whisper.cpp/examples/talk-llama/talk-llama.cpp
Przemysław Pawełczyk 3f7a03ebe3
ggml : do not use _GNU_SOURCE gratuitously (#1027)
* Do not use _GNU_SOURCE gratuitously.

What is needed to build whisper.cpp and examples is availability of
stuff defined in The Open Group Base Specifications Issue 6
(https://pubs.opengroup.org/onlinepubs/009695399/) known also as
Single Unix Specification v3 (SUSv3) or POSIX.1-2001 + XSI extensions.

There is no need to penalize musl libc which simply follows standards.

Not having feature test macros in source code gives greater flexibility
to those wanting to reuse it in 3rd party app, as they can build it with
minimal FTM (_XOPEN_SOURCE=600) or other FTM depending on their needs.

It builds without issues in Alpine (musl libc), Ubuntu (glibc), MSYS2.

* examples : include SDL headers before other headers

This is an attempt at fixing macOS build error coming from SDL2 relying
on Darwin extension memset_pattern4/8/16 coming from Apple's string.h.
2023-06-25 16:34:30 +03:00

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// Talk with AI
//
#include "common-sdl.h"
#include "common.h"
#include "whisper.h"
#include "llama.h"
#include <cassert>
#include <cstdio>
#include <fstream>
#include <regex>
#include <string>
#include <thread>
#include <vector>
#include <regex>
std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::string & text, bool add_bos) {
// initialize to prompt numer of chars, since n_tokens <= n_prompt_chars
std::vector<llama_token> res(text.size() + (int)add_bos);
int n = llama_tokenize(ctx, text.c_str(), res.data(), res.size(), add_bos);
assert(n >= 0);
res.resize(n);
return res;
}
// command-line parameters
struct whisper_params {
int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
int32_t voice_ms = 10000;
int32_t capture_id = -1;
int32_t max_tokens = 32;
int32_t audio_ctx = 0;
float vad_thold = 0.6f;
float freq_thold = 100.0f;
bool speed_up = false;
bool translate = false;
bool print_special = false;
bool print_energy = false;
bool no_timestamps = true;
bool verbose_prompt = false;
std::string person = "Georgi";
std::string language = "en";
std::string model_wsp = "models/ggml-base.en.bin";
std::string model_llama = "models/ggml-llama-7B.bin";
std::string speak = "./examples/talk-llama/speak";
std::string prompt = "";
std::string fname_out;
std::string path_session = ""; // path to file for saving/loading model eval state
};
void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
for (int i = 1; i < argc; i++) {
std::string arg = argv[i];
if (arg == "-h" || arg == "--help") {
whisper_print_usage(argc, argv, params);
exit(0);
}
else if (arg == "-t" || arg == "--threads") { params.n_threads = std::stoi(argv[++i]); }
else if (arg == "-vms" || arg == "--voice-ms") { params.voice_ms = std::stoi(argv[++i]); }
else if (arg == "-c" || arg == "--capture") { params.capture_id = std::stoi(argv[++i]); }
else if (arg == "-mt" || arg == "--max-tokens") { params.max_tokens = std::stoi(argv[++i]); }
else if (arg == "-ac" || arg == "--audio-ctx") { params.audio_ctx = std::stoi(argv[++i]); }
else if (arg == "-vth" || arg == "--vad-thold") { params.vad_thold = std::stof(argv[++i]); }
else if (arg == "-fth" || arg == "--freq-thold") { params.freq_thold = std::stof(argv[++i]); }
else if (arg == "-su" || arg == "--speed-up") { params.speed_up = true; }
else if (arg == "-tr" || arg == "--translate") { params.translate = true; }
else if (arg == "-ps" || arg == "--print-special") { params.print_special = true; }
else if (arg == "-pe" || arg == "--print-energy") { params.print_energy = true; }
else if (arg == "--verbose-prompt") { params.verbose_prompt = true; }
else if (arg == "-p" || arg == "--person") { params.person = argv[++i]; }
else if (arg == "--session") { params.path_session = argv[++i];}
else if (arg == "-l" || arg == "--language") { params.language = argv[++i]; }
else if (arg == "-mw" || arg == "--model-whisper") { params.model_wsp = argv[++i]; }
else if (arg == "-ml" || arg == "--model-llama") { params.model_llama = argv[++i]; }
else if (arg == "-s" || arg == "--speak") { params.speak = argv[++i]; }
else if (arg == "--prompt-file") {
std::ifstream file(argv[++i]);
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
if (params.prompt.back() == '\n') {
params.prompt.pop_back();
}
}
else if (arg == "-f" || arg == "--file") { params.fname_out = argv[++i]; }
else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
whisper_print_usage(argc, argv, params);
exit(0);
}
}
