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[src,scripts,egs] Misc small fixes, including fix to TDNN-F results in mini_librispeech (kaldi-asr#2565)
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‎.travis.yml‎

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@@ -21,6 +21,7 @@ addons:
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- gfortran-4.9
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- liblapack-dev
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- clang-3.8
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- sox
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branches:
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only:

‎egs/mini_librispeech/s5/local/chain/tuning/run_tdnn_1h.sh‎

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@@ -1,25 +1,26 @@
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#!/bin/bash
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# 1h is as 1g but a re-tuned model based on resnet-style TDNN-F layers with
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# bypass connections. Below, 1h2 is just a rerun of 1h with a different --affix
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# option, to give some idea of the run-to-run variation.
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# local/chain/compare_wer.sh --online exp/chain/tdnn1g_sp exp/chain/tdnn1h_sp exp/chain/tdnn1h2_sp
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# System tdnn1g_sp tdnn1h_sp tdnn1h2_sp
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#WER dev_clean_2 (tgsmall) 13.50 13.18 13.04
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# [online:] 13.52 13.03 12.97
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#WER dev_clean_2 (tglarge) 9.79 9.18 9.16
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# [online:] 9.79 9.29 9.24
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# Final train prob -0.0460 -0.0531 -0.0590
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# Final valid prob -0.0892 -0.0844 -0.0865
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# Final train prob (xent) -1.1739 -1.5244 -1.7771
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# Final valid prob (xent) -1.4487 -1.7447 -1.9611
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# Num-params 6234672 3512112 3512112
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# steps/info/chain_dir_info.pl exp/chain/tdnn1{g,h,h2}_sp
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# bypass connections. Below, 1h2 and 1h3 are just reruns of 1h with different
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# --affix options, to give some idea of the run-to-run variation.
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# local/chain/compare_wer.sh --online exp/chain/tdnn1g_sp exp/chain/tdnn1h_sp exp/chain/tdnn1h2_sp exp/chain/tdnn1h3_sp
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# System tdnn1g_sp tdnn1h_sp tdnn1h2_sp tdnn1h3_sp
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#WER dev_clean_2 (tgsmall) 13.50 12.09 12.23 12.19
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# [online:] 13.52 12.11 12.25 12.14
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#WER dev_clean_2 (tglarge) 9.79 8.59 8.64 8.73
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# [online:] 9.79 8.76 8.65 8.78
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# Final train prob -0.0460 -0.0493 -0.0490 -0.0493
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# Final valid prob -0.0892 -0.0805 -0.0803 -0.0813
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# Final train prob (xent) -1.1739 -1.1730 -1.1742 -1.1749
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# Final valid prob (xent) -1.4487 -1.3872 -1.3857 -1.3913
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# Num-params 6234672 5207856 5207856 5207856
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# exp/chain/tdnn1g_sp: num-iters=25 nj=2..5 num-params=6.2M dim=40+100->2328 combine=-0.056->-0.055 (over 3) xent:train/valid[15,24,final]=(-1.50,-1.23,-1.17/-1.73,-1.52,-1.45) logprob:train/valid[15,24,final]=(-0.063,-0.051,-0.046/-0.101,-0.094,-0.089)
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# exp/chain/tdnn1h_sp: num-iters=34 nj=2..5 num-params=3.5M dim=40+100->2328 combine=-0.055->-0.050 (over 4) xent:train/valid[21,33,final]=(-1.97,-1.57,-1.52/-2.11,-1.78,-1.74) logprob:train/valid[21,33,final]=(-0.080,-0.061,-0.053/-0.106,-0.096,-0.084)
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# exp/chain/tdnn1h2_sp: num-iters=34 nj=2..5 num-params=3.5M dim=40+100->2328 combine=-0.062->-0.056 (over 4) xent:train/valid[21,33,final]=(-2.21,-1.78,-1.78/-2.34,-1.96,-1.96) logprob:train/valid[21,33,final]=(-0.086,-0.066,-0.059/-0.110,-0.098,-0.087)
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# exp/chain/tdnn1h_sp: num-iters=34 nj=2..5 num-params=5.2M dim=40+100->2328 combine=-0.049->-0.046 (over 4) xent:train/valid[21,33,final]=(-1.50,-1.22,-1.17/-1.66,-1.44,-1.39) logprob:train/valid[21,33,final]=(-0.068,-0.055,-0.049/-0.097,-0.088,-0.080)
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# exp/chain/tdnn1h2_sp: num-iters=34 nj=2..5 num-params=5.2M dim=40+100->2328 combine=-0.049->-0.046 (over 4) xent:train/valid[21,33,final]=(-1.50,-1.22,-1.17/-1.67,-1.43,-1.39) logprob:train/valid[21,33,final]=(-0.068,-0.055,-0.049/-0.096,-0.087,-0.080)
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# exp/chain/tdnn1h3_sp: num-iters=34 nj=2..5 num-params=5.2M dim=40+100->2328 combine=-0.050->-0.046 (over 4) xent:train/valid[21,33,final]=(-1.51,-1.23,-1.17/-1.67,-1.45,-1.39) logprob:train/valid[21,33,final]=(-0.068,-0.055,-0.049/-0.097,-0.089,-0.081)
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# Set -e here so that we catch if any executable fails immediately
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set -euo pipefail

