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	#include "whisper.h"

	#ifdef WHISPER_USE_COREML
	#include "coreml/whisper-encoder.h"
	#endif

	#ifdef GGML_USE_METAL
	#include "ggml-metal.h"
	#endif

	#ifdef GGML_USE_CUDA
	#include "ggml-cuda.h"
	#endif

	#ifdef GGML_USE_SYCL
	#include "ggml-sycl.h"
	#endif

	#ifdef WHISPER_USE_OPENVINO
	#include "openvino/whisper-openvino-encoder.h"
	#endif

	#include "ggml.h"
	#include "ggml-cpu.h"
	#include "ggml-alloc.h"
	#include "ggml-backend.h"

	#include <atomic>
	#include <algorithm>
	#include <cassert>
	#define _USE_MATH_DEFINES
	#include <math.h>
	#include <cmath>
	#include <cstdio>
	#include <cstdarg>
	#include <cstring>
	#include <fstream>
	#include <map>
	#include <set>
	#include <string>
	#include <thread>
	#include <vector>
	#include <regex>
	#include <random>
	#include <functional>
	#include <codecvt>

	#if defined(_MSC_VER)
	#pragma warning(disable: 4244 4267) // possible loss of data
	#endif

	#if defined(GGML_BIG_ENDIAN)
	#include <bit>

	template<typename T>
	static T byteswap(T value) {
	return std::byteswap(value);
	}

	template<>
	float byteswap(float value) {
	return std::bit_cast<float>(byteswap(std::bit_cast<std::uint32_t>(value)));
	}

	template<typename T>
	static void byteswap_tensor_data(ggml_tensor * tensor) {
	T * datum = reinterpret_cast<T *>(tensor->data);
	for (int i = 0; i < ggml_nelements(tensor); i++) {
	datum[i] = byteswap(datum[i]);
	}
	}

	static void byteswap_tensor(ggml_tensor * tensor) {
	switch (tensor->type) {
	case GGML_TYPE_I16: {
	byteswap_tensor_data<int16_t>(tensor);
	break;
	}
	case GGML_TYPE_F16: {
	byteswap_tensor_data<ggml_fp16_t>(tensor);
	break;
	}
	case GGML_TYPE_I32: {
	byteswap_tensor_data<int32_t>(tensor);
	break;
	}
	case GGML_TYPE_F32: {
	byteswap_tensor_data<float>(tensor);
	break;
	}
	default: { // GML_TYPE_I8
	break;
	}
	}
	}

	#define BYTESWAP_VALUE(d) d = byteswap(d)
	#define BYTESWAP_FILTERS(f) \
	do { \
	for (auto & datum : f.data) { \
	datum = byteswap(datum); \
	} \
	} while (0)
	#define BYTESWAP_TENSOR(t) \
	do { \
	byteswap_tensor(t); \
	} while (0)
	#else
	#define BYTESWAP_VALUE(d) do {} while (0)
	#define BYTESWAP_FILTERS(f) do {} while (0)
	#define BYTESWAP_TENSOR(t) do {} while (0)
	#endif

	#ifdef __GNUC__
	#ifdef __MINGW32__
	#define WHISPER_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
	#else
	#define WHISPER_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
	#endif
	#else
	#define WHISPER_ATTRIBUTE_FORMAT(...)
	#endif

	//
	// logging
	//

	WHISPER_ATTRIBUTE_FORMAT(2, 3)
	static void whisper_log_internal (ggml_log_level level, const char * format, ...);
	static void whisper_log_callback_default(ggml_log_level level, const char * text, void * user_data);

	#define WHISPER_LOG_ERROR(...) whisper_log_internal(GGML_LOG_LEVEL_ERROR, __VA_ARGS__)
	#define WHISPER_LOG_WARN(...) whisper_log_internal(GGML_LOG_LEVEL_WARN , __VA_ARGS__)
	#define WHISPER_LOG_INFO(...) whisper_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__)

	// define this to enable verbose trace logging - useful for debugging purposes
	//#define WHISPER_DEBUG

	#if defined(WHISPER_DEBUG)
	#define WHISPER_LOG_DEBUG(...) whisper_log_internal(GGML_LOG_LEVEL_DEBUG, __VA_ARGS__)
	#else
	#define WHISPER_LOG_DEBUG(...)
	#endif

	#define WHISPER_ASSERT(x) \
	do { \
	if (!(x)) { \
	WHISPER_LOG_ERROR("WHISPER_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
	abort(); \
	} \
	} while (0)

	//#define WHISPER_USE_FLASH_FF
	#define WHISPER_MAX_DECODERS 8
	#define WHISPER_MAX_NODES 4096

	//
	// ggml helpers
	//

	static bool ggml_graph_compute_helper(
	struct ggml_cgraph * graph,
	std::vector<uint8_t> & buf,
	int n_threads,
	ggml_abort_callback abort_callback,
	void * abort_callback_data) {
	struct ggml_cplan plan = ggml_graph_plan(graph, n_threads, nullptr);

	plan.abort_callback = abort_callback;
	plan.abort_callback_data = abort_callback_data;

	if (plan.work_size > 0) {
	buf.resize(plan.work_size);
	plan.work_data = buf.data();
	}

	return ggml_graph_compute(graph, &plan);
	}

	static bool ggml_graph_compute_helper(
	struct ggml_backend * backend,
	struct ggml_cgraph * graph,
	int n_threads) {
	if (ggml_backend_is_cpu(backend)) {
	ggml_backend_cpu_set_n_threads(backend, n_threads);
	}
	// #ifdef GGML_USE_METAL
	// if (ggml_backend_is_metal(backend)) {
	// ggml_backend_metal_set_n_cb(backend, n_threads);
	// }
	// #endif
	return ggml_backend_graph_compute(backend, graph) == GGML_STATUS_SUCCESS;
	}

