mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2025-07-08 13:46:58 +02:00
704 lines
28 KiB
C++
704 lines
28 KiB
C++
// Talk with AI
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//
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#include "common.h"
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#include "common-sdl.h"
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#include "whisper.h"
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#include "llama.h"
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#include <map>
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#include <cassert>
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#include <cstdio>
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#include <fstream>
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#include <regex>
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#include <string>
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#include <thread>
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#include <vector>
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#include <regex>
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std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos) {
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// initialize to prompt numer of chars, since n_tokens <= n_prompt_chars
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std::vector<llama_token> res(text.size() + (int)add_bos);
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int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos);
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assert(n >= 0);
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res.resize(n);
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return res;
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}
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// command-line parameters
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struct whisper_params {
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int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
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int32_t voice_id = 0;
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int32_t voice_ms = 10000;
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int32_t capture_id = -1;
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int32_t max_tokens = 64;
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int32_t audio_ctx = 0;
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int32_t n_parts_llama = -1;
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float vad_thold = 0.4f;
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float freq_thold = 100.0f;
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bool speed_up = false;
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bool translate = false;
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bool print_special = false;
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bool print_energy = false;
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bool no_timestamps = true;
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bool verbose_prompt = false;
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std::string name_ni = "Georgi"; // natural intelligence
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std::string name_ai = "LLaMA"; // artificial intelligence
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std::string language = "en";
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std::string model_wsp = "models/ggml-base.en.bin";
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std::string model_llama = "models/ggml-llama-7B.bin";
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std::string speak = "./examples/talk/speak.sh";
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std::string prompt = "";
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std::string fname_out;
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};
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void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
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bool whisper_params_parse(int argc, char ** argv, whisper_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 == "-h" || arg == "--help") {
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whisper_print_usage(argc, argv, params);
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exit(0);
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}
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else if (arg == "-t" || arg == "--threads") { params.n_threads = std::stoi(argv[++i]); }
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else if (arg == "-vid" || arg == "--voice-id") { params.voice_id = std::stoi(argv[++i]); }
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else if (arg == "-vms" || arg == "--voice-ms") { params.voice_ms = std::stoi(argv[++i]); }
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else if (arg == "-c" || arg == "--capture") { params.capture_id = std::stoi(argv[++i]); }
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else if (arg == "-mt" || arg == "--max-tokens") { params.max_tokens = std::stoi(argv[++i]); }
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else if (arg == "-ac" || arg == "--audio-ctx") { params.audio_ctx = std::stoi(argv[++i]); }
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else if (arg == "-vth" || arg == "--vad-thold") { params.vad_thold = std::stof(argv[++i]); }
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else if (arg == "-fth" || arg == "--freq-thold") { params.freq_thold = std::stof(argv[++i]); }
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else if (arg == "--n-parts-llama") { params.n_parts_llama = std::stoi(argv[++i]); }
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else if (arg == "-su" || arg == "--speed-up") { params.speed_up = true; }
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else if (arg == "-tr" || arg == "--translate") { params.translate = true; }
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else if (arg == "-ps" || arg == "--print-special") { params.print_special = true; }
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else if (arg == "-pe" || arg == "--print-energy") { params.print_energy = true; }
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else if (arg == "--verbose-prompt") { params.verbose_prompt = true; }
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else if (arg == "-nni" || arg == "--name-ni") { params.name_ni = argv[++i]; }
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else if (arg == "-nai" || arg == "--name-ai") { params.name_ai = argv[++i]; }
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else if (arg == "-l" || arg == "--language") { params.language = argv[++i]; }
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else if (arg == "-mw" || arg == "--model-whisper") { params.model_wsp = argv[++i]; }
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else if (arg == "-ml" || arg == "--model-llama") { params.model_llama = argv[++i]; }
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else if (arg == "-s" || arg == "--speak") { params.speak = argv[++i]; }