return true;
}
void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & params) {
fprintf(stderr, "\n");
fprintf(stderr, "usage: %s [options]\n", argv[0]);
fprintf(stderr, "\n");
fprintf(stderr, "options:\n");
fprintf(stderr, " -h, --help [default] show this help message and exit\n");
fprintf(stderr, " -t N, --threads N [%-7d] number of threads to use during computation\n", params.n_threads);
fprintf(stderr, " -vms N, --voice-ms N [%-7d] voice duration in milliseconds\n", params.voice_ms);
fprintf(stderr, " -c ID, --capture ID [%-7d] capture device ID\n", params.capture_id);
fprintf(stderr, " -mt N, --max-tokens N [%-7d] maximum number of tokens per audio chunk\n", params.max_tokens);
fprintf(stderr, " -ac N, --audio-ctx N [%-7d] audio context size (0 - all)\n", params.audio_ctx);
fprintf(stderr, " -vth N, --vad-thold N [%-7.2f] voice activity detection threshold\n", params.vad_thold);
fprintf(stderr, " -fth N, --freq-thold N [%-7.2f] high-pass frequency cutoff\n", params.freq_thold);
fprintf(stderr, " -su, --speed-up [%-7s] speed up audio by x2 (reduced accuracy)\n", params.speed_up ? "true" : "false");
fprintf(stderr, " -tr, --translate [%-7s] translate from source language to english\n", params.translate ? "true" : "false");
fprintf(stderr, " -ps, --print-special [%-7s] print special tokens\n", params.print_special ? "true" : "false");
fprintf(stderr, " -pe, --print-energy [%-7s] print sound energy (for debugging)\n", params.print_energy ? "true" : "false");
fprintf(stderr, " -p NAME, --person NAME [%-7s] person name (for prompt selection)\n", params.person.c_str());
fprintf(stderr, " -l LANG, --language LANG [%-7s] spoken language\n", params.language.c_str());
fprintf(stderr, " -mw FILE, --model-whisper [%-7s] whisper model file\n", params.model_wsp.c_str());
fprintf(stderr, " -ml FILE, --model-llama [%-7s] llama model file\n", params.model_llama.c_str());
fprintf(stderr, " -s FILE, --speak TEXT [%-7s] command for TTS\n", params.speak.c_str());
fprintf(stderr, " --prompt-file FNAME [%-7s] file with custom prompt to start dialog\n", "");
fprintf(stderr, " --session FNAME file to cache model state in (may be large!) (default: none)\n");
fprintf(stderr, " --verbose-prompt [%-7s] print prompt at start\n", params.verbose_prompt ? "true" : "false");
fprintf(stderr, " -f FNAME, --file FNAME [%-7s] text output file name\n", params.fname_out.c_str());
fprintf(stderr, "\n");
}
std::string transcribe(
whisper_context * ctx,
const whisper_params & params,
const std::vector<float> & pcmf32,
const std::string prompt_text,
float & prob,
int64_t & t_ms) {
const auto t_start = std::chrono::high_resolution_clock::now();
prob = 0.0f;
t_ms = 0;
std::vector<whisper_token> prompt_tokens;
whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
prompt_tokens.resize(1024);
prompt_tokens.resize(whisper_tokenize(ctx, prompt_text.c_str(), prompt_tokens.data(), prompt_tokens.size()));
wparams.print_progress = false;
wparams.print_special = params.print_special;
wparams.print_realtime = false;
wparams.print_timestamps = !params.no_timestamps;
wparams.translate = params.translate;
wparams.no_context = true;
wparams.single_segment = true;
wparams.max_tokens = params.max_tokens;
wparams.language = params.language.c_str();
wparams.n_threads = params.n_threads;
wparams.prompt_tokens = prompt_tokens.empty() ? nullptr : prompt_tokens.data();
wparams.prompt_n_tokens = prompt_tokens.empty() ? 0 : prompt_tokens.size();
wparams.audio_ctx = params.audio_ctx;
wparams.speed_up = params.speed_up;
if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
return "";
}
int prob_n = 0;
std::string result;
const int n_segments = whisper_full_n_segments(ctx);
for (int i = 0; i < n_segments; ++i) {
const char * text = whisper_full_get_segment_text(ctx, i);
result += text;
const int n_tokens = whisper_full_n_tokens(ctx, i);
for (int j = 0; j < n_tokens; ++j) {
const auto token = whisper_full_get_token_data(ctx, i, j);
prob += token.p;
++prob_n;
}
}
if (prob_n > 0) {
prob /= prob_n;
}
const auto t_end = std::chrono::high_resolution_clock::now();
t_ms = std::chrono::duration_cast<std::chrono::milliseconds>(t_end - t_start).count();
return result;
}
const std::string k_prompt_whisper = R"(A conversation with a person called {1}.)";
const std::string k_prompt_llama = R"(Text transcript of a never ending dialog, where {0} interacts with an AI assistant named {1}.
{1} is helpful, kind, honest, friendly, good at writing and never fails to answer {0}s requests immediately and with details and precision.
