{"push_id":1244541540,"size":247,"distinct_size":225,"ref":"refs/heads/multi-recipe","head":"b5f35a70694c96a498a0cfe8d449d0618220471f","before":"a9fd68d6d6355ff30b43c7089ab17a24ec65413b","commits":[{"sha":"85b13f0be65bf9e40c8019a934c487fe80a630c9","author":{"name":"Xingyu Na","email":"2af6abebd31a4edb3487c94a58b07df219cca885@gmail.com"},"message":"Add biphone chain recipes for swbd (B)LSTM setups","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/85b13f0be65bf9e40c8019a934c487fe80a630c9"},{"sha":"61e7bfc9273da843e828018949fc2477ab5952b7","author":{"name":"Daniel Povey","email":"7175d5eac112cbb2a96bf4210565ecc79aff5856@gmail.com"},"message":"Minor improvements to documentation of data-preparation, to clarify about mono wav files and utterance/speaker order agreement.","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/61e7bfc9273da843e828018949fc2477ab5952b7"},{"sha":"fc5b00054aab98f6208d41bdcbb200cfaaf31fed","author":{"name":"Daniel Povey","email":"7175d5eac112cbb2a96bf4210565ecc79aff5856@gmail.com"},"message":"Add an extra check to mkgraph.sh to verify that the FST it produced is nonempty.","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/fc5b00054aab98f6208d41bdcbb200cfaaf31fed"},{"sha":"8a85b3b8df9b05f1ab76d84349c08e8bcef8cff4","author":{"name":"Daniel Povey","email":"7175d5eac112cbb2a96bf4210565ecc79aff5856@gmail.com"},"message":"Change cp -rH to cp -RH in some scripts in steps/, to work around BSD/Darwin limitation.","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/8a85b3b8df9b05f1ab76d84349c08e8bcef8cff4"},{"sha":"0b079042dda9a310f2d8f52c8fab2f7a95db0aeb","author":{"name":"Daniel Povey","email":"7175d5eac112cbb2a96bf4210565ecc79aff5856@gmail.com"},"message":"Add a few extra checks to utils/data/{get_frame_shift,get_utt2dur}.sh","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/0b079042dda9a310f2d8f52c8fab2f7a95db0aeb"},{"sha":"c72ac5a458261c304337c4e6d90c563d136c6b96","author":{"name":"Daniel Povey","email":"7175d5eac112cbb2a96bf4210565ecc79aff5856@gmail.com"},"message":"Merge pull request #805 from naxingyu/add-biphone-chain-lstm-swbd\n\nAdd biphone chain recipes for swbd (B)LSTM setups","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/c72ac5a458261c304337c4e6d90c563d136c6b96"},{"sha":"526fcad0a8a739be27687097b29dc9055d03db29","author":{"name":"Daniel Povey","email":"7175d5eac112cbb2a96bf4210565ecc79aff5856@gmail.com"},"message":"change nnet3 chain training script to be tolerant of missing models for combination.","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/526fcad0a8a739be27687097b29dc9055d03db29"},{"sha":"569da136fb5879da574bbf4abaf095d5d2015d83","author":{"name":"vesis84","email":"49ad3d9d96d714cec178ee74dc612c588b9ac38c@gmail.com"},"message":"cpplit.py : relocaing script to misc/maintenance,\n\n- adding script to fix whitespace in C++ code,","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/569da136fb5879da574bbf4abaf095d5d2015d83"},{"sha":"1e8c7b539cdb2274d23aa38b3f8a25c674ced522","author":{"name":"Harish Mallidi","email":"f605e13ed99fc42ce7cf234a6747308a3d64244a@gmail.com"},"message":"added option to compress features while resaving\nreduces space requirement in /tmp folders","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/1e8c7b539cdb2274d23aa38b3f8a25c674ced522"},{"sha":"6a8550745c48bd34f53ca8a2c382fd35579613fa","author":{"name":"Karel Vesely","email":"49ad3d9d96d714cec178ee74dc612c588b9ac38c@gmail.com"},"message":"Merge pull request #814 from mallidi/nnet1-copy-feats\n\nadded option to compress features while resaving in nnet/{train,pretrain_dbn}.sh","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/6a8550745c48bd34f53ca8a2c382fd35579613fa"},{"sha":"d8b196951c1cf3437b3fa6cd76edbbc0542b3db9","author":{"name":"Daniel Povey","email":"7175d5eac112cbb2a96bf4210565ecc79aff5856@gmail.com"},"message":"Merge pull request #812 from vesis84/cpplint_relocate\n\ncpplit.py : relocaing script to misc/maintenance (agreed with Dan 18.4. by email),","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/d8b196951c1cf3437b3fa6cd76edbbc0542b3db9"},{"sha":"b19ab6b3ed670fd1742dd1e4209e85b73548332f","author":{"name":"Shiyin Kang","email":"a425d3cbcfaaa0b6480f5ff2190680f964edf0ff@gmail.com"},"message":"_vector_reduce kernel template for CuVector Sum, Max and Min.\n\nAdd test to choose min length of vectors to be reduced on GPU.