8ee02c28b0
* code refactoring * Remove reference files * Update README and CI * small fixes * fix style issues * add style check for CI * fix style issues * remove kaldi-native-io
85 lines
3.0 KiB
C++
85 lines
3.0 KiB
C++
/**
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* Copyright (c) 2022 Xiaomi Corporation (authors: Fangjun Kuang)
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*
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* See LICENSE for clarification regarding multiple authors
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include "sherpa-onnx/csrc/decode.h"
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#include <assert.h>
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#include <algorithm>
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#include <vector>
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namespace sherpa_onnx {
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std::vector<int32_t> GreedySearch(RnntModel &model, // NOLINT
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const Ort::Value &encoder_out) {
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std::vector<int64_t> encoder_out_shape =
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encoder_out.GetTensorTypeAndShapeInfo().GetShape();
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assert(encoder_out_shape[0] == 1 && "Only batch_size=1 is implemented");
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Ort::Value projected_encoder_out =
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model.RunJoinerEncoderProj(encoder_out.GetTensorData<float>(),
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encoder_out_shape[1], encoder_out_shape[2]);
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const float *p_projected_encoder_out =
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projected_encoder_out.GetTensorData<float>();
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int32_t context_size = 2; // hard-code it to 2
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int32_t blank_id = 0; // hard-code it to 0
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std::vector<int32_t> hyp(context_size, blank_id);
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std::array<int64_t, 2> decoder_input{blank_id, blank_id};
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Ort::Value decoder_out = model.RunDecoder(decoder_input.data(), context_size);
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std::vector<int64_t> decoder_out_shape =
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decoder_out.GetTensorTypeAndShapeInfo().GetShape();
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Ort::Value projected_decoder_out = model.RunJoinerDecoderProj(
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decoder_out.GetTensorData<float>(), decoder_out_shape[2]);
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int32_t joiner_dim =
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projected_decoder_out.GetTensorTypeAndShapeInfo().GetShape()[1];
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int32_t T = encoder_out_shape[1];
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for (int32_t t = 0; t != T; ++t) {
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Ort::Value logit = model.RunJoiner(
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p_projected_encoder_out + t * joiner_dim,
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projected_decoder_out.GetTensorData<float>(), joiner_dim);
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int32_t vocab_size = logit.GetTensorTypeAndShapeInfo().GetShape()[1];
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const float *p_logit = logit.GetTensorData<float>();
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auto y = static_cast<int32_t>(std::distance(
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static_cast<const float *>(p_logit),
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std::max_element(static_cast<const float *>(p_logit),
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static_cast<const float *>(p_logit) + vocab_size)));
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if (y != blank_id) {
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decoder_input[0] = hyp.back();
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decoder_input[1] = y;
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hyp.push_back(y);
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decoder_out = model.RunDecoder(decoder_input.data(), context_size);
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projected_decoder_out = model.RunJoinerDecoderProj(
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decoder_out.GetTensorData<float>(), decoder_out_shape[2]);
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}
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}
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return {hyp.begin() + context_size, hyp.end()};
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}
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} // namespace sherpa_onnx
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