def preprocess(audio: Audio) -> np.ndarray:
features = [FEATURE_TYPES[feature_type](
audio.data, samplerate=audio.rate, **kwargs)]
for _ in range(delta_order):
features.append(delta(features[-1], delta_window))
return np.concatenate(features, axis=1)
def mk_MFB(filename, sample_rate=c.SAMPLE_RATE, use_delta=c.USE_DELTA):
audio, sr = librosa.load(filename, sr=sample_rate, mono=True)
audio = audio.flatten()
filter_banks, energies = fbank(audio,
samplerate=sample_rate,
nfilt=c.FILTER_BANK,
winlen=0.025)
delta_1 = delta(filter_banks, N=1)
delta_2 = delta(delta_1, N=1)
filter_banks = normalize_frames(filter_banks)
delta_1 = normalize_frames(delta_1)
delta_2 = normalize_frames(delta_2)
if use_delta:
frames_features = np.hstack([filter_banks, delta_1, delta_2])
else:
frames_features = filter_banks
np.save(filename.replace('.wav', '.npy'), frames_features)
return
def create_mfcc(filename):
"""Perform standard preprocessing, as described by Alex Graves (2012)
http://www.cs.toronto.edu/~graves/preprint.pdf
Output consists of 12 MFCC and 1 energy, as well as the first derivative of these.
[1 energy, 12 MFCC, 1 diff(energy), 12 diff(MFCC)
"""
(rate, sample) = wav.read(filename)
mfcc = features.mfcc(sample,
rate,
winlen=0.025,
winstep=0.01,
numcep=13,
nfilt=26,
preemph=0.97,
appendEnergy=True)
d_mfcc = features.delta(mfcc, 2)
a_mfcc = features.delta(d_mfcc, 2)
out = np.concatenate([mfcc, d_mfcc, a_mfcc], axis=1)
return out, out.shape[0]
def extract_features(samples,
sample_rate,
win_len,
win_shift,
win_fun=np.hamming):
"""
Computes 13 MFCC + delta + delta-delta features for an utterance.
:param samples: samples of the utterance, numpy array of shape (n_samples,)
:param sample_rate: sampling rate
:param win_len: window length (in seconds)
:param win_shift: window shift (in seconds)
:param win_fun: window function
:return: numpy array of shape (n_frames, n_features), where n_features=39
"""
mfcc = pss.mfcc(samples,
sample_rate,
winlen=win_len,
winstep=win_shift,
winfunc=win_fun)
delta = pss.delta(mfcc, 3)
delta_delta = pss.delta(delta, 3)
return np.concatenate((mfcc, delta, delta_delta), axis=1)
def mfcc(wav_path):
""" Grabs MFCC features with energy and derivates. """
(rate, sig) = wav.read(wav_path)
feat = python_speech_features.mfcc(sig, rate, appendEnergy=True)
delta_feat = python_speech_features.delta(feat, 2)
all_feats = [feat, delta_feat]
all_feats = np.array(all_feats)
