import os
# os.system("pip install ./ort_nightly_gpu-1.17.0.dev20240118002-cp310-cp310-manylinux_2_28_x86_64.whl")
os.system("pip install ort-nightly-gpu --index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ort-cuda-12-nightly/pypi/simple/")
import gc
import hashlib
import queue
import threading
import json
import shlex
import sys
import subprocess
import librosa
import numpy as np
import soundfile as sf
import torch
from tqdm import tqdm
from utils import (
remove_directory_contents,
create_directories,
download_manager,
)
import random
import spaces
from utils import logger
import onnxruntime as ort
import warnings
import spaces
import gradio as gr
import logging
import time
import traceback
from pedalboard import Pedalboard, Reverb, Delay, Chorus, Compressor, Gain, HighpassFilter, LowpassFilter
from pedalboard.io import AudioFile
import numpy as np
import yt_dlp
warnings.filterwarnings("ignore")
title = "<center><strong><font size='7'>Audio🔹separator</font></strong></center>"
description = "This demo uses the MDX-Net models for vocal and background sound separation."
theme = "NoCrypt/miku"
stem_naming = {
"Vocals": "Instrumental",
"Other": "Instruments",
"Instrumental": "Vocals",
"Drums": "Drumless",
"Bass": "Bassless",
}
class MDXModel:
def __init__(
self,
device,
dim_f,
dim_t,
n_fft,
hop=1024,
stem_name=None,
compensation=1.000,
):
self.dim_f = dim_f
self.dim_t = dim_t
self.dim_c = 4
self.n_fft = n_fft
self.hop = hop
self.stem_name = stem_name
self.compensation = compensation
self.n_bins = self.n_fft // 2 + 1
self.chunk_size = hop * (self.dim_t - 1)
self.window = torch.hann_window(
window_length=self.n_fft, periodic=True
).to(device)
out_c = self.dim_c
self.freq_pad = torch.zeros(
[1, out_c, self.n_bins - self.dim_f, self.dim_t]
).to(device)
def stft(self, x):
x = x.reshape([-1, self.chunk_size])
x = torch.stft(
x,
n_fft=self.n_fft,
hop_length=self.hop,
window=self.window,
center=True,
return_complex=True,
)
x = torch.view_as_real(x)
x = x.permute([0, 3, 1, 2])
x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape(
[-1, 4, self.n_bins, self.dim_t]
)
return x[:, :, : self.dim_f]
def istft(self, x, freq_pad=None):
freq_pad = (
self.freq_pad.repeat([x.shape[0], 1, 1, 1])
if freq_pad is None
else freq_pad
)
x = torch.cat([x, freq_pad], -2)
# c = 4*2 if self.target_name=='*' else 2
x = x.reshape([-1, 2, 2, self.n_bins, self.dim_t]).reshape(
[-1, 2, self.n_bins, self.dim_t]
)
x = x.permute([0, 2, 3, 1])
x = x.contiguous()
x = torch.view_as_complex(x)
x = torch.istft(
x,
n_fft=self.n_fft,
hop_length=self.hop,
window=self.window,
center=True,
)
return x.reshape([-1, 2, self.chunk_size])
class MDX:
DEFAULT_SR = 44100
# Unit: seconds
DEFAULT_CHUNK_SIZE = 0 * DEFAULT_SR
DEFAULT_MARGIN_SIZE = 1 * DEFAULT_SR
def __init__(
self, model_path: str, params: MDXModel, processor=0
):
# Set the device and the provider (CPU or CUDA)
self.device = (
torch.device(f"cuda:{processor}")
if processor >= 0
else torch.device("cpu")
)
self.provider = (
["CUDAExecutionProvider"]
if processor >= 0
else ["CPUExecutionProvider"]
)
self.model = params
# Load the ONNX model using ONNX Runtime
self.ort = ort.InferenceSession(model_path, providers=self.provider)
# Preload the model for faster performance
self.ort.run(
None,
{"input": torch.rand(1, 4, params.dim_f, params.dim_t).numpy()},
)
self.process = lambda spec: self.ort.run(
None, {"input": spec.cpu().numpy()}
)[0]
self.prog = None
@staticmethod
def get_hash(model_path):
try:
with open(model_path, "rb") as f:
f.seek(-10000 * 1024, 2)
model_hash = hashlib.md5(f.read()).hexdigest()
except: # noqa
model_hash = hashlib.md5(open(model_path, "rb").read()).hexdigest()
return model_hash
@staticmethod
def segment(
wave,
combine=True,
chunk_size=DEFAULT_CHUNK_SIZE,
margin_size=DEFAULT_MARGIN_SIZE,
):
"""
Segment or join segmented wave array
Args:
wave: (np.array) Wave array to be segmented or joined
combine: (bool) If True, combines segmented wave array.
