def __init__(self, model, logfile=None):
"""
Create a new sppasChunks instance.
@param model (str) the acoustic model directory name of the language of the text
@param modelL1 (str) the acoustic model directory name of the mother language of the speaker
@param logfile (sppasLog)
"""
sppasBase.__init__(self, logfile)
self.chunks = Chunks(model)
self._options["clean"] = True # Remove temporary files
self._options["silences"] = self.chunks.get_silences()
self._options["anchors"] = self.chunks.get_anchors()
self._options["ngram"] = self.chunks.get_ngram_init()
self._options["ngrammin"] = self.chunks.get_ngram_min()
self._options["windelay"] = self.chunks.get_windelay_init()
self._options["windelaymin"] = self.chunks.get_windelay_min()
self._options["chunksize"] = self.chunks.get_chunk_maxsize()
class sppasChunks(sppasBase):
"""
@author: Brigitte Bigi
@organization: Laboratoire Parole et Langage, Aix-en-Provence, France
@contact: [email protected]
@license: GPL, v3
@copyright: Copyright (C) 2011-2016 Brigitte Bigi
@summary: SPPAS integration of the Chunk Alignment automatic annotation.
"""
def __init__(self, model, logfile=None):
"""
Create a new sppasChunks instance.
@param model (str) the acoustic model directory name of the language of the text
@param modelL1 (str) the acoustic model directory name of the mother language of the speaker
@param logfile (sppasLog)
"""
sppasBase.__init__(self, logfile)
self.chunks = Chunks(model)
self._options["clean"] = True # Remove temporary files
self._options["silences"] = self.chunks.get_silences()
self._options["anchors"] = self.chunks.get_anchors()
self._options["ngram"] = self.chunks.get_ngram_init()
self._options["ngrammin"] = self.chunks.get_ngram_min()
self._options["windelay"] = self.chunks.get_windelay_init()
self._options["windelaymin"] = self.chunks.get_windelay_min()
self._options["chunksize"] = self.chunks.get_chunk_maxsize()
# ------------------------------------------------------------------------
# Methods to fix options
# ------------------------------------------------------------------------
def fix_options(self, options):
"""
Fix all options.
@param options (option)
"""
for opt in options:
key = opt.get_key()
if "clean" == key:
self.set_clean(opt.get_value())
elif "silences" == key:
self.chunks.set_silences(opt.get_value())
self._options["silences"] = opt.get_value()
elif "anchors" == key:
self.chunks.set_anchors(opt.get_value())
self._options["anchors"] = opt.get_value()
elif "ngram" == key:
self.chunks.set_ngram_init(opt.get_value())
self._options["ngram"] = opt.get_value()
elif "ngrammin" == key:
self.chunks.set_ngram_min(opt.get_value())
self._options["ngrammin"] = opt.get_value()
elif "windelay" == key:
self.chunks.set_windelay_init(opt.get_value())
self._options["windelay"] = opt.get_value()
elif "windelaymin" == key:
self.chunks.set_windelay_min(opt.get_value())
self._options["windelaymin"] = opt.get_value()
elif "chunksize" == key:
self.chunks.set_chunk_maxsize(opt.get_value())
self._options["chunksize"] = opt.get_value()
else:
raise KeyError("Unknown key option: %s" % key)
# ----------------------------------------------------------------------
def set_clean(self, clean):
"""
Fix the clean option.
@param clean (bool - IN) If clean is set to True then temporary files
will be removed.
"""
self._options["clean"] = clean
# ----------------------------------------------------------------------
def get_phonestier(self, trsinput):
"""
Return the tier with phonetization or None.
"""
if trsinput.GetSize() == 1 and trsinput[0].GetName().lower() == "rawtranscription":
return trsinput[0]
if trsinput.GetSize() == 1 and trsinput[0].GetName().lower() == "phonetization":
if trsinput[0].IsTimeInterval():
return None
return trsinput[0]
return None
# ------------------------------------------------------------------------
def get_tokenstier(self, trsinput):
"""
Return the tier with tokens, or None.
In case of EOT, 2 tiers with tokens are available: std and faked.
Priority is given to std.
"""
if trsinput.GetSize() == 1 and trsinput[0].GetName().lower() == "tokenization":
return trsinput[0]
return None
# ------------------------------------------------------------------------
def run(self, phonesname, tokensname, audioname, outputfilename):
"""
Execute SPPAS Chunks alignment.
@param phonesname (str - IN) file containing the phonetization
@param tokensname (str - IN) file containing the tokenization
@param audioname (str - IN) Audio file name
@param outputfilename (str - IN) the file name with the result
@return Transcription
"""
self.print_options()
self.print_diagnosis(audioname, phonesname, tokensname)
# Get the tiers to be time-aligned
# ---------------------------------------------------------------
trsinput = annotationdata.io.read(phonesname)
phontier = self.get_phonestier(trsinput)
if phontier is None:
raise IOError("No tier with the raw phonetization was found.")
try:
trsinputtok = annotationdata.io.read(tokensname)
toktier = self.get_tokenstier(trsinputtok)
except Exception:
raise IOError("No tier with the raw tokenization was found.")
# Prepare data
# -------------------------------------------------------------
inputaudio = fileutils.fix_audioinput(audioname)
workdir = fileutils.fix_workingdir(inputaudio)
if self._options["clean"] is False:
self.print_message("The working directory is: %s" % workdir, indent=3, status=None)
# Processing...
# ---------------------------------------------------------------
try:
trsoutput = self.chunks.create_chunks(inputaudio, phontier, toktier, workdir)
except Exception as e:
self.print_message(str(e))
self.print_message("WORKDIR=%s" % workdir)
if self._options["clean"] is True:
shutil.rmtree(workdir)
raise
# Set media
# --------------------------------------------------------------
extm = os.path.splitext(audioname)[1].lower()[1:]
media = Media(gen_id(), audioname, "audio/" + extm)
trsoutput.AddMedia(media)
for tier in trsoutput:
tier.SetMedia(media)
# Save results
# --------------------------------------------------------------
try:
self.print_message("Save automatic chunk alignment: ", indent=3)
# Save in a file
annotationdata.io.write(outputfilename, trsoutput)
except Exception:
if self._options["clean"] is True:
shutil.rmtree(workdir)
raise
# Clean!
# --------------------------------------------------------------
# if the audio file was converted.... remove the tmpaudio
if inputaudio != audioname:
os.remove(inputaudio)
# Remove the working directory we created
if self._options["clean"] is True:
shutil.rmtree(workdir)