def decode(tuple):
"""
Decode a speech waveform.
"""
(ark, g, pitch, hnr) = tuple
print("Frame padding:", opt.padding)
nFrames = len(ark)
assert(len(g) == nFrames)
assert(len(pitch) == nFrames)
assert(len(hnr) == nFrames)
# The original framer padded the ends so the number of samples to
# synthesise is a bit less than you might think
if opt.ola:
frameSize = framePeriod * 2
nSamples = framePeriod * (nFrames-1)
else:
frameSize = framePeriod
nSamples = frameSize * (nFrames-1)
ex = opt.glottal
if opt.glottal == 'cepgm' and (opt.encode or opt.decode or opt.pitch):
order = ark.shape[-1] - 2
ar = ark[:,0:order]
theta = ark[:,-2]
magni = np.exp(ark[:,-1])
else:
ar = ark
# Use the original AR residual; it should be a very good reconstruction.
if ex == 'ar':
e = ssp.ARExcitation(f, ar, g)
# Just noise. This is effectively a whisper synthesis.
elif ex == 'noise':
e = np.random.normal(size=(nFrames, frameSize))
# Just harmonics, and with a fixed F0. This is the classic robot
# synthesis.
elif ex == 'robot':
ew = np.zeros(nSamples)
period = int(1.0 / 200 * r)
for i in range(0, len(ew), period):
ew[i] = period
e = ssp.Frame(ew, size=frameSize, period=framePeriod)
# Synthesise harmonics plus noise in the ratio suggested by the HNR.
elif ex == 'synth':
# Harmonic part
mperiod = int(1.0 / np.mean(pitch) * r)
gm = ssp.GlottalModel(ssp.parameter('Pulse', 'impulse'))
pr, pg = ssp.pulse_response(gm, pcm, period=mperiod, order=lpOrder[r])
h = np.zeros(nSamples)
i = 0
frame = 0
while i < nSamples and frame < len(pitch):
period = int(1.0 / pitch[frame] * r)
if i + period > nSamples:
break
weight = np.sqrt(hnr[frame] / (hnr[frame] + 1))
h[i:i+period] = gm.pulse(period, pcm) * weight
i += period
frame = i // framePeriod
h = ssp.ARExcitation(h, pr, 1.0)
fh = ssp.Frame(h, size=frameSize, period=framePeriod, pad=opt.padding)
# Noise part
n = np.random.normal(size=nSamples)
n = ssp.ZeroFilter(n, 1.0) # Include the radiation impedance
fn = ssp.Frame(n, size=frameSize, period=framePeriod, pad=opt.padding)
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
hgain = ssp.parameter("HGain", 1.0)
e = fn + fh * hgain
# Like harmonics plus noise, but with explicit sinusoids instead of time
# domain impulses.
elif ex == 'sine':
order = 20
sine = ssp.Harmonics(r, order)
h = np.zeros(nSamples)
for i in range(0, len(h)-framePeriod, framePeriod):
frame = i // framePeriod
period = int(1.0 / pitch[frame] * r)
weight = np.sqrt(hnr[frame] / (hnr[frame] + 1))
h[i:i+framePeriod] = ( sine.sample(pitch[frame], framePeriod)
* weight )
fh = ssp.Frame(h, size=frameSize, period=framePeriod, pad=opt.padding)
n = np.random.normal(size=nSamples)
fn = ssp.Frame(n, size=frameSize, period=framePeriod, pad=opt.padding)
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
e = fn + fh*10
# High order linear prediction. Synthesise the harmonics using noise to
# excite a high order polynomial with roots resembling harmonics.
elif ex == 'holp':
# Some noise
n = np.random.normal(size=nSamples)
fn = ssp.Frame(n, size=frameSize, period=framePeriod)
# Use the noise to excite a high order AR model
fh = np.ndarray(fn.shape)
for i in range(len(fn)):
hoar = ssp.ARHarmonicPoly(pitch[i], r, 0.7)
fh[i] = ssp.ARResynthesis(fn[i], hoar, 1.0 / linalg.norm(hoar)**2)
print(i, pitch[i], linalg.norm(hoar), np.min(fh[i]), np.max(fh[i]))
print(' ', np.min(hoar), np.max(hoar))