If False, segments wave array.
chunk_size: (int) Size of each segment (in samples)
margin_size: (int) Size of margin between segments (in samples)
Returns:
numpy array: Segmented or joined wave array
"""
if combine:
# Initializing as None instead of [] for later numpy array concatenation
processed_wave = None
for segment_count, segment in enumerate(wave):
start = 0 if segment_count == 0 else margin_size
end = None if segment_count == len(wave) - 1 else -margin_size
if margin_size == 0:
end = None
if processed_wave is None: # Create array for first segment
processed_wave = segment[:, start:end]
else: # Concatenate to existing array for subsequent segments
processed_wave = np.concatenate(
(processed_wave, segment[:, start:end]), axis=-1
)
else:
processed_wave = []
sample_count = wave.shape[-1]
if chunk_size <= 0 or chunk_size > sample_count:
chunk_size = sample_count
if margin_size > chunk_size:
margin_size = chunk_size
for segment_count, skip in enumerate(
range(0, sample_count, chunk_size)
):
margin = 0 if segment_count == 0 else margin_size
end = min(skip + chunk_size + margin_size, sample_count)
start = skip - margin
cut = wave[:, start:end].copy()
processed_wave.append(cut)
if end == sample_count:
break
return processed_wave
def pad_wave(self, wave):
"""
Pad the wave array to match the required chunk size
Args:
wave: (np.array) Wave array to be padded
Returns:
tuple: (padded_wave, pad, trim)
- padded_wave: Padded wave array
- pad: Number of samples that were padded
- trim: Number of samples that were trimmed
"""
n_sample = wave.shape[1]
trim = self.model.n_fft // 2
gen_size = self.model.chunk_size - 2 * trim
pad = gen_size - n_sample % gen_size
# Padded wave
wave_p = np.concatenate(
(
np.zeros((2, trim)),
wave,
np.zeros((2, pad)),
np.zeros((2, trim)),
),
1,
)
mix_waves = []
for i in range(0, n_sample + pad, gen_size):
waves = np.array(wave_p[:, i:i + self.model.chunk_size])
mix_waves.append(waves)
mix_waves = torch.tensor(mix_waves, dtype=torch.float32).to(
self.device
)
return mix_waves, pad, trim
def _process_wave(self, mix_waves, trim, pad, q: queue.Queue, _id: int):
"""
Process each wave segment in a multi-threaded environment
Args:
mix_waves: (torch.Tensor) Wave segments to be processed
trim: (int) Number of samples trimmed during padding
pad: (int) Number of samples padded during padding
q: (queue.Queue) Queue to hold the processed wave segments
_id: (int) Identifier of the processed wave segment
Returns:
numpy array: Processed wave segment
"""
mix_waves = mix_waves.split(1)
with torch.no_grad():
pw = []
for mix_wave in mix_waves:
self.prog.update()
spec = self.model.stft(mix_wave)
processed_spec = torch.tensor(self.process(spec))
processed_wav = self.model.istft(
processed_spec.to(self.device)
)
processed_wav = (
processed_wav[:, :, trim:-trim]
.transpose(0, 1)
.reshape(2, -1)
.cpu()
.numpy()
)
pw.append(processed_wav)
processed_signal = np.concatenate(pw, axis=-1)[:, :-pad]
q.put({_id: processed_signal})
return processed_signal
def process_wave(self, wave: np.array, mt_threads=1):
"""
Process the wave array in a multi-threaded environment
Args:
wave: (np.array) Wave array to be processed
mt_threads: (int) Number of threads to be used for processing
Returns:
numpy array: Processed wave array
"""
self.prog = tqdm(total=0)
chunk = wave.shape[-1] // mt_threads
waves = self.segment(wave, False, chunk)
# Create a queue to hold the processed wave segments
q = queue.Queue()
threads = []
for c, batch in enumerate(waves):
mix_waves, pad, trim = self.pad_wave(batch)
self.prog.total = len(mix_waves) * mt_threads
thread = threading.Thread(
target=self._process_wave, args=(mix_waves, trim, pad, q, c)
)
thread.start()
threads.append(thread)
for thread in threads:
thread.join()
self.prog.close()
processed_batches = []
while not q.empty():
processed_batches.append(q.get())
processed_batches = [
list(wave.values())[0]
for wave in sorted(
processed_batches, key=lambda d: list(d.keys())[0]
)
]
assert len(processed_batches) == len(
waves
), "Incomplete processed batches, please reduce batch size!"
