ref #10 : quick-and-dirty attempt for real-time audio transciption

- Processes input in chunks of 3 seconds.
- Padding audio with silence
- Uses 1 second audio from previous pass
- No text context

.gitignore

@@ -1,3 +1,4 @@
 sync.sh
 main
+stream
 *.o

Makefile

@@ -1,3 +1,5 @@
+CC_SDL=`sdl2-config --cflags --libs`
+
 main: ggml.o main.o
 	g++ -pthread -o main ggml.o main.o
 	./main -h
@@ -8,6 +10,9 @@ ggml.o: ggml.c ggml.h
 main.o: main.cpp ggml.h
 	g++ -pthread -O3 -std=c++11 -c main.cpp
 
+stream: stream.cpp
+	g++ -pthread -O3 -std=c++11 -o stream stream.cpp ggml.o $(CC_SDL)
+
 # clean up the directory
 clean:
 	rm -f *.o main

stream.cpp

@@ -0,0 +1,2511 @@
+// Real-time speech recognition of input from a microphone
+//
+// A very quick-n-dirty implementation serving mainly as a proof of concept.
+
+#include "ggml.h"
+
+#define USE_FLASH_ATTN
+#define USE_FLASH_FF
+
+// third-party utilities
+// use your favorite implementations
+#define DR_WAV_IMPLEMENTATION
+#include "dr_wav.h"
+
+#include <SDL.h>
+#include <SDL_audio.h>
+
+#include <algorithm>
+#include <cassert>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <fstream>
+#include <map>
+#include <string>
+#include <thread>
+#include <vector>
+
+// available whisper models
+enum e_model {
+    MODEL_UNKNOWN,
+    MODEL_TINY,
+    MODEL_BASE,
+    MODEL_SMALL,
+    MODEL_MEDIUM,
+    MODEL_LARGE,
+};
+
+const std::map<std::string, std::pair<int, std::string>> g_lang = {
+    { "en",  { 0,  "english",         } },
+    { "zh",  { 1,  "chinese",         } },
+    { "de",  { 2,  "german",          } },
+    { "es",  { 3,  "spanish",         } },
+    { "ru",  { 4,  "russian",         } },
+    { "ko",  { 5,  "korean",          } },
+    { "fr",  { 6,  "french",          } },
+    { "ja",  { 7,  "japanese",        } },
+    { "pt",  { 8,  "portuguese",      } },
+    { "tr",  { 9,  "turkish",         } },
+    { "pl",  { 10, "polish",          } },
+    { "ca",  { 11,  "catalan",        } },
+    { "nl",  { 12,  "dutch",          } },
+    { "ar",  { 13,  "arabic",         } },
+    { "sv",  { 14,  "swedish",        } },
+    { "it",  { 15,  "italian",        } },
+    { "id",  { 16,  "indonesian",     } },
+    { "hi",  { 17,  "hindi",          } },
+    { "fi",  { 18,  "finnish",        } },
+    { "vi",  { 19,  "vietnamese",     } },
+    { "iw",  { 20,  "hebrew",         } },
+    { "uk",  { 21,  "ukrainian",      } },
+    { "el",  { 22,  "greek",          } },
+    { "ms",  { 23,  "malay",          } },
+    { "cs",  { 24,  "czech",          } },
+    { "ro",  { 25,  "romanian",       } },
+    { "da",  { 26,  "danish",         } },
+    { "hu",  { 27,  "hungarian",      } },
+    { "ta",  { 28,  "tamil",          } },
+    { "no",  { 29,  "norwegian",      } },
+    { "th",  { 30,  "thai",           } },
+    { "ur",  { 31,  "urdu",           } },
+    { "hr",  { 32,  "croatian",       } },
+    { "bg",  { 33,  "bulgarian",      } },
+    { "lt",  { 34,  "lithuanian",     } },
+    { "la",  { 35,  "latin",          } },
+    { "mi",  { 36,  "maori",          } },
+    { "ml",  { 37,  "malayalam",      } },
+    { "cy",  { 38,  "welsh",          } },
+    { "sk",  { 39,  "slovak",         } },
+    { "te",  { 40,  "telugu",         } },
+    { "fa",  { 41,  "persian",        } },
+    { "lv",  { 42,  "latvian",        } },
+    { "bn",  { 43,  "bengali",        } },
+    { "sr",  { 44,  "serbian",        } },
+    { "az",  { 45,  "azerbaijani",    } },
+    { "sl",  { 46,  "slovenian",      } },
+    { "kn",  { 47,  "kannada",        } },
+    { "et",  { 48,  "estonian",       } },
+    { "mk",  { 49,  "macedonian",     } },
+    { "br",  { 50,  "breton",         } },
+    { "eu",  { 51,  "basque",         } },
+    { "is",  { 52,  "icelandic",      } },
+    { "hy",  { 53,  "armenian",       } },
+    { "ne",  { 54,  "nepali",         } },
+    { "mn",  { 55,  "mongolian",      } },
+    { "bs",  { 56,  "bosnian",        } },
+    { "kk",  { 57,  "kazakh",         } },
+    { "sq",  { 58,  "albanian",       } },
+    { "sw",  { 59,  "swahili",        } },
+    { "gl",  { 60,  "galician",       } },
+    { "mr",  { 61,  "marathi",        } },
+    { "pa",  { 62,  "punjabi",        } },
+    { "si",  { 63,  "sinhala",        } },
+    { "km",  { 64,  "khmer",          } },
+    { "sn",  { 65,  "shona",          } },
+    { "yo",  { 66,  "yoruba",         } },
+    { "so",  { 67,  "somali",         } },
+    { "af",  { 68,  "afrikaans",      } },
+    { "oc",  { 69,  "occitan",        } },
+    { "ka",  { 70,  "georgian",       } },
+    { "be",  { 71,  "belarusian",     } },
+    { "tg",  { 72,  "tajik",          } },
+    { "sd",  { 73,  "sindhi",         } },
+    { "gu",  { 74,  "gujarati",       } },
+    { "am",  { 75,  "amharic",        } },
+    { "yi",  { 76,  "yiddish",        } },
+    { "lo",  { 77,  "lao",            } },
+    { "uz",  { 78,  "uzbek",          } },
+    { "fo",  { 79,  "faroese",        } },
+    { "ht",  { 80,  "haitian creole", } },
+    { "ps",  { 81,  "pashto",         } },
+    { "tk",  { 82,  "turkmen",        } },
+    { "nn",  { 83,  "nynorsk",        } },
+    { "mt",  { 84,  "maltese",        } },
+    { "sa",  { 85,  "sanskrit",       } },
+    { "lb",  { 86,  "luxembourgish",  } },
+    { "my",  { 87,  "myanmar",        } },
+    { "bo",  { 88,  "tibetan",        } },
+    { "tl",  { 89,  "tagalog",        } },
+    { "mg",  { 90,  "malagasy",       } },
+    { "as",  { 91,  "assamese",       } },
+    { "tt",  { 92,  "tatar",          } },
+    { "haw", { 93,  "hawaiian",       } },
+    { "ln",  { 94,  "lingala",        } },
+    { "ha",  { 95,  "hausa",          } },
+    { "ba",  { 96,  "bashkir",        } },
+    { "jw",  { 97,  "javanese",       } },
+    { "su",  { 98,  "sundanese",      } },
+};
+
+const size_t MB = 1024*1024;
+
+const std::map<e_model, size_t> MEM_REQ_MODEL = {
+    { MODEL_TINY,     86ull*MB },
+    { MODEL_BASE,    165ull*MB },
+    { MODEL_SMALL,   540ull*MB },
+    { MODEL_MEDIUM, 1650ull*MB },
+    { MODEL_LARGE,  3260ull*MB },
+};
+
+const std::map<e_model, size_t> MEM_REQ_ENCODE = {
+    { MODEL_TINY,     80ull*MB },
+    { MODEL_BASE,    128ull*MB },
+    { MODEL_SMALL,   300ull*MB },
+    { MODEL_MEDIUM,  680ull*MB },
+    { MODEL_LARGE,  1100ull*MB },
+};
+
+const std::map<e_model, size_t> MEM_REQ_ENCODE_LAYER = {
+    { MODEL_TINY,     64ull*MB },
+    { MODEL_BASE,     84ull*MB },
+    { MODEL_SMALL,   128ull*MB },
+    { MODEL_MEDIUM,  172ull*MB },
+    { MODEL_LARGE,   216ull*MB },
+};
+
+const std::map<e_model, size_t> MEM_REQ_DECODE = {
+    { MODEL_TINY,     94ull*MB },
+    { MODEL_BASE,     96ull*MB },
+    { MODEL_SMALL,    98ull*MB },
+    { MODEL_MEDIUM,  100ull*MB },
+    { MODEL_LARGE,   102ull*MB },
+};
+
+const std::map<e_model, size_t> MEM_REQ_DECODE_LAYER = {
+    { MODEL_TINY,     32ull*MB },
+    { MODEL_BASE,     44ull*MB },
+    { MODEL_SMALL,    64ull*MB },
+    { MODEL_MEDIUM,   84ull*MB },
+    { MODEL_LARGE,   110ull*MB },
+};
+
+// the memory buffers used to store the model in memory and perform the inference computations
+std::vector<uint8_t> g_buf_model;
+std::vector<uint8_t> g_buf_compute;
+std::vector<uint8_t> g_buf_compute_layer;
+
+const int SAMPLE_RATE = 16000;
+const int N_FFT       = 400;
+const int N_MEL       = 80;
+const int HOP_LENGTH  = 160;
+const int CHUNK_SIZE  = 30; // seconds
+
+struct whisper_mel {
+    int n_len;
+    int n_mel;
+
+    std::vector<float> data;
+};
+
+struct whisper_filters {
+    int32_t n_mel;
+    int32_t n_fft;
+
+    std::vector<float> data;
+};
+
+struct whisper_vocab {
+    using id    = int32_t;
+    using token = std::string;
+
+    int n_vocab = 51864;
+
+    std::map<token, id> token_to_id;
+    std::map<id, token> id_to_token;
+
+    id token_eot  = 50256;
+    id token_sot  = 50257;
+    id token_prev = 50360;
+    id token_solm = 50361; // ??