# fh[i] *= np.sqrt(r / pitch[i]) / linalg.norm(fh[i])
# fh[i] *= np.sqrt(hnr[i] / (hnr[i] + 1))
# Weight the noise as for the other methods
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
e = fh # fn + fh*30
# Shaped excitation. The pulses are shaped by a filter to have a
# rolloff, then added to the noise. The resulting signal is
# flattened using AR.
elif ex == 'shaped':
# Harmonic part
gm = ssp.GlottalModel(ssp.parameter('Pulse', 'impulse'))
gm.angle = pcm.hertz_to_radians(np.mean(pitch)*0.5)
h = np.zeros(nSamples)
i = 0
frame = 0
while i < nSamples and frame < len(pitch):
period = int(1.0 / pitch[frame] * r)
if i + period > nSamples:
break
weight = np.sqrt(hnr[frame] / (hnr[frame] + 1))
h[i:i+period] = gm.pulse(period, pcm) * weight
i += period
frame = i // framePeriod
# Filter to mimic the glottal pulse
hfilt = ssp.parameter("HFilt", None)
hpole1 = ssp.parameter("HPole1", 0.98)
hpole2 = ssp.parameter("HPole2", 0.8)
angle = pcm.hertz_to_radians(np.mean(pitch)) * ssp.parameter("Angle", 1.0)
if hfilt == 'pp':
h = ssp.ZeroFilter(h, 1.0)
h = ssp.PolePairFilter(h, hpole1, angle)
fh = ssp.Frame(h, size=frameSize, period=framePeriod)
# Noise part
n = np.random.normal(size=nSamples)
zero = ssp.parameter("NoiseZero", 1.0)
n = ssp.ZeroFilter(n, zero) # Include the radiation impedance
npole = ssp.parameter("NPole", None)
nf = ssp.parameter("NoiseFreq", 4000)
if npole is not None:
n = ssp.PolePairFilter(n, npole, pcm.hertz_to_radians(nf))
fn = ssp.Frame(n, size=frameSize, period=framePeriod, pad=opt.padding)
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
# Combination
assert(len(fh) == len(fn))
hgain = ssp.parameter("HGain", 1.0)
e = fn + fh * hgain
hnw = np.hanning(frameSize)
for i in range(len(e)):
ep = ssp.Window(e[i], hnw)
#ep = e[i]
eac = ssp.Autocorrelation(ep)
ea, eg = ssp.ARLevinson(eac, order=lpOrder[r])
e[i] = ssp.ARExcitation(e[i], ea, eg)
elif ex == 'ceplf':
omega, alpha = ssp.glottal_pole_lf(
f, pcm, pitch, hnr, visual=(opt.graphic == "ceplf"))
epsilon = ssp.parameter("Epsilon", 5000.0)
h = np.zeros(nSamples)
i = 0
frame = 0
while i < nSamples and frame < len(pitch):
period = int(1.0 / pitch[frame] * r)
if i + period > nSamples:
break
weight = np.sqrt(hnr[frame] / (hnr[frame] + 1))
pu = np.zeros((period))
T0 = pcm.period_to_seconds(period)
print(T0,)
Te = ssp.lf_te(T0, alpha[frame], omega[frame], epsilon)
if Te:
pu = ssp.pulse_lf(pu, T0, Te, alpha[frame], omega[frame], epsilon)
h[i:i+period] = pu * weight
i += period
frame = i // framePeriod
fh = ssp.Frame(h, size=frameSize, period=framePeriod, pad=opt.padding)
# Noise part
n = np.random.normal(size=nSamples)
zero = ssp.parameter("NoiseZero", 1.0)
n = ssp.ZeroFilter(n, zero) # Include the radiation impedance
fn = ssp.Frame(n, size=frameSize, period=framePeriod, pad=opt.padding)
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
# Combination
assert(len(fh) == len(fn))
hgain = ssp.parameter("HGain", 1.0)
e = fn + fh * hgain
hnw = np.hanning(frameSize)
for i in range(len(e)):
ep = ssp.Window(e[i], hnw)
#ep = e[i]
eac = ssp.Autocorrelation(ep)
ea, eg = ssp.ARLevinson(eac, order=lpOrder[r])
e[i] = ssp.ARExcitation(e[i], ea, eg)
elif ex == 'cepgm':