return self.segment(processed_batches, True, chunk)
@spaces.GPU()
def run_mdx(
model_params,
output_dir,
model_path,
filename,
exclude_main=False,
exclude_inversion=False,
suffix=None,
invert_suffix=None,
denoise=False,
keep_orig=True,
m_threads=2,
device_base="cuda",
):
if device_base == "cuda":
device = torch.device("cuda:0")
processor_num = 0
device_properties = torch.cuda.get_device_properties(device)
vram_gb = device_properties.total_memory / 1024**3
m_threads = 1 if vram_gb < 8 else (8 if vram_gb > 32 else 2)
logger.info(f"threads: {m_threads} vram: {vram_gb}")
else:
device = torch.device("cpu")
processor_num = -1
m_threads = 1
model_hash = MDX.get_hash(model_path)
mp = model_params.get(model_hash)
model = MDXModel(
device,
dim_f=mp["mdx_dim_f_set"],
dim_t=2 ** mp["mdx_dim_t_set"],
n_fft=mp["mdx_n_fft_scale_set"],
stem_name=mp["primary_stem"],
compensation=mp["compensate"],
)
mdx_sess = MDX(model_path, model, processor=processor_num)
wave, sr = librosa.load(filename, mono=False, sr=44100)
# normalizing input wave gives better output
peak = max(np.max(wave), abs(np.min(wave)))
wave /= peak
if denoise:
wave_processed = -(mdx_sess.process_wave(-wave, m_threads)) + (
mdx_sess.process_wave(wave, m_threads)
)
wave_processed *= 0.5
else:
wave_processed = mdx_sess.process_wave(wave, m_threads)
# return to previous peak
wave_processed *= peak
stem_name = model.stem_name if suffix is None else suffix
main_filepath = None
if not exclude_main:
main_filepath = os.path.join(
output_dir,
f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav",
)
sf.write(main_filepath, wave_processed.T, sr)
invert_filepath = None
if not exclude_inversion:
diff_stem_name = (
stem_naming.get(stem_name)
if invert_suffix is None
else invert_suffix
)
stem_name = (
f"{stem_name}_diff" if diff_stem_name is None else diff_stem_name
)
invert_filepath = os.path.join(
output_dir,
f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav",
)
sf.write(
invert_filepath,
(-wave_processed.T * model.compensation) + wave.T,
sr,
)
if not keep_orig:
os.remove(filename)
del mdx_sess, wave_processed, wave
gc.collect()
torch.cuda.empty_cache()
return main_filepath, invert_filepath
def run_mdx_beta(
model_params,
output_dir,
model_path,
filename,
exclude_main=False,
exclude_inversion=False,
suffix=None,
invert_suffix=None,
denoise=False,
keep_orig=True,
m_threads=2,
device_base="",
):
m_threads = 1
duration = librosa.get_duration(filename=filename)
if duration >= 60 and duration <= 120:
m_threads = 8
elif duration > 120:
m_threads = 16
logger.info(f"threads: {m_threads}")
model_hash = MDX.get_hash(model_path)
device = torch.device("cpu")
processor_num = -1
mp = model_params.get(model_hash)
model = MDXModel(
device,
dim_f=mp["mdx_dim_f_set"],
dim_t=2 ** mp["mdx_dim_t_set"],
n_fft=mp["mdx_n_fft_scale_set"],
stem_name=mp["primary_stem"],
compensation=mp["compensate"],
)
mdx_sess = MDX(model_path, model, processor=processor_num)
wave, sr = librosa.load(filename, mono=False, sr=44100)
# normalizing input wave gives better output
peak = max(np.max(wave), abs(np.min(wave)))
wave /= peak
if denoise:
wave_processed = -(mdx_sess.process_wave(-wave, m_threads)) + (
mdx_sess.process_wave(wave, m_threads)
)
wave_processed *= 0.5
else:
wave_processed = mdx_sess.process_wave(wave, m_threads)
# return to previous peak
wave_processed *= peak
stem_name = model.stem_name if suffix is None else suffix
main_filepath = None
if not exclude_main:
main_filepath = os.path.join(
output_dir,
f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav",
)
sf.write(main_filepath, wave_processed.T, sr)
invert_filepath = None
if not exclude_inversion:
diff_stem_name = (
stem_naming.get(stem_name)
if invert_suffix is None
else invert_suffix
)
stem_name = (
f"{stem_name}_diff" if diff_stem_name is None else diff_stem_name
)
invert_filepath = os.path.join(
output_dir,
f"{os.path.basename(os.path.splitext(filename)[0])}_{stem_name}.wav",
)
sf.write(
invert_filepath,
(-wave_processed.T * model.compensation) + wave.T,
sr,
)
if not keep_orig:
os.remove(filename)
del mdx_sess, wave_processed, wave
gc.collect()
torch.cuda.empty_cache()
return main_filepath, invert_filepath
MDX_DOWNLOAD_LINK = "https://github.com/TRvlvr/model_repo/releases/download/all_public_uvr_models/"
UVR_MODELS = [
"UVR-MDX-NET-Voc_FT.onnx",
"UVR_MDXNET_KARA_2.onnx",
"Reverb_HQ_By_FoxJoy.onnx",
"UVR-MDX-NET-Inst_HQ_4.onnx",
]
BASE_DIR = "." # os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
mdxnet_models_dir = os.path.join(BASE_DIR, "mdx_models")
output_dir = os.path.join(BASE_DIR, "clean_song_output")
def convert_to_stereo_and_wav(audio_path):
wave, sr = librosa.load(audio_path, mono=False, sr=44100)
# check if mono
if type(wave[0]) != np.ndarray or audio_path[-4:].lower() != ".wav": # noqa
stereo_path = f"{os.path.splitext(audio_path)[0]}_stereo.wav"
stereo_path = os.path.join(output_dir, stereo_path)
command = shlex.split(
f'ffmpeg -y -loglevel error -i "{audio_path}" -ac 2 -f wav "{stereo_path}"'
)
sub_params = {
"stdout": subprocess.PIPE,
"stderr": subprocess.PIPE,
"creationflags": subprocess.CREATE_NO_WINDOW
if sys.platform == "win32"
else 0,
}
process_wav = subprocess.Popen(command, **sub_params)
output, errors = process_wav.communicate()
if process_wav.returncode != 0 or not os.path.exists(stereo_path):
raise Exception("Error processing audio to stereo wav")
return stereo_path
else:
return audio_path
def get_hash(filepath):
with open(filepath, 'rb') as f:
file_hash = hashlib.blake2b()
while chunk := f.read(8192):
file_hash.update(chunk)
return file_hash.hexdigest()[:18]
def random_sleep():
sleep_time = round(random.uniform(5.2, 7.9), 1)
time.sleep(sleep_time)
def process_uvr_task(
orig_song_path: str = "aud_test.mp3",
main_vocals: bool = False,
dereverb: bool = True,
song_id: str = "mdx", # folder output name
only_voiceless: bool = False,
remove_files_output_dir: bool = False,
):
device_base = "cuda" if torch.cuda.is_available() else "cpu"
logger.info(f"Device: {device_base}")
if remove_files_output_dir:
remove_directory_contents(output_dir)
with open(os.path.join(mdxnet_models_dir, "data.json")) as infile:
mdx_model_params = json.load(infile)
song_output_dir = os.path.join(output_dir, song_id)
create_directories(song_output_dir)
orig_song_path = convert_to_stereo_and_wav(orig_song_path)
logger.info(f"onnxruntime device >> {ort.get_device()}")
if only_voiceless:
logger.info("Voiceless Track Separation...")
process = run_mdx(
mdx_model_params,
song_output_dir,
os.path.join(mdxnet_models_dir, "UVR-MDX-NET-Inst_HQ_4.onnx"),
orig_song_path,
suffix="Voiceless",
denoise=False,
keep_orig=True,
exclude_inversion=True,
device_base=device_base,
)
return process
logger.info("Vocal Track Isolation...")
vocals_path, instrumentals_path = run_mdx(
mdx_model_params,
song_output_dir,
os.path.join(mdxnet_models_dir, "UVR-MDX-NET-Voc_FT.onnx"),
orig_song_path,
denoise=True,
keep_orig=True,
device_base=device_base,
)
if main_vocals:
random_sleep()
msg_main = "Main Voice Separation from Supporting Vocals..."
logger.info(msg_main)
gr.Info(msg_main)
try:
backup_vocals_path, main_vocals_path = run_mdx(
mdx_model_params,
song_output_dir,
os.path.join(mdxnet_models_dir, "UVR_MDXNET_KARA_2.onnx"),
vocals_path,
suffix="Backup",
invert_suffix="Main",
denoise=True,
device_base=device_base,
)
except Exception as e:
backup_vocals_path, main_vocals_path = run_mdx_beta(
mdx_model_params,
song_output_dir,
os.path.join(mdxnet_models_dir, "UVR_MDXNET_KARA_2.onnx"),
vocals_path,
suffix="Backup",
invert_suffix="Main",
denoise=True,
device_base=device_base,
)
else:
backup_vocals_path, main_vocals_path = None, vocals_path
if dereverb:
random_sleep()
msg_dereverb = "Vocal Clarity Enhancement through De-Reverberation..."
logger.info(msg_dereverb)
gr.Info(msg_dereverb)
try:
_, vocals_dereverb_path = run_mdx(
mdx_model_params,
song_output_dir,
os.path.join(mdxnet_models_dir, "Reverb_HQ_By_FoxJoy.onnx"),
main_vocals_path,
invert_suffix="DeReverb",
exclude_main=True,
denoise=True,
device_base=device_base,
)
except Exception as e:
_, vocals_dereverb_path = run_mdx_beta(
mdx_model_params,
song_output_dir,
os.path.join(mdxnet_models_dir, "Reverb_HQ_By_FoxJoy.onnx"),
main_vocals_path,
invert_suffix="DeReverb",
exclude_main=True,
denoise=True,
device_base=device_base,
)
else:
vocals_dereverb_path = main_vocals_path
return (
vocals_path,
instrumentals_path,
backup_vocals_path,
main_vocals_path,
vocals_dereverb_path,
)
def add_vocal_effects(input_file, output_file, reverb_room_size=0.6, vocal_reverb_dryness=0.8, reverb_damping=0.6, reverb_wet_level=0.35,
delay_seconds=0.4, delay_mix=0.25,
compressor_threshold_db=-25, compressor_ratio=3.5, compressor_attack_ms=10, compressor_release_ms=60,
gain_db=3):
effects = [HighpassFilter()]
effects.append(Reverb(room_size=reverb_room_size, damping=reverb_damping, wet_level=reverb_wet_level, dry_level=vocal_reverb_dryness))
effects.append(Compressor(threshold_db=compressor_threshold_db, ratio=compressor_ratio,
attack_ms=compressor_attack_ms, release_ms=compressor_release_ms))
if delay_seconds > 0 or delay_mix > 0:
effects.append(Delay(delay_seconds=delay_seconds, mix=delay_mix))
print("delay applied")
# effects.append(Chorus())
if gain_db:
effects.append(Gain(gain_db=gain_db))
print("added gain db")
board = Pedalboard(effects)
with AudioFile(input_file) as f:
with AudioFile(output_file, 'w', f.samplerate, f.num_channels) as o:
# Read one second of audio at a time, until the file is empty:
while f.tell() < f.frames:
chunk = f.read(int(f.samplerate))
effected = board(chunk, f.samplerate, reset=False)
o.write(effected)
def add_instrumental_effects(input_file, output_file, highpass_freq=100, lowpass_freq=12000,
reverb_room_size=0.5, reverb_damping=0.5, reverb_wet_level=0.25,
compressor_threshold_db=-20, compressor_ratio=2.5, compressor_attack_ms=15, compressor_release_ms=80,
gain_db=2):
effects = [
HighpassFilter(cutoff_frequency_hz=highpass_freq),
LowpassFilter(cutoff_frequency_hz=lowpass_freq),
]
if reverb_room_size > 0 or reverb_damping > 0 or reverb_wet_level > 0:
effects.append(Reverb(room_size=reverb_room_size, damping=reverb_damping, wet_level=reverb_wet_level))
