def get_spectrogram_feature(filepath, train_mode=False):
(rate, width, sig) = wavio.readwav(filepath)
wavio.writewav24("test.wav", rate=rate, data=sig)
sig = sig.ravel()
sig = trim(sig)
stft = torch.stft(torch.FloatTensor(sig),
N_FFT,
hop_length=int(0.01 * SAMPLE_RATE),
win_length=int(0.030 * SAMPLE_RATE),
window=torch.hamming_window(int(0.030 * SAMPLE_RATE)),
center=False,
normalized=False,
onesided=True)
stft = (stft[:, :, 0].pow(2) + stft[:, :, 1].pow(2)).pow(0.5)
amag = stft.clone().detach()
amag = amag.view(
-1, amag.shape[0], amag.shape[1]
) # reshape spectrogram shape to [batch_size, time, frequency]
mel = melscale_pytorch.mel_scale(amag,
sample_rate=SAMPLE_RATE,
n_mels=N_FFT // 2 +
1) # melspec with same shape
plt.subplot(1, 2, 1)
plt.imshow(mel.transpose(1, 2).squeeze(), cmap='jet')
p = 1 # always augment
randp = np.random.uniform(0, 1)
do_aug = p > randp
if do_aug & train_mode: # apply augment
print("augment image")
mel = spec_augment_pytorch.spec_augment(mel,
time_warping_para=80,
frequency_masking_para=54,
time_masking_para=50,
frequency_mask_num=1,
time_mask_num=1)
feat = mel.view(mel.shape[1],
mel.shape[2]) # squeeze back to [frequency, time]
feat = feat.transpose(0, 1).clone().detach()
plt.subplot(1, 2, 2)
plt.imshow(feat, cmap='jet')
plt.show() # display it
del stft, amag, mel
return feat
def extract_drums(attack, decay, onset_array, wavefile):
"""
extracts the individual drum hits from a wav file and writes them as separate wav files.
attack and decay are in samples instead of milliseconds. So 44 samples is about 1 millisecond
for a second recorded at 44.1Khz samplerate.
The conditionals here need to be refined since the length of the subsequent wav files appear to be
slightly random. It was found that an attack time of 1500 samples works well for drum transients.
If the transient is too short (2200 samples =~50 ms not including the attack time), the file for
that drum hit is not written as it will not be useful.
"""
read_data = wavio.readwav(wavefile)
filename = os.path.splitext(wavefile)[0]
read_array = read_data[
2] # a list of sample values in sequential order for the wavefile
output_csv_filename = '{0}_samples_to_onset.csv'.format(filename)
samples_to_onset_array = []
output_dir = 'separated_input'
for i in range(len(onset_array) - 1):
if onset_array[i +
1] >= len(read_array) and onset_array[i] - attack < 0:
start = 0
write_array = read_array[0:]
elif onset_array[i] - attack < 0:
start = 0
write_array = read_array[0:onset_array[i + 1]]
elif onset_array[i + 1] >= len(read_array):
start = onset_array[i] - attack
write_array = read_array[onset_array[i] - attack:]
else:
start = onset_array[i] - attack
write_array = read_array[onset_array[i] - attack:onset_array[i +
1]]
if len(
write_array
) - attack >= decay: #if the drumhit file is long enough, write it into the unclassified_drums directory
output_filename = '{0}_{1}.wav'.format(filename, "%05d" % i)
wavio.writewav24(output_filename, read_data[0], write_array)
shutil.move(output_filename, '{0}'.format(output_dir))
samples_to_onset = onset_array[i] - start
samples_to_onset_array.append([output_filename, samples_to_onset])
with open(output_csv_filename, 'w') as csvfile:
a = csv.writer(csvfile)
a.writerows(samples_to_onset_array)
shutil.move(output_csv_filename, '{0}'.format(output_dir))
def test1(self):
path = tempfile.mkdtemp()
filename = os.path.join(path, "test1data.wav")
wavio.writewav24(filename, 44100, data1)
try:
f = wave.open(filename, 'r')
self.assertEqual(f.getnchannels(), 1)
self.assertEqual(f.getsampwidth(), 3)
self.assertEqual(f.getframerate(), 44100)
f.close()
rate, sampwidth, data = wavio.readwav(filename)
self.assertEqual(rate, 44100)
self.assertEqual(sampwidth, 3)
self.assertEqual(data.dtype, np.int32)
self.assertEqual(data.shape, (len(data1), 1))
np.testing.assert_equal(data, data1)
finally:
os.remove(filename)
os.removedirs(path)
def extract_drums(attack, decay, onset_array, wavefile):
"""
extracts the individual drum hits from a wav file and writes them as separate wav files.
attack and decay are in samples instead of milliseconds. So 44 samples is about 1 millisecond
for a second recorded at 44.1Khz samplerate.