# Infer the unstable poles via complex cepstrum, then build an explicit
# glottal model.
if not (opt.encode or opt.decode or opt.pitch):
theta, magni = ssp.glottal_pole_gm(
f, pcm, pitch, hnr, visual=(opt.graphic == "cepgm"))
h = np.zeros(nSamples)
i = 0
frame = 0
while i < nSamples and frame < len(pitch):
period = int(1.0 / pitch[frame] * r)
if i + period > nSamples:
break
h[i] = 1 # np.random.normal() ** 2
i += period
frame = i // framePeriod
fh = ssp.Frame(h, size=frameSize, period=framePeriod, pad=opt.padding)
gl = ssp.MinPhaseGlottis()
for i in range(len(fh)):
# This is minimum phase; the glotter will invert if required
gl.setpolepair(np.abs(magni[frame]), theta[frame])
fh[i] = gl.glotter(fh[i])
if linalg.norm(fh[i]) > 1e-6:
fh[i] *= np.sqrt(len(fh[i])) / linalg.norm(fh[i])
weight = np.sqrt(hnr[i] / (hnr[i] + 1))
fh[i] *= weight
if (opt.graphic == "h"):
fig = ssp.Figure(1, 1)
hPlot = fig.subplot()
hPlot.plot(h, 'r')
fig.show()
# Noise part
n = np.random.normal(size=nSamples)
zero = ssp.parameter("NoiseZero", 1.0)
n = ssp.ZeroFilter(n, zero) # Include the radiation impedance
fn = ssp.Frame(n, size=frameSize, period=framePeriod, pad=opt.padding)
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
# Combination
assert(len(fh) == len(fn))
hgain = ssp.parameter("HGain", 1.0)
e = fn + fh * hgain
hnw = np.hanning(frameSize)
for i in range(len(e)):
ep = ssp.Window(e[i], hnw)
#ep = e[i]
eac = ssp.Autocorrelation(ep)
ea, eg = ssp.ARLevinson(eac, order=lpOrder[r])
e[i] = ssp.ARExcitation(e[i], ea, eg)
else:
print("Unknown synthesis method")
exit
if opt.excitation:
s = e.flatten('C')/frameSize
else:
s = ssp.ARResynthesis(e, ar, g)
if opt.ola:
# Asymmetric window for OLA
sw = np.hanning(frameSize+1)
sw = np.delete(sw, -1)
s = ssp.Window(s, sw)
s = ssp.OverlapAdd(s)
else:
s = s.flatten('C')
gain = ssp.parameter("Gain", 1.0)
return s * gain
def decode((ar, g, pitch, hnr)):
"""
Decode a speech waveform.
"""
nFrames = len(ar)
assert(len(g) == nFrames)
assert(len(pitch) == nFrames)
assert(len(hnr) == nFrames)
# The original framer padded the ends so the number of samples to
# synthesise is a bit less than you might think
if opt.ola:
frameSize = framePeriod * 2
nSamples = framePeriod * (nFrames-1)
else:
frameSize = framePeriod
nSamples = frameSize * (nFrames-1)
ex = ssp.parameter('Excitation', 'synth')
# Use the original AR residual; it should be a very good
# reconstruction.
if ex == 'ar':
e = ssp.ARExcitation(f, ar, g)
# Just noise. This is effectively a whisper synthesis.
elif ex == 'noise':
e = np.random.normal(size=f.shape)
# Just harmonics, and with a fixed F0. This is the classic robot
# syntheisis.
elif ex == 'robot':
ew = np.zeros(nSamples)
period = int(1.0 / 200 * r)
for i in range(0, len(ew), period):
ew[i] = period
e = ssp.Frame(ew, size=frameSize, period=framePeriod)
# Synthesise harmonics plus noise in the ratio suggested by the
# HNR.
elif ex == 'synth':
# Harmonic part
mperiod = int(1.0 / np.mean(pitch) * r)
gm = ssp.GlottalModel(ssp.parameter('Pulse', 'impulse'))
pr, pg = ssp.pulse_response(gm, pcm, period=mperiod, order=lpOrder[r])
h = np.zeros(nSamples)
i = 0
frame = 0
while i < nSamples and frame < len(pitch):
period = int(1.0 / pitch[frame] * r)
if i + period > nSamples:
break
weight = np.sqrt(hnr[frame] / (hnr[frame] + 1))
h[i:i+period] = gm.pulse(period, pcm) * weight
i += period
frame = i // framePeriod
h = ssp.ARExcitation(h, pr, 1.0)
fh = ssp.Frame(h, size=frameSize, period=framePeriod)
# Noise part
n = np.random.normal(size=nSamples)
n = ssp.ZeroFilter(n, 1.0) # Include the radiation impedance
fn = ssp.Frame(n, size=frameSize, period=framePeriod)
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
hgain = ssp.parameter("HGain", 1.0)