effects.append(Compressor(threshold_db=compressor_threshold_db, ratio=compressor_ratio,
attack_ms=compressor_attack_ms, release_ms=compressor_release_ms))
if gain_db:
effects.append(Gain(gain_db=gain_db))
board = Pedalboard(effects)
with AudioFile(input_file) as f:
with AudioFile(output_file, 'w', f.samplerate, f.num_channels) as o:
# Read one second of audio at a time, until the file is empty:
while f.tell() < f.frames:
chunk = f.read(int(f.samplerate))
effected = board(chunk, f.samplerate, reset=False)
o.write(effected)
def sound_separate(media_file, stem, main, dereverb, vocal_effects=True, background_effects=True,
vocal_reverb_room_size=0.6, vocal_reverb_damping=0.6, vocal_reverb_wet_level=0.35,
vocal_delay_seconds=0.4, vocal_delay_mix=0.25,
vocal_compressor_threshold_db=-25, vocal_compressor_ratio=3.5, vocal_compressor_attack_ms=10, vocal_compressor_release_ms=60,
vocal_gain_db=4,
background_highpass_freq=120, background_lowpass_freq=11000,
background_reverb_room_size=0.5, background_reverb_damping=0.5, background_reverb_wet_level=0.25,
background_compressor_threshold_db=-20, background_compressor_ratio=2.5, background_compressor_attack_ms=15, background_compressor_release_ms=80,
background_gain_db=3):
if not media_file:
raise ValueError("The audio path is missing.")
if not stem:
raise ValueError("Please select 'vocal' or 'background' stem.")
hash_audio = str(get_hash(media_file))
media_dir = os.path.dirname(media_file)
outputs = []
start_time = time.time()
if stem == "vocal":
try:
_, _, _, _, vocal_audio = process_uvr_task(
orig_song_path=media_file,
song_id=hash_audio + "mdx",
main_vocals=main,
dereverb=dereverb,
remove_files_output_dir=False,
)
if vocal_effects:
suffix = '_effects'
file_name, file_extension = os.path.splitext(vocal_audio)
out_effects = file_name + suffix + file_extension
out_effects_path = os.path.join(media_dir, out_effects)
add_vocal_effects(vocal_audio, out_effects_path,
reverb_room_size=vocal_reverb_room_size, reverb_damping=vocal_reverb_damping, reverb_wet_level=vocal_reverb_wet_level,
delay_seconds=vocal_delay_seconds, delay_mix=vocal_delay_mix,
compressor_threshold_db=vocal_compressor_threshold_db, compressor_ratio=vocal_compressor_ratio, compressor_attack_ms=vocal_compressor_attack_ms, compressor_release_ms=vocal_compressor_release_ms,
gain_db=vocal_gain_db
)
vocal_audio = out_effects_path
outputs.append(vocal_audio)
except Exception as error:
logger.error(str(error))
traceback.print_exc()
if stem == "background":
background_audio, _ = process_uvr_task(
orig_song_path=media_file,
song_id=hash_audio + "voiceless",
only_voiceless=True,
remove_files_output_dir=False,
)
if background_effects:
suffix = '_effects'
file_name, file_extension = os.path.splitext(background_audio)
out_effects = file_name + suffix + file_extension
out_effects_path = os.path.join(media_dir, out_effects)
add_instrumental_effects(background_audio, out_effects_path,
highpass_freq=background_highpass_freq, lowpass_freq=background_lowpass_freq,
reverb_room_size=background_reverb_room_size, reverb_damping=background_reverb_damping, reverb_wet_level=background_reverb_wet_level,
compressor_threshold_db=background_compressor_threshold_db, compressor_ratio=background_compressor_ratio, compressor_attack_ms=background_compressor_attack_ms, compressor_release_ms=background_compressor_release_ms,
gain_db=background_gain_db
)
background_audio = out_effects_path
outputs.append(background_audio)
end_time = time.time()
execution_time = end_time - start_time
logger.info(f"Execution time: {execution_time} seconds")
if not outputs:
raise Exception("Error in sound separation.")
