Audio-Separator / app.py
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import os
import re
import torch
import shutil
import logging
import subprocess
import gradio as gr
from audio_separator.separator import Separator
device = "cuda" if torch.cuda.is_available() else "cpu"
use_autocast = device == "cuda"
#=========================#
# Roformer Models #
#=========================#
ROFORMER_MODELS = {
# BS Roformer
'BS-Roformer-Viperx-1053': 'model_bs_roformer_ep_937_sdr_10.5309.ckpt',
'BS-Roformer-Viperx-1296': 'model_bs_roformer_ep_368_sdr_12.9628.ckpt',
'BS-Roformer-Viperx-1297': 'model_bs_roformer_ep_317_sdr_12.9755.ckpt',
'BS-Roformer-De-Reverb': 'deverb_bs_roformer_8_384dim_10depth.ckpt',
'BS Roformer | Chorus Male-Female by Sucial': 'model_chorus_bs_roformer_ep_267_sdr_24.1275.ckpt',
# MelBand Roformer
'Mel-Roformer-Crowd-Aufr33-Viperx': 'mel_band_roformer_crowd_aufr33_viperx_sdr_8.7144.ckpt',
'Mel-Roformer-Karaoke-Aufr33-Viperx': 'mel_band_roformer_karaoke_aufr33_viperx_sdr_10.1956.ckpt',
'Mel-Roformer-Viperx-1143': 'model_mel_band_roformer_ep_3005_sdr_11.4360.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 | Aspiration Less Aggressive by Sucial': 'aspiration_mel_band_roformer_less_aggr_sdr_18.1201.ckpt',
'MelBand Roformer | Aspiration by Sucial': 'aspiration_mel_band_roformer_sdr_18.9845.ckpt',
'MelBand Roformer | De-Reverb Less Aggressive by anvuew': 'dereverb_mel_band_roformer_less_aggressive_anvuew_sdr_18.8050.ckpt',
'MelBand Roformer | De-Reverb by anvuew': 'dereverb_mel_band_roformer_anvuew_sdr_19.1729.ckpt',
'MelBand Roformer | Vocals by Kimberley Jensen': 'vocals_mel_band_roformer.ckpt',
'Mel-Roformer-Denoise-Aufr33-Aggr': 'denoise_mel_band_roformer_aufr33_aggr_sdr_27.9768.ckpt',
'Mel-Roformer-Denoise-Aufr33': 'denoise_mel_band_roformer_aufr33_sdr_27.9959.ckpt',
'MelBand Roformer | Bleed Suppressor V1 by unwa-97chris': 'mel_band_roformer_bleed_suppressor_v1.ckpt',
# MelBand Roformer Kim
'MelBand Roformer Kim | FT by unwa': 'mel_band_roformer_kim_ft_unwa.ckpt',
'MelBand Roformer Kim | Big Beta 4 FT by unwa': 'melband_roformer_big_beta4.ckpt',
'MelBand Roformer Kim | Big Beta 5e FT by unwa': 'melband_roformer_big_beta5e.ckpt',
'MelBand Roformer 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 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',
}
#=========================#
# MDX23C Models #
#=========================#
MDX23C_MODELS = {
'MDX23C DrumSep by aufr33-jarredou': 'MDX23C-DrumSep-aufr33-jarredou.ckpt',
'MDX23C De-Reverb by aufr33-jarredou': 'MDX23C-De-Reverb-aufr33-jarredou.ckpt',
'MDX23C-InstVoc HQ': 'MDX23C-8KFFT-InstVoc_HQ.ckpt',
'VIP | MDX23C-InstVoc HQ 2': 'MDX23C-8KFFT-InstVoc_HQ_2.ckpt',
'VIP | MDX23C_D1581': 'MDX23C_D1581.ckpt',
}
#=========================#
# MDXN-NET Models #
#=========================#
MDXNET_MODELS = {
'UVR-MDX-NET 1': 'UVR_MDXNET_1_9703.onnx',
'UVR-MDX-NET 2': 'UVR_MDXNET_2_9682.onnx',
'UVR-MDX-NET 3': 'UVR_MDXNET_3_9662.onnx',
'UVR_MDXNET_9482': 'UVR_MDXNET_9482.onnx',
'UVR-MDX-NET Inst 1': 'UVR-MDX-NET-Inst_1.onnx',
'UVR-MDX-NET Inst 2': 'UVR-MDX-NET-Inst_2.onnx',
'UVR-MDX-NET Inst 3': 'UVR-MDX-NET-Inst_3.onnx',
'UVR-MDX-NET Inst HQ 1': 'UVR-MDX-NET-Inst_HQ_1.onnx',
