768 lines
32 KiB
C++
768 lines
32 KiB
C++
// Talk with AI
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//
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#include "common-sdl.h"
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#include "common.h"
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#include "whisper.h"
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#include "llama.h"
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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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#include <sstream>
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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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auto * model = llama_get_model(ctx);
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// upper limit for the number of tokens
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int n_tokens = text.length() + add_bos;
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std::vector<llama_token> result(n_tokens);
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n_tokens = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, false);
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if (n_tokens < 0) {
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result.resize(-n_tokens);
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int check = llama_tokenize(model, text.data(), text.length(), result.data(), result.size(), add_bos, false);
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GGML_ASSERT(check == -n_tokens);
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} else {
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result.resize(n_tokens);
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}
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return result;
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}
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std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
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std::vector<char> result(8, 0);
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const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
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if (n_tokens < 0) {
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result.resize(-n_tokens);
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int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size());
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GGML_ASSERT(check == -n_tokens);
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} else {
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result.resize(n_tokens);
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}
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return std::string(result.data(), result.size());
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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_ms = 10000;
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int32_t capture_id = -1;
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int32_t max_tokens = 32;
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int32_t audio_ctx = 0;
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int32_t n_gpu_layers = 999;
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float vad_thold = 0.6f;
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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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bool use_gpu = true;
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std::string person = "Georgi";
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std::string bot_name = "LLaMA";
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std::string wake_cmd = "";
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std::string heard_ok = "";
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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-llama/speak";
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std::string speak_file = "./examples/talk-llama/to_speak.txt";
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std::string prompt = "";
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std::string fname_out;
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std::string path_session = ""; // path to file for saving/loading model eval state
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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 == "-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 == "-ngl" || arg == "--n-gpu-layers") { params.n_gpu_layers = 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 == "-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 == "-vp" || arg == "--verbose-prompt") { params.verbose_prompt = true; }
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else if (arg == "-ng" || arg == "--no-gpu") { params.use_gpu = false; }
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else if (arg == "-p" || arg == "--person") { params.person = argv[++i]; }
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else if (arg == "-bn" || arg == "--bot-name") { params.bot_name = argv[++i]; }
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else if (arg == "--session") { params.path_session = argv[++i]; }
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else if (arg == "-w" || arg == "--wake-command") { params.wake_cmd = argv[++i]; }
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else if (arg == "-ho" || arg == "--heard-ok") { params.heard_ok = 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 == "-sf" || arg == "--speak-file") { params.speak_file = 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 if (arg == "-ng" || arg == "--no-gpu") { params.use_gpu = false; }
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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, " -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, " -ngl N, --n-gpu-layers N [%-7d] number of layers to store in VRAM\n", params.n_gpu_layers);
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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, " -vp, --verbose-prompt [%-7s] print prompt at start\n", params.verbose_prompt ? "true" : "false");
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fprintf(stderr, " -ng, --no-gpu [%-7s] disable GPU\n", params.use_gpu ? "false" : "true");
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fprintf(stderr, " -p NAME, --person NAME [%-7s] person name (for prompt selection)\n", params.person.c_str());
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fprintf(stderr, " -bn NAME, --bot-name NAME [%-7s] bot name (to display)\n", params.bot_name.c_str());
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fprintf(stderr, " -w TEXT, --wake-command T [%-7s] wake-up command to listen for\n", params.wake_cmd.c_str());
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fprintf(stderr, " -ho TEXT, --heard-ok TEXT [%-7s] said by TTS before generating reply\n", params.heard_ok.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, " -s FILE, --speak TEXT [%-7s] command for TTS\n", params.speak.c_str());
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fprintf(stderr, " -sf FILE, --speak-file [%-7s] file to pass to TTS\n", params.speak_file.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, " --session FNAME file to cache model state in (may be large!) (default: none)\n");
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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 = params.n_threads;
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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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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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std::vector<std::string> get_words(const std::string &txt) {
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std::vector<std::string> words;
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std::istringstream iss(txt);
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std::string word;
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while (iss >> word) {
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words.push_back(word);
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}
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return words;
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}
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const std::string k_prompt_whisper = R"(A conversation with a person called {1}.)";
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const std::string k_prompt_llama = R"(Text transcript of a never ending dialog, where {0} interacts with an AI assistant named {1}.
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{1} is helpful, kind, honest, friendly, good at writing and never fails to answer {0}’s requests immediately and with details and precision.
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There are no annotations like (30 seconds passed...) or (to himself), just what {0} and {1} 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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{1} responds with short and concise answers.
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{0}{4} Hello, {1}!
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{1}{4} Hello {0}! How may I help you today?
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{0}{4} What time is it?
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{1}{4} It is {2} o'clock.
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{0}{4} What year is it?
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{1}{4} We are in {3}.
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{0}{4} What is a cat?
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{1}{4} A cat is a domestic species of small carnivorous mammal. It is the only domesticated species in the family Felidae.
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{0}{4} Name a color.
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{1}{4} Blue
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{0}{4})";
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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 (params.language != "auto" && 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_params cparams = whisper_context_default_params();
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cparams.use_gpu = params.use_gpu;
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struct whisper_context * ctx_wsp = whisper_init_from_file_with_params(params.model_wsp.c_str(), cparams);
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// llama init
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llama_backend_init();
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auto lmparams = llama_model_default_params();
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if (!params.use_gpu) {
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lmparams.n_gpu_layers = 0;
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} else {
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lmparams.n_gpu_layers = params.n_gpu_layers;
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}
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struct llama_model * model_llama = llama_load_model_from_file(params.model_llama.c_str(), lmparams);
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llama_context_params lcparams = llama_context_default_params();
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// tune these to your liking
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lcparams.n_ctx = 2048;
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lcparams.seed = 1;
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lcparams.n_threads = params.n_threads;
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struct llama_context * ctx_llama = llama_new_context_with_model(model_llama, lcparams);
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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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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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std::vector<float> pcmf32_cur;
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std::vector<float> pcmf32_prompt;
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const std::string prompt_whisper = ::replace(k_prompt_whisper, "{1}", params.bot_name);
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// construct the initial prompt for LLaMA inference
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std::string prompt_llama = params.prompt.empty() ? k_prompt_llama : 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, "{0}", params.person);
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prompt_llama = ::replace(prompt_llama, "{1}", params.bot_name);
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{
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// get time string
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std::string time_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), "%H:%M", now);
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time_str = buf;
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}
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prompt_llama = ::replace(prompt_llama, "{2}", time_str);
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}
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{
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// get year string
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std::string year_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), "%Y", now);
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year_str = buf;
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}
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prompt_llama = ::replace(prompt_llama, "{3}", year_str);
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}
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prompt_llama = ::replace(prompt_llama, "{4}", chat_symb);
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// init session
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std::string path_session = params.path_session;
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std::vector<llama_token> session_tokens;
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auto embd_inp = ::llama_tokenize(ctx_llama, prompt_llama, true);
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if (!path_session.empty()) {
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fprintf(stderr, "%s: attempting to load saved session from %s\n", __func__, path_session.c_str());
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// fopen to check for existing session
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FILE * fp = std::fopen(path_session.c_str(), "rb");
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if (fp != NULL) {
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std::fclose(fp);
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session_tokens.resize(llama_n_ctx(ctx_llama));
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size_t n_token_count_out = 0;
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if (!llama_load_session_file(ctx_llama, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
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fprintf(stderr, "%s: error: failed to load session file '%s'\n", __func__, path_session.c_str());
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return 1;
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}
|
||
session_tokens.resize(n_token_count_out);
|
||
for (size_t i = 0; i < session_tokens.size(); i++) {
|
||
embd_inp[i] = session_tokens[i];
|
||
}
|
||
|
||
fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n", __func__, (int) session_tokens.size());
|
||
} else {
|
||
fprintf(stderr, "%s: session file does not exist, will create\n", __func__);
|
||
}
|
||
}
|
||
|
||
// evaluate the initial prompt
|
||
|
||
printf("\n");
|
||
printf("%s : initializing - please wait ...\n", __func__);
|
||
|
||
if (llama_eval(ctx_llama, embd_inp.data(), embd_inp.size(), 0)) {
|
||
fprintf(stderr, "%s : failed to eval\n", __func__);
|
||
return 1;
|
||
}
|
||
|
||
if (params.verbose_prompt) {
|
||
fprintf(stdout, "\n");
|
||
fprintf(stdout, "%s", prompt_llama.c_str());
|
||
fflush(stdout);
|
||
}
|
||
|
||
// debug message about similarity of saved session, if applicable
|
||
size_t n_matching_session_tokens = 0;
|
||
if (session_tokens.size()) {
|
||
for (llama_token id : session_tokens) {
|
||
if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) {
|
||
break;
|
||
}
|
||
n_matching_session_tokens++;
|
||
}
|
||
if (n_matching_session_tokens >= embd_inp.size()) {
|
||
fprintf(stderr, "%s: session file has exact match for prompt!\n", __func__);
|
||
} else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
|
||
fprintf(stderr, "%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n",
|
||
__func__, n_matching_session_tokens, embd_inp.size());
|
||
} else {
|
||
fprintf(stderr, "%s: session file matches %zu / %zu tokens of prompt\n",
|
||
__func__, n_matching_session_tokens, embd_inp.size());
|
||
}
|
||
}
|
||
|
||
// HACK - because session saving incurs a non-negligible delay, for now skip re-saving session
|
||
// if we loaded a session with at least 75% similarity. It's currently just used to speed up the
|
||
// initial prompt so it doesn't need to be an exact match.