+    id token_not  = 50362; // no timestamps
+    id token_beg  = 50363;
+
+    // available tasks
+    const id token_translate  = 50358;
+    const id token_transcribe = 50359;
+
+    bool is_multilingual() const {
+        return n_vocab == 51865;
+    }
+};
+
+struct whisper_result {
+    whisper_vocab::id id;
+    int64_t t;
+};
+
+// command-line parameters
+struct whisper_params {
+    int32_t seed      = -1; // RNG seed, not used currently
+    int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
+
+    bool verbose              = false;
+    bool translate            = false;
+    bool print_special_tokens = false;
+    bool no_timestamps        = true;
+
+    std::string language  = "en";
+    std::string model     = "models/ggml-base.en.bin";
+    std::string fname_inp = "samples/jfk.wav";
+};
+
+void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
+
+bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
+    for (int i = 1; i < argc; i++) {
+        std::string arg = argv[i];
+
+        if (arg == "-s" || arg == "--seed") {
+            params.seed = std::stoi(argv[++i]);
+        } else if (arg == "-t" || arg == "--threads") {
+            params.n_threads = std::stoi(argv[++i]);
+        } else if (arg == "-v" || arg == "--verbose") {
+            params.verbose = true;
+        } else if (arg == "--translate") {
+            params.translate = true;
+        } else if (arg == "-l" || arg == "--language") {
+            params.language = argv[++i];
+            if (g_lang.find(params.language) == g_lang.end()) {
+                fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
+                whisper_print_usage(argc, argv, params);
+                exit(0);
+            }
+        } else if (arg == "-ps" || arg == "--print_special") {
+            params.print_special_tokens = true;
+        } else if (arg == "-nt" || arg == "--no_timestamps") {
+            params.no_timestamps = true;
+        } else if (arg == "-m" || arg == "--model") {
+            params.model = argv[++i];
+        } else if (arg == "-f" || arg == "--file") {
+            params.fname_inp = argv[++i];
+        } else if (arg == "-h" || arg == "--help") {
+            whisper_print_usage(argc, argv, params);
+            exit(0);
+        } else {
+            fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
+            whisper_print_usage(argc, argv, params);
+            exit(0);
+        }
+    }
+
+    return true;
+}
+
+void whisper_print_usage(int argc, char ** argv, const whisper_params & params) {
+    fprintf(stderr, "\n");
+    fprintf(stderr, "usage: %s [options]\n", argv[0]);
+    fprintf(stderr, "\n");
+    fprintf(stderr, "options:\n");
+    fprintf(stderr, "  -h,       --help           show this help message and exit\n");
+    fprintf(stderr, "  -s SEED,  --seed SEED      RNG seed (default: -1)\n");
+    fprintf(stderr, "  -t N,     --threads N      number of threads to use during computation (default: %d)\n", params.n_threads);
+    fprintf(stderr, "  -v,       --verbose        verbose output\n");
+    fprintf(stderr, "            --translate      translate from source language to english\n");
+    fprintf(stderr, "  -ps,      --print_special  print special tokens\n");
+    fprintf(stderr, "  -nt,      --no_timestamps  do not print timestamps\n");
+    fprintf(stderr, "  -l LANG,  --language LANG  spoken language (default: %s)\n", params.language.c_str());
+    fprintf(stderr, "  -m FNAME, --model FNAME    model path (default: %s)\n", params.model.c_str());
+    fprintf(stderr, "  -f FNAME, --file FNAME     input WAV file path (default: %s)\n", params.fname_inp.c_str());
+    fprintf(stderr, "\n");
+}
+
+
+// medium
+// hparams: {
+// 'n_mels': 80,
+// 'n_vocab': 51864,
+// 'n_audio_ctx': 1500,
+// 'n_audio_state': 1024,
+// 'n_audio_head': 16,
+// 'n_audio_layer': 24,
+// 'n_text_ctx': 448,
+// 'n_text_state': 1024,
+// 'n_text_head': 16,
+// 'n_text_layer': 24
+// }
+//
+// default hparams (Whisper tiny)
+struct whisper_hparams {
+    int32_t n_vocab       = 51864;
+    int32_t n_audio_ctx   = 1500;
+    int32_t n_audio_state = 384;
+    int32_t n_audio_head  = 6;
+    int32_t n_audio_layer = 4;
+    int32_t n_text_ctx    = 448;
+    int32_t n_text_state  = 384;
+    int32_t n_text_head   = 6;
+    int32_t n_text_layer  = 4;
+    int32_t n_mels        = 80;
+    int32_t f16           = 1;
+};
+
+// audio encoding layer
+struct whisper_layer_encoder {
+    // encoder.blocks.*.attn_ln
+    struct ggml_tensor * attn_ln_0_w;
+    struct ggml_tensor * attn_ln_0_b;
+
+    // encoder.blocks.*.attn.out
+    struct ggml_tensor * attn_ln_1_w;
+    struct ggml_tensor * attn_ln_1_b;
+
+    // encoder.blocks.*.attn.query
+    struct ggml_tensor * attn_q_w;
+    struct ggml_tensor * attn_q_b;
+
+    // encoder.blocks.*.attn.key
+    struct ggml_tensor * attn_k_w;
+
+    // encoder.blocks.*.attn.value
+    struct ggml_tensor * attn_v_w;
+    struct ggml_tensor * attn_v_b;
+
+    // encoder.blocks.*.mlp_ln
+    struct ggml_tensor * mlp_ln_w;
+    struct ggml_tensor * mlp_ln_b;
+
+    // encoder.blocks.*.mlp.0
+    struct ggml_tensor * mlp_0_w;
+    struct ggml_tensor * mlp_0_b;
+
+    // encoder.blocks.*.mlp.2
+    struct ggml_tensor * mlp_1_w;
+    struct ggml_tensor * mlp_1_b;
+};
+
+// token decoding layer
+struct whisper_layer_decoder {
+    // decoder.blocks.*.attn_ln
+    struct ggml_tensor * attn_ln_0_w;
+    struct ggml_tensor * attn_ln_0_b;
+
+    // decoder.blocks.*.attn.out
+    struct ggml_tensor * attn_ln_1_w;
+    struct ggml_tensor * attn_ln_1_b;
+
+    // decoder.blocks.*.attn.query
+    struct ggml_tensor * attn_q_w;
+    struct ggml_tensor * attn_q_b;
+
+    // decoder.blocks.*.attn.key
+    struct ggml_tensor * attn_k_w;
+
+    // decoder.blocks.*.attn.value
+    struct ggml_tensor * attn_v_w;
+    struct ggml_tensor * attn_v_b;
+
+    // decoder.blocks.*.cross_attn_ln
+    struct ggml_tensor * cross_attn_ln_0_w;
+    struct ggml_tensor * cross_attn_ln_0_b;
+
+    // decoder.blocks.*.cross_attn.out
+    struct ggml_tensor * cross_attn_ln_1_w;
+    struct ggml_tensor * cross_attn_ln_1_b;
+
+    // decoder.blocks.*.cross_attn.query
+    struct ggml_tensor * cross_attn_q_w;
+    struct ggml_tensor * cross_attn_q_b;
+
+    // decoder.blocks.*.cross_attn.key
+    struct ggml_tensor * cross_attn_k_w;
+
+    // decoder.blocks.*.cross_attn.value
+    struct ggml_tensor * cross_attn_v_w;
+    struct ggml_tensor * cross_attn_v_b;
+
+    // decoder.blocks.*.mlp_ln
+    struct ggml_tensor * mlp_ln_w;
+    struct ggml_tensor * mlp_ln_b;
+
+    // decoder.blocks.*.mlp.0
+    struct ggml_tensor * mlp_0_w;
+    struct ggml_tensor * mlp_0_b;
+
+    // decoder.blocks.*.mlp.2
+    struct ggml_tensor * mlp_1_w;
+    struct ggml_tensor * mlp_1_b;
+};
+
+struct whisper_model {
+    e_model type = MODEL_UNKNOWN;
+
+    whisper_hparams hparams;
+    whisper_filters filters;
+
+    // encoder.positional_embedding
+    struct ggml_tensor * e_pe;
+
+    // encoder.conv1
+    struct ggml_tensor * e_conv_1_w;
+    struct ggml_tensor * e_conv_1_b;
+
+    // encoder.conv2
+    struct ggml_tensor * e_conv_2_w;
+    struct ggml_tensor * e_conv_2_b;
+
+    // encoder.ln_post
+    struct ggml_tensor * e_ln_w;
+    struct ggml_tensor * e_ln_b;
+
+    // decoder.positional_embedding
+    struct ggml_tensor * d_pe; // DD
+
+    // decoder.token_embedding
+    struct ggml_tensor * d_te; // DD
+
+    // decoder.ln
+    struct ggml_tensor * d_ln_w; // DD
+    struct ggml_tensor * d_ln_b; // DD
+
+    std::vector<whisper_layer_encoder> layers_encoder;
+    std::vector<whisper_layer_decoder> layers_decoder;
+
+    // key + value memory
+    struct ggml_tensor * memory_k;
+    struct ggml_tensor * memory_v;
+
+    struct ggml_tensor * memory_cross_k;
+    struct ggml_tensor * memory_cross_v;
+
+    //
+    struct ggml_context * ctx;
+    std::map<std::string, struct ggml_tensor *> tensors;
+};
+
+// load the model from a ggml file
+//
+// file format:
+//
+//   - hparams
+//   - pre-computed mel filters
+//   - vocab
+//   - weights
+//
+// see the convert-pt-to-ggml.py script for details
+//
+bool whisper_model_load(const std::string & fname, whisper_model & model, whisper_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_audio_ctx,   sizeof(hparams.n_audio_ctx));
+        fin.read((char *) &hparams.n_audio_state, sizeof(hparams.n_audio_state));
+        fin.read((char *) &hparams.n_audio_head,  sizeof(hparams.n_audio_head));
+        fin.read((char *) &hparams.n_audio_layer, sizeof(hparams.n_audio_layer));
+        fin.read((char *) &hparams.n_text_ctx,    sizeof(hparams.n_text_ctx));
+        fin.read((char *) &hparams.n_text_state,  sizeof(hparams.n_text_state));
+        fin.read((char *) &hparams.n_text_head,   sizeof(hparams.n_text_head));
+        fin.read((char *) &hparams.n_text_layer,  sizeof(hparams.n_text_layer));
+        fin.read((char *) &hparams.n_mels,        sizeof(hparams.n_mels));
+        fin.read((char *) &hparams.f16,           sizeof(hparams.f16));
+
+        assert(hparams.n_text_state == hparams.n_audio_state);
+
+        if (hparams.n_audio_layer == 4) {
+            model.type = e_model::MODEL_TINY;
+        }
+
+        if (hparams.n_audio_layer == 6) {
+            model.type = e_model::MODEL_BASE;
+        }
+
+        if (hparams.n_audio_layer == 12) {
+            model.type = e_model::MODEL_SMALL;
+        }
+
+        if (hparams.n_audio_layer == 24) {
+            model.type = e_model::MODEL_MEDIUM;
+        }
+
+        if (hparams.n_audio_layer == 32) {
+            model.type = e_model::MODEL_LARGE;
+        }
+
+        printf("%s: n_vocab       = %d\n", __func__, hparams.n_vocab);
+        printf("%s: n_audio_ctx   = %d\n", __func__, hparams.n_audio_ctx);
+        printf("%s: n_audio_state = %d\n", __func__, hparams.n_audio_state);
+        printf("%s: n_audio_head  = %d\n", __func__, hparams.n_audio_head);
+        printf("%s: n_audio_layer = %d\n", __func__, hparams.n_audio_layer);
+        printf("%s: n_text_ctx    = %d\n", __func__, hparams.n_text_ctx);
+        printf("%s: n_text_state  = %d\n", __func__, hparams.n_text_state);
+        printf("%s: n_text_head   = %d\n", __func__, hparams.n_text_head);
+        printf("%s: n_text_layer  = %d\n", __func__, hparams.n_text_layer);
+        printf("%s: n_mels        = %d\n", __func__, hparams.n_mels);
+        printf("%s: f16           = %d\n", __func__, hparams.f16);
+        printf("%s: type          = %d\n", __func__, model.type);
+
+        g_buf_model.resize(MEM_REQ_MODEL.at(model.type));
+        g_buf_compute.resize(std::max(MEM_REQ_ENCODE.at(model.type), MEM_REQ_DECODE.at(model.type)));
+        g_buf_compute_layer.resize(std::max(MEM_REQ_ENCODE_LAYER.at(model.type), MEM_REQ_DECODE_LAYER.at(model.type)));
+
+        // this is the total memory required to run the inference
+        const size_t mem_required =
+                   g_buf_model.size() +
+                   g_buf_compute.size() +
+                   g_buf_compute_layer.size();
+
+        printf("%s: mem_required  = %.2f MB\n", __func__, mem_required / 1024.0 / 1024.0);
+    }
+
+    // load mel filters
+    {
+        auto & filters = model.filters;
+
+        fin.read((char *) &filters.n_mel, sizeof(filters.n_mel));
+        fin.read((char *) &filters.n_fft, sizeof(filters.n_fft));
+
+        filters.data.resize(filters.n_mel * filters.n_fft);
+        fin.read((char *) filters.data.data(), filters.data.size() * sizeof(float));
+    }
+
+    // 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;
+
+            //printf("%s: vocab[%d] = '%s'\n", __func__, i, word.c_str());
+        }
+
+        vocab.n_vocab = model.hparams.n_vocab;
+        if (vocab.is_multilingual()) {
+            vocab.token_eot++;
+            vocab.token_sot++;
+            vocab.token_prev++;
+            vocab.token_solm++;
+            vocab.token_not++;
+            vocab.token_beg++;
+        }
+
+        if (n_vocab < model.hparams.n_vocab) {
+            printf("%s: adding %d extra tokens\n", __func__, model.hparams.n_vocab - n_vocab);
+            for (int i = n_vocab; i < model.hparams.n_vocab; i++) {
+                if (i > vocab.token_beg) {
+                    word = "[_TT_" + std::to_string(i - vocab.token_beg) + "]";
+                } else if (i == vocab.token_eot) {
+                    word = "[_EOT_]";
+                } else if (i == vocab.token_sot) {
+                    word = "[_SOT_]";
+                } else if (i == vocab.token_prev) {
+                    word = "[_PREV_]";
+                } else if (i == vocab.token_not) {
+                    word = "[_NOT_]";
+                } else if (i == vocab.token_beg) {
+                    word = "[_BEG_]";
+                } else {
+                    word = "[_extra_token_" + std::to_string(i) + "]";
+                }
+                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_vocab = hparams.n_vocab;
+
+        const int n_audio_ctx   = hparams.n_audio_ctx;
+        const int n_audio_state = hparams.n_audio_state;
+        const int n_audio_layer = hparams.n_audio_layer;
+
+        const int n_text_ctx = hparams.n_text_ctx;
+        const int n_text_state = hparams.n_text_state;
+        const int n_text_layer = hparams.n_text_layer;
+
+        const int n_mels = hparams.n_mels;
+
+        // encoder
+        {
+            // TODO: F16 .. maybe not?
