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Merge pull request #406 from bygreencn/master

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Make the c code sample have the same function as the python code
This commit is contained in:
Alexander Veysov
2023-12-15 16:46:58 +03:00
committed by GitHub
parent 5e7ee10ee0 4a6d1701a4
commit 03dc3fae5c
2 changed files with 342 additions and 105 deletions
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436
examples/cpp/silero-vad-onnx.cpp
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@@ -2,13 +2,97 @@
#include <vector>
#include <sstream>
#include <cstring>
#include <limits>
#include <chrono>
#include <memory>
#include <string>
#include <stdexcept>
#include <iostream>
#include <string>
#include "onnxruntime_cxx_api.h"
#include "wav.h"
#include <cstdio>
#include <cstdarg>
#if __cplusplus < 201703L
#include <memory>
#endif
//#define __DEBUG_SPEECH_PROB___
class timestamp_t
{
public:
int start;
int end;
// default + parameterized constructor
timestamp_t(int start = 0, int end = 0)
: start(start), end(end)
{
};
// assignment operator modifies object, therefore non-const
timestamp_t& operator=(const timestamp_t& a)
{
start = a.start;
end = a.end;
return *this;
};
// equality comparison. doesn't modify object. therefore const.
bool operator==(const timestamp_t& a) const
{
return (start == a.start && end == a.end);
};
std::string c_str()
{
//return std::format("timestamp {:08d}, {:08d}", start, end);
return format("{start:%08d,end:%08d}", start, end);
};
private:
std::string format(const char* fmt, ...)
{
char buf[256];
va_list args;
va_start(args, fmt);
const auto r = std::vsnprintf(buf, sizeof buf, fmt, args);
va_end(args);
if (r < 0)
// conversion failed
return {};
const size_t len = r;
if (len < sizeof buf)
// we fit in the buffer
return { buf, len };
#if __cplusplus >= 201703L
// C++17: Create a string and write to its underlying array
std::string s(len, '\0');
va_start(args, fmt);
std::vsnprintf(s.data(), len + 1, fmt, args);
va_end(args);
return s;
#else
// C++11 or C++14: We need to allocate scratch memory
auto vbuf = std::unique_ptr<char[]>(new char[len + 1]);
va_start(args, fmt);
std::vsnprintf(vbuf.get(), len + 1, fmt, args);
va_end(args);
return { vbuf.get(), len };
#endif
};
};
class VadIterator
{
private:
// OnnxRuntime resources
Ort::Env env;
Ort::SessionOptions session_options;
@@ -16,48 +100,40 @@ class VadIterator
Ort::AllocatorWithDefaultOptions allocator;
Ort::MemoryInfo memory_info = Ort::MemoryInfo::CreateCpu(OrtArenaAllocator, OrtMemTypeCPU);
public:
private:
void init_engine_threads(int inter_threads, int intra_threads)
{
{
// The method should be called in each thread/proc in multi-thread/proc work
session_options.SetIntraOpNumThreads(intra_threads);
session_options.SetInterOpNumThreads(inter_threads);
session_options.SetGraphOptimizationLevel(GraphOptimizationLevel::ORT_ENABLE_ALL);
}
};
void init_onnx_model(const std::string &model_path)
{
void init_onnx_model(const std::wstring& model_path)
{
// Init threads = 1 for
init_engine_threads(1, 1);
// Load model
session = std::make_shared<Ort::Session>(env, model_path.c_str(), session_options);
}
};
void reset_states()
{
// Call reset before each audio start
std::memset(_h.data(), 0.0f, _h.size() * sizeof(float));
std::memset(_c.data(), 0.0f, _c.size() * sizeof(float));
triggerd = false;
triggered = false;
temp_end = 0;
current_sample = 0;
}
// Call it in predict func. if you prefer raw bytes input.
