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import matplotlib.pyplot as plt
import numpy as np
import soundfile as sf
from librosa.filters import mel
from scipy import signal
from scipy.fftpack import fft
class Audio:
"""
audio class which holds music data and timestamp for notes.
Args:
filename: file name.
stereo: True or False; wether you have Don/Ka streo file or not. normaly True.
Variables:
Example:
>>>from music_processor import *
>>>song = Audio(filename)
>>># to get audio data
>>>song.data
>>># to import .tja files:
>>>song.import_tja(filename)
>>># to get data converted
>>>song.data = (song.data[:,0]+song.data[:,1])/2
>>>fft_and_melscale(song, include_zero_cross=False)
"""
def __init__(self, data, samplerate, stereo=True):
self.data = data
self.samplerate = samplerate
if stereo is False:
self.data = (self.data[:, 0] + self.data[:, 1]) / 2
self.timestamp = []
def plotaudio(self, start_t, stop_t):
plt.plot(
np.linspace(start_t, stop_t, stop_t - start_t), self.data[start_t:stop_t, 0]
)
plt.show()
def save(self, filename, start_t=0, stop_t=None):
if stop_t is None:
stop_t = self.data.shape[0]
sf.write(filename, self.data[start_t:stop_t], self.samplerate)
def synthesize(self, diff=True, don="./asset/don.wav", ka="./asset/ka.wav"):
donsound = sf.read(don)[0]
donsound = (donsound[:, 0] + donsound[:, 1]) / 2
kasound = sf.read(ka)[0]
kasound = (kasound[:, 0] + kasound[:, 1]) / 2
donlen = len(donsound)
kalen = len(kasound)
if diff is True:
for stamp in self.timestamp:
timing = int(stamp[0] * self.samplerate)
try:
if stamp[1] in (1, 3, 5, 6, 7):
self.data[timing : timing + donlen] += donsound
elif stamp[1] in (2, 4):
self.data[timing : timing + kalen] += kasound
except ValueError:
pass
elif diff == "don":
if isinstance(self.timestamp[0], tuple):
for stamp in self.timestamp:
if stamp * self.samplerate + donlen < self.data.shape[0]:
self.data[
int(stamp[0] * self.samplerate) : int(
stamp[0] * self.samplerate
)
+ donlen
] += donsound
else:
for stamp in self.timestamp:
if stamp * self.samplerate + donlen < self.data.shape[0]:
self.data[
int(stamp * self.samplerate) : int(stamp * self.samplerate)
+ donlen
] += donsound
elif diff == "ka":
if isinstance(self.timestamp[0], tuple):
for stamp in self.timestamp:
if stamp * self.samplerate + kalen < self.data.shape[0]:
self.data[
int(stamp[0] * self.samplerate) : int(
stamp[0] * self.samplerate
)
+ kalen
] += kasound
else:
for stamp in self.timestamp:
if stamp * self.samplerate + kalen < self.data.shape[0]:
self.data[
int(stamp * self.samplerate) : int(stamp * self.samplerate)
+ kalen
] += kasound
def make_frame(data, nhop, nfft):
"""
helping function for fftandmelscale.
細かい時間に切り分けたものを学習データとするため，nhop(512)ずつずらしながらnfftサイズのデータを配列として返す
"""
length = data.shape[0]
framedata = np.concatenate((data, np.zeros(nfft))) # zero padding
return np.array(
[framedata[i * nhop : i * nhop + nfft] for i in range(length // nhop)]
)
# @jit
def fft_and_melscale(
song,
nhop=512,
nffts=[1024, 2048, 4096],
mel_nband=80,
mel_freqlo=27.5,
mel_freqhi=16000.0,
include_zero_cross=False,
):
"""
fft and melscale method.
fft: nfft = [1024, 2048, 4096]; サンプルの切り取る長さを変えながらデータからnp.arrayを抽出して高速フーリエ変換を行う．
melscale: 周波数の次元を削減するとともに，log10の値を取っている．
"""
feat_channels = []
for nfft in nffts:
feats = []
window = signal.windows.blackmanharris(nfft)
filt = mel(
sr=song.samplerate,
n_fft=nfft,
n_mels=mel_nband,
fmin=mel_freqlo,
fmax=mel_freqhi,
)
# get normal frame
frame = make_frame(song.data, nhop, nfft)
# print(frame.shape)
# melscaling
processedframe = fft(window * frame)[:, : nfft // 2 + 1]
processedframe = np.dot(filt, np.transpose(np.abs(processedframe) ** 2))
processedframe = 20 * np.log10(processedframe + 0.1)
# print(processedframe.shape)
feat_channels.append(processedframe)
if include_zero_cross:
song.zero_crossing = np.where(np.diff(np.sign(song.data)))[0]
print(song.zero_crossing)
return np.array(feat_channels)
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