+            ctx_size += n_audio_ctx*n_audio_state*ggml_type_size(GGML_TYPE_F32); // e_pe;
+
+            ctx_size += 3*n_mels*n_audio_state*ggml_type_size(wtype);         // e_conv_1_w
+            ctx_size +=          n_audio_state*ggml_type_size(GGML_TYPE_F32); // e_conv_1_b
+
+            ctx_size += 3*n_audio_state*n_audio_state*ggml_type_size(wtype);         // e_conv_2_w
+            ctx_size +=                 n_audio_state*ggml_type_size(GGML_TYPE_F32); // e_conv_2_b
+
+            ctx_size += n_audio_state*ggml_type_size(GGML_TYPE_F32); // e_ln_w;
+            ctx_size += n_audio_state*ggml_type_size(GGML_TYPE_F32); // e_ln_b;
+        }
+
+        // decoder
+        {
+            // TODO: F16 .. maybe not?
+            ctx_size += n_text_ctx*n_text_state*ggml_type_size(GGML_TYPE_F32); // d_pe;
+
+            ctx_size += n_vocab*n_text_state*ggml_type_size(wtype); // d_te;
+
+            ctx_size += n_text_state*ggml_type_size(GGML_TYPE_F32); // d_ln_w;
+            ctx_size += n_text_state*ggml_type_size(GGML_TYPE_F32); // d_ln_b;
+        }
+
+        // encoder layers
+        {
+            ctx_size += n_audio_layer*(n_audio_state*ggml_type_size(GGML_TYPE_F32)); // mlp_ln_w
+            ctx_size += n_audio_layer*(n_audio_state*ggml_type_size(GGML_TYPE_F32)); // mlp_ln_b
+
+            ctx_size += n_audio_layer*(4*n_audio_state*n_audio_state*ggml_type_size(wtype));         // mlp_0_w
+            ctx_size += n_audio_layer*(              4*n_audio_state*ggml_type_size(GGML_TYPE_F32)); // mlp_0_b
+
+            ctx_size += n_audio_layer*(4*n_audio_state*n_audio_state*ggml_type_size(wtype));         // mlp_1_w
+            ctx_size += n_audio_layer*(                n_audio_state*ggml_type_size(GGML_TYPE_F32)); // mlp_1_b
+
+            ctx_size += n_audio_layer*(n_audio_state*ggml_type_size(GGML_TYPE_F32)); // attn_ln_0_w
+            ctx_size += n_audio_layer*(n_audio_state*ggml_type_size(GGML_TYPE_F32)); // attn_ln_0_b
+
+            ctx_size += n_audio_layer*(n_audio_state*n_audio_state*ggml_type_size(wtype));         // attn_q_w
+            ctx_size += n_audio_layer*(              n_audio_state*ggml_type_size(GGML_TYPE_F32)); // attn_q_b
+
+            ctx_size += n_audio_layer*(n_audio_state*n_audio_state*ggml_type_size(wtype)); // attn_k_w
+
+            ctx_size += n_audio_layer*(n_audio_state*n_audio_state*ggml_type_size(wtype));         // attn_v_w
+            ctx_size += n_audio_layer*(              n_audio_state*ggml_type_size(GGML_TYPE_F32)); // attn_v_b
+
+            ctx_size += n_audio_layer*(n_audio_state*n_audio_state*ggml_type_size(wtype));         // attn_ln_1_w
+            ctx_size += n_audio_layer*(              n_audio_state*ggml_type_size(GGML_TYPE_F32)); // attn_ln_1_b
+        }
+
+        // decoder layers
+        {
+            ctx_size += n_text_layer*(n_text_state*ggml_type_size(GGML_TYPE_F32)); // mlp_ln_w
+            ctx_size += n_text_layer*(n_text_state*ggml_type_size(GGML_TYPE_F32)); // mlp_ln_b
+
+            ctx_size += n_text_layer*(4*n_text_state*n_text_state*ggml_type_size(wtype));         // mlp_0_w
+            ctx_size += n_text_layer*(             4*n_text_state*ggml_type_size(GGML_TYPE_F32)); // mlp_0_b
+
+            ctx_size += n_text_layer*(4*n_text_state*n_text_state*ggml_type_size(wtype));         // mlp_1_w
+            ctx_size += n_text_layer*(               n_text_state*ggml_type_size(GGML_TYPE_F32)); // mlp_1_b
+
+            ctx_size += n_text_layer*(n_text_state*ggml_type_size(GGML_TYPE_F32)); // attn_ln_0_w
+            ctx_size += n_text_layer*(n_text_state*ggml_type_size(GGML_TYPE_F32)); // attn_ln_0_b
+
+            ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype));         // attn_q_w
+            ctx_size += n_text_layer*(             n_text_state*ggml_type_size(GGML_TYPE_F32)); // attn_q_b
+
+            ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype)); // attn_k_w
+
+            ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype));         // attn_v_w
+            ctx_size += n_text_layer*(             n_text_state*ggml_type_size(GGML_TYPE_F32)); // attn_v_b
+
+            ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype));         // attn_ln_1_w
+            ctx_size += n_text_layer*(             n_text_state*ggml_type_size(GGML_TYPE_F32)); // attn_ln_1_b
+                                                                                                //
+            ctx_size += n_text_layer*(n_text_state*ggml_type_size(GGML_TYPE_F32)); // cross_attn_ln_0_w
+            ctx_size += n_text_layer*(n_text_state*ggml_type_size(GGML_TYPE_F32)); // cross_attn_ln_0_b
+
+            ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype));         // cross_attn_q_w
+            ctx_size += n_text_layer*(             n_text_state*ggml_type_size(GGML_TYPE_F32)); // cross_attn_q_b
+
+            ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype)); // cross_attn_k_w
+
+            ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype));         // cross_attn_v_w
+            ctx_size += n_text_layer*(             n_text_state*ggml_type_size(GGML_TYPE_F32)); // cross_attn_v_b
+
+            ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype));         // cross_attn_ln_1_w
+            ctx_size += n_text_layer*(             n_text_state*ggml_type_size(GGML_TYPE_F32)); // cross_attn_ln_1_b
+        }
+
+        ctx_size += n_text_layer*n_text_ctx*n_text_state*ggml_type_size(GGML_TYPE_F16); // memory_k
+        ctx_size += n_text_layer*n_text_ctx*n_text_state*ggml_type_size(GGML_TYPE_F16); // memory_v
+
+        ctx_size += n_text_layer*n_audio_ctx*n_text_state*ggml_type_size(GGML_TYPE_F16); // memory_cross_k
+        ctx_size += n_text_layer*n_audio_ctx*n_text_state*ggml_type_size(GGML_TYPE_F16); // memory_cross_v
+
+        ctx_size += (15 + 15*n_audio_layer + 24*n_text_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   = g_buf_model.size(),
+            .mem_buffer = g_buf_model.data(),
+        };
+
+        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_vocab = hparams.n_vocab;
+
+        const int n_audio_ctx   = hparams.n_audio_ctx;
+        const int n_audio_state = hparams.n_audio_state;
+        const int n_audio_layer = hparams.n_audio_layer;
+
+        const int n_text_ctx = hparams.n_text_ctx;
+        const int n_text_state = hparams.n_text_state;
+        const int n_text_layer = hparams.n_text_layer;
+
+        const int n_mels = hparams.n_mels;
+
+        model.layers_encoder.resize(n_audio_layer);
+        model.layers_decoder.resize(n_text_layer);
+
+        // encoder
+        {
+            model.e_pe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_audio_state, n_audio_ctx);
+
+            model.e_conv_1_w = ggml_new_tensor_3d(ctx, wtype,         3, n_mels, n_audio_state);
+            model.e_conv_1_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, n_audio_state);
+
+            model.e_conv_2_w = ggml_new_tensor_3d(ctx, wtype,         3, n_audio_state, n_audio_state);
+            model.e_conv_2_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, n_audio_state);
+
+            model.e_ln_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
+            model.e_ln_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
+
+            // map by name
+            model.tensors["encoder.positional_embedding"] = model.e_pe;
+
+            model.tensors["encoder.conv1.weight"] = model.e_conv_1_w;
+            model.tensors["encoder.conv1.bias"]   = model.e_conv_1_b;
+
+            model.tensors["encoder.conv2.weight"] = model.e_conv_2_w;
+            model.tensors["encoder.conv2.bias"]   = model.e_conv_2_b;
+
+            model.tensors["encoder.ln_post.weight"] = model.e_ln_w;
+            model.tensors["encoder.ln_post.bias"]   = model.e_ln_b;
+
+            for (int i = 0; i < n_audio_layer; ++i) {
+                auto & layer = model.layers_encoder[i];
+
+                layer.mlp_ln_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
+                layer.mlp_ln_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
+
+                layer.mlp_0_w = ggml_new_tensor_2d(ctx, wtype,           n_audio_state, 4*n_audio_state);
+                layer.mlp_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_audio_state);
+
+                layer.mlp_1_w = ggml_new_tensor_2d(ctx, wtype,         4*n_audio_state, n_audio_state);
+                layer.mlp_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_audio_state);
+
+                layer.attn_ln_0_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
+                layer.attn_ln_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
+
+                layer.attn_q_w = ggml_new_tensor_2d(ctx, wtype,         n_audio_state, n_audio_state);
+                layer.attn_q_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
+
+                layer.attn_k_w = ggml_new_tensor_2d(ctx, wtype,         n_audio_state, n_audio_state);
+
+                layer.attn_v_w = ggml_new_tensor_2d(ctx, wtype,         n_audio_state, n_audio_state);
+                layer.attn_v_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
+
+                layer.attn_ln_1_w = ggml_new_tensor_2d(ctx, wtype,         n_audio_state, n_audio_state);
+                layer.attn_ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
+
+                // map by name
+                model.tensors["encoder.blocks." + std::to_string(i) + ".mlp_ln.weight"] = layer.mlp_ln_w;
+                model.tensors["encoder.blocks." + std::to_string(i) + ".mlp_ln.bias"]   = layer.mlp_ln_b;
+
+                model.tensors["encoder.blocks." + std::to_string(i) + ".mlp.0.weight"] = layer.mlp_0_w;
+                model.tensors["encoder.blocks." + std::to_string(i) + ".mlp.0.bias"]   = layer.mlp_0_b;
+
+                model.tensors["encoder.blocks." + std::to_string(i) + ".mlp.2.weight"] = layer.mlp_1_w;
+                model.tensors["encoder.blocks." + std::to_string(i) + ".mlp.2.bias"]   = layer.mlp_1_b;
+
+                model.tensors["encoder.blocks." + std::to_string(i) + ".attn_ln.weight"] = layer.attn_ln_0_w;
+                model.tensors["encoder.blocks." + std::to_string(i) + ".attn_ln.bias"]   = layer.attn_ln_0_b;
+
+                model.tensors["encoder.blocks." + std::to_string(i) + ".attn.query.weight"] = layer.attn_q_w;
+                model.tensors["encoder.blocks." + std::to_string(i) + ".attn.query.bias"]   = layer.attn_q_b;
+
+                model.tensors["encoder.blocks." + std::to_string(i) + ".attn.key.weight"] = layer.attn_k_w;
+
+                model.tensors["encoder.blocks." + std::to_string(i) + ".attn.value.weight"] = layer.attn_v_w;
+                model.tensors["encoder.blocks." + std::to_string(i) + ".attn.value.bias"]   = layer.attn_v_b;
+
+                model.tensors["encoder.blocks." + std::to_string(i) + ".attn.out.weight"] = layer.attn_ln_1_w;
+                model.tensors["encoder.blocks." + std::to_string(i) + ".attn.out.bias"]   = layer.attn_ln_1_b;
+            }
+        }
+
+        // decoder
+        {