void bytes_to_float_tensor(const char *pcm_bytes)
{
const int16_t * in_data = reinterpret_cast<const int16_t*>(pcm_bytes);
for (int i = 0; i < window_size_samples; i++)
{
input[i] = static_cast<float>(in_data[i]) / 32768; // int16_t normalized to float
}
}
prev_end = next_start = 0;
speeches.clear();
current_speech = timestamp_t(0, 0);
};
void predict(const std::vector<float> &data)
{
// bytes_to_float_tensor(data);
// Infer
// Create ort tensors
input.assign(data.begin(), data.end());
@@ -84,7 +160,7 @@ public:
output_node_names.data(), output_node_names.size());
// Output probability & update h,c recursively
float output = ort_outputs[0].GetTensorMutableData<float>()[0];
float speech_prob = ort_outputs[0].GetTensorMutableData<float>()[0];
float *hn = ort_outputs[1].GetTensorMutableData<float>();
std::memcpy(_h.data(), hn, size_hc * sizeof(float));
float *cn = ort_outputs[2].GetTensorMutableData<float>();
@@ -92,75 +168,209 @@ public:
// Push forward sample index
current_sample += window_size_samples;
// Reset temp_end when > threshold
if ((output >= threshold) && (temp_end != 0))
if ((speech_prob >= threshold))
{
temp_end = 0;
}
// 1) Silence
if ((output < threshold) && (triggerd == false))
{
// printf("{ silence: %.3f s }\n", 1.0 * current_sample / sample_rate);
}
// 2) Speaking
if ((output >= (threshold - 0.15)) && (triggerd == true))
{
// printf("{ speaking_2: %.3f s }\n", 1.0 * current_sample / sample_rate);
#ifdef __DEBUG_SPEECH_PROB___
float speech = current_sample - window_size_samples; // minus window_size_samples to get precise start time point.
printf("{ start: %.3f s (%.3f) %08d}\n", 1.0 * speech / sample_rate, speech_prob, current_sample- window_size_samples);
#endif //__DEBUG_SPEECH_PROB___
if (temp_end != 0)
{
temp_end = 0;
if (next_start < prev_end)
next_start = current_sample - window_size_samples;
}
if (triggered == false)
{
triggered = true;
current_speech.start = current_sample - window_size_samples;
}
return;
}
// 3) Start
if ((output >= threshold) && (triggerd == false))
{
triggerd = true;
speech_start = current_sample - window_size_samples - speech_pad_samples; // minus window_size_samples to get precise start time point.
printf("{ start: %.3f s }\n", 1.0 * speech_start / sample_rate);
if (
(triggered == true)
&& ((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(0, 0);
// previously reached silence(< neg_thres) and is still not speech(< thres)
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(0, 0);
prev_end = 0;
next_start = 0;
temp_end = 0;
triggered = false;
}
return;
}
if ((speech_prob >= (threshold - 0.15)) && (speech_prob < threshold))
{
if (triggered) {
#ifdef __DEBUG_SPEECH_PROB___
float speech = current_sample - window_size_samples; // minus window_size_samples to get precise start time point.
printf("{ speeking: %.3f s (%.3f) %08d}\n", 1.0 * speech / sample_rate, speech_prob, current_sample - window_size_samples);
#endif //__DEBUG_SPEECH_PROB___
}
else {
#ifdef __DEBUG_SPEECH_PROB___
float speech = current_sample - window_size_samples; // minus window_size_samples to get precise start time point.
printf("{ silence: %.3f s (%.3f) %08d}\n", 1.0 * speech / sample_rate, speech_prob, current_sample - window_size_samples);
#endif //__DEBUG_SPEECH_PROB___
}
return;
}
// 4) End
if ((output < (threshold - 0.15)) && (triggerd == true))
if ((speech_prob < (threshold - 0.15)))
{
#ifdef __DEBUG_SPEECH_PROB___
float speech = current_sample - window_size_samples - speech_pad_samples; // minus window_size_samples to get precise start time point.
printf("{ end: %.3f s (%.3f) %08d}\n", 1.0 * speech / sample_rate, speech_prob, current_sample - window_size_samples);
#endif //__DEBUG_SPEECH_PROB___
if (triggered == true)
{
if (temp_end == 0)
{
temp_end = current_sample;
}
if (current_sample - temp_end > min_silence_samples_at_max_speech)
prev_end = temp_end;
// a. silence < min_slience_samples, continue speaking
if ((current_sample - temp_end) < min_silence_samples)
{
if (temp_end == 0)
{
temp_end = current_sample;
}
// b. silence >= min_slience_samples, end speaking
else
{
current_speech.end = temp_end;
if (current_speech.end - current_speech.start > min_speech_samples)
{
speeches.push_back(current_speech);
current_speech = timestamp_t(0, 0);
prev_end = 0;
next_start = 0;
temp_end = 0;
triggered = false;
}
}
}
// a. silence < min_slience_samples, continue speaking
if ((current_sample - temp_end) < min_silence_samples)
{
// printf("{ speaking_4: %.3f s }\n", 1.0 * current_sample / sample_rate);
// printf("");
}
// b. silence >= min_slience_samples, end speaking
else
{
speech_end = temp_end ? temp_end + speech_pad_samples : current_sample + speech_pad_samples;
temp_end = 0;
triggerd = false;
printf("{ end: %.3f s }\n", 1.0 * speech_end / sample_rate);
else {
// may first windows see end state.