	// faster matrix multiplications for tensors that do not have dimension 0 divisible by "pad"
	// the idea is to represent the original matrix multiplication:
	//
	// Z = X @ Y
	//
	// with the sum of two matrix multiplications:
	//
	// Z = (X_0 @ Y_0) + (X_1 @ Y_1)
	//
	// here X_0 and Y_0 are views of X and Y that have dimension 0 divisible by "pad"
	// and X_1 and Y_1 are the remaining views. X_1 and Y_1 end up being small matrices that can be processed with more
	// general-purpose kernels
	//
	static struct ggml_tensor * ggml_mul_mat_pad(struct ggml_context * ctx, struct ggml_tensor * x, struct ggml_tensor * y, int pad = 32) {
	// use padding only if dimension 0 is at least 8 times larger than the padding
	// else we won't get much benefit from the optimization
	const int n_pad_req = 8;

	if (x->ne[0] % pad == 0 \|\| x->ne[0] / pad < n_pad_req) {
	return ggml_mul_mat(ctx, x, y);
	}

	struct ggml_tensor * x_0 = ggml_view_3d(ctx, x, (x->ne[0]/pad)*pad, x->ne[1], x->ne[2], x->nb[1], x->nb[2], 0);
	struct ggml_tensor * x_1 = ggml_view_3d(ctx, x, x->ne[0]%pad, x->ne[1], x->ne[2], x->nb[1], x->nb[2], x_0->ne[0]*x_0->nb[0]);

	struct ggml_tensor * y_0 = ggml_view_3d(ctx, y, (y->ne[0]/pad)*pad, y->ne[1], y->ne[2], y->nb[1], y->nb[2], 0);
	struct ggml_tensor * y_1 = ggml_view_3d(ctx, y, y->ne[0]%pad, y->ne[1], y->ne[2], y->nb[1], y->nb[2], y_0->ne[0]*y_0->nb[0]);

	return ggml_add(ctx,
	ggml_mul_mat(ctx, x_0, y_0),
	ggml_mul_mat(ctx, x_1, y_1));
	}

	// TODO: check if other platforms can benefit from this optimization
	// TODO: CUDA is currently broken - seems ggml_mul_mat does not handle views correctly
	#if defined(GGML_USE_METAL)
	#define ggml_mul_mat ggml_mul_mat_pad
	#endif

	// available whisper models
	enum e_model {
	MODEL_UNKNOWN,
	MODEL_TINY,
	MODEL_BASE,
	MODEL_SMALL,
	MODEL_MEDIUM,
	MODEL_LARGE,
	};

	static const std::map<e_model, std::string> g_model_name = {
	{ MODEL_UNKNOWN, "unknown" },
	{ MODEL_TINY, "tiny" },
	{ MODEL_BASE, "base" },
	{ MODEL_SMALL, "small" },
	{ MODEL_MEDIUM, "medium" },
	{ MODEL_LARGE, "large" },
	};

	static 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", } },
	{ "he", { 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", } },
	{ "yue", { 99, "cantonese", } },
	};

	// [EXPERIMENTAL] Token-level timestamps with DTW
	static const whisper_ahead g_aheads_tiny_en[] = { {1, 0}, {2, 0}, {2, 5}, {3, 0}, {3, 1}, {3, 2}, {3, 3}, {3, 4} };
	static const whisper_ahead g_aheads_tiny[] = { {2, 2}, {3, 0}, {3, 2}, {3, 3}, {3, 4}, {3, 5} };
	static const whisper_ahead g_aheads_base_en[] = { {3, 3}, {4, 7}, {5, 1}, {5, 5}, {5, 7} };
	static const whisper_ahead g_aheads_base[] = { {3, 1}, {4, 2}, {4, 3}, {4, 7}, {5, 1}, {5, 2}, {5, 4}, {5, 6} };
	static const whisper_ahead g_aheads_small_en[] = { {6, 6}, {7, 0}, {7, 3}, {7, 8}, {8, 2}, {8, 5}, {8, 7}, {9, 0}, {9, 4}, {9, 8}, {9, 10}, {10, 0}, {10, 1}, {10, 2}, {10, 3}, {10, 6}, {10, 11}, {11, 2}, {11, 4} };
	static const whisper_ahead g_aheads_small[] = { {5, 3}, {5, 9}, {8, 0}, {8, 4}, {8, 7}, {8, 8}, {9, 0}, {9, 7}, {9, 9}, {10, 5} };
	static const whisper_ahead g_aheads_medium_en[] = { {11, 4}, {14, 1}, {14, 12}, {14, 14}, {15, 4}, {16, 0}, {16, 4}, {16, 9}, {17, 12}, {17, 14}, {18, 7}, {18, 10}, {18, 15}, {20, 0}, {20, 3}, {20, 9}, {20, 14}, {21, 12} };
	static const whisper_ahead g_aheads_medium[] = { {13, 15}, {15, 4}, {15, 15}, {16, 1}, {20, 0}, {23, 4} };
	static const whisper_ahead g_aheads_large_v1[] = { {9, 19}, {11, 2}, {11, 4}, {11, 17}, {22, 7}, {22, 11}, {22, 17}, {23, 2}, {23, 15} };
	static const whisper_ahead g_aheads_large_v2[] = { {10, 12}, {13, 17}, {16, 11}, {16, 12}, {16, 13}, {17, 15}, {17, 16}, {18, 4}, {18, 11}, {18, 19}, {19, 11}, {21, 2}, {21, 3}, {22, 3}, {22, 9}, {22, 12}, {23, 5}, {23, 7}, {23, 13}, {25, 5}, {26, 1}, {26, 12}, {27, 15} };
	static const whisper_ahead g_aheads_large_v3[] = { {7, 0}, {10, 17}, {12, 18}, {13, 12}, {16, 1}, {17, 14}, {19, 11}, {21, 4}, {24, 1}, {25, 6} };

