def get_mfcc_feat(filename, winstep=0.01, nfilt=40, numcep=13, preemph=0.95, appendEnergy=True, begin_index=-1,
end_index=-1):
"""
Compute MFCC features from an audio signal.
:param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds)
:param numcep: the number of cepstrum to return, default 13
:param nfilt: the number of filters in the filterbank, default 26.
:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.95.
:param appendEnergy: if this is true, the zeroth cepstral coefficient is replaced with the log of the total frame energy.
:param begin_index: the frame when record begin
:param end_index: the frame when record end
:return mfcc_feat: A numpy array of size (NUMFRAMES by numcep) containing features. Each row holds 1 feature vector.
:return fbank_feat: A numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector.
:return normalization_feature: the 39-dimensions feature after finite difference and normalization
"""
(rate, signal) = wav.read(filename)
if begin_index != -1:
signal = signal[begin_index * 44100 * 0.02:end_index * 44100 * 0.02]
# print signal
# print type(signal[0])
if rate == 44100:
winlen = 0.02
nfft = 1024
elif rate == 16000:
winlen = 0.025
nfft = 512
mfcc_feat = mfcc.mfcc(signal=signal, samplerate=rate, winlen=winlen, winstep=winstep, nfft=nfft,
nfilt=nfilt, numcep=numcep, preemph=preemph, appendEnergy=appendEnergy)
numpy.savetxt("result.txt", mfcc_feat, delimiter=",")
fbank_feat = mfcc.logfbank(signal=signal, samplerate=rate, winlen=winlen, winstep=winstep, nfft=nfft,
nfilt=nfilt, preemph=preemph)
# numpy.savetxt("result2.txt", fbank_feat, delimiter=",")
normalization_feature = mfcc.normalization(mfcc_feat)
return mfcc_feat, fbank_feat, normalization_feature
def get_class_proba_sound(clf_gb, clf_rf, data, interval, numFrames):
coeff = data.shape[0] / numFrames
features_nb = 2587
X = np.zeros((len(interval), features_nb))
for i, inter in enumerate(interval):
name, (beg, end) = inter
space = end - beg - 9
#print 'space:', space
limit = 40
data_interval = np.zeros(40 * coeff)
if (space > limit):
data_interval = data[(beg - 1) * coeff:(beg + limit - 1) *
coeff]
else:
data_interval[:space * coeff] = data[(beg - 1) *
coeff:(end - 10) * coeff]
ceps, mspec, spec = mf.mfcc(data_interval)
#print 'ceps, mspec, spec', ceps.shape, mspec.shape, spec.shape
X[i, :2587] = ceps.reshape(2587)
#X[i, 2587:10547] = mspec.reshape(7960)
#X[i, 10547:22835] = spec.reshape(12288)
X = np.nan_to_num(X)
X = X.clip(min=-100)
return clf_gb.predict_proba(X), clf_rf.predict_proba(X)
def __init__(self, audio):
self.audio = audio
self.window_length = 0.025
self.window_step = 0.01
self.num_cepstrals = 12
self.nfft = 512
self.mfcc = mfcc.mfcc()
def record(self, button):
text, ok = QtWidgets.QInputDialog().getText(self,
"Emotion Recognition",
"Enter Your Name:")
if ok and text:
self.speaker = text
print "Speaker: " + self.speaker
wFile = "Test/" + self.speaker + ".wav"
start_record(self, wFile, 5, self.state)
state = "File saved as " + self.speaker
self.repaint()
print 'File saved as ' + self.speaker + ".wav"
self.add_files()
fFile = "feat/Test/" + self.speaker + ".htk"
try:
# call MFCC feature extraction subroutine
f, E, fs = mfcc(wFile, self.winlen, self.ovrlen, self.pre_coef,
self.nfilter, self.nftt)
# VAD part
if self.opts == 1:
f = vad_thr(f, E)
# Energy threshold based VAD [comment this line if you would like to plugin the rVAD labels]
elif self.opts == 0:
l = numpy.loadtxt('..corresponding vad label file')
# [Plugin the VAD label generated by rVAD matlab]
if (len(f) - len(l)) == 1:
# 1-[end-frame] correction [matlab/python]
l = numpy.append(l, l[-1:, ])
elif (len(f) - len(l)) == -1:
l = numpy.delete(l, -1)
if (len(l)
== len(f)) and (len(numpy.argwhere(l == 1)) != 0):
idx = numpy.where(l == 1)
f = f[idx]
else:
print "mismatch frames between: label and feature files or no voice-frame in VAD"
exit()
# Zero mean unit variance normalize after VAD
f = cmvn(f)
# write the VAD+normalized features in file
if not os.path.exists(os.path.dirname(fFile)):
# create director for the feature file
os.makedirs(os.path.dirname(fFile))
writehtk(fFile, f, 0.01)
except:
print("Fail1..%s ---> %s\n" % (wFile, fFile))
if button.text() == "Live Detection":
self.test_emotion(name=self.speaker)
else:
self.state = "Not valid Name"
self.repaint()
return
def collect_mfcc(arg, dirname, fnames):
for fname in fnames:
if fname.endswith(".wav"):
signal, FS = waveio.wave_from_file(os.path.join(dirname, fname))
feature = list(mfcc.mfcc(signal, FS))
feature_collection[fname] = []
for point in feature:
feature_collection[fname].append(lbg.cluster_point(point, mu))
def calc_mfcc(arg, dirname, fnames):
for fname in fnames:
if fname.endswith(".wav"):
signal, FS = waveio.wave_from_file(os.path.join(
dirname, fname))
feature = list(mfcc(signal, FS))
print "File: %s generate %d number of MFCC" % (fname,
len(feature))
feature_collection["feature"] += feature
def make_fake_mfcc(construct_record):
"""制作fake录音\n
输入:fake录音的字符串描述\n
输出:(label，fake_mfcc_sequence)元组\n
e.g 输入123,输出(['1','2','3'],123的mfcc_sequence)"""
label = []
return_mfcc_sequence = []
for digit in construct_record:
label.append(digit)
return_mfcc_sequence += mfcc.mfcc(
readwave.read_wave("./fake/" + digit + ".wav"), "fm")
return label, return_mfcc_sequence
def get_mfcc_feat(filename,
winstep=0.01,
nfilt=40,
numcep=13,
preemph=0.95,
appendEnergy=True,
begin_index=-1,
end_index=-1):
"""
Compute MFCC features from an audio signal.