}
return;
}
};
public:
void process(const std::vector<float>& input_wav)
{
reset_states();
audio_length_samples = input_wav.size();
for (int j = 0; j < audio_length_samples; j += window_size_samples)
{
if (j + window_size_samples > audio_length_samples)
break;
std::vector<float> r{ &input_wav[0] + j, &input_wav[0] + j + window_size_samples };
predict(r);
}
if (current_speech.start > 0) {
current_speech.end = audio_length_samples;
speeches.push_back(current_speech);
current_speech = timestamp_t(0, 0);
prev_end = 0;
next_start = 0;
temp_end = 0;
triggered = false;
}
};
void process(const std::vector<float>& input_wav, std::vector<float>& output_wav)
{
process(input_wav);
collect_chunks(input_wav, output_wav);
}
void collect_chunks(const std::vector<float>& input_wav, std::vector<float>& output_wav)
{
output_wav.clear();
for (int i = 0; i < speeches.size(); i++) {
#ifdef __DEBUG_SPEECH_PROB___
std::cout << speeches[i].c_str() << std::endl;
#endif //#ifdef __DEBUG_SPEECH_PROB___
std::vector<float> slice(&input_wav[speeches[i].start], &input_wav[speeches[i].end]);
output_wav.insert(output_wav.end(),slice.begin(),slice.end());
}
};
const std::vector<timestamp_t> get_speech_timestamps() const
{
return speeches;
}
void drop_chunks(const std::vector<float>& input_wav, std::vector<float>& output_wav)
{
output_wav.clear();
int current_start = 0;
for (int i = 0; i < speeches.size(); i++) {
std::vector<float> slice(&input_wav[current_start],&input_wav[speeches[i].start]);
output_wav.insert(output_wav.end(), slice.begin(), slice.end());
current_start = speeches[i].end;
}
std::vector<float> slice(&input_wav[current_start], &input_wav[input_wav.size()]);
output_wav.insert(output_wav.end(), slice.begin(), slice.end());
};
private:
// model config
int64_t window_size_samples; // Assign when init, support 256 512 768 for 8k; 512 1024 1536 for 16k.
int sample_rate;
int sr_per_ms; // Assign when init, support 8 or 16
float threshold;
int sample_rate; //Assign when init support 16000 or 8000
int sr_per_ms; // Assign when init, support 8 or 16
float threshold;
int min_silence_samples; // sr_per_ms * #ms
int min_silence_samples_at_max_speech; // sr_per_ms * #98
int min_speech_samples; // sr_per_ms * #ms
float max_speech_samples;
int speech_pad_samples; // usually a
int audio_length_samples;
// model states
bool triggerd = false;
unsigned int speech_start = 0;
unsigned int speech_end = 0;
bool triggered = false;
unsigned int temp_end = 0;
unsigned int current_sample = 0;
// MAX 4294967295 samples / 8sample per ms / 1000 / 60 = 8947 minutes
float output;
int prev_end;
int next_start = 0;
//Output timestamp
std::vector<timestamp_t> speeches;
timestamp_t current_speech;
// Onnx model
// Inputs
@@ -180,75 +390,97 @@ private:
// Outputs
std::vector<Ort::Value> ort_outputs;
std::vector<const char *> output_node_names = {"output", "hn", "cn"};
public:
// Construction
VadIterator(const std::string ModelPath,
int Sample_rate, int frame_size,
float Threshold, int min_silence_duration_ms, int speech_pad_ms)
VadIterator(const std::wstring ModelPath,
int Sample_rate = 16000, int windows_frame_size = 64,
float Threshold = 0.5, int min_silence_duration_ms = 0,
int speech_pad_ms = 64, int min_speech_duration_ms = 64,
float max_speech_duration_s = std::numeric_limits<float>::infinity())
{
init_onnx_model(ModelPath);
threshold = Threshold;
sample_rate = Sample_rate;
sr_per_ms = sample_rate / 1000;
threshold = Threshold;
min_silence_samples = sr_per_ms * min_silence_duration_ms;
window_size_samples = windows_frame_size * sr_per_ms;
min_speech_samples = sr_per_ms * min_speech_duration_ms;
speech_pad_samples = sr_per_ms * speech_pad_ms;
window_size_samples = frame_size * sr_per_ms;
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;
input.resize(window_size_samples);