	static const std::map<whisper_alignment_heads_preset, whisper_aheads> g_aheads {
	{ WHISPER_AHEADS_TINY_EN, { 8, g_aheads_tiny_en } },
	{ WHISPER_AHEADS_TINY, { 6, g_aheads_tiny } },
	{ WHISPER_AHEADS_BASE_EN, { 5, g_aheads_base_en } },
	{ WHISPER_AHEADS_BASE, { 8, g_aheads_base } },
	{ WHISPER_AHEADS_SMALL_EN, { 19, g_aheads_small_en } },
	{ WHISPER_AHEADS_SMALL, { 10, g_aheads_small } },
	{ WHISPER_AHEADS_MEDIUM_EN, { 18, g_aheads_medium_en } },
	{ WHISPER_AHEADS_MEDIUM, { 6, g_aheads_medium } },
	{ WHISPER_AHEADS_LARGE_V1, { 9, g_aheads_large_v1 } },
	{ WHISPER_AHEADS_LARGE_V2, { 23, g_aheads_large_v2 } },
	{ WHISPER_AHEADS_LARGE_V3, { 10, g_aheads_large_v3 } },
	};

	static std::vector<uint32_t> get_alignment_heads_by_layer(const whisper_context_params & cparams, int il, int32_t n_text_layer, int32_t n_head);

	struct whisper_mel {
	int n_len;
	int n_len_org;
	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;

	// reference: https://github.com/openai/whisper/blob/248b6cb124225dd263bb9bd32d060b6517e067f8/whisper/tokenizer.py#L334-L349
	id token_eot = 50256;
	id token_sot = 50257;
	// task tokens (used only for multilingual models)
	id token_translate = 50357;
	id token_transcribe = 50358;
	// other special tokens
	id token_solm = 50359; // [TDRZ] used by tinydiarize models to indicate speaker turn
	id token_prev = 50360;
	id token_nosp = 50361;
	id token_not = 50362; // no timestamps
	id token_beg = 50363; // begin timestamps

	bool is_multilingual() const {
	return n_vocab >= 51865;
	}

	int num_languages() const {
	return n_vocab - 51765 - (is_multilingual() ? 1 : 0);
	}
	};

	struct whisper_segment {
	int64_t t0;
	int64_t t1;

	std::string text;

	std::vector<whisper_token_data> tokens;

	bool speaker_turn_next;
	};

	struct whisper_batch {
	int32_t n_tokens;

	whisper_token * token;
	whisper_pos * pos;
	int32_t * n_seq_id; // always 1, here for consistency with llama.cpp
	whisper_seq_id ** seq_id; // null terminated
	int8_t * logits;
	};

	static struct whisper_batch whisper_batch_init(int32_t n_tokens, int32_t n_seq_max) {
	whisper_batch batch = { 0, nullptr, nullptr, nullptr, nullptr, nullptr, };

	batch.token = (whisper_token * ) malloc(sizeof(whisper_token) * (n_tokens));
	batch.pos = (whisper_pos ) malloc(sizeof(whisper_pos) (n_tokens));
	batch.n_seq_id = (int32_t ) malloc(sizeof(int32_t) (n_tokens));
	batch.seq_id = (whisper_seq_id *) malloc(sizeof(whisper_seq_id ) * (n_tokens + 1));
	for (int i = 0; i < n_tokens; ++i) {
	batch.seq_id[i] = (whisper_seq_id ) malloc(sizeof(whisper_seq_id) n_seq_max);
	}
	batch.seq_id[n_tokens] = nullptr;
	batch.logits = (int8_t ) malloc(sizeof(int8_t) n_tokens);

	return batch;
	}

	static void whisper_batch_free(struct whisper_batch batch) {
	if (batch.token) free(batch.token);
	if (batch.pos) free(batch.pos);
	if (batch.n_seq_id) free(batch.n_seq_id);
	if (batch.seq_id) {
	for (int i = 0; batch.seq_id[i]; ++i) {
	free(batch.seq_id[i]);
	}
	free(batch.seq_id);
	}
	if (batch.logits) free(batch.logits);
	}

	static void whisper_batch_prep_legacy(whisper_batch & batch, const whisper_token * tokens, int n_tokens, int n_past, int seq_id) {
	batch.n_tokens = n_tokens;
	for (int i = 0; i < n_tokens; ++i) {
	if (tokens) {
	batch.token[i] = tokens[i];
	}
	batch.pos [i] = n_past + i;
	batch.n_seq_id[i] = 1;
	batch.seq_id [i][0] = seq_id;
	batch.logits [i] = 0;
	}
	batch.logits[n_tokens - 1] = 1;
	}

	// replace std::pair by using customized pair struct (reason: std::pair is very slow)
	template<typename A, typename B>
	struct whisper_pair {
	A first;
	B second;

	// Define a constructor that takes two arguments.
	whisper_pair(const A& a, const B& b) : first(a), second(b) {}
	// Define a constructor that takes no argument.
	whisper_pair() : first(A()), second(B()) {}
	};

	// ggml_allocr wrapper for whisper usage
	struct whisper_allocr {
	ggml_gallocr_t alloc = nullptr;

	std::vector<uint8_t> meta;
	};

	static size_t whisper_allocr_size(struct whisper_allocr & allocr) {
	return allocr.meta.size() + ggml_gallocr_get_buffer_size(allocr.alloc, 0);
	}

