int main(int argc, char *argv[]) {
try {
struct timeval start,stop,diff;
memset(&start,0,sizeof(struct timeval));
memset(&stop,0,sizeof(struct timeval));
memset(&diff,0,sizeof(struct timeval));
gettimeofday(&start,0);
printf("hello world xxx\n");
using namespace kaldi;
using namespace fst;
typedef kaldi::int32 int32;
typedef kaldi::int64 int64;
const char *usage =
"Reads in wav file(s) and simulates online decoding with neural nets\n"
"(nnet3 setup), with optional iVector-based speaker adaptation and\n"
"optional endpointing. Note: some configuration values and inputs are\n"
"set via config files whose filenames are passed as options\n"
"\n"
"Usage: online2-wav-nnet3-latgen-faster [options] <nnet3-in> <fst-in> "
"<spk2utt-rspecifier> <wav-rspecifier> <lattice-wspecifier>\n"
"The spk2utt-rspecifier can just be <utterance-id> <utterance-id> if\n"
"you want to decode utterance by utterance.\n";
ParseOptions po(usage);
std::string word_syms_rxfilename;
// feature_opts includes configuration for the iVector adaptation,
// as well as the basic features.
OnlineNnet2FeaturePipelineConfig feature_opts;
nnet3::NnetSimpleLoopedComputationOptions decodable_opts;
LatticeFasterDecoderConfig decoder_opts;
OnlineEndpointConfig endpoint_opts;
BaseFloat chunk_length_secs = 0.18;
bool do_endpointing = false;
bool online = true;
KALDI_LOG << " hello world test " <<endl;
po.Register("chunk-length", &chunk_length_secs,
"Length of chunk size in seconds, that we process. Set to <= 0 "
"to use all input in one chunk.");
po.Register("word-symbol-table", &word_syms_rxfilename,
"Symbol table for words [for debug output]");
po.Register("do-endpointing", &do_endpointing,
"If true, apply endpoint detection");
po.Register("online", &online,
"You can set this to false to disable online iVector estimation "
"and have all the data for each utterance used, even at "
"utterance start. This is useful where you just want the best "
"results and don't care about online operation. Setting this to "
"false has the same effect as setting "
"--use-most-recent-ivector=true and --greedy-ivector-extractor=true "
"in the file given to --ivector-extraction-config, and "
"--chunk-length=-1.");
po.Register("num-threads-startup", &g_num_threads,
"Number of threads used when initializing iVector extractor.");
feature_opts.Register(&po);
decodable_opts.Register(&po);
decoder_opts.Register(&po);
endpoint_opts.Register(&po);
po.Read(argc, argv);
if (po.NumArgs() != 5) {
po.PrintUsage();
return 1;
}
std::string nnet3_rxfilename = po.GetArg(1),
fst_rxfilename = po.GetArg(2),
spk2utt_rspecifier = po.GetArg(3),
wav_rspecifier = po.GetArg(4),
clat_wspecifier = po.GetArg(5);
OnlineNnet2FeaturePipelineInfo feature_info(feature_opts);
if (!online) {
feature_info.ivector_extractor_info.use_most_recent_ivector = true;
feature_info.ivector_extractor_info.greedy_ivector_extractor = true;
chunk_length_secs = -1.0;
}
TransitionModel trans_model;
nnet3::AmNnetSimple am_nnet;
{
bool binary;
Input ki(nnet3_rxfilename, &binary);
trans_model.Read(ki.Stream(), binary);
am_nnet.Read(ki.Stream(), binary);
SetBatchnormTestMode(true, &(am_nnet.GetNnet()));
SetDropoutTestMode(true, &(am_nnet.GetNnet()));
nnet3::CollapseModel(nnet3::CollapseModelConfig(), &(am_nnet.GetNnet()));
}
// this object contains precomputed stuff that is used by all decodable
// objects. It takes a pointer to am_nnet because if it has iVectors it has
// to modify the nnet to accept iVectors at intervals.
