encode() missing 1 required positional argument: 'src_mask' - Beam Decode about neural-transducer HOT 6 CLOSED

Check out the new master to get support of beam search with transformer. For transliteration, I would recommend using the transformer model. To speed up beam search, try using smaller decoding length --max_decode_len 32 or smaller beam size --decode_beam_size 3. To get the top-k output, you would need modify the return of beam search function to get the top-k prediction (instead of the best prediction)

and write that to file in the following function.

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Thank you for the pointer. I will try to implement it.

I am closing the ticket for time-being. Will open it again, in case I get stuck. Thanks again! 👍

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Even adding "src_mask" to line 364. also didn't help. New errors are poping up.

enc_hs = transducer.encode(src_sentence, src_mask)

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This is an example from my training data:

a c h z i g     a c h z g
v e r g l e i c h       v e r l i i c h
j o d l e r f e s t     j o d l e r f e s t
r o h r z u c k e r     g u t s c h

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Hi! It did not support beam search decoding with transformer at the moment due to the naive implementation of transformer with beam search would be much slower, and the gain is relatively small in preliminary experiment.

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Thank you @shijie-wu for the information.

It would be helpful if you could please help me with the below queries:

1. Any suggestions, what model from this list (soft,hard,approxihard,softinputfeed,largesoftinputfeed,approxihardinputfeed,hardmono,hmm,hmmfull,transformer,universaltransformer,tagtransformer,taguniversaltransformer) can I use for transliterations task (I have mentioned the example in the above comment)?
2. The current implementation for greedy decoding for transformers gives only one output (it takes only max probability). Is there any way if I can use the code of beam decoding implemented for other architectures and integrate with transformer pipeline?
3. At the end I want to have multiple possible outputs for my input (3-4 would good). Any suggestions how can I achieve so, with the current implementation?

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