def outputMorphPlot(fromTGFN, toTGFN, tierName, durationParameters, stepList,
outputImageFN):
if outputImageFN is not None:
utils.makeDir(os.path.split(outputImageFN)[0])
# Create the plot of the morph
if outputImageFN is not None:
_plotResults(durationParameters, fromTGFN, toTGFN, tierName, stepList,
outputImageFN, None, False)
def outputMorphPlot(fromTGFN, toTGFN, tierName, durationParameters, stepList,
outputImageFN):
if outputImageFN is not None:
utils.makeDir(os.path.split(outputImageFN)[0])
# Create the plot of the morph
if outputImageFN is not None:
_plotResults(durationParameters, fromTGFN, toTGFN,
tierName, stepList, outputImageFN,
None, False)
def changeDuration(fromWavFN, durationParameters, stepList, outputName,
outputMinPitch, outputMaxPitch, praatEXE):
'''
Uses praat to morph duration in one file to duration in another
Praat uses the PSOLA algorithm
'''
rootPath = os.path.split(fromWavFN)[0]
# Prep output directories
outputPath = join(rootPath, "duration_resynthesized_wavs")
utils.makeDir(outputPath)
durationTierPath = join(rootPath, "duration_tiers")
utils.makeDir(durationTierPath)
fromWavDuration = audio_scripts.getSoundFileDuration(fromWavFN)
durationParameters = copy.deepcopy(durationParameters)
# Pad any gaps with values of 1 (no change in duration)
# No need to stretch out any pauses at the beginning
if durationParameters[0][0] != 0:
tmpVar = (0, durationParameters[0][0] - PRAAT_TIME_DIFF, 1)
durationParameters.insert(0, tmpVar)
# Or the end
if durationParameters[-1][1] < fromWavDuration:
durationParameters.append((durationParameters[-1][1] + PRAAT_TIME_DIFF,
fromWavDuration, 1))
# Create the praat script for doing duration manipulation
for stepAmount in stepList:
durationPointList = []
for start, end, ratio in durationParameters:
percentChange = 1 + (ratio - 1) * stepAmount
durationPointList.append((start, percentChange))
durationPointList.append((end, percentChange))
outputPrefix = "%s_%0.3g" % (outputName, stepAmount)
durationTierFN = join(durationTierPath,
"%s.DurationTier" % outputPrefix)
outputWavFN = join(outputPath, "%s.wav" % outputPrefix)
durationTier = dataio.PointObject2D(durationPointList, dataio.DURATION,
0, fromWavDuration)
durationTier.save(durationTierFN)
praat_scripts.resynthesizeDuration(praatEXE,
fromWavFN,
durationTierFN,
outputWavFN,
outputMinPitch, outputMaxPitch)
def changeDuration(fromWavFN, durationParameters, stepList, outputName,
outputMinPitch, outputMaxPitch, praatEXE):
'''
Uses praat to morph duration in one file to duration in another
Praat uses the PSOLA algorithm
'''
rootPath = os.path.split(fromWavFN)[0]
# Prep output directories
outputPath = join(rootPath, "duration_resynthesized_wavs")
utils.makeDir(outputPath)
durationTierPath = join(rootPath, "duration_tiers")
utils.makeDir(durationTierPath)
fromWavDuration = audio_scripts.getSoundFileDuration(fromWavFN)
durationParameters = copy.deepcopy(durationParameters)
# Pad any gaps with values of 1 (no change in duration)
# No need to stretch out any pauses at the beginning
if durationParameters[0][0] != 0:
tmpVar = (0, durationParameters[0][0] - PRAAT_TIME_DIFF, 1)
durationParameters.insert(0, tmpVar)
# Or the end
if durationParameters[-1][1] < fromWavDuration:
durationParameters.append(
(durationParameters[-1][1] + PRAAT_TIME_DIFF, fromWavDuration, 1))
# Create the praat script for doing duration manipulation
for stepAmount in stepList:
durationPointList = []
for start, end, ratio in durationParameters:
percentChange = 1 + (ratio - 1) * stepAmount
durationPointList.append((start, percentChange))
durationPointList.append((end, percentChange))
outputPrefix = "%s_%0.3g" % (outputName, stepAmount)
durationTierFN = join(durationTierPath,