	// measure the memory usage of a graph and prepare the allocr's internal data buffer
	static bool whisper_allocr_graph_init(struct whisper_allocr & allocr, ggml_backend_t backend, std::function<struct ggml_cgraph *()> && get_graph) {
	auto & alloc = allocr.alloc;
	auto & meta = allocr.meta;

	alloc = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));

	meta.resize(ggml_tensor_overhead()*WHISPER_MAX_NODES + ggml_graph_overhead());

	// since there are dependencies between the different graphs,
	// we need to allocate them instead of only reserving to get the correct compute buffer size
	if (!ggml_gallocr_alloc_graph(alloc, get_graph())) {
	// failed to allocate the compute buffer
	WHISPER_LOG_ERROR("%s: failed to allocate the compute buffer\n", __func__);
	return false;
	}
	return true;
	}

	// 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 ftype = 1;
	float eps = 1e-5f;
	};

	// 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_kv_cell {
	whisper_pos pos = -1;

	std::set<whisper_seq_id> seq_id;

	bool has_seq_id(const whisper_seq_id & id) const {
	return seq_id.find(id) != seq_id.end();
	}
	};

	struct whisper_kv_cache {
	uint32_t head = 0;
	uint32_t size = 0;

	// computed before each graph build
	uint32_t n = 0;

	std::vector<whisper_kv_cell> cells;

	struct ggml_tensor * k;
	struct ggml_tensor * v;

	struct ggml_context * ctx = nullptr;

	ggml_backend_buffer_t buffer = nullptr;
	};

	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;

	// decoder.token_embedding
	struct ggml_tensor * d_te;

	// decoder.ln
	struct ggml_tensor * d_ln_w;
	struct ggml_tensor * d_ln_b;

	std::vector<whisper_layer_encoder> layers_encoder;
	std::vector<whisper_layer_decoder> layers_decoder;

	// ggml context that contains all the meta information about the model tensors
	struct ggml_context * ctx = nullptr;

	// the model backend data is read-only and can be shared between processors
	ggml_backend_buffer_t buffer = nullptr;

	// tensors
	int n_loaded;
	std::map<std::string, struct ggml_tensor *> tensors;
	};

	struct whisper_partial_utf8 {
	uint32_t value; // bit value so far (unshifted)
	int n_remain; // num bytes remaining; -1 indicates invalid sequence
	};

	struct whisper_grammar {
	/const/ std::vector<std::vector<whisper_grammar_element>> rules;
	std::vector<std::vector<const whisper_grammar_element *>> stacks;

	// buffer for partially generated UTF-8 sequence from accepted tokens
	whisper_partial_utf8 partial_utf8;
	};

	struct whisper_grammar_candidate {
	whisper_token id;
	const uint32_t * code_points;
	whisper_partial_utf8 partial_utf8;
	};

	struct whisper_sequence {
	std::vector<whisper_token_data> tokens;

	// the accumulated transcription in the current iteration (used to truncate the tokens array)
	int result_len;

	double sum_logprobs_all; // the sum of the log probabilities of the tokens
	double sum_logprobs; // the sum of the log probabilities of the tokens (first result_len tokens)
	double avg_logprobs; // the average log probability of the tokens
	double entropy; // the entropy of the tokens
	double score; // likelihood rank score
	};

	// TAGS: WHISPER_DECODER_INIT
	struct whisper_decoder {
	// the currently generated sequence of tokens
	whisper_sequence sequence;

	// grammar parse state of generated sequence of tokens
	whisper_grammar grammar;

	int i_batch; // the index of the token in the current batch
	int seek_delta; // the window shift found so far based on the decoded timestamp tokens

	bool failed; // has the current segment failed to decode?
	bool completed; // has the decoder completed the current segment?
	bool has_ts; // have we already sampled a non-beg timestamp token for the current segment?

	// new token probs, logits and logprobs after the last whisper_decode (1-dimensional array: [n_vocab])
	std::vector<float> probs;
	std::vector<float> logits;
	std::vector<float> logprobs;

	// work container used to avoid memory allocations
	std::vector<whisper_pair<double, whisper_vocab::id>> logits_id;

	mutable std::mt19937 rng; // used for sampling at t > 0.0
	};

	// [EXPERIMENTAL] Token-level timestamps with DTW
	struct whisper_aheads_masks {
	std::vector<struct ggml_tensor *> m; // One mask per text layer.
	struct ggml_context * ctx = nullptr;
	ggml_backend_buffer_t buffer = nullptr;
	};

	struct whisper_state {
	int64_t t_sample_us = 0;
	int64_t t_encode_us = 0;
	int64_t t_decode_us = 0;
	int64_t t_batchd_us = 0;
	int64_t t_prompt_us = 0;
	int64_t t_mel_us = 0;

	int32_t n_sample = 0; // number of tokens sampled
	int32_t n_encode = 0; // number of encoder calls
	int32_t n_decode = 0; // number of decoder calls with n_tokens == 1 (text-generation)
	int32_t n_batchd = 0; // number of decoder calls with n_tokens < 16 (batch decoding)
	int32_t n_prompt = 0; // number of decoder calls with n_tokens > 1 (prompt encoding)
	int32_t n_fail_p = 0; // number of logprob threshold failures
	int32_t n_fail_h = 0; // number of entropy threshold failures

	// unified self-attention KV cache for all decoders
	whisper_kv_cache kv_self;

	// cross-attention KV cache for the decoders
	// shared between all decoders
	whisper_kv_cache kv_cross;

	// padded buffer for flash-attention
	whisper_kv_cache kv_pad;

	whisper_mel mel;

	whisper_batch batch;

	whisper_decoder decoders[WHISPER_MAX_DECODERS];

	ggml_backend_t backend = nullptr;

