Use log probs for paraformer (#120)
* Use log probs for paraformer * Fix
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@@ -23,8 +23,7 @@ class OfflineParaformerDecoder {
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/** Run beam search given the output from the paraformer model.
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*
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* @param log_probs A 3-D tensor of shape (N, T, vocab_size)
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* @param token_num A 2-D tensor of shape (N, T). Its dtype is int64_t.
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* log_probs[i].argmax(axis=-1) equals to token_num[i]
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* @param token_num A 1-D tensor of shape (N). token_num equals to T.
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*
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* @return Return a vector of size `N` containing the decoded results.
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*/
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@@ -4,28 +4,33 @@
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#include "sherpa-onnx/csrc/offline-paraformer-greedy-search-decoder.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<OfflineParaformerDecoderResult>
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OfflineParaformerGreedySearchDecoder::Decode(Ort::Value /*log_probs*/,
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Ort::Value token_num) {
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std::vector<int64_t> shape = token_num.GetTensorTypeAndShapeInfo().GetShape();
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OfflineParaformerGreedySearchDecoder::Decode(Ort::Value log_probs,
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Ort::Value /*token_num*/) {
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std::vector<int64_t> shape = log_probs.GetTensorTypeAndShapeInfo().GetShape();
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int32_t batch_size = shape[0];
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int32_t num_tokens = shape[1];
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int32_t vocab_size = shape[2];
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std::vector<OfflineParaformerDecoderResult> results(batch_size);
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const int64_t *p = token_num.GetTensorData<int64_t>();
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for (int32_t i = 0; i != batch_size; ++i) {
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const float *p =
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log_probs.GetTensorData<float>() + i * num_tokens * vocab_size;
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for (int32_t k = 0; k != num_tokens; ++k) {
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if (p[k] == eos_id_) break;
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auto max_idx = static_cast<int64_t>(
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std::distance(p, std::max_element(p, p + vocab_size)));
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if (max_idx == eos_id_) break;
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results[i].tokens.push_back(p[k]);
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results[i].tokens.push_back(max_idx);
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p += vocab_size;
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}
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p += num_tokens;
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}
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return results;
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@@ -17,7 +17,7 @@ class OfflineParaformerGreedySearchDecoder : public OfflineParaformerDecoder {
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: eos_id_(eos_id) {}
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std::vector<OfflineParaformerDecoderResult> Decode(
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Ort::Value /*log_probs*/, Ort::Value token_num) override;
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Ort::Value log_probs, Ort::Value /*token_num*/) override;
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private:
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int32_t eos_id_;
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