133 lines
4.2 KiB
C++
133 lines
4.2 KiB
C++
// cxx-api-examples/streaming-zipformer-rtf-cxx-api.cc
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// Copyright (c) 2024 Xiaomi Corporation
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//
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// This file demonstrates how to use streaming Zipformer
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// with sherpa-onnx's C++ API.
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//
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// clang-format off
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//
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// cd /path/sherpa-onnx/
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// mkdir build
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// cd build
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// cmake ..
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// make
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//
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// wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20.tar.bz2
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// tar xvf sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20.tar.bz2
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// rm sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20.tar.bz2
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//
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// # 1. Test on CPU, run once
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//
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// ./bin/streaming-zipformer-rtf-cxx-api
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//
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// # 2. Test on CPU, run 10 times
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//
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// ./bin/streaming-zipformer-rtf-cxx-api 10
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//
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// # 3. Test on GPU, run 10 times
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//
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// ./bin/streaming-zipformer-rtf-cxx-api 10 cuda
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//
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// clang-format on
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#include <chrono> // NOLINT
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#include <iostream>
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#include <string>
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#include "sherpa-onnx/c-api/cxx-api.h"
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int32_t main(int argc, char *argv[]) {
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int32_t num_runs = 1;
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if (argc >= 2) {
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num_runs = atoi(argv[1]);
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if (num_runs < 0) {
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num_runs = 1;
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}
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}
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bool use_gpu = (argc == 3);
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using namespace sherpa_onnx::cxx; // NOLINT
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OnlineRecognizerConfig config;
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// please see
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// https://k2-fsa.github.io/sherpa/onnx/pretrained_models/online-transducer/zipformer-transducer-models.html#csukuangfj-sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20-bilingual-chinese-english
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config.model_config.transducer.encoder =
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"./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/"
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"encoder-epoch-99-avg-1.int8.onnx";
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// Note: We recommend not using int8.onnx for the decoder.
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config.model_config.transducer.decoder =
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"./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/"
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"decoder-epoch-99-avg-1.onnx";
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config.model_config.transducer.joiner =
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"./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/"
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"joiner-epoch-99-avg-1.int8.onnx";
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config.model_config.tokens =
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"./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/tokens.txt";
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config.model_config.num_threads = 1;
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config.model_config.provider = use_gpu ? "cuda" : "cpu";
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std::cout << "Loading model\n";
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OnlineRecognizer recognizer = OnlineRecognizer::Create(config);
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if (!recognizer.Get()) {
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std::cerr << "Please check your config\n";
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return -1;
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}
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std::cout << "Loading model done\n";
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std::string wave_filename =
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"./sherpa-onnx-streaming-zipformer-bilingual-zh-en-2023-02-20/test_wavs/"
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"0.wav";
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Wave wave = ReadWave(wave_filename);
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if (wave.samples.empty()) {
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std::cerr << "Failed to read: '" << wave_filename << "'\n";
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return -1;
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}
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std::cout << "Start recognition\n";
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float total_elapsed_seconds = 0;
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OnlineRecognizerResult result;
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for (int32_t i = 0; i < num_runs; ++i) {
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const auto begin = std::chrono::steady_clock::now();
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OnlineStream stream = recognizer.CreateStream();
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stream.AcceptWaveform(wave.sample_rate, wave.samples.data(),
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wave.samples.size());
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stream.InputFinished();
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while (recognizer.IsReady(&stream)) {
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recognizer.Decode(&stream);
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}
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result = recognizer.GetResult(&stream);
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auto end = std::chrono::steady_clock::now();
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float elapsed_seconds =
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std::chrono::duration_cast<std::chrono::milliseconds>(end - begin)
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.count() /
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1000.;
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printf("Run %d/%d, elapsed seconds: %.3f\n", i, num_runs, elapsed_seconds);
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total_elapsed_seconds += elapsed_seconds;
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}
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float average_elapsed_secodns = total_elapsed_seconds / num_runs;
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float duration = wave.samples.size() / static_cast<float>(wave.sample_rate);
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float rtf = total_elapsed_seconds / num_runs / duration;
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std::cout << "text: " << result.text << "\n";
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printf("Number of threads: %d\n", config.model_config.num_threads);
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printf("Duration: %.3fs\n", duration);
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printf("Total Elapsed seconds: %.3fs\n", total_elapsed_seconds);
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printf("Num runs: %d\n", num_runs);
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printf("Elapsed seconds per run: %.3f/%d=%.3f\n", total_elapsed_seconds,
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num_runs, average_elapsed_secodns);
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printf("(Real time factor) RTF = %.3f / %.3f = %.3f\n",
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average_elapsed_secodns, duration, rtf);
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return 0;
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}
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