	// ggml-alloc:
	// - stores meta info about the intermediate tensors into the `meta` buffers
	// - stores the actual tensor data into the `data` buffers
	whisper_allocr alloc_conv;
	whisper_allocr alloc_encode;
	whisper_allocr alloc_cross;
	whisper_allocr alloc_decode;

	// result of the encoder
	struct ggml_tensor * embd_conv = nullptr;
	struct ggml_tensor * embd_enc = nullptr;

	// helpers for GPU offloading
	std::vector<float> inp_mel;
	std::vector<float> inp_mask;

	// decode output (2-dimensional array: [n_tokens][n_vocab])
	std::vector<float> logits;

	std::vector<whisper_segment> result_all;
	std::vector<whisper_token> prompt_past;

	int lang_id = 0; // english by default

	std::string path_model; // populated by whisper_init_from_file_with_params()

	#ifdef WHISPER_USE_COREML
	whisper_coreml_context * ctx_coreml = nullptr;
	#endif

	#ifdef WHISPER_USE_OPENVINO
	whisper_openvino_context * ctx_openvino = nullptr;
	#endif

	// [EXPERIMENTAL] token-level timestamps data
	int64_t t_beg = 0;
	int64_t t_last = 0;

	whisper_token tid_last;

	std::vector<float> energy; // PCM signal energy

	// [EXPERIMENTAL] Token-level timestamps with DTW
	whisper_aheads_masks aheads_masks;
	ggml_tensor * aheads_cross_QKs = nullptr;
	std::vector<float> aheads_cross_QKs_data;

	// [EXPERIMENTAL] speed-up techniques
	int32_t exp_n_audio_ctx = 0; // 0 - use default
	};

	struct whisper_context {
	int64_t t_load_us = 0;
	int64_t t_start_us = 0;

	ggml_type wtype = ggml_type::GGML_TYPE_F16; // weight type (FP32 / FP16 / QX)
	ggml_type itype = ggml_type::GGML_TYPE_F16; // intermediate type (FP32 or FP16)

	whisper_context_params params;

	whisper_model model;
	whisper_vocab vocab;

	whisper_state * state = nullptr;

	ggml_backend_t backend = nullptr;

	std::string path_model; // populated by whisper_init_from_file_with_params()
	};

	struct whisper_global {
	// We save the log callback globally
	ggml_log_callback log_callback = whisper_log_callback_default;
	void * log_callback_user_data = nullptr;
	};

	static whisper_global g_state;

	template<typename T>
	static void read_safe(whisper_model_loader * loader, T & dest) {
	loader->read(loader->context, &dest, sizeof(T));
	BYTESWAP_VALUE(dest);
	}

	static bool kv_cache_init(
	struct whisper_kv_cache & cache,
	ggml_backend_t backend,
	ggml_type wtype,
	int64_t n_text_state,
	int64_t n_text_layer,
	int n_ctx) {
	const int64_t n_mem = n_text_layer*n_ctx;
	const int64_t n_elements = n_text_state*n_mem;

	struct ggml_init_params params = {
	/.mem_size =/ 2*ggml_tensor_overhead(),
	/.mem_buffer =/ nullptr,
	/.no_alloc =/ true,
	};

	cache.head = 0;
	cache.size = n_ctx;

	cache.cells.clear();
	cache.cells.resize(n_ctx);

	cache.ctx = ggml_init(params);

	if (!cache.ctx) {
	WHISPER_LOG_ERROR("%s: failed to allocate memory for the kv cache context\n", __func__);
	return false;
	}

	cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
	cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);

	cache.buffer = ggml_backend_alloc_ctx_tensors(cache.ctx, backend);
	if (!cache.buffer) {
	WHISPER_LOG_ERROR("%s: failed to allocate memory for the kv cache\n", __func__);
	return false;
	}

	ggml_backend_buffer_clear(cache.buffer, 0);

	return true;
	}

	static void kv_cache_free(struct whisper_kv_cache & cache) {
	ggml_free(cache.ctx);
	ggml_backend_buffer_free(cache.buffer);
	cache.ctx = nullptr;
	}

	static bool whisper_kv_cache_find_slot(
	struct whisper_kv_cache & cache,
	const struct whisper_batch & batch) {
	const uint32_t n_ctx = cache.size;
	const uint32_t n_tokens = batch.n_tokens;

	if (n_tokens > n_ctx) {
	WHISPER_LOG_ERROR("%s: n_tokens=%d > n_ctx=%d\n", __func__, n_tokens, n_ctx);
	return false;
	}

	uint32_t n_tested = 0;

	while (true) {
	if (cache.head + n_tokens > n_ctx) {
	n_tested += n_ctx - cache.head;
	cache.head = 0;
	continue;
	}

	bool found = true;
	for (uint32_t i = 0; i < n_tokens; i++) {
	if (cache.cells[cache.head + i].pos >= 0) {
	found = false;
	cache.head += i + 1;
	n_tested += i + 1;
	break;
	}
	}

	if (found) {
	break;
	}

	if (n_tested >= n_ctx) {
	//WHISPER_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens);
	return false;
	}
	}

	for (uint32_t i = 0; i < n_tokens; i++) {
	cache.cells[cache.head + i].pos = batch.pos[i];

	for (int32_t j = 0; j < batch.n_seq_id[i]; j++) {
	cache.cells[cache.head + i].seq_id.insert(batch.seq_id[i][j]);
	}
	}

	return true;
	}

	// find how many cells are currently in use
	static int32_t whisper_kv_cache_cell_max(const struct whisper_kv_cache & cache) {
	for (uint32_t i = cache.size - 1; i > 0; --i) {
	if (cache.cells[i].pos >= 0 && !cache.cells[i].seq_id.empty()) {
	return i + 1;
	}
	}

