src/sphinx_lmtools/lm_eval.c

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/* -*- c-basic-offset: 4; indent-tabs-mode: nil -*- */
/* ====================================================================
 * Copyright (c) 2008 Carnegie Mellon University.  All rights 
 * reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * modification, are permitted provided that the following conditions
 * are met:
 *
 * 1. Redistributions of source code must retain the above copyright
 *    notice, this list of conditions and the following disclaimer. 
 *
 * 2. Redistributions in binary form must reproduce the above copyright
 *    notice, this list of conditions and the following disclaimer in
 *    the documentation and/or other materials provided with the
 *    distribution.
 *
 * This work was supported in part by funding from the Defense Advanced 
 * Research Projects Agency and the National Science Foundation of the 
 * United States of America, and the CMU Sphinx Speech Consortium.
 *
 * THIS SOFTWARE IS PROVIDED BY CARNEGIE MELLON UNIVERSITY ``AS IS'' AND 
 * ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, 
 * THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
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#include <logmath.h>
#include <ngram_model.h>
#include <cmd_ln.h>
#include <ckd_alloc.h>
#include <err.h>
#include <strfuncs.h>

#include <stdio.h>
#include <string.h>
#include <math.h>

static const arg_t defn[] = {
  { "-help",
    ARG_BOOLEAN,
    "no",
    "Shows the usage of the tool"},

  { "-logbase",
    ARG_FLOAT64,
    "1.0001",
    "Base in which all log-likelihoods calculated" },

  { "-lm",
    ARG_STRING,
    NULL,
    "Language model file"},

  { "-probdef",
    ARG_STRING,
    NULL,
    "Probability definition file for classes in LM"},

  { "-lmctlfn",
    ARG_STRING,
    NULL,
    "Control file listing a set of language models"},

  { "-lmname",
    ARG_STRING,
    NULL,
    "Name of language model in -lmctlfn to use for all utterances" },

  { "-lsn",
    ARG_STRING,
    NULL,
    "Transcription file to evaluate"},

  { "-text",
    ARG_STRING,
    "Text string to evaluate"},

  { "-mmap",
    ARG_BOOLEAN,
    "no",
    "Use memory-mapped I/O for reading binary LM files"},

  { "-lw",
    ARG_FLOAT32,
    "1.0",
    "Language model weight" },

  { "-wip",
    ARG_FLOAT32,
    "1.0",
    "Word insertion probability" },

  { "-uw",
    ARG_FLOAT32,
    "1.0",
    "Unigram probability weight (interpolated with uniform distribution)"},

  /* FIXME: Support -lmstartsym, -lmendsym, -lmctlfn, -ctl_lm */
  { NULL, 0, NULL, NULL }
};

static int
calc_entropy(ngram_model_t *lm, char **words, int32 n,
             int32 *out_n_ccs, int32 *out_n_oovs)
{
        int32 *wids;
        int32 startwid;
        int32 i, ch, nccs, noovs, unk;

        if (n == 0)
            return 0;

        unk = ngram_unknown_wid(lm);

        /* Reverse this array into an array of word IDs. */
        wids = ckd_calloc(n, sizeof(*wids));
        for (i = 0; i < n; ++i)
                wids[n-i-1] = ngram_wid(lm, words[i]);
        /* Skip <s> as it's a context cue (HACK, this should be configurable). */
        startwid = ngram_wid(lm, "<s>");

        /* Now evaluate the list of words in reverse using the
         * remainder of the array as the history. */
        ch = noovs = nccs = 0;
        for (i = 0; i < n; ++i) {
                int32 n_used;
                int32 prob;

                /* Skip <s> as it's a context cue (HACK, this should be configurable). */
                if (wids[i] == startwid) {
                        ++nccs;
                        continue;
                }
                /* Skip and count OOVs. */
                if (wids[i] == NGRAM_INVALID_WID || wids[i] == unk) {
                        ++noovs;
                        continue;
                }
                /* Sum up information for each N-gram */
                prob = ngram_ng_score(lm,
                                      wids[i], wids + i + 1,
                                      n - i - 1, &n_used);
                ch -= prob;
        }

        if (out_n_ccs) *out_n_ccs = nccs;
        if (out_n_oovs) *out_n_oovs = noovs;

        /* Calculate cross-entropy CH = - 1/N sum log P(W|H) */
        n -= (nccs + noovs);
        if (n <= 0)
            return 0;
        return ch / n;
}

static void
evaluate_file(ngram_model_t *lm, logmath_t *lmath, const char *lsnfn)
{
        FILE *fh;
        char line[256];
        int32 nccs, noovs, nwords;
        float64 ch, log_to_log2;;

        if ((fh = fopen(lsnfn, "r")) == NULL)
                E_FATAL_SYSTEM("failed to open transcript file %s", lsnfn);