The conditionals here need to be refined since the length of the subsequent wav files appear to be
slightly random. It was found that an attack time of 1500 samples works well for drum transients.
If the transient is too short (2200 samples =~50 ms not including the attack time), the file for
that drum hit is not written as it will not be useful.
"""
read_data = wavio.readwav(wavefile)
filename = os.path.splitext(wavefile)[0]
read_array = read_data[2] # a list of sample values in sequential order for the wavefile
output_csv_filename = '{0}_samples_to_onset.csv'.format(filename)
samples_to_onset_array = []
output_dir = 'separated_input'
for i in range(len(onset_array) - 1):
if onset_array[i + 1] >= len(read_array) and onset_array[i] - attack < 0:
start = 0
write_array = read_array[0:]
elif onset_array[i] - attack < 0:
start = 0
write_array = read_array[0: onset_array[i + 1]]
elif onset_array[i + 1] >= len(read_array):
start = onset_array[i] - attack
write_array = read_array[onset_array[i] - attack:]
else:
start = onset_array[i] - attack
write_array = read_array[onset_array[i] - attack: onset_array[i+1]]
if len(write_array) - attack >= decay: #if the drumhit file is long enough, write it into the unclassified_drums directory
output_filename = '{0}_{1}.wav'.format(filename, "%05d" % i)
wavio.writewav24(output_filename, read_data[0], write_array)
shutil.move(output_filename, '{0}'.format(output_dir))
samples_to_onset = onset_array[i] - start
samples_to_onset_array.append([output_filename, samples_to_onset])
with open(output_csv_filename, 'w') as csvfile:
a = csv.writer(csvfile)
a.writerows(samples_to_onset_array)
shutil.move(output_csv_filename, '{0}'.format(output_dir))
def main():
"""
writes the new file which will show up in the current directory as {filename}replaced.wav
at the moment the replacement method is a simple prototype without using any ML yet.
Once we are able to find the most similar-sounding drumhit this will obviously have to change.
"""
similarDrums = os.listdir(
'./separated_corpus') # list of drumhit filenames directory.
similarDrums = filter(lambda f: '.wav' in f, similarDrums)
vSpace = vectSpace.vectSpace(
[], []) # the vectSpace object to find the closest-souding drumhit.
csvList = []
# csvList is made of a list of tuples of form ({filename}, {mfcc numpy array})
with open(corpus, 'rb') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
csvList.append(
[row[0], np.array(map(lambda e: float(e), row[1:]))])
# create drumVect objects from each entry in csvList and add said drumVect to vSpace
for elem in csvList:
dVect = drumVect.drumVect('{0}'.format(elem[0]), elem[1])
#print type(dVect)
vSpace.add_vect(dVect)
# create a drumVect objects from the input .wav
inputCsvList = []
with open('input_mfcc.csv', 'rb') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
inputCsvList.append(
[row[0], np.array(map(lambda e: float(e), row[1:]))])
inVectArray = [] #array of drumVect objects from the input .wav
for elem in inputCsvList:
dVect = drumVect.drumVect('{0}'.format(elem[0]), elem[1])
inVectArray.append(dVect)
w = wavio.readwav(sys.argv[1])
writeArray = w[
2] #a copy of the data from the original file supplied. modifying this.
hit = 0
while hit < len(inVectArray):
if onsets[hit] - attack < 0:
start = onsets[hit]
else:
start = onsets[hit] - 1500
if hit == len(onsets) - 1:
nextHit = None
else:
nextHit = onsets[hit + 1]
replacedHit = vSpace.k_closest(
1, inVectArray[hit])[1].get_filename() #our replacement hit.
repl = wavio.readwav(
'./{0}'.format(replacedHit)) #file of replacement hit
replacedHitArray = repl[2] #sample array
if nextHit != None:
#the replacedHitArray is longer than the distance between current and next hit
if len(writeArray[start:nextHit + 1]) < len(replacedHitArray):
writeArray[start:nextHit + 1] = replacedHitArray[
0:len(writeArray[start:nextHit + 1])]
#the replacedHitArray is shorter or equal to distance between current and next hit
else:
writeArray[start:start +
len(replacedHitArray)] = replacedHitArray
elif nextHit == None:
#if the replacedHitArray is longer than the rest of the writeArray or both equal length
if len(writeArray[start:]) <= len(replacedHitArray):
writeArray[start:] = replacedHitArray[0:len(writeArray[start:]
)]
#if the replacedHitArray is shorter than or equal to the rest of the writeArray
else:
writeArray[start:start +
len(replacedHitArray)] = replacedHitArray
hit += 1
wavio.writewav24(
'{0}replaced.wav'.format(filename[:len(filename) - 4]), w[0],
writeArray) #save the replaced drum file as a new file.