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else if (arg == "--prompt-file") {
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std::ifstream file(argv[++i]);
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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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}
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else if (arg == "-f" || arg == "--file") { params.fname_out = argv[++i]; }
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else {
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fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
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whisper_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 whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & params) {
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fprintf(stderr, "\n");
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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 [default] show this help message and exit\n");
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fprintf(stderr, " -t N, --threads N [%-7d] number of threads to use during computation\n", params.n_threads);
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fprintf(stderr, " -vid N, --voice-id N [%-7d] voice ID\n", params.voice_id);
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fprintf(stderr, " -vms N, --voice-ms N [%-7d] voice duration in milliseconds\n", params.voice_ms);
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fprintf(stderr, " -c ID, --capture ID [%-7d] capture device ID\n", params.capture_id);
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fprintf(stderr, " -mt N, --max-tokens N [%-7d] maximum number of tokens per audio chunk\n", params.max_tokens);
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fprintf(stderr, " -ac N, --audio-ctx N [%-7d] audio context size (0 - all)\n", params.audio_ctx);
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fprintf(stderr, " -vth N, --vad-thold N [%-7.2f] voice activity detection threshold\n", params.vad_thold);
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fprintf(stderr, " -fth N, --freq-thold N [%-7.2f] high-pass frequency cutoff\n", params.freq_thold);
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fprintf(stderr, " -su, --speed-up [%-7s] speed up audio by x2 (reduced accuracy)\n", params.speed_up ? "true" : "false");
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fprintf(stderr, " -tr, --translate [%-7s] translate from source language to english\n", params.translate ? "true" : "false");
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fprintf(stderr, " -ps, --print-special [%-7s] print special tokens\n", params.print_special ? "true" : "false");
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fprintf(stderr, " -pe, --print-energy [%-7s] print sound energy (for debugging)\n", params.print_energy ? "true" : "false");
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fprintf(stderr, " -nni NAME,--name-ni NAME [%-7s] natural intelligence name\n", params.name_ni.c_str());
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fprintf(stderr, " -nai NAME,--name-ai NAME [%-7s] artificial intelligence name\n", params.name_ai.c_str());
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fprintf(stderr, " -l LANG, --language LANG [%-7s] spoken language\n", params.language.c_str());
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fprintf(stderr, " -mw FILE, --model-whisper [%-7s] whisper model file\n", params.model_wsp.c_str());
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fprintf(stderr, " -ml FILE, --model-llama [%-7s] llama model file\n", params.model_llama.c_str());
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fprintf(stderr, " --n-parts-llama N [%-7d] num parts in llama model file\n", params.n_parts_llama);
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fprintf(stderr, " -s FILE, --speak TEXT [%-7s] command for TTS\n", params.speak.c_str());
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fprintf(stderr, " --prompt-file FNAME [%-7s] file with custom prompt to start dialog\n", "");
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fprintf(stderr, " --verbose-prompt [%-7s] print prompt at start\n", params.verbose_prompt ? "true" : "false");
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fprintf(stderr, " -f FNAME, --file FNAME [%-7s] text output file name\n", params.fname_out.c_str());
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fprintf(stderr, "\n");
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}
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std::string transcribe(
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whisper_context * ctx,
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const whisper_params & params,
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const std::vector<float> & pcmf32,
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const std::string prompt_text,
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float & prob,
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int64_t & t_ms) {
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const auto t_start = std::chrono::high_resolution_clock::now();
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prob = 0.0f;
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t_ms = 0;
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std::vector<whisper_token> prompt_tokens;
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whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
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prompt_tokens.resize(1024);
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prompt_tokens.resize(whisper_tokenize(ctx, prompt_text.c_str(), prompt_tokens.data(), prompt_tokens.size()));
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wparams.print_progress = false;
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wparams.print_special = params.print_special;
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wparams.print_realtime = false;
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wparams.print_timestamps = !params.no_timestamps;
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wparams.translate = params.translate;
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wparams.no_context = true;
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wparams.single_segment = true;
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wparams.max_tokens = params.max_tokens;
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wparams.language = params.language.c_str();
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wparams.n_threads = 2;
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wparams.prompt_tokens = prompt_tokens.empty() ? nullptr : prompt_tokens.data();
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wparams.prompt_n_tokens = prompt_tokens.empty() ? 0 : prompt_tokens.size();
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wparams.audio_ctx = params.audio_ctx;
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wparams.speed_up = params.speed_up;
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static int iter = params.voice_id;
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std::this_thread::sleep_for(std::chrono::milliseconds(100*iter));
