    )
+                with gr.Row():
+                    gr.Markdown(i18n("You can paste the link to the video/audio from many sites, check the complete list [here](https://github.com/yt-dlp/yt-dlp/blob/master/supportedsites.md)"))
+                with gr.Row():
+                    demucs_download_button = gr.Button(
+                        i18n("Download!"),
+                        variant = "primary"
+                    )
+
+            demucs_download_button.click(download_audio, [demucs_link], [demucs_audio])
+
+            with gr.Accordion(i18n("Batch separation"), open = False):
+                with gr.Row():
+                    demucs_input_path = gr.Textbox(
+                        label = i18n("Input path"),
+                        placeholder = i18n("Place the input path here"),
+                        interactive = True
+                    )
+                    demucs_output_path = gr.Textbox(
+                        label = i18n("Output path"),
+                        placeholder = i18n("Place the output path here"),
+                        interactive = True
+                    )
+                with gr.Row():
+                    demucs_bath_button = gr.Button(i18n("Separate!"), variant = "primary")
+                with gr.Row():
+                    demucs_info = gr.Textbox(
+                        label = i18n("Output information"),
+                        interactive = False
+                    )
+
+            demucs_bath_button.click(demucs_batch, [demucs_input_path, demucs_output_path, demucs_model, demucs_output_format, demucs_shifts, demucs_segment_size, demucs_segments_enabled, demucs_overlap, demucs_batch_size, demucs_normalization_threshold, demucs_amplification_threshold], [demucs_info])
+
+            with gr.Row():
+                demucs_button = gr.Button(i18n("Separate!"), variant = "primary")
+            with gr.Row():
+                demucs_stem1 = gr.Audio(
+                    show_download_button = True,
+                    interactive = False,
+                    type = "filepath",
+                    label = i18n("Stem 1")
+                )
+                demucs_stem2 = gr.Audio(
+                    show_download_button = True,
+                    interactive = False,
+                    type = "filepath",
+                    label = i18n("Stem 2")
+                )
+            with gr.Row():
+                demucs_stem3 = gr.Audio(
+                    show_download_button = True,
+                    interactive = False,
+                    type = "filepath",
+                    label = i18n("Stem 3")
+                )
+                demucs_stem4 = gr.Audio(
+                    show_download_button = True,
+                    interactive = False,
+                    type = "filepath",
+                    label = i18n("Stem 4")
+                )
+            with gr.Row(visible=False) as stem6:
+                demucs_stem5 = gr.Audio(
+                    show_download_button = True,
+                    interactive = False,
+                    type = "filepath",
+                    label = i18n("Stem 5")
+                )
+                demucs_stem6 = gr.Audio(
+                    show_download_button = True,
+                    interactive = False,
+                    type = "filepath",
+                    label = i18n("Stem 6")
+                )
+
+            demucs_model.change(update_stems, inputs=[demucs_model], outputs=stem6)
+                
+            demucs_button.click(demucs_separator, [demucs_audio, demucs_model, demucs_output_format, demucs_shifts, demucs_segment_size, demucs_segments_enabled, demucs_overlap, demucs_batch_size, demucs_normalization_threshold, demucs_amplification_threshold], [demucs_stem1, demucs_stem2, demucs_stem3, demucs_stem4, demucs_stem5, demucs_stem6])
+
+        with gr.TabItem(i18n("Leaderboard")):
+            with gr.Group():
+                with gr.Row(equal_height=True):
+                    list_filter = gr.Dropdown(
+                        label = i18n("List filter"),
+                        info = i18n("Filter and sort the model list by stem"),
+                        choices = ["vocals", "instrumental", "reverb", "echo", "noise", "crowd", "dry", "aspiration", "male", "woodwinds", "kick", "drums", "bass", "guitar", "piano", "other"],
+                        value = lambda : None
+                    )
+                    list_button = gr.Button(i18n("Show list!"), variant = "primary")
+            output_list = gr.HTML(label = i18n("Leaderboard"))
+
+            list_button.click(leaderboard, inputs=list_filter, outputs=output_list)
+
+        with gr.TabItem(i18n("Themes")):
+            themes_select = gr.Dropdown(
+                label = i18n("Theme"),
+                info = i18n("Select the theme you want to use. (Requires restarting the App)"),
+                choices = loadThemes.get_list(),
+                value = loadThemes.read_json(),
+                visible = True
+            )
+            dummy_output = gr.Textbox(visible = False)
+
+            themes_select.change(
+                fn = loadThemes.select_theme,
+                inputs = themes_select,
+                outputs = [dummy_output]
+            )
+
+        with gr.TabItem(i18n("Credits")):
+            gr.Markdown(
+                """
+                UVR5 UI created by **[Eddycrack 864](https://github.com/Eddycrack864).** Join **[AI HUB](https://discord.gg/aihub)** community.
+                * python-audio-separator by [beveradb](https://github.com/beveradb).
+                * Special thanks to [Ilaria](https://github.com/TheStingerX) for hosting this space and help.
+                * Thanks to [Mikus](https://github.com/cappuch) for the help with the code.
+                * Thanks to [Nick088](https://huggingface.co/Nick088) for the help to fix roformers.
+                * Thanks to [yt_dlp](https://github.com/yt-dlp/yt-dlp) devs.
+                * Separation by link source code and improvements by [Blane187](https://huggingface.co/Blane187).
+                * Thanks to [ArisDev](https://github.com/aris-py) for porting UVR5 UI to Kaggle and improvements.
+                * Thanks to [Bebra777228](https://github.com/Bebra777228)'s code for guiding me to improve my code.
+                * Thanks to Nick088, MrM0dZ, Ryouko-Yamanda65777, lucinamari, perariroswe, Enes, Léo and the_undead0 for helping translate UVR5 UI.
+                * Thanks to vadigr123 for creating the images for the Discord Rich Presence.
+                
+                You can donate to the original UVR5 project [here](https://www.buymeacoffee.com/uvr5)
+                """
+            )
+
+app.queue()
+app.launch()