talk.wasm : GPT-2 meets Whisper in WebAssembly (#155)

* talk : initial real-time transcription in the browser

* talk : polishing the UI

* talk : ready for beta testing

* talk.wasm : rename example

bindings/javascript/CMakeLists.txt

@@ -9,12 +9,13 @@ target_link_libraries(${TARGET} PRIVATE
     )
 
 unset(EXTRA_FLAGS)
+
 if (WHISPER_WASM_SINGLE_FILE)
     set(EXTRA_FLAGS "-s SINGLE_FILE=1")
     message(STATUS "Embedding WASM inside whisper.js")
 
     add_custom_command(
-        TARGET libwhisper POST_BUILD
+        TARGET ${TARGET} POST_BUILD
         COMMAND ${CMAKE_COMMAND} -E copy
         ${CMAKE_BINARY_DIR}/bin/libwhisper.js
         ${CMAKE_CURRENT_SOURCE_DIR}/whisper.js

examples/CMakeLists.txt

@@ -20,6 +20,7 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR})
 
 if (EMSCRIPTEN)
     add_subdirectory(whisper.wasm)
+    add_subdirectory(talk.wasm)
 else()
     add_subdirectory(main)
     add_subdirectory(stream)

examples/talk.wasm/CMakeLists.txt

@@ -0,0 +1,46 @@
+#
+# libtalk
+#
+
+set(TARGET libtalk)
+
+add_executable(${TARGET}
+    emscripten.cpp
+    )
+
+target_link_libraries(${TARGET} PRIVATE
+    whisper
+    )
+
+unset(EXTRA_FLAGS)
+
+if (WHISPER_WASM_SINGLE_FILE)
+    set(EXTRA_FLAGS "-s SINGLE_FILE=1")
+    message(STATUS "Embedding WASM inside talk.js")
+
+    add_custom_command(
+        TARGET ${TARGET} POST_BUILD
+        COMMAND ${CMAKE_COMMAND} -E copy
+        ${CMAKE_BINARY_DIR}/bin/libtalk.js
+        ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/talk.wasm/talk.js
+        )
+endif()
+
+set_target_properties(${TARGET} PROPERTIES LINK_FLAGS " \
+    --bind \
+    -s USE_PTHREADS=1 \
+    -s PTHREAD_POOL_SIZE=8 \
+    -s INITIAL_MEMORY=1400MB \
+    -s TOTAL_MEMORY=1400MB \
+    -s FORCE_FILESYSTEM=1 \
+    -s EXPORTED_RUNTIME_METHODS=\"['print', 'printErr', 'ccall', 'cwrap']\" \
+    ${EXTRA_FLAGS} \
+    ")
+
+#
+# talk.wasm
+#
+
+set(TARGET talk.wasm)
+
+configure_file(${CMAKE_CURRENT_SOURCE_DIR}/index-tmpl.html  ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${TARGET}/index.html @ONLY)

examples/talk.wasm/README.md

@@ -0,0 +1,7 @@
+# talk
+
+WIP IN PROGRESS
+
+ref: https://github.com/ggerganov/whisper.cpp/issues/154
+
+demo: https://talk.ggerganov.com

