884 lines
30 KiB
C++
884 lines
30 KiB
C++
#define _USE_MATH_DEFINES // for M_PI
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#include "common.h"
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// third-party utilities
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// use your favorite implementations
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#define DR_WAV_IMPLEMENTATION
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#include "dr_wav.h"
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#include <cmath>
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#include <cstring>
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#include <fstream>
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#include <regex>
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#include <locale>
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#include <codecvt>
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#include <sstream>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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// Function to check if the next argument exists
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std::string get_next_arg(int& i, int argc, char** argv, const std::string& flag, gpt_params& params) {
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if (i + 1 < argc && argv[i + 1][0] != '-') {
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return argv[++i];
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} else {
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fprintf(stderr, "error: %s requires one argument.\n", flag.c_str());
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gpt_print_usage(argc, argv, params);
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exit(0);
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}
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}
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
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for (int i = 1; i < argc; i++) {
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std::string arg = argv[i];
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if (arg == "-s" || arg == "--seed") {
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params.seed = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-t" || arg == "--threads") {
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params.n_threads = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-p" || arg == "--prompt") {
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params.prompt = get_next_arg(i, argc, argv, arg, params);
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} else if (arg == "-n" || arg == "--n_predict") {
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params.n_predict = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-np" || arg == "--n_parallel") {
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params.n_parallel = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "--top_k") {
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params.top_k = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "--top_p") {
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params.top_p = std::stof(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "--temp") {
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params.temp = std::stof(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "--repeat-last-n") {
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params.repeat_last_n = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "--repeat-penalty") {
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params.repeat_penalty = std::stof(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-b" || arg == "--batch_size") {
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params.n_batch= std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-c" || arg == "--context") {
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params.n_ctx= std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") {
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params.n_gpu_layers = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "--ignore-eos") {
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params.ignore_eos = true;
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} else if (arg == "-m" || arg == "--model") {
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params.model = get_next_arg(i, argc, argv, arg, params);
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} else if (arg == "-i" || arg == "--interactive") {
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params.interactive = true;
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} else if (arg == "-ip" || arg == "--interactive-port") {
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params.interactive = true;
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params.interactive_port = std::stoi(get_next_arg(i, argc, argv, arg, params));
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} else if (arg == "-h" || arg == "--help") {
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gpt_print_usage(argc, argv, params);
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exit(0);
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} else if (arg == "-f" || arg == "--file") {
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get_next_arg(i, argc, argv, arg, params);
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std::ifstream file(argv[i]);
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if (!file) {
