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data_fourier.py
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import config
from ext import parallel, pickle_save, pickle_load
from glob import glob
from math import ceil
from copy import deepcopy
from random import shuffle
from librosa import fft_frequencies, amplitude_to_db, db_to_amplitude
from librosa.core import load
from librosa.effects import split, trim
from librosa.core.spectrum import stft, istft, griffinlim
from librosa.feature import chroma_stft
from librosa.feature import mfcc
from librosa.feature.inverse import mfcc_to_audio
from numpy import abs, log, power, e, sum, clip, max, argmax, min, argmin, mean
from numpy import zeros_like, zeros, array, tile
from numpy import concatenate as cat
from numpy import stack
from librosa.display import specshow
from matplotlib.pyplot import plot, show
from scipy.io.wavfile import write
##
def main():
files = glob(config.data_path+'/*.wav') # + glob('data/*.mp3') # try ffmpeg -i input.mp3 output.wav
if not config.frequencies_to_pick:
# gather initial info from all files
frequency_strengths = zeros(len(config.frequencies_of_bins))
for file in files:
signal = load(file, config.sample_rate)[0]
spec = abs(stft(signal, config.fft_bins, config.fft_hop_len, config.fft_window_len))
# print('\tmax min initially:', max(spec), min(spec))
# show(specshow(spec, sr=config.sample_rate, hop_length=config.fft_hop_len))
frequency_strengths += spec.sum(1)/spec.shape[1]
max_strength = max(frequency_strengths)
strength_thr = max_strength/config.frequency_strength_thr
band_low_hz = 999_999
band_high_hz = -1
for frequency, strength in zip(config.frequencies_of_bins,frequency_strengths):
if strength>=strength_thr:
config.frequencies_to_pick.append(frequency)
if frequency < band_low_hz: band_low_hz = frequency
if frequency > band_high_hz: band_low_hz = frequency
# spec = cat([spec[config.frequencies_of_bins.index(i),:] for i in config.frequencies_to_pick], 0)
# print('\tmax min after bandpass:', max(spec), min(spec))
# show(specshow(spec, sr=config.sample_rate, hop_length=config.fft_hop_len))
print(f'with bandpass, timestep size: {len(config.frequencies_of_bins)} -> {len(config.frequencies_to_pick)}')
print(f'copy paste this line into frequencies_to_pick @ config: \n{config.frequencies_to_pick}')
# proceed to separately processing each file
converted = []
for file_id, file in enumerate(files):
print(f'reading: {file}')
song_id = [0 if i == file_id else 1 for i in range(len(files))]
# analysis
signal, sample_rate = load(file, config.sample_rate)
data, meta = audio_to_data(signal, song_id)
converted.append([data,meta])
# synthesis
signal_recons = data_to_audio(data,meta)
write(f'{file.split("/")[-1]}_{file_id}.wav', config.sample_rate, signal_recons)
signal_recons, sample_rate = load(f'{file.split("/")[-1]}_{file_id}.wav', config.sample_rate)
pickle_save(converted, config.data_path+'.pk')
print('saved data.')
def data_to_audio(data,meta):
spec = deepcopy(data[:,:len(config.frequencies_to_pick)])
spec = spec.T
if config.zscore_scale:
_, mean, std, scale = meta
spec *= scale
spec *= std
spec += mean
elif config.minmax_scale:
_, spec_min, spec_max = meta
spec *= spec_max - spec_min
spec += spec_min
elif config.log_scale:
spec = power(e,spec-1e-10)
spec = db_to_amplitude(spec)
spec = stack([spec[config.frequencies_to_pick.index(freq),:] if freq in config.frequencies_to_pick else zeros((spec.shape[1]))
for freq in config.frequencies_of_bins],0)
signal_recons = griffinlim(spec, hop_length=config.fft_hop_len, win_length=config.fft_window_len)
# signal_recons2 = mfcc_to_audio(mfccs, config.mel_bins)
return signal_recons
def audio_to_data(signal, song_id):
meta = [song_id]
if config.silence_thr_db:
signal, _ = trim(signal, config.silence_thr_db, frame_length=config.fft_bins, hop_length=config.fft_hop_len)
spec = abs(stft(signal, config.fft_bins, config.fft_hop_len, config.fft_window_len))
# mfccs = mfcc(signal, config.sample_rate, n_mfcc=config.mfcc_bins)
# chroma = chroma_stft(signal, config.sample_rate, n_fft=config.fft_bins, hop_length=config.fft_hop_len, win_length=config.fft_window_len)
# rows-frequencies cols-times
# show(specshow(spec, sr=config.sample_rate, hop_length=config.fft_hop_len))
# show(specshow(mfccs, sr=config.sample_rate, hop_length=config.fft_hop_len))
# show(plot(chroma))
spec_mod = deepcopy(spec)
print('\tmax min initially:', max(spec_mod), min(spec_mod))
spec_mod = stack([spec_mod[config.frequencies_of_bins.index(i),:] for i in config.frequencies_to_pick], 0)
print('\tmax min after bandpass:', max(spec_mod), min(spec_mod))
# show(specshow(spec, sr=config.sample_rate, hop_length=config.fft_hop_len))
spec_mod = amplitude_to_db(spec_mod)
print('\tmax min in db:', max(spec_mod), min(spec_mod))
# spec_mod = clip(spec_mod, config.amp_min_thr_db, config.amp_max_thr_db)
# print('db clipped.')
if config.zscore_scale:
mean = spec_mod.mean()
std = spec_mod.std()
spec_mod -= mean
spec_mod /= std
print('\tmax min after std:', max(spec_mod), min(spec_mod))
scale = max([abs(max(spec_mod)),abs(min(spec_mod))])
spec_mod /= scale
meta.extend([mean, std, scale])
elif config.minmax_scale:
spec_min = min(spec_mod)
spec_max = max(spec_mod)
spec_mod -= spec_min
spec_mod /= spec_max - spec_min
print('\tmax min after min/max:', max(spec_mod), min(spec_mod))
meta.extend([spec_min, spec_max])
elif config.log_scale:
spec_mod = log(spec_mod + 1e-10)
print('\tmax min after log:', max(spec_mod), min(spec_mod))
vector = spec_mod
# vector = concatenate([vector, chroma], 0)
vector = vector.T # now first index time, second index frequency
print('\tfinal vector shape:', vector.shape)
return vector, meta
def load_data(with_meta=False):
from torch import Tensor
data = pickle_load(config.data_path+'.pk')
data_tensors = []
for sequence,meta in data:
sequence = Tensor(sequence)
if config.use_gpu:
sequence = sequence.cuda()
data_tensors.append(sequence if not with_meta else [sequence,meta])
return data_tensors
def split_data(data, dev_ratio=None, do_shuffle=False):
if not dev_ratio: dev_ratio = config.dev_ratio
if do_shuffle: shuffle(data)
if dev_ratio:
hm_train = int(len(data)*(1-dev_ratio))
data_dev = data[hm_train:]
data = data[:hm_train]
return data, data_dev
else:
return data, []
def batchify_data(data, batch_size=None, do_shuffle=True):
if not batch_size: batch_size = config.batch_size
if do_shuffle: shuffle(data)
hm_batches = int(len(data)/batch_size)
return [data[i*batch_size:(i+1)*batch_size] for i in range(hm_batches)] \
if hm_batches else [data]
if __name__ == '__main__':
main()