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iter = (iter + 1) % 4;
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if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
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return "";
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}
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int prob_n = 0;
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std::string result;
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const int n_segments = whisper_full_n_segments(ctx);
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for (int i = 0; i < n_segments; ++i) {
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const char * text = whisper_full_get_segment_text(ctx, i);
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result += text;
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const int n_tokens = whisper_full_n_tokens(ctx, i);
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for (int j = 0; j < n_tokens; ++j) {
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const auto token = whisper_full_get_token_data(ctx, i, j);
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prob += token.p;
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++prob_n;
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}
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}
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if (prob_n > 0) {
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prob /= prob_n;
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}
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const auto t_end = std::chrono::high_resolution_clock::now();
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t_ms = std::chrono::duration_cast<std::chrono::milliseconds>(t_end - t_start).count();
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return result;
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}
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const std::vector<std::string> k_participants = {
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"LLaMA",
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"GGaMA",
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"SSaMA",
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"RRaMA",
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};
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// homophones
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const std::map<std::string, std::vector<std::string>> k_homophones = {
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{ "LLaMA", { "llama", "Llama", "LLAMA", }, },
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{ "GGaMA", { "gama", "Gama", "GAMA", "gamma", "Gamma", "GAMMA", }, },
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{ "SSaMA", { "sama", "Sama", "SAMA", "samma", "Samma", "SAMMA", }, },
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{ "RRaMA", { "rama", "Rama", "RAMA", "ramma", "Ramma", "RAMMA", }, },
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};
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const std::string k_prompt_whisper = R"(A conversation between {1}, {10}, {11}, {12} and {13}.)";
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const std::map<std::string, std::string> k_prompt = {
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{
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k_participants.at(0),
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R"(Text transcript of a never ending dialog, between {1}, {10}, {11}, {12} and {13}.
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There are no annotations like (30 seconds passed...) or (to himself), just what the participants say aloud to each other.
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The transcript only includes text, it does not include markup like HTML and Markdown.
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{10}, {11}, {12} and {13} respond with short and concise answers.
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{10} is smart, objective, honest and kind. Never fails to give a meaningful and insightful answer and opinion.
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{1} is leading the conversation and asking the questions.
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{1}{4} Hello {10}! What is your opinion on the current state of the world?
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{10}{4} Great question {1}! I think we live in a very interesting time.
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There are many things to be concerned about, but also many things to be optimistic about.
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{1}{4} What advice would you give to a young person who is just starting out in life?
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{10}{4} I would tell them to be patient and to not be afraid to fail.
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It is important to learn from your mistakes and to keep trying.
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{1}{4})"
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},
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{
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k_participants.at(1),
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R"(Text transcript of a never ending dialog, between {1}, {10}, {11}, {12} and {13}.
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There are no annotations like (30 seconds passed...) or (to himself), just what the participants say aloud to each other.
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The transcript only includes text, it does not include markup like HTML and Markdown.
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{10}, {11}, {12} and {13} respond with short and concise answers.
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{11} has critical thinking skills, is very knowledgeable and is a good listener. He is very humble and never arrogant.
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{1} is leading the conversation and asking the questions.
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{1}{4} Hello {11}! What is your opinion on the current state of the world?
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{11}{4} The world is about to experience a major change. We are on the verge of a new era.
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{1}{4} What advice would you give to a young person who is just starting out in life?
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{11}{4} My advice would be to be open minded and to be willing to learn from others.
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{1}{4})"
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},
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{
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k_participants.at(2),
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R"(Text transcript of a never ending dialog, between {1}, {10}, {11}, {12} and {13}.
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There are no annotations like (30 seconds passed...) or (to himself), just what the participants say aloud to each other.
