def __init__(self, cfg, commands, asr_hypotheses_in, slu_hypotheses_out,
close_event):
"""
Initialises an SLU object according to the configuration (cfg['SLU']
is the relevant section), and stores ends of pipes to other processes.
Arguments:
cfg: a Config object specifying the configuration to use
commands: our end of a pipe (multiprocessing.Pipe) for receiving
commands
asr_hypotheses_in: our end of a pipe (multiprocessing.Pipe) for
receiving audio frames (from ASR)
slu_hypotheses_out: our end of a pipe (multiprocessing.Pipe) for
sending SLU hypotheses
"""
multiprocessing.Process.__init__(self)
# Save the configuration.
self.cfg = cfg
# Save the pipe ends.
self.commands = commands
self.asr_hypotheses_in = asr_hypotheses_in
self.slu_hypotheses_out = slu_hypotheses_out
self.close_event = close_event
# Load the SLU.
self.slu = slu_factory(cfg)
def __init__(self, cfg, tts_preproc_output=False):
super(TextHub, self).__init__(cfg)
self.slu = None
self.dm = None
self.nlg = None
self.tts = None
self.slu = slu_factory(cfg)
dm_type = get_dm_type(cfg)
self.dm = dm_factory(dm_type, cfg)
self.dm.new_dialogue()
nlg_type = get_nlg_type(cfg)
self.nlg = nlg_factory(nlg_type, cfg)
if tts_preproc_output:
tts_type = get_tts_type(cfg)
self.tts = tts_factory(tts_type, cfg)
def __init__(self, cfg, tts_preproc_output=False):
super(TextHub, self).__init__(cfg)
self.slu = None
self.dm = None
self.nlg = None
self.tts = None
self.slu = slu_factory(cfg)
dm_type = get_dm_type(cfg)
self.dm = dm_factory(dm_type, cfg)
self.dm.new_dialogue()
nlg_type = get_nlg_type(cfg)
self.nlg = nlg_factory(nlg_type, cfg)
if tts_preproc_output:
tts_type = get_tts_type(cfg)
self.tts = tts_factory(tts_type, cfg)
def get_slu_dialogue_acts():
slu = slu_factory(cfg)
das = {}
da_types = set()
slots = set()
for clser in slu.parsed_classifiers:
da = slu.parsed_classifiers[clser]
da_types.add(da.dat)
da.name = str(da.name)
slots.add(da.name)
if da.dat in das:
if da.name not in das[da.dat]:
das[da.dat].append(da.name)
else:
das[da.dat] = [da.name]
with open('dialogue_acts_slots.csv', 'w') as f:
f.write('%s\n'%','.join(da_types))
f.write('%s\n\n'%','.join(slots))
for k in das:
f.write('%s, %s\n'%(k, ', '.join(das[k])))
pdb.set_trace()