EngineX-Iluvatar/enginex_bi_series-sherpa-onnx
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Support streaming zipformer CTC (#496)

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* Support streaming zipformer CTC

* test online zipformer2 CTC

* Update doc of sherpa-onnx.cc

* Add Python APIs for streaming zipformer2 ctc

* Add Python API examples for streaming zipformer2 ctc

* Swift API for streaming zipformer2 CTC

* NodeJS API for streaming zipformer2 CTC

* Kotlin API for streaming zipformer2 CTC

* Golang API for streaming zipformer2 CTC

* C# API for streaming zipformer2 CTC

* Release v1.9.6
This commit is contained in:
Fangjun Kuang
2023-12-22 13:46:33 +08:00
committed by GitHub
parent 7634f5f034
commit e475e750ac
70 changed files with 1517 additions and 211 deletions
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61
sherpa-onnx/csrc/online-recognizer-ctc-impl.h
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@@ -96,8 +96,67 @@ class OnlineRecognizerCtcImpl : public OnlineRecognizerImpl {
}
void DecodeStreams(OnlineStream **ss, int32_t n) const override {
if (n == 1 || !model_->SupportBatchProcessing()) {
for (int32_t i = 0; i != n; ++i) {
DecodeStream(ss[i]);
}
return;
}
// batch processing
int32_t chunk_length = model_->ChunkLength();
int32_t chunk_shift = model_->ChunkShift();
int32_t feat_dim = ss[0]->FeatureDim();
std::vector<OnlineCtcDecoderResult> results(n);
std::vector<float> features_vec(n * chunk_length * feat_dim);
std::vector<std::vector<Ort::Value>> states_vec(n);
std::vector<int64_t> all_processed_frames(n);
for (int32_t i = 0; i != n; ++i) {
DecodeStream(ss[i]);
const auto num_processed_frames = ss[i]->GetNumProcessedFrames();
std::vector<float> features =
ss[i]->GetFrames(num_processed_frames, chunk_length);
// Question: should num_processed_frames include chunk_shift?
ss[i]->GetNumProcessedFrames() += chunk_shift;
std::copy(features.begin(), features.end(),
features_vec.data() + i * chunk_length * feat_dim);
results[i] = std::move(ss[i]->GetCtcResult());
states_vec[i] = std::move(ss[i]->GetStates());
all_processed_frames[i] = num_processed_frames;
}
auto memory_info =
Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
std::array<int64_t, 3> x_shape{n, chunk_length, feat_dim};
Ort::Value x = Ort::Value::CreateTensor(memory_info, features_vec.data(),
features_vec.size(), x_shape.data(),
x_shape.size());
auto states = model_->StackStates(std::move(states_vec));
int32_t num_states = states.size();
auto out = model_->Forward(std::move(x), std::move(states));
std::vector<Ort::Value> out_states;
out_states.reserve(num_states);
for (int32_t k = 1; k != num_states + 1; ++k) {
out_states.push_back(std::move(out[k]));
}
std::vector<std::vector<Ort::Value>> next_states =
model_->UnStackStates(std::move(out_states));
decoder_->Decode(std::move(out[0]), &results);
for (int32_t k = 0; k != n; ++k) {
ss[k]->SetCtcResult(results[k]);
ss[k]->SetStates(std::move(next_states[k]));
}
}