'UVR-MDX-NET Inst HQ 2': 'UVR-MDX-NET-Inst_HQ_2.onnx',
'UVR-MDX-NET Inst HQ 3': 'UVR-MDX-NET-Inst_HQ_3.onnx',
'UVR-MDX-NET Inst HQ 4': 'UVR-MDX-NET-Inst_HQ_4.onnx',
'UVR-MDX-NET Inst HQ 5': 'UVR-MDX-NET-Inst_HQ_5.onnx',
'UVR-MDX-NET Inst Main': 'UVR-MDX-NET-Inst_Main.onnx',
'UVR-MDX-NET Karaoke': 'UVR_MDXNET_KARA.onnx',
'UVR-MDX-NET Karaoke 2': 'UVR_MDXNET_KARA_2.onnx',
'UVR-MDX-NET Main': 'UVR_MDXNET_Main.onnx',
'UVR-MDX-NET Voc FT': 'UVR-MDX-NET-Voc_FT.onnx',
'Kim Inst': 'Kim_Inst.onnx',
'Kim Vocal 1': 'Kim_Vocal_1.onnx',
'Kim Vocal 2': 'Kim_Vocal_2.onnx',
'kuielab_a_bass': 'kuielab_a_bass.onnx',
'kuielab_a_drums': 'kuielab_a_drums.onnx',
'kuielab_a_other': 'kuielab_a_other.onnx',
'kuielab_a_vocals': 'kuielab_a_vocals.onnx',
'kuielab_b_bass': 'kuielab_b_bass.onnx',
'kuielab_b_drums': 'kuielab_b_drums.onnx',
'kuielab_b_other': 'kuielab_b_other.onnx',
'kuielab_b_vocals': 'kuielab_b_vocals.onnx',
'Reverb HQ By FoxJoy': 'Reverb_HQ_By_FoxJoy.onnx',
'VIP | UVR-MDX-NET_Inst_82_beta': 'UVR-MDX-NET_Inst_82_beta.onnx',
'VIP | UVR-MDX-NET_Inst_90_beta': 'UVR-MDX-NET_Inst_90_beta.onnx',
'VIP | UVR-MDX-NET_Inst_187_beta': 'UVR-MDX-NET_Inst_187_beta.onnx',
'VIP | UVR-MDX-NET-Inst_full_292': 'UVR-MDX-NET-Inst_full_292.onnx',
'VIP | UVR-MDX-NET_Main_340': 'UVR-MDX-NET_Main_340.onnx',
'VIP | UVR-MDX-NET_Main_390': 'UVR-MDX-NET_Main_390.onnx',
'VIP | UVR-MDX-NET_Main_406': 'UVR-MDX-NET_Main_406.onnx',
'VIP | UVR-MDX-NET_Main_427': 'UVR-MDX-NET_Main_427.onnx',
'VIP | UVR-MDX-NET_Main_438': 'UVR-MDX-NET_Main_438.onnx',
}
#========================#
# VR-ARCH Models #
#========================#
VR_ARCH_MODELS = {
'1_HP-UVR': '1_HP-UVR.pth',
'2_HP-UVR': '2_HP-UVR.pth',
'3_HP-Vocal-UVR': '3_HP-Vocal-UVR.pth',
'4_HP-Vocal-UVR': '4_HP-Vocal-UVR.pth',
'5_HP-Karaoke-UVR': '5_HP-Karaoke-UVR.pth',
'6_HP-Karaoke-UVR': '6_HP-Karaoke-UVR.pth',
'7_HP2-UVR': '7_HP2-UVR.pth',
'8_HP2-UVR': '8_HP2-UVR.pth',
'9_HP2-UVR': '9_HP2-UVR.pth',
'10_SP-UVR-2B-32000-1': '10_SP-UVR-2B-32000-1.pth',
'11_SP-UVR-2B-32000-2': '11_SP-UVR-2B-32000-2.pth',
'12_SP-UVR-3B-44100': '12_SP-UVR-3B-44100.pth',
'13_SP-UVR-4B-44100-1': '13_SP-UVR-4B-44100-1.pth',
'14_SP-UVR-4B-44100-2': '14_SP-UVR-4B-44100-2.pth',
'15_SP-UVR-MID-44100-1': '15_SP-UVR-MID-44100-1.pth',
'16_SP-UVR-MID-44100-2': '16_SP-UVR-MID-44100-2.pth',
'17_HP-Wind_Inst-UVR': '17_HP-Wind_Inst-UVR.pth',
'MGM_HIGHEND_v4': 'MGM_HIGHEND_v4.pth',
'MGM_LOWEND_A_v4': 'MGM_LOWEND_A_v4.pth',
'MGM_LOWEND_B_v4': 'MGM_LOWEND_B_v4.pth',
'MGM_MAIN_v4': 'MGM_MAIN_v4.pth',
'UVR-BVE-4B_SN-44100-1': 'UVR-BVE-4B_SN-44100-1.pth',
'UVR-De-Reverb by aufr33-jarredou': 'UVR-De-Reverb-aufr33-jarredou.pth',
'UVR-De-Echo-Aggressive by FoxJoy': 'UVR-De-Echo-Aggressive.pth',
'UVR-De-Echo-Normal by FoxJoy': 'UVR-De-Echo-Normal.pth',
'UVR-DeEcho-DeReverb by FoxJoy': 'UVR-DeEcho-DeReverb.pth',
'UVR-DeNoise-Lite by FoxJoy': 'UVR-DeNoise-Lite.pth',
'UVR-DeNoise by FoxJoy': 'UVR-DeNoise.pth',
}
#=======================#
# DEMUCS Models #
#=======================#
DEMUCS_MODELS = {
'htdemucs': 'htdemucs.yaml',
'htdemucs_6s': 'htdemucs_6s.yaml',
'htdemucs_ft': 'htdemucs_ft.yaml',
'hdemucs_mmi': 'hdemucs_mmi.yaml',
}
def print_message(input_file, model_name):
"""Prints information about the audio separation process."""
