def test_pitch_io(self):
'''Tests for reading/writing pitch tiers'''
fn = "mary.PitchTier"
inputFN = join(self.dataRoot, fn)
outputFN = join(self.outputRoot, fn)
pp = dataio.open2DPointObject(inputFN)
pp.save(outputFN)
self.assertTrue(areTheSame(inputFN, outputFN, dataio.open2DPointObject))
def test_duration_tier_io(self):
'''Tests for reading/writing duration tiers'''
fn = "mary.DurationTier"
inputFN = join(self.dataRoot, fn)
outputFN = join(self.outputRoot, fn)
dt = dataio.open2DPointObject(inputFN)
dt.save(outputFN)
self.assertTrue(areTheSame(inputFN, outputFN, dataio.open2DPointObject))
def test_pitch_io(self):
'''Tests for reading/writing pitch tiers'''
fn = "mary.PitchTier"
inputFN = join(self.dataRoot, fn)
outputFN = join(self.outputRoot, fn)
pp = dataio.open2DPointObject(inputFN)
pp.save(outputFN)
self.assertTrue(areTheSame(inputFN, outputFN,
dataio.open2DPointObject))
def test_duration_tier_io(self):
'''Tests for reading/writing duration tiers'''
fn = "mary.DurationTier"
inputFN = join(self.dataRoot, fn)
outputFN = join(self.outputRoot, fn)
dt = dataio.open2DPointObject(inputFN)
dt.save(outputFN)
self.assertTrue(areTheSame(inputFN, outputFN,
dataio.open2DPointObject))
def extractPitchTier(wavFN,
outputFN,
praatEXE,
minPitch,
maxPitch,
sampleStep=0.01,
silenceThreshold=0.03,
forceRegenerate=True,
medianFilterWindowSize=0,
pitchQuadInterp=False):
'''
Extract pitch at regular intervals from the input wav file
Data is output to a text file and then returned in a list in the form
[(timeV1, pitchV1), (timeV2, pitchV2), ...]
sampleStep - the frequency to sample pitch at
silenceThreshold - segments with lower intensity won't be analyzed
for pitch
forceRegenerate - if running this function for the same file, if False
just read in the existing pitch file
pitchQuadInterp - if True, quadratically interpolate pitch
'''
outputPath = os.path.split(outputFN)[0]
utils.makeDir(outputPath)
if pitchQuadInterp is True:
doInterpolation = 1
else:
doInterpolation = 0
assert (os.path.exists(wavFN))
firstTime = not os.path.exists(outputFN)
if firstTime or forceRegenerate is True:
if os.path.exists(outputFN):
os.remove(outputFN)
argList = [
wavFN, outputFN, sampleStep, minPitch, maxPitch, silenceThreshold,
medianFilterWindowSize, doInterpolation
]
scriptName = "get_pitchtier.praat"
scriptFN = join(utils.scriptsPath, scriptName)
utils.runPraatScript(praatEXE, scriptFN, argList)
pitchTier = dataio.open2DPointObject(outputFN)
return pitchTier
def extractPitchTier(wavFN, outputFN, praatEXE,
minPitch, maxPitch, sampleStep=0.01,
silenceThreshold=0.03, forceRegenerate=True,
medianFilterWindowSize=0,
pitchQuadInterp=False):
'''
Extract pitch at regular intervals from the input wav file
Data is output to a text file and then returned in a list in the form
[(timeV1, pitchV1), (timeV2, pitchV2), ...]
sampleStep - the frequency to sample pitch at
silenceThreshold - segments with lower intensity won't be analyzed
for pitch
forceRegenerate - if running this function for the same file, if False
just read in the existing pitch file
pitchQuadInterp - if True, quadratically interpolate pitch
'''
outputPath = os.path.split(outputFN)[0]
utils.makeDir(outputPath)
if pitchQuadInterp is True:
doInterpolation = 1
else:
doInterpolation = 0
assert(os.path.exists(wavFN))
firstTime = not os.path.exists(outputFN)
if firstTime or forceRegenerate is True:
if os.path.exists(outputFN):
os.remove(outputFN)
argList = [wavFN, outputFN, sampleStep,
minPitch, maxPitch, silenceThreshold,
medianFilterWindowSize, doInterpolation]
scriptName = "get_pitchtier.praat"
scriptFN = join(utils.scriptsPath, scriptName)
utils.runPraatScript(praatEXE, scriptFN, argList)
pitchTier = dataio.open2DPointObject(outputFN)
return pitchTier
fromWavFN = fromName + ".wav"
fromPitchFN = fromName + ".txt"
fromTGFN = join(root, fromName + ".TextGrid")
toName = "mary1_stylized"
toPitchFN = toName + ".PitchTier"
# Prepare the data for morphing
# 1st load it into memory
fromPitchList = pitch_and_intensity.audioToPI(root, fromWavFN, root,
fromPitchFN, praatEXE, minPitch,
maxPitch, forceRegenerate=False)
fromPitchList = [(time, pitch) for time, pitch, _ in fromPitchList]
# Load in the target pitch contour
pitchTier = dataio.open2DPointObject(join(root, toPitchFN))
toPitchList = [(time, pitch) for time, pitch in pitchTier.pointList]
# The target contour doesn't contain enough sample points, so interpolate
# over the provided samples
# (this step can be skipped if there are enough sample points--a warning
# will be issued if there are any potential problems)
toPitchList = interpolation.quadraticInterpolation(toPitchList, 4, 1000, 0)
# 3rd select which sections to align.
# We'll use textgrids for this purpose.
tierName = "PhonAlign"
fromPitch = f0_morph.getPitchForIntervals(fromPitchList, fromTGFN, tierName)
toPitch = f0_morph.getPitchForIntervals(toPitchList, fromTGFN, tierName)
# Run the morph process
fromWavFN = fromName + ".wav"
fromPitchFN = fromName + ".txt"
fromTGFN = join(root, fromName + ".TextGrid")
toName = "mary1_stylized"
toPitchFN = toName + ".PitchTier"
# Prepare the data for morphing
# 1st load it into memory
fromPitchList = pitch_and_intensity.audioToPI(root, fromWavFN, root,
fromPitchFN, praatEXE, minPitch,
maxPitch, forceRegenerate=False)
fromPitchList = [(time, pitch) for time, pitch, _ in fromPitchList]
# Load in the target pitch contour
pitchTier = dataio.open2DPointObject(join(root, toPitchFN))
toPitchList = [(time, pitch) for time, pitch in pitchTier.pointList]
# The target contour doesn't contain enough sample points, so interpolate
# over the provided samples
# (this step can be skipped if there are enough sample points--a warning
# will be issued if there are any potential problems)
toPitchList = interpolation.quadraticInterpolation(toPitchList, 4, 1000, 0)
# 3rd select which sections to align.
# We'll use textgrids for this purpose.
tierName = "PhonAlign"
fromPitch = f0_morph.getPitchForIntervals(fromPitchList, fromTGFN, tierName)
toPitch = f0_morph.getPitchForIntervals(toPitchList, fromTGFN, tierName)
# Run the morph process