7f72c13d9a
* Don't reset model state and feature extractor on endpointing * support passing decoding_method from commandline * Add modified_beam_search to Python API * fix C API example * Fix style issues
127 lines
4.1 KiB
C++
127 lines
4.1 KiB
C++
// sherpa-onnx/csrc/online-transducer-greedy-search-decoder.cc
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//
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// Copyright (c) 2023 Xiaomi Corporation
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#include "sherpa-onnx/csrc/online-transducer-greedy-search-decoder.h"
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#include <algorithm>
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#include <utility>
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#include <vector>
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#include "sherpa-onnx/csrc/macros.h"
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#include "sherpa-onnx/csrc/onnx-utils.h"
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namespace sherpa_onnx {
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static void UseCachedDecoderOut(
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const std::vector<OnlineTransducerDecoderResult> &results,
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Ort::Value *decoder_out) {
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std::vector<int64_t> shape =
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decoder_out->GetTensorTypeAndShapeInfo().GetShape();
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float *dst = decoder_out->GetTensorMutableData<float>();
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for (const auto &r : results) {
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if (r.decoder_out) {
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const float *src = r.decoder_out.GetTensorData<float>();
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std::copy(src, src + shape[1], dst);
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}
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dst += shape[1];
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}
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}
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static void UpdateCachedDecoderOut(
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OrtAllocator *allocator, const Ort::Value *decoder_out,
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std::vector<OnlineTransducerDecoderResult> *results) {
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std::vector<int64_t> shape =
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decoder_out->GetTensorTypeAndShapeInfo().GetShape();
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auto memory_info =
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Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
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std::array<int64_t, 2> v_shape{1, shape[1]};
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const float *src = decoder_out->GetTensorData<float>();
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for (auto &r : *results) {
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if (!r.decoder_out) {
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r.decoder_out = Ort::Value::CreateTensor<float>(allocator, v_shape.data(),
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v_shape.size());
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}
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float *dst = r.decoder_out.GetTensorMutableData<float>();
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std::copy(src, src + shape[1], dst);
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src += shape[1];
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}
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}
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OnlineTransducerDecoderResult
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OnlineTransducerGreedySearchDecoder::GetEmptyResult() const {
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int32_t context_size = model_->ContextSize();
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int32_t blank_id = 0; // always 0
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OnlineTransducerDecoderResult r;
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r.tokens.resize(context_size, blank_id);
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return r;
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}
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void OnlineTransducerGreedySearchDecoder::StripLeadingBlanks(
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OnlineTransducerDecoderResult *r) const {
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int32_t context_size = model_->ContextSize();
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auto start = r->tokens.begin() + context_size;
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auto end = r->tokens.end();
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r->tokens = std::vector<int64_t>(start, end);
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}
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void OnlineTransducerGreedySearchDecoder::Decode(
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Ort::Value encoder_out,
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std::vector<OnlineTransducerDecoderResult> *result) {
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std::vector<int64_t> encoder_out_shape =
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encoder_out.GetTensorTypeAndShapeInfo().GetShape();
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if (encoder_out_shape[0] != result->size()) {
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SHERPA_ONNX_LOGE(
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"Size mismatch! encoder_out.size(0) %d, result.size(0): %d",
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static_cast<int32_t>(encoder_out_shape[0]),
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static_cast<int32_t>(result->size()));
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exit(-1);
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}
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int32_t batch_size = static_cast<int32_t>(encoder_out_shape[0]);
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int32_t num_frames = static_cast<int32_t>(encoder_out_shape[1]);
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int32_t vocab_size = model_->VocabSize();
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Ort::Value decoder_input = model_->BuildDecoderInput(*result);
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Ort::Value decoder_out = model_->RunDecoder(std::move(decoder_input));
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UseCachedDecoderOut(*result, &decoder_out);
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for (int32_t t = 0; t != num_frames; ++t) {
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Ort::Value cur_encoder_out =
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GetEncoderOutFrame(model_->Allocator(), &encoder_out, t);
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Ort::Value logit = model_->RunJoiner(
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std::move(cur_encoder_out), Clone(model_->Allocator(), &decoder_out));
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const float *p_logit = logit.GetTensorData<float>();
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bool emitted = false;
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for (int32_t i = 0; i < batch_size; ++i, p_logit += vocab_size) {
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auto y = static_cast<int32_t>(std::distance(
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static_cast<const float *>(p_logit),
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std::max_element(static_cast<const float *>(p_logit),
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static_cast<const float *>(p_logit) + vocab_size)));
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if (y != 0) {
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emitted = true;
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(*result)[i].tokens.push_back(y);
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(*result)[i].num_trailing_blanks = 0;
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} else {
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++(*result)[i].num_trailing_blanks;
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}
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}
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if (emitted) {
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Ort::Value decoder_input = model_->BuildDecoderInput(*result);
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decoder_out = model_->RunDecoder(std::move(decoder_input));
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}
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}
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UpdateCachedDecoderOut(model_->Allocator(), &decoder_out, result);
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}
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} // namespace sherpa_onnx
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