nnet3bin/nnet3-xvector-compute.cc

将特征在xvector神经网络模型中前向传播，并写出输出向量。我们将说话人识别的特定神经网络结构的输出向量或embedding称之为"Xvector"。该网络结构包括：帧级别的多个前馈层、帧级别之上的聚合层、统计池化层以及段级别的附加层。通常在统计池化层之后的输出层提取xvector。默认情况下，每个语句生成一个xvector。根据需要，可以chunk中提取多个xvector并求平均，以生成单个矢量。

   

Usage: nnet3-xvector-compute [options] <raw-nnet-in> <features-rspecifier> <vector-wspecifier>

e.g.: nnet3-xvector-compute final.raw scp:feats.scp ark:nnet_prediction.ark

   

对一个语音特征chunk，生成一个xvector

static void RunNnetComputation(const MatrixBase<BaseFloat> &features,

const Nnet &nnet, CachingOptimizingCompiler *compiler,

Vector<BaseFloat> *xvector) {

ComputationRequest request;

request.need_model_derivative = false;

request.store_component_stats = false;

request.inputs.push_back(

IoSpecification("input", 0, features.NumRows()));

IoSpecification output_spec;

output_spec.name = "output";

output_spec.has_deriv = false;

   

将output-node所请求的输出Cindex索引数限制为1，这样，一个chunk（segment）只输出一个结果，即xvector

output_spec.indexes.resize(1);

   

request.outputs.resize(1);

request.outputs[0].Swap(&output_spec);

std::shared_ptr<const NnetComputation> computation(std::move(compiler->Compile(request)));

Nnet *nnet_to_update = NULL; // we're not doing any update.

NnetComputer computer(NnetComputeOptions(), *computation,

nnet, nnet_to_update);

CuMatrix<BaseFloat> input_feats_cu(features);

computer.AcceptInput("input", &input_feats_cu);

computer.Run();

CuMatrix<BaseFloat> cu_output;

//输出的cu_output为行数为1的矩阵

computer.GetOutputDestructive("output", &cu_output);

xvector->Resize(cu_output.NumCols());

//取输出矩阵的第一行向量作为xvector

xvector->CopyFromVec(cu_output.Row(0));

}

   

ParseOptions po(usage);

Timer timer;

   

NnetSimpleComputationOptions opts;

CachingOptimizingCompilerOptions compiler_config;

   

opts.acoustic_scale = 1.0; // by default do no scaling in this recipe.

   

std::string use_gpu = "no";

int32 chunk_size = -1,

min_chunk_size = 100;

//若帧组不足一个chunk，则对input进行左右padding。

bool pad_input = true;

   

opts.Register(&po);

compiler_config.Register(&po);

   

po.Register("use-gpu", &use_gpu,

"yes|no|optional|wait, only has effect if compiled with CUDA");

po.Register("chunk-size", &chunk_size,

"If set, extracts xectors from specified chunk-size, and averages. "

"If not set, extracts an xvector from all available features.");

po.Register("min-chunk-size", &min_chunk_size,

"Minimum chunk-size allowed when extracting xvectors.");

po.Register("pad-input", &pad_input, "If true, duplicate the first and "

"last frames of the input features as required to equal min-chunk-size.");

   

po.Read(argc, argv);

   

if (po.NumArgs() != 3) {

po.PrintUsage();

exit(1);

}

   

#if HAVE_CUDA==1

CuDevice::Instantiate().SelectGpuId(use_gpu);

#endif

   

std::string nnet_rxfilename = po.GetArg(1),

feature_rspecifier = po.GetArg(2),

vector_wspecifier = po.GetArg(3);

   

Nnet nnet;

ReadKaldiObject(nnet_rxfilename, &nnet);

SetBatchnormTestMode(true, &nnet);

SetDropoutTestMode(true, &nnet);

CollapseModel(CollapseModelConfig(), &nnet);

   

CachingOptimizingCompiler compiler(nnet, opts.optimize_config, compiler_config);

   

BaseFloatVectorWriter vector_writer(vector_wspecifier);

   

int32 num_success = 0, num_fail = 0;

int64 frame_count = 0;

int32 xvector_dim = nnet.OutputDim("output");

   

SequentialBaseFloatMatrixReader feature_reader(feature_rspecifier);

for (; !feature_reader.Done(); feature_reader.Next()) {

std::string utt = feature_reader.Key();

const Matrix<BaseFloat> &features (feature_reader.Value());

if (features.NumRows() == 0) {

KALDI_WARN << "Zero-length utterance: " << utt;

num_fail++;

continue;

}

int32 num_rows = features.NumRows(),

feat_dim = features.NumCols(),

this_chunk_size = chunk_size;

if (!pad_input && num_rows < min_chunk_size) {

KALDI_WARN << "Minimum chunk size of " << min_chunk_size

<< " is greater than the number of rows "

<< "in utterance: " << utt;

num_fail++;

continue;

} else if (num_rows < chunk_size) {

KALDI_LOG << "Chunk size of " << chunk_size << " is greater than "

<< "the number of rows in utterance: " << utt

<< ", using chunk size of " << num_rows;

this_chunk_size = num_rows;

} else if (chunk_size == -1) {

this_chunk_size = num_rows;

}

//num_chunks=1

int32 num_chunks = ceil(

num_rows / static_cast<BaseFloat>(this_chunk_size));

Vector<BaseFloat> xvector_avg(xvector_dim, kSetZero);

BaseFloat tot_weight = 0.0;

   

// Iterate over the feature chunks.

for (int32 chunk_indx = 0; chunk_indx < num_chunks; chunk_indx++) {

//若接近输入的末尾，需要考虑剩余的帧是否足以凑足一个chunk。

int32 offset = std::min(

this_chunk_size, num_rows - chunk_indx * this_chunk_size);

if (!pad_input && offset < min_chunk_size)

continue;

SubMatrix<BaseFloat> sub_features(

features, chunk_indx * this_chunk_size, offset, 0, feat_dim);

Vector<BaseFloat> xvector;

tot_weight += offset;

   

// Pad input if the offset is less than the minimum chunk size

if (pad_input && offset < min_chunk_size) {

Matrix<BaseFloat> padded_features(min_chunk_size, feat_dim);

int32 left_context = (min_chunk_size - offset) / 2;

int32 right_context = min_chunk_size - offset - left_context;

for (int32 i = 0; i < left_context; i++) {

padded_features.Row(i).CopyFromVec(sub_features.Row(0));

}

for (int32 i = 0; i < right_context; i++) {

padded_features.Row(min_chunk_size - i - 1).CopyFromVec(sub_features.Row(offset - 1));

}

padded_features.Range(left_context, offset, 0, feat_dim).CopyFromMat(sub_features);

//一个chunk生成一个xvector

RunNnetComputation(padded_features, nnet, &compiler, &xvector);

} else {

RunNnetComputation(sub_features, nnet, &compiler, &xvector);

}

//将所有chunk的xvectors进行累加

xvector_avg.AddVec(offset, xvector);

}

//求所有chunk的平均xvector

xvector_avg.Scale(1.0 / tot_weight);

vector_writer.Write(utt, xvector_avg);

   

frame_count += features.NumRows();

num_success++;

}