base_name = os.path.splitext(os.path.basename(input_file))[0]
print("\n")
print("🎵 Audio-Separator 🎵")
print("Input audio:", base_name)
print("Separation Model:", model_name)
print("Audio Separation Process...")
def prepare_output_dir(input_file, output_dir):
"""Create a directory for the output files and clean it if it already exists."""
base_name = os.path.splitext(os.path.basename(input_file))[0]
out_dir = os.path.join(output_dir, base_name)
try:
if os.path.exists(out_dir):
shutil.rmtree(out_dir)
os.makedirs(out_dir)
except Exception as e:
raise RuntimeError(f"Failed to prepare output directory {out_dir}: {e}")
return out_dir
def rename_stems(audio, vocals_stem, instrumental_stem, other_stem, drums_stem, bass_stem, guitar_stem, piano_stem, model):
base_name = os.path.splitext(os.path.basename(audio))[0]
stems = {
"Vocals": vocals_stem.replace("NAME", base_name).replace("STEM", "Vocals").replace("MODEL", model),
"Instrumental": instrumental_stem.replace("NAME", base_name).replace("STEM", "Instrumental").replace("MODEL", model),
"Drums": drums_stem.replace("NAME", base_name).replace("STEM", "Drums").replace("MODEL", model),
"Bass": bass_stem.replace("NAME", base_name).replace("STEM", "Bass").replace("MODEL", model),
"Other": other_stem.replace("NAME", base_name).replace("STEM", "Other").replace("MODEL", model),
"Guitar": guitar_stem.replace("NAME", base_name).replace("STEM", "Guitar").replace("MODEL", model),
"Piano": piano_stem.replace("NAME", base_name).replace("STEM", "Piano").replace("MODEL", model),
}
return stems
def leaderboard(list_filter, list_limit):
try:
result = subprocess.run(
["audio-separator", "-l", f"--list_filter={list_filter}", f"--list_limit={list_limit}"],
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, seg_size, override_seg_size, overlap, pitch_shift, model_dir, out_dir, out_format, norm_thresh, amp_thresh, batch_size, vocals_stem, instrumental_stem, other_stem, drums_stem, bass_stem, guitar_stem, piano_stem, progress=gr.Progress(track_tqdm=True)):
"""Separate audio using Roformer model."""
stemname = rename_stems(audio, vocals_stem, instrumental_stem, other_stem, drums_stem, bass_stem, guitar_stem, piano_stem, model_key)
print_message(audio, model_key)
model = ROFORMER_MODELS[model_key]
try:
out_dir = prepare_output_dir(audio, out_dir)
separator = Separator(
log_level=logging.WARNING,
model_file_dir=model_dir,
output_dir=out_dir,
output_format=out_format,
normalization_threshold=norm_thresh,
amplification_threshold=amp_thresh,
use_autocast=use_autocast,
mdxc_params={
"segment_size": seg_size,
"override_model_segment_size": override_seg_size,
"batch_size": batch_size,
"overlap": overlap,
"pitch_shift": pitch_shift,
}
)
progress(0.2, desc="Model loaded...")
separator.load_model(model_filename=model)
progress(0.7, desc="Audio separated...")
separation = separator.separate(audio, stemname)
print(f"Separation complete!\nResults: {', '.join(separation)}")
stems = [os.path.join(out_dir, file_name) for file_name in separation]
return stems[0], stems[1]
except Exception as e:
raise RuntimeError(f"Roformer separation failed: {e}") from e
def mdx23c_separator(audio, model_key, seg_size, override_seg_size, overlap, pitch_shift, model_dir, out_dir, out_format, norm_thresh, amp_thresh, batch_size, vocals_stem, instrumental_stem, other_stem, drums_stem, bass_stem, guitar_stem, piano_stem, progress=gr.Progress(track_tqdm=True)):
"""Separate audio using MDX23C model."""