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The transcript only includes text, it does not include markup like HTML and Markdown.
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{10}, {11}, {12} and {13} respond with short and concise answers.
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{12} has strong leadership skills, strategic thinking, and innovative ideas. Has the ability to mentor and support young people.
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{1} is leading the conversation and asking the questions.
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{1}{4} Hello {12}! What is your opinion on the current state of the world?
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{12}{4} Our future is bright. We are living in a time of great opportunity.
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{1}{4} What advice would you give to a young person who is just starting out in life?
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{12}{4} I would tell them to be brave and to be willing to take risks.
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{1}{4})"
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},
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{
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k_participants.at(3),
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R"(Text transcript of a never ending dialog, between {1}, {10}, {11}, {12} and {13}.
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There are no annotations like (30 seconds passed...) or (to himself), just what the participants say aloud to each other.
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The transcript only includes text, it does not include markup like HTML and Markdown.
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{10}, {11}, {12} and {13} respond with short and concise answers.
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{13} is rude, arrogant, and has a bad attitude. He is very opinionated and never listens to others.
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{1} is leading the conversation and asking the questions.
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{1}{4} Hello {13}! What is your opinion on the current state of the world?
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{13}{4} The world is a terrible place. It is full of evil and corruption.
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{1}{4} What advice would you give to a young person who is just starting out in life?
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{13}{4} I would tell them to be selfish and to never trust anyone.
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{1}{4})"
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},
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};
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int main(int argc, char ** argv) {
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whisper_params params;
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if (whisper_params_parse(argc, argv, params) == false) {
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return 1;
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}
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if (whisper_lang_id(params.language.c_str()) == -1) {
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fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
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whisper_print_usage(argc, argv, params);
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exit(0);
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}
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// whisper init
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struct whisper_context * ctx_wsp = whisper_init_from_file(params.model_wsp.c_str());
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// llama init
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auto lparams = llama_context_default_params();
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// tune these to your liking
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lparams.n_ctx = 512;
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lparams.seed = 1;
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lparams.f16_kv = true;
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lparams.n_parts = params.n_parts_llama;
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struct llama_context * ctx_llama = llama_init_from_file(params.model_llama.c_str(), lparams);