        /* We have to keep ch in floating-point to avoid overflows, so
         * we might as well use log2. */
        log_to_log2 = log(logmath_get_base(lmath)) / log(2);
        nccs = noovs = nwords = 0;
        ch = 0.0;
        while (fgets(line, sizeof(line), fh)) {
                char **words;
                int32 n, tmp_ch, tmp_noovs, tmp_nccs;

                n = str2words(line, NULL, 0);
                if (n < 0)
                        E_FATAL("str2words(line, NULL, 0) = %d, should not happen\n", n);
                if (n == 0) /* Do nothing! */
                        continue;
                words = ckd_calloc(n, sizeof(*words));
                str2words(line, words, n);

                /* Remove any utterance ID (FIXME: has to be a single "word") */
                if (words[n-1][0] == '('
                    && words[n-1][strlen(words[n-1])-1] == ')')
                        n = n - 1;

                tmp_ch = calc_entropy(lm, words, n, &tmp_nccs, &tmp_noovs);

                ch += (float64) tmp_ch * (n - tmp_nccs - tmp_noovs) * log_to_log2;
                nccs += tmp_nccs;
                noovs += tmp_noovs;
                nwords += n;
                
                ckd_free(words);
        }

        ch /= (nwords - nccs - noovs);
        printf("cross-entropy: %f bits\n", ch);

        /* Calculate perplexity pplx = exp CH */
        printf("perplexity: %f\n", pow(2.0, ch));

        /* Report OOVs and CCs */
        printf("%d words evaluated\n", nwords);
        printf("%d OOVs (%.2f%%), %d context cues removed\n",
               noovs, (double)noovs / nwords * 100, nccs);
}

static void
evaluate_string(ngram_model_t *lm, logmath_t *lmath, const char *text)
{
        char *textfoo;
        char **words;
        int32 n, ch, noovs, nccs;

        /* Split it into an array of strings. */
        textfoo = ckd_salloc(text);
        n = str2words(textfoo, NULL, 0);
        if (n < 0)
                E_FATAL("str2words(textfoo, NULL, 0) = %d, should not happen\n", n);
        if (n == 0) /* Do nothing! */
                return;
        words = ckd_calloc(n, sizeof(*words));
        str2words(textfoo, words, n);

        ch = calc_entropy(lm, words, n, &nccs, &noovs);

        printf("input: %s\n", text);
        printf("cross-entropy: %f bits\n",
               ch * log(logmath_get_base(lmath)) / log(2));

        /* Calculate perplexity pplx = exp CH */
        printf("perplexity: %f\n", logmath_exp(lmath, ch));

        /* Report OOVs and CCs */
        printf("%d words evaluated\n", n);
        printf("%d OOVs, %d context cues removed\n",
              noovs, nccs);

        ckd_free(textfoo);
        ckd_free(words);
}

int
main(int argc, char *argv[])
{
        cmd_ln_t *config;
        ngram_model_t *lm = NULL;
        logmath_t *lmath;
        const char *lmfn, *probdefn, *lsnfn, *text;

        if ((config = cmd_ln_parse_r(NULL, defn, argc, argv, TRUE)) == NULL)
                return 1;

        /* Create log math object. */
        if ((lmath = logmath_init
             (cmd_ln_float64_r(config, "-logbase"), 0, 0)) == NULL) {
                E_FATAL("Failed to initialize log math\n");
        }

        /* Load the language model. */
        lmfn = cmd_ln_str_r(config, "-lm");
        if (lmfn == NULL
            || (lm = ngram_model_read(config, lmfn,
                                      NGRAM_AUTO, lmath)) == NULL) {
                E_FATAL("Failed to load language model from %s\n",
                        cmd_ln_str_r(config, "-lm"));
        }
        if ((probdefn = cmd_ln_str_r(config, "-probdef")) != NULL)
            ngram_model_read_classdef(lm, probdefn);
        ngram_model_apply_weights(lm,
                                  cmd_ln_float32_r(config, "-lw"),
                                  cmd_ln_float32_r(config, "-wip"),
                                  cmd_ln_float32_r(config, "-uw"));


        /* Now evaluate some text. */
        lsnfn = cmd_ln_str_r(config, "-lsn");
        text = cmd_ln_str_r(config, "-text");
        if (lsnfn) {
                evaluate_file(lm, lmath, lsnfn);
        }
        else if (text) {
                evaluate_string(lm, lmath, text);
        }

        return 0;
}

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