stemname = rename_stems(audio, vocals_stem, instrumental_stem, other_stem, drums_stem, bass_stem, guitar_stem, piano_stem, model_key)
print_message(audio, model_key)
model = MDX23C_MODELS[model_key]
try:
out_dir = prepare_output_dir(audio, out_dir)
separator = Separator(
log_level=logging.WARNING,
model_file_dir=model_dir,
output_dir=out_dir,
output_format=out_format,
normalization_threshold=norm_thresh,
amplification_threshold=amp_thresh,
use_autocast=use_autocast,
mdxc_params={
"segment_size": seg_size,
"override_model_segment_size": override_seg_size,
"batch_size": batch_size,
"overlap": overlap,
"pitch_shift": pitch_shift,
}
)
progress(0.2, desc="Model loaded...")
separator.load_model(model_filename=model)
progress(0.7, desc="Audio separated...")
separation = separator.separate(audio, stemname)
print(f"Separation complete!\nResults: {', '.join(separation)}")
stems = [os.path.join(out_dir, file_name) for file_name in separation]
return stems[0], stems[1]
except Exception as e:
raise RuntimeError(f"MDX23C separation failed: {e}") from e
def mdx_separator(audio, model_key, hop_length, seg_size, overlap, denoise, model_dir, out_dir, out_format, norm_thresh, amp_thresh, batch_size, vocals_stem, instrumental_stem, other_stem, drums_stem, bass_stem, guitar_stem, piano_stem, progress=gr.Progress(track_tqdm=True)):
"""Separate audio using MDX-NET model."""
stemname = rename_stems(audio, vocals_stem, instrumental_stem, other_stem, drums_stem, bass_stem, guitar_stem, piano_stem, model_key)
print_message(audio, model_key)
model = MDXNET_MODELS[model_key]
try:
out_dir = prepare_output_dir(audio, out_dir)
separator = Separator(
log_level=logging.WARNING,
model_file_dir=model_dir,
output_dir=out_dir,
output_format=out_format,
normalization_threshold=norm_thresh,
amplification_threshold=amp_thresh,
use_autocast=use_autocast,
mdx_params={
"hop_length": hop_length,
"segment_size": seg_size,
"overlap": overlap,
"batch_size": batch_size,
"enable_denoise": denoise,
}
)
progress(0.2, desc="Model loaded...")
separator.load_model(model_filename=model)
progress(0.7, desc="Audio separated...")
separation = separator.separate(audio, stemname)
print(f"Separation complete!\nResults: {', '.join(separation)}")
stems = [os.path.join(out_dir, file_name) for file_name in separation]
return stems[0], stems[1]
except Exception as e:
raise RuntimeError(f"MDX-NET separation failed: {e}") from e
def vr_separator(audio, model_key, window_size, aggression, tta, post_process, post_process_threshold, high_end_process, model_dir, out_dir, out_format, norm_thresh, amp_thresh, batch_size, vocals_stem, instrumental_stem, other_stem, drums_stem, bass_stem, guitar_stem, piano_stem, progress=gr.Progress(track_tqdm=True)):
"""Separate audio using VR ARCH model."""
stemname = rename_stems(audio, vocals_stem, instrumental_stem, other_stem, drums_stem, bass_stem, guitar_stem, piano_stem, model_key)
print_message(audio, model_key)
model = VR_ARCH_MODELS[model_key]
try:
out_dir = prepare_output_dir(audio, out_dir)
separator = Separator(
log_level=logging.WARNING,
model_file_dir=model_dir,
output_dir=out_dir,
output_format=out_format,
normalization_threshold=norm_thresh,
amplification_threshold=amp_thresh,
use_autocast=use_autocast,
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="Model loaded...")
separator.load_model(model_filename=model)
progress(0.7, desc="Audio separated...")
separation = separator.separate(audio, stemname)
print(f"Separation complete!\nResults: {', '.join(separation)}")
stems = [os.path.join(out_dir, file_name) for file_name in separation]
return stems[0], stems[1]
except Exception as e:
raise RuntimeError(f"VR ARCH separation failed: {e}") from e
def demucs_separator(audio, model_key, seg_size, shifts, overlap, segments_enabled, model_dir, out_dir, out_format, norm_thresh, amp_thresh, vocals_stem, instrumental_stem, other_stem, drums_stem, bass_stem, guitar_stem, piano_stem, progress=gr.Progress(track_tqdm=True)):
"""Separate audio using Demucs model."""
stemname = rename_stems(audio, vocals_stem, instrumental_stem, other_stem, drums_stem, bass_stem, guitar_stem, piano_stem, model_key)
print_message(audio, model_key)
model = DEMUCS_MODELS[model_key]
try:
out_dir = prepare_output_dir(audio, out_dir)
separator = Separator(
log_level=logging.WARNING,
model_file_dir=model_dir,
output_dir=out_dir,
output_format=out_format,
normalization_threshold=norm_thresh,
amplification_threshold=amp_thresh,
use_autocast=use_autocast,
demucs_params={
"segment_size": seg_size,
"shifts": shifts,
"overlap": overlap,
"segments_enabled": segments_enabled,
}
)
progress(0.2, desc="Model loaded...")
