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- #include "ggml/ggml.h"
- #include "ggml/ggml-alloc.h"
- #include "math.h"
- #include "model_loader.h"
- #include "fairseq2.h"
- #include "lib/unity_lib.h"
- #include <sndfile.h>
- #include <cstdlib>
- #include "ggml-alloc.h"
- #include <numeric>
- #include <algorithm>
- struct unity_params {
- int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
- std::string model = "seamlessM4T_medium.ggml"; // model path
- std::string input_text = "";
- std::string tgt_lang = "eng";
- std::vector<std::string> files = {};
- bool text = false;
- SequenceGeneratorOptions opts = {
- /*beam_size*/ 5,
- /*min_seq_len*/ 1,
- /*soft_max_seq_len_a*/ 1,
- /*soft_max_seq_len_b*/ 200,
- /*hard_max_seq_len*/ 1000,
- /*len_penalty*/ 1.0,
- /*unk_penalty*/ 0.0,
- /*normalize_scores*/ true,
- /*mem_mb*/ 512
- };
- int32_t max_audio_s = 30;
- bool verbose = false;
- };
- void unity_print_usage(int /*argc*/, char ** argv, const unity_params & params) {
- fprintf(stderr, "usage: %s [options] file1 file2 ...\n", argv[0]);
- fprintf(stderr, "\n");
- fprintf(stderr, "options:\n");
- fprintf(stderr, " -h, --help show this help message and exit\n");
- fprintf(stderr, " -i, --input Input text for the text-2-text translation\n");
- fprintf(stderr, " -l, --tgt-lang Target translation lang (default: %s\n", params.tgt_lang);
- fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
- fprintf(stderr, " -v, --verbose Print out word level confidence score and LID score (default: off)");
- fprintf(stderr, " -m FNAME, --model FNAME\n");
- fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
- fprintf(stderr, " --text text output\n");
- fprintf(stderr, " --beam-size beam size (default: %d)\n", params.opts.beam_size);
- fprintf(stderr, " -M, --mem memory buffer, increase for long inputs (default: %d)\n", params.opts.mem_mb);
- fprintf(stderr, " --max-audio max duration of audio in seconds (default: %d)\n", params.max_audio_s);
- fprintf(stderr, "\n");
- }
- std::string get_next_arg(int& i, int argc, char** argv, const std::string& flag, unity_params& params) {
- if (i + 1 < argc && argv[i + 1][0] != '-') {
- return argv[++i];
- } else {
- fprintf(stderr, "error: %s requires one argument.\n", flag.c_str());
- unity_print_usage(argc, argv, params);
- exit(0);
- }
- }
- bool unity_params_parse(int argc, char ** argv, unity_params & params) {
- for (int i = 1; i < argc; i++) {
- std::string arg = argv[i];
- if (arg == "-h" || arg == "--help") {
- unity_print_usage(argc, argv, params);
- } else if (arg == "-t" || arg == "--threads") {
- params.n_threads = std::stoi(get_next_arg(i, argc, argv, arg, params));
- } else if (arg == "-m" || arg == "--model") {
- params.model = get_next_arg(i, argc, argv, arg, params);
- } else if (arg == "-i" || arg == "--input") {
- params.input_text = get_next_arg(i, argc, argv, arg, params);
- } else if (arg == "-l" || arg == "--tgt-lang") {
- params.tgt_lang = get_next_arg(i, argc, argv, arg, params);
- } else if (arg == "--text") {
- params.text = true;
- } else if (arg == "-b" || arg == "--beam-size") {
- params.opts.beam_size = std::stoi(get_next_arg(i, argc, argv, arg, params));
- } else if (arg == "-v" || arg == "--verbose") {
- params.verbose = true;
- } else if (arg == "-M" || arg == "--mem") {
- params.opts.mem_mb = std::stoi(get_next_arg(i, argc, argv, arg, params));
- } else {
- params.files.push_back(std::string(arg));
- }
- }
- return true;
- }
- int main(int argc, char ** argv) {
- unity_params params;
- if (unity_params_parse(argc, argv, params) == false) {
- return 1;
- }
- fairseq2_model model;
- // load the model
- if (load_fairseq2_ggml_file(model, params.model.c_str())) {
- fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
- return 1;
- }
- // The ctx_size_mb mostly depends of input length and model dim.
