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sherpa-onnx-offline.cc
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sherpa-onnx-offline.cc
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// sherpa-onnx/csrc/sherpa-onnx-offline.cc
//
// Copyright (c) 2022-2023 Xiaomi Corporation
#include <stdio.h>
#include <chrono> // NOLINT
#include <string>
#include <vector>
#include "sherpa-onnx/csrc/offline-recognizer.h"
#include "sherpa-onnx/csrc/parse-options.h"
#include "sherpa-onnx/csrc/wave-reader.h"
int main(int32_t argc, char *argv[]) {
const char *kUsageMessage = R"usage(
Speech recognition using non-streaming models with sherpa-onnx.
Usage:
(1) Transducer from icefall
See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-transducer/index.html
./bin/sherpa-onnx-offline \
--tokens=/path/to/tokens.txt \
--encoder=/path/to/encoder.onnx \
--decoder=/path/to/decoder.onnx \
--joiner=/path/to/joiner.onnx \
--num-threads=1 \
--decoding-method=greedy_search \
/path/to/foo.wav [bar.wav foobar.wav ...]
(2) Paraformer from FunASR
See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-paraformer/index.html
./bin/sherpa-onnx-offline \
--tokens=/path/to/tokens.txt \
--paraformer=/path/to/model.onnx \
--num-threads=1 \
--decoding-method=greedy_search \
/path/to/foo.wav [bar.wav foobar.wav ...]
(3) Whisper models
See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/whisper/tiny.en.html
./bin/sherpa-onnx-offline \
--whisper-encoder=./sherpa-onnx-whisper-base.en/base.en-encoder.int8.onnx \
--whisper-decoder=./sherpa-onnx-whisper-base.en/base.en-decoder.int8.onnx \
--tokens=./sherpa-onnx-whisper-base.en/base.en-tokens.txt \
--num-threads=1 \
/path/to/foo.wav [bar.wav foobar.wav ...]
(4) NeMo CTC models
See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/index.html
./bin/sherpa-onnx-offline \
--tokens=./sherpa-onnx-nemo-ctc-en-conformer-medium/tokens.txt \
--nemo-ctc-model=./sherpa-onnx-nemo-ctc-en-conformer-medium/model.onnx \
--num-threads=2 \
--decoding-method=greedy_search \
--debug=false \
./sherpa-onnx-nemo-ctc-en-conformer-medium/test_wavs/0.wav \
./sherpa-onnx-nemo-ctc-en-conformer-medium/test_wavs/1.wav \
./sherpa-onnx-nemo-ctc-en-conformer-medium/test_wavs/8k.wav
(5) TDNN CTC model for the yesno recipe from icefall
See https://k2-fsa.github.io/sherpa/onnx/pretrained_models/offline-ctc/yesno/index.html
//
./build/bin/sherpa-onnx-offline \
--sample-rate=8000 \
--feat-dim=23 \
--tokens=./sherpa-onnx-tdnn-yesno/tokens.txt \
--tdnn-model=./sherpa-onnx-tdnn-yesno/model-epoch-14-avg-2.onnx \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_0_1_0_0_0_1.wav \
./sherpa-onnx-tdnn-yesno/test_wavs/0_0_1_0_0_0_1_0.wav
Note: It supports decoding multiple files in batches
foo.wav should be of single channel, 16-bit PCM encoded wave file; its
sampling rate can be arbitrary and does not need to be 16kHz.
Please refer to
https://k2-fsa.github.io/sherpa/onnx/pretrained_models/index.html
for a list of pre-trained models to download.
)usage";
sherpa_onnx::ParseOptions po(kUsageMessage);
sherpa_onnx::OfflineRecognizerConfig config;
config.Register(&po);
po.Read(argc, argv);
if (po.NumArgs() < 1) {
fprintf(stderr, "Error: Please provide at least 1 wave file.\n\n");
po.PrintUsage();
exit(EXIT_FAILURE);
}
fprintf(stderr, "%s\n", config.ToString().c_str());
if (!config.Validate()) {
fprintf(stderr, "Errors in config!\n");
return -1;
}
fprintf(stderr, "Creating recognizer ...\n");
sherpa_onnx::OfflineRecognizer recognizer(config);
fprintf(stderr, "Started\n");
const auto begin = std::chrono::steady_clock::now();
std::vector<std::unique_ptr<sherpa_onnx::OfflineStream>> ss;
std::vector<sherpa_onnx::OfflineStream *> ss_pointers;
float duration = 0;
for (int32_t i = 1; i <= po.NumArgs(); ++i) {
const std::string wav_filename = po.GetArg(i);
int32_t sampling_rate = -1;
bool is_ok = false;
const std::vector<float> samples =
sherpa_onnx::ReadWave(wav_filename, &sampling_rate, &is_ok);
if (!is_ok) {
fprintf(stderr, "Failed to read '%s'\n", wav_filename.c_str());
return -1;
}
duration += samples.size() / static_cast<float>(sampling_rate);
auto s = recognizer.CreateStream();
s->AcceptWaveform(sampling_rate, samples.data(), samples.size());
ss.push_back(std::move(s));
ss_pointers.push_back(ss.back().get());
}
recognizer.DecodeStreams(ss_pointers.data(), ss_pointers.size());
const auto end = std::chrono::steady_clock::now();
fprintf(stderr, "Done!\n\n");
for (int32_t i = 1; i <= po.NumArgs(); ++i) {
fprintf(stderr, "%s\n%s\n----\n", po.GetArg(i).c_str(),
ss[i - 1]->GetResult().AsJsonString().c_str());
}
float elapsed_seconds =
std::chrono::duration_cast<std::chrono::milliseconds>(end - begin)
.count() /
1000.;
fprintf(stderr, "num threads: %d\n", config.model_config.num_threads);
fprintf(stderr, "decoding method: %s\n", config.decoding_method.c_str());
if (config.decoding_method == "modified_beam_search") {
fprintf(stderr, "max active paths: %d\n", config.max_active_paths);
}
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;
}