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whisper.cpp
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talk : improve prompting

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pull/271/head
Georgi Gerganov 1 year ago
parent 930c693989
commit a613f16aec
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GPG Key ID: 449E073F9DC10735
4 changed files with 39 additions and 79 deletions
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2
examples/talk/README.md
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@ -31,7 +31,7 @@ To run this, you will need a ggml GPT-2 model: [instructions](https://github.com
Alternatively, you can simply download the smallest ggml GPT-2 117M model (240 MB) like this:
```
wget --quiet --show-progress -O models/ggml-gpt-2-117M.bin https://ggml.ggerganov.com/ggml-model-gpt-2-117M.bin
wget --quiet --show-progress -O models/ggml-gpt-2-117M.bin https://huggingface.co/datasets/ggerganov/ggml/raw/main/ggml-model-gpt-2-117M.bin
```
## TTS

13
examples/talk/gpt-2.cpp
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@ -139,7 +139,7 @@ gpt_vocab::id gpt_sample_top_k_top_p(
}
//printf("\n");
//for (int i = 0; i < (int)logits_id.size(); i++) {
//for (int i = 0; i < (int) logits_id.size(); i++) {
// printf("%d: '%s' %f\n", i, vocab.id_to_token.at(logits_id[i].second).c_str(), logits_id[i].first);
//}
//exit(0);
@ -825,8 +825,8 @@ Me too.
int32_t n_threads = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
// sampling parameters
int32_t top_k = 20;
float top_p = 0.98f;
int32_t top_k = 5;
float top_p = 0.9f;
float temp = 1.0f;
};
@ -840,7 +840,7 @@ struct gpt2_context * gpt2_init(const char * path_model) {
const int64_t t_start_us = ggml_time_us();
if (!gpt2_model_load(path_model, ctx->model, ctx->vocab)) {
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, "gpt-2.bin");
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, path_model);
return nullptr;
}
@ -913,10 +913,7 @@ std::string gpt2_gen_text(gpt2_context * ctx, const char * text, int max_tokens)
result += ctx->vocab.id_to_token[embd[0]];
// end of text token
if (embd.back() == 50256 ||
ctx->vocab.id_to_token[embd.back()] == "." ||
ctx->vocab.id_to_token[embd.back()] == "!" ||
ctx->vocab.id_to_token[embd.back()] == "?") {
if (embd.back() == 50256) {
break;
}
}