# Make time the first dimension for easy length normalization padding later.
all_feats = np.swapaxes(all_feats, 0, 1)
all_feats = np.swapaxes(all_feats, 1, 2)
feat_fn = wav_path[:-3] + "mfcc13_d.npy"
np.save(feat_fn, all_feats)
def get_mfcc_v2(y,
sr,
n_mfcc=13,
tgt_sr=16000,
win_len=0.025,
hop_len=0.010,
n_fft=512,
n_mels=22,
fmin=0.0,
fmax=None,
cep_lifter=22,
pre_emph=0.97,
win_func=lambda x: np.ones((x, )),
append_energy=True,
delta=True,
delta_delta=True):
if sr != 16000.0:
y = librosa.core.resample(y, orig_sr=sr, target_sr=16000)
mfccs = python_speech_features.mfcc(y,
tgt_sr,
winlen=win_len,
winstep=hop_len,
numcep=n_mfcc,
nfilt=n_mels,
nfft=n_fft,
lowfreq=fmin,
highfreq=fmax,
preemph=pre_emph,
ceplifter=cep_lifter,
appendEnergy=append_energy,
winfunc=win_func)
features = [mfccs]
if delta:
features.append(python_speech_features.delta(mfccs, 1))
if delta_delta:
features.append(python_speech_features.delta(mfccs, 2))
return np.hstack(features)
def extract_feature_for_audio(self, audio_file_path):
# load the wav file to an array
signal, sr = librosa.load(audio_file_path,
mono=True,
sr=self.sample_rate)
# trim the leading and trailing slience
signal_trimed, index = librosa.effects.trim(signal,
top_db=self.silence_cutoff)
# extract the mfcc feature, for details about mfcc,
# see: http://www.practicalcryptography.com/miscellaneous/machine-learning/guide-mel-frequency-cepstral-coefficients-mfccs/
MFCC = mfcc(signal_trimed,
self.sample_rate,
winlen=self.winlen,
winstep=self.winlen / 2,
winfunc=numpy.hamming,
nfft=self.nfft,
numcep=self.numcep)
# do not use the first mfcc coefficient
features = MFCC[:, 1:self.numcep]
# caculate the delta of the mfcc and add to the features
Delta = delta(MFCC, 2)
features = numpy.column_stack((features, Delta))
# caculate the delta of the delta of the mfcc and add to the features
Acc = delta(Delta, 2)
features = numpy.column_stack((features, Acc))
# total number of features would be the number of columns of the `features` array
self.num_features = features.shape[1]
# each audio file will be transformed to an numpy array with a shape(N, self.num_features)
# where N is the number of frames that are extracted from the audio file by the mfcc function
return features
def process_mel(self, mel_input):
#mel_input [80,344]
mel_input = mel_input.T #[344,80]
delta1 = ps.delta(mel_input, 2)
delta2 = ps.delta(delta1, 2)
time = mel_input.shape[0]
mel = np.pad(mel_input, ((0, 800 - time), (0, 0)),
'constant',
constant_values=0) #[800,80]
delta1 = np.pad(delta1, ((0, 800 - time), (0, 0)),
'constant',
constant_values=0)
delta2 = np.pad(delta2, ((0, 800 - time), (0, 0)),
'constant',
constant_values=0)
mel_output = np.zeros((3, 800, 80))
mel_output[0, :, :] = mel
mel_output[1, :, :] = delta1
mel_output[2, :, :] = delta2
return mel_output
def get_mfcc(self, data, fs):
wav_feature = mfcc(data,
fs,
numcep=self.numc,
winlen=c.FRAME_LEN,
winstep=c.FRAME_STEP,
nfilt=26,
nfft=c.NUM_FFT)
#print(wav_feature.shape," before",type(wav_feature))
reserve_length = wav_feature.shape[0] - wav_feature.shape[0] % 100
d_wav_feature_1 = delta(wav_feature, 2)
d_wav_feature_2 = delta(d_wav_feature_1, 2)
mfcc_feat_normal = normalize_frames(wav_feature.T)
d_mfcc_feat_1_normal = normalize_frames(d_wav_feature_1.T)
d_mfcc_feat_2_normal = normalize_frames(d_wav_feature_2.T)
mfcc_feature = [
mfcc_feat_normal, d_mfcc_feat_1_normal, d_mfcc_feat_2_normal
]
mfcc_feature = torch.tensor(mfcc_feature)
length = (reserve_length / 100 - 1) / 2
total_length = (int(length) * 2 + 1) * 2
index = torch.randperm(total_length)
if self.mode == "train":
feature = torch.zeros([int(length), 3, self.numc, 300])
for r in range(int(length)):
for i in range(6):
feature[r, :, :, i * 50:(i + 1) * 50] =\
mfcc_feature[:, :, index[r * 4 + i] * 50:(index[r * 4 + i] + 1) * 50]
feature = feature.permute(0, 1, 3, 2)
else:
feature = mfcc_feature[:, :, 0:reserve_length]
feature = feature.unsqueeze(0)
feature = feature.permute(0, 1, 3, 2)
return feature
def audio_feature(signal,
samplerate=16000,
winlen=0.025,
winstep=0.01,
numcep=13,
nfilt=40,
nfft=512,
lowfreq=0,
highfreq=None,
preemph=0.97,
ceplifter=22,
appendEnergy=True,
winfunc=np.hamming):
feat, energy = fbank(signal, samplerate, winlen, winstep, nfilt, nfft,
lowfreq, highfreq, preemph, winfunc)
log_fbank = np.log(feat)
# discard the 0-th dct coefficient
mfcc = dct(log_fbank, type=2, axis=1, norm='ortho')[:, 1:numcep]
mfcc = lifter(mfcc, ceplifter)
d1_mfcc = delta(mfcc, 1)
d2_mfcc = delta(d1_mfcc, 1)
energy = np.reshape(np.log(energy), (energy.shape[0], 1))
mixed = np.concatenate((mfcc, d1_mfcc, d2_mfcc, energy), axis=1)
return mixed
def fbank(wav_path, flat=True):
""" Currently grabs log Mel filterbank, deltas and double deltas."""