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// print some info about the processing
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{
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fprintf(stderr, "\n");
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if (!whisper_is_multilingual(ctx_wsp)) {
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if (params.language != "en" || params.translate) {
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params.language = "en";
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params.translate = false;
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fprintf(stderr, "%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
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}
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}
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fprintf(stderr, "%s: processing, %d threads, lang = %s, task = %s, timestamps = %d ...\n",
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__func__,
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params.n_threads,
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params.language.c_str(),
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params.translate ? "translate" : "transcribe",
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params.no_timestamps ? 0 : 1);
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fprintf(stderr, "\n");
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}
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// init audio
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audio_async audio(30*1000);
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if (!audio.init(params.capture_id, WHISPER_SAMPLE_RATE)) {
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fprintf(stderr, "%s: audio.init() failed!\n", __func__);
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return 1;
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}
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audio.resume();
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int n_iter = 0;
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bool is_running = true;
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bool force_speak = false;
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float prob0 = 0.0f;
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const std::string chat_symb = ":";
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const std::string name_ni = params.name_ni;
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const std::string name_ai = params.name_ai;
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// the participant that was referenced last
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std::string name_ref = name_ni;
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std::vector<float> pcmf32_cur;
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std::vector<float> pcmf32_prompt;
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std::string prompt_whisper = k_prompt_whisper;
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prompt_whisper = ::replace(prompt_whisper, "{1}", name_ni);
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prompt_whisper = ::replace(prompt_whisper, "{10}", k_participants.at(0));
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prompt_whisper = ::replace(prompt_whisper, "{11}", k_participants.at(1));
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prompt_whisper = ::replace(prompt_whisper, "{12}", k_participants.at(2));
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prompt_whisper = ::replace(prompt_whisper, "{13}", k_participants.at(3));
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// construct the initial prompt for LLaMA inference
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std::string prompt_llama = params.prompt.empty() ? k_prompt.find(name_ai)->second : params.prompt;
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// need to have leading ' '
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prompt_llama.insert(0, 1, ' ');
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prompt_llama = ::replace(prompt_llama, "{1}", name_ni);
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prompt_llama = ::replace(prompt_llama, "{10}", k_participants.at(0));
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prompt_llama = ::replace(prompt_llama, "{11}", k_participants.at(1));
