66 lines
1.8 KiB
C++
66 lines
1.8 KiB
C++
// sherpa-onnx/csrc/online-ctc-decoder.h
|
|
//
|
|
// Copyright (c) 2023 Xiaomi Corporation
|
|
|
|
#ifndef SHERPA_ONNX_CSRC_ONLINE_CTC_DECODER_H_
|
|
#define SHERPA_ONNX_CSRC_ONLINE_CTC_DECODER_H_
|
|
|
|
#include <memory>
|
|
#include <vector>
|
|
|
|
#include "kaldi-decoder/csrc/faster-decoder.h"
|
|
#include "onnxruntime_cxx_api.h" // NOLINT
|
|
|
|
namespace sherpa_onnx {
|
|
|
|
class OnlineStream;
|
|
|
|
struct OnlineCtcDecoderResult {
|
|
/// Number of frames after subsampling we have decoded so far
|
|
int32_t frame_offset = 0;
|
|
|
|
/// The decoded token IDs
|
|
std::vector<int64_t> tokens;
|
|
|
|
/// The decoded word IDs
|
|
/// Note: tokens.size() is usually not equal to words.size()
|
|
/// words is empty for greedy search decoding.
|
|
/// it is not empty when an HLG graph or an HLG graph is used.
|
|
std::vector<int32_t> words;
|
|
|
|
/// timestamps[i] contains the output frame index where tokens[i] is decoded.
|
|
/// Note: The index is after subsampling
|
|
///
|
|
/// tokens.size() == timestamps.size()
|
|
std::vector<int32_t> timestamps;
|
|
|
|
int32_t num_trailing_blanks = 0;
|
|
};
|
|
|
|
class OnlineCtcDecoder {
|
|
public:
|
|
virtual ~OnlineCtcDecoder() = default;
|
|
|
|
/** Run streaming CTC decoding given the output from the encoder model.
|
|
*
|
|
* @param log_probs A 3-D tensor of shape
|
|
* (batch_size, num_frames, vocab_size) containing
|
|
* lob_probs in row major.
|
|
*
|
|
* @param results Input & Output parameters..
|
|
*/
|
|
virtual void Decode(const float *log_probs, int32_t batch_size,
|
|
int32_t num_frames, int32_t vocab_size,
|
|
std::vector<OnlineCtcDecoderResult> *results,
|
|
OnlineStream **ss = nullptr, int32_t n = 0) = 0;
|
|
|
|
virtual std::unique_ptr<kaldi_decoder::FasterDecoder> CreateFasterDecoder()
|
|
const {
|
|
return nullptr;
|
|
}
|
|
};
|
|
|
|
} // namespace sherpa_onnx
|
|
|
|
#endif // SHERPA_ONNX_CSRC_ONLINE_CTC_DECODER_H_
|