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fprintf(stderr, "error: failed to open file '%s'\n", argv[i]);
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break;
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}
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std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
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if (params.prompt.back() == '\n') {
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params.prompt.pop_back();
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}
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} else if (arg == "-tt" || arg == "--token_test") {
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params.token_test = get_next_arg(i, argc, argv, arg, params);
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}
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else {
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fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
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gpt_print_usage(argc, argv, params);
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exit(0);
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}
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}
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return true;
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}
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void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
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fprintf(stderr, "usage: %s [options]\n", argv[0]);
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fprintf(stderr, "\n");
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fprintf(stderr, "options:\n");
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fprintf(stderr, " -h, --help show this help message and exit\n");
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fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n");
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fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
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fprintf(stderr, " -p PROMPT, --prompt PROMPT\n");
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fprintf(stderr, " prompt to start generation with (default: random)\n");
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fprintf(stderr, " -f FNAME, --file FNAME\n");
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fprintf(stderr, " load prompt from a file\n");
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fprintf(stderr, " -tt TOKEN_TEST, --token_test TOKEN_TEST\n");
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fprintf(stderr, " test tokenization\n");
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fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d)\n", params.n_predict);
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fprintf(stderr, " --top_k N top-k sampling (default: %d)\n", params.top_k);
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fprintf(stderr, " --top_p N top-p sampling (default: %.1f)\n", params.top_p);
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fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp);
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fprintf(stderr, " --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled)\n", params.repeat_last_n);
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fprintf(stderr, " --repeat-penalty N penalize repeat sequence of tokens (default: %.2f, 1.0 = disabled)\n", (double)params.repeat_penalty);
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fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch);
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fprintf(stderr, " -c N, --context N context / KV cache size (default: %d)\n", params.n_ctx);
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fprintf(stderr, " --ignore-eos ignore EOS token during generation\n");
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fprintf(stderr, " -ngl N, --gpu-layers N number of layers to offload to GPU on supported models (default: %d)\n", params.n_gpu_layers);
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fprintf(stderr, " -m FNAME, --model FNAME\n");
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fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
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fprintf(stderr, "\n");
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}
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std::string gpt_random_prompt(std::mt19937 & rng) {
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const int r = rng() % 10;
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switch (r) {
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case 0: return "So";
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case 1: return "Once upon a time";
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case 2: return "When";
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case 3: return "The";
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case 4: return "After";
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case 5: return "If";
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case 6: return "import";
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case 7: return "He";
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case 8: return "She";
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case 9: return "They";
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default: return "To";
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}