input_node_dims[0] = 1;
input_node_dims[1] = window_size_samples;
// std::cout << "== Input size" << input.size() << std::endl;
_h.resize(size_hc);
_c.resize(size_hc);
sr.resize(1);
sr[0] = sample_rate;
}
};
};
int main()
{
std::vector<timestamp_t> stamps;
// Read wav
wav::WavReader wav_reader("./test_for_vad.wav");
std::vector<int16_t> data(wav_reader.num_samples());
wav::WavReader wav_reader("recorder.wav"); //16000,1,32float
std::vector<float> input_wav(wav_reader.num_samples());
std::vector<float> output_wav;
for (int i = 0; i < wav_reader.num_samples(); i++)
{
data[i] = static_cast<int16_t>(*(wav_reader.data() + i));
input_wav[i] = static_cast<float>(*(wav_reader.data() + i));
}
for (int i = 0; i < wav_reader.num_samples(); i++)
{
input_wav[i] = static_cast<float>(data[i]) / 32768;
}
// ===== Test configs =====
std::string path = "../files/silero_vad.onnx";
int test_sr = 8000;
int test_frame_ms = 64;
float test_threshold = 0.5f;
int test_min_silence_duration_ms = 0;
int test_speech_pad_ms = 0;
int test_window_samples = test_frame_ms * (test_sr/1000);
std::wstring path = L"silero_vad.onnx";
VadIterator vad(path);
VadIterator vad(
path, test_sr, test_frame_ms, test_threshold,
test_min_silence_duration_ms, test_speech_pad_ms);
// ==============================================
// ==== = Example 1 of full function =====
// ==============================================
vad.process(input_wav);
for (int j = 0; j < wav_reader.num_samples(); j += test_window_samples)
{
// std::cout << "== 4" << std::endl;
std::vector<float> r{&input_wav[0] + j, &input_wav[0] + j + test_window_samples};
auto start = std::chrono::high_resolution_clock::now();
// Predict and print throughout process time
vad.predict(r);
auto end = std::chrono::high_resolution_clock::now();
auto elapsed_time = std::chrono::duration_cast<std::chrono::nanoseconds>(end-start);
// std::cout << "== Elapsed time: " << 1.0*elapsed_time.count()/1000000 << "ms" << " ==" <<std::endl;
// 1.a get_speech_timestamps
stamps = vad.get_speech_timestamps();
for (int i = 0; i < stamps.size(); i++) {
std::cout << stamps[i].c_str() << std::endl;
}
// 1.b collect_chunks output wav
vad.collect_chunks(input_wav, output_wav);
// 1.c drop_chunks output wav
vad.drop_chunks(input_wav, output_wav);
// ==============================================
// ===== Example 2 of simple full function =====
// ==============================================
vad.process(input_wav, output_wav);
stamps = vad.get_speech_timestamps();
for (int i = 0; i < stamps.size(); i++) {
std::cout << stamps[i].c_str() << std::endl;
}
// ==============================================
// ===== Example 3 of full function =====
// ==============================================
for(int i = 0; i<2; i++)
vad.process(input_wav, output_wav);
}

11
examples/cpp/wav.h
View File

@@ -59,8 +59,7 @@ class WavReader {
WavHeader header;
fread(&header, 1, sizeof(header), fp);
if (header.fmt_size < 16) {
fprintf(stderr,
"WaveData: expect PCM format data "
printf("WaveData: expect PCM format data "
"to have fmt chunk of at least size 16.\n");
return false;
} else if (header.fmt_size > 16) {
@@ -87,6 +86,12 @@ class WavReader {
data_ = new float[num_data]; // Create 1-dim array
num_samples_ = num_data / num_channel_;
std::cout << "num_channel_ :" << num_channel_ << std::endl;
std::cout << "sample_rate_ :" << sample_rate_ << std::endl;
std::cout << "bits_per_sample_:" << bits_per_sample_ << std::endl;
std::cout << "num_samples :" << num_data << std::endl;
std::cout << "num_data size :" << header.data_size << std::endl;
for (int i = 0; i < num_data; ++i) {
switch (bits_per_sample_) {
case 8: {
@@ -110,7 +115,7 @@ class WavReader {
break;
}
default:
fprintf(stderr, "unsupported quantization bits");
printf("unsupported quantization bits\n");
exit(1);
}
}