	return 1;
	}

	static void whisper_kv_cache_clear(struct whisper_kv_cache & cache) {
	for (int32_t i = 0; i < (int32_t) cache.size; ++i) {
	cache.cells[i].pos = -1;
	cache.cells[i].seq_id.clear();
	}
	cache.head = 0;
	}

	static void whisper_kv_cache_seq_rm(
	struct whisper_kv_cache & cache,
	whisper_seq_id seq_id,
	whisper_pos p0,
	whisper_pos p1) {
	uint32_t new_head = cache.size;

	if (p0 < 0) p0 = 0;
	if (p1 < 0) p1 = std::numeric_limits<whisper_pos>::max();

	for (uint32_t i = 0; i < cache.size; ++i) {
	if (cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
	if (seq_id < 0) {
	cache.cells[i].seq_id.clear();
	} else if (cache.cells[i].has_seq_id(seq_id)) {
	cache.cells[i].seq_id.erase(seq_id);
	} else {
	continue;
	}
	if (cache.cells[i].seq_id.empty()) {
	cache.cells[i].pos = -1;
	if (new_head == cache.size) new_head = i;
	}
	}
	}

	// If we freed up a slot, set head to it so searching can start there.
	if (new_head != cache.size) cache.head = new_head;
	}

	static void whisper_kv_cache_seq_cp(
	struct whisper_kv_cache & cache,
	whisper_seq_id seq_id_src,
	whisper_seq_id seq_id_dst,
	whisper_pos p0,
	whisper_pos p1) {
	if (p0 < 0) p0 = 0;
	if (p1 < 0) p1 = std::numeric_limits<whisper_pos>::max();

	cache.head = 0;

	for (uint32_t i = 0; i < cache.size; ++i) {
	if (cache.cells[i].has_seq_id(seq_id_src) && cache.cells[i].pos >= p0 && cache.cells[i].pos < p1) {
	cache.cells[i].seq_id.insert(seq_id_dst);
	}
	}
	}

	static uint32_t whisper_kv_cache_get_padding(const struct whisper_context & wctx) {
	if (!wctx.params.flash_attn) {
	return 1u;
	}

	#ifdef GGML_USE_METAL
	if (ggml_backend_is_metal(wctx.backend)) {
	return 32u;
	}
	#endif

	#ifdef GGML_USE_CUDA
	if (ggml_backend_is_cuda(wctx.backend)) {
	return 256u;
	}
	#endif

	return 1u;
	}

	// [EXPERIMENTAL] Token-level timestamps with DTW
	static bool aheads_masks_init(
	const whisper_context_params & cparams,
	const whisper_hparams & hparams,
	struct whisper_aheads_masks & aheads_masks,
	ggml_backend_t backend) {

	const int32_t n_text_layer = hparams.n_text_layer;
	const int32_t n_head = hparams.n_text_head;

	// Sanity checks
	if (cparams.dtw_aheads_preset == WHISPER_AHEADS_NONE) {
	WHISPER_LOG_ERROR("%s: dtw_aheads_preset should be != DTW_AHEADS_NONE\n", __func__);
	return false;
	} else if (cparams.dtw_aheads_preset == WHISPER_AHEADS_N_TOP_MOST) {
	if (cparams.dtw_n_top > n_text_layer \|\| cparams.dtw_n_top <= 0) {
	WHISPER_LOG_ERROR("%s: dtw_n_top must be between %d and %d for this model.", __func__, 1, n_text_layer);
	return false;
	}
	} else {
	const auto aheads = cparams.dtw_aheads_preset == WHISPER_AHEADS_CUSTOM ? cparams.dtw_aheads : g_aheads.at(cparams.dtw_aheads_preset);
	if (cparams.dtw_aheads_preset == WHISPER_AHEADS_CUSTOM) {
	if (aheads.n_heads == 0) {
	WHISPER_LOG_ERROR("%s: dtw_aheads.n_heads should be > 0", __func__);
	return false;
	}
	if (aheads.heads == NULL) {
	WHISPER_LOG_ERROR("%s: dtw_aheads.heads unset", __func__);
	return false;
	}
	}
	for (size_t i = 0; i < aheads.n_heads; ++i) {
	if (aheads.heads[i].n_text_layer >= n_text_layer) {
	WHISPER_LOG_ERROR("%s: tried to set alignment head on text layer %d, but model only has %d text layers", __func__, aheads.heads[i].n_text_layer + 1, n_text_layer);
	return false;
	}
	if (aheads.heads[i].n_text_layer < 0) {
	WHISPER_LOG_ERROR("%s: tried to set alignment head on text layer < 0", __func__);
	return false;
	}
	if (aheads.heads[i].n_head >= n_head) {
	WHISPER_LOG_ERROR("%s: tried to set alignment head on head %d, but model only has %d heads", __func__, aheads.heads[i].n_head + 1, n_head);
	return false;
	}
	if (aheads.heads[i].n_head < 0) {
	WHISPER_LOG_ERROR("%s: tried to set alignment head on head < 0", __func__);
	return false;
	}
	}
	}

	struct ggml_init_params params = {
	/.mem_size =/ (size_t) static_cast<size_t>(n_text_layer)*ggml_tensor_overhead(),
	/.mem_buffer =/ nullptr,
	/.no_alloc =/ true,
	};

	aheads_masks.ctx = ggml_init(params);

	if (!aheads_masks.ctx) {
	WHISPER_LOG_ERROR("%s: failed to allocate memory for the aheads_masks context\n", __func__);
	return false;
	}

	for (int64_t il = 0; il < n_text_layer; ++il) {
	auto aheads = get_alignment_heads_by_layer(cparams, il, n_text_layer, n_head);
	if (!aheads.empty()) {
	aheads_masks.m.push_back(ggml_new_tensor_2d(aheads_masks.ctx, GGML_TYPE_F32, n_head, aheads.size()));
	} else {
	aheads_masks.m.push_back(nullptr);
	}
	}

	aheads_masks.buffer = ggml_backend_alloc_ctx_tensors(aheads_masks.ctx, backend);
	if (!aheads_masks.buffer) {
	WHISPER_LOG_ERROR("%s: failed to allocate memory for aheads_masks\n", __func__);
	return false;
	}

