File size: 6,983 Bytes
d21d362 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 | #include "vad_iterator.h"
#include <cmath>
#include <cstdio>
#include <cstring>
#include <memory>
void VadIterator::init_onnx_model(const std::string& model_path) {
init_engine_threads(1, 1);
session = std::make_shared<Ort::Session>(env, model_path.c_str(), session_options);
}
void VadIterator::init_engine_threads(int inter_threads, int intra_threads) {
session_options.SetIntraOpNumThreads(intra_threads);
session_options.SetInterOpNumThreads(inter_threads);
session_options.SetGraphOptimizationLevel(ORT_ENABLE_ALL);
}
void VadIterator::reset_states() {
std::memset(_state.data(), 0, _state.size() * sizeof(float));
triggered = false;
temp_end = 0;
current_sample = 0;
prev_end = next_start = 0;
speeches.clear();
current_speech = timestamp_t();
std::fill(_context.begin(), _context.end(), 0.0f);
}
void VadIterator::predict(const std::vector<float>& data_chunk) {
std::vector<float> new_data(effective_window_size, 0.0f);
std::copy(_context.begin(), _context.end(), new_data.begin());
std::copy(data_chunk.begin(), data_chunk.end(), new_data.begin() + context_samples);
input = new_data;
Ort::Value input_ort = Ort::Value::CreateTensor<float>(
memory_info, input.data(), input.size(), input_node_dims, 2);
Ort::Value state_ort = Ort::Value::CreateTensor<float>(
memory_info, _state.data(), _state.size(), state_node_dims, 3);
Ort::Value sr_ort = Ort::Value::CreateTensor<int64_t>(
memory_info, sr.data(), sr.size(), sr_node_dims, 1);
ort_inputs.clear();
ort_inputs.emplace_back(std::move(input_ort));
ort_inputs.emplace_back(std::move(state_ort));
ort_inputs.emplace_back(std::move(sr_ort));
ort_outputs = session->Run(
Ort::RunOptions{nullptr},
input_node_names.data(), ort_inputs.data(), ort_inputs.size(),
output_node_names.data(), output_node_names.size());
float speech_prob = ort_outputs[0].GetTensorMutableData<float>()[0];
float* stateN = ort_outputs[1].GetTensorMutableData<float>();
std::memcpy(_state.data(), stateN, size_state * sizeof(float));
current_sample += static_cast<unsigned int>(window_size_samples);
if (speech_prob >= threshold) {
if (temp_end != 0) {
temp_end = 0;
if (next_start < prev_end)
next_start = current_sample - window_size_samples;
}
if (!triggered) {
triggered = true;
current_speech.start = current_sample - window_size_samples;
}
std::copy(new_data.end() - context_samples, new_data.end(), _context.begin());
return;
}
if (triggered && ((current_sample - current_speech.start) > max_speech_samples)) {
if (prev_end > 0) {
current_speech.end = prev_end;
speeches.push_back(current_speech);
current_speech = timestamp_t();
if (next_start < prev_end)
triggered = false;
else
current_speech.start = next_start;
prev_end = 0;
next_start = 0;
temp_end = 0;
} else {
current_speech.end = current_sample;
speeches.push_back(current_speech);
current_speech = timestamp_t();
prev_end = 0;
next_start = 0;
temp_end = 0;
triggered = false;
}
std::copy(new_data.end() - context_samples, new_data.end(), _context.begin());
return;
}
if ((speech_prob >= (threshold - 0.15)) && (speech_prob < threshold)) {
std::copy(new_data.end() - context_samples, new_data.end(), _context.begin());
return;
}
if (speech_prob < (threshold - 0.15)) {
if (triggered) {
if (temp_end == 0)
temp_end = current_sample;
if (current_sample - temp_end > min_silence_samples_at_max_speech)
prev_end = temp_end;
if ((current_sample - temp_end) >= min_silence_samples) {
current_speech.end = temp_end;
if (current_speech.end - current_speech.start > min_speech_samples) {
speeches.push_back(current_speech);
current_speech = timestamp_t();
prev_end = 0;
next_start = 0;
temp_end = 0;
triggered = false;
}
}
}
std::copy(new_data.end() - context_samples, new_data.end(), _context.begin());
return;
}
}
void VadIterator::process(const std::vector<float>& input_wav) {
reset_states();
audio_length_samples = static_cast<int>(input_wav.size());
for (size_t j = 0; j < static_cast<size_t>(audio_length_samples); j += static_cast<size_t>(window_size_samples)) {
if (j + static_cast<size_t>(window_size_samples) > static_cast<size_t>(audio_length_samples))
break;
std::vector<float> chunk(&input_wav[j], &input_wav[j] + window_size_samples);
predict(chunk);
}
if (current_speech.start >= 0) {
current_speech.end = audio_length_samples;
speeches.push_back(current_speech);
current_speech = timestamp_t();
prev_end = 0;
next_start = 0;
temp_end = 0;
triggered = false;
}
}
const std::vector<timestamp_t>& VadIterator::get_speech_timestamps() const {
return speeches;
}
void VadIterator::reset() {
reset_states();
}
// 构造函数实现
VadIterator::VadIterator(const std::string ModelPath,
int Sample_rate,
int windows_frame_size,
float Threshold,
int min_silence_duration_ms,
int speech_pad_ms,
int min_speech_duration_ms,
float max_speech_duration_s)
: sample_rate(Sample_rate),
threshold(Threshold),
speech_pad_samples(speech_pad_ms),
prev_end(0),
memory_info(Ort::MemoryInfo::CreateCpu(OrtAllocatorType::OrtDeviceAllocator, OrtMemType::OrtMemTypeDefault))
{
sr_per_ms = sample_rate / 1000;
window_size_samples = windows_frame_size * sr_per_ms;
effective_window_size = window_size_samples + context_samples;
input_node_dims[0] = 1;
input_node_dims[1] = effective_window_size;
_state.resize(size_state);
sr.resize(1);
sr[0] = sample_rate;
_context.assign(context_samples, 0.0f);
min_speech_samples = sr_per_ms * min_speech_duration_ms;
if (max_speech_duration_s < 0) {
max_speech_samples = std::numeric_limits<float>::infinity();
} else {
max_speech_samples = (sample_rate * max_speech_duration_s - window_size_samples - 2 * speech_pad_samples);
}
min_silence_samples = sr_per_ms * min_silence_duration_ms;
min_silence_samples_at_max_speech = sr_per_ms * 98;
init_onnx_model(ModelPath);
} |