Open joshhansen opened 5 years ago
it depends on tensor2tensor version, they break it every month
I am facing a similar issue as well:
> kittipeumpoonwong
S IH T IY P IY AH M P UW N W AO N W AO N W AO N W AO N W AO N W AO N W AO N W AO N W AO N W AO N W AO NG
Is this a model issue or a bug in the decoder code?
I tried using the suggestion that it might be due to the tensor2tensor lib but I am getting the same results for tensor2tensor==1.6.6 and tensor2tensor==1.7.0
@joshhansen I solved this issue by adjusting the beam size of the decoding from 1 to 5.
g2p-seq2seq --decode wordlist.txt --model_dir g2p-seq2seq-model-6.2-cmudict-nostress --return_beams --beam_size 5
ysl IY EH S EH S EH L
ysl IH S AH L
ysl IY EH S EH S EH S EH S EH S EH L
ysl IY EH S EH S EH S EH S EH L
ysl IY EH S EH S EH S EH L
ybr W AY B ER
ybr IH B ER
ybr IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY
ybr IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY
ybr IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY B IY
xsn EH K S EH S EH N
xsn EH K S EH S EH S EH S EH N
xsn EH K S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH N
xsn EH K S EH S EH S EH N
xsn EH K S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH S EH N
wsq D AH B AH L Y UW EH S K Y UW EH S K Y UW
wsq D AH B AH L Y UW EH S K Y UW
wsq D AH B AH L Y UW EH S K Y UW EH S K EY
wsq D AH B AH L Y UW EH S IY
wsq D AH B AH L Y UW EH S K Y UW EH S K
wsk D AH B AH L Y UW EH S K Y UW EH S K EY
wsk D AH B AH Y UW EH S K Y UW EH S K EY
wsk W EH S K
wsk D AH B AH L Y UW EH S K Y UW EH S K Y UW EH S K EY
wsk D AH B AH L Y UW EH S K Y UW EH S K Y UW EH S K Y
wjr W ER
wjr W AA R
wjr W AY R
wjr W JH UW N Y ER
wjr D AH B AH L Y UW JH UW JH IY AA R
lqr EH L K Y UW EH S AA R
lqr EH L K Y UW EH R
lqr EH L K Y UW EH L AA R
lqr EH L K Y UW EH L Y ER
lqr EH L K Y UW EH L Y UW AA R
I'm finding repeatedly that the g2p-seq2seq model generates strangely long pronunciations using the included model. For all sequences up to three letters long, the following strange outputs occur:
But these are all fairly arbitrary. Actual words get such results, too:
The recurring theme seems to be that for whatever reason these words get stuck in a loop for a long time.
These are pretty rare, but are so egregiously bad that it makes me wonder if there is a bug somewhere? If not, guidance would be appreciated on how to train a model that avoids these issues.