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from scipy.io import wavfile
from scipy import signal
import numpy as np
import matplotlib.pyplot as plt
class SingleSong:
# To load one excerpt with arbitrary length, or one full song, for test or validation
def __init__(self, chunk_len, filter_, hq_path, cutoff, duration=None, start=8):
hq, sr = read_audio(hq_path) # high quality target
lq = lowpass(hq, cutoff, filter_=filter_) # low quality input
# CROP
song_len = lq.shape[-1]
if duration is None: # save entire song
test_start = 0
test_len = song_len
else:
test_start = start * sr # start from n th second
test_len = duration * sr
test_len = min(test_len, song_len - test_start)
lq = lq[:, test_start:test_start + test_len]
hq = hq[:, test_start:test_start + test_len]
self.x_full = lq.copy()
self.t_full = hq.copy()
# To have equal length chunks for minibatching
time_len = lq.shape[-1]
n_chunks, rem = divmod(time_len, chunk_len)
lq = lq[..., :-rem or None] # or None handles rem=0
hq = hq[..., :-rem or None]
# adjust lengths
self.x_full = self.x_full[..., :lq.shape[-1] or None]
self.t_full = self.t_full[..., :lq.shape[-1] or None]
# Save full samples
self.lq = np.split(lq, n_chunks, axis=-1) # create a lists of chunks
self.hq = np.split(hq, n_chunks, axis=-1) # create a lists of chunks
def get_full_signals(self):
# Returns full length input and target
return self.x_full, self.t_full
def preallocate(self):
"""
Preallocates the matrix to save all minibatch outputs.
It is faster to transfer all minibatches from GPU to CPU at once.
"""
return np.zeros((len(self.lq), *self.lq[0].shape))
def __len__(self):
return len(self.lq)
def __getitem__(self, idx):
return self.lq[idx], self.hq[idx]
def lowpass(sig, cutoff, filter_=('cheby1', 8), sr=44100):
"""Lowpasses input signal based on a cutoff frequency
Arguments:
sig {numpy 1d array} -- input signal
cutoff {int} -- cutoff frequency
Keyword Arguments:
sr {int} -- sampling rate of the input signal (default: {44100})
filter_type {str} -- type of filter, only butter and cheby1 are implemented (default: {'butter'})
Returns:
numpy 1d array -- lowpassed signal
"""
nyq = sr / 2
cutoff /= nyq
if filter_[0] == 'butter':
B, A = signal.butter(filter_[1], cutoff)
elif filter_[0] == 'cheby1':
B, A = signal.cheby1(filter_[1], 0.05, cutoff)
elif filter_[0] == 'bessel':
B, A = signal.bessel(filter_[1], cutoff, norm='mag')
elif filter_[0] == 'ellip':
B, A = signal.ellip(filter_[1], 0.05, 20, cutoff)
sig_lp = signal.filtfilt(B, A, sig)
return sig_lp.astype(np.float32)
def read_audio(path, make_stereo=True):
sr, audio = wavfile.read(path)
audio = audio.T
if np.issubdtype(audio.dtype, np.int16):
audio = audio.astype(np.float32) / 32768.0
if len(audio.shape) == 1: # if mono
audio = np.expand_dims(audio, axis=0)
if make_stereo:
audio = np.repeat(audio, 2, axis=0)
return audio, sr |