e = fn + fh * hgain
# Like harmonics plus noise, but with explicit sinusoids instead
# of time domain impulses.
elif ex == 'sine':
order = 20
sine = ssp.Harmonics(r, order)
h = np.zeros(nSamples)
for i in range(0, len(h)-framePeriod, framePeriod):
frame = i // framePeriod
period = int(1.0 / pitch[frame] * r)
weight = np.sqrt(hnr[frame] / (hnr[frame] + 1))
h[i:i+framePeriod] = ( sine.sample(pitch[frame], framePeriod)
* weight )
fh = ssp.Frame(h, size=frameSize, period=framePeriod)
n = np.random.normal(size=nSamples)
fn = ssp.Frame(n, size=frameSize, period=framePeriod)
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
e = fn + fh*10
# High order linear prediction. Synthesise the harmonics using
# noise to excite a high order polynomial with roots resembling
# harmonics.
elif ex == 'holp':
# Some noise
n = np.random.normal(size=nSamples)
fn = ssp.Frame(n, size=frameSize, period=framePeriod)
# Use the noise to excite a high order AR model
fh = np.ndarray(fn.shape)
for i in range(len(fn)):
hoar = ssp.ARHarmonicPoly(pitch[i], r, 0.7)
fh[i] = ssp.ARResynthesis(fn[i], hoar, 1.0 / linalg.norm(hoar)**2)
print i, pitch[i], linalg.norm(hoar), np.min(fh[i]), np.max(fh[i])
print ' ', np.min(hoar), np.max(hoar)
# fh[i] *= np.sqrt(r / pitch[i]) / linalg.norm(fh[i])
# fh[i] *= np.sqrt(hnr[i] / (hnr[i] + 1))
# Weight the noise as for the other methods
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
e = fh # fn + fh*30
# Shaped excitation. The pulses are shaped by a filter to have a
# rolloff, then added to the noise. The resulting signal is
# flattened using AR.
elif ex == 'shaped':
# Harmonic part
gm = ssp.GlottalModel(ssp.parameter('Pulse', 'impulse'))
gm.angle = pcm.hertz_to_radians(np.mean(pitch)*0.5)
h = np.zeros(nSamples)
i = 0
frame = 0
while i < nSamples and frame < len(pitch):
period = int(1.0 / pitch[frame] * r)
if i + period > nSamples:
break
weight = np.sqrt(hnr[frame] / (hnr[frame] + 1))
h[i:i+period] = gm.pulse(period, pcm) * weight
i += period
frame = i // framePeriod
# Filter to mimic the glottal pulse
hfilt = ssp.parameter("HFilt", None)
hpole1 = ssp.parameter("HPole1", 0.98)
hpole2 = ssp.parameter("HPole2", 0.8)
angle = pcm.hertz_to_radians(np.mean(pitch)) * ssp.parameter("Angle", 1.0)
# Noise part
n = np.random.normal(size=nSamples)
zero = ssp.parameter("NoiseZero", 1.0)
n = ssp.ZeroFilter(n, zero) # Include the radiation impedance
npole = ssp.parameter("NPole", None)
nf = ssp.parameter("NoiseFreq", 4000)
if npole is not None:
n = ssp.PolePairFilter(n, npole, pcm.hertz_to_radians(nf))
fn = ssp.Frame(n, size=frameSize, period=framePeriod)
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
# Combination
assert(len(fh) == len(fn))
hgain = ssp.parameter("HGain", 1.0)
e = fn + fh * hgain
hnw = np.hanning(frameSize)
for i in range(len(e)):
ep = ssp.Window(e[i], hnw)
#ep = e[i]
eac = ssp.Autocorrelation(ep)
ea, eg = ssp.ARLevinson(eac, order=lpOrder[r])
e[i] = ssp.ARExcitation(e[i], ea, eg)
else:
print "Unknown synthesis method"
exit
if opt.excitation:
s = e.flatten('C')/frameSize
else:
s = ssp.ARResynthesis(e, ar, g)
if opt.ola:
# Asymmetric window for OLA
sw = np.hanning(frameSize+1)
sw = np.delete(sw, -1)
s = ssp.Window(s, sw)
s = ssp.OverlapAdd(s)
else:
s = s.flatten('C')
gain = ssp.parameter("Gain", 1.0)
return s * gain
def decode(tuple):
"""
Decode a speech waveform.