"%s.DurationTier" % outputPrefix)
outputWavFN = join(outputPath, "%s.wav" % outputPrefix)
durationTier = dataio.PointObject2D(durationPointList, dataio.DURATION,
0, fromWavDuration)
durationTier.save(durationTierFN)
praat_scripts.resynthesizeDuration(praatEXE, fromWavFN, durationTierFN,
outputWavFN, outputMinPitch,
outputMaxPitch)
def outputMorphTextgrids(fromTGFN, durationParameters, stepList, outputTGName):
if outputTGName is not None:
utils.makeDir(os.path.split(outputTGName)[0])
# Create the adjusted textgrids
if outputTGName is not None:
for stepFactor in stepList:
stepDurationParameters = [
(start, end, 1 + (ratio - 1) * stepFactor)
for start, end, ratio in durationParameters
]
adjustedTG = textgridManipulateDuration(fromTGFN,
stepDurationParameters)
outputTGFN = "%s_%0.3g.TextGrid" % (outputTGName, stepFactor)
adjustedTG.save(outputTGFN)
def outputMorphTextgrids(fromTGFN, durationParameters, stepList,
outputTGName):
if outputTGName is not None:
utils.makeDir(os.path.split(outputTGName)[0])
# Create the adjusted textgrids
if outputTGName is not None:
for stepFactor in stepList:
stepDurationParameters = [(start,
end,
1 + (ratio - 1) * stepFactor)
for start, end, ratio
in durationParameters]
adjustedTG = textgridManipulateDuration(fromTGFN,
stepDurationParameters)
outputTGFN = "%s_%0.3g.TextGrid" % (outputTGName, stepFactor)
adjustedTG.save(outputTGFN)
def f0Morph(fromWavFN,
pitchPath,
stepList,
outputName,
doPlotPitchSteps,
fromPitchData,
toPitchData,
outputMinPitch,
outputMaxPitch,
praatEXE,
keepPitchRange=False,
keepAveragePitch=False):
'''
Resynthesizes the pitch track from a source to a target wav file
fromPitchData and toPitchData should be segmented according to the
portions that you want to morph. The two lists must have the same
number of sublists.
Occurs over a three-step process.
This function can act as a template for how to use the function
morph_sequence.morphChunkedDataLists to morph pitch contours or
other data.
By default, everything is morphed, but it is possible to maintain elements
of the original speaker's pitch (average pitch and pitch range) by setting
the appropriate flag)
'''
fromDuration = audio_scripts.getSoundFileDuration(fromWavFN)
# Iterative pitch tier data path
pitchTierPath = join(pitchPath, "pitchTiers")
resynthesizedPath = join(pitchPath, "f0_resynthesized_wavs")
for tmpPath in [pitchTierPath, resynthesizedPath]:
utils.makeDir(tmpPath)
# 1. Prepare the data for morphing - acquire the segments to merge
# (Done elsewhere, with the input fed into this function)
# 2. Morph the fromData to the toData
try:
finalOutputList = morph_sequence.morphChunkedDataLists(
fromPitchData, toPitchData, stepList)
except IndexError:
raise MissingPitchDataException()
fromPitchData = [row for subList in fromPitchData for row in subList]
toPitchData = [row for subList in toPitchData for row in subList]
# 3. Save the pitch data and resynthesize the pitch
mergedDataList = []
for i in range(0, len(finalOutputList)):
outputDataList = finalOutputList[i]
if keepPitchRange is True:
outputDataList = morph_sequence.morphRange(outputDataList,
fromPitchData)
if keepAveragePitch is True:
outputDataList = morph_sequence.morphAveragePitch(
outputDataList, fromPitchData)
stepOutputName = "%s_%0.3g" % (outputName, stepList[i])
pitchFNFullPath = join(pitchTierPath, "%s.PitchTier" % stepOutputName)
outputFN = join(resynthesizedPath, "%s.wav" % stepOutputName)
pointObj = dataio.PointObject2D(outputDataList, dataio.PITCH, 0,
fromDuration)
pointObj.save(pitchFNFullPath)
outputTime, outputVals = list(zip(*outputDataList))
mergedDataList.append((outputTime, outputVals))
praat_scripts.resynthesizePitch(praatEXE, fromWavFN, pitchFNFullPath,