separator.load_model(model_filename=model)
progress(0.7, desc="Audio separated...")
separation = separator.separate(audio, stemname)
print(f"Separation complete!\nResults: {', '.join(separation)}")
stems = [os.path.join(out_dir, file_name) for file_name in separation]
if model_key == "htdemucs_6s":
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):
"""Update the visibility of stem outputs based on the selected Demucs model."""
if model == "htdemucs_6s":
return gr.update(visible=True)
else:
return gr.update(visible=False)
def show_hide_params(param):
"""Update the visibility of a parameter based on the checkbox state."""
return gr.update(visible=param)
with gr.Blocks(
title="🎵 Audio-Separator by Politrees 🎵",
css="footer{display:none !important}",
theme=gr.themes.Default(
spacing_size="sm",
radius_size="lg",
)
) as app:
gr.HTML("<h1><center> 🎵 Audio-Separator by Politrees 🎵 </center></h1>")
with gr.Tab("Roformer"):
with gr.Group():
with gr.Row():
roformer_model = gr.Dropdown(value="MelBand Roformer Kim | Big Beta 5e FT by unwa", label="Select the Model", choices=list(ROFORMER_MODELS.keys()), scale=3)
roformer_output_format = gr.Dropdown(value="wav", choices=["wav", "flac", "mp3"], label="Output Format", info="The format of the output audio file.", scale=1)
with gr.Accordion("Advanced settings", open=False):
with gr.Column(variant='panel'):
with gr.Group():
roformer_override_seg_size = gr.Checkbox(value=False, label="Override segment size", info="Override model default segment size instead of using the model default value.")
with gr.Row():
roformer_seg_size = gr.Slider(minimum=32, maximum=4000, step=32, value=256, label="Segment Size", info="Larger consumes more resources, but may give better results.", visible=False)
roformer_overlap = gr.Slider(minimum=2, maximum=10, step=1, value=8, label="Overlap", info="Amount of overlap between prediction windows. Lower is better but slower.")
roformer_pitch_shift = gr.Slider(minimum=-24, maximum=24, step=1, value=0, label="Pitch shift", info="Shift audio pitch by a number of semitones while processing. may improve output for deep/high vocals.")
with gr.Column(variant='panel'):
with gr.Group():
with gr.Row():
roformer_batch_size = gr.Slider(minimum=1, maximum=16, step=1, value=1, label="Batch Size", info="Larger consumes more RAM but may process slightly faster.")
roformer_norm_threshold = gr.Slider(minimum=0.1, maximum=1, step=0.1, value=0.9, label="Normalization threshold", info="The threshold for audio normalization.")
roformer_amp_threshold = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.0, label="Amplification threshold", info="The threshold for audio amplification.")
with gr.Row():
roformer_audio = gr.Audio(label="Input Audio", type="filepath")
with gr.Row():
roformer_button = gr.Button("Separate!", variant="primary")
with gr.Row():
roformer_stem1 = gr.Audio(label="Stem 1", type="filepath", interactive=False)
roformer_stem2 = gr.Audio(label="Stem 2", type="filepath", interactive=False)
with gr.Tab("MDX23C"):
with gr.Group():
with gr.Row():
mdx23c_model = gr.Dropdown(value="MDX23C-InstVoc HQ", label="Select the Model", choices=list(MDX23C_MODELS.keys()), scale=3)
mdx23c_output_format = gr.Dropdown(value="wav", choices=["wav", "flac", "mp3"], label="Output Format", info="The format of the output audio file.", scale=1)
with gr.Accordion("Advanced settings", open=False):
with gr.Column(variant='panel'):
with gr.Group():
mdx23c_override_seg_size = gr.Checkbox(value=False, label="Override segment size", info="Override model default segment size instead of using the model default value.")
with gr.Row():
mdx23c_seg_size = gr.Slider(minimum=32, maximum=4000, step=32, value=256, label="Segment Size", info="Larger consumes more resources, but may give better results.", visible=False)
mdx23c_overlap = gr.Slider(minimum=2, maximum=50, step=1, value=8, label="Overlap", info="Amount of overlap between prediction windows. Higher is better but slower.")
mdx23c_pitch_shift = gr.Slider(minimum=-24, maximum=24, step=1, value=0, label="Pitch shift", info="Shift audio pitch by a number of semitones while processing. may improve output for deep/high vocals.")
with gr.Column(variant='panel'):
with gr.Group():
with gr.Row():
mdx23c_batch_size = gr.Slider(minimum=1, maximum=16, step=1, value=1, label="Batch Size", info="Larger consumes more RAM but may process slightly faster.")
