online-transducer-greedy-search-decoder.cc
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// sherpa/csrc/online-transducer-greedy-search-decoder.cc
//
// Copyright (c) 2023 Xiaomi Corporation
#include "sherpa-onnx/csrc/online-transducer-greedy-search-decoder.h"
#include <assert.h>
#include <algorithm>
#include <utility>
#include <vector>
#include "sherpa-onnx/csrc/onnx-utils.h"
namespace sherpa_onnx {
static Ort::Value GetFrame(Ort::Value *encoder_out, int32_t t) {
std::vector<int64_t> encoder_out_shape =
encoder_out->GetTensorTypeAndShapeInfo().GetShape();
assert(encoder_out_shape[0] == 1);
int32_t encoder_out_dim = encoder_out_shape[2];
auto memory_info =
Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
std::array<int64_t, 2> shape{1, encoder_out_dim};
return Ort::Value::CreateTensor(
memory_info,
encoder_out->GetTensorMutableData<float>() + t * encoder_out_dim,
encoder_out_dim, shape.data(), shape.size());
}
OnlineTransducerDecoderResult
OnlineTransducerGreedySearchDecoder::GetEmptyResult() {
int32_t context_size = model_->ContextSize();
int32_t blank_id = 0; // always 0
OnlineTransducerDecoderResult r;
r.tokens.resize(context_size, blank_id);
return r;
}
void OnlineTransducerGreedySearchDecoder::StripLeadingBlanks(
OnlineTransducerDecoderResult *r) {
int32_t context_size = model_->ContextSize();
auto start = r->tokens.begin() + context_size;
auto end = r->tokens.end();
r->tokens = std::vector<int64_t>(start, end);
}
void OnlineTransducerGreedySearchDecoder::Decode(
Ort::Value encoder_out,
std::vector<OnlineTransducerDecoderResult> *result) {
std::vector<int64_t> encoder_out_shape =
encoder_out.GetTensorTypeAndShapeInfo().GetShape();
if (encoder_out_shape[0] != result->size()) {
fprintf(stderr,
"Size mismatch! encoder_out.size(0) %d, result.size(0): %d\n",
static_cast<int32_t>(encoder_out_shape[0]),
static_cast<int32_t>(result->size()));
exit(-1);
}
if (result->size() != 1) {
fprintf(stderr, "only batch size == 1 is implemented. Given: %d",
static_cast<int32_t>(result->size()));
exit(-1);
}
auto &hyp = (*result)[0].tokens;
int32_t num_frames = encoder_out_shape[1];
int32_t vocab_size = model_->VocabSize();
Ort::Value decoder_input = model_->BuildDecoderInput(hyp);
Ort::Value decoder_out = model_->RunDecoder(std::move(decoder_input));
for (int32_t t = 0; t != num_frames; ++t) {
Ort::Value cur_encoder_out = GetFrame(&encoder_out, t);
Ort::Value logit =
model_->RunJoiner(std::move(cur_encoder_out), Clone(&decoder_out));
const float *p_logit = logit.GetTensorData<float>();
auto y = static_cast<int32_t>(std::distance(
static_cast<const float *>(p_logit),
std::max_element(static_cast<const float *>(p_logit),
static_cast<const float *>(p_logit) + vocab_size)));
if (y != 0) {
hyp.push_back(y);
decoder_input = model_->BuildDecoderInput(hyp);
decoder_out = model_->RunDecoder(std::move(decoder_input));
}
}
}
} // namespace sherpa_onnx