‎egs/wsj/s5/steps/decode.sh‎

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@@ -80,7 +80,7 @@ if [ $(basename $model) != final.alimdl ] ; then
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fi
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for f in $sdata/1/feats.scp $sdata/1/cmvn.scp $model $graphdir/HCLG.fst; do
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[ ! -f $f ] && echo "decode.sh: no such file $f" && exit 1;
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[ ! -f $f ] && echo "$0: Error: no such file $f" && exit 1;
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done
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if [ -f $srcdir/final.mat ]; then feat_type=lda; else feat_type=delta; fi
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case $feat_type in
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delta) feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | add-deltas $delta_opts ark:- ark:- |";;
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lda) feats="ark,s,cs:apply-cmvn $cmvn_opts --utt2spk=ark:$sdata/JOB/utt2spk scp:$sdata/JOB/cmvn.scp scp:$sdata/JOB/feats.scp ark:- | splice-feats $splice_opts ark:- ark:- | transform-feats $srcdir/final.mat ark:- ark:- |";;
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*) echo "Invalid feature type $feat_type" && exit 1;
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*) echo "$0: Error: Invalid feature type $feat_type" && exit 1;
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esac
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if [ ! -z "$transform_dir" ]; then # add transforms to features...
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echo "Using fMLLR transforms from $transform_dir"
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[ ! -f $transform_dir/trans.1 ] && echo "Expected $transform_dir/trans.1 to exist."
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[ ! -s $transform_dir/num_jobs ] && \
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echo "$0: expected $transform_dir/num_jobs to contain the number of jobs." && exit 1;
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echo "$0: Error: expected $transform_dir/num_jobs to contain the number of jobs." && exit 1;
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nj_orig=$(cat $transform_dir/num_jobs)
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if [ $nj -ne $nj_orig ]; then
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# Copy the transforms into an archive with an index.
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if [ $stage -le 0 ]; then
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if [ -f "$graphdir/num_pdfs" ]; then
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[ "`cat $graphdir/num_pdfs`" -eq `am-info --print-args=false $model | grep pdfs | awk '{print $NF}'` ] || \
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{ echo "Mismatch in number of pdfs with $model"; exit 1; }
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{ echo "$0: Error: Mismatch in number of pdfs with $model"; exit 1; }
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fi
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$cmd --num-threads $num_threads JOB=1:$nj $dir/log/decode.JOB.log \
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gmm-latgen-faster$thread_string --max-active=$max_active --beam=$beam --lattice-beam=$lattice_beam \
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if ! $skip_scoring ; then
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[ ! -x local/score.sh ] && \
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echo "Not scoring because local/score.sh does not exist or not executable." && exit 1;
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echo "$0: Not scoring because local/score.sh does not exist or not executable." && exit 1;
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local/score.sh --cmd "$cmd" $scoring_opts $data $graphdir $dir ||
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{ echo "$0: Scoring failed. (ignore by '--skip-scoring true')"; exit 1; }
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{ echo "$0: Error: scoring failed. (ignore by '--skip-scoring true')"; exit 1; }
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fi
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exit 0;

‎egs/wsj/s5/steps/nnet3/remove_egs.sh‎

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../nnet2/remove_egs.sh

‎src/nnet3/convolution.cc‎

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temp_rows = new_num_t_out * computation->num_images;
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BaseFloat new_num_megabytes = (4 * temp_rows * temp_cols) / 1000000.0;
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// make sure we're within the memory limit.
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if (new_num_megabytes > megabyte_limit) {
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if (new_num_megabytes > 1.01 * megabyte_limit) {
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KALDI_WARN << "Memory consumed in convolution is more than requested "
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<< "(maybe very long time sequence?)";
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}