"""
(ark, g, pitch, hnr) = tuple
print("Frame padding:", opt.padding)
nFrames = len(ark)
assert (len(g) == nFrames)
assert (len(pitch) == nFrames)
assert (len(hnr) == nFrames)
# The original framer padded the ends so the number of samples to
# synthesise is a bit less than you might think
if opt.ola:
frameSize = framePeriod * 2
nSamples = framePeriod * (nFrames - 1)
else:
frameSize = framePeriod
nSamples = frameSize * (nFrames - 1)
ex = opt.glottal
if opt.glottal == 'cepgm' and (opt.encode or opt.decode or opt.pitch):
order = ark.shape[-1] - 2
ar = ark[:, 0:order]
theta = ark[:, -2]
magni = np.exp(ark[:, -1])
else:
ar = ark
# Use the original AR residual; it should be a very good reconstruction.
if ex == 'ar':
e = ssp.ARExcitation(f, ar, g)
# Just noise. This is effectively a whisper synthesis.
elif ex == 'noise':
e = np.random.normal(size=(nFrames, frameSize))
# Just harmonics, and with a fixed F0. This is the classic robot
# synthesis.
elif ex == 'robot':
ew = np.zeros(nSamples)
period = int(1.0 / 200 * r)
for i in range(0, len(ew), period):
ew[i] = period
e = ssp.Frame(ew, size=frameSize, period=framePeriod)
# Synthesise harmonics plus noise in the ratio suggested by the HNR.
elif ex == 'synth':
# Harmonic part
mperiod = int(1.0 / np.mean(pitch) * r)
gm = ssp.GlottalModel(ssp.parameter('Pulse', 'impulse'))
pr, pg = ssp.pulse_response(gm, pcm, period=mperiod, order=lpOrder[r])
h = np.zeros(nSamples)
i = 0
frame = 0
while i < nSamples and frame < len(pitch):
period = int(1.0 / pitch[frame] * r)
if i + period > nSamples:
break
weight = np.sqrt(hnr[frame] / (hnr[frame] + 1))
h[i:i + period] = gm.pulse(period, pcm) * weight
i += period
frame = i // framePeriod
h = ssp.ARExcitation(h, pr, 1.0)
fh = ssp.Frame(h, size=frameSize, period=framePeriod, pad=opt.padding)
# Noise part
n = np.random.normal(size=nSamples)
n = ssp.ZeroFilter(n, 1.0) # Include the radiation impedance
fn = ssp.Frame(n, size=frameSize, period=framePeriod, pad=opt.padding)
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
hgain = ssp.parameter("HGain", 1.0)
e = fn + fh * hgain
# Like harmonics plus noise, but with explicit sinusoids instead of time
# domain impulses.
elif ex == 'sine':
order = 20
sine = ssp.Harmonics(r, order)
h = np.zeros(nSamples)
for i in range(0, len(h) - framePeriod, framePeriod):
frame = i // framePeriod
period = int(1.0 / pitch[frame] * r)
weight = np.sqrt(hnr[frame] / (hnr[frame] + 1))
h[i:i + framePeriod] = (sine.sample(pitch[frame], framePeriod) *
weight)
fh = ssp.Frame(h, size=frameSize, period=framePeriod, pad=opt.padding)
n = np.random.normal(size=nSamples)
fn = ssp.Frame(n, size=frameSize, period=framePeriod, pad=opt.padding)
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
e = fn + fh * 10
# High order linear prediction. Synthesise the harmonics using noise to
# excite a high order polynomial with roots resembling harmonics.
elif ex == 'holp':
# Some noise
n = np.random.normal(size=nSamples)
fn = ssp.Frame(n, size=frameSize, period=framePeriod)
# Use the noise to excite a high order AR model
fh = np.ndarray(fn.shape)
for i in range(len(fn)):
hoar = ssp.ARHarmonicPoly(pitch[i], r, 0.7)
fh[i] = ssp.ARResynthesis(fn[i], hoar, 1.0 / linalg.norm(hoar)**2)
print(i, pitch[i], linalg.norm(hoar), np.min(fh[i]), np.max(fh[i]))
print(' ', np.min(hoar), np.max(hoar))
