Datasets:
shibing624
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// ctc expects the blank label last
if (FLAGS_criterion == kCtcCriterion) {
tokenDict.addEntry(kBlankToken);
} else {
LOG(FATAL) << "CTC-trained model required for VAD-CTC.";
}
int numClasses = tokenDict.indexSize();
LOG(INFO) << "Number of classes (network): " << numClasses;
fl::lib::text::Dictionary wordDict;
text::LexiconMap lexicon;
if (!FLAGS_lexicon.empty()) {
lexicon = text::loadWords(FLAGS_lexicon, FLAGS_maxword);
wordDict = text::createWordDict(lexicon);
LOG(INFO) << "Number of words: " << wordDict.indexSize();
wordDict.setDefaultIndex(wordDict.getIndex(text::kUnkToken));
}
/* ===================== Create Dataset ===================== */
fl::lib::audio::FeatureParams featParams(
FLAGS_samplerate,
FLAGS_framesizems,
FLAGS_framestridems,
FLAGS_filterbanks,
FLAGS_lowfreqfilterbank,
FLAGS_highfreqfilterbank,
FLAGS_mfcccoeffs,
kLifterParam /* lifterparam */,
FLAGS_devwin /* delta window */,
FLAGS_devwin /* delta-delta window */);
featParams.useEnergy = false;
featParams.usePower = false;
featParams.zeroMeanFrame = false;
FeatureType featType =
getFeatureType(FLAGS_features_type, FLAGS_channels, featParams).second;
TargetGenerationConfig targetGenConfig(
FLAGS_wordseparator,
FLAGS_sampletarget,
FLAGS_criterion,
FLAGS_surround,
false /* isSeq2SeqCrit */,
FLAGS_replabel,
true /* skip unk */,
FLAGS_usewordpiece /* fallback2LetterWordSepLeft */,
!FLAGS_usewordpiece /* fallback2LetterWordSepLeft */);
auto inputTransform = inputFeatures(
featParams,
featType,
{FLAGS_localnrmlleftctx, FLAGS_localnrmlrightctx},
{});
auto targetTransform = targetFeatures(tokenDict, lexicon, targetGenConfig);
auto wordTransform = wordFeatures(wordDict);
int targetpadVal = kTargetPadValue;
int wordpadVal = kTargetPadValue;
auto ds = createDataset(
{FLAGS_test},
FLAGS_datadir,
1,
inputTransform,
targetTransform,
wordTransform,
std::make_tuple(0, targetpadVal, wordpadVal),
0,
1);
LOG(INFO) << "[Dataset] Dataset loaded.";
/* ===================== Build LM ===================== */
std::shared_ptr<text::LM> lm;
if (!FLAGS_lm.empty()) {
if (FLAGS_lmtype == "kenlm") {
lm = std::make_shared<text::KenLM>(FLAGS_lm, wordDict);
if (!lm) {
throw std::runtime_error(
"[LM constructing] Failed to load LM: " + FLAGS_lm);
}
} else {
throw std::runtime_error(
"[LM constructing] Invalid LM Type: " + FLAGS_lmtype);
}
}
/* ===================== Test ===================== */
int cnt = 0;
auto prefetchds =
loadPrefetchDataset(ds, FLAGS_nthread, false /* shuffle */, 0 /* seed */);
for (auto& sample : *prefetchds) {
auto rawEmission = fl::pkg::runtime::forwardSequentialModuleWithPadMask(
fl::input(sample[kInputIdx]), network, sample[kDurationIdx]);
auto sampleId = readSampleIds(sample[kSampleIdx]).front();
LOG(INFO) << "Processing sample ID " << sampleId;
// Hypothesis
auto tokenPrediction =
afToVector<int>(criterion->viterbiPath(rawEmission.array()));
auto letterPrediction = tknPrediction2Ltr(
tokenPrediction,
tokenDict,