(rate, sig) = wav.read(wav_path)
if len(sig) == 0:
logger.warning("Empty wav: {}".format(wav_path))
fbank_feat = python_speech_features.logfbank(sig, rate, nfilt=40)
energy = extract_energy(rate, sig)
feat = np.hstack([energy, fbank_feat])
delta_feat = python_speech_features.delta(feat, 2)
delta_delta_feat = python_speech_features.delta(delta_feat, 2)
all_feats = [feat, delta_feat, delta_delta_feat]
if not flat:
all_feats = np.array(all_feats)
# Make time the first dimension for easy length normalization padding
# later.
all_feats = np.swapaxes(all_feats, 0, 1)
all_feats = np.swapaxes(all_feats, 1, 2)
else:
all_feats = np.concatenate(all_feats, axis=1)
# Log Mel Filterbank, with delta, and double delta
feat_fn = wav_path[:-3] + "fbank.npy"
np.save(feat_fn, all_feats)
def mfccProc2(self, results_dict):
(rate, sig) = audioBasicIO.readAudioFile(self.fname)
#Create 2d array for MFCC features
mfcc_feat = mfcc(sig, samplerate=44100, nfft=1103)
#Create 2d array for the delta of MFCC features
d_mfcc_feat = delta(mfcc_feat, 2)
#Create 2d array for the log of fbank features
fbank_feat = logfbank(sig, rate)
dev_array = []
for i in mfcc_feat:
temp = stdev(i)
dev_array.append(temp)
tone = stdev(dev_array)
results_dict["tone"] = tone
return (mfcc_feat)
def log_mel_filterbank(audio, sample_rate, window_size, step_size):
"""Returns the log of the mel filterbank energies as well as the first and second order deltas.
Hanning window used for parity with log_spectrogram function.
Args:
audio (np.ndarray): audio signal array
sample_rate (int): sample_rate of signal
window_size (int): window size
step_size (int): step size
Returns:
np.ndarray: log mel filterbank, delta, and delta-deltas
"""
delta_window = 1
log_mel = python_speech_features.base.logfbank(audio,
sample_rate,
winlen=window_size / 1000,
winstep=step_size / 1000,
winfunc=np.hanning)
delta = python_speech_features.delta(log_mel, N=delta_window)
delta_delta = python_speech_features.delta(delta, N=delta_window)
output = np.concatenate((log_mel, delta, delta_delta), axis=1)
return output.astype(np.float32)
def mfcc(y, sr, numcep=13, delta=False, delta_delta=False, width=2, **kwargs):
"""
Compute MFCCs of Audio Signal
:param y: Audio signal
:param sr: Original sample rate
:param numcep: Number of MFCCs to compute
:param delta: If delta of MFCCs are required
:param delta_delta: If acceleration of MFCCs are required
:param width: Number of samples to consider for computing delta
:param kwargs: Other parameters to pass on python_speech_features like hop length etc.