# fh[i] *= np.sqrt(r / pitch[i]) / linalg.norm(fh[i])
# fh[i] *= np.sqrt(hnr[i] / (hnr[i] + 1))
# Weight the noise as for the other methods
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
e = fh # fn + fh*30
# Shaped excitation. The pulses are shaped by a filter to have a
# rolloff, then added to the noise. The resulting signal is
# flattened using AR.
elif ex == 'shaped':
# Harmonic part
gm = ssp.GlottalModel(ssp.parameter('Pulse', 'impulse'))
gm.angle = pcm.hertz_to_radians(np.mean(pitch) * 0.5)
h = np.zeros(nSamples)
i = 0
frame = 0
while i < nSamples and frame < len(pitch):
period = int(1.0 / pitch[frame] * r)
if i + period > nSamples:
break
weight = np.sqrt(hnr[frame] / (hnr[frame] + 1))
h[i:i + period] = gm.pulse(period, pcm) * weight
i += period
frame = i // framePeriod
# Filter to mimic the glottal pulse
hfilt = ssp.parameter("HFilt", None)
hpole1 = ssp.parameter("HPole1", 0.98)
hpole2 = ssp.parameter("HPole2", 0.8)
angle = pcm.hertz_to_radians(np.mean(pitch)) * ssp.parameter(
"Angle", 1.0)
if hfilt == 'pp':
h = ssp.ZeroFilter(h, 1.0)
h = ssp.PolePairFilter(h, hpole1, angle)
fh = ssp.Frame(h, size=frameSize, period=framePeriod)
# Noise part
n = np.random.normal(size=nSamples)
zero = ssp.parameter("NoiseZero", 1.0)
n = ssp.ZeroFilter(n, zero) # Include the radiation impedance
npole = ssp.parameter("NPole", None)
nf = ssp.parameter("NoiseFreq", 4000)
if npole is not None:
n = ssp.PolePairFilter(n, npole, pcm.hertz_to_radians(nf))
fn = ssp.Frame(n, size=frameSize, period=framePeriod, pad=opt.padding)
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
# Combination
assert (len(fh) == len(fn))
hgain = ssp.parameter("HGain", 1.0)
e = fn + fh * hgain
hnw = np.hanning(frameSize)
for i in range(len(e)):
ep = ssp.Window(e[i], hnw)
#ep = e[i]
eac = ssp.Autocorrelation(ep)
ea, eg = ssp.ARLevinson(eac, order=lpOrder[r])
e[i] = ssp.ARExcitation(e[i], ea, eg)
elif ex == 'ceplf':
omega, alpha = ssp.glottal_pole_lf(f,
pcm,
pitch,
hnr,
visual=(opt.graphic == "ceplf"))
epsilon = ssp.parameter("Epsilon", 5000.0)
h = np.zeros(nSamples)
i = 0
frame = 0
while i < nSamples and frame < len(pitch):
period = int(1.0 / pitch[frame] * r)
if i + period > nSamples:
break
weight = np.sqrt(hnr[frame] / (hnr[frame] + 1))
pu = np.zeros((period))
T0 = pcm.period_to_seconds(period)
print(T0, )
Te = ssp.lf_te(T0, alpha[frame], omega[frame], epsilon)
if Te:
pu = ssp.pulse_lf(pu, T0, Te, alpha[frame], omega[frame],
epsilon)
h[i:i + period] = pu * weight
i += period
frame = i // framePeriod
fh = ssp.Frame(h, size=frameSize, period=framePeriod, pad=opt.padding)
# Noise part
n = np.random.normal(size=nSamples)
zero = ssp.parameter("NoiseZero", 1.0)
n = ssp.ZeroFilter(n, zero) # Include the radiation impedance
fn = ssp.Frame(n, size=frameSize, period=framePeriod, pad=opt.padding)
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
# Combination
assert (len(fh) == len(fn))
hgain = ssp.parameter("HGain", 1.0)
e = fn + fh * hgain
hnw = np.hanning(frameSize)
for i in range(len(e)):
ep = ssp.Window(e[i], hnw)
#ep = e[i]
eac = ssp.Autocorrelation(ep)
ea, eg = ssp.ARLevinson(eac, order=lpOrder[r])
e[i] = ssp.ARExcitation(e[i], ea, eg)
elif ex == 'cepgm':
