Fix batch decoding for greedy search (#71)
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@@ -10,47 +10,39 @@
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#include <utility>
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#include <utility>
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#include <vector>
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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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#include "sherpa-onnx/csrc/onnx-utils.h"
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namespace sherpa_onnx {
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namespace sherpa_onnx {
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static Ort::Value GetFrame(Ort::Value *encoder_out, int32_t t) {
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static Ort::Value GetFrame(OrtAllocator *allocator, Ort::Value *encoder_out,
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int32_t t) {
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std::vector<int64_t> encoder_out_shape =
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std::vector<int64_t> encoder_out_shape =
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encoder_out->GetTensorTypeAndShapeInfo().GetShape();
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encoder_out->GetTensorTypeAndShapeInfo().GetShape();
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assert(encoder_out_shape[0] == 1);
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int32_t encoder_out_dim = encoder_out_shape[2];
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auto batch_size = encoder_out_shape[0];
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auto num_frames = encoder_out_shape[1];
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assert(t < num_frames);
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auto encoder_out_dim = encoder_out_shape[2];
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auto offset = num_frames * encoder_out_dim;
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auto memory_info =
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auto memory_info =
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Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
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Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
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std::array<int64_t, 2> shape{1, encoder_out_dim};
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std::array<int64_t, 2> shape{batch_size, encoder_out_dim};
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return Ort::Value::CreateTensor(
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Ort::Value ans =
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memory_info,
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Ort::Value::CreateTensor<float>(allocator, shape.data(), shape.size());
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encoder_out->GetTensorMutableData<float>() + t * encoder_out_dim,
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encoder_out_dim, shape.data(), shape.size());
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}
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static Ort::Value Repeat(OrtAllocator *allocator, Ort::Value *cur_encoder_out,
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int32_t n) {
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if (n == 1) {
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return std::move(*cur_encoder_out);
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}
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std::vector<int64_t> cur_encoder_out_shape =
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cur_encoder_out->GetTensorTypeAndShapeInfo().GetShape();
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std::array<int64_t, 2> ans_shape{n, cur_encoder_out_shape[1]};
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Ort::Value ans = Ort::Value::CreateTensor<float>(allocator, ans_shape.data(),
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ans_shape.size());
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const float *src = cur_encoder_out->GetTensorData<float>();
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float *dst = ans.GetTensorMutableData<float>();
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float *dst = ans.GetTensorMutableData<float>();
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for (int32_t i = 0; i != n; ++i) {
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const float *src = encoder_out->GetTensorData<float>();
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std::copy(src, src + cur_encoder_out_shape[1], dst);
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dst += cur_encoder_out_shape[1];
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for (int32_t i = 0; i != batch_size; ++i) {
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std::copy(src + t * encoder_out_dim, src + (t + 1) * encoder_out_dim, dst);
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src += offset;
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dst += encoder_out_dim;
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}
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}
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return ans;
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return ans;
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@@ -83,10 +75,10 @@ void OnlineTransducerGreedySearchDecoder::Decode(
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encoder_out.GetTensorTypeAndShapeInfo().GetShape();
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encoder_out.GetTensorTypeAndShapeInfo().GetShape();
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if (encoder_out_shape[0] != result->size()) {
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if (encoder_out_shape[0] != result->size()) {
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fprintf(stderr,
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SHERPA_ONNX_LOGE(
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"Size mismatch! encoder_out.size(0) %d, result.size(0): %d\n",
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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>(encoder_out_shape[0]),
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static_cast<int32_t>(result->size()));
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static_cast<int32_t>(result->size()));
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exit(-1);
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exit(-1);
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}
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}
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@@ -98,10 +90,10 @@ void OnlineTransducerGreedySearchDecoder::Decode(
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Ort::Value decoder_out = model_->RunDecoder(std::move(decoder_input));
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Ort::Value decoder_out = model_->RunDecoder(std::move(decoder_input));
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for (int32_t t = 0; t != num_frames; ++t) {
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for (int32_t t = 0; t != num_frames; ++t) {
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Ort::Value cur_encoder_out = GetFrame(&encoder_out, t);
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Ort::Value cur_encoder_out = GetFrame(model_->Allocator(), &encoder_out, t);
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cur_encoder_out = Repeat(model_->Allocator(), &cur_encoder_out, batch_size);
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Ort::Value logit = model_->RunJoiner(
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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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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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const float *p_logit = logit.GetTensorData<float>();
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bool emitted = false;
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bool emitted = false;
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