There are no annotations like (30 seconds passed...) or (to himself), just what {0} and {1} say aloud to each other.
The transcript only includes text, it does not include markup like HTML and Markdown.
{1} responds with short and concise answers.
{0}{4} Hello, {1}!
{1}{4} Hello {0}! How may I help you today?
{0}{4} What time is it?
{1}{4} It is {2} o'clock.
{0}{4} What year is it?
{1}{4} We are in {3}.
{0}{4} What is a cat?
{1}{4} A cat is a domestic species of small carnivorous mammal. It is the only domesticated species in the family Felidae.
{0}{4} Name a color.
{1}{4} Blue
{0}{4})";
int main(int argc, char ** argv) {
whisper_params params;
if (whisper_params_parse(argc, argv, params) == false) {
return 1;
}
if (whisper_lang_id(params.language.c_str()) == -1) {
fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
whisper_print_usage(argc, argv, params);
exit(0);
}
// whisper init
struct whisper_context * ctx_wsp = whisper_init_from_file(params.model_wsp.c_str());
// llama init
llama_init_backend();
auto lparams = llama_context_default_params();
// tune these to your liking
lparams.n_ctx = 2048;
lparams.seed = 1;
lparams.f16_kv = true;
struct llama_context * ctx_llama = llama_init_from_file(params.model_llama.c_str(), lparams);
// print some info about the processing
{
fprintf(stderr, "\n");
if (!whisper_is_multilingual(ctx_wsp)) {
if (params.language != "en" || params.translate) {
params.language = "en";
params.translate = false;
fprintf(stderr, "%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
}
}
fprintf(stderr, "%s: processing, %d threads, lang = %s, task = %s, timestamps = %d ...\n",
__func__,
params.n_threads,
params.language.c_str(),
params.translate ? "translate" : "transcribe",
params.no_timestamps ? 0 : 1);
fprintf(stderr, "\n");
}
// init audio
audio_async audio(30*1000);
if (!audio.init(params.capture_id, WHISPER_SAMPLE_RATE)) {
fprintf(stderr, "%s: audio.init() failed!\n", __func__);
return 1;
}
audio.resume();
int n_iter = 0;
bool is_running = true;
bool force_speak = false;
float prob0 = 0.0f;
const std::string chat_symb = ":";
const std::string bot_name = "LLaMA";
std::vector<float> pcmf32_cur;
std::vector<float> pcmf32_prompt;
const std::string prompt_whisper = ::replace(k_prompt_whisper, "{1}", bot_name);
// construct the initial prompt for LLaMA inference
std::string prompt_llama = params.prompt.empty() ? k_prompt_llama : params.prompt;
// need to have leading ' '
prompt_llama.insert(0, 1, ' ');
prompt_llama = ::replace(prompt_llama, "{0}", params.person);
prompt_llama = ::replace(prompt_llama, "{1}", bot_name);
{
// 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);
// init session
std::string path_session = params.path_session;
std::vector<llama_token> session_tokens;
auto embd_inp = ::llama_tokenize(ctx_llama, prompt_llama, true);
if (!path_session.empty()) {
fprintf(stderr, "%s: attempting to load saved session from %s\n", __func__, path_session.c_str());
// fopen to check for existing session
FILE * fp = std::fopen(path_session.c_str(), "rb");
if (fp != NULL) {
std::fclose(fp);
session_tokens.resize(lparams.n_ctx);
size_t n_token_count_out = 0;
if (!llama_load_session_file(ctx_llama, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
fprintf(stderr, "%s: error: failed to load session file '%s'\n", __func__, path_session.c_str());
return 1;
}
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, params.n_threads)) {
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__);
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 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;
text_heard.insert(0, 1, ' ');
text_heard += "\n" + 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_str(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, params.n_threads)) {
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(ctx_llama);
logits[llama_token_eos()] = 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()];
llama_sample_repetition_penalty(ctx_llama, &candidates_p,
embd_inp.data() + std::max(0, n_past - repeat_last_n),
repeat_last_n, repeat_penalty);
logits[llama_token_nl()] = 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_temperature(ctx_llama, &candidates_p, temp);
id = llama_sample_token(ctx_llama, &candidates_p);
}
}
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));
}
}
{
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 (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;
}
}
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;
}