nnet3::DecodableNnetSimpleLoopedInfo decodable_info(decodable_opts,
&am_nnet);
fst::Fst<fst::StdArc> *decode_fst = ReadFstKaldiGeneric(fst_rxfilename);
fst::SymbolTable *word_syms = NULL;
if (word_syms_rxfilename != "")
if (!(word_syms = fst::SymbolTable::ReadText(word_syms_rxfilename)))
KALDI_ERR << "Could not read symbol table from file "
<< word_syms_rxfilename;
int32 num_done = 0, num_err = 0;
double tot_like = 0.0;
int64 num_frames = 0;
SequentialTokenVectorReader spk2utt_reader(spk2utt_rspecifier);
RandomAccessTableReader<WaveHolder> wav_reader(wav_rspecifier);
CompactLatticeWriter clat_writer(clat_wspecifier);
OnlineTimingStats timing_stats;
for (; !spk2utt_reader.Done(); spk2utt_reader.Next()) {
std::string spk = spk2utt_reader.Key();
const std::vector<std::string> &uttlist = spk2utt_reader.Value();
OnlineIvectorExtractorAdaptationState adaptation_state(
feature_info.ivector_extractor_info);
for (size_t i = 0; i < uttlist.size(); i++) {
std::string utt = uttlist[i];
if (!wav_reader.HasKey(utt)) {
KALDI_WARN << "Did not find audio for utterance " << utt;
num_err++;
continue;
}
const WaveData &wave_data = wav_reader.Value(utt);
// get the data for channel zero (if the signal is not mono, we only
// take the first channel).
SubVector<BaseFloat> data(wave_data.Data(), 0);
OnlineNnet2FeaturePipeline feature_pipeline(feature_info);
feature_pipeline.SetAdaptationState(adaptation_state);
OnlineSilenceWeighting silence_weighting(
trans_model,
feature_info.silence_weighting_config,
decodable_opts.frame_subsampling_factor);
SingleUtteranceNnet3Decoder decoder(decoder_opts, trans_model,
decodable_info,
*decode_fst, &feature_pipeline);
OnlineTimer decoding_timer(utt);
BaseFloat samp_freq = wave_data.SampFreq();
int32 chunk_length;
if (chunk_length_secs > 0) {
chunk_length = int32(samp_freq * chunk_length_secs);
if (chunk_length == 0) chunk_length = 1;
} else {
chunk_length = std::numeric_limits<int32>::max();
}
int32 samp_offset = 0;
std::vector<std::pair<int32, BaseFloat> > delta_weights;
while (samp_offset < data.Dim()) {
int32 samp_remaining = data.Dim() - samp_offset;
int32 num_samp = chunk_length < samp_remaining ? chunk_length
: samp_remaining;
SubVector<BaseFloat> wave_part(data, samp_offset, num_samp);
feature_pipeline.AcceptWaveform(samp_freq, wave_part);
samp_offset += num_samp;
decoding_timer.WaitUntil(samp_offset / samp_freq);
if (samp_offset == data.Dim()) {
// no more input. flush out last frames
feature_pipeline.InputFinished();
}
if (silence_weighting.Active() &&
feature_pipeline.IvectorFeature() != NULL) {
silence_weighting.ComputeCurrentTraceback(decoder.Decoder());
silence_weighting.GetDeltaWeights(feature_pipeline.NumFramesReady(),
&delta_weights);
feature_pipeline.IvectorFeature()->UpdateFrameWeights(delta_weights);
}
decoder.AdvanceDecoding();
if (do_endpointing && decoder.EndpointDetected(endpoint_opts)) {
break;
}
}
decoder.FinalizeDecoding();
CompactLattice clat;
bool end_of_utterance = true;
decoder.GetLattice(end_of_utterance, &clat);
GetDiagnosticsAndPrintOutput(utt, word_syms, clat,
&num_frames, &tot_like);
decoding_timer.OutputStats(&timing_stats);
// In an application you might avoid updating the adaptation state if
// you felt the utterance had low confidence. See lat/confidence.h
feature_pipeline.GetAdaptationState(&adaptation_state);
// we want to output the lattice with un-scaled acoustics.
BaseFloat inv_acoustic_scale =
1.0 / decodable_opts.acoustic_scale;
ScaleLattice(AcousticLatticeScale(inv_acoustic_scale), &clat);
clat_writer.Write(utt, clat);
printf("step num_done = %d\r\n", num_done);
KALDI_LOG << "Decoded utterance " << utt;
num_done++;
gettimeofday(&stop,0);
time_substract(&diff,&start,&stop);
printf("Total time : %d s,%d us\n",(int)diff.tv_sec,(int)diff.tv_usec);
}
}
//timing_stats.Print(online);
printf("step a\r\n");
KALDI_LOG << "Decoded " << num_done << " utterances, "
<< num_err << " with errors.";
printf("step b\r\n");
KALDI_LOG << "Overall likelihood per frame was " << (tot_like / num_frames)
<< " per frame over " << num_frames << " frames.";
delete decode_fst;
delete word_syms; // will delete if non-NULL.
return (num_done != 0 ? 0 : 1);
} catch(const std::exception& e) {
std::cerr << e.what();
return -1;
}
} /
kaldi nnet3 online2-wav-nnet3-latgen-faster decoder
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转载自blog.csdn.net/lbaihao/article/details/84860978
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