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g2p module can't parse sentences like:
g2p("HTTP") ['T', 'AE1', 'P', 'T', 'IY1'] g2p("RFC") ['R', 'EH1', 'F', 'S', 'IY1']
and can't split words like taxidriver -> taxi driver.
taxidriver -> taxi driver
Is it possible to tune predict function of g2p to get a correct grapheme to phoneme conversion for abbreviations and composite words?
predict
g2p
The text was updated successfully, but these errors were encountered:
The only workaround I found is to add this in __call__ function:
__call__
self.exceptions = {'http', 'https', 'rfc'} elif word in self.exceptions: pron = [] w_len = len(word) for i in range(w_len): pron.extend(self.cmu[word[i]][0]) if i != w_len - 1: pron.extend([" "]) g2p("http, rfc") ['EY1', 'CH', ' ', 'T', 'IY1', ' ', 'T', 'IY1', ' ', 'P', 'IY1', ' ', ',', ' ', 'AA1', 'R', ' ', 'EH1', 'F', ' ', 'S', 'IY1']
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g2p module can't parse sentences like:
and can't split words like
taxidriver -> taxi driver
.Is it possible to tune
predict
function ofg2p
to get a correct grapheme to phoneme conversion for abbreviations and composite words?The text was updated successfully, but these errors were encountered: