This PR adds support for non-streaming Zipformer CTC ASR models across multiple language bindings, WebAssembly, examples, and CI workflows. - Introduces a new OfflineZipformerCtcModelConfig in C/C++, Python, Swift, Java, Kotlin, Go, Dart, Pascal, and C# APIs - Updates initialization, freeing, and recognition logic to include Zipformer CTC in WASM and Node.js - Adds example scripts and CI steps for downloading, building, and running Zipformer CTC models Model doc is available at https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/icefall/zipformer.html
238 lines
7.2 KiB
C++
238 lines
7.2 KiB
C++
// cxx-api-examples/zipformer-ctc-simulate-streaming-microphone-cxx-api.cc
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// Copyright (c) 2025 Xiaomi Corporation
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//
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// This file demonstrates how to use Zipformer CTC with sherpa-onnx's C++ API
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// for streaming speech recognition from a microphone.
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//
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// clang-format off
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//
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// wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/silero_vad.onnx
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//
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// wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
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// tar xvf sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
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// rm sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03.tar.bz2
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//
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// clang-format on
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#include <signal.h>
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#include <stdio.h>
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#include <stdlib.h>
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#include <chrono> // NOLINT
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#include <condition_variable> // NOLINT
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#include <iostream>
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#include <mutex> // NOLINT
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#include <queue>
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#include <vector>
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#include "portaudio.h" // NOLINT
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#include "sherpa-display.h" // NOLINT
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#include "sherpa-onnx/c-api/cxx-api.h"
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#include "sherpa-onnx/csrc/microphone.h"
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std::queue<std::vector<float>> samples_queue;
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std::condition_variable condition_variable;
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std::mutex mutex;
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bool stop = false;
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static void Handler(int32_t /*sig*/) {
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stop = true;
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condition_variable.notify_one();
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fprintf(stderr, "\nCaught Ctrl + C. Exiting...\n");
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}
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static int32_t RecordCallback(const void *input_buffer,
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void * /*output_buffer*/,
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unsigned long frames_per_buffer, // NOLINT
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const PaStreamCallbackTimeInfo * /*time_info*/,
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PaStreamCallbackFlags /*status_flags*/,
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void * /*user_data*/) {
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std::lock_guard<std::mutex> lock(mutex);
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samples_queue.emplace(
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reinterpret_cast<const float *>(input_buffer),
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reinterpret_cast<const float *>(input_buffer) + frames_per_buffer);
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condition_variable.notify_one();
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return stop ? paComplete : paContinue;
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}
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static sherpa_onnx::cxx::VoiceActivityDetector CreateVad() {
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using namespace sherpa_onnx::cxx; // NOLINT
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VadModelConfig config;
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config.silero_vad.model = "./silero_vad.onnx";
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config.silero_vad.threshold = 0.5;
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config.silero_vad.min_silence_duration = 0.1;
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config.silero_vad.min_speech_duration = 0.25;
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config.silero_vad.max_speech_duration = 8;
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config.sample_rate = 16000;
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config.debug = false;
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VoiceActivityDetector vad = VoiceActivityDetector::Create(config, 20);
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if (!vad.Get()) {
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std::cerr << "Failed to create VAD. Please check your config\n";
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exit(-1);
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}
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return vad;
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}
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static sherpa_onnx::cxx::OfflineRecognizer CreateOfflineRecognizer() {
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using namespace sherpa_onnx::cxx; // NOLINT
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OfflineRecognizerConfig config;
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config.model_config.zipformer_ctc.model =
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"./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/model.int8.onnx";
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config.model_config.tokens =
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"./sherpa-onnx-zipformer-ctc-zh-int8-2025-07-03/tokens.txt";
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config.model_config.num_threads = 2;
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config.model_config.debug = false;
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std::cout << "Loading model\n";
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OfflineRecognizer recognizer = OfflineRecognizer::Create(config);
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if (!recognizer.Get()) {
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std::cerr << "Please check your config\n";
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exit(-1);
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}
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std::cout << "Loading model done\n";
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return recognizer;
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}
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int32_t main() {
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signal(SIGINT, Handler);
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using namespace sherpa_onnx::cxx; // NOLINT
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auto vad = CreateVad();
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auto recognizer = CreateOfflineRecognizer();
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sherpa_onnx::Microphone mic;
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PaDeviceIndex num_devices = Pa_GetDeviceCount();
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if (num_devices == 0) {
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std::cerr << " If you are using Linux, please try "
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"./build/bin/zipformer-ctc-simulate-streaming-alsa-cxx-api\n";
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return -1;
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}
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int32_t device_index = Pa_GetDefaultInputDevice();
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const char *pDeviceIndex = std::getenv("SHERPA_ONNX_MIC_DEVICE");
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if (pDeviceIndex) {
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fprintf(stderr, "Use specified device: %s\n", pDeviceIndex);
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device_index = atoi(pDeviceIndex);
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}
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mic.PrintDevices(device_index);
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float mic_sample_rate = 16000;
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const char *sample_rate_str = std::getenv("SHERPA_ONNX_MIC_SAMPLE_RATE");
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if (sample_rate_str) {
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fprintf(stderr, "Use sample rate %f for mic\n", mic_sample_rate);
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mic_sample_rate = atof(sample_rate_str);
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}
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float sample_rate = 16000;
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LinearResampler resampler;
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if (mic_sample_rate != sample_rate) {
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float min_freq = std::min(mic_sample_rate, sample_rate);
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float lowpass_cutoff = 0.99 * 0.5 * min_freq;
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int32_t lowpass_filter_width = 6;
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resampler = LinearResampler::Create(mic_sample_rate, sample_rate,
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lowpass_cutoff, lowpass_filter_width);
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}
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if (mic.OpenDevice(device_index, mic_sample_rate, 1, RecordCallback,
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nullptr) == false) {
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std::cerr << "Failed to open microphone device\n";
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return -1;
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}
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int32_t window_size = 512; // samples, please don't change
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int32_t offset = 0;
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std::vector<float> buffer;
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bool speech_started = false;
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auto started_time = std::chrono::steady_clock::now();
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SherpaDisplay display;
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std::cout << "Started! Please speak\n";
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while (!stop) {
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{
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std::unique_lock<std::mutex> lock(mutex);
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while (samples_queue.empty() && !stop) {
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condition_variable.wait(lock);
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}
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const auto &s = samples_queue.front();
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if (!resampler.Get()) {
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buffer.insert(buffer.end(), s.begin(), s.end());
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} else {
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auto resampled = resampler.Resample(s.data(), s.size(), false);
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buffer.insert(buffer.end(), resampled.begin(), resampled.end());
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}
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samples_queue.pop();
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}
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for (; offset + window_size < buffer.size(); offset += window_size) {
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vad.AcceptWaveform(buffer.data() + offset, window_size);
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if (!speech_started && vad.IsDetected()) {
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speech_started = true;
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started_time = std::chrono::steady_clock::now();
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}
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}
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if (!speech_started) {
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if (buffer.size() > 10 * window_size) {
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offset -= buffer.size() - 10 * window_size;
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buffer = {buffer.end() - 10 * window_size, buffer.end()};
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}
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}
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auto current_time = std::chrono::steady_clock::now();
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const float elapsed_seconds =
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std::chrono::duration_cast<std::chrono::milliseconds>(current_time -
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started_time)
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.count() /
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1000.;
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if (speech_started && elapsed_seconds > 0.2) {
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OfflineStream stream = recognizer.CreateStream();
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stream.AcceptWaveform(sample_rate, buffer.data(), buffer.size());
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recognizer.Decode(&stream);
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OfflineRecognizerResult result = recognizer.GetResult(&stream);
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display.UpdateText(result.text);
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display.Display();
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started_time = std::chrono::steady_clock::now();
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}
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while (!vad.IsEmpty()) {
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auto segment = vad.Front();
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vad.Pop();
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OfflineStream stream = recognizer.CreateStream();
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stream.AcceptWaveform(sample_rate, segment.samples.data(),
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segment.samples.size());
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recognizer.Decode(&stream);
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OfflineRecognizerResult result = recognizer.GetResult(&stream);
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display.UpdateText(result.text);
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display.FinalizeCurrentSentence();
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display.Display();
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buffer.clear();
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offset = 0;
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speech_started = false;
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}
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}
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return 0;
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}
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