Go to the source code of this file.
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| kaldi |
| This code computes Goodness of Pronunciation (GOP) and extracts phone-level pronunciation feature for mispronunciations detection tasks, the reference:
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| kaldi::nnet2 |
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void | FormatNnetInput (const Nnet &nnet, const std::vector< NnetExample > &data, Matrix< BaseFloat > *mat) |
| Takes the input to the nnet for a minibatch of examples, and formats as a single matrix. More...
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BaseFloat | TotalNnetTrainingWeight (const std::vector< NnetExample > &egs) |
| Returns the total weight summed over all the examples... More...
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double | ComputeNnetObjf (const Nnet &nnet, const std::vector< NnetExample > &examples, double *tot_accuracy=NULL) |
| Computes objective function over a minibatch. More...
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double | DoBackprop (const Nnet &nnet, const std::vector< NnetExample > &examples, Nnet *nnet_to_update, double *tot_accuracy=NULL) |
| This function computes the objective function and either updates the model or adds to parameter gradients. More...
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double | DoBackprop (const Nnet &nnet, const std::vector< NnetExample > &examples, Matrix< BaseFloat > *examples_formatted, Nnet *nnet_to_update, double *tot_accuracy=NULL) |
| This version of DoBackprop allows you to separately call FormatNnetInput and provide the result to DoBackprop; this can be useful when using GPUs because the call to FormatNnetInput can be in a separate thread from the one that uses the GPU. More...
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double | ComputeNnetGradient (const Nnet &nnet, const std::vector< NnetExample > &examples, int32 batch_size, Nnet *gradient) |
| ComputeNnetGradient is mostly used to compute gradients on validation sets; it divides the example into batches and calls DoBackprop() on each. More...
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double | ComputeNnetObjf (const Nnet &nnet, const std::vector< NnetExample > &examples, int32 minibatch_size, double *tot_accuracy=NULL) |
| This version of ComputeNnetObjf breaks up the examples into multiple minibatches to do the computation. More...
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