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"""
Implementation of Audio Effects Chain Manipulation for the task 'Mixing Style Transfer'
"""
from glob import glob
import os
import sys
currentdir = os.path.dirname(os.path.realpath(__file__))
sys.path.append(currentdir)
sys.path.append(os.path.dirname(currentdir))
from common_audioeffects import *
from common_dataprocessing import create_dataset
# create augmentation effects chain according to targeted effects with their applying probability
def create_effects_augmentation_chain(effects, \
ir_dir_path=None, \
sample_rate=44100, \
shuffle=False, \
parallel=False, \
parallel_weight_factor=None):
'''
Args:
effects (list of tuples or string) : First tuple element is string denoting the target effects.
Second tuple element is probability of applying current effects.
ir_dir_path (string) : directory path that contains directories of impulse responses organized according to RT60
sample_rate (int) : using sampling rate
shuffle (boolean) : shuffle FXs inside current FX chain
parallel (boolean) : compute parallel FX computation (alpha * input + (1-alpha) * manipulated output)
parallel_weight_factor : the value of alpha for parallel FX computation. default=None : random value in between (0.0, 0.5)
'''
fx_list = []
apply_prob = []
for cur_fx in effects:
# store probability to apply current effects. default is to set as 100%
if isinstance(cur_fx, tuple):
apply_prob.append(cur_fx[1])
cur_fx = cur_fx[0]
else:
apply_prob.append(1)
# processors of each audio effects
if isinstance(cur_fx, AugmentationChain) or isinstance(cur_fx, Processor):
fx_list.append(cur_fx)
elif cur_fx.lower()=='gain':
fx_list.append(Gain())
elif 'eq' in cur_fx.lower():
fx_list.append(Equaliser(n_channels=2, sample_rate=sample_rate))
elif 'comp' in cur_fx.lower():
fx_list.append(Compressor(sample_rate=sample_rate))
elif 'expand' in cur_fx.lower():
fx_list.append(Expander(sample_rate=sample_rate))
elif 'pan' in cur_fx.lower():
fx_list.append(Panner())
elif 'image'in cur_fx.lower():
fx_list.append(MidSideImager())
elif 'algorithmic' in cur_fx.lower():
fx_list.append(AlgorithmicReverb(sample_rate=sample_rate))
elif 'reverb' in cur_fx.lower():
# apply algorithmic reverberation if ir_dir_path is not defined
if ir_dir_path==None:
fx_list.append(AlgorithmicReverb(sample_rate=sample_rate))
# apply convolution reverberation
else:
IR_paths = glob(f"{ir_dir_path}*/RT60_avg/[!0-]*")
IR_list = []
IR_dict = {}
for IR_path in IR_paths:
cur_rt = IR_path.split('/')[-1]
if cur_rt not in IR_dict:
IR_dict[cur_rt] = []
IR_dict[cur_rt].extend(create_dataset(path=IR_path, \
accepted_sampling_rates=[sample_rate], \
sources=['impulse_response'], \
mapped_sources={}, load_to_memory=True, debug=False)[0])
long_ir_list = []
for cur_rt in IR_dict:
cur_rt_len = int(cur_rt.split('-')[0])
if cur_rt_len < 3000:
IR_list.append(IR_dict[cur_rt])
else:
long_ir_list.extend(IR_dict[cur_rt])
IR_list.append(long_ir_list)
fx_list.append(ConvolutionalReverb(IR_list, sample_rate))
else:
raise ValueError(f"make sure the target effects are in the Augment FX chain : received fx called {cur_fx}")
aug_chain_in = []
for cur_i, cur_fx in enumerate(fx_list):
normalize = False if isinstance(cur_fx, AugmentationChain) or cur_fx.name=='Gain' else True
aug_chain_in.append((cur_fx, apply_prob[cur_i], normalize))
return AugmentationChain(fxs=aug_chain_in, shuffle=shuffle, parallel=parallel, parallel_weight_factor=parallel_weight_factor)
# create audio FX-chain according to input instrument
def create_inst_effects_augmentation_chain(inst, \
apply_prob_dict, \
ir_dir_path=None, \
algorithmic=False, \
sample_rate=44100):
'''
Args:
inst (string) : FXmanipulator for target instrument. Current version only distinguishes 'drums' for applying reverberation
apply_prob_dict (dictionary of (FX name, probability)) : applying proababilities for each FX
ir_dir_path (string) : directory path that contains directories of impulse responses organized according to RT60
algorithmic (boolean) : rather to use algorithmic reverberation (True) or convolution reverberation (False)
sample_rate (int) : using sampling rate
'''
reverb_type = 'algorithmic' if algorithmic else 'reverb'
eq_comp_rand = create_effects_augmentation_chain([('eq', apply_prob_dict['eq']), ('comp', apply_prob_dict['comp'])], \
ir_dir_path=ir_dir_path, \
sample_rate=sample_rate, \
shuffle=True)
pan_image_rand = create_effects_augmentation_chain([('pan', apply_prob_dict['pan']), ('imager', apply_prob_dict['imager'])], \
ir_dir_path=ir_dir_path, \
sample_rate=sample_rate, \
shuffle=True)
if inst=='drums':
# apply reverberation to low frequency with little probability
low_pass_eq_params = ParameterList()
low_pass_eq_params.add(Parameter('high_shelf_gain', -50.0, 'float', minimum=-50.0, maximum=-50.0))
low_pass_eq_params.add(Parameter('high_shelf_freq', 100.0, 'float', minimum=100.0, maximum=100.0))
low_pass_eq = Equaliser(n_channels=2, \
sample_rate=sample_rate, \
bands=['high_shelf'], \
parameters=low_pass_eq_params)
reverb_parallel_low = create_effects_augmentation_chain([low_pass_eq, (reverb_type, apply_prob_dict['reverb']*0.01)], \
ir_dir_path=ir_dir_path, \
sample_rate=sample_rate, \
parallel=True, \
parallel_weight_factor=0.8)
# high pass eq for drums reverberation
high_pass_eq_params = ParameterList()
high_pass_eq_params.add(Parameter('low_shelf_gain', -50.0, 'float', minimum=-50.0, maximum=-50.0))
high_pass_eq_params.add(Parameter('low_shelf_freq', 100.0, 'float', minimum=100.0, maximum=100.0))
high_pass_eq = Equaliser(n_channels=2, \
sample_rate=sample_rate, \
bands=['low_shelf'], \
parameters=high_pass_eq_params)
reverb_parallel_high = create_effects_augmentation_chain([high_pass_eq, (reverb_type, apply_prob_dict['reverb'])], \
ir_dir_path=ir_dir_path, \
sample_rate=sample_rate, \
parallel=True, \
parallel_weight_factor=0.6)
reverb_parallel = create_effects_augmentation_chain([reverb_parallel_low, reverb_parallel_high], \
ir_dir_path=ir_dir_path, \
sample_rate=sample_rate)
else:
reverb_parallel = create_effects_augmentation_chain([(reverb_type, apply_prob_dict['reverb'])], \
ir_dir_path=ir_dir_path, \
sample_rate=sample_rate, \
parallel=True)
# full effects chain
effects_chain = create_effects_augmentation_chain([eq_comp_rand, \
pan_image_rand, \
reverb_parallel, \
('gain', apply_prob_dict['gain'])], \
ir_dir_path=ir_dir_path, \
sample_rate=sample_rate)
return effects_chain
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