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return "The";
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}
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std::string trim(const std::string & s) {
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std::regex e("^\\s+|\\s+$");
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return std::regex_replace(s, e, "");
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}
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std::string replace(const std::string & s, const std::string & from, const std::string & to) {
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std::string result = s;
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size_t pos = 0;
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while ((pos = result.find(from, pos)) != std::string::npos) {
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result.replace(pos, from.length(), to);
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pos += to.length();
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}
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return result;
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}
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void gpt_vocab::add_special_token(const std::string & token) {
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special_tokens.push_back(token);
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}
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std::map<std::string, int32_t> json_parse(const std::string & fname) {
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std::map<std::string, int32_t> result;
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// read file into string
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std::string json;
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{
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std::ifstream ifs(fname);
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if (!ifs) {
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fprintf(stderr, "Failed to open %s\n", fname.c_str());
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exit(1);
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}
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json = std::string((std::istreambuf_iterator<char>(ifs)),
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(std::istreambuf_iterator<char>()));
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}
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if (json[0] != '{') {
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return result;
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}
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// parse json
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{
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bool has_key = false;
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bool in_token = false;
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std::string str_key = "";
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std::string str_val = "";
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int n = json.size();
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for (int i = 1; i < n; ++i) {
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if (!in_token) {
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if (json[i] == ' ') continue;
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if (json[i] == '"') {
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in_token = true;
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continue;
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}
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} else {
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if (json[i] == '\\' && i+1 < n) {
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if (has_key == false) {
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str_key += json[i];
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} else {
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str_val += json[i];
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}
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++i;
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} else if (json[i] == '"') {
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if (has_key == false) {
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has_key = true;
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++i;
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while (json[i] == ' ') ++i;
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++i; // :
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while (json[i] == ' ') ++i;
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if (json[i] != '\"') {
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while (json[i] != ',' && json[i] != '}') {
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str_val += json[i++];
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}
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has_key = false;
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} else {
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in_token = true;
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continue;
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}
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} else {
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has_key = false;
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}
