def getf0_python(wav, fs, frame_len, frame_step):
def lowpassfilter(x, cutoff, gain):
h = scipy.signal.firwin(31, cutoff)
h /= h.sum()
return gain * scipy.signal.filtfilt(h, np.array([1.0]), x)
frame_len = int(frame_len * fs)
frame_step = int(frame_step * fs)
frames = frame.framesig(wav, frame_len, frame_step, winfunc=lambda x:np.ones((1,x)))
# convert dtype ro float64
wav = wav.astype(np.float64)
wav /= 30000.0
# convert dtype ro float64
wav = wav.astype(np.float64)
# lowpass at 900Hz
wav = lowpassfilter(wav, 2.0*900.0/fs, 1.0)
#frame the signal
ENERGY_THRESHOLD = 0.1
wsize = frame_len
wrate = frame_step
st = 0
en = wsize
correlogram = np.zeros((0, wsize//2))
pit = np.zeros(0)
while True:
if en > wav.shape[0]:
break
frm = wav[st:en].copy()
# classify as speech/non-speech using enerfy
speech = True
rms = np.sqrt(np.mean(frm**2))
if rms < ENERGY_THRESHOLD:
speech = False
# center clipping
C = frm.max() * 0.4
frm[np.abs(frm) < C] = 0.0
frm[frm > C] = 1.0
frm[frm < -C] = -1.0
# autocorrelation
r = np.correlate(frm, frm, 'same')[wsize//2:]
if r[0] > 0:
r /= r[0]
correlogram = np.r_[correlogram, r.reshape((1,r.shape[0]))]
# find peak
r_limit = r[50:150]
r_limit -= r_limit.min()
r_limit /= r_limit.max()
peak = np.argmax(r_limit)+50
if r_limit.max() < 0.2:
cur_pit = 0
else:
cur_pit = fs / peak
pit = np.r_[pit, cur_pit]
st += wrate
en += wrate
return pit
def encode(self, wav, frame_len=0.025, frame_step=0.010):
winfunc=lambda x:np.hamming(x).reshape((1,x))
self.frame_len = int(frame_len * self.fs)
self.frame_step = int(frame_step * self.fs)
wav = frame.preemphasis(wav,coeff=0.97)##
self.energy = frame.get_energy(wav, self.frame_len, self.frame_step, winfunc=winfunc)
self.frames = frame.framesig(wav, self.frame_len, self.frame_step, winfunc=winfunc)
param_size = len(self._encode_frame(self.frames[0]))
self.params = np.zeros((self.frames.shape[0], param_size))
for i in range(self.frames.shape[0]):
self.params[i, :] = self._encode_frame(self.frames[i])
def decode(self, src_signal):
winfunc=lambda x:np.hamming(x).reshape((1,x))
src_energy = frame.get_energy(src_signal, self.frame_len, self.frame_step)
gain = self.energy / src_energy
gain_interp = np.interp(np.linspace(0,1,src_signal.shape[0]), np.linspace(0, 1, gain.shape[0]), gain)
src_signal *= gain_interp
src_frames = frame.framesig(src_signal, self.frame_len, self.frame_step)
for i in range(self.frames.shape[0]):
self.frames[i, :] = self._decode_frame(self.params[i, :], np.r_[src_frames[max(0,i-1)],src_frames[i]])[self.frame_len:]
wav = frame.deframesig(self.frames, src_signal.shape[0], self.frame_len, self.frame_step, winfunc=winfunc)
wav = frame.deemphasis(wav,coeff=0.97)##
#wav *= gain_interp
return wav
def encode(self, wav, frame_len=0.025, frame_step=0.010):
winfunc = lambda x: np.hamming(x).reshape((1, x))
self.frame_len = int(frame_len * self.fs)
self.frame_step = int(frame_step * self.fs)
wav = frame.preemphasis(wav, coeff=0.97) ##
self.energy = frame.get_energy(wav,
self.frame_len,
self.frame_step,
winfunc=winfunc)
self.frames = frame.framesig(wav,
self.frame_len,
self.frame_step,
winfunc=winfunc)
param_size = len(self._encode_frame(self.frames[0]))
self.params = np.zeros((self.frames.shape[0], param_size))
for i in range(self.frames.shape[0]):
self.params[i, :] = self._encode_frame(self.frames[i])
def decode(self, src_signal):
winfunc = lambda x: np.hamming(x).reshape((1, x))
src_energy = frame.get_energy(src_signal, self.frame_len,
self.frame_step)
gain = self.energy / src_energy
gain_interp = np.interp(np.linspace(0, 1, src_signal.shape[0]),
np.linspace(0, 1, gain.shape[0]), gain)
src_signal *= gain_interp
src_frames = frame.framesig(src_signal, self.frame_len,
self.frame_step)
for i in range(self.frames.shape[0]):
self.frames[i, :] = self._decode_frame(
self.params[i, :], np.r_[src_frames[max(0, i - 1)],
src_frames[i]])[self.frame_len:]
wav = frame.deframesig(self.frames,
src_signal.shape[0],
self.frame_len,
self.frame_step,
winfunc=winfunc)
wav = frame.deemphasis(wav, coeff=0.97) ##
#wav *= gain_interp
return wav
def getf0_python(wav, fs, frame_len, frame_step):
def lowpassfilter(x, cutoff, gain):
h = scipy.signal.firwin(31, cutoff)
h /= h.sum()
return gain * scipy.signal.filtfilt(h, np.array([1.0]), x)
frame_len = int(frame_len * fs)
frame_step = int(frame_step * fs)
frames = frame.framesig(wav,
frame_len,
frame_step,
winfunc=lambda x: np.ones((1, x)))
# convert dtype ro float64
wav = wav.astype(np.float64)
wav /= 30000.0
# convert dtype ro float64
wav = wav.astype(np.float64)
# lowpass at 900Hz
wav = lowpassfilter(wav, 2.0 * 900.0 / fs, 1.0)
#frame the signal
ENERGY_THRESHOLD = 0.1
wsize = frame_len
wrate = frame_step
st = 0
en = wsize
correlogram = np.zeros((0, wsize // 2))
pit = np.zeros(0)
while True:
if en > wav.shape[0]:
break
frm = wav[st:en].copy()
# classify as speech/non-speech using enerfy
speech = True
rms = np.sqrt(np.mean(frm**2))
if rms < ENERGY_THRESHOLD:
speech = False
# center clipping
C = frm.max() * 0.4
frm[np.abs(frm) < C] = 0.0
frm[frm > C] = 1.0
frm[frm < -C] = -1.0
# autocorrelation
r = np.correlate(frm, frm, 'same')[wsize // 2:]
if r[0] > 0:
r /= r[0]
correlogram = np.r_[correlogram, r.reshape((1, r.shape[0]))]
# find peak
r_limit = r[50:150]
r_limit -= r_limit.min()
r_limit /= r_limit.max()
peak = np.argmax(r_limit) + 50
if r_limit.max() < 0.2:
cur_pit = 0
else:
cur_pit = fs / peak
pit = np.r_[pit, cur_pit]
st += wrate
en += wrate
return pit