:return: MFCCs (numpy array of shape n_frames * n_mfccs)
"""
mfccs = python_speech_features.mfcc(signal=y,
samplerate=sr,
numcep=numcep,
**kwargs)
if delta:
d1 = python_speech_features.delta(mfccs, N=width)
mfccs = np.hstack((mfccs, d1))
if delta_delta:
d2 = python_speech_features.delta(mfccs[:, mfccs.shape[1] / 2:],
N=width)
mfccs = np.hstack((mfccs, d2))
return mfccs
def test_all_files(path, hmm_list):
global false_list, word_list, false_number, correct_number
file_list = []
files = os.listdir(path)
for file_name in files:
for u in word_list:
if file_name.__contains__(u):
file_list.append(file_name)
for file in file_list:
for labels in word_list:
if file.__contains__(labels):
tested_label = labels
break
#tested_label = file[ :-6]
print("Tested label : ", tested_label)
audio = AudioSegment.from_file(path + "/" + file,
format="wav",
frame_rate=32000)
audio = audio.set_frame_rate(16000)
audio.export("filtered-talk1.wav", format="wav")
(rate, sig) = wav.read("filtered-talk1.wav")
# print "Read : " , ses_yol + word + "/" + word + str ( i / 10 ) + str ( i %10 ) +".wav"
mfcc_feat = mfcc(sig, rate, nfft=1536)
d_mfcc_feat = delta(mfcc_feat, 2)
data = np.concatenate((mfcc_feat, d_mfcc_feat), axis=1).tolist()
for label in word_list:
if label in file:
break
data = scalers[label].transform(data)
vector = get_score_vector(data, hmm_list)
vector = nn_scaler.transform([vector])
print("Vector : ", vector.shape)
predicted = trained.predict(vector)
print("type : ", type(predicted[0]))
print("Res : ", str(predicted[0]))
print("Prediction ", trained.predict_proba(vector))
if predicted[0] != tested_label:
false_list.append(file + " predicted answer : " + predicted[0])
false_number += 1
else:
correct_number += 1
def convert_to_vector(filename):
(rate, sig) = wav.read(filename)
mfcc_feat = mfcc(sig, rate)
d_mfcc_feat = delta(mfcc_feat, 2)
fbank_feat = logfbank(sig, rate)
#print(fbank_feat)
print("######################")
vector1 = (fbank_feat[1:3, :][1])
#print(vector1)
vector2 = (fbank_feat[1:3, :][0])
#print vector2
print("######################")
z = np.hstack((vector1, vector2))
# vector.extend(list(fbank_feat[1:3,:][1]))
return z
def mfcc_delta_feature_extraction(self, data):
vectorized_data = []
hop_length = int(self.wnd_step * self.sample_rate)
win_length = int(self.wnd_len * self.sample_rate)
for d in data:
mfcc_librosa = mfcc(d,
sr=self.sample_rate,
n_mfcc=self.num_features,
hop_length=hop_length,
win_length=win_length)
# mfcc_librosa = mfcc(d, sr=self.sample_rate, n_mfcc=self.num_features)
mfcc_f = np.transpose(mfcc_librosa)
delta_f = delta(mfcc_f, 8)
v_d = np.append(mfcc_f, delta_f, axis=1)
vectorized_data.append(v_d)
return vectorized_data
def get_24_coefficients(filenames,
data_dir=os.path.join(
'Support_CentraleDigitale_Lab_201920',
'Data_Submarin', 'Dataset_J1')):
"""Input : audio recordings filenames list of a same speaker
For instance, filenames_MJPM = ['MJPM-1','MJPM-2','MJPM-3']
Output : Array with 24 columns corresponding to the MFC Coefficients 2-13,
and the delta MFC Coefficients of these MFC Coefficients.
It has I rows corresponding to speaking frames of the recordings."""