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str_key = ::replace(str_key, "\\u0120", " " ); // \u0120 -> space
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str_key = ::replace(str_key, "\\u010a", "\n"); // \u010a -> new line
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str_key = ::replace(str_key, "\\\"", "\""); // \\\" -> "
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try {
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result[str_key] = std::stoi(str_val);
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} catch (...) {
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//fprintf(stderr, "%s: ignoring key '%s' with value '%s'\n", fname.c_str(), str_key.c_str(), str_val.c_str());
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}
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str_key = "";
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str_val = "";
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in_token = false;
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continue;
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}
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if (has_key == false) {
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str_key += json[i];
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} else {
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str_val += json[i];
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}
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}
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}
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}
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return result;
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}
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std::string convert_to_utf8(const std::wstring & input) {
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std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
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return converter.to_bytes(input);
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}
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std::wstring convert_to_wstring(const std::string & input) {
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std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
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return converter.from_bytes(input);
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}
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void gpt_split_words(std::string str, std::vector<std::string>& words) {
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const std::string pattern = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
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const std::regex re(pattern);
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std::smatch m;
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while (std::regex_search(str, m, re)) {
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for (auto x : m) {
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words.push_back(x);
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}
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str = m.suffix();
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}
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}
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std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) {
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std::vector<std::string> words;
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// first split the text into words
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{
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std::string str = text;
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// Generate the subpattern from the special_tokens vector if it's not empty
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if (!vocab.special_tokens.empty()) {
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const std::regex escape(R"([\[\\\^\$\.\|\?\*\+\(\)\{\}])");
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std::string special_tokens_subpattern;
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for (const auto & token : vocab.special_tokens) {
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if (!special_tokens_subpattern.empty()) {
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special_tokens_subpattern += "|";
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}
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special_tokens_subpattern += std::regex_replace(token, escape, R"(\$&)");
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}
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std::regex re(special_tokens_subpattern);
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std::smatch m;
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// Split the text by special tokens.
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while (std::regex_search(str, m, re)) {
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// Split the substrings in-between special tokens into words.
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gpt_split_words(m.prefix(), words);
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// Add matched special tokens as words.
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for (auto x : m) {
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words.push_back(x);
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}
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str = m.suffix();
