offline-recognizer-moonshine-impl.h
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// sherpa-onnx/csrc/offline-recognizer-moonshine-impl.h
//
// Copyright (c) 2024 Xiaomi Corporation
#ifndef SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_MOONSHINE_IMPL_H_
#define SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_MOONSHINE_IMPL_H_
#include <algorithm>
#include <cmath>
#include <memory>
#include <string>
#include <utility>
#include <vector>
#include "sherpa-onnx/csrc/offline-model-config.h"
#include "sherpa-onnx/csrc/offline-moonshine-decoder.h"
#include "sherpa-onnx/csrc/offline-moonshine-greedy-search-decoder.h"
#include "sherpa-onnx/csrc/offline-moonshine-model.h"
#include "sherpa-onnx/csrc/offline-recognizer-impl.h"
#include "sherpa-onnx/csrc/offline-recognizer.h"
#include "sherpa-onnx/csrc/symbol-table.h"
#include "sherpa-onnx/csrc/transpose.h"
namespace sherpa_onnx {
static OfflineRecognitionResult Convert(
const OfflineMoonshineDecoderResult &src, const SymbolTable &sym_table) {
OfflineRecognitionResult r;
r.tokens.reserve(src.tokens.size());
std::string text;
for (auto i : src.tokens) {
if (!sym_table.Contains(i)) {
continue;
}
const auto &s = sym_table[i];
text += s;
r.tokens.push_back(s);
}
r.text = text;
return r;
}
class OfflineRecognizerMoonshineImpl : public OfflineRecognizerImpl {
public:
explicit OfflineRecognizerMoonshineImpl(const OfflineRecognizerConfig &config)
: OfflineRecognizerImpl(config),
config_(config),
symbol_table_(config_.model_config.tokens),
model_(std::make_unique<OfflineMoonshineModel>(config.model_config)) {
Init();
}
template <typename Manager>
OfflineRecognizerMoonshineImpl(Manager *mgr,
const OfflineRecognizerConfig &config)
: OfflineRecognizerImpl(mgr, config),
config_(config),
symbol_table_(mgr, config_.model_config.tokens),
model_(
std::make_unique<OfflineMoonshineModel>(mgr, config.model_config)) {
Init();
}
void Init() {
if (config_.decoding_method == "greedy_search") {
decoder_ =
std::make_unique<OfflineMoonshineGreedySearchDecoder>(model_.get());
} else {
SHERPA_ONNX_LOGE(
"Only greedy_search is supported at present for moonshine. Given %s",
config_.decoding_method.c_str());
exit(-1);
}
}
std::unique_ptr<OfflineStream> CreateStream() const override {
MoonshineTag tag;
return std::make_unique<OfflineStream>(tag);
}
void DecodeStreams(OfflineStream **ss, int32_t n) const override {
// batch decoding is not implemented yet
for (int32_t i = 0; i != n; ++i) {
DecodeStream(ss[i]);
}
}
OfflineRecognizerConfig GetConfig() const override { return config_; }
private:
void DecodeStream(OfflineStream *s) const {
auto memory_info =
Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
std::vector<float> audio = s->GetFrames();
try {
std::array<int64_t, 2> shape{1, static_cast<int64_t>(audio.size())};
Ort::Value audio_tensor = Ort::Value::CreateTensor(
memory_info, audio.data(), audio.size(), shape.data(), shape.size());
Ort::Value features =
model_->ForwardPreprocessor(std::move(audio_tensor));
int32_t features_len = features.GetTensorTypeAndShapeInfo().GetShape()[1];
int64_t features_shape = 1;
Ort::Value features_len_tensor = Ort::Value::CreateTensor(
memory_info, &features_len, 1, &features_shape, 1);
Ort::Value encoder_out = model_->ForwardEncoder(
std::move(features), std::move(features_len_tensor));
auto results = decoder_->Decode(std::move(encoder_out));
auto r = Convert(results[0], symbol_table_);
r.text = ApplyInverseTextNormalization(std::move(r.text));
r.text = ApplyHomophoneReplacer(std::move(r.text));
s->SetResult(r);
} catch (const Ort::Exception &ex) {
SHERPA_ONNX_LOGE(
"\n\nCaught exception:\n\n%s\n\nReturn an empty result. Number of "
"audio samples: %d",
ex.what(), static_cast<int32_t>(audio.size()));
return;
}
}
private:
OfflineRecognizerConfig config_;
SymbolTable symbol_table_;
std::unique_ptr<OfflineMoonshineModel> model_;
std::unique_ptr<OfflineMoonshineDecoder> decoder_;
};
} // namespace sherpa_onnx
#endif // SHERPA_ONNX_CSRC_OFFLINE_RECOGNIZER_MOONSHINE_IMPL_H_