+            model.d_pe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_text_state, n_text_ctx);
+
+            model.d_te = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_vocab);
+
+            model.d_ln_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
+            model.d_ln_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
+
+            // map by name
+            model.tensors["decoder.positional_embedding"] = model.d_pe;
+
+            model.tensors["decoder.token_embedding.weight"] = model.d_te;
+
+            model.tensors["decoder.ln.weight"] = model.d_ln_w;
+            model.tensors["decoder.ln.bias"]   = model.d_ln_b;
+
+            for (int i = 0; i < n_text_layer; ++i) {
+                auto & layer = model.layers_decoder[i];
+
+                layer.mlp_ln_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
+                layer.mlp_ln_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
+
+                layer.mlp_0_w = ggml_new_tensor_2d(ctx, wtype,           n_text_state, 4*n_text_state);
+                layer.mlp_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_text_state);
+
+                layer.mlp_1_w = ggml_new_tensor_2d(ctx, wtype,         4*n_text_state, n_text_state);
+                layer.mlp_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32,   n_text_state);
+
+                layer.attn_ln_0_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
+                layer.attn_ln_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
+
+                layer.attn_q_w = ggml_new_tensor_2d(ctx, wtype,         n_text_state, n_text_state);
+                layer.attn_q_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
+
+                layer.attn_k_w = ggml_new_tensor_2d(ctx, wtype,         n_text_state, n_text_state);
+
+                layer.attn_v_w = ggml_new_tensor_2d(ctx, wtype,         n_text_state, n_text_state);
+                layer.attn_v_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
+
+                layer.attn_ln_1_w = ggml_new_tensor_2d(ctx, wtype,         n_text_state, n_text_state);
+                layer.attn_ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
+
+                layer.cross_attn_ln_0_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
+                layer.cross_attn_ln_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
+
+                layer.cross_attn_q_w = ggml_new_tensor_2d(ctx, wtype,         n_text_state, n_text_state);
+                layer.cross_attn_q_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
+
+                layer.cross_attn_k_w = ggml_new_tensor_2d(ctx, wtype,         n_text_state, n_text_state);
+
+                layer.cross_attn_v_w = ggml_new_tensor_2d(ctx, wtype,         n_text_state, n_text_state);
+                layer.cross_attn_v_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
+
+                layer.cross_attn_ln_1_w = ggml_new_tensor_2d(ctx, wtype,         n_text_state, n_text_state);
+                layer.cross_attn_ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
+
+                // map by name
+                model.tensors["decoder.blocks." + std::to_string(i) + ".mlp_ln.weight"] = layer.mlp_ln_w;
+                model.tensors["decoder.blocks." + std::to_string(i) + ".mlp_ln.bias"]   = layer.mlp_ln_b;
+
+                model.tensors["decoder.blocks." + std::to_string(i) + ".mlp.0.weight"] = layer.mlp_0_w;
+                model.tensors["decoder.blocks." + std::to_string(i) + ".mlp.0.bias"]   = layer.mlp_0_b;
+
+                model.tensors["decoder.blocks." + std::to_string(i) + ".mlp.2.weight"] = layer.mlp_1_w;
+                model.tensors["decoder.blocks." + std::to_string(i) + ".mlp.2.bias"]   = layer.mlp_1_b;
+
+                model.tensors["decoder.blocks." + std::to_string(i) + ".attn_ln.weight"] = layer.attn_ln_0_w;
+                model.tensors["decoder.blocks." + std::to_string(i) + ".attn_ln.bias"]   = layer.attn_ln_0_b;
+
+                model.tensors["decoder.blocks." + std::to_string(i) + ".attn.query.weight"] = layer.attn_q_w;
+                model.tensors["decoder.blocks." + std::to_string(i) + ".attn.query.bias"]   = layer.attn_q_b;
+
+                model.tensors["decoder.blocks." + std::to_string(i) + ".attn.key.weight"] = layer.attn_k_w;
+
+                model.tensors["decoder.blocks." + std::to_string(i) + ".attn.value.weight"] = layer.attn_v_w;
+                model.tensors["decoder.blocks." + std::to_string(i) + ".attn.value.bias"]   = layer.attn_v_b;
+
+                model.tensors["decoder.blocks." + std::to_string(i) + ".attn.out.weight"] = layer.attn_ln_1_w;
+                model.tensors["decoder.blocks." + std::to_string(i) + ".attn.out.bias"]   = layer.attn_ln_1_b;
+
+                model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn_ln.weight"] = layer.cross_attn_ln_0_w;
+                model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn_ln.bias"]   = layer.cross_attn_ln_0_b;
+
+                model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.query.weight"] = layer.cross_attn_q_w;
+                model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.query.bias"]   = layer.cross_attn_q_b;
+
+                model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.key.weight"] = layer.cross_attn_k_w;
+
+                model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.value.weight"] = layer.cross_attn_v_w;
+                model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.value.bias"]   = layer.cross_attn_v_b;
+
+                model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.out.weight"] = layer.cross_attn_ln_1_w;
+                model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.out.bias"]   = layer.cross_attn_ln_1_b;
+            }
+        }
+    }
+
+    // key + value memory
+    {
+        const auto & hparams = model.hparams;
+
+        const int n_text_state = hparams.n_text_state;
+        const int n_text_layer = hparams.n_text_layer;
+        const int n_text_ctx   = hparams.n_text_ctx;
+
+        // key/value memory for the self-attention layer
+        {
+            const int n_mem      = n_text_layer*n_text_ctx;
+            const int n_elements = n_text_state*n_mem;
+
+            model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
+            model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
+        }
+
+        // key/value memory for the cross-attention layer
+        {
+            const int n_audio_ctx   = hparams.n_audio_ctx;
+
+            const int n_mem      = n_text_layer*n_audio_ctx;
+            const int n_elements = n_text_state*n_mem;
+
+            model.memory_cross_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
+            model.memory_cross_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
+        }
+
+        const size_t memory_size =
+            ggml_nbytes(model.memory_k)       + ggml_nbytes(model.memory_v) +
+            ggml_nbytes(model.memory_cross_k) + ggml_nbytes(model.memory_cross_v);
+
+        printf("%s: memory size = %8.2f MB \n", __func__, memory_size/1024.0/1024.0);
+    }
+
+    // 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[3] = { 1, 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] || tensor->ne[2] != ne[2]) {
+                fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d, %d], expected [%d, %d, %d]\n",
+                        __func__, name.data(), tensor->ne[0], tensor->ne[1], tensor->ne[2], ne[0], ne[1], ne[2]);
+                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 encoder
+//
+// given audio recording (more specifically, its log mel spectrogram), runs forward pass of the encoder
+// part of the transformer model and returns the encoded features
+//
+//   - model:      the model
+//   - n_threads:  number of threads to use
+//   - mel_offset: offset in the mel spectrogram (i.e. audio offset)
+//   - mel_inp:    input mel spectrogram
+//   - features:   output encoded features
+//
+bool whisper_encode(
+        const whisper_model & model,
+        const int n_threads,
+        const int mel_offset,
+        const whisper_mel & mel_inp,
+              std::vector<float> & features) {
+    const auto & hparams = model.hparams;
+
+    const int n_vocab = hparams.n_vocab;
+
+    const int n_ctx   = hparams.n_audio_ctx;
+    const int n_state = hparams.n_audio_state;
+    const int n_head  = hparams.n_audio_head;
+    const int n_layer = hparams.n_audio_layer;
+
+    const int N = n_ctx;
+
+    const int n_mels = hparams.n_mels;
+    assert(mel_inp.n_mel == n_mels);
+
+    struct ggml_init_params params = {
+        .mem_size   = g_buf_compute.size(),
+        .mem_buffer = g_buf_compute.data(),
+    };
+
+    struct ggml_context * ctx0 = ggml_init(params);
+
+    struct ggml_tensor * mel = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 2*n_ctx, n_mels);
+    assert(mel->type == GGML_TYPE_F32);
+    {
+        float * dst = (float *) mel->data;
+        memset(dst, 0, ggml_nbytes(mel));
+
+        const int i0 = std::min(mel_offset, mel_inp.n_len);
+        const int i1 = std::min(mel_offset + 2*n_ctx, mel_inp.n_len);
+
+        for (int j = 0; j < mel_inp.n_mel; ++j) {
+            for (int i = i0; i < i1; ++i) {
+                dst[j*2*n_ctx + (i - i0)] = mel_inp.data[j*mel_inp.n_len + i];
+            }
+        }
+    }
+
+    struct ggml_tensor * cur;
+
+    // convolution + gelu
+    {
+        cur = ggml_conv_1d_1s(ctx0, model.e_conv_1_w, mel);
+        cur = ggml_add(ctx0,
+                ggml_repeat(ctx0,
+                    model.e_conv_1_b,
+                    cur),
+                cur);
+
+        cur = ggml_gelu(ctx0, cur);
+
+        cur = ggml_conv_1d_2s(ctx0, model.e_conv_2_w, cur);
+        cur = ggml_add(ctx0,
+                ggml_repeat(ctx0,
+                    model.e_conv_2_b,
+                    cur),
+                cur);
+
+        cur = ggml_gelu(ctx0, cur);
+    }
+
+    cur = ggml_add(ctx0, model.e_pe, ggml_transpose(ctx0, cur));
+
+    struct ggml_tensor * inpL = cur;
+
+    for (int il = 0; il < n_layer; ++il) {
+        const auto & layer = model.layers_encoder[il];
+
+        // create separate context for each layer to reduce memory usage
+
+        struct ggml_init_params paramsL = {
+            .mem_size   = g_buf_compute_layer.size(),
+            .mem_buffer = g_buf_compute_layer.data(),
+        };
+
+        struct ggml_context * ctxL = ggml_init(paramsL);
+
+        // norm
+        {
+            cur = ggml_norm(ctxL, inpL);
+
+            // cur = ln_0_w*cur + ln_0_b
+            cur = ggml_add(ctxL,
+                    ggml_mul(ctxL,
+                        ggml_repeat(ctxL, layer.attn_ln_0_w, cur),
+                        cur),
+                    ggml_repeat(ctxL, layer.attn_ln_0_b, cur));
+        }
+
+        // self-attention
+        {
+            struct ggml_tensor * Qcur = ggml_mul_mat(ctxL,
+                    layer.attn_q_w,
+                    cur);
+
+            Qcur = ggml_add(ctxL,
+                    ggml_repeat(ctxL,
+                        layer.attn_q_b,
+                        Qcur),
+                    Qcur);
+
+            //Qcur = ggml_scale(ctxL, Qcur, ggml_new_f32(ctxL, pow(float(n_state)/n_head, -0.25)));
+
+            // note: no bias for Key
+            struct ggml_tensor * Kcur = ggml_mul_mat(ctxL,
+                    layer.attn_k_w,
+                    cur);
+
+            //Kcur = ggml_scale(ctxL, Kcur, ggml_new_f32(ctxL, pow(float(n_state)/n_head, -0.25)));
+
+            struct ggml_tensor * Vcur = ggml_mul_mat(ctxL,
+                    layer.attn_v_w,
+                    cur);
+
+            Vcur = ggml_add(ctxL,
+                    ggml_repeat(ctxL,
+                        layer.attn_v_b,
+                        Vcur),
+                    Vcur);
+
+            // ------
+
+#ifdef USE_FLASH_ATTN
+            struct ggml_tensor * Q =
+                ggml_permute(ctxL,
+                        ggml_cpy(ctxL,
+                            Qcur,
+                            ggml_new_tensor_3d(ctxL, GGML_TYPE_F16, n_state/n_head, n_head, N)),
+                        0, 2, 1, 3);
+
+            struct ggml_tensor * K =
+                ggml_permute(ctxL,
+                        ggml_cpy(ctxL,
+                            Kcur,
+                            ggml_new_tensor_3d(ctxL, GGML_TYPE_F16, n_state/n_head, n_head, N)),
+                        0, 2, 1, 3);
+
+            struct ggml_tensor * V =
+                ggml_cpy(ctxL,
+                        ggml_permute(ctxL,
+                            ggml_reshape_3d(ctxL,
+                                Vcur,
+                                n_state/n_head, n_head, N),
+                            1, 2, 0, 3),
+                        ggml_new_tensor_3d(ctxL, GGML_TYPE_F16, N, n_state/n_head, n_head)
+                        );
+
+            struct ggml_tensor * KQV = ggml_flash_attn(ctxL, Q, K, V, false);
+#else
+            struct ggml_tensor * Q =
+                ggml_permute(ctxL,
+                        ggml_cpy(ctxL,
+                            Qcur,
+                            ggml_new_tensor_3d(ctxL, GGML_TYPE_F32, n_state/n_head, n_head, N)),
+                        0, 2, 1, 3);
+
+            struct ggml_tensor * K =
+                ggml_permute(ctxL,
+                        ggml_cpy(ctxL,
+                            Kcur,
+                            ggml_new_tensor_3d(ctxL, GGML_TYPE_F16, n_state/n_head, n_head, N)),
+                        0, 2, 1, 3);
+
+            // K * Q
+            struct ggml_tensor * KQ = ggml_mul_mat(ctxL, K, Q);
+
+            struct ggml_tensor * KQ_scaled =
+                ggml_scale(ctxL,
+                        KQ,
+                        ggml_new_f32(ctxL, 1.0f/sqrt(float(n_state)/n_head))
+                        );
+
+            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctxL, KQ_scaled);
+
+            //struct ggml_tensor * V_trans =
+            //    ggml_permute(ctxL,
+            //            ggml_cpy(ctxL,
+            //                Vcur,
+            //                ggml_new_tensor_3d(ctxL, GGML_TYPE_F16, n_state/n_head, n_head, N)),
+            //            1, 2, 0, 3);
+
+            //struct ggml_tensor * KQV = ggml_mul_mat(ctxL, V_trans, KQ_soft_max);
+
+            struct ggml_tensor * V =
+                ggml_cpy(ctxL,
+                        ggml_permute(ctxL,
+                            ggml_reshape_3d(ctxL,
+                                Vcur,
+                                n_state/n_head, n_head, N),
+                            0, 2, 1, 3),
+                        ggml_new_tensor_3d(ctxL, GGML_TYPE_F16, n_state/n_head, N, n_head)
+                        );
+
+            struct ggml_tensor * KQV = ggml_mul_mat(ctxL, ggml_transpose(ctxL, V), KQ_soft_max);
+#endif
+
+            struct ggml_tensor * KQV_merged = ggml_permute(ctxL, KQV, 0, 2, 1, 3);
+
+            cur = ggml_cpy(ctxL,
+                    KQV_merged,
+                    ggml_new_tensor_2d(ctxL, GGML_TYPE_F32, n_state, N));
+        }
+
+        // projection
+        {
+            cur = ggml_mul_mat(ctxL,
+                    layer.attn_ln_1_w,
+                    cur);
+
+            cur = ggml_add(ctxL,
+                    ggml_repeat(ctxL, layer.attn_ln_1_b, cur),
+                    cur);
+        }
+
+        // add the input
+        cur = ggml_add(ctxL, cur, inpL);
+
+        struct ggml_tensor * inpFF = cur;
+
+        // feed-forward network
+        {
+            // norm
+            {
+                cur = ggml_norm(ctxL, inpFF);
+
+                // cur = mlp_ln_w*cur + mlp_ln_b
+                cur = ggml_add(ctxL,
+                        ggml_mul(ctxL,
+                            ggml_repeat(ctxL, layer.mlp_ln_w, cur),
+                            cur),
+                        ggml_repeat(ctxL, layer.mlp_ln_b, cur));
+            }
+
+#ifdef USE_FLASH_FF
+            cur = ggml_flash_ff(ctxL,
+                    ggml_cpy(ctxL, cur, ggml_new_tensor_2d(ctxL, GGML_TYPE_F16, n_state, N)),
+                    layer.mlp_0_w, layer.mlp_0_b, layer.mlp_1_w, layer.mlp_1_b);
+#else
+            // fully connected
+            cur = ggml_mul_mat(ctxL,
+                    layer.mlp_0_w,
+                    cur);
+
+            cur = ggml_add(ctxL,
+                    ggml_repeat(ctxL, layer.mlp_0_b, cur),
+                    cur);
+
+            // GELU activation
+            cur = ggml_gelu(ctxL, cur);
+
+            // projection
+            cur = ggml_mul_mat(ctxL,
+                    layer.mlp_1_w,
+                    cur);
+
+            cur = ggml_add(ctxL,
+                    ggml_repeat(ctxL, layer.mlp_1_b, cur),
+                    cur);
+#endif
+        }
+
+        // output from this layer
+        struct ggml_tensor * inpO = ggml_add(ctxL, cur, inpFF);
+
+        {
+            struct ggml_cgraph gf = { .n_threads = n_threads };
+
+            ggml_build_forward_expand(&gf, inpO);
+            ggml_graph_compute       (ctxL, &gf);
+
+            //ggml_graph_print(&gf);
+        }
+
+        // TODO: this is a hack to have per-layer computation graphs - need to come up with something better
+        // input for next layer (inpO -> inpL)
+        memcpy(inpL->data, inpO->data, ggml_nbytes(inpL));
+        inpL->op = GGML_OP_NONE;
+        inpL->src0 = NULL;
+        inpL->src1 = NULL;
+
+        //printf("%s: - used_mem(%d) = %f MB\n", __func__, il, ggml_used_mem(ctxL)/1024.0/1024.0);
+
+        ggml_free(ctxL);
+    }
+
+    cur = inpL;
+
+    // norm
+    {
+        cur = ggml_norm(ctx0, cur);
+
+        // cur = ln_f_g*cur + ln_f_b
+        cur = ggml_add(ctx0,
+                ggml_mul(ctx0,
+                    ggml_repeat(ctx0, model.e_ln_w, cur),
+                    cur),
+                ggml_repeat(ctx0, model.e_ln_b, cur));
+    }
+
+    // run the computation
+    {
+        struct ggml_cgraph gf = { .n_threads = n_threads };
+
+        ggml_build_forward_expand(&gf, cur);
+        ggml_graph_compute       (ctx0, &gf);
+
+        //ggml_graph_print(&gf);
+    }
+
+    // cur
+    //{
+    //    printf("ne0 = %d\n", cur->ne[0]);
+    //    printf("ne1 = %d\n", cur->ne[1]);
+    //    for (int i = 0; i < 10; ++i) {
+    //        printf("%8.4f ", ((float *)(cur->data))[i]);
+    //    }
+    //    printf("... ");
+    //    for (int i = cur->ne[0] - 10; i < cur->ne[0]; ++i) {
+    //        printf("%8.4f ", ((float *)(cur->data))[i]);
+    //    }
+    //    printf("\n");
+    //}
+
+    // pre-compute cross-attention memory
+    {
+        struct ggml_cgraph gf = { .n_threads = n_threads };
+
+        // TODO: hack to disconnect the encoded features from the previous graph
+        cur->op = GGML_OP_NONE;
+        cur->src0 = NULL;
+        cur->src1 = NULL;
+
+        for (int il = 0; il < model.hparams.n_text_layer; ++il) {
+            auto & layer = model.layers_decoder[il];
+
+            struct ggml_tensor * Kcross = ggml_mul_mat(ctx0,
+                    layer.cross_attn_k_w,
+                    cur);
+
+            Kcross = ggml_scale(ctx0, Kcross, ggml_new_f32(ctx0, pow(float(n_state)/n_head, -0.25)));
+
+            struct ggml_tensor * Vcross = ggml_mul_mat(ctx0,
+                    layer.cross_attn_v_w,
+                    cur);
+
+            Vcross = ggml_add(ctx0,
+                    ggml_repeat(ctx0,
+                        layer.cross_attn_v_b,
+                        Vcross),
+                    Vcross);
+
+            struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_cross_k, n_state*n_ctx, (ggml_element_size(model.memory_cross_k)*n_state)*(il*n_ctx));
+            struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_cross_v, n_state*n_ctx, (ggml_element_size(model.memory_cross_v)*n_state)*(il*n_ctx));
+
+            ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcross, k));
+            ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcross, v));
+        }
+
+        ggml_graph_compute(ctx0, &gf);
+    }
+
+    ////////////////////////////////////////////////////////////////////////////
+
+    // output the features
+    assert(cur->type == GGML_TYPE_F32);
+    features.resize(cur->ne[0]*cur->ne[1]);
+    memcpy(features.data(), cur->data, features.size()*sizeof(float));
+
+    //printf("%s: used_mem = %f MB\n", __func__, ggml_used_mem(ctx0)/1024.0/1024.0);
+
+    ggml_free(ctx0);
+
+    return true;
+}
+
+// evaluate the decoder
+//
+// given text prompt + audio features -> predicts the probabilities for the next token
+//
+//   - model:      the model
+//   - n_threads:  number of threads to use
+//   - n_past:     prompt length
+//   - prompt:     text prompt
+//   - logits_out: output logits
+//   - probs_out:  output probabilities
+//
+bool whisper_decode(
+        const whisper_model & model,
+        const int n_threads,
+        const int n_past,
+        const std::vector<whisper_vocab::id> & prompt,
+              std::vector<float> & logits_out,
+              std::vector<float> & probs_out) {
+    const auto & hparams = model.hparams;
+
+    const int n_vocab = hparams.n_vocab;
+
+    const int n_ctx   = hparams.n_text_ctx;
+    const int n_state = hparams.n_text_state;
+    const int n_head  = hparams.n_text_head;
+    const int n_layer = hparams.n_text_layer;
+
+    const int N = prompt.size();
+    const int M = hparams.n_audio_ctx;
+
+    struct ggml_init_params params = {
+            .mem_size   = g_buf_compute.size(),
+            .mem_buffer = g_buf_compute.data(),
+        };
+
+    struct ggml_context * ctx0 = ggml_init(params);
+
+    struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
+    memcpy(embd->data, prompt.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;
+    }
+
+    // token encoding + position encoding
+    struct ggml_tensor * cur =
+        ggml_add(ctx0,
+                ggml_get_rows(ctx0, model.d_te, embd),
+                ggml_get_rows(ctx0, model.d_pe, position));
+
+    struct ggml_tensor * inpL = cur;
+
+    for (int il = 0; il < n_layer; ++il) {
+        const auto & layer = model.layers_decoder[il];
+
+        struct ggml_init_params paramsL = {
+            .mem_size   = g_buf_compute_layer.size(),
+            .mem_buffer = g_buf_compute_layer.data(),
+        };
+
+        struct ggml_context * ctxL = ggml_init(paramsL);
+        struct ggml_cgraph gf = { .n_threads = n_threads };
+
+        // norm
+        {
+            cur = ggml_norm(ctxL, inpL);
+
+            // cur = ln_0_w*cur + ln_0_b
+            cur = ggml_add(ctxL,
+                    ggml_mul(ctxL,
+                        ggml_repeat(ctxL, layer.attn_ln_0_w, cur),
+                        cur),
+                    ggml_repeat(ctxL, layer.attn_ln_0_b, cur));
+        }
+
+        // self-attention
+        {
+            struct ggml_tensor * Qcur = ggml_mul_mat(ctxL,
+                    layer.attn_q_w,
+                    cur);
+
+            Qcur = ggml_add(ctxL,
+                    ggml_repeat(ctxL,
+                        layer.attn_q_b,
+                        Qcur),
+                    Qcur);
+
+            Qcur = ggml_scale(ctxL, Qcur, ggml_new_f32(ctxL, pow(float(n_state)/n_head, -0.25)));
+
+            // note: no bias for Key
+            struct ggml_tensor * Kcur = ggml_mul_mat(ctxL,
+                    layer.attn_k_w,
+                    cur);
+
+            Kcur = ggml_scale(ctxL, Kcur, ggml_new_f32(ctxL, pow(float(n_state)/n_head, -0.25)));
+
+            struct ggml_tensor * Vcur = ggml_mul_mat(ctxL,
+                    layer.attn_v_w,
+                    cur);
+
+            Vcur = ggml_add(ctxL,
+                    ggml_repeat(ctxL,
+                        layer.attn_v_b,
+                        Vcur),
+                    Vcur);
+
+            // store key and value to memory
+            {
+                struct ggml_tensor * k = ggml_view_1d(ctxL, model.memory_k, N*n_state, (ggml_element_size(model.memory_k)*n_state)*(il*n_ctx + n_past));
+                struct ggml_tensor * v = ggml_view_1d(ctxL, model.memory_v, N*n_state, (ggml_element_size(model.memory_v)*n_state)*(il*n_ctx + n_past));
+
+                ggml_build_forward_expand(&gf, ggml_cpy(ctxL, Kcur, k));
+                ggml_build_forward_expand(&gf, ggml_cpy(ctxL, Vcur, v));
+            }
+
+            // ------
+
+            struct ggml_tensor * Q =
+                ggml_permute(ctxL,
+                        ggml_cpy(ctxL,
+                            Qcur,
+                            ggml_new_tensor_3d(ctxL, GGML_TYPE_F32, n_state/n_head, n_head, N)),
+                        0, 2, 1, 3);
+
+            struct ggml_tensor * K =
+                ggml_permute(ctxL,
+                        ggml_reshape_3d(ctxL,
+                            ggml_view_1d(ctxL, model.memory_k, (n_past + N)*n_state, il*n_ctx*ggml_element_size(model.memory_k)*n_state),
+                            n_state/n_head, n_head, n_past + N),
+                        0, 2, 1, 3);
+
+            // K * Q
+            struct ggml_tensor * KQ = ggml_mul_mat(ctxL, K, Q);
+
+            //struct ggml_tensor * KQ_scaled =
+            //    ggml_scale(ctxL,
+            //            KQ,
+            //            ggml_new_f32(ctxL, 1.0f/sqrt(float(n_state)/n_head))
+            //            );
+
+            struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctxL, KQ, n_past);
+
+            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctxL, KQ_masked);
+
+            struct ggml_tensor * V_trans =
+                ggml_permute(ctxL,
+                        ggml_reshape_3d(ctxL,
+                            ggml_view_1d(ctxL, model.memory_v, (n_past + N)*n_state, il*n_ctx*ggml_element_size(model.memory_v)*n_state),
+                            n_state/n_head, n_head, n_past + N),
+                        1, 2, 0, 3);
+
+            struct ggml_tensor * KQV = ggml_mul_mat(ctxL, V_trans, KQ_soft_max);
+
+            struct ggml_tensor * KQV_merged = ggml_permute(ctxL, KQV, 0, 2, 1, 3);
+
+            cur = ggml_cpy(ctxL,
+                    KQV_merged,
+                    ggml_new_tensor_2d(ctxL, GGML_TYPE_F32, n_state, N));
+        }
+
+        {
+            cur = ggml_mul_mat(ctxL,
+                    layer.attn_ln_1_w,
+                    cur);
+
+            cur = ggml_add(ctxL,
+                    ggml_repeat(ctxL, layer.attn_ln_1_b, cur),
+                    cur);
+        }
+
+        // add the input
+        struct ggml_tensor * inpCA = ggml_add(ctxL, cur, inpL);
+
+        // norm
+        {
+            cur = ggml_norm(ctxL, inpCA); // note: we use inpCA here
+
+            // cur = ln_0_w*cur + ln_0_b
+            cur = ggml_add(ctxL,
+                    ggml_mul(ctxL,
+                        ggml_repeat(ctxL, layer.cross_attn_ln_0_w, cur),
+                        cur),
+                    ggml_repeat(ctxL, layer.cross_attn_ln_0_b, cur));
+        }
+
+        // cross-attention
+        {
+            struct ggml_tensor * Qcur = ggml_mul_mat(ctxL,
+                    layer.cross_attn_q_w,
+                    cur);
+
+            Qcur = ggml_add(ctxL,
+                    ggml_repeat(ctxL,
+                        layer.cross_attn_q_b,
+                        Qcur),
+                    Qcur);
+
+            Qcur = ggml_scale(ctxL, Qcur, ggml_new_f32(ctxL, pow(float(n_state)/n_head, -0.25)));
+
+            // Kcross is already scaled
+            struct ggml_tensor * Kcross =
+                ggml_reshape_3d(ctxL,
+                        ggml_view_1d(ctxL, model.memory_cross_k, M*n_state, il*M*ggml_element_size(model.memory_cross_k)*n_state),
+                        n_state/n_head, n_head, M);
+
+            struct ggml_tensor * Vcross =
+                ggml_reshape_3d(ctxL,
+                        ggml_view_1d(ctxL, model.memory_cross_v, M*n_state, il*M*ggml_element_size(model.memory_cross_v)*n_state),
+                        n_state/n_head, n_head, M);
+
+            // ------
+
+            struct ggml_tensor * Q =
+                ggml_permute(ctxL,
+                        ggml_cpy(ctxL,
+                            Qcur,
+                            ggml_new_tensor_3d(ctxL, GGML_TYPE_F32, n_state/n_head, n_head, N)),
+                        0, 2, 1, 3);
+
+            struct ggml_tensor * K = ggml_permute(ctxL, Kcross, 0, 2, 1, 3);
+
+            // K * Q
+            struct ggml_tensor * KQ = ggml_mul_mat(ctxL, K, Q);
+
+            //struct ggml_tensor * KQ_scaled =
+            //    ggml_scale(ctxL,
+            //            KQ,
+            //            ggml_new_f32(ctxL, 1.0f/sqrt(float(n_state)/n_head))
+            //            );
+
+            // no masking for cross-attention
+            //struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctxL, KQ_scaled, n_past);
+
+            struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctxL, KQ);
+
+            struct ggml_tensor * V_trans = ggml_permute(ctxL, Vcross, 1, 2, 0, 3);
+
+            struct ggml_tensor * KQV = ggml_mul_mat(ctxL, V_trans, KQ_soft_max);
+
+            struct ggml_tensor * KQV_merged = ggml_permute(ctxL, KQV, 0, 2, 1, 3);
+
+            // cur = KQV_merged.contiguous().view(n_state, N)
+            cur = ggml_cpy(ctxL,
+                    KQV_merged,
+                    ggml_new_tensor_2d(ctxL, GGML_TYPE_F32, n_state, N));
+        }
+
+        // projection
+        {
+            cur = ggml_mul_mat(ctxL,
+                    layer.cross_attn_ln_1_w,
+                    cur);
+
+            cur = ggml_add(ctxL,
+                    ggml_repeat(ctxL, layer.cross_attn_ln_1_b, cur),
+                    cur);
+        }
+
+        // add the input
+        cur = ggml_add(ctxL, cur, inpCA);
+
+        struct ggml_tensor * inpFF = cur;
+
+        // feed-forward network
+        {
+            // norm
+            {
+                cur = ggml_norm(ctxL, inpFF);
+
+                // cur = mlp_ln_w*cur + mlp_ln_b
+                cur = ggml_add(ctxL,
+                        ggml_mul(ctxL,
+                            ggml_repeat(ctxL, layer.mlp_ln_w, cur),
+                            cur),
+                        ggml_repeat(ctxL, layer.mlp_ln_b, cur));
+            }
+
+            // fully connected
+            cur = ggml_mul_mat(ctxL,
+                    layer.mlp_0_w,
+                    cur);
+
+            cur = ggml_add(ctxL,
+                    ggml_repeat(ctxL, layer.mlp_0_b, cur),
+                    cur);
+
+            // GELU activation
+            cur = ggml_gelu(ctxL, cur);
+
+            // projection
+            cur = ggml_mul_mat(ctxL,
+                    layer.mlp_1_w,
+                    cur);
+
+            cur = ggml_add(ctxL,
+                    ggml_repeat(ctxL, layer.mlp_1_b, cur),
+                    cur);
+        }
+
+        // output from this layer
+        struct ggml_tensor * inpO = ggml_add(ctxL, cur, inpFF);
+
+        {
+            ggml_build_forward_expand(&gf, inpO);
+            ggml_graph_compute       (ctxL, &gf);
+
+            //ggml_graph_print(&gf);
+        }
+
+        // TODO: this is a hack to have per-layer computation graphs - need to come up with something better
+        // input for next layer (inpO -> inpL)
+        memcpy(inpL->data, inpO->data, ggml_nbytes(inpL));
+        inpL->op = GGML_OP_NONE;
+        inpL->src0 = NULL;
+        inpL->src1 = NULL;
+
+        if (N > 1) {
+            //printf("%s: - used_mem(%d) = %f MB\n", __func__, il, ggml_used_mem(ctxL)/1024.0/1024.0);
+        }
+
+        ggml_free(ctxL);
+    }
+
+    cur = inpL;
+
+    // norm
+    {
+        cur = ggml_norm(ctx0, cur);
+
+        cur = ggml_add(ctx0,
+                ggml_mul(ctx0,
+                    ggml_repeat(ctx0, model.d_ln_w, cur),
+                    cur),
+                ggml_repeat(ctx0, model.d_ln_b, cur));
+    }
+
+    struct ggml_tensor * logits = ggml_mul_mat(ctx0, model.d_te, cur);
+
+    // logits -> probs
+    cur = ggml_dup(ctx0, logits);
+    cur = ggml_soft_max(ctx0, cur); // in-place
+
+    // run the computation
+    {
+        struct ggml_cgraph gf = { .n_threads = n_threads };
+
+        ggml_build_forward_expand(&gf, cur);
+        ggml_graph_compute       (ctx0, &gf);
+    }
+
+    logits_out.resize(N*n_vocab);
+    memcpy(logits_out.data(), ggml_get_data(logits), sizeof(float)*N*n_vocab);
+
+    probs_out.resize(N*n_vocab);
+    memcpy(probs_out.data(), ggml_get_data(cur), sizeof(float)*N*n_vocab);
+
+    if (N > 1) {
+        //const float mem_per_token = ggml_used_mem(ctx0)/1024.0/1024.0/N;
+        //printf("%s: used_mem = %f MB / %f per token\n", __func__, ggml_used_mem(ctx0)/1024.0/1024.0, mem_per_token);
+        //printf("%s: max mem = %f MB\n", __func__, mem_per_token*model.hparams.n_text_ctx);
+    }
+
+    ggml_free(ctx0);
+
+    return true;
+}
+
+// the most basic sampling scheme - select the top token
+// TODO: beam search
+// TODO: temperature
+whisper_vocab::id whisper_sample_best(
+        const whisper_vocab & vocab,
+        const float * probs, bool need_timestamp) {
+    int n_logits = vocab.id_to_token.size();
+
+    std::vector<std::pair<double, whisper_vocab::id>> probs_id;
+    probs_id.reserve(n_logits);
+
+    for (int i = 0; i < n_logits; i++) {
+        probs_id.push_back(std::make_pair(probs[i], i));
+    }
+
+    const int top_k = 4;
+
+    // find the top K tokens
+    std::partial_sort(
+            probs_id.begin(),
+            probs_id.begin() + top_k, probs_id.end(),
+            [](const std::pair<double, whisper_vocab::id> & a, const std::pair<double, whisper_vocab::id> & b) {
+        return a.first > b.first;
+    });
+
+    probs_id.resize(top_k);
+
+    //printf("\n");
+    //for (int i = 0; i < (int) probs_id.size(); i++) {
+    //    printf("%d: '%s' %f, %d\n", i, vocab.id_to_token.at(probs_id[i].second).c_str(), probs_id[i].first, probs_id[i].second);
+    //}
+
+    if (need_timestamp) {
+        // at the end of the 30-second audio segment, we start giving preference to time tokens
+        for (int i = 0; i < top_k; i++) {
+            if (probs_id[i].second > vocab.token_beg + 1300 && probs_id[i].first > 0.01*probs_id[0].first) {
+                return probs_id[i].second;
+            }
+        }
+    }
+
+    int res = 0;
+    while ((probs_id[res].second == vocab.token_sot ||
+            probs_id[res].second == vocab.token_solm ||
+            probs_id[res].second == vocab.token_not) &&
+            res < (int) probs_id.size() - 1) {
+        res++;
+    }
+
+    return probs_id[res].second;
+}
+
+// samples only from the timestamps tokens
+whisper_vocab::id whisper_sample_timestamp(
+        const whisper_vocab & vocab,
+        const float * probs) {
+    int n_logits = vocab.id_to_token.size();
+
+    std::vector<std::pair<double, whisper_vocab::id>> probs_id;
+    probs_id.reserve(n_logits);
+
+    for (int i = vocab.token_beg + 1; i < n_logits; i++) {
+        probs_id.push_back(std::make_pair(probs[i], i));
+    }
+
+    const int top_k = 10;
+
+    // find the top K tokens
+    std::partial_sort(
+            probs_id.begin(),
+            probs_id.begin() + top_k, probs_id.end(),
+            [](const std::pair<double, whisper_vocab::id> & a, const std::pair<double, whisper_vocab::id> & b) {
+        return a.first > b.first;
+    });
+
+    probs_id.resize(top_k);
+
+    //printf("\n");
+    //for (int i = 0; i < (int) probs_id.size(); i++) {
+    //    printf("%d: '%s' %f, %d\n", i, vocab.id_to_token.at(probs_id[i].second).c_str(), probs_id[i].first, probs_id[i].second);
+    //}
+
+    return probs_id[0].second;
+}
+
+// naive Discrete Fourier Transform
+// input is real-valued
+// output is complex-valued
+void dft(const std::vector<float> & in, std::vector<float> & out) {
+    int N = in.size();
+
+    out.resize(N*2);
+
+    for (int k = 0; k < N; k++) {
+        float re = 0;
+        float im = 0;
+
+        for (int n = 0; n < N; n++) {
+            float angle = 2*M_PI*k*n/N;
+            re += in[n]*cos(angle);
+            im -= in[n]*sin(angle);
+        }
+
+        out[k*2 + 0] = re;
+        out[k*2 + 1] = im;
+    }
+}
+
+// Cooley-Tukey FFT
+// poor man's implmentation - use something better
+// input is real-valued
+// output is complex-valued
+void fft(const std::vector<float> & in, std::vector<float> & out) {
+    out.resize(in.size()*2);
+
+    int N = in.size();
+
+    if (N == 1) {
+        out[0] = in[0];
+        out[1] = 0;
+        return;
+    }
+
+    if (N%2 == 1) {
+        dft(in, out);
+        return;
+    }
+
+    std::vector<float> even;
+    std::vector<float> odd;
+
+    for (int i = 0; i < N; i++) {
+        if (i % 2 == 0) {
+            even.push_back(in[i]);
+        } else {
+            odd.push_back(in[i]);
+        }
+    }
+
+    std::vector<float> even_fft;
+    std::vector<float> odd_fft;
+
+    fft(even, even_fft);
+    fft(odd, odd_fft);
+
+    for (int k = 0; k < N/2; k++) {
+        float theta = 2*M_PI*k/N;
+
+        float re = cos(theta);
+        float im = -sin(theta);
+
+        float re_odd = odd_fft[2*k + 0];
+        float im_odd = odd_fft[2*k + 1];
+
+        out[2*k + 0] = even_fft[2*k + 0] + re*re_odd - im*im_odd;
+        out[2*k + 1] = even_fft[2*k + 1] + re*im_odd + im*re_odd;
+
+        out[2*(k + N/2) + 0] = even_fft[2*k + 0] - re*re_odd + im*im_odd;
+        out[2*(k + N/2) + 1] = even_fft[2*k + 1] - re*im_odd - im*re_odd;
+    }
+}
+
+// ref: https://github.com/openai/whisper/blob/main/whisper/audio.py#L92-L124
+bool log_mel_spectrogram(
+    const std::vector<float> sf32,
+    const int sample_rate,
+    const int fft_size,
+    const int fft_step,
+    const int n_mel,
+    const int n_threads,
+    const whisper_filters & filters,
+    whisper_mel & mel) {
+    const int n_sample = sf32.size();
+    const float * samples = sf32.data();
+
+    // Hanning window
+    std::vector<float> hann;
+    hann.resize(fft_size);
+    for (int i = 0; i < fft_size; i++) {
+        hann[i] = 0.5*(1.0 - cos((2.0*M_PI*i)/(fft_size)));
+    }
+
+    mel.n_mel = n_mel;
+    mel.n_len = (n_sample)/fft_step;
+    mel.data.resize(mel.n_mel*mel.n_len);
+
+    const int n_fft = 1 + fft_size/2;
+
+    //printf("%s: n_sample = %d, n_len = %d\n", __func__, n_sample, mel.n_len);
+    //printf("%s: recording length: %f s\n", __func__, (float) n_sample/sample_rate);
+
+    std::vector<std::thread> workers(n_threads);
+    for (int iw = 0; iw < n_threads; ++iw) {
+        workers[iw] = std::thread([&](int ith) {
+            std::vector<float> fft_in;
+            fft_in.resize(fft_size);
+            for (int i = 0; i < fft_size; i++) {
+                fft_in[i] = 0.0;
+            }
+
+            std::vector<float> fft_out;
+            fft_out.resize(2*fft_size);
+
+            for (int i = ith; i < mel.n_len; i += n_threads) {
+                const int offset = i*fft_step;
+
+                // apply Hanning window
+                for (int j = 0; j < fft_size; j++) {
+                    if (offset + j < n_sample) {
+                        fft_in[j] = hann[j]*samples[offset + j];
+                    } else {
+                        fft_in[j] = 0.0;
+                    }
+                }
+
+                // FFT -> mag^2
+                fft(fft_in, fft_out);
+
+                for (int j = 0; j < fft_size; j++) {
+                    fft_out[j] = (fft_out[2*j + 0]*fft_out[2*j + 0] + fft_out[2*j + 1]*fft_out[2*j + 1]);
+                }
+                for (int j = 1; j < fft_size/2; j++) {
+                    fft_out[j] += fft_out[fft_size - j];
+                }
+
+                // mel spectrogram
+                for (int j = 0; j < mel.n_mel; j++) {
+                    double sum = 0.0;
+
+                    for (int k = 0; k < n_fft; k++) {
+                        sum += fft_out[k]*filters.data[j*n_fft + k];
+                    }
+                    if (sum < 1e-10) {
+                        sum = 1e-10;
+                    }
+
+                    sum = log10(sum);
+
+                    mel.data[j*mel.n_len + i] = sum;
+                }
+            }
+        }, iw);
+    }
+
+    for (int iw = 0; iw < n_threads; ++iw) {
+        workers[iw].join();
+    }
+
+    // clamping and normalization
+    double mmax = -1e20;
+    for (int i = 0; i < mel.n_mel*mel.n_len; i++) {
+        if (mel.data[i] > mmax) {
+            mmax = mel.data[i];
+        }
+    }
+
+    mmax -= 8.0;
+
+    for (int i = 0; i < mel.n_mel*mel.n_len; i++) {
+        if (mel.data[i] < mmax) {
+            mel.data[i] = mmax;
+        }
+
+        mel.data[i] = (mel.data[i] + 4.0)/4.0;
+    }
+
+    return true;
+}
+
+//  500 -> 00:05.000
+// 6000 -> 01:00.000
+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);
+}
+
+//
+// SDL Audio capture
+//
+
+SDL_AudioDeviceID g_dev_id_in = 0;
+
+bool audio_sdl_init(const int capture_id) {
+    if (g_dev_id_in) {
+        fprintf(stderr, "%s: already initialized\n", __func__);
+        return false;
+    }
+
+    if (g_dev_id_in == 0) {
+        SDL_LogSetPriority(SDL_LOG_CATEGORY_APPLICATION, SDL_LOG_PRIORITY_INFO);
+
+        if (SDL_Init(SDL_INIT_AUDIO) < 0) {
+            SDL_LogError(SDL_LOG_CATEGORY_APPLICATION, "Couldn't initialize SDL: %s\n", SDL_GetError());
+            return (1);
+        }
+
+        SDL_SetHintWithPriority(SDL_HINT_AUDIO_RESAMPLING_MODE, "medium", SDL_HINT_OVERRIDE);
+
+        {
+            int nDevices = SDL_GetNumAudioDevices(SDL_TRUE);
+            printf("%s: found %d capture devices:\n", __func__, nDevices);
+            for (int i = 0; i < nDevices; i++) {
+                printf("%s:    - Capture device #%d: '%s'\n", __func__, i, SDL_GetAudioDeviceName(i, SDL_TRUE));
+            }
+        }
+    }
+
+    if (g_dev_id_in == 0) {
+        SDL_AudioSpec capture_spec_requested;
+        SDL_AudioSpec capture_spec_obtained;
+
+        SDL_zero(capture_spec_requested);
+        SDL_zero(capture_spec_obtained);
+
+        capture_spec_requested.freq     = SAMPLE_RATE;
+        capture_spec_requested.format   = AUDIO_F32;
+        capture_spec_requested.channels = 1;
+        capture_spec_requested.samples  = 1024;
+
+        if (capture_id >= 0) {
+            printf("%s: attempt to open capture device %d : '%s' ...\n", __func__, capture_id, SDL_GetAudioDeviceName(capture_id, SDL_TRUE));
+            g_dev_id_in = SDL_OpenAudioDevice(SDL_GetAudioDeviceName(capture_id, SDL_TRUE), SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
+        } else {
+            printf("%s: attempt to open default capture device ...\n", __func__);
+            g_dev_id_in = SDL_OpenAudioDevice(nullptr, SDL_TRUE, &capture_spec_requested, &capture_spec_obtained, 0);
+        }
+        if (!g_dev_id_in) {
+            printf("%s: couldn't open an audio device for capture: %s!\n", __func__, SDL_GetError());
+            g_dev_id_in = 0;
+        } else {
+            printf("%s: obtained spec for input device (SDL Id = %d):\n", __func__, g_dev_id_in);
+            printf("%s:     - sample rate:       %d\n", __func__, capture_spec_obtained.freq);
+            printf("%s:     - format:            %d (required: %d)\n", __func__, capture_spec_obtained.format, capture_spec_requested.format);
+            printf("%s:     - channels:          %d (required: %d)\n", __func__, capture_spec_obtained.channels, capture_spec_requested.channels);
+            printf("%s:     - samples per frame: %d\n", __func__, capture_spec_obtained.samples);
+        }
+    }
+
+
+    return true;
+}
+
+///////////////////////////
+
+int main(int argc, char ** argv) {
+    const int64_t t_main_start_us = ggml_time_us();
+
+    whisper_params params;
+
+    if (whisper_params_parse(argc, argv, params) == false) {
+        return 1;
+    }
+
+    if (params.seed < 0) {
+        params.seed = time(NULL);
+    }
+
+    // init audio
+
+    if (!audio_sdl_init(-1)) {
+        fprintf(stderr, "%s: audio_sdl_init() failed!\n", __func__);
+        return 1;
+    }
+
+    // model loading
+
+    //printf("%s: seed = %d\n", __func__, params.seed);
+
+    int64_t t_load_us   = 0;
+    int64_t t_mel_us    = 0;
+    int64_t t_sample_us = 0;
+    int64_t t_encode_us = 0;
+    int64_t t_decode_us = 0;
+
+    whisper_vocab vocab;
+    whisper_model model;
+
+    // load the model
+    {
+        const int64_t t_start_us = ggml_time_us();
+