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}
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// Remaining text without special tokens will be handled below.
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}
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gpt_split_words(str, words);
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}
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// find the longest token that forms each word in words:
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std::vector<gpt_vocab::id> tokens;
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for (const auto & word : words) {
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for (int i = 0; i < (int) word.size(); ){
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for (int j = word.size() - 1; j >= i; j--){
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auto cand = word.substr(i, j-i+1);
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auto it = vocab.token_to_id.find(cand);
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if (it != vocab.token_to_id.end()){ // word.substr(i, j-i+1) in vocab
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tokens.push_back(it->second);
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i = j + 1;
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break;
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}
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else if (j == i){ // word.substr(i, 1) has no matching
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fprintf(stderr, "%s: unknown token '%s'\n", __func__, word.substr(i, 1).data());
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i++;
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}
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}
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}
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}
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return tokens;
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}
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std::vector<gpt_vocab::id> parse_tokens_from_string(const std::string& input, char delimiter) {
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std::vector<gpt_vocab::id> output;
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std::stringstream ss(input);
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std::string token;
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while (std::getline(ss, token, delimiter)) {
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output.push_back(std::stoi(token));
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}
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return output;
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}
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std::map<std::string, std::vector<gpt_vocab::id>> extract_tests_from_file(const std::string & fpath_test){
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if (fpath_test.empty()){
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fprintf(stderr, "%s : No test file found.\n", __func__);
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return std::map<std::string, std::vector<gpt_vocab::id>>();
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}
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std::map<std::string, std::vector<gpt_vocab::id>> tests;
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auto fin = std::ifstream(fpath_test, std::ios_base::in);
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const char * delimeter = " => ";
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const char del_tok = ',';
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std::string line;
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while (std::getline(fin, line)) {
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size_t delimiterPos = line.find(delimeter);
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if (delimiterPos != std::string::npos) {
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std::string text = line.substr(0, delimiterPos);
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std::string s_tokens = line.substr(delimiterPos + std::strlen(delimeter));
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tests[text] = parse_tokens_from_string(s_tokens, del_tok);
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}
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}
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return tests;
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}
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void test_gpt_tokenizer(gpt_vocab & vocab, const std::string & fpath_test){
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std::map<std::string, std::vector<gpt_vocab::id>> tests = extract_tests_from_file(fpath_test);
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size_t n_fails = 0;
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for (const auto & test : tests) {
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std::vector<gpt_vocab::id> tokens = gpt_tokenize(vocab, test.first);
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if (tokens != test.second){
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n_fails++;
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// print out failure cases
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fprintf(stderr, "%s : failed test: '%s'\n", __func__, test.first.c_str());
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fprintf(stderr, "%s : tokens in hf: ", __func__);
