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https://github.com/ggerganov/llama.cpp.git
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Merge c9d1eb3a06
into 9a483999a6
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49382ff2fa
@ -2214,6 +2214,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex
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params.vocoder.model = value;
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}
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).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER}));
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add_opt(common_arg(
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{"--tts-use-guide-tokens"},
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"Use guide tokens to improve TTS word recall",
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[](common_params & params) {
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params.vocoder.use_guide_tokens = true;
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}
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).set_examples({LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_SERVER}));
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// model-specific
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add_opt(common_arg(
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@ -178,6 +178,8 @@ struct common_params_vocoder {
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std::string model = ""; // model path // NOLINT
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std::string model_url = ""; // model url to download // NOLINT
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bool use_guide_tokens = false; // enable guide tokens to improve TTS accuracy // NOLINT
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};
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struct common_params {
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@ -425,6 +425,29 @@ static void prompt_init(llama_tokens & prompt, const llama_vocab * vocab) {
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prompt_add(prompt, vocab, "<|im_start|>\n", true, true);
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}
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static std::vector<llama_token> prepare_guide_tokens(const llama_model * model, const std::string& str)
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{
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const std::string& delimiter = "<|text_sep|>";
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std::vector<llama_token> result;
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size_t start = 0;
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size_t end = str.find(delimiter);
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while (end != std::string::npos) {
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std::string current_word = str.substr(start, end - start);
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auto tmp = common_tokenize(model, current_word, false, true);
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result.push_back(tmp[0]);
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start = end + delimiter.length();
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end = str.find(delimiter, start);
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}
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// Add the last part
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std::string current_word = str.substr(start);
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auto tmp = common_tokenize(model, current_word, false, true);
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result.push_back(tmp[0]);
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return result;
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}
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int main(int argc, char ** argv) {
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common_params params;
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@ -494,6 +517,7 @@ int main(int argc, char ** argv) {
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const auto t_main_start = ggml_time_us();
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std::vector<llama_token> codes;
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std::vector<llama_token> guide_tokens;
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// process prompt and generate voice codes
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{
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@ -508,6 +532,10 @@ int main(int argc, char ** argv) {
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// convert the input text into the necessary format expected by OuteTTS
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{
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std::string prompt_clean = process_text(params.prompt);
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if(params.vocoder.use_guide_tokens)
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{
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guide_tokens = prepare_guide_tokens(model_ttc,prompt_clean);
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}
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LOG_INF("%s: prompt: '%s'\n", __func__, prompt_clean.c_str());
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@ -717,6 +745,8 @@ lovely<|t_0.56|><|code_start|><|634|><|596|><|1766|><|1556|><|1306|><|1285|><|14
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int n_past = batch.n_tokens;
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int n_decode = 0;
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bool next_token_uses_guide_token = true;
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while (n_decode <= n_predict) {
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// prepare the next batch
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common_batch_clear(batch);
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@ -728,7 +758,18 @@ lovely<|t_0.56|><|code_start|><|634|><|596|><|1766|><|1556|><|1306|><|1285|><|14
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continue;
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}
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const llama_token new_token_id = common_sampler_sample(smpl[i], ctx_ttc, i_batch[i]);
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llama_token new_token_id = common_sampler_sample(smpl[i], ctx_ttc, i_batch[i]);
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//guide tokens help prevent hallucinations by forcing the TTS to use the correct word
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if(!guide_tokens.empty() && next_token_uses_guide_token && !llama_token_is_control(model_ttc, new_token_id) && !llama_token_is_eog(model_ttc, new_token_id))
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{
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llama_token guide_token = guide_tokens[0];
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guide_tokens.erase(guide_tokens.begin());
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new_token_id = guide_token; //ensure correct word fragment is used
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}
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//this is the token id that always precedes a new word
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next_token_uses_guide_token = (new_token_id == 198);
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common_sampler_accept(smpl[i], new_token_id, true);
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