def run(self, d, binary=True): #Execute Kaldi command inputting dict d and return KaldiFormat handler ark_in = KaldiArk('/tmp/pipe.ark', 'wb') for key, mat in d: ark_in.write(key, mat) ark_in.p.close() return self.run_file('/tmp/pipe.ark', binary)
def run(self, d, binary=True): #Execute Kaldi command inputting dict d and return KaldiFormat handler ark_in = KaldiArk('/tmp/pipe.ark','wb') for key, mat in d: ark_in.write(key, mat) ark_in.p.close() return self.run_file('/tmp/pipe.ark', binary)
KaldiScp('/data2/tawara/work/ttic/MyPython/src/kaldi/timit/data/fbank/train_tr90/feats.scp'), \ offsets = offset) N_test=x_test.shape[0] N_train=x_train.shape[0] print "Applying batch normalization" for i in xrange(0, N_train, batchsize): x_batch = x_train[i : i + batchsize] model.forward(x_batch,test=False) logger.info("Extracting final layer") save_to = args.save_to print 'Saving output layer to %s' % filename+'.post.ark' ark=KaldiArk(filename+'.post.ark','wb') for key in frame_index: x_batch = x_test[frame_index[key][0] : frame_index[key][1]] ark.write(key, cuda.to_cpu(model.forward(x_batch,test=True).data)) elif args.cmd == "train": for d in option_dict: print d + ": " + str(option_dict[d]) ratio = float(args.ratio) n_epoch = args.epoch seed = args.seed batchsize = args.batchsize model_to = args.save_to numpy.random.seed(seed) context_length = args.context offset = range(-context_length, context_length+1) ali_to_pdf=KaldiCommand('bin/ali-to-pdf', option='/data2/tawara/work/ttic/MyPython/src/kaldi/timit/exp/tri3_ali/final.mdl')