examples/talk.wasm/emscripten.cpp

@@ -0,0 +1,1379 @@
+#include "ggml.h"
+#include "whisper.h"
+
+#include <emscripten.h>
+#include <emscripten/bind.h>
+
+#include <atomic>
+#include <cassert>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <map>
+#include <mutex>
+#include <string>
+#include <thread>
+#include <vector>
+#include <regex>
+#include <random>
+
+std::string to_timestamp(int64_t t) {
+    int64_t sec = t/100;
+    int64_t msec = t - sec*100;
+    int64_t min = sec/60;
+    sec = sec - min*60;
+
+    char buf[32];
+    snprintf(buf, sizeof(buf), "%02d:%02d.%03d", (int) min, (int) sec, (int) msec);
+
+    return std::string(buf);
+}
+
+/////////////////////// GPT-2 BEGIN /////////////////////////
+// TODO: move to a separate file
+
+//
+// Vocab utils
+//
+
+struct gpt_vocab {
+    using id    = int32_t;
+    using token = std::string;
+
+    std::map<token, id> token_to_id;
+    std::map<id, token> id_to_token;
+};
+
+void replace(std::string & str, const std::string & needle, const std::string & replacement) {
+    size_t pos = 0;
+    while ((pos = str.find(needle, pos)) != std::string::npos) {
+        str.replace(pos, needle.length(), replacement);
+        pos += replacement.length();
+    }
+}
+
+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;
+                    }
+
+                    ::replace(str_key, "\\u0120", " " ); // \u0120 -> space
+                    ::replace(str_key, "\\u010a", "\n"); // \u010a -> new line
+                    ::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::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;
+        std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
+
+        std::regex re(pat);
+        std::smatch m;
+
+        while (std::regex_search(str, m, re)) {
+            for (auto x : m) {
+                words.push_back(x);
+            }
+            str = m.suffix();
+        }
+    }
+
+    // find the longest tokens that form the words:
+    std::vector<gpt_vocab::id> tokens;
+    for (const auto & word : words) {
+        if (word.size() == 0) continue;
+
+        int i = 0;
+        int n = word.size();
+        while (i < n) {
+            int j = n;
+            while (j > i) {
+                auto it = vocab.token_to_id.find(word.substr(i, j-i));
+                if (it != vocab.token_to_id.end()) {
+                    tokens.push_back(it->second);
+                    i = j;
+                    break;
+                }
+                --j;
+            }
+            if (i == n) {
+                break;
+            }
+            if (j == i) {
+                auto sub = word.substr(i, 1);
+                if (vocab.token_to_id.find(sub) != vocab.token_to_id.end()) {
+                    tokens.push_back(vocab.token_to_id.at(sub));
+                } else {
+                    fprintf(stderr, "%s: unknown token '%s'\n", __func__, sub.data());
+                }
+                ++i;
+            }
+        }
+    }
+
+    return tokens;
+}
+
+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);
+
+    for (int i = 0; i < n_logits; i++) {
+        logits_id.push_back(std::make_pair(logits[i], 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);
+
+    // normalize
+    {
+        double sum = 0.0f;
+        for (int i = 0; i < (int)logits_id.size(); i++) {
+            sum += logits_id[i].first;
+        }
+
+        sum = 1.0/sum;
+        for (int i = 0; i < (int)logits_id.size(); i++) {
+            logits_id[i].first *= sum;
+        }
+    }
+
+    if (top_p < 1.0f) {
+        {
+            double cumsum = 0.0f;
+            for (int i = 0; i < top_k; i++) {
+                cumsum += logits_id[i].first;
+                if (cumsum >= top_p) {
+                    logits_id.resize(i+1);
+                    break;
+                }
+            }
+        }
+
+        // normalize again
+        {
+            double sum = 0.0f;
+            for (int i = 0; i < (int)logits_id.size(); i++) {
+                sum += logits_id[i].first;
+            }
+
+            sum = 1.0/sum;
+            for (int i = 0; i < (int)logits_id.size(); i++) {
+                logits_id[i].first *= sum;
+            }
+        }
+    }
+
+    //printf("\n");
+    //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);
+
+    // sample from the obtained distribution
+    std::vector<double> probs;
+    probs.reserve(logits_id.size());
+
+    for (int i = 0; i < (int) logits_id.size(); i++) {
+        probs.push_back(logits_id[i].first);
+    }
+
+    std::discrete_distribution<> dist(probs.begin(), probs.end());
+    int idx = dist(rng);
+
+    return logits_id[idx].second;
+}
+
+// default hparams (GPT-2 117M)
+struct gpt2_hparams {
+    int32_t n_vocab = 50257;
+    int32_t n_ctx   = 1024;
+    int32_t n_embd  = 768;
+    int32_t n_head  = 12;
+    int32_t n_layer = 12;
+    int32_t f16     = 1;
+};
+
+struct gpt2_layer {
+    // normalization
+    struct ggml_tensor * ln_1_g;
+    struct ggml_tensor * ln_1_b;
+
+    struct ggml_tensor * ln_2_g;
+    struct ggml_tensor * ln_2_b;
+
+    // attention
+    struct ggml_tensor * c_attn_attn_w;
+    struct ggml_tensor * c_attn_attn_b;
+
+    struct ggml_tensor * c_attn_proj_w;
+    struct ggml_tensor * c_attn_proj_b;
+
+    // mlp
+    struct ggml_tensor * c_mlp_fc_w;
+    struct ggml_tensor * c_mlp_fc_b;
+
+    struct ggml_tensor * c_mlp_proj_w_trans; // transposed for efficiency
+    struct ggml_tensor * c_mlp_proj_b;
+};
+
+struct gpt2_model {
+    gpt2_hparams hparams;
+
+    // normalization
+    struct ggml_tensor * ln_f_g;
+    struct ggml_tensor * ln_f_b;
+
+    struct ggml_tensor * wte; // position embedding
+    struct ggml_tensor * wpe; //    token embedding
+
+    std::vector<gpt2_layer> layers;
+
+    // key + value memory
+    struct ggml_tensor * memory_k;
+    struct ggml_tensor * memory_v;
+
+    //
+    struct ggml_context * ctx;
+    std::map<std::string, struct ggml_tensor *> tensors;
+};
+
+// load the model's weights from a file
+bool gpt2_model_load(const std::string & fname, gpt2_model & model, gpt_vocab & vocab) {
+    printf("%s: loading model from '%s'\n", __func__, fname.c_str());
+
+    auto fin = std::ifstream(fname, std::ios::binary);
+    if (!fin) {
+        fprintf(stderr, "%s: failed to open '%s'\n", __func__, fname.c_str());
+        return false;
+    }
+
+    // verify magic
+    {
+        uint32_t magic;
+        fin.read((char *) &magic, sizeof(magic));
+        if (magic != 0x67676d6c) {
+            fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
+            return false;
+        }
+    }
+
+    // load hparams
+    {
+        auto & hparams = model.hparams;
+
+        fin.read((char *) &hparams.n_vocab, sizeof(hparams.n_vocab));
+        fin.read((char *) &hparams.n_ctx,   sizeof(hparams.n_ctx));
+        fin.read((char *) &hparams.n_embd,  sizeof(hparams.n_embd));
+        fin.read((char *) &hparams.n_head,  sizeof(hparams.n_head));
+        fin.read((char *) &hparams.n_layer, sizeof(hparams.n_layer));
+        fin.read((char *) &hparams.f16,     sizeof(hparams.f16));
+
+        printf("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
+        printf("%s: n_ctx   = %d\n", __func__, hparams.n_ctx);
+        printf("%s: n_embd  = %d\n", __func__, hparams.n_embd);
+        printf("%s: n_head  = %d\n", __func__, hparams.n_head);
+        printf("%s: n_layer = %d\n", __func__, hparams.n_layer);
+        printf("%s: f16     = %d\n", __func__, hparams.f16);
+    }
+
+    // load vocab
+    {
+        int32_t n_vocab = 0;
+        fin.read((char *) &n_vocab, sizeof(n_vocab));
+
+        if (n_vocab != model.hparams.n_vocab) {
+            fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
+                    __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
+            return false;
+        }
+
+        std::string word;
+        for (int i = 0; i < n_vocab; i++) {
+            uint32_t len;
+            fin.read((char *) &len, sizeof(len));
+
+            word.resize(len);
+            fin.read((char *) word.data(), len);
+
+            vocab.token_to_id[word] = i;
+            vocab.id_to_token[i] = word;
+        }
+    }
+
+    // for the big tensors, we have the option to store the data in 16-bit floats
