80060c276d
Please see https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/nemo/index.html for a list of pre-trained CTC models from NeMo.
129 lines
4.0 KiB
C++
129 lines
4.0 KiB
C++
// sherpa-onnx/csrc/offline-recognizer-ctc-impl.h
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//
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// Copyright (c) 2022-2023 Xiaomi Corporation
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#ifndef SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_CTC_IMPL_H_
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#define SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_CTC_IMPL_H_
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#include <memory>
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#include <string>
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#include <utility>
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#include <vector>
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#include "sherpa-onnx/csrc/offline-ctc-decoder.h"
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#include "sherpa-onnx/csrc/offline-ctc-greedy-search-decoder.h"
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#include "sherpa-onnx/csrc/offline-ctc-model.h"
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#include "sherpa-onnx/csrc/offline-recognizer-impl.h"
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#include "sherpa-onnx/csrc/pad-sequence.h"
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#include "sherpa-onnx/csrc/symbol-table.h"
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namespace sherpa_onnx {
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static OfflineRecognitionResult Convert(const OfflineCtcDecoderResult &src,
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const SymbolTable &sym_table) {
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OfflineRecognitionResult r;
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r.tokens.reserve(src.tokens.size());
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std::string text;
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for (int32_t i = 0; i != src.tokens.size(); ++i) {
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auto sym = sym_table[src.tokens[i]];
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text.append(sym);
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r.tokens.push_back(std::move(sym));
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}
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r.text = std::move(text);
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return r;
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}
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class OfflineRecognizerCtcImpl : public OfflineRecognizerImpl {
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public:
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explicit OfflineRecognizerCtcImpl(const OfflineRecognizerConfig &config)
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: config_(config),
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symbol_table_(config_.model_config.tokens),
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model_(OfflineCtcModel::Create(config_.model_config)) {
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config_.feat_config.nemo_normalize_type =
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model_->FeatureNormalizationMethod();
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if (config.decoding_method == "greedy_search") {
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if (!symbol_table_.contains("<blk>")) {
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SHERPA_ONNX_LOGE(
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"We expect that tokens.txt contains "
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"the symbol <blk> and its ID.");
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exit(-1);
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}
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int32_t blank_id = symbol_table_["<blk>"];
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decoder_ = std::make_unique<OfflineCtcGreedySearchDecoder>(blank_id);
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} else {
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SHERPA_ONNX_LOGE("Only greedy_search is supported at present. Given %s",
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config.decoding_method.c_str());
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exit(-1);
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}
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}
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std::unique_ptr<OfflineStream> CreateStream() const override {
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return std::make_unique<OfflineStream>(config_.feat_config);
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}
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void DecodeStreams(OfflineStream **ss, int32_t n) const override {
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auto memory_info =
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Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
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int32_t feat_dim = config_.feat_config.feature_dim;
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std::vector<Ort::Value> features;
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features.reserve(n);
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std::vector<std::vector<float>> features_vec(n);
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std::vector<int64_t> features_length_vec(n);
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for (int32_t i = 0; i != n; ++i) {
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std::vector<float> f = ss[i]->GetFrames();
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int32_t num_frames = f.size() / feat_dim;
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features_vec[i] = std::move(f);
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features_length_vec[i] = num_frames;
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std::array<int64_t, 2> shape = {num_frames, feat_dim};
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Ort::Value x = Ort::Value::CreateTensor(
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memory_info, features_vec[i].data(), features_vec[i].size(),
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shape.data(), shape.size());
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features.push_back(std::move(x));
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} // for (int32_t i = 0; i != n; ++i)
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std::vector<const Ort::Value *> features_pointer(n);
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for (int32_t i = 0; i != n; ++i) {
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features_pointer[i] = &features[i];
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}
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std::array<int64_t, 1> features_length_shape = {n};
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Ort::Value x_length = Ort::Value::CreateTensor(
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memory_info, features_length_vec.data(), n,
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features_length_shape.data(), features_length_shape.size());
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Ort::Value x = PadSequence(model_->Allocator(), features_pointer,
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-23.025850929940457f);
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auto t = model_->Forward(std::move(x), std::move(x_length));
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auto results = decoder_->Decode(std::move(t.first), std::move(t.second));
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for (int32_t i = 0; i != n; ++i) {
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auto r = Convert(results[i], symbol_table_);
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ss[i]->SetResult(r);
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}
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}
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private:
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OfflineRecognizerConfig config_;
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SymbolTable symbol_table_;
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std::unique_ptr<OfflineCtcModel> model_;
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std::unique_ptr<OfflineCtcDecoder> decoder_;
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};
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} // namespace sherpa_onnx
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#endif // SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_CTC_IMPL_H_
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