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app.py
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import os
import subprocess
import re
import platform
import torch
import logging
import yt_dlp
import gradio as gr
import assets.themes.loadThemes as loadThemes
from audio_separator.separator import Separator
from assets.i18n.i18n import I18nAuto
from argparse import ArgumentParser
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"
if __name__ == "__main__":
parser = ArgumentParser(
description="Separate audio into multiple stems",
epilog="Example: python app.py --share --listen-port 8080 --open"
)
parser.add_argument(
"--share",
action="store_true",
help="Enable sharing of the interface through Gradio's temporary URLs"
)
parser.add_argument(
"--listen-port",
type=int,
default=9999,
help="The listening port that the server will use (default: 9999)"
)
parser.add_argument(
"--open",
action="store_true",
help="Automatically open the interface in the default web browser"
)
args = parser.parse_args()
#=========================#
# 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 "<table border='1'>" + "".join(
f"<tr style='{'font-weight: bold; font-size: 1.2em;' if i == 0 else ''}'>" +
"".join(f"<td>{cell}</td>" for cell in re.split(r"\s{2,}", line.strip())) +
"</tr>"
for i, line in enumerate(re.findall(r"^(?!-+)(.+)$", result.stdout.strip(), re.MULTILINE))
) + "</table>"
except Exception as e:
return f"Error: {e}"
def 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
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
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
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
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)
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
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
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
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
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 like UVR5 UI you can star my repo on [GitHub](https://github.com/Eddycrack864/UVR5-UI)"))
gr.Markdown(i18n("Try UVR5 UI on Hugging Face with A100 [here](https://huggingface.co/spaces/TheStinger/UVR5_UI)"))
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"),