+    // in order to save memory and also to speed up the computation
+    const ggml_type wtype = model.hparams.f16 ? GGML_TYPE_F16 : GGML_TYPE_F32;
+
+    auto & ctx = model.ctx;
+
+    size_t ctx_size = 0;
+
+    {
+        const auto & hparams = model.hparams;
+
+        const int n_embd  = hparams.n_embd;
+        const int n_layer = hparams.n_layer;
+        const int n_ctx   = hparams.n_ctx;
+        const int n_vocab = hparams.n_vocab;
+
+        ctx_size += n_embd*ggml_type_size(GGML_TYPE_F32); // ln_f_g
+        ctx_size += n_embd*ggml_type_size(GGML_TYPE_F32); // ln_f_b
+
+        ctx_size += n_vocab*n_embd*ggml_type_size(wtype);         // wte
+        ctx_size +=   n_ctx*n_embd*ggml_type_size(GGML_TYPE_F32); // wpe
+
+        ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_1_g
+        ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_1_b
+
+        ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_2_g
+        ctx_size += n_layer*(n_embd*ggml_type_size(GGML_TYPE_F32)); // ln_2_b
+
+        ctx_size += n_layer*(3*n_embd*n_embd*ggml_type_size(wtype));         // c_attn_attn_w
+        ctx_size += n_layer*(       3*n_embd*ggml_type_size(GGML_TYPE_F32)); // c_attn_attn_b
+
+        ctx_size += n_layer*(n_embd*n_embd*ggml_type_size(wtype));           // c_attn_proj_w
+        ctx_size += n_layer*(       n_embd*ggml_type_size(GGML_TYPE_F32));   // c_attn_proj_b
+
+        ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_size(wtype));         // c_mlp_fc_w
+        ctx_size += n_layer*(       4*n_embd*ggml_type_size(GGML_TYPE_F32)); // c_mlp_fc_b
+
+        ctx_size += n_layer*(4*n_embd*n_embd*ggml_type_size(wtype));         // c_mlp_proj_w
+        ctx_size += n_layer*(         n_embd*ggml_type_size(GGML_TYPE_F32)); // c_mlp_proj_b
+
+        ctx_size += n_ctx*n_layer*n_embd*ggml_type_size(GGML_TYPE_F32); // memory_k
+        ctx_size += n_ctx*n_layer*n_embd*ggml_type_size(GGML_TYPE_F32); // memory_v
+
+        ctx_size += (6 + 12*n_layer)*256; // object overhead
+
+        printf("%s: ggml ctx size = %6.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
+    }
+
+    // create the ggml context
+    {
+        struct ggml_init_params params = {
+            .mem_size   = ctx_size,
+            .mem_buffer = NULL,
+        };
+
+        model.ctx = ggml_init(params);
+        if (!model.ctx) {
+            fprintf(stderr, "%s: ggml_init() failed\n", __func__);
+            return false;
+        }
+    }
+
+    // prepare memory for the weights
+    {
+        const auto & hparams = model.hparams;
+
+        const int n_embd  = hparams.n_embd;
+        const int n_layer = hparams.n_layer;
+        const int n_ctx   = hparams.n_ctx;
+        const int n_vocab = hparams.n_vocab;
+
+        model.layers.resize(n_layer);
+
+        model.ln_f_g = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+        model.ln_f_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
+
+        model.wte = ggml_new_tensor_2d(ctx, wtype,         n_embd, n_vocab);
+        model.wpe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ctx);
+
+        // map by name
+        model.tensors["model/ln_f/g"] = model.ln_f_g;
+        model.tensors["model/ln_f/b"] = model.ln_f_b;
+
+        model.tensors["model/wte"] = model.wte;
+        model.tensors["model/wpe"] = model.wpe;
+
+        for (int i = 0; i < n_layer; ++i) {
+            auto & layer = model.layers[i];
+
+            layer.ln_1_g             = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+            layer.ln_1_b             = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+
+            layer.ln_2_g             = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+            layer.ln_2_b             = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+
+            layer.c_attn_attn_w      = ggml_new_tensor_2d(ctx, wtype,         3*n_embd, n_embd);
+            layer.c_attn_attn_b      = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 3*n_embd);
+
+            layer.c_attn_proj_w      = ggml_new_tensor_2d(ctx, wtype,           n_embd, n_embd);
+            layer.c_attn_proj_b      = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+
+            layer.c_mlp_fc_w         = ggml_new_tensor_2d(ctx, wtype,         4*n_embd, n_embd);
+            layer.c_mlp_fc_b         = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_embd);
+
+            layer.c_mlp_proj_w_trans = ggml_new_tensor_2d(ctx, wtype,         4*n_embd, n_embd);
+            layer.c_mlp_proj_b       = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_embd);
+
+            // map by name
+            model.tensors["model/h" + std::to_string(i) + "/ln_1/g"]        = layer.ln_1_g;
+            model.tensors["model/h" + std::to_string(i) + "/ln_1/b"]        = layer.ln_1_b;
+
+            model.tensors["model/h" + std::to_string(i) + "/ln_2/g"]        = layer.ln_2_g;
+            model.tensors["model/h" + std::to_string(i) + "/ln_2/b"]        = layer.ln_2_b;
+
+            model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/w"] = layer.c_attn_attn_w;
+            model.tensors["model/h" + std::to_string(i) + "/attn/c_attn/b"] = layer.c_attn_attn_b;
+
+            model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/w"] = layer.c_attn_proj_w;
+            model.tensors["model/h" + std::to_string(i) + "/attn/c_proj/b"] = layer.c_attn_proj_b;
+
+            model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/w"]    = layer.c_mlp_fc_w;
+            model.tensors["model/h" + std::to_string(i) + "/mlp/c_fc/b"]    = layer.c_mlp_fc_b;
+
+            model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/w"]  = layer.c_mlp_proj_w_trans;
+            model.tensors["model/h" + std::to_string(i) + "/mlp/c_proj/b"]  = layer.c_mlp_proj_b;
+        }
+    }
+
+    // key + value memory
+    {
+        const auto & hparams = model.hparams;
+
+        const int n_embd  = hparams.n_embd;
+        const int n_layer = hparams.n_layer;
+        const int n_ctx   = hparams.n_ctx;
+
+        const int n_mem      = n_layer*n_ctx;
+        const int n_elements = n_embd*n_mem;
+
+        model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
+        model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
+
+        const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
+
+        printf("%s: memory size = %8.2f MB, n_mem = %d\n", __func__, memory_size/1024.0/1024.0, n_mem);
+    }
+
+    // load weights
+    {
+        size_t total_size = 0;
+
+        while (true) {
+            int32_t n_dims;
+            int32_t length;
+            int32_t ftype;
+
+            fin.read(reinterpret_cast<char *>(&n_dims), sizeof(n_dims));
+            fin.read(reinterpret_cast<char *>(&length), sizeof(length));
+            fin.read(reinterpret_cast<char *>(&ftype),  sizeof(ftype));
+
+            if (fin.eof()) {
+                break;
+            }
+
+            int32_t nelements = 1;
+            int32_t ne[2] = { 1, 1 };
+            for (int i = 0; i < n_dims; ++i) {
+                fin.read(reinterpret_cast<char *>(&ne[i]), sizeof(ne[i]));
+                nelements *= ne[i];
+            }
+
+            std::string name(length, 0);
+            fin.read(&name[0], length);
+
+            if (model.tensors.find(name.data()) == model.tensors.end()) {
+                fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
+                return false;
+            }
+
+            auto tensor = model.tensors[name.data()];
+            if (ggml_nelements(tensor) != nelements) {
+                fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
+                return false;
+            }
+
+            if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1]) {
+                fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d], expected [%d, %d]\n",
+                        __func__, name.data(), tensor->ne[0], tensor->ne[1], ne[0], ne[1]);
+                return false;
+            }
+
+            const size_t bpe = (ftype == 0) ? sizeof(float) : sizeof(ggml_fp16_t);
+
+            if (nelements*bpe != ggml_nbytes(tensor)) {
+                fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
