online-transducer-greedy-search-nemo-decoder.cc
4.1 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
// sherpa-onnx/csrc/online-transducer-greedy-search-nemo-decoder.cc
//
// Copyright (c) 2024 Xiaomi Corporation
// Copyright (c) 2024 Sangeet Sagar
#include "sherpa-onnx/csrc/online-transducer-greedy-search-nemo-decoder.h"
#include <algorithm>
#include <iterator>
#include <utility>
#include "sherpa-onnx/csrc/macros.h"
#include "sherpa-onnx/csrc/online-stream.h"
#include "sherpa-onnx/csrc/onnx-utils.h"
namespace sherpa_onnx {
static Ort::Value BuildDecoderInput(int32_t token, OrtAllocator *allocator) {
std::array<int64_t, 2> shape{1, 1};
Ort::Value decoder_input =
Ort::Value::CreateTensor<int32_t>(allocator, shape.data(), shape.size());
int32_t *p = decoder_input.GetTensorMutableData<int32_t>();
p[0] = token;
return decoder_input;
}
static void DecodeOne(const float *encoder_out, int32_t num_rows,
int32_t num_cols, OnlineTransducerNeMoModel *model,
float blank_penalty, OnlineStream *s) {
auto memory_info =
Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
int32_t vocab_size = model->VocabSize();
int32_t blank_id = vocab_size - 1;
auto &r = s->GetResult();
Ort::Value decoder_out{nullptr};
auto decoder_input = BuildDecoderInput(
r.tokens.empty() ? blank_id : r.tokens.back(), model->Allocator());
std::vector<Ort::Value> &last_decoder_states = s->GetNeMoDecoderStates();
std::vector<Ort::Value> tmp_decoder_states;
tmp_decoder_states.reserve(last_decoder_states.size());
for (auto &v : last_decoder_states) {
tmp_decoder_states.push_back(View(&v));
}
// decoder_output_pair.second returns the next decoder state
std::pair<Ort::Value, std::vector<Ort::Value>> decoder_output_pair =
model->RunDecoder(std::move(decoder_input),
std::move(tmp_decoder_states));
std::array<int64_t, 3> encoder_shape{1, num_cols, 1};
bool emitted = false;
for (int32_t t = 0; t != num_rows; ++t) {
Ort::Value cur_encoder_out = Ort::Value::CreateTensor(
memory_info, const_cast<float *>(encoder_out) + t * num_cols, num_cols,
encoder_shape.data(), encoder_shape.size());
Ort::Value logit = model->RunJoiner(std::move(cur_encoder_out),
View(&decoder_output_pair.first));
float *p_logit = logit.GetTensorMutableData<float>();
if (blank_penalty > 0) {
p_logit[blank_id] -= blank_penalty;
}
auto y = static_cast<int32_t>(std::distance(
static_cast<const float *>(p_logit),
std::max_element(static_cast<const float *>(p_logit),
static_cast<const float *>(p_logit) + vocab_size)));
if (y != blank_id) {
emitted = true;
r.tokens.push_back(y);
r.timestamps.push_back(t + r.frame_offset);
r.num_trailing_blanks = 0;
decoder_input = BuildDecoderInput(y, model->Allocator());
// last decoder state becomes the current state for the first chunk
decoder_output_pair = model->RunDecoder(
std::move(decoder_input), std::move(decoder_output_pair.second));
} else {
++r.num_trailing_blanks;
}
}
if (emitted) {
s->SetNeMoDecoderStates(std::move(decoder_output_pair.second));
}
r.frame_offset += num_rows;
}
void OnlineTransducerGreedySearchNeMoDecoder::Decode(Ort::Value encoder_out,
OnlineStream **ss,
int32_t n) const {
auto shape = encoder_out.GetTensorTypeAndShapeInfo().GetShape();
int32_t batch_size = static_cast<int32_t>(shape[0]); // bs = 1
if (batch_size != n) {
SHERPA_ONNX_LOGE("Size mismatch! encoder_out.size(0) %d, n: %d",
static_cast<int32_t>(shape[0]), n);
exit(-1);
}
int32_t dim1 = static_cast<int32_t>(shape[1]); // T
int32_t dim2 = static_cast<int32_t>(shape[2]); // encoder_out_dim
const float *p = encoder_out.GetTensorData<float>();
for (int32_t i = 0; i != batch_size; ++i) {
const float *this_p = p + dim1 * dim2 * i;
DecodeOne(this_p, dim1, dim2, model_, blank_penalty_, ss[i]);
}
}
} // namespace sherpa_onnx