108 lines
3.2 KiB
C++
108 lines
3.2 KiB
C++
// sherpa-onnx/csrc/sherpa-onnx-offline-language-identification.cc
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//
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// Copyright (c) 2022-2024 Xiaomi Corporation
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#include <stdio.h>
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#include <chrono> // NOLINT
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#include <string>
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#include <vector>
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#include "sherpa-onnx/csrc/parse-options.h"
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#include "sherpa-onnx/csrc/spoken-language-identification.h"
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#include "sherpa-onnx/csrc/wave-reader.h"
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int main(int32_t argc, char *argv[]) {
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const char *kUsageMessage = R"usage(
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Spoken language identification with sherpa-onnx.
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Usage:
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(1) Use a whisper multilingual model
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wget https://github.com/k2-fsa/sherpa-onnx/releases/download/asr-models/sherpa-onnx-whisper-tiny.tar.bz2
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tar xvf sherpa-onnx-whisper-tiny.tar.bz2
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rm sherpa-onnx-whisper-tiny.tar.bz2
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We only use the int8.onnx models below.
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./bin/sherpa-onnx-offline-spoken-language-identification \
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--whisper-encoder=sherpa-onnx-whisper-tiny/tiny-encoder.int8.onnx \
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--whisper-decoder=sherpa-onnx-whisper-tiny/tiny-decoder.int8.onnx \
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--num-threads=1 \
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/path/to/foo.wav
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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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You can find test waves for different languages at
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https://hf-mirror.com/spaces/k2-fsa/spoken-language-identification/tree/main/test_wavs
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Please refer to
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https://k2-fsa.github.io/sherpa/onnx/pretrained_models/whisper/index.html
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Note that only whisper multilingual models are supported. For instance,
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"tiny" is supported but "tiny.en" is not.
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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::SpokenLanguageIdentificationConfig 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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fprintf(stderr, "Error: Please provide 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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fprintf(stderr, "Creating spoken language identifier ...\n");
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sherpa_onnx::SpokenLanguageIdentification slid(config);
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fprintf(stderr, "Started\n");
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const std::string wav_filename = po.GetArg(1);
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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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float duration = samples.size() / static_cast<float>(sampling_rate);
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const auto begin = std::chrono::steady_clock::now();
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auto s = slid.CreateStream();
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s->AcceptWaveform(sampling_rate, samples.data(), samples.size());
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auto language = slid.Compute(s.get());
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const auto end = std::chrono::steady_clock::now();
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fprintf(stderr, "Done!\n\n");
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fprintf(stderr, "%s\nDetected language: %s\n", wav_filename.c_str(),
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language.c_str());
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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, "num threads: %d\n", config.num_threads);
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fprintf(stderr, "Elapsed seconds: %.3f s\n", elapsed_seconds);
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float rtf = elapsed_seconds / duration;
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fprintf(stderr, "Real time factor (RTF): %.3f / %.3f = %.3f\n",
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elapsed_seconds, duration, rtf);
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return 0;
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}
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