outputFN, outputMinPitch,
outputMaxPitch)
# 4. (Optional) Plot the generated contours
if doPlotPitchSteps:
fromTime, fromVals = list(zip(*fromPitchData))
toTime, toVals = list(zip(*toPitchData))
plot_morphed_data.plotF0((fromTime, fromVals), (toTime, toVals),
mergedDataList,
join(pitchTierPath, "%s.png" % outputName))
fromName = "mary1"
toName = "mary2"
fromWavFN = join(root, fromName + ".wav")
toWavFN = join(root, toName + ".wav")
fromPitchFN = fromName + ".txt"
toPitchFN = toName + ".txt"
tierName = "PhonAlign"
fromTGFN = join(root, fromName + ".TextGrid")
toTGFN = join(root, toName + ".TextGrid")
outputPath = join(root, "duration_morph")
utils.makeDir(outputPath)
outputName = "%s_%s_dur_morph" % (fromName, toName)
outputTG = join(outputPath, "%s.TextGrid" % outputName)
outputImageFN = join(outputPath, "%s.png" % outputName)
filterFunc = None
includeUnlabeledRegions = False
# Morph the duration from one file to another
durationParams = duration_morph.getMorphParameters(fromTGFN, toTGFN, tierName,
filterFunc,
includeUnlabeledRegions)
morphedTG = duration_morph.textgridMorphDuration(fromTGFN, toTGFN)
morphedTG.save(outputTG)
duration_morph.changeDuration(fromWavFN,
durationParams,
stepList,
def f0Morph(fromWavFN,
pitchPath,
stepList,
outputName,
doPlotPitchSteps,
fromPitchData,
toPitchData,
outputMinPitch,
outputMaxPitch,
praatEXE,
keepPitchRange=False,
keepAveragePitch=False,
sourcePitchDataList=None,
minIntervalLength=0.3):
'''
Resynthesizes the pitch track from a source to a target wav file
fromPitchData and toPitchData should be segmented according to the
portions that you want to morph. The two lists must have the same
number of sublists.
Occurs over a three-step process.
This function can act as a template for how to use the function
morph_sequence.morphChunkedDataLists to morph pitch contours or
other data.
By default, everything is morphed, but it is possible to maintain elements
of the original speaker's pitch (average pitch and pitch range) by setting
the appropriate flag)
sourcePitchDataList: if passed in, any regions unspecified by
fromPitchData will be sampled from this list. In
essence, this allows one to leave segments of
the original pitch contour untouched by the
morph process.
'''
fromDuration = audio_scripts.getSoundFileDuration(fromWavFN)
# Find source pitch samples that will be mixed in with the target
# pitch samples later
nonMorphPitchData = []
if sourcePitchDataList is not None:
timeList = sorted(fromPitchData)
timeList = [(row[0][0], row[-1][0]) for row in timeList]
endTime = sourcePitchDataList[-1][0]
invertedTimeList = praatio_utils.invertIntervalList(timeList, endTime)
invertedTimeList = [(start, stop) for start, stop in invertedTimeList
if stop - start > minIntervalLength]
for start, stop in invertedTimeList:
pitchList = praatio_utils.getValuesInInterval(
sourcePitchDataList, start, stop)
nonMorphPitchData.extend(pitchList)
# Iterative pitch tier data path
pitchTierPath = join(pitchPath, "pitchTiers")
resynthesizedPath = join(pitchPath, "f0_resynthesized_wavs")
for tmpPath in [pitchTierPath, resynthesizedPath]:
utils.makeDir(tmpPath)
# 1. Prepare the data for morphing - acquire the segments to merge
# (Done elsewhere, with the input fed into this function)
# 2. Morph the fromData to the toData
try:
finalOutputList = morph_sequence.morphChunkedDataLists(
fromPitchData, toPitchData, stepList)
except IndexError:
raise MissingPitchDataException()
fromPitchData = [row for subList in fromPitchData for row in subList]
toPitchData = [row for subList in toPitchData for row in subList]
# 3. Save the pitch data and resynthesize the pitch
mergedDataList = []
for i in range(0, len(finalOutputList)):
outputDataList = finalOutputList[i]