return outputs
def sound_separate(media_file, stem, main, dereverb, vocal_effects=True, background_effects=True,
vocal_reverb_room_size=0.6, vocal_reverb_damping=0.6, vocal_reverb_dryness=0.8 ,vocal_reverb_wet_level=0.35,
vocal_delay_seconds=0.4, vocal_delay_mix=0.25,
vocal_compressor_threshold_db=-25, vocal_compressor_ratio=3.5, vocal_compressor_attack_ms=10, vocal_compressor_release_ms=60,
vocal_gain_db=4,
background_highpass_freq=120, background_lowpass_freq=11000,
background_reverb_room_size=0.5, background_reverb_damping=0.5, background_reverb_wet_level=0.25,
background_compressor_threshold_db=-20, background_compressor_ratio=2.5, background_compressor_attack_ms=15, background_compressor_release_ms=80,
background_gain_db=3,
):
if not media_file:
raise ValueError("The audio path is missing.")
if not stem:
raise ValueError("Please select 'vocal' or 'background' stem.")
hash_audio = str(get_hash(media_file))
media_dir = os.path.dirname(media_file)
outputs = []
try:
duration_base_ = librosa.get_duration(filename=media_file)
print("Duration audio:", duration_base_)
except Exception as e:
print(e)
start_time = time.time()
if stem == "vocal":
try:
_, _, _, _, vocal_audio = process_uvr_task(
orig_song_path=media_file,
song_id=hash_audio + "mdx",
main_vocals=main,
dereverb=dereverb,
remove_files_output_dir=False,
)
if vocal_effects:
suffix = '_effects'
file_name, file_extension = os.path.splitext(os.path.abspath(vocal_audio))
out_effects = file_name + suffix + file_extension
out_effects_path = os.path.join(media_dir, out_effects)
add_vocal_effects(vocal_audio, out_effects_path,
reverb_room_size=vocal_reverb_room_size, reverb_damping=vocal_reverb_damping, vocal_reverb_dryness=vocal_reverb_dryness, reverb_wet_level=vocal_reverb_wet_level,
delay_seconds=vocal_delay_seconds, delay_mix=vocal_delay_mix,
compressor_threshold_db=vocal_compressor_threshold_db, compressor_ratio=vocal_compressor_ratio, compressor_attack_ms=vocal_compressor_attack_ms, compressor_release_ms=vocal_compressor_release_ms,
gain_db=vocal_gain_db
)
vocal_audio = out_effects_path
outputs.append(vocal_audio)
except Exception as error:
gr.Info(str(error))
logger.error(str(error))
if stem == "background":
background_audio, _ = process_uvr_task(
orig_song_path=media_file,
song_id=hash_audio + "voiceless",
only_voiceless=True,
remove_files_output_dir=False,
)
if background_effects:
suffix = '_effects'
file_name, file_extension = os.path.splitext(os.path.abspath(background_audio))
out_effects = file_name + suffix + file_extension
out_effects_path = os.path.join(media_dir, out_effects)
print(file_name, file_extension, out_effects, out_effects_path)
add_instrumental_effects(background_audio, out_effects_path,
highpass_freq=background_highpass_freq, lowpass_freq=background_lowpass_freq,
reverb_room_size=background_reverb_room_size, reverb_damping=background_reverb_damping, reverb_wet_level=background_reverb_wet_level,
compressor_threshold_db=background_compressor_threshold_db, compressor_ratio=background_compressor_ratio, compressor_attack_ms=background_compressor_attack_ms, compressor_release_ms=background_compressor_release_ms,
gain_db=background_gain_db
)
background_audio = out_effects_path
outputs.append(background_audio)
end_time = time.time()
execution_time = end_time - start_time
logger.info(f"Execution time: {execution_time} seconds")
if not outputs:
raise Exception("Error in sound separation.")
return outputs
def audio_downloader(
url_media,
):
url_media = url_media.strip()
if not url_media:
return None
print(url_media[:10])
dir_output_downloads = "downloads"
os.makedirs(dir_output_downloads, exist_ok=True)
media_info = yt_dlp.YoutubeDL(
{"quiet": True, "no_warnings": True, "noplaylist": True}
).extract_info(url_media, download=False)
download_path = f"{os.path.join(dir_output_downloads, media_info['title'])}.m4a"
ydl_opts = {
'format': 'm4a/bestaudio/best',
'postprocessors': [{ # Extract audio using ffmpeg
'key': 'FFmpegExtractAudio',
'preferredcodec': 'm4a',
}],
'force_overwrites': True,
'noplaylist': True,
'no_warnings': True,
'quiet': True,
'ignore_no_formats_error': True,
'restrictfilenames': True,
'outtmpl': download_path,
}
with yt_dlp.YoutubeDL(ydl_opts) as ydl_download:
ydl_download.download([url_media])
return download_path
def downloader_conf():
return gr.Checkbox(
False,
label="URL-to-Audio",
# info="",
container=False,
)
def url_media_conf():
return gr.Textbox(
value="",
label="Enter URL",