+                        __func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
+                return false;
+            }
+
+            fin.read(reinterpret_cast<char *>(tensor->data), ggml_nbytes(tensor));
+
+            //printf("%24s - [%5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
+            total_size += ggml_nbytes(tensor);
+        }
+
+        printf("%s: model size  = %8.2f MB\n", __func__, total_size/1024.0/1024.0);
+    }
+
+    fin.close();
+
+    return true;
+}
+
+// evaluate the transformer
+//
+//   - model:     the model
+//   - n_threads: number of threads to use
+//   - n_past:    the context size so far
+//   - embd_inp:  the embeddings of the tokens in the context
+//   - embd_w:    the predicted probabilities of the next token
+//
+bool gpt2_eval(
+        const gpt2_model & model,
+        const int n_threads,
+        const int n_past,
+        const std::vector<gpt_vocab::id> & embd_inp,
+              std::vector<float>         & embd_w,
+              size_t                     & mem_per_token) {
+    const int N = embd_inp.size();
+
+    const auto & hparams = model.hparams;
+
+    const int n_embd  = hparams.n_embd;
+    const int n_layer = hparams.n_layer;
+    const int n_ctx   = hparams.n_ctx;
+    const int n_head  = hparams.n_head;
+    const int n_vocab = hparams.n_vocab;
+
+    static size_t buf_size = 512u*1024*1024;
+    static void * buf = malloc(buf_size);
+
+    if (mem_per_token > 0 && mem_per_token*N > buf_size) {
+        const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
+        printf("\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
+
+        // reallocate
+        buf_size = buf_size_new;
+        buf = realloc(buf, buf_size);
+        if (buf == nullptr) {
+            fprintf(stderr, "%s: failed to allocate %zu bytes\n", __func__, buf_size);
+            return false;
+        }
+    }
+
+    struct ggml_init_params params = {
+        .mem_size   = buf_size,
+        .mem_buffer = buf,
+    };
+
+    struct ggml_context * ctx0 = ggml_init(params);
+    struct ggml_cgraph gf = { .n_threads = n_threads };
+
+    struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
+    memcpy(embd->data, embd_inp.data(), N*ggml_element_size(embd));
+
+    struct ggml_tensor * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
+    for (int i = 0; i < N; ++i) {
+        ((int32_t *) position->data)[i] = n_past + i;
+    }
+
+    // wte + wpe
+    struct ggml_tensor * inpL =
+        ggml_add(ctx0,
+                ggml_get_rows(ctx0, model.wte, embd),
+                ggml_get_rows(ctx0, model.wpe, position));
+
+    for (int il = 0; il < n_layer; ++il) {
+        struct ggml_tensor * cur;
+
+        // norm
+        {
+            // [ 768, N]
+            cur = ggml_norm(ctx0, inpL);
+
+            // cur = ln_1_g*cur + ln_1_b
+            // [ 768, N]
+            cur = ggml_add(ctx0,
+                    ggml_mul(ctx0,
+                        ggml_repeat(ctx0, model.layers[il].ln_1_g, cur),
+                        cur),
+                    ggml_repeat(ctx0, model.layers[il].ln_1_b, cur));
+        }
+
+        // attn
+        // [2304, 768] - model.layers[il].c_attn_attn_w
+        // [2304,   1] - model.layers[il].c_attn_attn_b
+        // [ 768,   N] - cur (in)
+        // [2304,   N] - cur (out)
+        //
+        // cur = attn_w*cur + attn_b
+        // [2304, N]
+        {
+            cur = ggml_mul_mat(ctx0,
+                    ggml_transpose(ctx0, model.layers[il].c_attn_attn_w),
+                    cur);
+
+            cur = ggml_add(ctx0,
+                    ggml_repeat(ctx0, model.layers[il].c_attn_attn_b, cur),
+                    cur);
+        }
+
+        // self-attention
+        {
+            struct ggml_tensor * Qcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 0*sizeof(float)*n_embd);
+            struct ggml_tensor * Kcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 1*sizeof(float)*n_embd);
+            struct ggml_tensor * Vcur = ggml_view_2d(ctx0, cur, n_embd, N, cur->nb[1], 2*sizeof(float)*n_embd);
+
+            // store key and value to memory
+            if (N >= 1) {
+                struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_k, N*n_embd, (ggml_element_size(model.memory_k)*n_embd)*(il*n_ctx + n_past));
+                struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_v, N*n_embd, (ggml_element_size(model.memory_v)*n_embd)*(il*n_ctx + n_past));
+
+                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
+                ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
+            }
+
+            // Q = Qcur.contiguous().view(n_embd/n_head, n_head, N).permute(0, 2, 1, 3)
+            // [64, N, 12]
+            struct ggml_tensor * Q =
+                ggml_permute(ctx0,
+                        ggml_cpy(ctx0,
+                            Qcur,
+                            ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_embd/n_head, n_head, N)),
+                        0, 2, 1, 3);
+
+            // K = Kmem.view(n_embd/n_head, n_head, n_past + N).permute(0, 2, 1, 3)
+            // [64, n_past + N, 12]
+            struct ggml_tensor * K =
+                ggml_permute(ctx0,
+                        ggml_reshape_3d(ctx0,
+                            ggml_view_1d(ctx0, model.memory_k, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_k)*n_embd),
+                            n_embd/n_head, n_head, n_past + N),
+                        0, 2, 1, 3);
+
+            // GG: flash attention
+            //struct ggml_tensor * V =
+            //    ggml_cpy(ctx0,
+            //            ggml_permute(ctx0,
+            //                ggml_reshape_3d(ctx0,
+            //                    ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),
+            //                    n_embd/n_head, n_head, n_past + N),
+            //                1, 2, 0, 3),
+            //            ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_past + N, n_embd/n_head, n_head));
+
+            //struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, true);
+
+            // K * Q
+            // [n_past + N, N, 12]
+            struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
+
+            // KQ_scaled = KQ / sqrt(n_embd/n_head)
+            // [n_past + N, N, 12]
+            struct ggml_tensor * KQ_scaled =
+                ggml_scale(ctx0,
+                        KQ,
+                        ggml_new_f32(ctx0, 1.0f/sqrt(float(n_embd)/n_head))
+                        );
+
+            // KQ_masked = mask_past(KQ_scaled)
+            // [n_past + N, N, 12]
+            struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
+
+            // KQ = soft_max(KQ_masked)
+            // [n_past + N, N, 12]
+            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
+
+            // V_trans = Vmem.view(n_embd/n_head, n_head, n_past + N).permute(1, 2, 0, 3).contiguous()
+            // [n_past + N, 64, 12]
+            struct ggml_tensor * V_trans =
+                ggml_permute(ctx0,
+                        ggml_reshape_3d(ctx0,
+                            ggml_view_1d(ctx0, model.memory_v, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(model.memory_v)*n_embd),
+                            n_embd/n_head, n_head, n_past + N),
+                        1, 2, 0, 3);
+
+            // KQV = transpose(V) * KQ_soft_max
+            // [64, N, 12]
+            struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
+
+            // KQV_merged = KQV.permute(0, 2, 1, 3)
+            // [64, 12, N]
+            struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
+
+            // cur = KQV_merged.contiguous().view(n_embd, N)
+            // [768, N]
+            cur = ggml_cpy(ctx0,
+                    KQV_merged,
+                    ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N));
+        }
+
+        // projection
+        // [ 768, 768] - model.layers[il].c_attn_proj_w
+        // [ 768,   1] - model.layers[il].c_attn_proj_b
+        // [ 768,   N] - cur (in)
+        // [ 768,   N] - cur (out)
+        //
+        // cur = proj_w*cur + proj_b
+        // [768, N]
+        {
+            cur = ggml_mul_mat(ctx0,
+                    ggml_transpose(ctx0, model.layers[il].c_attn_proj_w),
+                    cur);
+
+            cur = ggml_add(ctx0,
+                    ggml_repeat(ctx0, model.layers[il].c_attn_proj_b, cur),
+                    cur);
+        }
+
+        // add the input
+        cur = ggml_add(ctx0, cur, inpL);
+
+        struct ggml_tensor * inpFF = cur;
+
+        // feed-forward network
+        {
+            // norm
+            {
+                cur = ggml_norm(ctx0, inpFF);
+
+                // cur = ln_2_g*cur + ln_2_b
+                // [ 768, N]
+                cur = ggml_add(ctx0,
+                        ggml_mul(ctx0,
+                            ggml_repeat(ctx0, model.layers[il].ln_2_g, cur),
+                            cur),
+                        ggml_repeat(ctx0, model.layers[il].ln_2_b, cur));
+            }
+
+            // fully connected
+            // [3072, 768] - model.layers[il].c_mlp_fc_w
+            // [3072,   1] - model.layers[il].c_mlp_fc_b
+            // [ 768,   N] - cur (in)
+            // [3072,   N] - cur (out)
+            //
+            // cur = fc_w*cur + fc_b
+            // [3072, N]
+            cur = ggml_mul_mat(ctx0,
+                    ggml_transpose(ctx0, model.layers[il].c_mlp_fc_w),
+                    cur);
+
+            cur = ggml_add(ctx0,
+                    ggml_repeat(ctx0, model.layers[il].c_mlp_fc_b, cur),
+                    cur);
+
+            // GELU activation
+            // [3072, N]
+            cur = ggml_gelu(ctx0, cur);
+
+            // projection
+            // [ 768, 3072] - model.layers[il].c_mlp_proj_w
+            // [ 768,    1] - model.layers[il].c_mlp_proj_b
+            // [3072,    N] - cur (in)
+            // [ 768,    N] - cur (out)
+            //
+            // cur = proj_w*cur + proj_b
+            // [768, N]
+            cur = ggml_mul_mat(ctx0,
+                    model.layers[il].c_mlp_proj_w_trans,
+                    cur);
+
+            cur = ggml_add(ctx0,
+                    ggml_repeat(ctx0, model.layers[il].c_mlp_proj_b, cur),
+                    cur);
+        }
+
+        // input for next layer
+        inpL = ggml_add(ctx0, cur, inpFF);
+    }
+
+    // norm
+    {
+        // [ 768, N]
+        inpL = ggml_norm(ctx0, inpL);
+
+        // inpL = ln_f_g*inpL + ln_f_b
+        // [ 768, N]
+        inpL = ggml_add(ctx0,
+                ggml_mul(ctx0,
+                    ggml_repeat(ctx0, model.ln_f_g, inpL),
+                    inpL),
+                ggml_repeat(ctx0, model.ln_f_b, inpL));
+    }
+
+    // inpL = WTE * inpL
+    // [ 768, 50257] - model.wte
+    // [ 768, N]     - inpL
+    inpL = ggml_mul_mat(ctx0, model.wte, inpL);
+
+    // logits -> probs
+    inpL = ggml_soft_max(ctx0, inpL);
+
+    // run the computation
+    ggml_build_forward_expand(&gf, inpL);
+    ggml_graph_compute       (ctx0, &gf);
+
+    //if (n_past%100 == 0) {
+    //    ggml_graph_print   (&gf);
+    //    ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
+    //}
+
+    //embd_w.resize(n_vocab*N);
+    //memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
+
+    // return result for just the last token
+    embd_w.resize(n_vocab);
+    memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
+
+    if (mem_per_token == 0) {
+        mem_per_token = ggml_used_mem(ctx0)/N;
+    }
+    //printf("used_mem = %zu\n", ggml_used_mem(ctx0));
+
+    ggml_free(ctx0);
+
+    return true;
+}
+
+/////////////////////////////// GPT-2 END ////////////////////////////////
+
+constexpr int N_THREAD = 8;
+
+struct gpt2_state {
+    std::string prompt_base = R"(Hello, how are you?
+I'm fine, thanks. How are you?
+Thanks, I'm fine too. What are you doing?
+I'm just sitting here.
+It's a lovely day, isn't it?
+Yes, it is.
+Did you know that I'm a robot?
+I wasn't aware of that.
+)";
+
+    std::mt19937 rng;
+
+    gpt_vocab vocab;
+    gpt2_model model;
+
+    int32_t n_threads = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
+    int32_t n_predict = 32; // new tokens to predict
+
+    // sampling parameters
+    int32_t top_k = 40;
+    float   top_p = 0.9f;
+    float   temp  = 1.0f;
+};
+
+struct gpt2_state g_gpt2;
+
+std::vector<float> g_pcmf32;
+
+std::vector<struct whisper_context *> g_contexts(4, nullptr);
+
+std::mutex g_mutex;
+std::thread g_worker;
+std::atomic<bool> g_running(false);
+
+bool g_force_speak = false;
+std::string g_text_to_speak = "";
+std::string g_status = "idle";
+std::string g_status_forced = "";
+
+std::string gpt2_gen_text(const std::string & prompt) {
+    int n_past = 0;
+
+    std::vector<float> embd_w;
+
+    // tokenize the prompt
+    std::vector<gpt_vocab::id> embd_inp = ::gpt_tokenize(g_gpt2.vocab, g_gpt2.prompt_base + prompt);
+
+    g_gpt2.n_predict = std::min(g_gpt2.n_predict, g_gpt2.model.hparams.n_ctx - (int) embd_inp.size());
+
+    std::vector<gpt_vocab::id> embd = embd_inp;
+
+    size_t mem_per_token = 3000000;
+
+    std::string result;
+
+    for (int i = embd.size(); i < embd_inp.size() + g_gpt2.n_predict; i++) {
+        // predict
+        if (embd.size() > 0) {
+            if (!gpt2_eval(g_gpt2.model, g_gpt2.n_threads, n_past, embd, embd_w, mem_per_token)) {
+                printf("gpt-2: failed to generate text\n");
+                return "";
+            }
+        }
+
+        n_past += embd.size();
+        embd.clear();
+
+        {
+            // sample next token
+            const int   top_k = g_gpt2.top_k;
+            const float top_p = g_gpt2.top_p;
+            const float temp  = g_gpt2.temp;
+
+            const int n_vocab = g_gpt2.model.hparams.n_vocab;
+
+            const gpt_vocab::id id = gpt_sample_top_k_top_p(g_gpt2.vocab, embd_w.data() + (embd_w.size() - n_vocab), top_k, top_p, temp, g_gpt2.rng);
+
+            // add it to the context
+            embd.push_back(id);
+        }
+
+        result += g_gpt2.vocab.id_to_token[embd[0]];
+
+        // end of text token
+        if (embd.back() == 50256 ||
+            g_gpt2.vocab.id_to_token[embd.back()] == "." ||
+            g_gpt2.vocab.id_to_token[embd.back()] == "!" ||
+            g_gpt2.vocab.id_to_token[embd.back()] == "?") {
+            break;
+        }
+    }
+
+    return result;
+}
+
+void talk_set_status(const std::string & status) {
+    std::lock_guard<std::mutex> lock(g_mutex);
+    g_status = status;
+}
+
+void talk_main(size_t index) {
+    talk_set_status("loading data ...");
+
+    struct whisper_full_params wparams = whisper_full_default_params(whisper_sampling_strategy::WHISPER_SAMPLING_GREEDY);
+
+    wparams.n_threads            = std::min(N_THREAD, (int) std::thread::hardware_concurrency());
+    wparams.offset_ms            = 0;
+    wparams.translate            = false;
+    wparams.no_context           = true;
+    wparams.single_segment       = true;
+    wparams.print_realtime       = false;
+    wparams.print_progress       = false;
+    wparams.print_timestamps     = true;
+    wparams.print_special_tokens = false;
+
+    wparams.max_tokens           = 32;
+    wparams.audio_ctx            = 768;
+
+    wparams.language             = "en";
+
+    g_gpt2.rng = std::mt19937(time(NULL));
+
+    // load the model
+    {
+        const int64_t t_start_us = ggml_time_us();
+
+        if (!gpt2_model_load("gpt-2.bin", g_gpt2.model, g_gpt2.vocab)) {
+            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, "gpt-2.bin");
+            return;
+        }
+
+        const int64_t t_load_us = ggml_time_us() - t_start_us;
+
+        printf("gpt-2: model loaded in %d ms\n", (int) (t_load_us/1000));
+    }
+
+    std::vector<float> pcmf32;
+
+    auto & ctx = g_contexts[index];
+
+    const int64_t step_samples = 2*WHISPER_SAMPLE_RATE;
+    const int64_t step_ms = (step_samples*1000)/WHISPER_SAMPLE_RATE;
+    const int64_t window_samples = 9*WHISPER_SAMPLE_RATE;
+
+    auto t_last = std::chrono::high_resolution_clock::now();
+
+    talk_set_status("listening ...");
+
+    while (g_running) {
+
+        const auto t_now = std::chrono::high_resolution_clock::now();
+        if (std::chrono::duration_cast<std::chrono::milliseconds>(t_now - t_last).count() < step_ms) {