# The 12 MFC Coefficients 2-13
mfcc_speaker_speaking = mfcc_locuteur_speaking(filenames, data_dir)
# The 12 delta MFC Coefficients corresponding to the previous coefficients
d_mfcc_speaker_speaking = delta(mfcc_speaker_speaking, 2)
mfcc_24_coeffs = np.hstack(
(mfcc_speaker_speaking, d_mfcc_speaker_speaking))
return mfcc_24_coeffs
def test_features(fname):
#os.chdir(path)
#feat=np.zeros((1,27))
#fnames=[x for x in os.listdir(path) if x[-3:]=="wav"]
(rate, sig) = wav.read(fname)
#sig=sig[:,1]
fr_l = math.floor(rate * 0.025)
mfcc_feat = mfcc(sig, rate, nfft=fr_l + 1)
d_mfcc_feat = delta(mfcc_feat, 2)
feat = np.zeros((len(d_mfcc_feat), 26)) # first row is the tag
feat[:, :13] = mfcc_feat
feat[:, 13:] = d_mfcc_feat
feat_std = preprocessing.scale(feat)
return feat
def compute_mfcc(filename):
(rate, sig) = wav.read(filename)
m = mfcc(sig,
samplerate=rate,
winlen=0.025,
winstep=0.01,
numcep=13,
nfilt=40,
nfft=512,
lowfreq=0,
highfreq=None,
preemph=0,
ceplifter=22,
appendEnergy=True)
m = delta(m, 6)
#m = delta(m, 2)
return m
def mfcc_with_delta(
audio, samplerate, n_features, n_channels,
**kwargs): # 이 함수가 호출될때 위의 함수 (preprocessor)에 이 함수가 붙어서 호출됨.
"""Calculate Mel-frequency cepstral coefficients, and calculate delta
features if requested."""
tmp = _features.mfcc(
audio, samplerate, numcep=n_features, **kwargs
) # python_speech_features.mfcc 여기서 audio 는 1d array 여야하는데.. 언제 바뀜?
# return a numpy array of size containing features.
# Each row holds 1 feature vector.
tmp -= _np.mean(tmp, axis=0) + 1e-8
result = [tmp]
for _ in range(1, n_channels):
tmp = _features.delta(tmp, 2)
result.append(tmp)
result = _np.stack(result, axis=2)
return result
def featureList(self, path):
# obj = Silence()
# newpath = os.path.splitext(path)[0] + "_silenced" + os.path.splitext(path)[1]
# obj.silencemain(path,newpath)
(rate, sig) = wav.read(path)
print "___________________path_________________"
print sig.shape
print rate
print path
mfcc_feat = mfcc(sig, rate)
d_mfcc_feat = delta(mfcc_feat, 2)
fbank_feat = logfbank(sig, rate)
print "file:feature.py line:24"
print fbank_feat.shape
return fbank_feat
def extract_features(audio, rate):
"""extract 20 dim mfcc features from an audio, performs CMS and combines
delta to make it 40 dim feature vector"""
mfcc_feature = mfcc.mfcc(audio,
rate,
0.025,
0.01,
26,
nfft=1200,
preemph=0.97,
appendEnergy=True)
# mfcc_feature = preprocessing.scale(mfcc_feature)
mfcc_feature1 = mfcc.logfbank(audio, rate, 0.025, 0.01, 26, nfft=1200)
mfcc_feature2 = mfcc.ssc(audio, rate, 0.025, 0.01, 26, nfft=1200)
delta = mfcc.delta(mfcc_feature, 26)
combined = np.hstack((mfcc_feature, delta, mfcc_feature1, mfcc_feature2))
return combined
def extract_feature(self, path):
fs, y = wavfile.read(path)
y = y / np.max(abs(y))
mfcc_feat = mfcc(y, fs)
mfcc_feat = delta(mfcc_feat, 2)
data = pad_sequences(mfcc_feat.T,
self.max_frames,
dtype=float,
padding='post',
truncating='post').T
if (path.split('/')[-2] == '0.Background'):
target = 0
elif (path.split('/')[-2] == '1.Bat den'):
target = 1
elif (path.split('/')[-2] == '2.Tat den'):
target = 2
elif (path.split('/')[-2] == '3.Bat dieu hoa'):
target = 3
elif (path.split('/')[-2] == '4.Tat dieu hoa'):
target = 4
elif (path.split('/')[-2] == '5.Bat quat'):
target = 5
elif (path.split('/')[-2] == '6.Tat quat'):
target = 6
elif (path.split('/')[-2] == '7.Bat tivi'):
target = 7
elif (path.split('/')[-2] == '8.Tat tivi'):
target = 8
elif (path.split('/')[-2] == '9.Mo cua'):
target = 9
elif (path.split('/')[-2] == '10.Dong cua'):
target = 10
elif (path.split('/')[-2] == '11.Khoa cua'):
target = 11
elif (path.split('/')[-2] == '12.Mo cong'):
target = 12
elif (path.split('/')[-2] == '13.Dong cong'):
target = 13
elif (path.split('/')[-2] == '14.Khoa cong'):
target = 14
elif (path.split('/')[-2] == '15.Doremon'):
target = 15
return data, target
def extract_features(self,
signal,
rate,
winlen=WINLEN,
winstep=WINSTEP,
nfft=NFFT,
n=N):