:param winstep: the step between successive windows in seconds. Default is 0.01s (10 milliseconds)
:param numcep: the number of cepstrum to return, default 13
:param nfilt: the number of filters in the filterbank, default 26.
:param preemph: apply preemphasis filter with preemph as coefficient. 0 is no filter. Default is 0.95.
:param appendEnergy: if this is true, the zeroth cepstral coefficient is replaced with the log of the total frame energy.
:param begin_index: the frame when record begin
:param end_index: the frame when record end
:return mfcc_feat: A numpy array of size (NUMFRAMES by numcep) containing features. Each row holds 1 feature vector.
:return fbank_feat: A numpy array of size (NUMFRAMES by nfilt) containing features. Each row holds 1 feature vector.
:return normalization_feature: the 39-dimensions feature after finite difference and normalization
"""
(rate, signal) = wav.read(filename)
if begin_index != -1:
signal = signal[begin_index * 44100 * 0.02:end_index * 44100 * 0.02]
# print signal
# print type(signal)
if rate == 44100:
winlen = 0.02
nfft = 1024
elif rate == 16000:
winlen = 0.025
nfft = 512
mfcc_feat = mfcc.mfcc(signal=signal,
samplerate=rate,
winlen=winlen,
winstep=winstep,
nfft=nfft,
nfilt=nfilt,
numcep=numcep,
preemph=preemph,
appendEnergy=appendEnergy)
numpy.savetxt("result.txt", mfcc_feat, delimiter=",")
fbank_feat = mfcc.logfbank(signal=signal,
samplerate=rate,
winlen=winlen,
winstep=winstep,
nfft=nfft,
nfilt=nfilt,
preemph=preemph)
# numpy.savetxt("result2.txt", fbank_feat, delimiter=",")
normalization_feature = mfcc.normalization(mfcc_feat)
return mfcc_feat, fbank_feat, normalization_feature
def loop(self):
self.working = True
while self.working:
frames = self.input_audio.read()
if self.input_frames is not None:
self.input_frames = numpy.append(self.input_frames, frames)
mfcc_features = mfcc.mfcc(frames)
self.state.step(self, mfcc_features)
if self.output_queue is not None:
output = self.network.capture_output(1)
self.output_queue.put(output)
self.input_audio.close()
def compare(arg, dirname, fnames):
for fname in fnames:
if test in fname:
# Get MFCC for each test sample
test_features = mfcc(signal = waveio.wave_from_file(os.path.join(dirname, fname))[0], fs = waveio.wave_from_file(os.path.join(dirname, fname))[1])
# Record overhead and score for each test sample versus each template.(Each sample is of a dict corresponding to each template)
overhead[fname]= {}
score[fname] = {}
for template in template_feature.keys():
time_start = time()
score[fname][template] = dtw(template_feature[template], test_features)
overhead[fname][template] = time() - time_start
def training(nfiltbank):
nSpeaker = 1
nCentroid = 16
codebooks_mfcc = np.empty((nSpeaker, nfiltbank, nCentroid))
directory = 'train'
fname = str()
fname = '/shefali1.wav'
(fs, s) = read(directory + fname)
mel_coeff = mfcc(s, fs, nfiltbank)
codebook = lbg(mel_coeff, nCentroid)
print('Training complete')
return (codebook)
def training(nfiltbank):
nSpeaker = 8
nCentroid = 16
codebooks = np.empty((nSpeaker, nfiltbank, nCentroid))
directory = 'train'
fname = str()
for i in range(nSpeaker):
fname = '/s' + str(i + 1) + '.wav'
(fs, s) = read(directory + fname)
mel_coeff = mfcc(s, fs, nfiltbank)
codebooks[i, :, :] = lbg(mel_coeff, nCentroid)
print('Training complete')
return (codebooks)