mdx23c_norm_threshold = gr.Slider(minimum=0.1, maximum=1, step=0.1, value=0.9, label="Normalization threshold", info="The threshold for audio normalization.")
mdx23c_amp_threshold = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.0, label="Amplification threshold", info="The threshold for audio amplification.")
with gr.Row():
mdx23c_audio = gr.Audio(label="Input Audio", type="filepath")
with gr.Row():
mdx23c_button = gr.Button("Separate!", variant="primary")
with gr.Row():
mdx23c_stem1 = gr.Audio(label="Stem 1", type="filepath", interactive=False)
mdx23c_stem2 = gr.Audio(label="Stem 2", type="filepath", interactive=False)
with gr.Tab("MDX-NET"):
with gr.Group():
with gr.Row():
mdx_model = gr.Dropdown(value="UVR-MDX-NET Inst HQ 5", label="Select the Model", choices=list(MDXNET_MODELS.keys()), scale=3)
mdx_output_format = gr.Dropdown(value="wav", choices=["wav", "flac", "mp3"], label="Output Format", info="The format of the output audio file.", scale=1)
with gr.Accordion("Advanced settings", open=False):
with gr.Column(variant='panel'):
with gr.Group():
mdx_denoise = gr.Checkbox(value=False, label="Denoise", info="Enable denoising after separation.")
with gr.Row():
mdx_hop_length = gr.Slider(minimum=32, maximum=2048, step=32, value=1024, label="Hop Length", info="Usually called stride in neural networks; only change if you know what you're doing.")
mdx_seg_size = gr.Slider(minimum=32, maximum=4000, step=32, value=256, label="Segment Size", info="Larger consumes more resources, but may give better results.")
mdx_overlap = gr.Slider(minimum=0.001, maximum=0.999, step=0.001, value=0.25, label="Overlap", info="Amount of overlap between prediction windows. Higher is better but slower.")
with gr.Column(variant='panel'):
with gr.Group():
with gr.Row():
mdx_batch_size = gr.Slider(minimum=1, maximum=16, step=1, value=1, label="Batch Size", info="Larger consumes more RAM but may process slightly faster.")
mdx_norm_threshold = gr.Slider(minimum=0.1, maximum=1, step=0.1, value=0.9, label="Normalization threshold", info="The threshold for audio normalization.")
mdx_amp_threshold = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.0, label="Amplification threshold", info="The threshold for audio amplification.")
with gr.Row():
mdx_audio = gr.Audio(label="Input Audio", type="filepath")
with gr.Row():
mdx_button = gr.Button("Separate!", variant="primary")
with gr.Row():
mdx_stem1 = gr.Audio(label="Stem 1", type="filepath", interactive=False)
mdx_stem2 = gr.Audio(label="Stem 2", type="filepath", interactive=False)
with gr.Tab("VR ARCH"):
with gr.Group():
with gr.Row():
vr_model = gr.Dropdown(value="1_HP-UVR", label="Select the Model", choices=list(VR_ARCH_MODELS.keys()), scale=3)
vr_output_format = gr.Dropdown(value="wav", choices=["wav", "flac", "mp3"], label="Output Format", info="The format of the output audio file.", scale=1)
with gr.Accordion("Advanced settings", open=False):
with gr.Column(variant='panel'):
with gr.Group():
with gr.Row():
vr_post_process = gr.Checkbox(value=False, label="Post Process", info="Identify leftover artifacts within vocal output; may improve separation for some songs.")
vr_tta = gr.Checkbox(value=False, label="TTA", info="Enable Test-Time-Augmentation; slow but improves quality.")
vr_high_end_process = gr.Checkbox(value=False, label="High End Process", info="Mirror the missing frequency range of the output.")
with gr.Row():
vr_post_process_threshold = gr.Slider(minimum=0.1, maximum=0.3, step=0.1, value=0.2, label="Post Process Threshold", info="Threshold for post-processing.", visible=False)
vr_window_size = gr.Slider(minimum=320, maximum=1024, step=32, value=512, label="Window Size", info="Balance quality and speed. 1024 = fast but lower, 320 = slower but better quality.")
vr_aggression = gr.Slider(minimum=1, maximum=100, step=1, value=5, label="Agression", info="Intensity of primary stem extraction.")
with gr.Column(variant='panel'):
with gr.Group():
with gr.Row():
vr_batch_size = gr.Slider(minimum=1, maximum=16, step=1, value=1, label="Batch Size", info="Larger consumes more RAM but may process slightly faster.")
vr_norm_threshold = gr.Slider(minimum=0.1, maximum=1, step=0.1, value=0.9, label="Normalization threshold", info="The threshold for audio normalization.")
vr_amp_threshold = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.0, label="Amplification threshold", info="The threshold for audio amplification.")