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for (const auto & t : test.second) {
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fprintf(stderr, "%s(%d), ", vocab.id_to_token[t].c_str(), t);
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}
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fprintf(stderr, "\n");
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fprintf(stderr, "%s : tokens in ggml: ", __func__);
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for (const auto & t : tokens) {
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fprintf(stderr, "%s(%d), ", vocab.id_to_token[t].c_str(), t);
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}
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fprintf(stderr, "\n");
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}
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}
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fprintf(stderr, "%s : %zu tests failed out of %zu tests.\n", __func__, n_fails, tests.size());
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}
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bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {
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printf("%s: loading vocab from '%s'\n", __func__, fname.c_str());
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vocab.token_to_id = ::json_parse(fname);
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for (const auto & kv : vocab.token_to_id) {
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vocab.id_to_token[kv.second] = kv.first;
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}
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printf("%s: vocab size = %d\n", __func__, (int) vocab.token_to_id.size());
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// print the vocabulary
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//for (auto kv : vocab.token_to_id) {
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// printf("'%s' -> %d\n", kv.first.data(), kv.second);
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//}
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return true;
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}
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gpt_vocab::id gpt_sample_top_k_top_p(
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const gpt_vocab & vocab,
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const float * logits,
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int top_k,
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double top_p,
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double temp,
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std::mt19937 & rng) {
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int n_logits = vocab.id_to_token.size();
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std::vector<std::pair<double, gpt_vocab::id>> logits_id;
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logits_id.reserve(n_logits);
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{
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const double scale = 1.0/temp;
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for (int i = 0; i < n_logits; ++i) {
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logits_id.push_back(std::make_pair(logits[i]*scale, i));
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}
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}
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// find the top K tokens
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std::partial_sort(
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logits_id.begin(),
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logits_id.begin() + top_k, logits_id.end(),
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[](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
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return a.first > b.first;
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});
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logits_id.resize(top_k);
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double maxl = -INFINITY;
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for (const auto & kv : logits_id) {
|
|
maxl = std::max(maxl, kv.first);
|
|
}
|
|
|
|
// compute probs for the top K tokens
|
|
std::vector<double> probs;
|
|
probs.reserve(logits_id.size());
|
|
|
|
double sum = 0.0;
|
|
for (const auto & kv : logits_id) {
|
|
double p = exp(kv.first - maxl);
|
|
probs.push_back(p);
|
|
sum += p;
|
|
}
|
|
|
|
// normalize the probs
|
|
for (auto & p : probs) {
|
|
p /= sum;
|
|
}
|
|
|
|
if (top_p < 1.0f) {
|
|
double cumsum = 0.0f;
|
|
for (int i = 0; i < top_k; i++) {
|
|
cumsum += probs[i];
|
|
if (cumsum >= top_p) {
|
|
top_k = i + 1;
|
|
probs.resize(top_k);
|
|
logits_id.resize(top_k);
|
|
break;
|
|
}
|
|
}
|
|
|
|
cumsum = 1.0/cumsum;
|
|
for (int i = 0; i < (int) probs.size(); i++) {
|
|
probs[i] *= cumsum;
|
|
}
|
|
}
|
|
|
|
//printf("\n");
|
|
//for (int i = 0; i < (int) probs.size(); i++) {
|
|
// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
|
|
//}
|
|
//exit(0);
|
|
|
|
std::discrete_distribution<> dist(probs.begin(), probs.end());
|
|
int idx = dist(rng);
|
|
|
|
return logits_id[idx].second;
|
|
}
|
|
|
|
gpt_vocab::id gpt_sample_top_k_top_p_repeat(
|
|
const gpt_vocab & vocab,
|
|
const float * logits,
|
|
const int32_t * last_n_tokens_data,
|
|
size_t last_n_tokens_data_size,
|
|
int top_k,
|
|
double top_p,
|
|