\n\nNew:\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 16, speed was 0.000886179 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 32, speed was 0.00119834 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 64, speed was 0.00182674 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 128, speed was 0.00721178 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 256, speed was 0.0166563 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 1024, speed was 0.0626621 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 2048, speed was 0.108495 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 4096, speed was 0.162914 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 8192, speed was 0.248687 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 16384, speed was 0.491677 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 32768, speed was 0.931507 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 65536, speed was 1.75797 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 16, speed was 0.00116685 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 32, speed was 0.00229885 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 64, speed was 0.00430313 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 128, speed was 0.00840191 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 256, speed was 0.0156417 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 1024, speed was 0.051799 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 2048, speed was 0.09064 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 4096, speed was 0.122844 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 8192, speed was 0.241084 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 16384, speed was 0.468114 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 32768, speed was 0.859946 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 65536, speed was 1.53817 gigaflops.\n\nOld:\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 16, speed was 0.000461866 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 32, speed was 0.000936284 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 64, speed was 0.00180461 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 128, speed was 0.00350883 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 256, speed was 0.00700597 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 1024, speed was 0.0273135 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 2048, speed was 0.0529984 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 4096, speed was 0.0930953 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 8192, speed was 0.149376 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 16384, speed was 0.197131 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 32768, speed was 0.492249 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<float>, for dim = 65536, speed was 0.657485 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 16, speed was 0.000406633 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 32, speed was 0.000836551 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 64, speed was 0.00167463 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 128, speed was 0.00338708 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 256, speed was 0.00668978 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 1024, speed was 0.0253556 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 2048, speed was 0.0510465 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 4096, speed was 0.081494 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 8192, speed was 0.156451 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 16384, speed was 0.311666 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 32768, speed was 0.545834 gigaflops.\nLOG (TestCuVectorSum():cu-vector-speed-test.cc:72) For CuVector::Sum<double>, for dim = 65536, speed was 0.914985 gigaflops.\n\nfix vector sum bug.\n\ndel old kernels\n\ncorrect way for inline.\n\nonly do this when we have cuda.","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/b19ab6b3ed670fd1742dd1e4209e85b73548332f"},{"sha":"b7a35e2abfc5ef3c488fe1a0a1b3b61eb5adaa30","author":{"name":"Joshua Milas","email":"b53684993f25a164725a6ca5981ce7aa465ccf8b@gmail.com"},"message":"Added a patch for pthreads and updated the INSTALL.md for windows\nUpdated guide to say it works on MSVS 2015.\nFixed some grammar as well.","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/b7a35e2abfc5ef3c488fe1a0a1b3b61eb5adaa30"},{"sha":"64523b927a87f0a97ec4bfed5daf4a6d4b43c8a1","author":{"name":"Jan \"yenda\" Trmal","email":"f3b4420c2ad3e981044e0d0bb4237359b92cb368@gmail.com"},"message":"Merge pull request #815 from DeepHorizons/update_windows_install_guide\n\nMSVS 2015 pthread patch and guide update","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/64523b927a87f0a97ec4bfed5daf4a6d4b43c8a1"},{"sha":"1b7e44b3b6d8153d7878aa65d1df554c5ff8d565","author":{"name":"Daniel Povey","email":"7175d5eac112cbb2a96bf4210565ecc79aff5856@gmail.com"},"message":"two minor fixes: fix to formulas in comments in chain-denominator.h (thanks: hossein hadian), and to import in nnet3_chain_lib.py (thanks: sameer khurana)","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/1b7e44b3b6d8153d7878aa65d1df554c5ff8d565"},{"sha":"59a38c59353e3ba3c1006a3485bf08ae05e11f15","author":{"name":"Daniel Povey","email":"7175d5eac112cbb2a96bf4210565ecc79aff5856@gmail.com"},"message":"Add new #include to kaldi-math.cc to fix cygwin compilation problem [thanks to Prijazen Dom]","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/59a38c59353e3ba3c1006a3485bf08ae05e11f15"},{"sha":"07ab4f63c2d9a44c527253983e49a7fc5c7ead6d","author":{"name":"Shiyin