def predict(model_name, filename):
samples, sample_rate = audio.decode_wav(filename)
coefficients = mfcc(samples, sample_rate)
coefficients = tf.reshape(tf.cast(coefficients, tf.float32),
[1, 98, 13, 1])
model = tf.keras.models.load_model(model_name)
prediction = model.predict(coefficients)
if np.argmax(prediction) >= 0.8:
output = constant.train_commands[np.argmax(prediction)]
else:
output = None
return output
def train(n):
k = 16
import wave
code = []
for i in range(n):
wav_file=wave.open('{}'.format(i+1) + '.wav')
raw_frames = wav_file.readframes(-1)
num_frames = wav_file.getnframes()
num_channels = wav_file.getnchannels()
sample_rate = wav_file.getframerate()
sample_width = wav_file.getsampwidth()
temp_buffer = np.empty((num_frames, num_channels, 4), dtype=np.uint8)
raw_bytes = np.frombuffer(raw_frames, dtype=np.uint8)
temp_buffer[:, :, :sample_width] = raw_bytes.reshape(-1, num_channels,
sample_width)
temp_buffer[:, :, sample_width:] = \
(temp_buffer[:, :, sample_width-1:sample_width] >> 7) * 255
frames = temp_buffer.view('<i4').reshape(temp_buffer.shape[:-1])
#sample_rate = wav_file.getframerate()
sample_rate = 16000;
signal=frames[::3,0]
sample_rate, signal = cut.cut(sample_rate, signal)
# sample_rate, signal = scipy.io.wavfile.read('{}'.format(i+1) + '.wav')
v = mfcc(signal, sample_rate)
code.append(vqlbg(v, k))
print(i)
if n == 3:
d1 = disteu(code[0], code[1])
d2 = disteu(code[1], code[2])
d3 = disteu(code[2], code[0])
dk = []
dk.append(sum(d1.min(1)) / d1.shape[0])
dk.append(sum(d2.min(1)) / d2.shape[0])
dk.append(sum(d3.min(1)) / d3.shape[0])
dmax = max(dk)
dmin = min(dk)
else:
dmax = 0
dmin = 0
return code, dmax, dmin
def compare_files(filenames, save_preprocessed_files):
class Sim:
def __init__(self, name1, name2):
self.name1 = name1
self.name2 = name2
characteristics = {}
for filename in filenames:
print("processing {}".format(filename))
signal = sound_file.load(filename)
signal = pre_processing.process(signal)
if save_preprocessed_files:
sound_file.save(filename[:-4] + "_p.wav", signal)
characteristics[filename] = mfcc.mfcc(signal)
metrics = _metrics_of_characteristics(characteristics, filenames)
_simple_metrics_comparison(metrics)
_dbscan_metrics_comparison(metrics)
return metrics
def get_mpc(path,filename,ncep,dataset):
signal, rate = get_audio_info(path)
mfcc_feat,mpc_feat,spectrum = mfcc(signal,fs=rate,nfft=512,nceps=ncep,nwin=512)
feat = mpc_feat[:,:ncep]
mean_feat = np.mean(feat, axis=0)
covvar = np.cov(feat,rowvar=0)
cov_feat = []
#cov_feat = np.empty([0,0])
for i in range(0,covvar.shape[0]):
for j in range(0,i+1):
cov_feat.append(covvar[i][j])
cov_feat = np.array(cov_feat)
feature_vector = np.append(mean_feat,cov_feat)
data.append(feature_vector)
if dataset == 'ismir':
labels.append(re.split('-', filename, maxsplit=1)[0])
elif dataset == 'gtzan':
labels.append(re.split('\.', filename, maxsplit=1)[0])
else:
print 'unrecognised dataset type'
sys.exit()
def test(isOnline, test_data):
if isOnline:
test_sequence = mfcc.mfcc(record.record(), "fast_mode")
hmm(test_sequence, 3, "test0", True)
else:
for i in xrange(10):
### 俊优的test sequence
for index in test_data[0]:
name = ".\\txtDictionary\\junyo_" + str(i) + "_" + str(
index) + ".txt"
sequence = np.loadtxt(name)
sequence = sequence[1:]
hmm(sequence, i, name, False)
### 健炜的test sequence
for index in test_data[1]:
name = ".\\txtDictionary\\" + str(i) + "_" + str(
index) + ".txt"
sequence = np.loadtxt(name)
sequence = sequence[1:]
hmm(sequence, i, name, False)
def test(name, code):
import wave
wav_file = wave.open(name + '.wav')
raw_frames = wav_file.readframes(-1)
num_frames = wav_file.getnframes()
num_channels = wav_file.getnchannels()
sample_rate = wav_file.getframerate()
sample_width = wav_file.getsampwidth()
temp_buffer = np.empty((num_frames, num_channels, 4), dtype=np.uint8)
raw_bytes = np.frombuffer(raw_frames, dtype=np.uint8)
temp_buffer[:, :, :sample_width] = raw_bytes.reshape(
-1, num_channels, sample_width)
temp_buffer[:, :, sample_width:] = \
(temp_buffer[:, :, sample_width-1:sample_width] >> 7) * 255
frames = temp_buffer.view('<i4').reshape(temp_buffer.shape[:-1])
#sample_rate = wav_file.getframerate()/3
sample_rate = 16000
signal = frames[::3, 0]
sample_rate, signal = cut.cut(sample_rate, signal)
v = mfcc(signal, sample_rate)
distmin = float('inf')
dist = 0
for i in range(len(code)):
d = disteu(v, code[i])
dist += sum(d.min(1)) / d.shape[0]
dist = dist / len(code)
# dist = sum(d.min(1)) / d.shape[0]
#
# if dist < distmin:
# distmin = dist
print(dist)
if dist < 4.0:
return True
else:
return False
def GetNumber(self, AudioIn, Fs):
#对输入信号求mfcc
ReceivedSignal = mfcc.mfcc(AudioIn, Fs)
value = [] #开始进行模板匹配
value.append(dtw.dtw(self.mfcc0, ReceivedSignal))
value.append(dtw.dtw(self.mfcc1, ReceivedSignal))
value.append(dtw.dtw(self.mfcc2, ReceivedSignal))
value.append(dtw.dtw(self.mfcc3, ReceivedSignal))
value.append(dtw.dtw(self.mfcc4, ReceivedSignal))
value.append(dtw.dtw(self.mfcc5, ReceivedSignal))
value.append(dtw.dtw(self.mfcc6, ReceivedSignal))
value.append(dtw.dtw(self.mfcc7, ReceivedSignal))
value.append(dtw.dtw(self.mfcc8, ReceivedSignal))
value.append(dtw.dtw(self.mfcc9, ReceivedSignal))
value.append(dtw.dtw(self.mfccAuther, ReceivedSignal))
#print(value)
number = value.index(min(value)) #获取最接近的值
#print(number)
return number
def features(audio, rate, ncp=ncep):
audio = clip(audio.astype(np.float64))
feats = np.empty(ncp + nother)
coefs = mfcc.mfcc(audio,
samplerate=rate,
numcep=ncp,
nfilt=2 * ncp,
nfft=4096,
winlen=4096 / rate,
winstep=4096 / 3 / rate)
feats[0:ncp] = np.mean(coefs, axis=0)
fft = np.abs(np.fft.rfft(audio))
pwr = fft**2
feats[ncp] = ms.gmean(pwr) / np.mean(pwr)
freq = np.fft.rfftfreq(len(audio), 1 / rate)
feats[ncp + 1] = np.sum(fft * freq) / np.sum(fft)
feats[ncp + 2] = np.sqrt(np.mean(np.square(audio)))
return feats
def get_class_proba_sound(clf_gb, clf_rf, data, interval, numFrames):
coeff = data.shape[0] / numFrames
features_nb = 2587
X = np.zeros((len(interval), features_nb))
for i, inter in enumerate(interval):
name, (beg, end) = inter
space = end - beg - 9
#print 'space:', space
limit = 40
data_interval = np.zeros(40*coeff)
if(space > limit):
data_interval = data[(beg-1)*coeff:(beg+limit-1)*coeff]
else:
data_interval[:space*coeff] = data[(beg-1)*coeff:(end-10)*coeff]
ceps, mspec, spec = mf.mfcc(data_interval)
#print 'ceps, mspec, spec', ceps.shape, mspec.shape, spec.shape
X[i, :2587] = ceps.reshape(2587)
#X[i, 2587:10547] = mspec.reshape(7960)
#X[i, 10547:22835] = spec.reshape(12288)
X = np.nan_to_num(X)
X = X.clip(min=-100)
return clf_gb.predict_proba(X), clf_rf.predict_proba(X)
min_cost = node.cost
for node in trellis:
if node.cost > min_cost + PRUNING_THRESHOLD:
node.active = False
node.cost = sys.float_info.max
# pruning done, now start next loop, taking in next mfcc in the sequence.
# collect result
pointer = trellis[-1].backPointer
result = []
while pointer != 0:
result.insert(0, back_pointer_table[pointer].meaning)
pointer = back_pointer_table[pointer].previous_index
for i in range(len(result)):
ob = result[i].find('\"')
result[i] = result[i][ob+1]
return result
if __name__ == '__main__':
generated_trellis = trellis_generate("network_gen.txt")
print('trellis construct done')
# continuous_speech = mfcc.mfcc(record.record(), "fm")
continuous_speech = mfcc.mfcc(readwave.read_wave("./fake/9.wav"),"fm")
continuous_speech += mfcc.mfcc(readwave.read_wave("./fake/1.wav"),"fm")
continuous_speech += mfcc.mfcc(readwave.read_wave("./fake/0.wav"),"fm")
continuous_speech += mfcc.mfcc(readwave.read_wave("./fake/9.wav"),"fm")
continuous_speech += mfcc.mfcc(readwave.read_wave("./fake/6.wav"), "fm")
continuous_speech += mfcc.mfcc(readwave.read_wave("./fake/7.wav"), "fm")
continuous_speech += mfcc.mfcc(readwave.read_wave("./fake/8.wav"), "fm")
getresult = back_pointer_dtw(continuous_speech, generated_trellis)
print(getresult)
def SystemInit(self):
#输入模板信号
self.sample_rate, self.signal1 = scipy.io.wavfile.read('1.wav')
self.sample_rate, self.signal2 = scipy.io.wavfile.read('2.wav')
self.sample_rate, self.signal3 = scipy.io.wavfile.read('3.wav')
self.sample_rate, self.signal4 = scipy.io.wavfile.read('4.wav')
self.sample_rate, self.signal5 = scipy.io.wavfile.read('5.wav')
self.sample_rate, self.signal6 = scipy.io.wavfile.read('6.wav')
self.sample_rate, self.signal7 = scipy.io.wavfile.read('7.wav')
self.sample_rate, self.signal8 = scipy.io.wavfile.read('8.wav')
self.sample_rate, self.signal9 = scipy.io.wavfile.read('9.wav')
self.sample_rate, self.signal0 = scipy.io.wavfile.read('0.wav')
self.sample_rate, self.signalAuther = scipy.io.wavfile.read(
'Auther.wav')
#对模板信号求mfcc
self.mfcc0 = mfcc.mfcc(self.signal0, self.sample_rate)
self.mfcc1 = mfcc.mfcc(self.signal1, self.sample_rate)
self.mfcc2 = mfcc.mfcc(self.signal2, self.sample_rate)
self.mfcc3 = mfcc.mfcc(self.signal3, self.sample_rate)
self.mfcc4 = mfcc.mfcc(self.signal4, self.sample_rate)
self.mfcc5 = mfcc.mfcc(self.signal5, self.sample_rate)
self.mfcc6 = mfcc.mfcc(self.signal6, self.sample_rate)
self.mfcc7 = mfcc.mfcc(self.signal7, self.sample_rate)
self.mfcc8 = mfcc.mfcc(self.signal8, self.sample_rate)
self.mfcc9 = mfcc.mfcc(self.signal9, self.sample_rate)
self.mfccAuther = mfcc.mfcc(self.signalAuther, self.sample_rate)
clf = pickle.load(open('model_svm', 'rb'))
#读取标签对应的乐器名称
instruments = pickle.load(open('names', 'rb'))
#读取测试文件目录
musicpath = input("Enter the path of the folder containing testing audios :")
print("Computing...")
filename = [name for name in os.listdir(musicpath) if re.match(r'.*wav', name)]
result = []
for i in range(len(filename)):
fs, music = wavfile.read(os.path.join(musicpath, filename[i]))
if music.dtype.type not in [np.int16, np.int32, np.float32]:
raise TypeError(
'only 16bit,32bit PCM and 32bit floating-point wavefiles are supported'
)
#只支持这三种精度的wav文件，注意不支持8bit与24bit
#预处理
frameTime = 0.02 #(s)
#f：处理后的各帧对象 frameLength：以取样点数表示的帧长
f, frameLength = preprocessing(music, fs, frameTime)
#计算出mfcc
ceps = mfcc(f, fs, frameLength)
result.append(clf.predict(ceps)) #得到对每一帧的预测结果
for i in range(len(result)):
result[i] = np.argmax(np.bincount(result[i])) #每帧投票，选出得票最多的结果
print('The instrument played in "' + filename[i] + '" is detected as: ' +
instruments[result[i]])
import readwave
import mfcc
import numpy as np
for digit in range(0, 10):
for index in range(0, 5):
file_name = "./junyo_iso_11_1/" + str(digit) + "_" + str(
index) + ".wav"
speech = mfcc.mfcc(readwave.read_wave(file_name), "fm")
np.savetxt(str(digit) + "_" + str(index) + ".txt", speech)
def main():
sample_rate, signal11 = scipy.io.wavfile.read('1.wav')
sample_rate, signal12 = scipy.io.wavfile.read('2.wav')
sample_rate, signal13 = scipy.io.wavfile.read('3.wav')
sample_rate, signal14 = scipy.io.wavfile.read('4.wav')
sample_rate, signal15 = scipy.io.wavfile.read('5.wav')
