path: root/kaldi_io/tools/kaldi_to_nerv.cpp

#include <cstdio>
#include <fstream>
#include <string>
#include <cstring>
#include <cassert>

char token[1024];
char output[1024];
double mat[4096][4096];
int main(int argc, char **argv) {
    std::ofstream fout;
    fout.open(argv[1]);
    int cnt = 0;
    bool shift;
    while (scanf("%s", token) != EOF)
    {
        int nrow, ncol;
        int i, j;
        if (strcmp(token, "<AffineTransform>") == 0)
        {
            double lrate, blrate, mnorm;
            scanf("%d %d", &ncol, &nrow);
            scanf("%s %lf %s %lf %s %lf",
                    token, &lrate, token, &blrate, token, &mnorm);
            scanf("%s", token);
            assert(*token == '[');
            printf("%d %d\n", nrow, ncol);
            for (j = 0; j < ncol; j++)
                for (i = 0; i < nrow; i++)
                    scanf("%lf", mat[i] + j);
            long base = fout.tellp();
            sprintf(output, "%16d", 0);
            fout << output;
            sprintf(output, "{type=\"nerv.LinearTransParam\",id=\"affine%d_ltp\"}\n",
                    cnt);
            fout << output;
            sprintf(output, "%d %d\n", nrow, ncol);
            fout << output;
            for (i = 0; i < nrow; i++)
            {
                for (j = 0; j < ncol; j++)
                    fout << mat[i][j] << " ";
                fout << std::endl;
            }
            long length = fout.tellp() - base;
            fout.seekp(base);
            sprintf(output, "[%13lu]\n", length);
            fout << output;
            fout.seekp(0, std::ios_base::end);
            scanf("%s", token);
            assert(*token == ']');
            if (scanf("%s", token) == 1 && *token == '[')
            {
                base = fout.tellp();
                for (j = 0; j < ncol; j++)
                    scanf("%lf", mat[0] + j);
                sprintf(output, "%16d", 0);
                fout << output;
                sprintf(output, "{type=\"nerv.BiasParam\",id=\"affine%d_bp\"}\n",
                        cnt);
                fout << output;
                sprintf(output, "1 %d\n", ncol);
                fout << output;
                for (j = 0; j < ncol; j++)
                    fout << mat[0][j] << " ";
                fout << std::endl;
                length = fout.tellp() - base;
                fout.seekp(base);
                sprintf(output, "[%13lu]\n", length);
                fout << output;
                fout.seekp(0, std::ios_base::end);
                cnt++;
            }
        }
        else if ((shift = (strcmp(token, "<AddShift>") == 0)) ||
                strcmp(token, "<Rescale>") == 0)
        {
            double lrate, blrate, mnorm;
            scanf("%d %d", &ncol, &ncol);
            scanf("%s %lf",
                    token, &lrate);
            scanf("%s", token);
            assert(*token == '[');
            printf("%d\n", ncol);
            for (j = 0; j < ncol; j++)
                    scanf("%lf", mat[0] + j);
            long base = fout.tellp();
            sprintf(output, "%16d", 0);
            fout << output;
            sprintf(output, "{type=\"nerv.BiasParam\",id=\"%s%d\"}\n",
                    shift ? "bias" : "window",
                    cnt);
            fout << output;
            sprintf(output, "%d %d\n", 1, ncol);
            fout << output;
            for (j = 0; j < ncol; j++)
                fout << mat[0][j] << " ";
            fout << std::endl;
            long length = fout.tellp() - base;
            fout.seekp(base);
            sprintf(output, "[%13lu]\n", length);
            fout << output;
            fout.seekp(0, std::ios_base::end);
            scanf("%s", token);
            assert(*token == ']');
        }
    }
    return 0;
}