double temp,
|
|
int repeat_last_n,
|
|
float repeat_penalty,
|
|
std::mt19937 & rng) {
|
|
|
|
int n_logits = vocab.id_to_token.size();
|
|
|
|
const auto * plogits = logits;
|
|
|
|
const auto last_n_tokens = std::vector<int32_t>(last_n_tokens_data, last_n_tokens_data + last_n_tokens_data_size);
|
|
|
|
if (temp <= 0) {
|
|
// select the token with the highest logit directly
|
|
float max_logit = plogits[0];
|
|
gpt_vocab::id max_id = 0;
|
|
|
|
for (int i = 1; i < n_logits; ++i) {
|
|
if (plogits[i] > max_logit) {
|
|
max_logit = plogits[i];
|
|
max_id = i;
|
|
}
|
|
}
|
|
return max_id;
|
|
}
|
|
|
|
|
|
std::vector<std::pair<double, gpt_vocab::id>> logits_id;
|
|
logits_id.reserve(n_logits);
|
|
|
|
{
|
|
const float scale = 1.0f/temp;
|
|
for (int i = 0; i < n_logits; ++i) {
|
|
// repetition penalty from ctrl paper (https://arxiv.org/abs/1909.05858)
|
|
// credit https://github.com/facebookresearch/llama/compare/main...shawwn:llama:main
|
|
if (repeat_last_n > 0 && std::find(last_n_tokens.end()-repeat_last_n, last_n_tokens.end(), i) != last_n_tokens.end()) {
|
|
// if score < 0 then repetition penalty has to multiplied to reduce the previous token probability
|
|
if (plogits[i] < 0.0f) {
|
|
logits_id.push_back(std::make_pair(plogits[i]*scale*repeat_penalty, i));
|
|
} else {
|
|
logits_id.push_back(std::make_pair(plogits[i]*scale/repeat_penalty, i));
|
|
}
|
|
} else {
|
|
logits_id.push_back(std::make_pair(plogits[i]*scale, i));
|
|
}
|
|
}
|
|
}
|
|
|
|
// find the top K tokens
|
|
std::partial_sort(
|
|
logits_id.begin(),
|
|
logits_id.begin() + top_k, logits_id.end(),
|
|
[](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
|
|
return a.first > b.first;
|
|
});
|
|
|
|
logits_id.resize(top_k);
|
|
|
|
double maxl = -INFINITY;
|
|
for (const auto & kv : logits_id) {
|
|
maxl = std::max(maxl, kv.first);
|
|
}
|
|
|
|
// compute probs for the top K tokens
|
|
std::vector<double> probs;
|
|
probs.reserve(logits_id.size());
|
|
|
|
double sum = 0.0;
|
|
for (const auto & kv : logits_id) {
|
|
double p = exp(kv.first - maxl);
|
|
probs.push_back(p);
|
|
sum += p;
|
|
}
|
|
|
|
// normalize the probs
|
|
for (auto & p : probs) {
|
|
p /= sum;
|
|
}
|
|
|
|
if (top_p < 1.0f) {
|
|
double cumsum = 0.0f;
|
|
for (int i = 0; i < top_k; i++) {
|
|
cumsum += probs[i];
|
|
if (cumsum >= top_p) {
|
|
top_k = i + 1;
|
|
probs.resize(top_k);
|
|
logits_id.resize(top_k);
|
|
break;
|
|
}
|
|
}
|
|
|
|
cumsum = 1.0/cumsum;
|
|
for (int i = 0; i < (int) probs.size(); i++) {
|
|
probs[i] *= cumsum;
|
|
}
|
|
}
|
|
|
|
// printf("\n");
|
|
// for (int i = 0; i < (int) probs.size(); i++) {
|
|
// for (int i = 0; i < 10; i++) {
|
|
// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), probs[i]);
|
|
// }
|
|
|
|
std::discrete_distribution<> dist(probs.begin(), probs.end());
|
|
int idx = dist(rng);
|
|
|
|
return logits_id[idx].second;
|
|
|
|
}
|
|
|
|
bool is_wav_buffer(const std::string buf) {
|
|
// RIFF ref: https://en.wikipedia.org/wiki/Resource_Interchange_File_Format
|
|
// WAV ref: https://www.mmsp.ece.mcgill.ca/Documents/AudioFormats/WAVE/WAVE.html
|
|
if (buf.size() < 12 || buf.substr(0, 4) != "RIFF" || buf.substr(8, 4) != "WAVE") {
|
|
return false;
|
|
}
|
|
|
|
uint32_t chunk_size = *reinterpret_cast<const uint32_t*>(buf.data() + 4);
|
|
if (chunk_size + 8 != buf.size()) {
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
bool read_wav(const std::string & fname, std::vector<float>& pcmf32, std::vector<std::vector<float>>& pcmf32s, bool stereo) {
|
|
drwav wav;
|
|
std::vector<uint8_t> wav_data; // used for pipe input from stdin
|
|
|
|
if (fname == "-") {
|
|
{
|
|
uint8_t buf[1024];
|
|
while (true)
|
|
{
|
|
const size_t n = fread(buf, 1, sizeof(buf), stdin);
|
|
if (n == 0) {
|
|
break;
|
|
}
|
|
wav_data.insert(wav_data.end(), buf, buf + n);
|
|
}
|
|
}
|
|
|
|
if (drwav_init_memory(&wav, wav_data.data(), wav_data.size(), nullptr) == false) {
|
|
fprintf(stderr, "error: failed to open WAV file from stdin\n");
|
|
return false;
|
|
}
|
|
|
|
fprintf(stderr, "%s: read %zu bytes from stdin\n", __func__, wav_data.size());
|
|
}
|
|
else if (is_wav_buffer(fname)) {
|
|
if (drwav_init_memory(&wav, fname.c_str(), fname.size(), nullptr) == false) {
|
|
fprintf(stderr, "error: failed to open WAV file from fname buffer\n");
|
|
return false;
|
|
}
|
|
}
|
|
else if (drwav_init_file(&wav, fname.c_str(), nullptr) == false) {
|
|
fprintf(stderr, "error: failed to open '%s' as WAV file\n", fname.c_str());
|
|
return false;
|
|
}
|
|
|
|
if (wav.channels != 1 && wav.channels != 2) {
|
|
fprintf(stderr, "%s: WAV file '%s' must be mono or stereo\n", __func__, fname.c_str());
|
|
return false;
|
|
}
|
|
|
|
if (stereo && wav.channels != 2) {
|
|
fprintf(stderr, "%s: WAV file '%s' must be stereo for diarization\n", __func__, fname.c_str());
|
|
return false;
|
|
}
|
|
|
|
if (wav.sampleRate != COMMON_SAMPLE_RATE) {
|
|
fprintf(stderr, "%s: WAV file '%s' must be %i kHz\n", __func__, fname.c_str(), COMMON_SAMPLE_RATE/1000);
|
|
return false;
|
|
}
|
|
|
|
if (wav.bitsPerSample != 16) {
|
|
fprintf(stderr, "%s: WAV file '%s' must be 16-bit\n", __func__, fname.c_str());
|
|
return false;
|
|
}
|
|
|
|
const uint64_t n = wav_data.empty() ? wav.totalPCMFrameCount : wav_data.size()/(wav.channels*wav.bitsPerSample/8);
|
|
|
|
std::vector<int16_t> pcm16;
|
|
pcm16.resize(n*wav.channels);
|
|
drwav_read_pcm_frames_s16(&wav, n, pcm16.data());
|
|
drwav_uninit(&wav);
|
|
|
|
// convert to mono, float
|
|
pcmf32.resize(n);
|
|
if (wav.channels == 1) {
|
|
for (uint64_t i = 0; i < n; i++) {
|
|
pcmf32[i] = float(pcm16[i])/32768.0f;