|
||
bool need_to_save_session = !path_session.empty() && n_matching_session_tokens < (embd_inp.size() * 3 / 4);
|
||
|
||
printf("%s : done! start speaking in the microphone\n", __func__);
|
||
|
||
// show wake command if enabled
|
||
const std::string wake_cmd = params.wake_cmd;
|
||
const int wake_cmd_length = get_words(wake_cmd).size();
|
||
const bool use_wake_cmd = wake_cmd_length > 0;
|
||
|
||
if (use_wake_cmd) {
|
||
printf("%s : the wake-up command is: '%s%s%s'\n", __func__, "\033[1m", wake_cmd.c_str(), "\033[0m");
|
||
}
|
||
|
||
printf("\n");
|
||
printf("%s%s", params.person.c_str(), chat_symb.c_str());
|
||
fflush(stdout);
|
||
|
||
// clear audio buffer
|
||
audio.clear();
|
||
|
||
// text inference variables
|
||
const int voice_id = 2;
|
||
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
|
||
int n_session_consumed = !path_session.empty() && session_tokens.size() > 0 ? session_tokens.size() : 0;
|
||
|
||
std::vector<llama_token> embd;
|
||
|
||
// reverse prompts for detecting when it's time to stop speaking
|
||
std::vector<std::string> antiprompts = {
|
||
params.person + chat_symb,
|
||
};
|
||
|
||
// 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(2000, 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 all_heard;
|
||
|
||
if (!force_speak) {
|
||
all_heard = ::trim(::transcribe(ctx_wsp, params, pcmf32_cur, prompt_whisper, prob0, t_ms));
|
||
}
|
||
|
||
const auto words = get_words(all_heard);
|
||
|
||
std::string wake_cmd_heard;
|
||
std::string text_heard;
|
||
|
||
for (int i = 0; i < (int) words.size(); ++i) {
|
||
if (i < wake_cmd_length) {
|
||
wake_cmd_heard += words[i] + " ";
|
||
} else {
|
||
text_heard += words[i] + " ";
|
||
}
|
||
}
|
||
|
||
// check if audio starts with the wake-up command if enabled
|
||
if (use_wake_cmd) {
|
||
const float sim = similarity(wake_cmd_heard, wake_cmd);
|
||
|
||
if ((sim < 0.7f) || (text_heard.empty())) {
|
||
audio.clear();
|
||
continue;
|
||
}
|
||
}
|
||
|
||
// optionally give audio feedback that the current text is being processed
|
||
if (!params.heard_ok.empty()) {
|
||
speak_with_file(params.speak, params.heard_ok, params.speak_file, voice_id);
|
||
}
|
||
|
||
// 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;
|
||
|
||
text_heard.insert(0, 1, ' ');
|
||
text_heard += "\n" + params.bot_name + chat_symb;
|
||
fprintf(stdout, "%s%s%s", "\033[1m", text_heard.c_str(), "\033[0m");
|
||
fflush(stdout);
|
||
|
||
embd = ::llama_tokenize(ctx_llama, text_heard, false);
|
||
|
||
// Append the new input tokens to the session_tokens vector
|
||
if (!path_session.empty()) {
|
||
session_tokens.insert(session_tokens.end(), tokens.begin(), tokens.end());
|
||
}
|
||
|
||
// text inference
|
||
bool done = false;
|
||
std::string text_to_speak;
|
||
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());
|
||
// stop saving session if we run out of context
|
||
path_session = "";
|
||
//printf("\n---\n");
|
||
//printf("resetting: '");
|
||
//for (int i = 0; i < (int) embd.size(); i++) {
|
||
// printf("%s", llama_token_to_piece(ctx_llama, embd[i]));
|
||
//}
|
||
//printf("'\n");
|
||
//printf("\n---\n");
|
||
}
|
||
|
||
// try to reuse a matching prefix from the loaded session instead of re-eval (via n_past)
|
||
// REVIEW
|
||
if (n_session_consumed < (int) session_tokens.size()) {
|
||
size_t i = 0;
|
||