+            {
+                std::lock_guard<std::mutex> lock(g_mutex);
+                g_pcmf32.clear();
+            }
+            std::this_thread::sleep_for(std::chrono::milliseconds(10));
+            continue;
+        }
+
+        talk_set_status("listening ...");
+
+        {
+            std::unique_lock<std::mutex> lock(g_mutex);
+
+            if (g_pcmf32.size() < step_samples) {
+                lock.unlock();
+
+                std::this_thread::sleep_for(std::chrono::milliseconds(10));
+
+                continue;
+            }
+
+            pcmf32 = std::vector<float>(g_pcmf32.end() - std::min((int64_t) g_pcmf32.size(), window_samples), g_pcmf32.end());
+        }
+
+        // if energy in during last second is above threshold, then skip
+        {
+            float energy_all = 0.0f;
+            float energy_1s  = 0.0f;
+
+            for (size_t i = 0; i < pcmf32.size(); i++) {
+                energy_all += fabsf(pcmf32[i]);
+
+                if (i >= pcmf32.size() - WHISPER_SAMPLE_RATE) {
+                    energy_1s += fabsf(pcmf32[i]);
+                }
+            }
+
+            energy_all /= pcmf32.size();
+            energy_1s  /= WHISPER_SAMPLE_RATE;
+
+            if (energy_1s > 0.1f*energy_all && !g_force_speak) {
+                std::this_thread::sleep_for(std::chrono::milliseconds(10));
+                continue;
+            }
+        }
+
+        talk_set_status("processing ...");
+
+        g_force_speak = false;
+
+        t_last = t_now;
+
+        {
+            const auto t_start = std::chrono::high_resolution_clock::now();
+
+            int ret = whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size());
+            if (ret != 0) {
+                printf("whisper_full() failed: %d\n", ret);
+                break;
+            }
+
+            const auto t_end = std::chrono::high_resolution_clock::now();
+
+            printf("whisper_full() returned %d in %f seconds\n", ret, std::chrono::duration<double>(t_end - t_start).count());
+        }
+
+        {
+            std::string text_heard;
+
+            const int n_segments = whisper_full_n_segments(ctx);
+            for (int i = n_segments - 1; i < n_segments; ++i) {
+                const char * text = whisper_full_get_segment_text(ctx, i);
+
+                const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
+                const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
+
+                printf ("[%s --> %s]  %s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), text);
+
+                text_heard += text;
+            }
+
+            // 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+$"), "");
+
+            talk_set_status("'" + text_heard + "' - thinking how to respond ...");
+
+            const std::vector<gpt_vocab::id> tokens = ::gpt_tokenize(g_gpt2.vocab, text_heard);
+
+            printf("whisper: number of tokens: %d, '%s'\n", (int) tokens.size(), text_heard.c_str());
+
+            std::string text_to_speak;
+
+            if (tokens.size() > 0) {
+                text_to_speak = gpt2_gen_text(text_heard + "\n");
+                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"));
+
+                std::lock_guard<std::mutex> lock(g_mutex);
+
+                // remove first 2 lines of base prompt
+                {
+                    const size_t pos = g_gpt2.prompt_base.find_first_of("\n");
+                    if (pos != std::string::npos) {
+                        g_gpt2.prompt_base = g_gpt2.prompt_base.substr(pos + 1);
+                    }
+                }
+                {
+                    const size_t pos = g_gpt2.prompt_base.find_first_of("\n");
+                    if (pos != std::string::npos) {
+                        g_gpt2.prompt_base = g_gpt2.prompt_base.substr(pos + 1);
+                    }
+                }
+                g_gpt2.prompt_base += text_heard + "\n" + text_to_speak + "\n";
+            } else {
+                text_to_speak = gpt2_gen_text("");
+                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"));
+
+                std::lock_guard<std::mutex> lock(g_mutex);
+
+                const size_t pos = g_gpt2.prompt_base.find_first_of("\n");
+                if (pos != std::string::npos) {
+                    g_gpt2.prompt_base = g_gpt2.prompt_base.substr(pos + 1);
+                }
+                g_gpt2.prompt_base += text_to_speak + "\n";
+            }
+
+            printf("gpt-2: %s\n", text_to_speak.c_str());
+
+            //printf("========================\n");
+            //printf("gpt-2: prompt_base:\n'%s'\n", g_gpt2.prompt_base.c_str());
+            //printf("========================\n");
+
+            {
+                std::lock_guard<std::mutex> lock(g_mutex);
+                t_last = std::chrono::high_resolution_clock::now();
+                g_text_to_speak = text_to_speak;
+                g_pcmf32.clear();
+            }
+
+            talk_set_status("speaking ...");
+        }
+    }
+
+    if (index < g_contexts.size()) {
+        whisper_free(g_contexts[index]);
+        g_contexts[index] = nullptr;
+    }
+}
+
+EMSCRIPTEN_BINDINGS(talk) {
+    emscripten::function("init", emscripten::optional_override([](const std::string & path_model) {
+        for (size_t i = 0; i < g_contexts.size(); ++i) {
+            if (g_contexts[i] == nullptr) {
+                g_contexts[i] = whisper_init(path_model.c_str());
+                if (g_contexts[i] != nullptr) {
+                    g_running = true;
+                    if (g_worker.joinable()) {
+                        g_worker.join();
+                    }
+                    g_worker = std::thread([i]() {
+                        talk_main(i);
+                    });
+
+                    return i + 1;
+                } else {
+                    return (size_t) 0;
+                }
+            }
+        }
+
+        return (size_t) 0;
+    }));
+
+    emscripten::function("free", emscripten::optional_override([](size_t index) {
+        if (g_running) {
+            g_running = false;
+        }
+    }));
+
+    emscripten::function("set_audio", emscripten::optional_override([](size_t index, const emscripten::val & audio) {
+        --index;
+
+        if (index >= g_contexts.size()) {
+            return -1;
+        }
+
+        if (g_contexts[index] == nullptr) {
+            return -2;
+        }
+
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            const int n = audio["length"].as<int>();
+
+            emscripten::val heap = emscripten::val::module_property("HEAPU8");
+            emscripten::val memory = heap["buffer"];
+
+            g_pcmf32.resize(n);
+
+            emscripten::val memoryView = audio["constructor"].new_(memory, reinterpret_cast<uintptr_t>(g_pcmf32.data()), n);
+            memoryView.call<void>("set", audio);
+        }
+
+        return 0;
+    }));
+
+    emscripten::function("force_speak", emscripten::optional_override([](size_t index) {
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            g_force_speak = true;
+        }
+    }));
+
+    emscripten::function("get_text_context", emscripten::optional_override([]() {
+        std::string text_context;
+
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            text_context = g_gpt2.prompt_base;
+        }
+
+        return text_context;
+    }));
+
+    emscripten::function("get_text_to_speak", emscripten::optional_override([]() {
+        std::string text_to_speak;
+
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            text_to_speak = std::move(g_text_to_speak);
+        }
+
+        return text_to_speak;
+    }));
+
+    emscripten::function("get_status", emscripten::optional_override([]() {
+        std::string status;
+
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            status = g_status_forced.empty() ? g_status : g_status_forced;
+        }
+
+        return status;
+    }));
+
+    emscripten::function("set_status", emscripten::optional_override([](const std::string & status) {
+        {
+            std::lock_guard<std::mutex> lock(g_mutex);
+            g_status_forced = status;
+        }
+    }));
+}

