sherpa-onnx.cc
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// sherpa-onnx/csrc/sherpa-onnx.cc
//
// Copyright (c) 2022-2023 Xiaomi Corporation
#include <stdio.h>
#include <chrono> // NOLINT
#include <string>
#include <vector>
#include "sherpa-onnx/csrc/online-stream.h"
#include "sherpa-onnx/csrc/online-transducer-greedy-search-decoder.h"
#include "sherpa-onnx/csrc/online-transducer-model-config.h"
#include "sherpa-onnx/csrc/online-transducer-model.h"
#include "sherpa-onnx/csrc/symbol-table.h"
#include "sherpa-onnx/csrc/wave-reader.h"
int main(int32_t argc, char *argv[]) {
if (argc < 6 || argc > 7) {
const char *usage = R"usage(
Usage:
./bin/sherpa-onnx \
/path/to/tokens.txt \
/path/to/encoder.onnx \
/path/to/decoder.onnx \
/path/to/joiner.onnx \
/path/to/foo.wav [num_threads]
Please refer to
https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
for a list of pre-trained models to download.
)usage";
fprintf(stderr, "%s\n", usage);
return 0;
}
std::string tokens = argv[1];
sherpa_onnx::OnlineTransducerModelConfig config;
config.debug = false;
config.encoder_filename = argv[2];
config.decoder_filename = argv[3];
config.joiner_filename = argv[4];
std::string wav_filename = argv[5];
config.num_threads = 2;
if (argc == 7) {
config.num_threads = atoi(argv[6]);
}
fprintf(stderr, "%s\n", config.ToString().c_str());
auto model = sherpa_onnx::OnlineTransducerModel::Create(config);
sherpa_onnx::SymbolTable sym(tokens);
Ort::AllocatorWithDefaultOptions allocator;
int32_t chunk_size = model->ChunkSize();
int32_t chunk_shift = model->ChunkShift();
auto memory_info =
Ort::MemoryInfo::CreateCpu(OrtDeviceAllocator, OrtMemTypeDefault);
std::vector<Ort::Value> states = model->GetEncoderInitStates();
float expected_sampling_rate = 16000;
bool is_ok = false;
std::vector<float> samples =
sherpa_onnx::ReadWave(wav_filename, expected_sampling_rate, &is_ok);
if (!is_ok) {
fprintf(stderr, "Failed to read %s\n", wav_filename.c_str());
return -1;
}
float duration = samples.size() / expected_sampling_rate;
fprintf(stderr, "wav filename: %s\n", wav_filename.c_str());
fprintf(stderr, "wav duration (s): %.3f\n", duration);
auto begin = std::chrono::steady_clock::now();
fprintf(stderr, "Started\n");
sherpa_onnx::OnlineStream stream;
stream.AcceptWaveform(expected_sampling_rate, samples.data(), samples.size());
std::vector<float> tail_paddings(
static_cast<int>(0.2 * expected_sampling_rate));
stream.AcceptWaveform(expected_sampling_rate, tail_paddings.data(),
tail_paddings.size());
stream.InputFinished();
int32_t num_frames = stream.NumFramesReady();
int32_t feature_dim = stream.FeatureDim();
std::array<int64_t, 3> x_shape{1, chunk_size, feature_dim};
sherpa_onnx::OnlineTransducerGreedySearchDecoder decoder(model.get());
std::vector<sherpa_onnx::OnlineTransducerDecoderResult> result = {
decoder.GetEmptyResult()};
while (stream.NumFramesReady() - stream.GetNumProcessedFrames() >
chunk_size) {
std::vector<float> features =
stream.GetFrames(stream.GetNumProcessedFrames(), chunk_size);
stream.GetNumProcessedFrames() += chunk_shift;
Ort::Value x =
Ort::Value::CreateTensor(memory_info, features.data(), features.size(),
x_shape.data(), x_shape.size());
auto pair = model->RunEncoder(std::move(x), states);
states = std::move(pair.second);
decoder.Decode(std::move(pair.first), &result);
}
decoder.StripLeadingBlanks(&result[0]);
const auto &hyp = result[0].tokens;
std::string text;
for (auto t : hyp) {
text += sym[t];
}
fprintf(stderr, "Done!\n");
fprintf(stderr, "Recognition result for %s:\n%s\n", wav_filename.c_str(),
text.c_str());
auto end = std::chrono::steady_clock::now();
float elapsed_seconds =
std::chrono::duration_cast<std::chrono::milliseconds>(end - begin)
.count() /
1000.;
fprintf(stderr, "num threads: %d\n", config.num_threads);
fprintf(stderr, "Elapsed seconds: %.3f s\n", elapsed_seconds);
float rtf = elapsed_seconds / duration;
fprintf(stderr, "Real time factor (RTF): %.3f / %.3f = %.3f\n",
elapsed_seconds, duration, rtf);
return 0;
}