sample_rate, signal16 = scipy.io.wavfile.read('6.wav')
sample_rate, signal17 = scipy.io.wavfile.read('7.wav')
sample_rate, signal18 = scipy.io.wavfile.read('8.wav')
sample_rate, signal19 = scipy.io.wavfile.read('9.wav')
sample_rate, signal10 = scipy.io.wavfile.read('0.wav')
print('ok1')
mfcc10 = mfcc(signal10, sample_rate)
mfcc11 = mfcc(signal11, sample_rate)
mfcc12 = mfcc(signal12, sample_rate)
mfcc13 = mfcc(signal13, sample_rate)
mfcc14 = mfcc(signal14, sample_rate)
mfcc15 = mfcc(signal15, sample_rate)
mfcc16 = mfcc(signal16, sample_rate)
mfcc17 = mfcc(signal17, sample_rate)
mfcc18 = mfcc(signal18, sample_rate)
mfcc19 = mfcc(signal19, sample_rate)
print('ok2')
value = []
sample_rate, signalinput = scipy.io.wavfile.read('1.wav')
ReceivedSignal = mfcc(signalinput, sample_rate)
value.append(dtw(mfcc10, ReceivedSignal))
value.append(dtw(mfcc11, ReceivedSignal))
value.append(dtw(mfcc12, ReceivedSignal))
value.append(dtw(mfcc13, ReceivedSignal))
value.append(dtw(mfcc14, ReceivedSignal))
value.append(dtw(mfcc15, ReceivedSignal))
value.append(dtw(mfcc16, ReceivedSignal))
value.append(dtw(mfcc17, ReceivedSignal))
value.append(dtw(mfcc18, ReceivedSignal))
value.append(dtw(mfcc19, ReceivedSignal))
print(value)
number = value.index(min(value))
print(number)
import matplotlib.pyplot as plt import numpy as np from mfcc import mfcc from read import read from stft import stft fname = "sineSweep.wav" (srate, data) = read(fname, "mono") N = 1024 X= stft(data, N) X = np.abs(X) X = X[:N/2+1] X = mfcc(X, 44100) #mag to dec conversion #X = 10 * np.log10(X) plt.imshow(X[1:], interpolation='nearest', aspect='auto', origin='lower') plt.show()
getwav = [line.replace('\n','') for line in wavedata] # Remove to '\n'
print('Get wave data list: ', getwav)
""" Read wave datas """
wavdata_, fs_, time_shift = read_wav_data(getwav[0])
''' Compute features
Used functions:
MFCC, Delta MFCC, AveMFCC, AveDeltaMFCC
MFCC: mfcc.mfcc(line)
Delta MFCC: mfcc.delta(line)
Averages: line.mean(axis=1)
'''
# MFCC
wavdata, fs, time_song, ceps = mfcc.mfcc(wavdata_, fs_)
# Delta MFCC
delta_mfcc = np.empty((0, len(ceps)), int)
for line in range(len(ceps.T)):
delta_mfcc = np.append(delta_mfcc, [mfcc.delta(ceps.T[line])], axis=0)
# MFCC Average
ave_mfcc = []
ave_mfcc.append(ceps.T.mean(axis=1))
# Delta MFCC Average
ave_DeltaMfcc = []
ave_DeltaMfcc.append(delta_mfcc.mean(axis=1))
def get_mfcc(filename):
samples, sample_rate = decode_wav(filename)
return mfcc(samples, sample_rate)
ubmDir= 'GMM' + str(nmix)
wFile = "Test_dec.wav"
start_record(wFile,3)
fFile= "feat/Test/"+wFile+".htk"
with open(ubmDir + '/' + 'ubm') as f:
print "lood ubm .. %s" %(f)
ubm_mu, ubm_cov, ubm_w = pickle.load(f)
winlen, ovrlen, pre_coef, nfilter, nftt = 0.025, 0.02, 0.97, 26 , 512
opts=1
try:
#call MFCC feature extraction subroutine
f, E, fs=mfcc(wFile,winlen, ovrlen, pre_coef, nfilter, nftt)
# VAD part
if opts == 1:
f=vad_thr(f,E) #Energy threshold based VAD [comment this line if you would like to plugin the rVAD labels]
elif opts == 0:
l=numpy.loadtxt('..corresponding vad label file'); #[Pluggin the VAD label generated by rVAD matlab]
if (len(f) - len(l)) ==1: #1-[end-frame] correction [matlab/python]
l= numpy.append(l,l[-1:,])
elif (len(f) -len(l)) == -1:
l=numpy.delete(l,-1)
fft = numpy.fft.rfft(buf.data, n_fft, axis=-1)
# Power spectrum and flitering
mspec = numpy.log(numpy.dot(fft.real**2 + fft.imag**2, fbank.T))
# Use the DCT to 'compress' the coefficients (spectrum -> cepstrum domain)
# The C0 term is removed as it is the constant term
buf.data = scipy.fftpack.realtransforms.dct(mspec,
type=2,
norm='ortho',
axis=-1)[1:nceps + 1]
buf.data = buf.data[:, numpy.newaxis]
next.send(buf)
except GeneratorExit:
next.close()
if __name__ == '__main__':
import numpy
import ringBuffer
from testPipeline import audio_reader, sink
from mfcc import mfcc
output = sink()
buf_cep = ringBuffer.ring_buffer(output, 2, 0, 13)
cep = mfcc(buf_cep)
buf_sig = ringBuffer.ring_buffer(cep, 200, 80)
inp = audio_reader(buf_sig, "taaa.wav")
next(inp)
def test_emotion(self, name):
if name:
value = name
else:
file = self.files.currentItem()
print file.text()