examples/talk.wasm/index-tmpl.html

@@ -0,0 +1,646 @@
+<!doctype html>
+<html lang="en-us">
+    <head>
+        <title>Talk - GPT-2 meets Whisper in WebAssembly</title>
+
+        <style>
+            #output {
+                width: 100%;
+                height: 100%;
+                margin: 0 auto;
+                margin-top: 10px;
+                border-left: 0px;
+                border-right: 0px;
+                padding-left: 0px;
+                padding-right: 0px;
+                display: block;
+                background-color: black;
+                color: white;
+                font-size: 10px;
+                font-family: 'Lucida Console', Monaco, monospace;
+                outline: none;
+                white-space: pre;
+                overflow-wrap: normal;
+                overflow-x: scroll;
+            }
+        </style>
+    </head>
+    <body>
+        <div id="main-container">
+            <b>Talk - GPT-2 meets Whisper in WebAssembly</b>
+
+            <br><br>
+
+            On this page you can talk with an AI entity. It uses:
+
+            <ul>
+                <li><a href="https://github.com/ggerganov/whisper.cpp">OpenAI's Whisper</a> model to listen to you as you speak in the microphone</li>
+                <li><a href="https://github.com/ggerganov/ggml/tree/master/examples/gpt-2">OpenAI's GPT-2</a> model to generate a text response</li>
+                <li><a href="https://developer.mozilla.org/en-US/docs/Web/API/Web_Speech_API">Web Speech API</a> to speak the response to you through the speakers</li>
+            </ul>
+
+            All of this runs <b>locally in your browser</b> using WebAssembly.<br>
+            You can find more about this project on <a href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/talk.wasm">GitHub</a>.
+
+            <br><br>
+
+            <hr>
+
+            <div id="model-whisper">
+                <span id="model-whisper-status">Whisper model:</span>
+                <button id="fetch-whisper-tiny-en" onclick="loadWhisper('tiny.en')">tiny.en (75 MB)</button>
+                <button id="fetch-whisper-base-en" onclick="loadWhisper('base.en')">base.en (142 MB)</button>
+                <span id="fetch-whisper-progress"></span>
+
+                <!--
+                    <input type="file" id="file" name="file" onchange="loadFile(event, 'whisper.bin')" />
+                -->
+            </div>
+
+            <br>
+
+            <div id="model-gpt-2">
+                <span id="model-gpt-2-status">GPT-2 model:</span>
+                <button id="fetch-gpt-2-small" onclick="loadGPT2('small')">small 117M (240 MB)</button>
+                <!--<button id="fetch-gpt-2-medium" onclick="loadGPT2('medium')">medium 345M (720 MB)</button>-->
+                <span id="fetch-gpt-2-progress"></span>
+
+                <!--
+                <input type="file" id="file" name="file" onchange="loadFile(event, 'gpt-2.bin')" />
+                -->
+            </div>
+
+            <br>
+
+            <div id="input">
+                <button id="start" onclick="onStart()">Start</button>
+                <button id="stop" onclick="onStop()" disabled>Stop</button>
+                <select id="voice" onchange="onVoiceChange()">
+                    <option value="0">Default</option>
+                </select>
+                <button id="speak" onclick="onSpeak('Hello')">Say hello</button>
+                <button id="speak" onclick="onSpeakRandom()">Say something</button>
+                <button id="speak" onclick="clearCache()">Clear Cache</button>
+            </div>
+
+            <br>
+
+            <div id="state">
+                Status: <b><span id="state-status">idle</span></b>
+
+                <pre id="state-context">[The text context will be displayed here]</pre>
+            </div>
+
+            <hr>
+
+            Debug output:
+            <textarea id="output" rows="20"></textarea>
+
+            <br>
+
+            <b>Troubleshooting</b>
+
+            <br><br>
+
+            The page does some heavy computations, so make sure:
+
+            <ul>
+                <li>To use a modern web browser (e.g. Chrome, Firefox)</li>
+                <li>To use a fast desktop or laptop computer (e.g. not a mobile phone)</li>
+                <li>Your browser supports WASM <a href="https://webassembly.org/roadmap/">Fixed-width SIMD</a></li>
+            </ul>
+
+            <br><br>
+
+            <div class="cell-version">
+                <span>
+                    |
+                    Build time: <span class="nav-link">@GIT_DATE@</span> |
+                    Commit hash: <a class="nav-link" href="https://github.com/ggerganov/whisper.cpp/commit/@GIT_SHA1@">@GIT_SHA1@</a> |
+                    Commit subject: <span class="nav-link">@GIT_COMMIT_SUBJECT@</span> |
+                    <a class="nav-link" href="https://github.com/ggerganov/whisper.cpp/tree/master/examples/talk.wasm">Source Code</a> |
+                </span>
+            </div>
+        </div>
+
+        <script type='text/javascript'>
+            var printTextarea = (function() {
+                    var element = document.getElementById('output');
+                    if (element) element.alue = ''; // clear browser cache
+                    return function(text) {
+                        if (arguments.length > 1) text = Array.prototype.slice.call(arguments).join(' ');
+                        console.log(text);
+                        if (element) {
+                            element.value += text + "\n";
+                            element.scrollTop = element.scrollHeight; // focus on bottom
+                        }
+                    };
+                })();
+
+            const kRestartRecording_s = 15;
+            const kSampleRate = 16000;
+
+            window.AudioContext = window.AudioContext || window.webkitAudioContext;
+            window.OfflineAudioContext = window.OfflineAudioContext || window.webkitOfflineAudioContext;
+
+            // web audio context
+            var context = null;
+
+            // audio data
+            var audio = null;
+            var audio0 = null;
+
+            // the talk instance
+            var instance = null;
+
+            // model names
+            var model_whisper = null;
+            var model_gpt_2 = null;
+
+            // speech synthesis
+            const synth = window.speechSynthesis;
+            var voice = null;
+
+            var Module = {
+                print: printTextarea,
+                printErr: printTextarea,
+                setStatus: function(text) {
+                    printTextarea('js: ' + text);
+                },
+                monitorRunDependencies: function(left) {
+                },
+                preRun: function() {
+                    printTextarea('js: Preparing ...');
+                },
+                postRun: function() {
+                    printTextarea('js: Initialized successfully!');
+
+                    // populate the voice list
+                    var voices = synth.getVoices();
+                    var el = document.getElementById('voice');
+
+                    var n = 0;
+                    voices.forEach(function(voice, i) {
+                        if (!voice.lang.startsWith('en')) return;
+                        var option = document.createElement('option');
+                        option.value = i;
+                        option.innerHTML = voice.name + ' (' + voice.lang + ')';
+                        el.appendChild(option);
+                        n++;
+                    });
+
+                    // select random voice
+                    if (n > 0) {
+                        for (var k = 0; k < 10; k++) {
+                            var i = Math.floor(Math.random() * n);
+                            el.selectedIndex = i;
+                            voice = voices[document.getElementById('voice').options[i].value];
+
+                            // give preference to Google voices
+                            if (voice.name.startsWith('Google')) break;
+                        }
+                    }
+                }
+            };
+
+            // helper function
+            function convertTypedArray(src, type) {
+                var buffer = new ArrayBuffer(src.byteLength);
+                var baseView = new src.constructor(buffer).set(src);
+                return new type(buffer);
+            }
+
+            //
+            // fetch models
+            //
+
+            function storeFS(fname, buf) {
+                // write to WASM file using FS_createDataFile
+                // if the file exists, delete it
+                try {
+                    Module.FS_unlink(fname);
+                } catch (e) {
+                    // ignore
+                }
+
+                Module.FS_createDataFile("/", fname, buf, true, true);
+
+                printTextarea('js: stored model: ' + fname + ' size: ' + buf.length);
+
+                if (fname == 'whisper.bin') {
+                    document.getElementById('model-whisper').innerHTML = 'Whisper model: loaded "' + model_whisper + '"!';
+                } else if (fname == 'gpt-2.bin') {
+                    document.getElementById('model-gpt-2').innerHTML = 'GPT-2 model: loaded "' + model_gpt_2 + '"!';
+                }
+            }
+
+            let dbVersion = 1
+            let dbName    = 'talk.ggerganov.com';
+            let indexedDB = window.indexedDB || window.mozIndexedDB || window.webkitIndexedDB || window.msIndexedDB
+
+            // fetch a remote file from remote URL using the Fetch API
+            async function fetchRemote(url, elProgress) {
+                printTextarea('js: downloading with fetch()...');
+
+                const response = await fetch(
+                    url,
+                    {
+                        method: 'GET',
+                        headers: {
+                            'Content-Type': 'application/octet-stream',
+                        },
+                    }
+                );
+
+                if (!response.ok) {
+                    printTextarea('js: failed to fetch ' + url);
+                    return;
+                }
+
+                const contentLength = response.headers.get('content-length');
+                const total = parseInt(contentLength, 10);
+                const reader = response.body.getReader();
+
+                var chunks = [];
+                var receivedLength = 0;
+                var progressLast = -1;
+
+                while (true) {
+                    const { done, value } = await reader.read();
+
+                    if (done) {
+                        break;
+                    }
+
+                    chunks.push(value);
+                    receivedLength += value.length;
+
+                    if (contentLength) {
+                        // update progress bar element with the new percentage
+                        elProgress.innerHTML = Math.round((receivedLength / total) * 100) + '%';
+
+                        var progressCur = Math.round((receivedLength / total) * 10);
+                        if (progressCur != progressLast) {
+                            printTextarea('js: fetching ' + 10*progressCur + '% ...');
+                            progressLast = progressCur;
+                        }
+                    }
+                }
+
+                var chunksAll = new Uint8Array(receivedLength);
+                var position = 0;
+                for (var chunk of chunks) {
+                    chunksAll.set(chunk, position);
+                    position += chunk.length;
+                }
+
+                return chunksAll;
+            }
+
+            // load remote data
+            // - check if the data is already in the IndexedDB
+            // - if not, fetch it from the remote URL and store it in the IndexedDB
+            // - store it in WASM memory
+            function loadRemote(url, dst, elProgress, size_mb) {
+                // query the storage quota and print it
+                navigator.storage.estimate().then(function (estimate) {
+                    printTextarea('js: storage quota: ' + estimate.quota + ' bytes');
+                    printTextarea('js: storage usage: ' + estimate.usage + ' bytes');
+                });
+
+                // check if the data is already in the IndexedDB
+                var request = indexedDB.open(dbName, dbVersion);
+
+                request.onupgradeneeded = function (event) {
+                    var db = event.target.result;
+                    if (db.version == 1) {
+                        var objectStore = db.createObjectStore('models', { autoIncrement: false });
+                        printTextarea('js: created IndexedDB ' + db.name + ' version ' + db.version);
+                    } else {
+                        // clear the database
+                        var objectStore = event.currentTarget.transaction.objectStore('models');
+                        objectStore.clear();
+                        printTextarea('js: cleared IndexedDB ' + db.name + ' version ' + db.version);
+                    }
+                };
+
+                request.onsuccess = function (event) {
+                    var db = event.target.result;
+                    var transaction = db.transaction(['models'], 'readonly');
+                    var objectStore = transaction.objectStore('models');
+                    var request = objectStore.get(url);
+
+                    request.onsuccess = function (event) {
+                        if (request.result) {