+        if (!whisper_model_load(params.model, model, vocab)) {
+            fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
+            whisper_print_usage(argc, argv, {});
+            return 1;
+        }
+
+        t_load_us = ggml_time_us() - t_start_us;
+    }
+
+    const int n_samples_30s = 30*SAMPLE_RATE;
+    std::vector<float> pcmf32(n_samples_30s, 0.0f);
+    std::vector<float> pcmf32_old;
+
+    // print some info about the processing
+    {
+        printf("\n");
+        if (!vocab.is_multilingual()) {
+            if (params.language != "en" || params.translate) {
+                params.language = "en";
+                params.translate = false;
+                printf("%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
+            }
+        }
+        printf("%s: processing %d samples (%.1f sec), %d threads, lang = %s, task = %s, timestamps = %d ...\n",
+                __func__, int(pcmf32.size()), float(pcmf32.size())/SAMPLE_RATE, params.n_threads,
+                g_lang.at(params.language).second.c_str(),
+                params.translate ? "translate" : "transcribe",
+                params.no_timestamps ? 0 : 1);
+        printf("\n");
+    }
+
+    SDL_PauseAudioDevice(g_dev_id_in, 0);
+
+    // main audio loop
+    while (true) {
+        // process 3 seconds of new audio
+        while ((int) SDL_GetQueuedAudioSize(g_dev_id_in) < 3*SAMPLE_RATE*sizeof(float)) {
+            SDL_Delay(1);
+        }
+        const int n_samples_new = SDL_GetQueuedAudioSize(g_dev_id_in)/sizeof(float);
+
+        // take one second from previous iteration
+        // TODO: better strategy
+        const int n_samples_take = std::min((int) pcmf32_old.size(), std::max(0, n_samples_30s/30 - n_samples_new));
+
+        //printf("processing: take = %d, new = %d, old = %d\n", n_samples_take, n_samples_new, (int) pcmf32_old.size());
+
+        pcmf32.resize(n_samples_new + n_samples_take);
+
+        for (int i = 0; i < n_samples_take; i++) {
+            pcmf32[i] = pcmf32_old[pcmf32_old.size() - n_samples_take + i];
+        }
+
+        SDL_DequeueAudio(g_dev_id_in, pcmf32.data() + n_samples_take, n_samples_new*sizeof(float));
+
+        pcmf32_old = pcmf32;
+
+        // compute log mel spectrogram
+        whisper_mel mel_inp;
+        {
+            const int64_t t_start_us = ggml_time_us();
+
+            log_mel_spectrogram(pcmf32, SAMPLE_RATE, N_FFT, HOP_LENGTH, N_MEL, params.n_threads, model.filters, mel_inp);
+
+            t_mel_us = ggml_time_us() - t_start_us;
+        }
+
+        // the accumulated text context so far
+        std::vector<whisper_vocab::id> prompt_past = { };
+
+        // these tokens determine the task that will be performed
+        std::vector<whisper_vocab::id> prompt_init = { vocab.token_sot };
+        if (vocab.is_multilingual()) {
+            prompt_init.push_back(vocab.token_sot + 1 + g_lang.at(params.language).first);
+            if (params.translate) {
+                prompt_init.push_back(vocab.token_translate);
+            } else {
+                prompt_init.push_back(vocab.token_transcribe);
+            }
+        }
+
+        // the generated text including timestamps
+        //std::vector<whisper_result> result_all;
+
+        // main loop
+        int seek = 0;
+        while (true) {
+            if (seek >= mel_inp.n_len) {
+                break;
+            }
+
+            // encode audio features starting at offset seek
+            std::vector<float> features;
+            {
+                const int64_t t_start_us = ggml_time_us();
+
+                if (!whisper_encode(model, params.n_threads, seek, mel_inp, features)) {
+                    fprintf(stderr, "%s: failed to eval\n", __func__);
+                    return 1;
+                }
+
+                t_encode_us += ggml_time_us() - t_start_us;
+            }
+
+            std::vector<float> probs;
+            std::vector<float> logits;
+
+            std::vector<whisper_vocab::id> prompt;
+
+            int n_past = 0;
+
+            // if we have already generated some text, use it as a prompt to condition the next generation
+            if (prompt_past.size() > 0) {
+                int n_take = std::min(model.hparams.n_text_ctx/2, int(prompt_past.size()));
+
+                prompt = { vocab.token_prev };
+                prompt.insert(prompt.begin() + 1, prompt_past.end() - n_take, prompt_past.end());
+
+                prompt_past.clear();
+                prompt_past.insert(prompt_past.end(), prompt.begin() + 1, prompt.end());
+            }
+
+            prompt.insert(prompt.end(), prompt_init.begin(), prompt_init.end());
+
+            bool done = false;
+            int seek_delta = 100*CHUNK_SIZE;
+            whisper_vocab::id last_id = 0;
+
+            // print the prompt
+            //printf("\n\n");
+            //for (int i = 0; i < prompt.size(); i++) {
+            //    printf("%s: prompt[%d] = %s\n", __func__, i, vocab.id_to_token[prompt[i]].c_str());
+            //}
+            //printf("\n\n");
+
+            // the accumulated transcription in the current interation
+            int result_len = 0;
+            std::vector<whisper_result> result_cur;
+
+            for (int i = 0; i < model.hparams.n_text_ctx/2 - 4; ++i) {
+                // decode
+                if (prompt.size() > 0) {
+                    const int64_t t_start_us = ggml_time_us();
+
+                    if (!whisper_decode(model, params.n_threads, n_past, prompt, logits, probs)) {
+                        fprintf(stderr, "%s: failed to eval\n", __func__);
+                        return 1;
+                    }
+
+                    t_decode_us += ggml_time_us() - t_start_us;
+                }
+
+                n_past += prompt.size();
+                prompt.clear();
+
+                // very basic greedy sampling strategy:
+                //
+                //   - always take the most probable token
+                //
+                // more sophisticated sampling strategies could be implemented here, but we keep it simple
+                // feel free to experiment!
+                //
+                {
+                    const int n_vocab = model.hparams.n_vocab;
+
+                    whisper_vocab::id id  = 0;
+                    whisper_vocab::id tid = vocab.token_beg;
+
+                    {
+                        const int64_t t_start_sample_us = ggml_time_us();
+
+                        id = whisper_sample_best(vocab, probs.data() + (probs.size() - n_vocab), result_len == 0);
+                        if (i > 0) {
+                            tid = whisper_sample_timestamp(vocab, probs.data() + (probs.size() - n_vocab));
+                        }
+
+                        t_sample_us += ggml_time_us() - t_start_sample_us;
+                    }
+
+                    // update sliding window
+                    if (id > vocab.token_beg) {
+                        seek_delta = 2*(id - vocab.token_beg);
+                        result_len = i + 1;
+                    }
+                    last_id = id;
+
+                    // add it to the context
+                    prompt.push_back(id);
+                    result_cur.push_back({ id, seek + 2*(tid - vocab.token_beg) });
+
+                    //printf("%s: %s\n", __func__, vocab.id_to_token[id].c_str());
+
+                    // end of text token
+                    if (id == vocab.token_eot) {
+                        break;
+                    }
+                }
+
+                if (done) {
+                    break;
+                }
+            }
+
+            result_cur.resize(result_len);
+            //result_all.insert(result_all.end(), result_cur.begin(), result_cur.end());
+
+            for (const auto & r : result_cur) {
+                prompt_past.push_back(r.id);
+            }
+
+            // print the text from this iteration
+            if (result_cur.size() > 0) {
+                auto t0 = result_cur.front().t;
+
+                std::string text = "";
+                for (int i = 0; i < result_cur.size(); i++) {
+                    if (params.print_special_tokens == false && result_cur[i].id >= vocab.token_eot) {
+                    } else {
+                        text += vocab.id_to_token[result_cur[i].id];
+                    }
+                    if (result_cur[i].id > vocab.token_beg) {
+                        const auto t1 = result_cur[i].t;
+                        if (!text.empty()) {
+                            if (params.no_timestamps) {
+                                printf ("%s", text.c_str());
+                                fflush(stdout);
+                            } else {
+                                printf ("[%s --> %s]  %s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), text.c_str());
+                            }
+                        }
+                        text = "";
+                        while (result_cur[i].id > vocab.token_beg && i < result_cur.size()) {
+                            i++;
+                        }
+                        i--;
+                        t0 = result_cur[i].t;
+                    }
+                }
+
+                if (!text.empty()) {
+                    printf ("[%s --> %s]  %s\n", to_timestamp(t0).c_str(), to_timestamp(seek + seek_delta).c_str(), text.c_str());
+                }
+            }
+
+            seek += seek_delta;
+        }
+    }
+
+    // WIP: attempt for per-token timestamps
+    //if (!params.no_timestamps && result_all.size() > 0) {
+    //    const int64_t dt = 500; // 5 second intervals
+
+    //    int i0 = 0;
+
+    //    int64_t t0 = result_all[0].t;
+    //    int64_t t1 = t0;
+
+    //    printf("\n\n");
+    //    for (int i = 0; i < result_all.size(); ++i) {
+    //        printf("'%s' -> %lld\n", vocab.id_to_token[result_all[i].id].c_str(), result_all[i].t);
+    //        if (result_all[i].t - t0 > dt) {
+    //            t1 = result_all[i - 1].t;
+    //            printf("[%s --> %s] ", to_timestamp(t0).c_str(), to_timestamp(t1).c_str());
+    //            for (int j = i0; j < i; ++j) {
+    //                printf("%s", vocab.id_to_token.at(result_all[j].id).c_str());
+    //            }
+    //            printf("\n");
+    //            i0 = i;
+    //            t0 = result_all[i].t;
+    //        }
+    //    }
+    //}
+
+    // report timing
+    {
+        const int64_t t_main_end_us = ggml_time_us();
+
+        printf("\n\n");
+        printf("%s:     load time = %8.2f ms\n", __func__, t_load_us/1000.0f);
+        printf("%s:      mel time = %8.2f ms\n", __func__, t_mel_us/1000.0f);
+        printf("%s:   sample time = %8.2f ms\n", __func__, t_sample_us/1000.0f);
+        printf("%s:   encode time = %8.2f ms / %.2f ms per layer\n", __func__, t_encode_us/1000.0f, t_encode_us/1000.0f/model.hparams.n_audio_layer);
+        printf("%s:   decode time = %8.2f ms\n", __func__, t_decode_us/1000.0f);
+        printf("%s:    total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0f);
+    }
+
+    ggml_free(model.ctx);
+
+    return 0;
+}