read = csv.reader(c)
for row in read:
samples_to_onset_dict[os.path.basename(row[0])] = row[1]
hop_s = win_s / 4
temp_files = []
for wav in wav_files:
wav_file_path = "separated_input/{0}".format(wav)
samples_to_onset = int(samples_to_onset_dict[wav])
readwav = wavio.readwav(wav_file_path)
readwav_array = readwav[2]
mfccwav_array = readwav_array[samples_to_onset:samples_to_onset +
samples_for_mfcc]
temp_filename = 'separated_input/{0}_temp.wav'.format(
os.path.splitext(wav)[0])
wavio.writewav24(temp_filename, readwav[0], mfccwav_array)
temp_files.append(temp_filename)
csv_output = []
for temp_file in temp_files:
samplerate = 0
s = a.source(temp_file, samplerate, hop_s)
samplerate = s.samplerate
p = a.pvoc(win_s, hop_s)
m = a.mfcc(win_s, n_filters, n_coeffs, samplerate)
mfccs = np.zeros([
n_coeffs,
])
frames_read = 0
while True:
def main():
"""
writes the new file which will show up in the current directory as {filename}replaced.wav
at the moment the replacement method is a simple prototype without using any ML yet.
Once we are able to find the most similar-sounding drumhit this will obviously have to change.
"""
similarDrums = os.listdir('./separated_corpus') # list of drumhit filenames directory.
similarDrums = filter(lambda f : '.wav' in f, similarDrums)
vSpace = vectSpace.vectSpace([],[]) # the vectSpace object to find the closest-souding drumhit.
csvList = []
# csvList is made of a list of tuples of form ({filename}, {mfcc numpy array})
with open(corpus, 'rb') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
csvList.append([row[0], np.array(map(lambda e : float(e), row[1:]))])
# create drumVect objects from each entry in csvList and add said drumVect to vSpace
for elem in csvList:
dVect = drumVect.drumVect('{0}'.format(elem[0]), elem[1])
#print type(dVect)
vSpace.add_vect(dVect)
# create a drumVect objects from the input .wav
inputCsvList = []
with open('input_mfcc.csv', 'rb') as csvfile:
reader = csv.reader(csvfile)
for row in reader:
inputCsvList.append([row[0], np.array(map(lambda e : float(e), row[1:]))])
inVectArray = [] #array of drumVect objects from the input .wav
for elem in inputCsvList:
dVect = drumVect.drumVect('{0}'.format(elem[0]), elem[1])
inVectArray.append(dVect)
w = wavio.readwav(sys.argv[1])
writeArray = w[2] #a copy of the data from the original file supplied. modifying this.
hit = 0
while hit < len(inVectArray):
if onsets[hit] - attack < 0:
start = onsets[hit]
else:
start = onsets[hit] - 1500
if hit == len(onsets) - 1:
nextHit = None
else:
nextHit = onsets[hit + 1]
replacedHit = vSpace.k_closest(1, inVectArray[hit])[1].get_filename() #our replacement hit.
repl = wavio.readwav('./{0}'.format(replacedHit)) #file of replacement hit
replacedHitArray = repl[2] #sample array
if nextHit != None:
#the replacedHitArray is longer than the distance between current and next hit
if len(writeArray[start: nextHit + 1]) < len(replacedHitArray):
writeArray[start: nextHit + 1] = replacedHitArray[0: len(writeArray[start: nextHit + 1])]
#the replacedHitArray is shorter or equal to distance between current and next hit
else:
writeArray[start: start + len(replacedHitArray)] = replacedHitArray
elif nextHit == None:
#if the replacedHitArray is longer than the rest of the writeArray or both equal length
if len(writeArray[start:]) <= len(replacedHitArray):
writeArray[start:] = replacedHitArray[0: len(writeArray[start:])]
#if the replacedHitArray is shorter than or equal to the rest of the writeArray
else:
writeArray[start: start + len(replacedHitArray)] = replacedHitArray
hit += 1
wavio.writewav24('{0}replaced.wav'.format(filename[:len(filename) - 4]), w[0], writeArray) #save the replaced drum file as a new file.
