146 lines
4.0 KiB
C++
146 lines
4.0 KiB
C++
// sherpa-onnx/csrc/sherpa-onnx.cc
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//
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// Copyright (c) 2022-2023 Xiaomi Corporation
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#include <stdio.h>
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#include <chrono> // NOLINT
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#include <iomanip>
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#include <iostream>
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#include <string>
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#include <vector>
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#include "sherpa-onnx/csrc/online-recognizer.h"
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#include "sherpa-onnx/csrc/online-stream.h"
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#include "sherpa-onnx/csrc/parse-options.h"
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#include "sherpa-onnx/csrc/symbol-table.h"
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#include "sherpa-onnx/csrc/wave-reader.h"
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typedef struct {
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std::unique_ptr<sherpa_onnx::OnlineStream> online_stream;
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float duration;
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float elapsed_seconds;
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} Stream;
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int main(int32_t argc, char *argv[]) {
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const char *kUsageMessage = R"usage(
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Usage:
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./bin/sherpa-onnx \
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--tokens=/path/to/tokens.txt \
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--encoder=/path/to/encoder.onnx \
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--decoder=/path/to/decoder.onnx \
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--joiner=/path/to/joiner.onnx \
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--provider=cpu \
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--num-threads=2 \
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--decoding-method=greedy_search \
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/path/to/foo.wav [bar.wav foobar.wav ...]
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Note: It supports decoding multiple files in batches
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Default value for num_threads is 2.
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Valid values for decoding_method: greedy_search (default), modified_beam_search.
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Valid values for provider: cpu (default), cuda, coreml.
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foo.wav should be of single channel, 16-bit PCM encoded wave file; its
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sampling rate can be arbitrary and does not need to be 16kHz.
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Please refer to
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https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
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for a list of pre-trained models to download.
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)usage";
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sherpa_onnx::ParseOptions po(kUsageMessage);
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sherpa_onnx::OnlineRecognizerConfig config;
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config.Register(&po);
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po.Read(argc, argv);
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if (po.NumArgs() < 1) {
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po.PrintUsage();
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exit(EXIT_FAILURE);
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}
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fprintf(stderr, "%s\n", config.ToString().c_str());
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if (!config.Validate()) {
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fprintf(stderr, "Errors in config!\n");
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return -1;
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}
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sherpa_onnx::OnlineRecognizer recognizer(config);
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std::vector<Stream> ss;
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const auto begin = std::chrono::steady_clock::now();
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std::vector<float> durations;
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for (int32_t i = 1; i <= po.NumArgs(); ++i) {
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const std::string wav_filename = po.GetArg(i);
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int32_t sampling_rate = -1;
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bool is_ok = false;
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const std::vector<float> samples =
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sherpa_onnx::ReadWave(wav_filename, &sampling_rate, &is_ok);
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if (!is_ok) {
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fprintf(stderr, "Failed to read %s\n", wav_filename.c_str());
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return -1;
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}
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const float duration = samples.size() / static_cast<float>(sampling_rate);
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auto s = recognizer.CreateStream();
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s->AcceptWaveform(sampling_rate, samples.data(), samples.size());
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std::vector<float> tail_paddings(static_cast<int>(0.8 * sampling_rate));
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// Note: We can call AcceptWaveform() multiple times.
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s->AcceptWaveform(sampling_rate, tail_paddings.data(),
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tail_paddings.size());
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// Call InputFinished() to indicate that no audio samples are available
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s->InputFinished();
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ss.push_back({std::move(s), duration, 0});
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}
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std::vector<sherpa_onnx::OnlineStream *> ready_streams;
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for (;;) {
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ready_streams.clear();
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for (auto &s : ss) {
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const auto p_ss = s.online_stream.get();
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if (recognizer.IsReady(p_ss)) {
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ready_streams.push_back(p_ss);
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} else if (s.elapsed_seconds == 0) {
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const auto end = std::chrono::steady_clock::now();
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const 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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s.elapsed_seconds = elapsed_seconds;
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}
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}
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if (ready_streams.empty()) {
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break;
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}
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recognizer.DecodeStreams(ready_streams.data(), ready_streams.size());
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}
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std::ostringstream os;
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for (int32_t i = 1; i <= po.NumArgs(); ++i) {
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const auto &s = ss[i - 1];
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const float rtf = s.elapsed_seconds / s.duration;
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os << po.GetArg(i) << "\n";
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os << std::setprecision(2) << "Elapsed seconds: " << s.elapsed_seconds
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<< ", Real time factor (RTF): " << rtf << "\n";
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const auto r = recognizer.GetResult(s.online_stream.get());
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os << r.text << "\n";
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os << r.AsJsonString() << "\n\n";
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}
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std::cerr << os.str();
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return 0;
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}
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