# Infer the unstable poles via complex cepstrum, then build an explicit
# glottal model.
if not (opt.encode or opt.decode or opt.pitch):
theta, magni = ssp.glottal_pole_gm(f,
pcm,
pitch,
hnr,
visual=(opt.graphic == "cepgm"))
h = np.zeros(nSamples)
i = 0
frame = 0
while i < nSamples and frame < len(pitch):
period = int(1.0 / pitch[frame] * r)
if i + period > nSamples:
break
h[i] = 1 # np.random.normal() ** 2
i += period
frame = i // framePeriod
fh = ssp.Frame(h, size=frameSize, period=framePeriod, pad=opt.padding)
gl = ssp.MinPhaseGlottis()
for i in range(len(fh)):
# This is minimum phase; the glotter will invert if required
gl.setpolepair(np.abs(magni[frame]), theta[frame])
fh[i] = gl.glotter(fh[i])
if linalg.norm(fh[i]) > 1e-6:
fh[i] *= np.sqrt(len(fh[i])) / linalg.norm(fh[i])
weight = np.sqrt(hnr[i] / (hnr[i] + 1))
fh[i] *= weight
if (opt.graphic == "h"):
fig = ssp.Figure(1, 1)
hPlot = fig.subplot()
hPlot.plot(h, 'r')
fig.show()
# Noise part
n = np.random.normal(size=nSamples)
zero = ssp.parameter("NoiseZero", 1.0)
n = ssp.ZeroFilter(n, zero) # Include the radiation impedance
fn = ssp.Frame(n, size=frameSize, period=framePeriod, pad=opt.padding)
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
# Combination
assert (len(fh) == len(fn))
hgain = ssp.parameter("HGain", 1.0)
e = fn + fh * hgain
hnw = np.hanning(frameSize)
for i in range(len(e)):
ep = ssp.Window(e[i], hnw)
#ep = e[i]
eac = ssp.Autocorrelation(ep)
ea, eg = ssp.ARLevinson(eac, order=lpOrder[r])
e[i] = ssp.ARExcitation(e[i], ea, eg)
else:
print("Unknown synthesis method")
exit
if opt.excitation:
s = e.flatten('C') / frameSize
else:
s = ssp.ARResynthesis(e, ar, g)
if opt.ola:
# Asymmetric window for OLA
sw = np.hanning(frameSize + 1)
sw = np.delete(sw, -1)
s = ssp.Window(s, sw)
s = ssp.OverlapAdd(s)
else:
s = s.flatten('C')
gain = ssp.parameter("Gain", 1.0)
return s * gain
def decode((ar, g, pitch, hnr)):
"""
Decode a speech waveform.
"""
nFrames = len(ar)
assert(len(g) == nFrames)
assert(len(pitch) == nFrames)
assert(len(hnr) == nFrames)
# The original framer padded the ends so the number of samples to
# synthesise is a bit less than you might think
if opt.ola:
frameSize = framePeriod * 2
nSamples = framePeriod * (nFrames-1)
else:
frameSize = framePeriod
nSamples = frameSize * (nFrames-1)
ex = ssp.parameter('Excitation', 'synth')
# Use the original AR residual; it should be a very good
# reconstruction.
if ex == 'ar':
e = ssp.ARExcitation(f, ar, g)
# Just noise. This is effectively a whisper synthesis.
elif ex == 'noise':
e = np.random.normal(size=f.shape)
# Just harmonics, and with a fixed F0. This is the classic robot
# syntheisis.
elif ex == 'robot':
ew = np.zeros(nSamples)
period = int(1.0 / 200 * r)
for i in range(0, len(ew), period):
ew[i] = period
e = ssp.Frame(ew, size=frameSize, period=framePeriod)
# Synthesise harmonics plus noise in the ratio suggested by the
# HNR.
elif ex == 'synth':
# Harmonic part
mperiod = int(1.0 / np.mean(pitch) * r)
gm = ssp.GlottalModel(ssp.parameter('Pulse', 'impulse'))
pr, pg = ssp.pulse_response(gm, pcm, period=mperiod, order=lpOrder[r])
h = np.zeros(nSamples)
i = 0
frame = 0
while i < nSamples and frame < len(pitch):
period = int(1.0 / pitch[frame] * r)
if i + period > nSamples:
break
weight = np.sqrt(hnr[frame] / (hnr[frame] + 1))
h[i:i+period] = gm.pulse(period, pcm) * weight
i += period
frame = i // framePeriod
h = ssp.ARExcitation(h, pr, 1.0)
fh = ssp.Frame(h, size=frameSize, period=framePeriod)
# Noise part
n = np.random.normal(size=nSamples)
n = ssp.ZeroFilter(n, 1.0) # Include the radiation impedance
fn = ssp.Frame(n, size=frameSize, period=framePeriod)
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
hgain = ssp.parameter("HGain", 1.0)