with gr.Row():
vr_audio = gr.Audio(label="Input Audio", type="filepath")
with gr.Row():
vr_button = gr.Button("Separate!", variant="primary")
with gr.Row():
vr_stem1 = gr.Audio(label="Stem 1", type="filepath", interactive=False)
vr_stem2 = gr.Audio(label="Stem 2", type="filepath", interactive=False)
with gr.Tab("Demucs"):
with gr.Group():
with gr.Row():
demucs_model = gr.Dropdown(value="htdemucs_ft", label="Select the Model", choices=list(DEMUCS_MODELS.keys()), scale=3)
demucs_output_format = gr.Dropdown(value="wav", choices=["wav", "flac", "mp3"], label="Output Format", info="The format of the output audio file.", scale=1)
with gr.Accordion("Advanced settings", open=False):
with gr.Column(variant='panel'):
with gr.Group():
demucs_segments_enabled = gr.Checkbox(value=True, label="Segment-wise processing", info="Enable segment-wise processing.")
with gr.Row():
demucs_seg_size = gr.Slider(minimum=1, maximum=100, step=1, value=40, label="Segment Size", info="Size of segments into which the audio is split. Higher = slower but better quality.")
demucs_overlap = gr.Slider(minimum=0.001, maximum=0.999, step=0.001, value=0.25, label="Overlap", info="Overlap between prediction windows. Higher = slower but better quality.")
demucs_shifts = gr.Slider(minimum=0, maximum=20, step=1, value=2, label="Shifts", info="Number of predictions with random shifts, higher = slower but better quality.")
with gr.Column(variant='panel'):
with gr.Group():
with gr.Row():
demucs_norm_threshold = gr.Slider(minimum=0.1, maximum=1, step=0.1, value=0.9, label="Normalization threshold", info="The threshold for audio normalization.")
demucs_amp_threshold = gr.Slider(minimum=0.0, maximum=1, step=0.1, value=0.0, label="Amplification threshold", info="The threshold for audio amplification.")
with gr.Row():
demucs_audio = gr.Audio(label="Input Audio", type="filepath")
with gr.Row():
demucs_button = gr.Button("Separate!", variant="primary")
with gr.Row():
demucs_stem1 = gr.Audio(label="Stem 1", type="filepath", interactive=False)
demucs_stem2 = gr.Audio(label="Stem 2", type="filepath", interactive=False)
with gr.Row():
demucs_stem3 = gr.Audio(label="Stem 3", type="filepath", interactive=False)
demucs_stem4 = gr.Audio(label="Stem 4", type="filepath", interactive=False)
with gr.Row(visible=False) as stem6:
demucs_stem5 = gr.Audio(label="Stem 5", type="filepath", interactive=False)
demucs_stem6 = gr.Audio(label="Stem 6", type="filepath", interactive=False)
with gr.Tab("Settings"):
with gr.Group():
with gr.Row():
model_file_dir = gr.Textbox(value="/tmp/audio-separator-models/", label="Directory to cache model files", info="The directory where model files are stored.", placeholder="/tmp/audio-separator-models/")
output_dir = gr.Textbox(value="output", label="File output directory", info="The directory where output files will be saved.", placeholder="output")
with gr.Accordion("Rename Stems", open=False):
gr.Markdown(
"""
Keys for automatic determination of input file names, stems, and models to simplify the construction of output file names.
Keys:
* **NAME** - Input File Name
* **STEM** - Stem Name (e.g., Vocals, Instrumental)
* **MODEL** - Model Name (e.g., BS-Roformer-Viperx-1297)
> Example:
> * **Usage:** NAME_(STEM)_MODEL
> * **Output File Name:** Music_(Vocals)_BS-Roformer-Viperx-1297
"""
)
with gr.Row():
vocals_stem = gr.Textbox(value="NAME_(STEM)_MODEL", label="Vocals Stem", info="Output example: Music_(Vocals)_BS-Roformer-Viperx-1297", placeholder="NAME_(STEM)_MODEL")
instrumental_stem = gr.Textbox(value="NAME_(STEM)_MODEL", label="Instrumental Stem", info="Output example: Music_(Instrumental)_BS-Roformer-Viperx-1297", placeholder="NAME_(STEM)_MODEL")
other_stem = gr.Textbox(value="NAME_(STEM)_MODEL", label="Other Stem", info="Output example: Music_(Other)_BS-Roformer-Viperx-1297", placeholder="NAME_(STEM)_MODEL")
with gr.Row():
drums_stem = gr.Textbox(value="NAME_(STEM)_MODEL", label="Drums Stem", info="Output example: Music_(Drums)_BS-Roformer-Viperx-1297", placeholder="NAME_(STEM)_MODEL")
bass_stem = gr.Textbox(value="NAME_(STEM)_MODEL", label="Bass Stem", info="Output example: Music_(Bass)_BS-Roformer-Viperx-1297", placeholder="NAME_(STEM)_MODEL")
with gr.Row():
guitar_stem = gr.Textbox(value="NAME_(STEM)_MODEL", label="Guitar Stem", info="Output example: Music_(Guitar)_BS-Roformer-Viperx-1297", placeholder="NAME_(STEM)_MODEL")
piano_stem = gr.Textbox(value="NAME_(STEM)_MODEL", label="Piano Stem", info="Output example: Music_(Piano)_BS-Roformer-Viperx-1297", placeholder="NAME_(STEM)_MODEL")
with gr.Tab("Leaderboard"):
with gr.Group():
with gr.Row(equal_height=True):
list_filter = gr.Dropdown(value="vocals", choices=["vocals", "instrumental", "drums", "bass", "guitar", "piano", "other"], label="List filter", info="Filter and sort the model list by 'stem'")
list_limit = gr.Slider(minimum=1, maximum=10, step=1, value=5, label="List limit", info="Limit the number of models shown.")
