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Also, let run MNIST tests against the default (end-user) data location. |
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| BrainScript | ||
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| Python | ||
| README.md | ||
README.md
This example demonstrates the use of CNTK for grapheme-to-phoneme (letter-to-sound) conversion using a sequence-to-sequence model with attention, using the CMUDict dictionary.
The code supports a number of alternative configurations. As configured currently, it implements
- a 3-hidden layer unidirectional LSTM encoder network, all hidden dimensions are 512
- a 3-hidden layer unidirectional LSTM decoder network, all hidden dimensions are 512
- encoder state is passed to the decoder by means of attention, with projection dimension 128 and maximum input length of 20 tokens
- embedding is disabled (because the 'vocabulary' of the task, letters and phonemes, is very small)
- beam decoder with beam width 3
To Use
Modify the following in G2P.cntk as needed:
- pathnames
- deviceId to specify CPU (-1) or GPU (>=0 or "auto")
Run:
- command line:
cntk configFile=Examples/SequenceToSequence/CMUDict/BrainScript/G2P.cntk RunRootDir=g2p - VS Debugger:
configFile=$(SolutionDir)Examples/SequenceToSequence/CMUDict/BrainScript/G2P.cntk RunRootDir=$(SolutionDir)Examples/SequenceToSequence/CMUDict