nnet.py 文件源码

def decode(self, reader, writer):
        '''
        compute pseudo likelihoods the testing set

        Args:
            reader: a feature reader object to read features to decode
            writer: a writer object to write likelihoods
        '''

        #create a decoder
        decoder = Decoder(self.dnn, self.input_dim, reader.max_input_length)

        #read the prior
        prior = np.load(self.conf['savedir'] + '/prior.npy')

        #start tensorflow session
        config = tf.ConfigProto()
        config.gpu_options.allow_growth = True #pylint: disable=E1101
        with tf.Session(graph=decoder.graph, config=config):

            #load the model
            decoder.restore(self.conf['savedir'] + '/final')

            #feed the utterances one by one to the neural net
            while True:
                utt_id, utt_mat, looped = reader.get_utt()

                if looped:
                    break

                #compute predictions
                output = decoder(utt_mat)

                #get state likelihoods by dividing by the prior
                output = output/prior

                #floor the values to avoid problems with log
                np.where(output == 0, np.finfo(float).eps, output)

                #write the pseudo-likelihoods in kaldi feature format
                writer.write_next_utt(utt_id, np.log(output))

        #close the writer
        writer.close()