306 lines
10 KiB
C++
306 lines
10 KiB
C++
// sherpa-onnx/csrc/sherpa-onnx-offline-parallel.cc
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//
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// Copyright (c) 2022-2023 cuidc
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#include <stdio.h>
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#include <atomic>
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#include <chrono> // NOLINT
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#include <fstream>
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#include <mutex> // NOLINT
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#include <string>
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#include <thread> // NOLINT
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#include <vector>
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#include "sherpa-onnx/csrc/offline-recognizer.h"
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#include "sherpa-onnx/csrc/parse-options.h"
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#include "sherpa-onnx/csrc/wave-reader.h"
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std::atomic<int> wav_index(0);
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std::mutex mtx;
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std::vector<std::vector<std::string>> SplitToBatches(
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const std::vector<std::string> &input, int32_t batch_size) {
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std::vector<std::vector<std::string>> outputs;
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auto itr = input.cbegin();
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int32_t process_num = 0;
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while (process_num + batch_size <= static_cast<int32_t>(input.size())) {
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auto chunk_end = itr + batch_size;
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outputs.emplace_back(itr, chunk_end);
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itr = chunk_end;
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process_num += batch_size;
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}
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if (itr != input.cend()) {
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outputs.emplace_back(itr, input.cend());
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}
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return outputs;
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}
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std::vector<std::string> LoadScpFile(const std::string &wav_scp_path) {
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std::vector<std::string> wav_paths;
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std::ifstream in(wav_scp_path);
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if (!in.is_open()) {
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fprintf(stderr, "Failed to open file: %s.\n", wav_scp_path.c_str());
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return wav_paths;
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}
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std::string line, column1, column2;
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while (std::getline(in, line)) {
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std::istringstream iss(line);
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iss >> column1 >> column2;
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wav_paths.emplace_back(std::move(column2));
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}
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return wav_paths;
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}
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void AsrInference(const std::vector<std::vector<std::string>> &chunk_wav_paths,
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sherpa_onnx::OfflineRecognizer *recognizer,
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float *total_length, float *total_time) {
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std::vector<std::unique_ptr<sherpa_onnx::OfflineStream>> ss;
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std::vector<sherpa_onnx::OfflineStream *> ss_pointers;
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float duration = 0.0f;
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float elapsed_seconds_batch = 0.0f;
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// warm up
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for (const auto &wav_filename : chunk_wav_paths[0]) {
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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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continue;
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}
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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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ss.push_back(std::move(s));
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ss_pointers.push_back(ss.back().get());
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}
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recognizer->DecodeStreams(ss_pointers.data(), ss_pointers.size());
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ss_pointers.clear();
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ss.clear();
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while (true) {
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int chunk = wav_index.fetch_add(1);
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if (chunk >= chunk_wav_paths.size()) {
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break;
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}
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const auto &wav_paths = chunk_wav_paths[chunk];
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const auto begin = std::chrono::steady_clock::now();
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for (const auto &wav_filename : wav_paths) {
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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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continue;
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}
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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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ss.push_back(std::move(s));
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ss_pointers.push_back(ss.back().get());
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}
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recognizer->DecodeStreams(ss_pointers.data(), ss_pointers.size());
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const 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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elapsed_seconds_batch += elapsed_seconds;
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int i = 0;
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for (const auto &wav_filename : wav_paths) {
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fprintf(stderr, "%s\n%s\n----\n", wav_filename.c_str(),
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ss[i]->GetResult().AsJsonString().c_str());
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i = i + 1;
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}
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ss_pointers.clear();
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ss.clear();
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}
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{
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std::lock_guard<std::mutex> guard(mtx);
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*total_length += duration;
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if (*total_time < elapsed_seconds_batch) {
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*total_time = elapsed_seconds_batch;
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}
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}
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}
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int main(int32_t argc, char *argv[]) {
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const char *kUsageMessage = R"usage(
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Speech recognition using non-streaming models with sherpa-onnx.
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Usage:
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(1) Transducer from icefall
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See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-transducer/index.html
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./bin/sherpa-onnx-offline-parallel \
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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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--num-threads=1 \
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--decoding-method=greedy_search \
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--batch-size=8 \
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--nj=1 \
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--wav-scp=wav.scp
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./bin/sherpa-onnx-offline-parallel \
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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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--num-threads=1 \
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--decoding-method=greedy_search \
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--batch-size=1 \
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--nj=8 \
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/path/to/foo.wav [bar.wav foobar.wav ...]
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(2) Paraformer from FunASR
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See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-paraformer/index.html
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./bin/sherpa-onnx-offline-parallel \
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--tokens=/path/to/tokens.txt \
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--paraformer=/path/to/model.onnx \
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--num-threads=1 \
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--decoding-method=greedy_search \
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/path/to/foo.wav [bar.wav foobar.wav ...]