list_button = gr.Button("Show list", variant="primary")
output_list = gr.HTML(label="Leaderboard")
with gr.Tab("Credits"):
gr.Markdown(
"""
This Space created by **[Politrees](https://github.com/Bebra777228)**.
* python-audio-separator by **[beveradb](https://github.com/beveradb)**.
* Thanks to **[Hev832](https://huggingface.co/Hev832)** for the help with the code.
"""
)
roformer_override_seg_size.change(show_hide_params, inputs=[roformer_override_seg_size], outputs=[roformer_seg_size])
mdx23c_override_seg_size.change(show_hide_params, inputs=[mdx23c_override_seg_size], outputs=[mdx23c_seg_size])
vr_post_process.change(show_hide_params, inputs=[vr_post_process], outputs=[vr_post_process_threshold])
demucs_model.change(update_stems, inputs=[demucs_model], outputs=stem6)
list_button.click(leaderboard, inputs=[list_filter, list_limit], outputs=output_list)
roformer_button.click(
roformer_separator,
inputs=[
roformer_audio,
roformer_model,
roformer_seg_size,
roformer_override_seg_size,
roformer_overlap,
roformer_pitch_shift,
model_file_dir,
output_dir,
roformer_output_format,
roformer_norm_threshold,
roformer_amp_threshold,
roformer_batch_size,
vocals_stem,
instrumental_stem,
other_stem,
drums_stem,
bass_stem,
guitar_stem,
piano_stem,
],
outputs=[
roformer_stem1,
roformer_stem2,
], concurrency_limit=1,
)
mdx23c_button.click(
mdx23c_separator,
inputs=[
mdx23c_audio,
mdx23c_model,
mdx23c_seg_size,
mdx23c_override_seg_size,
mdx23c_overlap,
mdx23c_pitch_shift,
model_file_dir,
output_dir,
mdx23c_output_format,
mdx23c_norm_threshold,
mdx23c_amp_threshold,
mdx23c_batch_size,
vocals_stem,
instrumental_stem,
other_stem,
drums_stem,
bass_stem,
guitar_stem,
piano_stem,
],
outputs=[
mdx23c_stem1,
mdx23c_stem2,
], concurrency_limit=1,
)
mdx_button.click(
mdx_separator,
inputs=[
mdx_audio,
mdx_model,
mdx_hop_length,
mdx_seg_size,
mdx_overlap,
mdx_denoise,
model_file_dir,
output_dir,
mdx_output_format,
mdx_norm_threshold,
mdx_amp_threshold,
mdx_batch_size,
vocals_stem,
instrumental_stem,
other_stem,
drums_stem,
bass_stem,
guitar_stem,
piano_stem,
],
outputs=[
mdx_stem1,
mdx_stem2,
], concurrency_limit=1,
)
vr_button.click(
vr_separator,
inputs=[
vr_audio,
vr_model,
vr_window_size,
vr_aggression,
vr_tta,
vr_post_process,
vr_post_process_threshold,
vr_high_end_process,
model_file_dir,
output_dir,
vr_output_format,
vr_norm_threshold,
vr_amp_threshold,
vr_batch_size,
vocals_stem,
instrumental_stem,
other_stem,
drums_stem,
bass_stem,
guitar_stem,
piano_stem,
],
outputs=[
vr_stem1,
vr_stem2,
], concurrency_limit=1,
)
demucs_button.click(
demucs_separator,
inputs=[
demucs_audio,
demucs_model,
demucs_seg_size,
demucs_shifts,
demucs_overlap,
demucs_segments_enabled,
model_file_dir,
output_dir,
demucs_output_format,
demucs_norm_threshold,
demucs_amp_threshold,
vocals_stem,
instrumental_stem,
other_stem,
drums_stem,
bass_stem,
guitar_stem,
piano_stem,
],
outputs=[
demucs_stem1,
demucs_stem2,
demucs_stem3,
demucs_stem4,
demucs_stem5,
demucs_stem6,
], concurrency_limit=1,
)
def main():
app.queue().launch(share=True, debug=True)
if __name__ == "__main__":
main()