	// Set data on mask tensors
	// Since this must be backend agnostic, we write our desired values on mask_data,
	// and send it to backend with ggml_backend_tensor_set.
	// Each mask in N_HEADS*N_ALIGNMENT_HEADS, one per text layer containing alignment
	// heads. Each row of the mask "marks" one alignment head. E.g. if some text layer
	// has a total of 10 heads and of those, heads 0,5,6 are alignment heads, the mask
	// should read:
	// 1 0 0 0 0 0 0 0 0 0
	// 0 0 0 0 0 1 0 0 0 0
	// 0 0 0 0 0 0 1 0 0 0
	std::vector<float> mask_data;
	for (int64_t il = 0; il < n_text_layer; ++il) {
	if (aheads_masks.m[il] != nullptr) {
	auto aheads = get_alignment_heads_by_layer(cparams, il, n_text_layer, n_head);

	size_t data_size = aheads_masks.m[il]->ne[0] * aheads_masks.m[il]->ne[1];
	size_t data_size_bytes = data_size * sizeof(float);
	mask_data.resize(data_size);

	std::fill(mask_data.begin(), mask_data.end(), 0);
	for (size_t ih = 0; ih < aheads.size(); ++ih) {
	size_t pos = (aheads[ih] + (ih * aheads_masks.m[il]->ne[0]));
	mask_data[pos] = 1.0f;
	}

	ggml_backend_tensor_set(aheads_masks.m[il], mask_data.data(), 0, data_size_bytes);
	}
	}

	if (aheads_masks.m.empty()) {
	WHISPER_LOG_ERROR("%s: \n", __func__);
	return false;
	}

	return true;
	}

	static void aheads_masks_free(struct whisper_aheads_masks & aheads_masks) {
	ggml_free(aheads_masks.ctx);
	ggml_backend_buffer_free(aheads_masks.buffer);
	aheads_masks.ctx = nullptr;
	}

	static size_t aheads_masks_nbytes(struct whisper_aheads_masks & aheads_masks) {
	size_t size = 0;
	for (size_t i = 0; i < aheads_masks.m.size(); ++i) {
	if (aheads_masks.m[i] != nullptr)
	size += ggml_nbytes(aheads_masks.m[i]);
	}
	return size;
	}

	static ggml_backend_t whisper_backend_init(const whisper_context_params & params) {
	ggml_backend_t backend_gpu = NULL;

	// initialize the backends
	#ifdef GGML_USE_CUDA
	if (params.use_gpu) {
	WHISPER_LOG_INFO("%s: using CUDA backend\n", __func__);
	backend_gpu = ggml_backend_cuda_init(params.gpu_device);
	if (!backend_gpu) {
	WHISPER_LOG_ERROR("%s: ggml_backend_cuda_init() failed\n", __func__);
	}
	}
	#endif

	#ifdef GGML_USE_METAL
	if (params.use_gpu) {
	WHISPER_LOG_INFO("%s: using Metal backend\n", __func__);
	backend_gpu = ggml_backend_metal_init();
	if (!backend_gpu) {
	WHISPER_LOG_ERROR("%s: ggml_backend_metal_init() failed\n", __func__);
	} else if (!ggml_backend_metal_supports_family(backend_gpu, 7)) {
	WHISPER_LOG_ERROR("%s: Metal GPU does not support family 7 - falling back to CPU\n", __func__);
	ggml_backend_free(backend_gpu);
	backend_gpu = NULL;
	}
	}
	#endif

	#ifdef GGML_USE_SYCL
	if (params.use_gpu) {
	WHISPER_LOG_INFO("%s: using SYCL backend\n", __func__);
	backend_gpu = ggml_backend_sycl_init(params.gpu_device);
	if (!backend_gpu) {
	WHISPER_LOG_ERROR("%s: ggml_backend_sycl_init() failed\n", __func__);
	}
	}
	#endif

	if (backend_gpu) {
	return backend_gpu;
	}
	return ggml_backend_cpu_init();
	}

	// 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
	//
	static bool whisper_model_load(struct whisper_model_loader * loader, whisper_context & wctx) {
	WHISPER_LOG_INFO("%s: loading model\n", __func__);

	const int64_t t_start_us = ggml_time_us();

	wctx.t_start_us = t_start_us;

	auto & model = wctx.model;
	auto & vocab = wctx.vocab;

	// verify magic
	{
	uint32_t magic;
	read_safe(loader, magic);
	if (magic != GGML_FILE_MAGIC) {
	WHISPER_LOG_ERROR("%s: invalid model data (bad magic)\n", __func__);
	return false;
	}
	}

	//load hparams
	{
	auto & hparams = model.hparams;

	read_safe(loader, hparams.n_vocab);
	read_safe(loader, hparams.n_audio_ctx);
	read_safe(loader, hparams.n_audio_state);
	read_safe(loader, hparams.n_audio_head);
	read_safe(loader, hparams.n_audio_layer);
	read_safe(loader, hparams.n_text_ctx);
	read_safe(loader, hparams.n_text_state);
	read_safe(loader, hparams.n_text_head);
	read_safe(loader, hparams.n_text_layer);
	read_safe(loader, hparams.n_mels);
	read_safe(loader, hparams.ftype);

	assert(hparams.n_text_state == hparams.n_audio_state);

	std::string mver = "";