if keepPitchRange is True:
outputDataList = morph_sequence.morphRange(outputDataList,
fromPitchData)
if keepAveragePitch is True:
outputDataList = morph_sequence.morphAveragePitch(
outputDataList, fromPitchData)
if sourcePitchDataList is not None:
outputDataList.extend(nonMorphPitchData)
outputDataList.sort()
stepOutputName = "%s_%0.3g" % (outputName, stepList[i])
pitchFNFullPath = join(pitchTierPath, "%s.PitchTier" % stepOutputName)
outputFN = join(resynthesizedPath, "%s.wav" % stepOutputName)
pointObj = dataio.PointObject2D(outputDataList, dataio.PITCH, 0,
fromDuration)
pointObj.save(pitchFNFullPath)
outputTime, outputVals = zip(*outputDataList)
mergedDataList.append((outputTime, outputVals))
praat_scripts.resynthesizePitch(praatEXE, fromWavFN, pitchFNFullPath,
outputFN, outputMinPitch,
outputMaxPitch)
# 4. (Optional) Plot the generated contours
if doPlotPitchSteps:
fromTime, fromVals = zip(*fromPitchData)
toTime, toVals = zip(*toPitchData)
plot_morphed_data.plotF0((fromTime, fromVals), (toTime, toVals),
mergedDataList,
join(pitchTierPath, "%s.png" % outputName))
def f0Morph(fromWavFN, pitchPath, stepList,
outputName, doPlotPitchSteps, fromPitchData, toPitchData,
outputMinPitch, outputMaxPitch, praatEXE, keepPitchRange=False,
keepAveragePitch=False):
'''
Resynthesizes the pitch track from a source to a target wav file
fromPitchData and toPitchData should be segmented according to the
portions that you want to morph. The two lists must have the same
number of sublists.
Occurs over a three-step process.
This function can act as a template for how to use the function
morph_sequence.morphChunkedDataLists to morph pitch contours or
other data.
By default, everything is morphed, but it is possible to maintain elements
of the original speaker's pitch (average pitch and pitch range) by setting
the appropriate flag)
'''
fromDuration = audio_scripts.getSoundFileDuration(fromWavFN)
# Iterative pitch tier data path
pitchTierPath = join(pitchPath, "pitchTiers")
resynthesizedPath = join(pitchPath, "f0_resynthesized_wavs")
for tmpPath in [pitchTierPath, resynthesizedPath]:
utils.makeDir(tmpPath)
# 1. Prepare the data for morphing - acquire the segments to merge
# (Done elsewhere, with the input fed into this function)
# 2. Morph the fromData to the toData
try:
finalOutputList = morph_sequence.morphChunkedDataLists(fromPitchData,
toPitchData,
stepList)
except IndexError:
raise MissingPitchDataException()
fromPitchData = [row for subList in fromPitchData for row in subList]
toPitchData = [row for subList in toPitchData for row in subList]
# 3. Save the pitch data and resynthesize the pitch
mergedDataList = []
for i in range(0, len(finalOutputList)):
outputDataList = finalOutputList[i]
if keepPitchRange is True:
outputDataList = morph_sequence.morphRange(outputDataList,
fromPitchData)
if keepAveragePitch is True:
outputDataList = morph_sequence.morphAveragePitch(outputDataList,
fromPitchData)
stepOutputName = "%s_%0.3g" % (outputName, stepList[i])
pitchFNFullPath = join(pitchTierPath, "%s.PitchTier" % stepOutputName)
outputFN = join(resynthesizedPath, "%s.wav" % stepOutputName)
pointObj = dataio.PointObject2D(outputDataList, dataio.PITCH,
0, fromDuration)
pointObj.save(pitchFNFullPath)
outputTime, outputVals = zip(*outputDataList)
mergedDataList.append((outputTime, outputVals))
praat_scripts.resynthesizePitch(praatEXE, fromWavFN, pitchFNFullPath,
outputFN, outputMinPitch,
outputMaxPitch)
# 4. (Optional) Plot the generated contours
if doPlotPitchSteps:
fromTime, fromVals = zip(*fromPitchData)
toTime, toVals = zip(*toPitchData)
plot_morphed_data.plotF0((fromTime, fromVals),
(toTime, toVals),
mergedDataList,
join(pitchTierPath,
"%s.png" % outputName))