|
|
}
|
|
} else {
|
|
for (uint64_t i = 0; i < n; i++) {
|
|
pcmf32[i] = float(pcm16[2*i] + pcm16[2*i + 1])/65536.0f;
|
|
}
|
|
}
|
|
|
|
if (stereo) {
|
|
// convert to stereo, float
|
|
pcmf32s.resize(2);
|
|
|
|
pcmf32s[0].resize(n);
|
|
pcmf32s[1].resize(n);
|
|
for (uint64_t i = 0; i < n; i++) {
|
|
pcmf32s[0][i] = float(pcm16[2*i])/32768.0f;
|
|
pcmf32s[1][i] = float(pcm16[2*i + 1])/32768.0f;
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
void high_pass_filter(std::vector<float> & data, float cutoff, float sample_rate) {
|
|
const float rc = 1.0f / (2.0f * M_PI * cutoff);
|
|
const float dt = 1.0f / sample_rate;
|
|
const float alpha = dt / (rc + dt);
|
|
|
|
float y = data[0];
|
|
|
|
for (size_t i = 1; i < data.size(); i++) {
|
|
y = alpha * (y + data[i] - data[i - 1]);
|
|
data[i] = y;
|
|
}
|
|
}
|
|
|
|
bool vad_simple(std::vector<float> & pcmf32, int sample_rate, int last_ms, float vad_thold, float freq_thold, bool verbose) {
|
|
const int n_samples = pcmf32.size();
|
|
const int n_samples_last = (sample_rate * last_ms) / 1000;
|
|
|
|
if (n_samples_last >= n_samples) {
|
|
// not enough samples - assume no speech
|
|
return false;
|
|
}
|
|
|
|
if (freq_thold > 0.0f) {
|
|
high_pass_filter(pcmf32, freq_thold, sample_rate);
|
|
}
|
|
|
|
float energy_all = 0.0f;
|
|
float energy_last = 0.0f;
|
|
|
|
for (int i = 0; i < n_samples; i++) {
|
|
energy_all += fabsf(pcmf32[i]);
|
|
|
|
if (i >= n_samples - n_samples_last) {
|
|
energy_last += fabsf(pcmf32[i]);
|
|
}
|
|
}
|
|
|
|
energy_all /= n_samples;
|
|
energy_last /= n_samples_last;
|
|
|
|
if (verbose) {
|
|
fprintf(stderr, "%s: energy_all: %f, energy_last: %f, vad_thold: %f, freq_thold: %f\n", __func__, energy_all, energy_last, vad_thold, freq_thold);
|
|
}
|
|
|
|
if (energy_last > vad_thold*energy_all) {
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
float similarity(const std::string & s0, const std::string & s1) {
|
|
const size_t len0 = s0.size() + 1;
|
|
const size_t len1 = s1.size() + 1;
|
|
|
|
std::vector<int> col(len1, 0);
|
|
std::vector<int> prevCol(len1, 0);
|
|
|
|
for (size_t i = 0; i < len1; i++) {
|
|
prevCol[i] = i;
|
|
}
|
|
|
|
for (size_t i = 0; i < len0; i++) {
|
|
col[0] = i;
|
|
for (size_t j = 1; j < len1; j++) {
|
|
col[j] = std::min(std::min(1 + col[j - 1], 1 + prevCol[j]), prevCol[j - 1] + (i > 0 && s0[i - 1] == s1[j - 1] ? 0 : 1));
|
|
}
|
|
col.swap(prevCol);
|
|
}
|
|
|
|
const float dist = prevCol[len1 - 1];
|
|
|
|
return 1.0f - (dist / std::max(s0.size(), s1.size()));
|
|
}
|
|
|
|
bool sam_params_parse(int argc, char ** argv, sam_params & params) {
|
|
for (int i = 1; i < argc; i++) {
|
|
std::string arg = argv[i];
|
|
|
|
if (arg == "-s" || arg == "--seed") {
|
|
params.seed = std::stoi(argv[++i]);
|
|
} else if (arg == "-t" || arg == "--threads") {
|
|
params.n_threads = std::stoi(argv[++i]);
|
|
} else if (arg == "-m" || arg == "--model") {
|
|
params.model = argv[++i];
|
|
} else if (arg == "-i" || arg == "--inp") {
|
|
params.fname_inp = argv[++i];
|
|
} else if (arg == "-o" || arg == "--out") {
|
|
params.fname_out = argv[++i];
|
|
} else if (arg == "-h" || arg == "--help") {
|
|
sam_print_usage(argc, argv, params);
|
|
exit(0);
|
|
} else {
|
|
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
|
|
sam_print_usage(argc, argv, params);
|
|
exit(0);
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
void sam_print_usage(int /*argc*/, char ** argv, const sam_params & params) {
|
|
fprintf(stderr, "usage: %s [options]\n", argv[0]);
|
|
fprintf(stderr, "\n");
|
|
fprintf(stderr, "options:\n");
|
|
fprintf(stderr, " -h, --help show this help message and exit\n");
|
|
fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n");
|
|
fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
|
|
fprintf(stderr, " -m FNAME, --model FNAME\n");
|
|
fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
|
|
fprintf(stderr, " -i FNAME, --inp FNAME\n");
|
|
fprintf(stderr, " input file (default: %s)\n", params.fname_inp.c_str());
|
|
fprintf(stderr, " -o FNAME, --out FNAME\n");
|
|
fprintf(stderr, " output file (default: %s)\n", params.fname_out.c_str());
|
|
fprintf(stderr, "\n");
|
|
}
|
|
|
|
// 500 -> 00:05.000
|
|
// 6000 -> 01:00.000
|
|
std::string to_timestamp(int64_t t, bool comma) {
|
|
int64_t msec = t * 10;
|
|
int64_t hr = msec / (1000 * 60 * 60);
|
|
msec = msec - hr * (1000 * 60 * 60);
|
|
int64_t min = msec / (1000 * 60);
|
|
msec = msec - min * (1000 * 60);
|
|
int64_t sec = msec / 1000;
|
|
msec = msec - sec * 1000;
|
|
|
|
char buf[32];
|
|
snprintf(buf, sizeof(buf), "%02d:%02d:%02d%s%03d", (int) hr, (int) min, (int) sec, comma ? "," : ".", (int) msec);
|
|
|
|
return std::string(buf);
|
|
}
|
|
|
|
int timestamp_to_sample(int64_t t, int n_samples, int whisper_sample_rate) {
|
|
return std::max(0, std::min((int) n_samples - 1, (int) ((t*whisper_sample_rate)/100)));
|
|
}
|
|
|
|
bool is_file_exist(const char *fileName)
|
|
{
|
|
std::ifstream infile(fileName);
|
|
return infile.good();
|
|
}
|
|
|
|
bool speak_with_file(const std::string & command, const std::string & text, const std::string & path, int voice_id)
|
|
{
|
|
std::ofstream speak_file(path.c_str());
|
|
if (speak_file.fail()) {
|
|
fprintf(stderr, "%s: failed to open speak_file\n", __func__);
|
|
return false;
|
|
} else {
|
|
speak_file.write(text.c_str(), text.size());
|
|
speak_file.close();
|
|
int ret = system((command + " " + std::to_string(voice_id) + " " + path).c_str());
|
|
if (ret != 0) {
|
|
fprintf(stderr, "%s: failed to speak\n", __func__);
|
|
return false;
|
|
}
|
|
}
|
|
return true;
|
|
}
|