- int ctx_size_mb = params.opts.mem_mb;
- auto encoder_buf = std::vector<uint8_t>(8 * 1024 * 1024); // Only tensor metadata goes in there
- auto encoder_fwd_buf = std::vector<uint8_t>(ctx_size_mb * 1024 * 1024 / 2);
- ggml_allocr* fwd_alloc = ggml_allocr_new(encoder_fwd_buf.data(), encoder_fwd_buf.capacity(), 8);
- char result_str[4096];
- std::string input;
- bool interactive = (params.files.size() == 0 && params.input_text.length() == 0);
- auto next_file = params.files.begin();
- // Flag for the input case: true --> s2st, false --> t2tt
- bool s2st_or_t2tt = true;
- // S2ST
- while (true) {
- if (interactive) {
- std::cout << "\nEnter audio_path and tgt_lang, separated by space (or 'exit' to quit):\n";
- std::getline(std::cin, input);
- if (input == "exit") {
- break;
- }
- } else {
- if (params.input_text.length() > 0) {
- break;
- }
- if (next_file == params.files.end() && s2st_or_t2tt) break;
- input = *(next_file++);
- }
- std::istringstream iss(input);
- std::string audio_path;
- std::string tgt_lang = params.tgt_lang;
- iss >> audio_path >> tgt_lang;
- if (audio_path == "-") {
- audio_path = "/proc/self/fd/0";
- }
- std::cerr << "Translating (Transcribing) " << audio_path << " to " << tgt_lang << "\n";
- SF_INFO info;
- SNDFILE* sndfile = sf_open(audio_path.c_str(), SFM_READ, &info);
- if (!sndfile) {
- std::cerr << "Could not open file\n";
- if (interactive) continue;
- else return 1;
- }
- // Load audio input
- GGML_ASSERT(info.samplerate == 16000);
- GGML_ASSERT(info.channels == 1);
- // Truncate audio input. Ideally we should chunk it, but this will prevent most obvious OOM.
- int n_frames = std::min(info.samplerate * params.max_audio_s, (int)info.frames);
- std::vector<float> data(n_frames * info.channels);
- sf_readf_float(sndfile, data.data(), n_frames);
- Result result = unity_eval_speech(model, data, params.opts, tgt_lang, params.n_threads);
- std::string concat_transcription = std::accumulate(std::next(result.transcription.begin()), result.transcription.end(), result.transcription[0],
- [](const std::string& a, const std::string& b) {
- return a + " " + b;
- }
- );
- if (params.verbose) {
- std::cout << "Final transcription: " << concat_transcription << std::endl;
- std::cout << std::endl;
- std::cout << "Word level confidence score:" << std::endl;
- for (size_t i = 0; i < result.transcription.size(); ++i) {
- std::cout << "Word: " << result.transcription[i] << " | Score: " << result.word_confidence_scores[i] << std::endl;
- }
- std::cout << std::endl;
- std::cout << "LID scores: " << std::endl;
- for (const auto& kv : result.lid_scores) {
- std::cout << "Language: " << kv.first << "| Score: " << kv.second << std::endl;
- }
- } else {
- std::cout << concat_transcription << std::endl;
- }
- }
- // T2TT
- if (params.input_text.length() > 0) {
- // tokenize the input text
- Result result = unity_eval_text(model, params.input_text, params.opts, params.tgt_lang, params.n_threads);
- std::string concat_translation = std::accumulate(std::next(result.transcription.begin()), result.transcription.end(), result.transcription[0],
- [](const std::string& a, const std::string& b) {
- return a + " " + b;
- }
- );
- std::cout << "Translation: " << concat_translation << std::endl;
- }
- return 0;
- }
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