value = file.text().split(".")[0]
fFile = "feat/Test/" + value + ".htk"
self.emotion = test(fFile, self.Tardest, self.ubm_mu, self.ubm_cov,
self.ubm_w)
self.result = "Detected as: "
self.repaint()
start_play(self.emotion + "_dec.wav")
self.state = "Say Yes or No"
self.repaint()
wFile = "Test_dec.wav"
start_record(self, wFile, 3, self.state)
fFile = "feat/Test/" + wFile + ".htk"
try:
# call MFCC feature extraction subroutine
f, E, fs = mfcc(wFile, self.winlen, self.ovrlen, self.pre_coef,
self.nfilter, self.nftt)
# VAD part
if self.opts == 1:
f = vad_thr(f, E)
# Energy threshold based VAD [comment this line if you would like to plugin the rVAD labels]
elif self.opts == 0:
l = numpy.loadtxt('..corresponding vad label file')
# [Plugin the VAD label generated by rVAD matlab]
if (len(f) - len(l)) == 1:
# 1-[end-frame] correction [matlab/python]
l = numpy.append(l, l[-1:, ])
elif (len(f) - len(l)) == -1:
l = numpy.delete(l, -1)
if (len(l) == len(f)) and (len(numpy.argwhere(l == 1)) != 0):
idx = numpy.where(l == 1)
f = f[idx]
else:
print "mismatch frames between: label and feature files or no voice-frame in VAD"
exit()
# Zero mean unit variance normalize after VAD
f = cmvn(f)
# write the VAD+normalized features in file
if not os.path.exists(os.path.dirname(fFile)):
# create director for the feature file
os.makedirs(os.path.dirname(fFile))
#print("%s --> %s\n" %(wFile,fFile))
writehtk(fFile, f, 0.01)
except:
print("Fail ..%s ---> %s\n" % (wFile, fFile))
decision = test_decision(fFile, 'DEC3_Tau10.0', self.ubm_mu,
self.ubm_cov, self.ubm_w)
if decision == "YES":
start_play(self.emotion + ".wav")
self.result = ""
self.emotion = ""
self.state = ""
self.repaint()
return
def feature_fetch(target, dirname, fnames):
for fname in fnames:
if target in fname:
with open(os.path.join(dirname, fname)) as f:
signal, FS = waveio.wave_from_file(f)
samples.append(mfcc.mfcc(signal, FS))
import os
import waveio
from mfcc import mfcc
from dtw import dtw
from time import time
voice_dir = "voiceMaterial"
templates = ["strawberry", "apple", "beef", "egg", "lemon", "mushroom", "noodle", "orange", "spaghetti", "spam", "watermelon"]
test = "mushroom"
# Get MFCC for each template, using the 1st sample of them
print "Get MFCC for each template, begin..."
template_feature = {}
for template in templates:
template_feature[template] = mfcc(signal = waveio.wave_from_file(os.path.join(voice_dir, template+"1.wav"))[0], fs = waveio.wave_from_file(os.path.join(voice_dir, template+"1.wav"))[1])
print "Get MFCC for each template, finish."
overhead = {}
score = {}
def compare(arg, dirname, fnames):
for fname in fnames:
if test in fname:
# Get MFCC for each test sample
test_features = mfcc(signal = waveio.wave_from_file(os.path.join(dirname, fname))[0], fs = waveio.wave_from_file(os.path.join(dirname, fname))[1])
# Record overhead and score for each test sample versus each template.(Each sample is of a dict corresponding to each template)
overhead[fname]= {}
score[fname] = {}
for template in template_feature.keys():
time_start = time()
from scipy.io.wavfile import read
from vqlbg import EUDistance
from mfcc import mfcc
from train_id import training
nSpeaker = 1
nfiltbank = 20
(codebooks) = training(nfiltbank)
directory = 'test'
fname = str()
nCorrect_MFCC = 0
def minDistance(features, codebooks):
speaker = 0
distmin = np.inf
for k in range(np.shape(codebooks)[0]):
D = EUDistance(features, codebooks[k, :, :])
dist = np.sum(np.min(D, axis=1)) / (np.shape(D)[0])
if dist < distmin:
distmin = dist
speaker = k
return speaker
fname = '/s1.wav'
(fs, s) = read(directory + fname)
mel_coefs = mfcc(s, fs, nfiltbank)
sp_mfcc = minDistance(mel_coefs, codebooks)
print('Speaker', ' is matches with speaker', (sp_mfcc + 1))
def get_mfccs(index, paths=csv['path']):
import mfcc as mf
mf.set_param_values()
mfccs = np.asarray([mf.mfcc(i) for i in paths[index]], dtype=np.float32)
return mfccs
import argparse
import traceback
import numpy as np
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--input",
default=None,
type=str,
help="Input path for mp3 file")
parser.add_argument("--output",