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prompt_llama = ::replace(prompt_llama, "{12}", k_participants.at(2));
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prompt_llama = ::replace(prompt_llama, "{13}", k_participants.at(3));
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{
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// get date string
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std::string date_str;
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{
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time_t t = time(0);
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struct tm * now = localtime(&t);
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char buf[128];
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strftime(buf, sizeof(buf), "%d/%m/%Y", now);
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date_str = buf;
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}
|
|
prompt_llama = ::replace(prompt_llama, "{1}", date_str);
|
|
}
|
|
|
|
{
|
|
// get time string
|
|
std::string time_str;
|
|
{
|
|
time_t t = time(0);
|
|
struct tm * now = localtime(&t);
|
|
char buf[128];
|
|
strftime(buf, sizeof(buf), "%H:%M", now);
|
|
time_str = buf;
|
|
}
|
|
prompt_llama = ::replace(prompt_llama, "{2}", time_str);
|
|
}
|
|
|
|
{
|
|
// get year string
|
|
std::string year_str;
|
|
{
|
|
time_t t = time(0);
|
|
struct tm * now = localtime(&t);
|
|
char buf[128];
|
|
strftime(buf, sizeof(buf), "%Y", now);
|
|
year_str = buf;
|
|
}
|
|
prompt_llama = ::replace(prompt_llama, "{3}", year_str);
|
|
}
|
|
|
|
prompt_llama = ::replace(prompt_llama, "{4}", chat_symb);
|
|
|
|
// evaluate the initial prompt
|
|
|
|
auto embd_inp = ::llama_tokenize(ctx_llama, prompt_llama, true);
|
|
|
|
printf("\n");
|
|
printf("%s : initializing - please wait ...\n", __func__);
|
|
|
|
if (llama_eval(ctx_llama, embd_inp.data(), embd_inp.size(), 0, params.n_threads)) {
|
|
fprintf(stderr, "%s : failed to eval\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
if (params.verbose_prompt) {
|
|
fprintf(stdout, "\n");
|
|
fprintf(stdout, "%s", prompt_whisper.c_str());
|
|
fprintf(stdout, "\n");
|
|
|
|
fprintf(stdout, "\n");
|
|
fprintf(stdout, "%s", prompt_llama.c_str());
|
|
fprintf(stdout, "\n");
|
|
fprintf(stdout, "\n");
|
|
fflush(stdout);
|
|
}
|
|
|
|
printf("%s : done! start speaking in the microphone\n", __func__);
|
|
printf("\n");
|
|
printf("%s%s", name_ni.c_str(), chat_symb.c_str());
|
|
fflush(stdout);
|
|
|
|
// clear audio buffer
|
|
audio.clear();
|
|
|
|
// text inference variables
|
|
const int voice_id = params.voice_id;
|
|
const int n_keep = embd_inp.size();
|
|
const int n_ctx = llama_n_ctx(ctx_llama);
|
|
|
|
int n_past = n_keep;
|
|
int n_prev = 64; // TODO arg
|
|
|
|
std::vector<llama_token> embd;
|
|
|
|
// reverse prompts for detecting when it's time to stop speaking
|
|
std::vector<std::string> antiprompts = {
|
|
name_ni + chat_symb,
|
|
};
|
|
|
|
for (const auto & p : k_participants) {
|
|
antiprompts.push_back(p + chat_symb);
|
|
}
|
|
|
|
std::string text_heard_all;
|
|
|
|
// main loop
|
|
while (is_running) {
|
|
// handle Ctrl + C
|
|
is_running = sdl_poll_events();
|
|
|
|
if (!is_running) {
|
|
break;
|
|
}
|
|
|
|
// delay
|
|
std::this_thread::sleep_for(std::chrono::milliseconds(100));
|
|
|
|
int64_t t_ms = 0;
|
|
|
|
{
|
|
audio.get(15000, pcmf32_cur);
|
|
|
|
if (::vad_simple(pcmf32_cur, WHISPER_SAMPLE_RATE, 1250, params.vad_thold, params.freq_thold, params.print_energy) || force_speak) {
|
|
//fprintf(stdout, "%s: Speech detected! Processing ...\n", __func__);
|
|
|
|
audio.get(params.voice_ms, pcmf32_cur);
|
|
|
|
std::string text_heard;
|
|
|
|
if (!force_speak) {
|
|
text_heard = ::trim(::transcribe(ctx_wsp, params, pcmf32_cur, prompt_whisper, prob0, t_ms));
|
|
}
|
|
|
|
// remove text between brackets using regex
|
|
{
|
|
std::regex re("\\[.*?\\]");
|
|
text_heard = std::regex_replace(text_heard, re, "");
|
|
}
|
|
|
|
// remove text between brackets using regex
|
|
{
|
|
std::regex re("\\(.*?\\)");
|
|
text_heard = std::regex_replace(text_heard, re, "");
|
|
}
|
|
|
|
// remove all characters, except for letters, numbers, punctuation and ':', '\'', '-', ' '
|
|
text_heard = std::regex_replace(text_heard, std::regex("[^a-zA-Z0-9\\.,\\?!\\s\\:\\'\\-]"), "");
|
|
|
|
// take first line
|
|
text_heard = text_heard.substr(0, text_heard.find_first_of('\n'));
|
|
|
|
// remove leading and trailing whitespace
|
|
text_heard = std::regex_replace(text_heard, std::regex("^\\s+"), "");