101
examples/talk/talk.cpp
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@ -473,56 +473,15 @@ std::string transcribe(whisper_context * ctx, const whisper_params & params, con
return result;
}
// compute similarity between two strings using Levenshtein distance
float similarity(const std::string & s0, const std::string & s1) {
const size_t len0 = s0.size() + 1;
const size_t len1 = s1.size() + 1;
const std::string k_prompt =
R"(This is a dialogue between {0} (A) and a person (B). The dialogue so far is:
std::vector<int> col(len1, 0);
std::vector<int> prevCol(len1, 0);
B: Hello {0}, how are you?
A: I'm fine, thank you.
{1}
Here is how {0} (A) continues the dialogue:
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] + (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()));
}
// generated with ChatGPT
std::map<std::string, std::string> k_prompts = {
{ "Santa",
R"(Kid: Hi Santa! Are you real?
Santa: Of course I am, my dear! Ho ho ho!
Kid: Can you please bring me a new toy for Christmas?
Santa: I'll see what I can do, but you have to make sure to be a good boy or girl and listen to your parents.
Kid: I will, Santa! Thank you!
Santa: You're welcome, little one. Merry Christmas! Ho ho ho!
Kid: Can you tell me how you deliver all the presents to all the kids in the world in one night?
Santa: It's a secret, but I have a lot of help from my elves and my magical sleigh. And I have a special route that I follow to make sure I visit every child.
Kid: Wow, that's amazing! Can I please have a ride in your sleigh sometime?
Santa: I'm sorry, but only good boys and girls get to ride in my sleigh.
)" },
{ "Kid",
R"(Kid: Hi Santa! Are you real?
Santa: Of course I am, my dear! Ho ho ho!
Kid: Can you please bring me a new toy for Christmas?
Santa: I'll see what I can do, but you have to make sure to be a good boy or girl and listen to your parents.
Kid: I will, Santa! Thank you!
Kid: Can you tell me how you deliver all the presents to all the kids in the world in one night?
Santa: It's a secret, but I have a lot of help from my elves and my magical sleigh. And I have a special route that I follow to make sure I visit every child.
Kid: Wow, that's amazing! Can I please have a ride in your sleigh sometime?
)" },
};
A:)";
int main(int argc, char ** argv) {
whisper_params params;
@ -579,7 +538,7 @@ int main(int argc, char ** argv) {
int n_iter = 0;
bool is_running = true;
bool force_speak = params.person == "Kid";
bool force_speak = false;
float prob0 = 0.0f;
float prob = 0.0f;
@ -587,19 +546,13 @@ int main(int argc, char ** argv) {
std::vector<float> pcmf32_cur;
std::vector<float> pcmf32_prompt;
if (k_prompts.find(params.person) == k_prompts.end()) {
fprintf(stderr, "%s: unknown person '%s'\n", __func__, params.person.c_str());
return 1;
}
gpt2_set_prompt(ctx_gpt, k_prompts.at(params.person).c_str());
gpt2_set_prompt(ctx_gpt, "");
const std::string person_other = params.person == "Santa" ? "Kid" : "Santa";
const int voice_id = params.person == "Santa" ? 5 : 2;
const int voice_id = rand()%6;
fprintf(stderr, "gpt-2: prompt_base:\n");
fprintf(stderr, "gpt-2: prompt:\n");
fprintf(stderr, "========================\n\n");
fprintf(stderr, "%s\n", gpt2_get_prompt(ctx_gpt));
fprintf(stderr, "%s\n", ::replace(k_prompt, "{0}", params.person).c_str());
fprintf(stderr, "========================\n\n");
// main loop
@ -636,13 +589,12 @@ int main(int argc, char ** argv) {
audio.get(params.voice_ms, pcmf32_cur);
std::string text_heard = "Hey little one, what do you want for Christmas?";
std::string text_heard = "";
if (!force_speak) {
text_heard = ::trim(::transcribe(ctx_wsp, params, pcmf32_cur, prob0, t_ms));
}
force_speak = false;
// remove text between brackets using regex
{
std::regex re("\\[.*?\\]");
@ -667,13 +619,15 @@ int main(int argc, char ** argv) {
const std::vector<gpt_vocab::id> tokens = gpt2_tokenize(ctx_gpt, text_heard.c_str());
if (text_heard.empty() || tokens.empty()) {
if (text_heard.empty() || tokens.empty() || force_speak) {
fprintf(stdout, "%s: Heard nothing, skipping ...\n", __func__);
audio.clear();
continue;
}
force_speak = false;
fprintf(stdout, "%s: Heard '%s%s%s', (t = %d ms)\n", __func__, "\033[1m", text_heard.c_str(), "\033[0m", (int) t_ms);
std::string prompt_base = gpt2_get_prompt(ctx_gpt);
@ -681,9 +635,11 @@ int main(int argc, char ** argv) {
std::string text_to_speak;
{
text_heard = person_other + ": " + text_heard;
prompt_base += "B: " + text_heard + "\n";
text_to_speak = gpt2_gen_text(ctx_gpt, (prompt_base + text_heard + "\n").c_str(), params.max_tokens);
std::string prompt = ::replace(::replace(k_prompt, "{0}", params.person), "{1}", prompt_base);
text_to_speak = gpt2_gen_text(ctx_gpt, prompt.c_str(), params.max_tokens);
text_to_speak = std::regex_replace(text_to_speak, std::regex("[^a-zA-Z0-9\\.,\\?!\\s\\:\\'\\-]"), "");
text_to_speak = text_to_speak.substr(0, text_to_speak.find_first_of("\n"));
@ -703,13 +659,20 @@ int main(int argc, char ** argv) {
}
}
prompt_base += text_heard + "\n" + text_to_speak + "\n";
}
prompt_base += "A:" + text_to_speak + "\n";
{
prompt = ::replace(::replace(k_prompt, "{0}", params.person), "{1}", prompt_base);
printf("%s\n", text_to_speak.c_str());
printf("===============\n");
printf("prompt:\n");
printf("%s\n", prompt.c_str());
printf("===============\n");
}
}
//printf("========================\n");
//printf("gpt-2: prompt_base:\n'%s'\n", prompt_base.c_str());
//printf("gpt-2: prompt_base:\n%s\n", prompt_base.c_str());
//printf("========================\n");
gpt2_set_prompt(ctx_gpt, prompt_base.c_str());

2
models/download-ggml-model.cmd
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@ -40,7 +40,7 @@ if exist "ggml-%model%.bin" (
goto :eof
)
PowerShell -NoProfile -ExecutionPolicy Bypass -Command "Invoke-WebRequest -Uri https://ggml.ggerganov.com/ggml-model-whisper-%model%.bin -OutFile ggml-%model%.bin"
PowerShell -NoProfile -ExecutionPolicy Bypass -Command "Invoke-WebRequest -Uri https://huggingface.co/datasets/ggerganov/whisper.cpp/raw/main/ggml-%model%.bin -OutFile ggml-%model%.bin"
if %ERRORLEVEL% neq 0 (
echo Failed to download ggml model %model%

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