Kang","email":"a425d3cbcfaaa0b6480f5ff2190680f964edf0ff@gmail.com"},"message":"vec max/min/sum tests cover both long and short vector cases; use the smallest length threshold tested on different machines.","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/07ab4f63c2d9a44c527253983e49a7fc5c7ead6d"},{"sha":"6229763dc1a177d6bd9c0f443d601e157184bac3","author":{"name":"Daniel Povey","email":"7175d5eac112cbb2a96bf4210565ecc79aff5856@gmail.com"},"message":"Merge pull request #813 from kangshiyin/reduce-vector\n\nImplement CuVector::Sum, Max, Min with a _vector_reudce kernel template.","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/6229763dc1a177d6bd9c0f443d601e157184bac3"},{"sha":"a831e9af866877a12505effd964d5992f56311b3","author":{"name":"Shiyin Kang","email":"a425d3cbcfaaa0b6480f5ff2190680f964edf0ff@gmail.com"},"message":"Speed tests and unit tests for cu matrix max/min/sum\n\nOld sum:\nLOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<float>, for dim = 16, speed was 0.00340611 gigaflops, result = 26.2034\nLOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<float>, for dim = 32, speed was 0.0141018 gigaflops, result = 17.9455\nLOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<float>, for dim = 64, speed was 0.0575425 gigaflops, result = 31.6159\nLOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<float>, for dim = 128, speed was 0.229418 gigaflops, result = 81.3117\nLOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<float>, for dim = 256, speed was 0.778943 gigaflops, result = 572.224\nLOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<float>, for dim = 512, speed was 3.11055 gigaflops, result = 39.6668\nLOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<float>, for dim = 1024, speed was 7.50506 gigaflops, result = 518.842\nLOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<double>, for dim = 16, speed was 0.00216499 gigaflops, result = -2.71724\nLOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<double>, for dim = 32, speed was 0.00863257 gigaflops, result = 43.261\nLOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<double>, for dim = 64, speed was 0.0513208 gigaflops, result = 30.3323\nLOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<double>, for dim = 128, speed was 0.20313 gigaflops, result = -152.665\nLOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<double>, for dim = 256, speed was 0.759338 gigaflops, result = -355.256\nLOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<double>, for dim = 512, speed was 2.60159 gigaflops, result = 744.185\nLOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<double>, for dim = 1024, speed was 7.2258 gigaflops, result = -857.049","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/a831e9af866877a12505effd964d5992f56311b3"},{"sha":"8816d95d9010ef80fb0320abd0d48ca91cbb780e","author":{"name":"Shiyin Kang","email":"a425d3cbcfaaa0b6480f5ff2190680f964edf0ff@gmail.com"},"message":"CuMatrixBase::Max, Min and Sum by reduce_mat_cols\n\n    New sum:\n    LOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<float>, for dim = 16, speed was 0.00954969 gigaflops, result = 26.2034\n    LOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<float>, for dim = 32, speed was 0.0400381 gigaflops, result = 17.9455\n    LOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<float>, for dim = 64, speed was 0.152595 gigaflops, result = 31.6159\n    LOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<float>, for dim = 128, speed was 0.546459 gigaflops, result = 81.3117\n    LOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<float>, for dim = 256, speed was 1.94432 gigaflops, result = 572.224\n    LOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<float>, for dim = 512, speed was 6.23377 gigaflops, result = 39.6669\n    LOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<float>, for dim = 1024, speed was 16.0119 gigaflops, result = 518.841\n    LOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<double>, for dim = 16, speed was 0.00916145 gigaflops, result = -2.71724\n    LOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<double>, for dim = 32, speed was 0.0366853 gigaflops, result = 43.261\n    LOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<double>, for dim = 64, speed was 0.144912 gigaflops, result = 30.3323\n    LOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<double>, for dim = 128, speed was 0.501765 gigaflops, result = -152.665\n    LOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<double>, for dim = 256, speed was 1.83353 gigaflops, result = -355.256\n    LOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<double>, for dim = 512, speed was 5.609 gigaflops, result = 744.185\n    LOG (TestCuMatrixSum():cu-matrix-speed-test.cc:57) For CuMatrix::TestCuMatrixSum<double>, for dim = 1024, speed was 12.6693 gigaflops, result = -857.049","distinct":false,"url":"https://api.github.com/repos/guoguo12/kaldi/commits/8816d95d9010ef80fb0320abd0d48ca91cbb780e"}]}
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