import os import subprocess import re import platform import torch import logging import yt_dlp import spaces import gradio as gr import assets.themes.loadThemes as loadThemes from audio_separator.separator import Separator from assets.i18n.i18n import I18nAuto i18n = I18nAuto() 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 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"" for cell in re.split(r"\s{2,}", line.strip())) + "" for i, line in enumerate(re.findall(r"^(?!-+)(.+)$", result.stdout.strip(), re.MULTILINE)) ) + "
{cell}
" except Exception as e: return f"Error: {e}" @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 @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 @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 @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 @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) @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 @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 @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 @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 @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("

🎵 UVR5 UI 🎵

") gr.Markdown(i18n("If you liked this HF Space you can give me a ❤️")) gr.Markdown(i18n("Try UVR5 UI using Colab [here](https://colab.research.google.com/github/Eddycrack864/UVR5-UI/blob/main/UVR_UI.ipynb)")) 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 and Enes for helping translate UVR5 UI. You can donate to the original UVR5 project here: [!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/uvr5) """ ) app.queue() app.launch()