offline-transducer-greedy-search-nemo-decoder.cc
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// sherpa-onnx/csrc/offline-transducer-greedy-search-nemo-decoder.cc
//
// Copyright (c) 2024 Xiaomi Corporation
#include "sherpa-onnx/csrc/offline-transducer-greedy-search-nemo-decoder.h"
#include <algorithm>
#include <iterator>
#include <utility>
#include "sherpa-onnx/csrc/macros.h"
#include "sherpa-onnx/csrc/onnx-utils.h"
namespace sherpa_onnx {
static std::pair<Ort::Value, Ort::Value> BuildDecoderInput(
int32_t token, OrtAllocator *allocator) {
std::array<int64_t, 2> shape{1, 1};
Ort::Value decoder_input =
Ort::Value::CreateTensor<int32_t>(allocator, shape.data(), shape.size());
std::array<int64_t, 1> length_shape{1};
Ort::Value decoder_input_length = Ort::Value::CreateTensor<int32_t>(
allocator, length_shape.data(), length_shape.size());
int32_t *p = decoder_input.GetTensorMutableData<int32_t>();
int32_t *p_length = decoder_input_length.GetTensorMutableData<int32_t>();
p[0] = token;
p_length[0] = 1;
return {std::move(decoder_input), std::move(decoder_input_length)};
}
static OfflineTransducerDecoderResult DecodeOne(
const float *p, int32_t num_rows, int32_t num_cols,
OfflineTransducerNeMoModel *model, float blank_penalty) {
auto memory_info =
Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
OfflineTransducerDecoderResult ans;
int32_t vocab_size = model->VocabSize();
int32_t blank_id = vocab_size - 1;
int32_t max_symbols_per_frame = 10;
auto decoder_input_pair = BuildDecoderInput(blank_id, model->Allocator());
std::pair<Ort::Value, std::vector<Ort::Value>> decoder_output_pair =
model->RunDecoder(std::move(decoder_input_pair.first),
std::move(decoder_input_pair.second),
model->GetDecoderInitStates(1));
std::array<int64_t, 3> encoder_shape{1, num_cols, 1};
for (int32_t t = 0; t != num_rows; ++t) {
Ort::Value cur_encoder_out = Ort::Value::CreateTensor(
memory_info, const_cast<float *>(p) + t * num_cols, num_cols,
encoder_shape.data(), encoder_shape.size());
for (int32_t q = 0; q != max_symbols_per_frame; ++q) {
Ort::Value logit = model->RunJoiner(View(&cur_encoder_out),
View(&decoder_output_pair.first));
float *p_logit = logit.GetTensorMutableData<float>();
if (blank_penalty > 0) {
p_logit[blank_id] -= blank_penalty;
}
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 != blank_id) {
ans.tokens.push_back(y);
ans.timestamps.push_back(t);
decoder_input_pair = BuildDecoderInput(y, model->Allocator());
decoder_output_pair =
model->RunDecoder(std::move(decoder_input_pair.first),
std::move(decoder_input_pair.second),
std::move(decoder_output_pair.second));
} else {
break;
} // if (y != blank_id)
}
} // for (int32_t i = 0; i != num_rows; ++i)
return ans;
}
std::vector<OfflineTransducerDecoderResult>
OfflineTransducerGreedySearchNeMoDecoder::Decode(
Ort::Value encoder_out, Ort::Value encoder_out_length,
OfflineStream ** /*ss = nullptr*/, int32_t /*n= 0*/) {
auto shape = encoder_out.GetTensorTypeAndShapeInfo().GetShape();
int32_t batch_size = static_cast<int32_t>(shape[0]);
int32_t dim1 = static_cast<int32_t>(shape[1]);
int32_t dim2 = static_cast<int32_t>(shape[2]);
const int32_t *p_length = encoder_out_length.GetTensorData<int32_t>();
const float *p = encoder_out.GetTensorData<float>();
std::vector<OfflineTransducerDecoderResult> ans(batch_size);
for (int32_t i = 0; i != batch_size; ++i) {
const float *this_p = p + dim1 * dim2 * i;
int32_t this_len = p_length[i];
ans[i] = DecodeOne(this_p, this_len, dim2, model_, blank_penalty_);
}
return ans;
}
} // namespace sherpa_onnx