placeholder="www.youtube.com/watch?v=g_9rPvbENUw",
visible=False,
lines=1,
)
def url_button_conf():
return gr.Button(
"Go",
variant="secondary",
visible=False,
)
def show_components_downloader(value_active):
return gr.update(
visible=value_active
), gr.update(
visible=value_active
)
def audio_conf():
return gr.File(
label="Audio file",
# file_count="multiple",
type="filepath",
container=True,
)
def stem_conf():
return gr.Radio(
choices=["vocal", "background"],
value="vocal",
label="Stem",
# info="",
)
def main_conf():
return gr.Checkbox(
False,
label="Main",
# info="",
)
def dereverb_conf():
return gr.Checkbox(
False,
label="Dereverb",
# info="",
visible=True,
)
def vocal_effects_conf():
return gr.Checkbox(
False,
label="Vocal Effects",
# info="",
visible=True,
)
def background_effects_conf():
return gr.Checkbox(
False,
label="Background Effects",
# info="",
visible=False,
)
def vocal_reverb_room_size_conf():
return gr.Number(
0.15,
label="Vocal Reverb Room Size",
minimum=0.0,
maximum=1.0,
step=0.05,
visible=True,
)
def vocal_reverb_damping_conf():
return gr.Number(
0.7,
label="Vocal Reverb Damping",
minimum=0.0,
maximum=1.0,
step=0.01,
visible=True,
)
def vocal_reverb_wet_level_conf():
return gr.Number(
0.2,
label="Vocal Reverb Wet Level",
minimum=0.0,
maximum=1.0,
step=0.05,
visible=True,
)
def vocal_reverb_dryness_level_conf():
return gr.Number(
0.8,
label="Vocal Reverb Dryness Level",
minimum=0.0,
maximum=1.0,
step=0.05,
visible=True,
)
def vocal_delay_seconds_conf():
return gr.Number(
0.,
label="Vocal Delay Seconds",
minimum=0.0,
maximum=1.0,
step=0.01,
visible=True,
)
def vocal_delay_mix_conf():
return gr.Number(
0.,
label="Vocal Delay Mix",
minimum=0.0,
maximum=1.0,
step=0.01,
visible=True,
)
def vocal_compressor_threshold_db_conf():
return gr.Number(
-15,
label="Vocal Compressor Threshold (dB)",
minimum=-60,
maximum=0,
step=1,
visible=True,
)
def vocal_compressor_ratio_conf():
return gr.Number(
4.,
label="Vocal Compressor Ratio",
minimum=0,
maximum=20,
step=0.1,
visible=True,
)
def vocal_compressor_attack_ms_conf():
return gr.Number(
1.0,
label="Vocal Compressor Attack (ms)",
minimum=0,
maximum=1000,
step=1,
visible=True,
)
def vocal_compressor_release_ms_conf():
return gr.Number(
100,
label="Vocal Compressor Release (ms)",
minimum=0,
maximum=3000,
step=1,
visible=True,
)
def vocal_gain_db_conf():
return gr.Number(
0,
label="Vocal Gain (dB)",
minimum=-40,
maximum=40,
step=1,
visible=True,
)
def background_highpass_freq_conf():
return gr.Number(
120,
label="Background Highpass Frequency (Hz)",
minimum=0,
maximum=1000,
step=1,
visible=True,
)
def background_lowpass_freq_conf():
return gr.Number(
11000,
label="Background Lowpass Frequency (Hz)",
minimum=0,
maximum=20000,
step=1,
visible=True,
)
def background_reverb_room_size_conf():
return gr.Number(
0.1,
label="Background Reverb Room Size",
minimum=0.0,
maximum=1.0,
step=0.1,
visible=True,
)
def background_reverb_damping_conf():
return gr.Number(
0.5,
label="Background Reverb Damping",
minimum=0.0,
maximum=1.0,
step=0.1,
visible=True,
)
def background_reverb_wet_level_conf():
return gr.Number(
0.25,
label="Background Reverb Wet Level",
minimum=0.0,
maximum=1.0,
step=0.05,
visible=True,
)
def background_compressor_threshold_db_conf():
return gr.Number(
-15,
label="Background Compressor Threshold (dB)",
minimum=-60,
maximum=0,
step=1,
visible=True,
)
def background_compressor_ratio_conf():
return gr.Number(
4.,
label="Background Compressor Ratio",
minimum=0,
maximum=20,
step=0.1,
visible=True,
)
def background_compressor_attack_ms_conf():
return gr.Number(
15,
label="Background Compressor Attack (ms)",
minimum=0,
maximum=1000,
step=1,
visible=True,
)
def background_compressor_release_ms_conf():
return gr.Number(
60,
label="Background Compressor Release (ms)",
minimum=0,
maximum=3000,
step=1,
visible=True,
)
def background_gain_db_conf():
return gr.Number(
0,
label="Background Gain (dB)",
minimum=-40,
maximum=40,
step=1,
visible=True,
)
def button_conf():
return gr.Button(
"Inference",
variant="primary",
)
def output_conf():
return gr.File(
label="Result",
file_count="multiple",
interactive=False,
)
def show_vocal_components(value_name):
if value_name == "vocal":
return gr.update(visible=True), gr.update(
visible=True
), gr.update(visible=True), gr.update(
visible=False
)
else:
return gr.update(visible=False), gr.update(
visible=False
), gr.update(visible=False), gr.update(
visible=True
)
def get_gui(theme):
with gr.Blocks(theme=theme) as app:
gr.Markdown(title)
gr.Markdown(description)
downloader_gui = downloader_conf()
with gr.Row():
with gr.Column(scale=2):
url_media_gui = url_media_conf()
with gr.Column(scale=1):
url_button_gui = url_button_conf()
downloader_gui.change(
show_components_downloader,
[downloader_gui],
[url_media_gui, url_button_gui]
)
aud = audio_conf()
url_button_gui.click(
audio_downloader,
[url_media_gui],
[aud]
)
with gr.Column():
with gr.Row():
stem_gui = stem_conf()
with gr.Column():
with gr.Row():
main_gui = main_conf()
dereverb_gui = dereverb_conf()
vocal_effects_gui = vocal_effects_conf()
background_effects_gui = background_effects_conf()
# with gr.Column():
with gr.Accordion("Vocal Effects Parameters", open=False): # with gr.Row():
# gr.Label("Vocal Effects Parameters")
with gr.Row():
vocal_reverb_room_size_gui = vocal_reverb_room_size_conf()
vocal_reverb_damping_gui = vocal_reverb_damping_conf()
vocal_reverb_dryness_gui = vocal_reverb_dryness_level_conf()
vocal_reverb_wet_level_gui = vocal_reverb_wet_level_conf()
vocal_delay_seconds_gui = vocal_delay_seconds_conf()
vocal_delay_mix_gui = vocal_delay_mix_conf()
vocal_compressor_threshold_db_gui = vocal_compressor_threshold_db_conf()
vocal_compressor_ratio_gui = vocal_compressor_ratio_conf()
vocal_compressor_attack_ms_gui = vocal_compressor_attack_ms_conf()
vocal_compressor_release_ms_gui = vocal_compressor_release_ms_conf()
vocal_gain_db_gui = vocal_gain_db_conf()
with gr.Accordion("Background Effects Parameters", open=False): # with gr.Row():
# gr.Label("Background Effects Parameters")
with gr.Row():
background_highpass_freq_gui = background_highpass_freq_conf()
background_lowpass_freq_gui = background_lowpass_freq_conf()
background_reverb_room_size_gui = background_reverb_room_size_conf()
background_reverb_damping_gui = background_reverb_damping_conf()
background_reverb_wet_level_gui = background_reverb_wet_level_conf()
background_compressor_threshold_db_gui = background_compressor_threshold_db_conf()
background_compressor_ratio_gui = background_compressor_ratio_conf()
background_compressor_attack_ms_gui = background_compressor_attack_ms_conf()
background_compressor_release_ms_gui = background_compressor_release_ms_conf()
background_gain_db_gui = background_gain_db_conf()
stem_gui.change(
show_vocal_components,
[stem_gui],
[main_gui, dereverb_gui, vocal_effects_gui, background_effects_gui],
)
button_base = button_conf()
output_base = output_conf()
button_base.click(
sound_separate,
inputs=[
aud,
stem_gui,
main_gui,
dereverb_gui,
vocal_effects_gui,
background_effects_gui,
vocal_reverb_room_size_gui, vocal_reverb_damping_gui, vocal_reverb_dryness_gui, vocal_reverb_wet_level_gui,
vocal_delay_seconds_gui, vocal_delay_mix_gui, vocal_compressor_threshold_db_gui, vocal_compressor_ratio_gui,
vocal_compressor_attack_ms_gui, vocal_compressor_release_ms_gui, vocal_gain_db_gui,
background_highpass_freq_gui, background_lowpass_freq_gui, background_reverb_room_size_gui,
background_reverb_damping_gui, background_reverb_wet_level_gui, background_compressor_threshold_db_gui,
background_compressor_ratio_gui, background_compressor_attack_ms_gui, background_compressor_release_ms_gui,
background_gain_db_gui,
],
outputs=[output_base],
)
gr.Examples(
examples=[
[
"./test.mp3",
"vocal",
False,
False,
False,
False,
0.15, 0.7, 0.8, 0.2,
0., 0., -15, 4., 1, 100, 0,
120, 11000, 0.5, 0.1, 0.25, -15, 4., 15, 60, 0,
],
],
fn=sound_separate,
inputs=[
aud,
stem_gui,
main_gui,
dereverb_gui,
vocal_effects_gui,
background_effects_gui,
vocal_reverb_room_size_gui, vocal_reverb_damping_gui, vocal_reverb_dryness_gui, vocal_reverb_wet_level_gui,
vocal_delay_seconds_gui, vocal_delay_mix_gui, vocal_compressor_threshold_db_gui, vocal_compressor_ratio_gui,
vocal_compressor_attack_ms_gui, vocal_compressor_release_ms_gui, vocal_gain_db_gui,
background_highpass_freq_gui, background_lowpass_freq_gui, background_reverb_room_size_gui,
background_reverb_damping_gui, background_reverb_wet_level_gui, background_compressor_threshold_db_gui,
background_compressor_ratio_gui, background_compressor_attack_ms_gui, background_compressor_release_ms_gui,
background_gain_db_gui,
],
outputs=[output_base],
cache_examples=False,
)
return app
if __name__ == "__main__":
for id_model in UVR_MODELS:
download_manager(
os.path.join(MDX_DOWNLOAD_LINK, id_model), mdxnet_models_dir
)
app = get_gui(theme)
app.queue(default_concurrency_limit=40)
app.launch(
max_threads=40,
share=False,
show_error=True,
quiet=False,
debug=False,
)