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