# signal = self._scale_signal(signal) # obniza o 4% !?
# signal = self._cut_the_tips(signal) # obniza o 1%
mfcc_values = mfcc(signal=signal,
samplerate=rate,
winlen=winlen,
winstep=winstep,
nfft=nfft)
dmfcc_values = delta(mfcc_values, n)
result = np.append(mfcc_values, dmfcc_values, axis=1)
return result
def mfcc_features(self, audio, rate, numcep = 20, nfft = 2000, N = 2):
"""
Returns the MFCC and delta MFCC features of the given audio, stacked together horizontally
Parameters:
:audio: The audio file for which MFCC features must be computed
:rate: The sample rate of the audio file
:numcep: The number of cepstrum to return, default 20
:nfft: The FFT size, default 2000
:N: Calculate delta features based on preceding and following N frames, default 2
Return Value: A numpy array which has the scaled MFCC and delta MFCC features, stacked horizontally
"""
self.mfcc = python_speech_features.mfcc(audio, rate, numcep = numcep, nfft = nfft)
#self.mfcc = preprocessing.scale(self.mfcc)
self.delta_mfcc = python_speech_features.delta(self.mfcc, N)
self.mfcc_feature = np.hstack((self.mfcc, self.delta_mfcc))
return self.mfcc_feature
def load_all_wav_into_csv():
metadata = open_file_read('../accents_data/speakers_all.csv')
count = 0
for file in metadata:
if file[FILE_MISSING_IDX] == "FALSE" and file[
FIRST_LANGUAGE_IDX] in ACCEPTED_LANGUAGES:
count += 1
filename_wav = os.path.join(
dirname, '../accents_data/recordings_wav/' +
file[FILE_NAME_IDX] + '.wav')
rate, sig = make_standard_length(filename_wav)
mfcc_feat = mfcc(sig, rate, nfft=1200, nfilt=13)
d_mfcc_feat = delta(mfcc_feat, 2)
fbank_feat = logfbank(sig, rate, nfft=1200, nfilt=13)
write_to_csv('../accents_data/recordings_csv/' + file[3] + '.csv',
fbank_feat)
progress(count, NUMBER_OF_FILES, "Generating MFCCs")
def run(sec) :
n = int(rate * sec) # 冒頭無発話区間とフレームレートをかけあわせて，計算範囲だけを残す
for x, pcmname in enumerate(pcmnames) :
data = read_pcm(pcmname) # pcmファイルを読み込み
data = data[: n] # secに合わせて，冒頭区間のみを残す
print("[%s]\nstart: open %s" %(ctime(), pcmname))
# MFCCの計算とその平均
mfcc_feature = mfcc(data, rate, winlen=length, winstep=step, numcep=n_feature)
mfcc_mean = np.mean(mfcc_feature.T, axis=1).astype(np.str)
# MFCCからデルタとその平均を求める
d_mfcc_feat = delta(mfcc_feature, 2)
d_mfcc_mean = np.mean(d_mfcc_feat.T, axis=1).astype(np.str)
#print(d_mfcc_mean)
# delta-deltaを計算する
#d2_mfcc_feat = delta(d_mfcc_feat, 2)
#d2_mfcc_mean = np.mean(d2_mfcc_feat.T, axis=1).astype(np.str)
# 結果（pcmファイル名，MFCC，デルタ）をまとめる
rslt = np.array([pcmname], dtype=np.str)
rslt = np.append(rslt, mfcc_mean)
rslt = np.append(rslt, d_mfcc_mean)
# 結果を1つの大きな配列に結合する
if x == 0 :
out = np.array([rslt])
else :
out = np.append(out, np.array([out]), axis=0)
print("[%s]\ndone: get features from %s" %(ctime(), pcmname))
# 全てのpcmファイルを読み込んだら，tsv形式で出力する
ms = sec * 1000
outname = "mfcc_feature_%sms.tsv" %ms
np.savetxt(outname, out, delimiter="\t", fmt="%s")
def __mfcc(audio_data, sampling_rate, win_len, win_step, num_features, n_fft,
f_min, f_max):
"""
Convert a wav signal into Mel Frequency Cepstral Coefficients (MFCC).