+                            printTextarea('js: "' + url + '" is already in the IndexedDB');
+                            storeFS(dst, request.result);
+                        } else {
+                            // data is not in the IndexedDB
+                            printTextarea('js: "' + url + '" is not in the IndexedDB');
+
+                            // alert and ask the user to confirm
+                            if (!confirm('You are about to download ' + size_mb + ' MB of data.\nThe model data will be cached in the browser for future use.\n\nPress OK to continue.')) {
+                                document.getElementById('fetch-whisper-tiny-en').style.display = 'inline-block';
+                                document.getElementById('fetch-whisper-base-en').style.display = 'inline-block';
+                                document.getElementById('fetch-gpt-2-small').style.display = 'inline-block';
+                                return;
+                            }
+
+                            fetchRemote(url, elProgress).then(function (data) {
+                                if (data) {
+                                    // store the data in the IndexedDB
+                                    var request = indexedDB.open(dbName, dbVersion);
+                                    request.onsuccess = function (event) {
+                                        var db = event.target.result;
+                                        var transaction = db.transaction(['models'], 'readwrite');
+                                        var objectStore = transaction.objectStore('models');
+                                        var request = objectStore.put(data, url);
+
+                                        request.onsuccess = function (event) {
+                                            printTextarea('js: "' + url + '" stored in the IndexedDB');
+                                            storeFS(dst, data);
+                                        };
+
+                                        request.onerror = function (event) {
+                                            printTextarea('js: failed to store "' + url + '" in the IndexedDB');
+                                        };
+                                    };
+                                }
+                            });
+                        }
+                    };
+
+                    request.onerror = function (event) {
+                        printTextarea('js: failed to get data from the IndexedDB');
+                    };
+                };
+
+                request.onerror = function (event) {
+                    printTextarea('js: failed to open IndexedDB');
+                };
+
+                request.onblocked = function (event) {
+                    printTextarea('js: failed to open IndexedDB: blocked');
+                };
+
+                request.onabort = function (event) {
+                    printTextarea('js: failed to open IndexedDB: abort');
+                };
+            }
+
+            function loadWhisper(model) {
+                let urls = {
+                    'tiny.en': 'https://talk.ggerganov.com/ggml-model-whisper-tiny.en.bin',
+                    'base.en': 'https://talk.ggerganov.com/ggml-model-whisper-base.en.bin',
+                };
+
+                let sizes = {
+                    'tiny.en': 75,
+                    'base.en': 142,
+                };
+
+                let url     = urls[model];
+                let dst     = 'whisper.bin';
+                let el      = document.getElementById('fetch-whisper-progress');
+                let size_mb = sizes[model];
+
+                model_whisper = model;
+
+                document.getElementById('fetch-whisper-tiny-en').style.display = 'none';
+                document.getElementById('fetch-whisper-base-en').style.display = 'none';
+                document.getElementById('model-whisper-status').innerHTML = 'Whisper model: loading "' + model + '" ... ';
+
+                loadRemote(url, dst, el, size_mb);
+            }
+
+            function loadGPT2(model) {
+                let urls = {
+                    'small':  'https://talk.ggerganov.com/ggml-model-gpt-2-117M.bin',
+                    'medium': 'https://talk.ggerganov.com/ggml-model-gpt-2-345M.bin',
+                };
+
+                let sizes = {
+                    'small':  240,
+                    'medium': 712,
+                };
+
+                let url     = urls[model];
+                let dst     = 'gpt-2.bin';
+                let el      = document.getElementById('fetch-gpt-2-progress');
+                let size_mb = sizes[model];
+
+                model_gpt_2 = model;
+
+                document.getElementById('fetch-gpt-2-small').style.display = 'none';
+                document.getElementById('model-gpt-2-status').innerHTML = 'GPT-2 model: loading "' + model + '" ... ';
+
+                loadRemote(url, dst, el, size_mb);
+            }
+
+            //
+            // microphone
+            //
+
+            var mediaRecorder = null;
+            var doRecording = false;
+            var startTime = 0;
+
+            function stopRecording() {
+                Module.set_status("paused");
+                doRecording = false;
+                audio0 = null;
+                audio = null;
+            }
+
+            function startRecording() {
+                if (!context) {
+                    context = new AudioContext({sampleRate: 16000});
+                }
+
+                Module.set_status("");
+
+                document.getElementById('start').disabled = true;
+                document.getElementById('stop').disabled = false;
+
+                doRecording = true;
+                startTime = Date.now();
+
+                var chunks = [];
+                var stream = null;
+
+                navigator.mediaDevices.getUserMedia({audio: true, video: false})
+                    .then(function(s) {
+                        stream = s;
+                        mediaRecorder = new MediaRecorder(stream);
+                        mediaRecorder.ondataavailable = function(e) {
+                            chunks.push(e.data);
+
+                            var blob = new Blob(chunks, { 'type' : 'audio/ogg; codecs=opus' });
+                            var reader = new FileReader();
+
+                            reader.onload = function(event) {
+                                var buf = new Uint8Array(reader.result);
+
+                                context.decodeAudioData(buf.buffer, function(audioBuffer) {
+                                    var offlineContext = new OfflineAudioContext(audioBuffer.numberOfChannels, audioBuffer.length, audioBuffer.sampleRate);
+                                    var source = offlineContext.createBufferSource();
+                                    source.buffer = audioBuffer;
+                                    source.connect(offlineContext.destination);
+                                    source.start(0);
+
+                                    offlineContext.startRendering().then(function(renderedBuffer) {
+                                        audio = renderedBuffer.getChannelData(0);
+
+                                        //printTextarea('js: audio recorded, size: ' + audio.length + ', old size: ' + (audio0 == null ? 0 : audio0.length));
+
+                                        var audioAll = new Float32Array(audio0 == null ? audio.length : audio0.length + audio.length);
+                                        if (audio0 != null) {
+                                            audioAll.set(audio0, 0);
+                                        }
+                                        audioAll.set(audio, audio0 == null ? 0 : audio0.length);
+
+                                        if (instance) {
+                                            Module.set_audio(instance, audioAll);
+                                        }
+                                    });
+                                }, function(e) {
+                                    audio = null;
+                                });
+                            }
+
+                            reader.readAsArrayBuffer(blob);
+                        };
+
+                        mediaRecorder.onstop = function(e) {
+                            if (doRecording) {
+                                setTimeout(function() {
+                                    startRecording();
+                                });
+                            }
+                        };
+
+                        mediaRecorder.start(250);
+                    })
+                    .catch(function(err) {
+                        printTextarea('js: error getting audio stream: ' + err);
+                    });
+
+                var interval = setInterval(function() {
+                    if (!doRecording) {
+                        clearInterval(interval);
+                        mediaRecorder.stop();
+                        stream.getTracks().forEach(function(track) {
+                            track.stop();
+                        });
+
+                        document.getElementById('start').disabled = false;
+                        document.getElementById('stop').disabled = true;
+
+                        mediaRecorder = null;
+                    }
+
+                    // if audio length is more than kRestartRecording_s seconds, restart recording
+                    if (audio != null && audio.length > kSampleRate*kRestartRecording_s) {
+                        if (doRecording) {
+                            //printTextarea('js: restarting recording');
+
+                            clearInterval(interval);
+                            audio0 = audio;
+                            audio = null;
+                            mediaRecorder.stop();
+                            stream.getTracks().forEach(function(track) {
+                                track.stop();
+                            });
+                        }
+                    }
+                }, 250);
+            }
+
+            //
+            // speak
+            //
+
+            function onSpeak(text) {
+                var voices = synth.getVoices();
+                var msg = new SpeechSynthesisUtterance(text);
+
+                if (voice == null) {
+                    voice = voices[0];
+                }
+
+                msg.voice = voice;
+                synth.speak(msg);
+
+                if (doRecording) {
+                    Module.set_status("speaking ...");
+                    printTextarea('js: speaking');
+                    stopRecording();
+                    var interval = setInterval(function() {
+                        if (!synth.speaking) {
+                            printTextarea('js: done speaking');
+                            clearInterval(interval);
+                            startRecording();
+                        } else {
+                            Module.set_status("");
+                        }
+                    }, 100);
+                }
+            }
+
+            function onSpeakRandom() {
+                Module.force_speak(instance);
+            }
+
+            async function clearCache() {
+                if (confirm('Are you sure you want to clear the cache?\nAll the models will be downloaded again.')) {
+                    indexedDB.deleteDatabase(dbName);
+                }
+            }
+
+            //
+            // main
+            //
+
+            var intervalUpdate = null;
+
+            function onStart() {
+                if (!instance) {
+                    instance = Module.init('whisper.bin');
+
+                    if (instance) {
+                        printTextarea("js: whisper initialized, instance: " + instance);
+                    }
+                }
+
+                if (!instance) {
+                    printTextarea("js: failed to initialize whisper");
+                    return;
+                }
+
+                startRecording();
+
+                intervalUpdate = setInterval(function() {
+                    var textToSpeak = Module.get_text_to_speak();
+
+                    if (textToSpeak != null && textToSpeak.length > 1) {
+                        onSpeak(textToSpeak);
+                    }
+
+                    document.getElementById('state-status').innerHTML = Module.get_status();
+                    document.getElementById('state-context').innerHTML = Module.get_text_context();
+                }, 100);
+            }
+
+            function onStop() {
+                stopRecording();
+            }
+
+            function onVoiceChange() {
+                printTextarea('js: voice changed to: ' + document.getElementById('voice').value);
+                voice = synth.getVoices()[document.getElementById('voice').value];
+            }
+
+        </script>
+        <script type="text/javascript" src="talk.js"></script>
+    </body>
+</html>

whisper.cpp

@@ -2750,7 +2750,7 @@ int whisper_full(
                 } else {
                     text += whisper_token_to_str(ctx, tokens_cur[i].id);
                 }
-                if (tokens_cur[i].id > whisper_token_beg(ctx)) {
+                if (tokens_cur[i].id > whisper_token_beg(ctx) && !params.single_segment) {
                     const auto t1 = seek + 2*(tokens_cur[i].tid - whisper_token_beg(ctx));
                     if (!text.empty()) {
                         const auto tt0 = params.speed_up ? 2*t0 : t0;