‎src/nnet3/nnet-analyze.cc‎

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@@ -1441,22 +1441,35 @@ int64 GetMaxMemoryUse(const NnetComputation &computation) {
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max_memory_use = 0;
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int32 num_commands = computation.commands.size(),
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num_submatrices = computation.submatrices.size();
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// the vector 'num_compressed_bytes' is used to remember the number of bytes
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// in the compressed matrices for each submatrix (this will only be used for
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// those that correspond to a 'whole matrix). It's needed because the
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// decompression command doesn't tell us what compression type was used for
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// that matrix.
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std::vector<int32> num_compressed_bytes(num_submatrices, -100000000);
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for (int32 command_index = 0; command_index < num_commands; ++command_index) {
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const NnetComputation::Command &c = computation.commands[command_index];
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int64 this_num_bytes = -100000000,
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this_compressed_num_bytes = -10000000;
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if (c.arg1 >= 0 && c.arg1 < num_submatrices) {
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// if arg1 could plausibly be a sub-matrix index...
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const NnetComputation::SubMatrixInfo &submat_info =
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computation.submatrices[c.arg1];
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this_num_bytes = static_cast<int64>(sizeof(BaseFloat)) *
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submat_info.num_rows * submat_info.num_cols;
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this_compressed_num_bytes =
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((c.arg2 == static_cast<int32>(kCompressedMatrixInt8) ||
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c.arg2 == static_cast<int32>(kCompressedMatrixUint8)) ?
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1 : 2) * static_cast<int64>(submat_info.num_rows) *
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submat_info.num_cols;
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if (c.command_type == kCompressMatrix) {
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this_compressed_num_bytes =
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((c.arg2 == static_cast<int32>(kCompressedMatrixInt8) ||
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c.arg2 == static_cast<int32>(kCompressedMatrixUint8)) ?
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1 : 2) * static_cast<int64>(submat_info.num_rows) *
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submat_info.num_cols;
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num_compressed_bytes[c.arg1] = this_compressed_num_bytes;
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} else if (c.command_type == kDecompressMatrix) {
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this_compressed_num_bytes = num_compressed_bytes[c.arg1];
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}
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}
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switch (c.command_type) {
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case kAllocMatrix:

‎src/nnet3/nnet-optimize-utils.cc‎

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/** @param [in] nnet The neural net the computation is for.
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@param [in] memory_compression_level. The level of compression:
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0 = no compression (the constructor should not be calle with this value).
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0 = no compression (the constructor should not be called with this value).
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1 = compression that doesn't affect the results (but still takes time).
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2 = compression that affects the results only very slightly
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3 = compression that affects the results a little more.

‎tools/extras/check_dependencies.sh‎

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add_packages zlib-devel zlib1g-dev zlib-devel
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fi
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for f in make automake autoconf patch grep bzip2 gzip wget git; do
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for f in make automake autoconf patch grep bzip2 gzip wget git sox; do
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if ! which $f >&/dev/null; then
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echo "$0: $f is not installed."
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add_packages $f $f $f
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pythonok=false
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fi
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(
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(
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#Use a subshell so that sourcing env.sh does not have an influence on the rest of the script
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[ -f ./env.sh ] && . ./env.sh
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if $pythonok && ! which python2 >&/dev/null; then
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fi
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if [[ -f $PWD/python/.use_default_python && -f $PWD/python/python ]]; then
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rm $PWD/python/python
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rm $PWD/python/python
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fi
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if $pythonok && which python >&/dev/null && [[ ! -f $PWD/python/.use_default_python ]]; then
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version=`python 2>&1 --version | awk '{print $2}' `
119119
if [[ $version != "2.7"* ]] ; then
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echo "$0: WARNING python 2.7 is not the default python. We fixed this by adding a correct symlink more prominently on the path."
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echo "$0: If you really want to use python $version as default, add an empty file $PWD/python/.use_default_python and run this script again."
121+
echo "$0: If you really want to use python $version as default, add an empty file $PWD/python/.use_default_python and run this script again."
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mkdir -p $PWD/python
123123
ln -s $(which python2.7) $PWD/python/python
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echo "export PATH=$PWD/python:\${PATH}" >> env.sh

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