def write_audio_tracks(self, inputaudio, units, diralign, silence=0.):
"""
Write the first channel of an audio file into separated track files.
Re-sample to 16000 Hz, 16 bits.
@param inputaudio (src - IN) File name of the audio file.
@param units (list - IN) List of tuples (start-time,end-time) of tracks.
@param diralign (str - IN) Directory to write audio tracks.
@param silence float - IN) Duration of a silence to surround the tracks.
"""
channel = autils.extract_audio_channel( inputaudio,0 )
channel = autils.format_channel( channel,16000,2 )
for track,u in enumerate(units):
(s,e) = u
trackchannel = autils.extract_channel_fragment( channel, s, e, silence)
trackname = self._tracknames.audiofilename(diralign, track+1)
autils.write_channel(trackname, trackchannel)
def create_chunks(self, inputaudio, phontier, toktier, diralign):
"""
Create time-aligned tiers from raw intput tiers.
@param phontier (Tier - IN) the tier with phonetization
@param toktier (Tier - IN) the tier with tokenization to split
@param diralign (str - IN) the directory to work.
"""
trsoutput = Transcription("Chunks")
# Extract the audio channel
channel = autils.extract_audio_channel( inputaudio,0 )
channel = autils.format_channel( channel,16000,2 )
# Extract the lists of tokens and their corresponding pronunciations
pronlist = self._tier2raw( phontier,map=True ).split()
toklist = self._tier2raw( toktier, map=False ).split()
if len(pronlist) != len(toklist):
raise IOError("Inconsistency between the number of items in phonetization %d and tokenization %d."%(len(pronlist),len(toklist)))
# At a first stage, we'll find anchors.
anchortier = AnchorTier()
anchortier.set_duration( channel.get_duration() )
anchortier.set_extdelay(1.)
anchortier.set_outdelay(0.5)
# Search silences and use them as anchors.
if self.SILENCES is True:
anchortier.append_silences( channel )
# Estimates the speaking rate (amount of tokens/sec. in average)
self._spkrate.eval_from_duration( channel.get_duration(), len(toklist) )
# Multi-pass ASR to find anchors
A = -1 # number of anchors in the preceding pass
N = self.N # decreasing N-gram value
W = self.W # decreasing window length
while A != anchortier.GetSize() and anchortier.check_holes_ntokens( self.NBT ) is False:
anchortier.set_windelay( W )
A = anchortier.GetSize()
logging.debug(" =========================================================== ")
logging.debug(" Number of anchors: %d"%A)
logging.debug(" N: %d"%N)
logging.debug(" W: %d"%W)
# perform ASR and append new anchors in the anchor tier (if any)
self._asr(toklist, pronlist, anchortier, channel, diralign, N)
# append the anchor tier as intermediate result
if self.ANCHORS is True and A != anchortier.GetSize():
self._append_tier(anchortier,trsoutput)
annotationdata.io.write( os.path.join(diralign,"ANCHORS-%d.xra"%anchortier.GetSize()),trsoutput )
# prepare next pass
W = max(W-1., self.WMIN)
N = max(N-1, self.NMIN)
# Then, anchors are exported as tracks.
tiert = anchortier.export(toklist)
tiert.SetName("Chunks-Tokenized")
tierp = anchortier.export(pronlist)
tierp.SetName("Chunks-Phonetized")
trsoutput.Append(tiert)
trsoutput.Append(tierp)
return trsoutput