default=None,
type=str,
help="Output path for numpy array")
args = parser.parse_args()
try:
audio, sr = librosa.load(args.input,
sr=16000,
mono=True,
dtype=np.float32)
except Exception as ex:
print("Load autio failed:", ex)
traceback.print_exc(file=sys.stdout)
sys.exit(-1)
audio = (audio * 32768).astype(np.int16)
output = mfcc.mfcc(sr, audio)
np.save(args.output, output)
def train_model_on_sound(wav_list):
gestures = {'vattene':0, 'vieniqui':1, 'perfetto':2, 'furbo':3, 'cheduepalle':4,
'chevuoi':5, 'daccordo':6, 'seipazzo':7, 'combinato':8, 'freganiente':9,
'ok':10, 'cosatifarei':11, 'basta':12, 'prendere':13, 'noncenepiu':14,
'fame':15, 'tantotempo':16, 'buonissimo':17, 'messidaccordo':18, 'sonostufo':19}
dataX = []
for wav in wav_list:
path = re.sub('\_audio.wav$', '', wav)
print '\n', '##############'
print path[-25:]
sample = VideoMat(path, True)
sk = Skelet(sample)
rate, data = get_data(wav)
labels = sample.labels
coeff = data.shape[0] / sample.numFrames
interval = get_interval(data, sample.numFrames) #comment to use true interval data
interval = interval_analysis(interval, sk)
interval = [['', (beg, end)] for name, (beg, end) in interval if end-beg>10]
features_nb = 2588 #Change to add mspec and spec features
for value in labels:
if value != 0:
name, (beg, end) = value
for inter in interval:
name2, (beg2, end2) = inter
if beg2>beg-5 and end2<end+5:
space = end2 - beg2 - 9
limit = 40
data_interval = np.zeros(40*coeff)
if(space > limit):
data_interval = data[(beg2-1)*coeff:(beg2+limit-1)*coeff]
else:
data_interval[:space*coeff] = data[(beg2-1)*coeff:(end2-10)*coeff]
ceps, mspec, spec = mf.mfcc(data_interval)
#print ceps.shape, mspec.shape, spec.shape
data_tmp = np.zeros(features_nb)
data_tmp[0] = gestures[name]
data_tmp[1:2588] = ceps.reshape(2587)
#data_tmp[2588:10548] = mspec.reshape(7960)
#data_tmp[1273:13561] = spec.reshape(12288)
data_tmp = np.nan_to_num(data_tmp)
dataX.append(copy.copy(data_tmp))
break
print len(dataX)
data = np.asarray(dataX)
Y = data[:, 0]
X = data[:, 1:2588]
X = X.clip(min=-100)
clf = GradientBoostingClassifier(n_estimators=200, verbose=2, max_depth=7, min_samples_leaf=10, min_samples_split=20, random_state=0)
clf = clf.fit(X, Y)
pickle.dump(clf, open('gradient_boosting_model_sound.pkl','wb'))
clf = RandomForestClassifier(n_estimators=300, criterion='entropy', min_samples_split=10, min_samples_leaf=1, verbose=2, random_state=1) #n_jobs=2
clf = clf.fit(X, Y)
pickle.dump(clf, open('random_forest_model_sound.pkl','wb'))
clf = ExtraTreesClassifier(n_estimators=300, min_samples_split=10, min_samples_leaf=1, verbose=2, random_state=1) #n_jobs=2
clf = clf.fit(X, Y)
pickle.dump(clf, open('extra_trees_model_sound.pkl','wb'))
RATE=16000
RECORD_SECONDS = 2
CHUNKSIZE = 1024
# initialize portaudio
p = pyaudio.PyAudio()
stream = p.open(format=pyaudio.paInt16, channels=2, rate=RATE, input=True, frames_per_buffer=CHUNKSIZE)
frames = []
print "Record"
for _ in range(0, int(RATE / CHUNKSIZE * RECORD_SECONDS)):
data = stream.read(CHUNKSIZE)
frames.append(np.fromstring(data, dtype=np.int16))
print "EIEI"
numpydata = np.hstack(frames)
signal = numpydata.astype(np.int64)
mfcc_feat = mfcc.mfcc(signal, samplerate=RATE, winlen=0.03, winstep=0.01, numcep=12, nfilt=15, lowfreq=300, highfreq=3700)
print mfcc_feat.shape
# close stream
stream.stop_stream()
stream.close()
p.terminate()
#~~~~~~~~~~~~~
# Real world demo
#~~~~~~~~~~~~~
from time import time
M_set = [64, 128, 256]
for M in M_set:
print "Generating VQ with M == %d" % M
timer = time()
# Train VQ
mu, clusters = vq_generator(
dirname="/home/magodo/code/voiceMaterial/word",
M=M,
return_clusters=True)
# Store the trained VQ(tuple of mu(list of 256 different vectors) and clusters(dict of 256 different vector sets))
import pickle
with open("VQ.pkl" + "-%d" % M, "wb") as f:
pickle.dump((mu, clusters), f)
train_time = (time() - timer) / 60.0
print "Used %f minutes" % train_time
# Perform the trained VQ to a demo.wav
signal, FS = waveio.wave_from_file("demo.wav")
feature = list(mfcc(signal, FS))
vq_array = []
for i in feature:
vq_array.append(lbg.cluster_point(i, mu))
print vq_array