samples_to_onset_dict = {}
with open('separated_input/{0}'.format(csv_file), 'r') as c:
read = csv.reader(c)
for row in read:
samples_to_onset_dict[os.path.basename(row[0])] = row[1]
hop_s = win_s / 4
temp_files = []
for wav in wav_files:
wav_file_path = "separated_input/{0}".format(wav)
samples_to_onset = int(samples_to_onset_dict[wav])
readwav = wavio.readwav(wav_file_path)
readwav_array = readwav[2]
mfccwav_array = readwav_array[samples_to_onset : samples_to_onset + samples_for_mfcc]
temp_filename = 'separated_input/{0}_temp.wav'.format(os.path.splitext(wav)[0])
wavio.writewav24(temp_filename, readwav[0],mfccwav_array)
temp_files.append(temp_filename)
csv_output = []
for temp_file in temp_files:
samplerate = 0
s = a.source(temp_file, samplerate, hop_s)
samplerate = s.samplerate
p = a.pvoc(win_s, hop_s)
m = a.mfcc(win_s, n_filters, n_coeffs, samplerate)
mfccs = np.zeros([n_coeffs,])
frames_read = 0
while True:
samples, read = s()
spec = p(samples)
for csvfile in csv_files:
with open('{0}/{1}'.format(source_directory, csvfile), 'r') as c:
reader = csv.reader(c)
for row in reader:
samples_to_onset_dict[os.path.basename(row[0])] = row[1]
temp_files = []
for wav in wav_files:
wav_file_path = "{0}/{1}".format(source_directory, wav)
samples_to_onset = int(samples_to_onset_dict[wav])
readwav = wavio.readwav(wav_file_path)
readwav_array = readwav[2] # sample data from the wav file of interest
mfccwav_array = readwav_array[samples_to_onset: samples_to_onset + samples_for_mfcc]
temp_filename = '{0}{1}_temp.wav'.format(source_directory, os.path.splitext(wav)[0])
wavio.writewav24(temp_filename, readwav[0], mfccwav_array) # temp wav file that the mfcc's are calculated from
temp_files.append(temp_filename)
csv_output = []
for temp_file in temp_files:
# run mfcc analysis on the temp file
samplerate = 0
s = source(temp_file, samplerate, hop_s)
samplerate = s.samplerate
p = pvoc(win_s, hop_s)
m = mfcc(win_s, n_filters, n_coeffs, samplerate)
mfccs = zeros([n_coeffs,])
frames_read = 0
while True:
samples, read = s()
import wavio
from scipy.signal import decimate
rate, sampwidth, data=wavio.readwav('/home/eudocio/Escritorio/borrar/EHB/Cuestionarios/121 (AUDIO A).wav')
decimationFactor=2
nframes=len(data)
subSamplingData=decimate(data, decimationFactor, 1,'fir', axis=0)
monoAverage=data.mean(axis=1)
monoLeft=data[:, 0]
monoRight=data[:, 1]
wavio.writewav24('/home/eudocio/Escritorio/borrar/EHB/Cuestionarios/121_AUDIO_A_decimado.wav', 24000, subSamplingData)
wavio.writewav24('/home/eudocio/Escritorio/borrar/EHB/Cuestionarios/121_AUDIO_A_nuevo.wav', 48000, monoAverage)
wavio.writewav24('/home/eudocio/Escritorio/borrar/EHB/Cuestionarios/121_AUDIO_A_izquierdo.wav', 48000, monoLeft)
wavio.writewav24('/home/eudocio/Escritorio/borrar/EHB/Cuestionarios/121_AUDIO_A_derecho.wav', 48000, monoRight)
print(subSamplingData)
gmm1 = makeGMM(50,sample,"gmm_jazz_50")
print("学習を実行します。混合数=100")
gmm2 = makeGMM(100,sample,"gmm_jazz_100") #100以降はメモリエラーが発生する場合あり
'''
#高階調化処理
wav = read_wav_cd("../wav/Moanin.wav",BEGIN_FLAME,END_FLAME)
print("変換する振幅データを読み込みます。")
cdAmp_l = makeAmpArrayCD(wav['amp_l'], BEGIN_FLAME, END_FLAME) #Lチャネル
#cdAmp_r = makeAmpArrayCD(wav['amp_r'], BEGIN_FLAME, END_FLAME) #Rチャネル
#変換前
inputArray = []
for j in cdAmp_l : inputArray.extend(j * 5000000.0)
wavio.writewav24("input.wav", 96000, inputArray)
#print("正解振幅データを作成しています。") #正解を見たい時のオプション
#originAmp = makeAmpArrayHi(wav['amp_l'], BEGIN_FLAME, END_FLAME)
#origin = []
#for j in originAmp : origin.extend(j * 5000000.0)
gmm = joblib.load("gmm_jazz_100") #GMMファイルの読み込み
debug_gmm(gmm)
print("Lチャネルを変換しています。")
result_l = convertAmp(gmm, cdAmp_l, 50)
#print("Rチャネルを変換しています。")
#result_r = convertAmp(gmm, cdAmp_r, components)
print("\n変換が終了しました。\n")
