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"""
Implementation of the 'audio effects chain normalization'
"""
import numpy as np
import scipy
import os
import sys
currentdir = os.path.dirname(os.path.realpath(__file__))
sys.path.append(currentdir)
from utils_data_normalization import *
from normalization_imager import *
'''
Audio Effects Chain Normalization
process: normalizes input stems according to given precomputed features
'''
class Audio_Effects_Normalizer:
def __init__(self, precomputed_feature_path, \
STEMS=['drums', 'bass', 'other', 'vocals'], \
EFFECTS=['eq', 'compression', 'imager', 'loudness']):
self.STEMS = STEMS # Stems to be normalized
self.EFFECTS = EFFECTS # Effects to be normalized, order matters
# Audio settings
self.SR = 44100
self.SUBTYPE = 'PCM_16'
# General Settings
self.FFT_SIZE = 2**16
self.HOP_LENGTH = self.FFT_SIZE//4
# Loudness
self.NTAPS = 1001
self.LUFS = -30
self.MIN_DB = -40 # Min amplitude to apply EQ matching
# Compressor
self.COMP_USE_EXPANDER = False
self.COMP_PEAK_NORM = -10.0
self.COMP_TRUE_PEAK = False
self.COMP_PERCENTILE = 75 # features_mean (v1) was done with 25
self.COMP_MIN_TH = -40
self.COMP_MAX_RATIO = 20
comp_settings = {key:{} for key in self.STEMS}
for key in comp_settings:
if key == 'vocals':
comp_settings[key]['attack'] = 7.5
comp_settings[key]['release'] = 400.0
comp_settings[key]['ratio'] = 4
comp_settings[key]['n_mels'] = 128
elif key == 'drums':
comp_settings[key]['attack'] = 10.0
comp_settings[key]['release'] = 180.0
comp_settings[key]['ratio'] = 6
comp_settings[key]['n_mels'] = 128
elif key == 'bass':
comp_settings[key]['attack'] = 10.0
comp_settings[key]['release'] = 500.0
comp_settings[key]['ratio'] = 5
comp_settings[key]['n_mels'] = 16
elif key == 'other':
comp_settings[key]['attack'] = 15.0
comp_settings[key]['release'] = 666.0
comp_settings[key]['ratio'] = 4
comp_settings[key]['n_mels'] = 128
self.comp_settings = comp_settings
# Load Pre-computed Audio Effects Features
features_mean = np.load(precomputed_feature_path, allow_pickle='TRUE')[()]
self.features_mean = self.smooth_feature(features_mean)
# normalize current audio input with the order of designed audio FX
def normalize_audio(self, audio, src):
assert src in self.STEMS
normalized_audio = audio
for cur_effect in self.EFFECTS:
normalized_audio = self.normalize_audio_per_effect(normalized_audio, src=src, effect=cur_effect)
return normalized_audio
# normalize current audio input with current targeted audio FX
def normalize_audio_per_effect(self, audio, src, effect):
audio = audio.astype(dtype=np.float32)
audio_track = np.pad(audio, ((self.FFT_SIZE, self.FFT_SIZE), (0, 0)), mode='constant')
assert len(audio_track.shape) == 2 # Always expects two dimensions
if audio_track.shape[1] == 1: # Converts mono to stereo with repeated channels
audio_track = np.repeat(audio_track, 2, axis=-1)
output_audio = audio_track.copy()
max_db = amp_to_db(np.max(np.abs(output_audio)))
if max_db > self.MIN_DB:
if effect == 'eq':
# normalize each channel
for ch in range(audio_track.shape[1]):
audio_eq_matched = get_eq_matching(output_audio[:, ch],
self.features_mean[effect][src],
sr=self.SR,
n_fft=self.FFT_SIZE,
hop_length=self.HOP_LENGTH,
min_db=self.MIN_DB,
ntaps=self.NTAPS,
lufs=self.LUFS)
np.copyto(output_audio[:,ch], audio_eq_matched)
elif effect == 'compression':
assert(len(self.features_mean[effect][src])==2)
# normalize each channel
for ch in range(audio_track.shape[1]):
try:
audio_comp_matched = get_comp_matching(output_audio[:, ch],
self.features_mean[effect][src][0],
self.features_mean[effect][src][1],
self.comp_settings[src]['ratio'],
self.comp_settings[src]['attack'],
self.comp_settings[src]['release'],
sr=self.SR,
min_db=self.MIN_DB,
min_th=self.COMP_MIN_TH,
comp_peak_norm=self.COMP_PEAK_NORM,
max_ratio=self.COMP_MAX_RATIO,
n_mels=self.comp_settings[src]['n_mels'],
true_peak=self.COMP_TRUE_PEAK,
percentile=self.COMP_PERCENTILE,
expander=self.COMP_USE_EXPANDER)
np.copyto(output_audio[:,ch], audio_comp_matched[:, 0])
except:
break
elif effect == 'loudness':
output_audio = fx_utils.lufs_normalize(output_audio, self.SR, self.features_mean[effect][src], log=False)
elif effect == 'imager':
# threshold of applying Haas effects
mono_threshold = 0.99 if src=='bass' else 0.975
audio_imager_matched = normalize_imager(output_audio, \
target_side_mid_bal=self.features_mean[effect][src], \
mono_threshold=mono_threshold, \
sr=self.SR)
np.copyto(output_audio, audio_imager_matched)
output_audio = output_audio[self.FFT_SIZE:self.FFT_SIZE+audio.shape[0]]
return output_audio
def smooth_feature(self, feature_dict_):
for effect in self.EFFECTS:
for key in self.STEMS:
if effect == 'eq':
if key in ['other', 'vocals']:
f = 401
else:
f = 151
feature_dict_[effect][key] = scipy.signal.savgol_filter(feature_dict_[effect][key],
f, 1, mode='mirror')
elif effect == 'panning':
feature_dict_[effect][key] = scipy.signal.savgol_filter(feature_dict_[effect][key],
501, 1, mode='mirror')
return feature_dict_
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