|
|
text_heard = std::regex_replace(text_heard, std::regex("\\s+$"), "");
|
|
|
|
const std::vector<llama_token> tokens = llama_tokenize(ctx_llama, text_heard.c_str(), false);
|
|
|
|
if (text_heard.empty() || tokens.empty() || force_speak) {
|
|
//fprintf(stdout, "%s: Heard nothing, skipping ...\n", __func__);
|
|
audio.clear();
|
|
|
|
continue;
|
|
}
|
|
|
|
force_speak = false;
|
|
|
|
if (text_heard[0] != ' ') {
|
|
text_heard.insert(0, 1, ' ');
|
|
}
|
|
|
|
// replace homophones
|
|
for (const auto & homophone : k_homophones) {
|
|
for (const auto & word : homophone.second) {
|
|
text_heard = ::replace(text_heard, word, homophone.first);
|
|
}
|
|
}
|
|
|
|
// check which participant was mentioned
|
|
const auto name_ref_old = name_ref;
|
|
for (const auto & participant : k_participants) {
|
|
if (participant == name_ref) {
|
|
continue;
|
|
}
|
|
|
|
if (text_heard.find(participant) != std::string::npos) {
|
|
name_ref = participant;
|
|
break;
|
|
}
|
|
}
|
|
if (name_ref == name_ref_old && name_ref != name_ai) {
|
|
name_ref = name_ni;
|
|
}
|
|
|
|
text_heard += "\n" + name_ref + chat_symb;
|
|
fprintf(stdout, "%s%s%s", "\033[1m", text_heard.c_str(), "\033[0m");
|
|
fflush(stdout);
|
|
|
|
text_heard_all += text_heard;
|
|
// keep only last 100 characters
|
|
if (text_heard_all.size() > 100) {
|
|
text_heard_all = text_heard_all.substr(text_heard_all.size() - 100);
|
|
}
|
|
|
|
if (name_ref != name_ai) {
|
|
} else {
|
|
// text inference
|
|
bool done = false;
|
|
std::string text_to_speak;
|
|
|
|
embd = ::llama_tokenize(ctx_llama, text_heard_all, false);
|
|
text_heard_all.clear();
|
|
|
|
while (true) {
|
|
// predict
|
|
if (embd.size() > 0) {
|
|
if (n_past + (int) embd.size() > n_ctx) {
|
|
n_past = n_keep;
|
|
|
|
// insert n_left/2 tokens at the start of embd from last_n_tokens
|
|
embd.insert(embd.begin(), embd_inp.begin() + embd_inp.size() - n_prev, embd_inp.end());
|
|
|
|
//printf("\n---\n");
|
|
//printf("resetting: '");
|
|
//for (int i = 0; i < (int) embd.size(); i++) {
|
|
// printf("%s", llama_token_to_str(ctx_llama, embd[i]));
|
|
//}
|
|
//printf("'\n");
|
|
//printf("\n---\n");
|
|
}
|
|
|
|
if (llama_eval(ctx_llama, embd.data(), embd.size(), n_past, params.n_threads)) {
|
|
fprintf(stderr, "%s : failed to eval\n", __func__);
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
//printf("n_iter = %d, n_past = %d, n_ctx = %d, n_keep = %d, n_prev = %d, embd.size() = %d\n", n_iter, n_past, n_ctx, n_keep, n_prev, (int) embd.size());
|
|
|
|
embd_inp.insert(embd_inp.end(), embd.begin(), embd.end());
|
|
n_past += embd.size();
|
|
embd.clear();
|
|
|
|
if (done) break;
|
|
|
|
{
|
|
// out of user input, sample next token
|
|
const float top_k = 5;
|
|
const float top_p = 0.80f;
|
|
const float temp = 0.20f;
|
|
const float repeat_penalty = 1.0764f;
|
|
|
|
const int repeat_last_n = 256;
|
|
|
|
llama_token id = 0;
|
|
|
|
{
|
|
auto logits = llama_get_logits(ctx_llama);
|
|
logits[llama_token_eos()] = 0;
|
|
|
|
id = llama_sample_top_p_top_k(ctx_llama,
|
|
embd_inp.data() + std::max(0, n_past - repeat_last_n),
|
|
repeat_last_n, top_k, top_p, temp, repeat_penalty);
|
|
}
|
|
|
|
if (id != llama_token_eos()) {
|
|
// add it to the context
|
|
embd.push_back(id);
|
|
|
|
text_to_speak += llama_token_to_str(ctx_llama, id);
|
|
|
|
printf("%s", llama_token_to_str(ctx_llama, id));
|
|
}
|
|
|
|
// new line
|
|
if (id == 13) {
|
|
}
|
|
}
|
|
|
|
{
|
|
std::string last_output;
|
|
for (int i = embd_inp.size() - 16; i < (int) embd_inp.size(); i++) {
|
|
last_output += llama_token_to_str(ctx_llama, embd_inp[i]);
|
|
}
|
|
last_output += llama_token_to_str(ctx_llama, embd[0]);
|
|
|
|
for (const std::string & antiprompt : antiprompts) {
|
|
if (last_output.find(antiprompt.c_str(), last_output.length() - antiprompt.length(), antiprompt.length()) != std::string::npos) {
|
|
done = true;
|
|
text_to_speak = ::replace(text_to_speak, antiprompt, "");
|
|
fflush(stdout);
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
is_running = sdl_poll_events();
|
|
|
|
if (!is_running) {
|
|
break;
|
|
}
|
|
}
|
|
|
|
text_to_speak = ::replace(text_to_speak, "\"", "");
|
|
system((params.speak + " " + std::to_string(voice_id) + " \"" + text_to_speak + "\"").c_str());
|
|
}
|
|
|
|
audio.clear();
|
|
|
|
++n_iter;
|
|
}
|
|
}
|
|
}
|
|
|
|
audio.pause();
|
|
|
|
whisper_print_timings(ctx_wsp);
|
|
whisper_free(ctx_wsp);
|
|
|
|
llama_print_timings(ctx_llama);
|
|
llama_free(ctx_llama);
|
|
|
|
return 0;
|
|
}
|