EngineX-Iluvatar/enginex-mr_series-sherpa-onnx
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Support building GPU-capable sherpa-onnx on Linux aarch64. (#1500)

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Thanks to @Peakyxh for providing pre-built onnxruntime libraries 
with CUDA support for Linux aarch64.

Tested on Jetson nano b01
This commit is contained in:
Fangjun Kuang
2024-11-01 11:16:28 +08:00
committed by GitHub
parent a3c89aa0d8
commit 9ab89c33bc
41 changed files with 537 additions and 291 deletions
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10
sherpa-onnx/csrc/online-nemo-ctc-model.cc
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@@ -102,7 +102,7 @@ class OnlineNeMoCtcModel::Impl {
int32_t ChunkShift() const { return chunk_shift_; }
OrtAllocator *Allocator() const { return allocator_; }
OrtAllocator *Allocator() { return allocator_; }
// Return a vector containing 3 tensors
// - cache_last_channel
@@ -119,7 +119,7 @@ class OnlineNeMoCtcModel::Impl {
}
std::vector<Ort::Value> StackStates(
std::vector<std::vector<Ort::Value>> states) const {
std::vector<std::vector<Ort::Value>> states) {
int32_t batch_size = static_cast<int32_t>(states.size());
if (batch_size == 1) {
return std::move(states[0]);
@@ -157,6 +157,8 @@ class OnlineNeMoCtcModel::Impl {
std::vector<Ort::Value> states) const {
assert(states.size() == 3);
auto allocator = const_cast<Impl *>(this)->allocator_;
std::vector<std::vector<Ort::Value>> ans;
auto shape = states[0].GetTensorTypeAndShapeInfo().GetShape();
@@ -171,9 +173,9 @@ class OnlineNeMoCtcModel::Impl {
for (int32_t i = 0; i != 3; ++i) {
std::vector<Ort::Value> v;
if (i == 2) {
v = Unbind<int64_t>(allocator_, &states[i], 0);
v = Unbind<int64_t>(allocator, &states[i], 0);
} else {
v = Unbind(allocator_, &states[i], 0);
v = Unbind(allocator, &states[i], 0);
}
assert(v.size() == batch_size);