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(3) Whisper models
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See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/whisper/tiny.en.html
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./bin/sherpa-onnx-offline-parallel \
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--whisper-encoder=./sherpa-onnx-whisper-base.en/base.en-encoder.int8.onnx \
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--whisper-decoder=./sherpa-onnx-whisper-base.en/base.en-decoder.int8.onnx \
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--tokens=./sherpa-onnx-whisper-base.en/base.en-tokens.txt \
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--num-threads=1 \
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/path/to/foo.wav [bar.wav foobar.wav ...]
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(4) NeMo CTC models
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See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/index.html
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./bin/sherpa-onnx-offline-parallel \
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--tokens=./sherpa-onnx-nemo-ctc-en-conformer-medium/tokens.txt \
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--nemo-ctc-model=./sherpa-onnx-nemo-ctc-en-conformer-medium/model.onnx \
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--num-threads=2 \
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--decoding-method=greedy_search \
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--debug=false \
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./sherpa-onnx-nemo-ctc-en-conformer-medium/test_wavs/0.wav \
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./sherpa-onnx-nemo-ctc-en-conformer-medium/test_wavs/1.wav \
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./sherpa-onnx-nemo-ctc-en-conformer-medium/test_wavs/8k.wav
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(5) TDNN CTC model for the yesno recipe from icefall
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See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/yesno/index.html
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//
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./bin/sherpa-onnx-offline-parallel \
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--sample-rate=8000 \
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--feat-dim=23 \
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--tokens=./sherpa-onnx-tdnn-yesno/tokens.txt \
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--tdnn-model=./sherpa-onnx-tdnn-yesno/model-epoch-14-avg-2.onnx \
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./sherpa-onnx-tdnn-yesno/test_wavs/0_0_0_1_0_0_0_1.wav \
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./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_0_0_1_0.wav
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Note: It supports decoding multiple files in batches
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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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std::string wav_scp = ""; // file path, kaldi style wav list.
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int32_t nj = 1; // thread number
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int32_t batch_size = 1; // number of wav files processed at once.
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sherpa_onnx::ParseOptions po(kUsageMessage);
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sherpa_onnx::OfflineRecognizerConfig config;
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config.Register(&po);
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po.Register("wav-scp", &wav_scp,
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"a file including wav-id and wav-path, kaldi style wav list."
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"default="
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". when it is not empty, wav files which positional "
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"parameters provide are invalid.");
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po.Register("nj", &nj, "multi-thread num for decoding, default=1");
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po.Register("batch-size", &batch_size,
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"number of wav files processed at once during the decoding"
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"process. default=1");
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po.Read(argc, argv);
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if (po.NumArgs() < 1 && wav_scp.empty()) {
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fprintf(stderr, "Error: Please provide at least 1 wave file.\n\n");
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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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std::this_thread::sleep_for(std::chrono::seconds(10)); // sleep 10s
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fprintf(stderr, "Creating recognizer ...\n");
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const auto begin = std::chrono::steady_clock::now();
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sherpa_onnx::OfflineRecognizer recognizer(config);
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const 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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fprintf(stderr,
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"Started nj: %d, batch_size: %d, wav_path: %s. recognizer init time: "
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"%.6f\n",
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nj, batch_size, wav_scp.c_str(), elapsed_seconds);
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std::this_thread::sleep_for(std::chrono::seconds(10)); // sleep 10s
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std::vector<std::string> wav_paths;
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if (!wav_scp.empty()) {
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wav_paths = LoadScpFile(wav_scp);
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} else {
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for (int32_t i = 1; i <= po.NumArgs(); ++i) {
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wav_paths.emplace_back(po.GetArg(i));
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}
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}
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if (wav_paths.empty()) {
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fprintf(stderr, "wav files is empty.\n");
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return -1;
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}
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std::vector<std::thread> threads;
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std::vector<std::vector<std::string>> batch_wav_paths =
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SplitToBatches(wav_paths, batch_size);
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float total_length = 0.0f;
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float total_time = 0.0f;
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for (int i = 0; i < nj; i++) {
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threads.emplace_back(std::thread(AsrInference, batch_wav_paths, &recognizer,
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&total_length, &total_time));
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}
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for (auto &thread : threads) {
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thread.join();
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}
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fprintf(stderr, "num threads: %d\n", config.model_config.num_threads);
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fprintf(stderr, "decoding method: %s\n", config.decoding_method.c_str());
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if (config.decoding_method == "modified_beam_search") {
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fprintf(stderr, "max active paths: %d\n", config.max_active_paths);
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}
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fprintf(stderr, "Elapsed seconds: %.3f s\n", total_time);
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float rtf = total_time / total_length;
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fprintf(stderr, "Real time factor (RTF): %.6f / %.6f = %.4f\n", total_time,
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total_length, rtf);
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fprintf(stderr, "SPEEDUP: %.4f\n", 1.0 / rtf);
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return 0;
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}
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