Args:
audio_data (np.ndarray): Wav signal.
sampling_rate (int): Sampling rate.
win_len (float): Window length in seconds.
win_step (float): Window stride in seconds.
num_features (int): Number of features to generate.
n_fft (int): Number of Fast Fourier Transforms.
f_min (float): Minimum frequency to consider.
f_max (float): Maximum frequency to consider.
Returns:
np.ndarray: MFCC feature vectors. Shape: [time, num_features]
"""
if num_features % 2 != 0:
raise ValueError('num_features is not a multiple of 2.')
# Compute MFCC features.
mfcc = psf.mfcc(signal=audio_data,
samplerate=sampling_rate,
winlen=win_len,
winstep=win_step,
numcep=num_features // 2,
nfilt=num_features,
nfft=n_fft,
lowfreq=f_min,
highfreq=f_max,
preemph=0.97,
ceplifter=22,
appendEnergy=True)
# And the first-order differences (delta features).
mfcc_delta = psf.delta(mfcc, 2)
# Combine MFCC with MFCC_delta
return np.concatenate([mfcc, mfcc_delta], axis=1)
def process_timit_psf(path, output_path):
for dirpath, dirnames, filenames in os.walk(path):
for filename in filenames:
if filename[-4:] == ".wav":
full_path = os.path.join(dirpath, filename)
wave, sr = librosa.load(full_path, mono=True, sr=16000)
mfcc_features = python_speech_features.mfcc(wave,
samplerate=sr,
numcep=13,
nfilt=26,
appendEnergy=True,
winlen=0.025,
winstep=0.01)
delta_features = python_speech_features.delta(mfcc_features, 9)
output_filename = os.path.join(dirpath,
filename[:-4] + "_mfcc")
print(output_filename)
concat_features = np.concatenate(
(mfcc_features, delta_features), axis=1).T
print(concat_features.shape)
np.save(output_filename, concat_features, allow_pickle=False)
def MFCC(LOCATION, SAVELOCATION):
# NOTE: Currently using a library for generating MFCCs, to make sure that
# the implementation of the EM algorithm is based on correct values. Own
# implementation of MFCCs can be found commented out below.
data = genfromtxt(LOCATION, delimiter=",")
signal = np.zeros([64000, 1])
for i in range(0, 64000):
signal[i, 0] = data[i]
mfcc_feat = np.transpose(mfcc(signal, 16000))
d_mfcc_feat = (delta(mfcc_feat, 2))
rows, columns = np.shape(mfcc_feat)
# Concatenate matrices
# MFCC matrix is on top of the delta matrix
mfcc_deltas = np.zeros([rows * 2, columns])
for i in range(0, rows):
mfcc_deltas[i, :] = mfcc_feat[i, :]
mfcc_deltas[i + rows, :] = d_mfcc_feat[i, :]
np.savetxt(SAVELOCATION, mfcc_deltas, delimiter=",")
return mfcc_deltas
#!/usr/bin/env python
from python_speech_features import mfcc
from python_speech_features import delta
from python_speech_features import logfbank
import scipy.io.wavfile as wav
(rate,sig) = wav.read("english.wav")
mfcc_feat = mfcc(sig,rate)
d_mfcc_feat = delta(mfcc_feat, 2)
fbank_feat = logfbank(sig,rate)
print(fbank_feat[1:3,:])