for ( ; i < embd.size(); i++) {
|
||
if (embd[i] != session_tokens[n_session_consumed]) {
|
||
session_tokens.resize(n_session_consumed);
|
||
break;
|
||
}
|
||
|
||
n_past++;
|
||
n_session_consumed++;
|
||
|
||
if (n_session_consumed >= (int) session_tokens.size()) {
|
||
i++;
|
||
break;
|
||
}
|
||
}
|
||
if (i > 0) {
|
||
embd.erase(embd.begin(), embd.begin() + i);
|
||
}
|
||
}
|
||
|
||
if (embd.size() > 0 && !path_session.empty()) {
|
||
session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
|
||
n_session_consumed = session_tokens.size();
|
||
}
|
||
|
||
if (llama_eval(ctx_llama, embd.data(), embd.size(), n_past)) {
|
||
fprintf(stderr, "%s : failed to eval\n", __func__);
|
||
return 1;
|
||
}
|
||
}
|
||
|
||
|
||
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 = 0.30f;
|
||
const float repeat_penalty = 1.1764f;
|
||
|
||
const int repeat_last_n = 256;
|
||
|
||
if (!path_session.empty() && need_to_save_session) {
|
||
need_to_save_session = false;
|
||
llama_save_session_file(ctx_llama, path_session.c_str(), session_tokens.data(), session_tokens.size());
|
||
}
|
||
|
||
llama_token id = 0;
|
||
|
||
{
|
||
auto logits = llama_get_logits(ctx_llama);
|
||
auto n_vocab = llama_n_vocab(model_llama);
|
||
|
||
logits[llama_token_eos(model_llama)] = 0;
|
||
|
||
std::vector<llama_token_data> candidates;
|
||
candidates.reserve(n_vocab);
|
||
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
|
||
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
|
||
}
|
||
|
||
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
|
||
|
||
// apply repeat penalty
|
||
const float nl_logit = logits[llama_token_nl(model_llama)];
|
||
|
||
llama_sample_repetition_penalties(ctx_llama, &candidates_p,
|
||
embd_inp.data() + std::max(0, n_past - repeat_last_n),
|
||
repeat_last_n, repeat_penalty, 0.0, 0.0f);
|
||
|
||
logits[llama_token_nl(model_llama)] = nl_logit;
|
||
|
||
if (temp <= 0) {
|
||
// Greedy sampling
|
||
id = llama_sample_token_greedy(ctx_llama, &candidates_p);
|
||
} else {
|
||
// Temperature sampling
|
||
llama_sample_top_k(ctx_llama, &candidates_p, top_k, 1);
|
||
llama_sample_top_p(ctx_llama, &candidates_p, top_p, 1);
|
||
llama_sample_temp (ctx_llama, &candidates_p, temp);
|
||
id = llama_sample_token(ctx_llama, &candidates_p);
|
||
}
|
||
}
|
||
|
||
if (id != llama_token_eos(model_llama)) {
|
||
// add it to the context
|
||
embd.push_back(id);
|
||
|
||
text_to_speak += llama_token_to_piece(ctx_llama, id);
|
||
|
||
printf("%s", llama_token_to_piece(ctx_llama, id).c_str());
|
||
fflush(stdout);
|
||
}
|
||
}
|
||
|
||
{
|
||
std::string last_output;
|
||
for (int i = embd_inp.size() - 16; i < (int) embd_inp.size(); i++) {
|
||
last_output += llama_token_to_piece(ctx_llama, embd_inp[i]);
|
||
}
|
||
last_output += llama_token_to_piece(ctx_llama, embd[0]);
|
||
|
||
for (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);
|
||
need_to_save_session = true;
|
||
break;
|
||
}
|
||
}
|
||
}
|
||
|
||
is_running = sdl_poll_events();
|
||
|
||
if (!is_running) {
|
||
break;
|
||
}
|
||
}
|
||
|
||
speak_with_file(params.speak, text_to_speak, params.speak_file, voice_id);
|
||
|
||
audio.clear();
|
||
}
|
||
}
|
||
}
|
||
|
||
audio.pause();
|
||
|
||
whisper_print_timings(ctx_wsp);
|
||
whisper_free(ctx_wsp);
|
||
|
||
llama_print_timings(ctx_llama);
|
||
llama_free(ctx_llama);
|
||
|
||
return 0;
|
||
}
|