e = fn + fh * hgain
# Like harmonics plus noise, but with explicit sinusoids instead
# of time domain impulses.
elif ex == 'sine':
order = 20
sine = ssp.Harmonics(r, order)
h = np.zeros(nSamples)
for i in range(0, len(h)-framePeriod, framePeriod):
frame = i // framePeriod
period = int(1.0 / pitch[frame] * r)
weight = np.sqrt(hnr[frame] / (hnr[frame] + 1))
h[i:i+framePeriod] = ( sine.sample(pitch[frame], framePeriod)
* weight )
fh = ssp.Frame(h, size=frameSize, period=framePeriod)
n = np.random.normal(size=nSamples)
fn = ssp.Frame(n, size=frameSize, period=framePeriod)
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
e = fn + fh*10
# High order linear prediction. Synthesise the harmonics using
# noise to excite a high order polynomial with roots resembling
# harmonics.
elif ex == 'holp':
# Some noise
n = np.random.normal(size=nSamples)
fn = ssp.Frame(n, size=frameSize, period=framePeriod)
# Use the noise to excite a high order AR model
fh = np.ndarray(fn.shape)
for i in range(len(fn)):
hoar = ssp.ARHarmonicPoly(pitch[i], r, 0.7)
fh[i] = ssp.ARResynthesis(fn[i], hoar, 1.0 / linalg.norm(hoar)**2)
print i, pitch[i], linalg.norm(hoar), np.min(fh[i]), np.max(fh[i])
print ' ', np.min(hoar), np.max(hoar)
# fh[i] *= np.sqrt(r / pitch[i]) / linalg.norm(fh[i])
# fh[i] *= np.sqrt(hnr[i] / (hnr[i] + 1))
# Weight the noise as for the other methods
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
e = fh # fn + fh*30
# Shaped excitation. The pulses are shaped by a filter to have a
# rolloff, then added to the noise. The resulting signal is
# flattened using AR.
elif ex == 'shaped':
# Harmonic part
gm = ssp.GlottalModel(ssp.parameter('Pulse', 'impulse'))
gm.angle = pcm.hertz_to_radians(np.mean(pitch)*0.5)
h = np.zeros(nSamples)
i = 0
frame = 0
while i < nSamples and frame < len(pitch):
period = int(1.0 / pitch[frame] * r)
if i + period > nSamples:
break
weight = np.sqrt(hnr[frame] / (hnr[frame] + 1))
h[i:i+period] = gm.pulse(period, pcm) * weight
i += period
frame = i // framePeriod
# Filter to mimic the glottal pulse
hfilt = ssp.parameter("HFilt", None)
hpole1 = ssp.parameter("HPole1", 0.98)
hpole2 = ssp.parameter("HPole2", 0.8)
angle = pcm.hertz_to_radians(np.mean(pitch)) * ssp.parameter("Angle", 1.0)
if hfilt == 'pp':
h = ssp.ZeroFilter(h, 1.0)
h = ssp.PolePairFilter(h, hpole1, angle)
if hfilt == 'g':
h = ssp.GFilter(h, hpole1, angle, hpole2)
if hfilt == 'p':
h = ssp.PFilter(h, hpole1, angle, hpole2)
fh = ssp.Frame(h, size=frameSize, period=framePeriod)
# Noise part
n = np.random.normal(size=nSamples)
zero = ssp.parameter("NoiseZero", 1.0)
n = ssp.ZeroFilter(n, zero) # Include the radiation impedance
npole = ssp.parameter("NPole", None)
nf = ssp.parameter("NoiseFreq", 4000)
if npole is not None:
n = ssp.PolePairFilter(n, npole, pcm.hertz_to_radians(nf))
fn = ssp.Frame(n, size=frameSize, period=framePeriod)
for i in range(len(fn)):
fn[i] *= np.sqrt(1.0 / (hnr[i] + 1))
# Combination
assert(len(fh) == len(fn))
hgain = ssp.parameter("HGain", 1.0)
e = fn + fh * hgain
hnw = np.hanning(frameSize)
for i in range(len(e)):
ep = ssp.Window(e[i], hnw)
#ep = e[i]
eac = ssp.Autocorrelation(ep)
ea, eg = ssp.ARLevinson(eac, order=lpOrder[r])
e[i] = ssp.ARExcitation(e[i], ea, eg)
else:
print "Unknown synthesis method"
exit
if opt.excitation:
s = e.flatten('C')/frameSize
else:
s = ssp.ARResynthesis(e, ar, g)
if opt.ola:
# Asymmetric window for OLA
sw = np.hanning(frameSize+1)
sw = np.delete(sw, -1)
s = ssp.Window(s, sw)
s = ssp.OverlapAdd(s)
else:
s = s.flatten('C')
gain = ssp.parameter("Gain", 1.0)
return s * gain