	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;

	if (hparams.n_vocab == 51866) {
	mver = " v3";
	}
	}

	const int32_t qntvr = hparams.ftype / GGML_QNT_VERSION_FACTOR;

	hparams.ftype %= GGML_QNT_VERSION_FACTOR;

	// for the big tensors, we have the option to store the data in 16-bit floats or quantized
	// in order to save memory and also to speed up the computation
	wctx.wtype = ggml_ftype_to_ggml_type((ggml_ftype) (model.hparams.ftype));
	if (wctx.wtype == GGML_TYPE_COUNT) {
	WHISPER_LOG_ERROR("%s: invalid model (bad ftype value %d)\n", __func__, model.hparams.ftype);
	return false;
	}

	WHISPER_LOG_INFO("%s: n_vocab = %d\n", __func__, hparams.n_vocab);
	WHISPER_LOG_INFO("%s: n_audio_ctx = %d\n", __func__, hparams.n_audio_ctx);
	WHISPER_LOG_INFO("%s: n_audio_state = %d\n", __func__, hparams.n_audio_state);
	WHISPER_LOG_INFO("%s: n_audio_head = %d\n", __func__, hparams.n_audio_head);
	WHISPER_LOG_INFO("%s: n_audio_layer = %d\n", __func__, hparams.n_audio_layer);
	WHISPER_LOG_INFO("%s: n_text_ctx = %d\n", __func__, hparams.n_text_ctx);
	WHISPER_LOG_INFO("%s: n_text_state = %d\n", __func__, hparams.n_text_state);
	WHISPER_LOG_INFO("%s: n_text_head = %d\n", __func__, hparams.n_text_head);
	WHISPER_LOG_INFO("%s: n_text_layer = %d\n", __func__, hparams.n_text_layer);
	WHISPER_LOG_INFO("%s: n_mels = %d\n", __func__, hparams.n_mels);
	WHISPER_LOG_INFO("%s: ftype = %d\n", __func__, model.hparams.ftype);
	WHISPER_LOG_INFO("%s: qntvr = %d\n", __func__, qntvr);
	WHISPER_LOG_INFO("%s: type = %d (%s%s)\n", __func__, model.type, g_model_name.at(model.type).c_str(), mver.c_str());
	}

	// load mel filters
	{
	auto & filters = wctx.model.filters;

	read_safe(loader, filters.n_mel);
	read_safe(loader, filters.n_fft);

	filters.data.resize(filters.n_mel * filters.n_fft);
	loader->read(loader->context, filters.data.data(), filters.data.size() * sizeof(float));
	BYTESWAP_FILTERS(filters);
	}

	// load vocab
	{
	int32_t n_vocab = 0;
	read_safe(loader, n_vocab);

	//if (n_vocab != model.hparams.n_vocab) {
	// WHISPER_LOG_ERROR("%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;
	std::vector<char> tmp;

	tmp.reserve(128);

	for (int i = 0; i < n_vocab; i++) {
	uint32_t len;
	read_safe(loader, len);

	if (len > 0) {
	tmp.resize(len);
	loader->read(loader->context, &tmp[0], tmp.size()); // read to buffer
	word.assign(&tmp[0], tmp.size());
	} else {
	// seems like we have an empty-string token in multi-language models (i = 50256)
	//WHISPER_LOG_WARN("%s: warning: empty-string token in vocab, i = %d\n", __func__, i);
	word = "";
	}

	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++;

	// account for variable number of language tokens
	const int dt = vocab.num_languages() - 98;

	vocab.token_translate += dt;
	vocab.token_transcribe += dt;
	vocab.token_solm += dt;
	vocab.token_prev += dt;
	vocab.token_nosp += dt;
	vocab.token_not += dt;
	vocab.token_beg += dt;
	}

	if (n_vocab < model.hparams.n_vocab) {
	WHISPER_LOG_INFO("%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_translate) {
	word = "[_TRANSLATE_]";
	} else if (i == vocab.token_transcribe) {
	word = "[_TRANSCRIBE_]";
	} else if (i == vocab.token_solm) {
	word = "[_SOLM_]";
	} else if (i == vocab.token_prev) {
	word = "[_PREV_]";
	} else if (i == vocab.token_nosp) {
	word = "[_NOSP_]";
	} else if (i == vocab.token_not) {
	word = "[_NOT_]";
	} else if (i == vocab.token_beg) {
	word = "[_BEG_]";
	} else if (i > vocab.token_sot && i <= vocab.token_sot + vocab.num_languages()) {
	word = "[_LANG_" + std::string(whisper_lang_str(i - vocab.token_sot - 1)) + "]";
	} else {
	word = "[_extra_token_" + std::to_string(i) + "]";
	}
	vocab.token_to_id[word] = i;
	vocab.id_to_token[i] = word;
	}
	}

	WHISPER_LOG_INFO("%s: n_langs = %d\n", __func__, vocab.num_languages());
	}

	const ggml_type wtype = wctx.wtype;
	const ggml_type vtype = wctx.wtype == GGML_TYPE_F32 ? GGML_TYPE_F32 : GGML_TYPE_F16; // conv type

	// create the ggml context
	{
	const auto & hparams = model.hparams;

	const int n_audio_layer = hparams.n_audio_layer;
	const int n_text_layer = hparams.n_text_layer;

	const size_t n_tensors = 10 /* input / + 15 + 15n_audio_layer + 24*n_text_layer;

	struct ggml_init_params params = {
	/.mem_size =/ n_tensors*ggml_tensor_overhead(),
	/.mem_buffer =/ nullptr,
	/.no_alloc =/ true,
	};

	model.ctx = ggml_init(params);
	if (!model.ctx) {
	WHISPER_LOG_ERROR("%s: ggml_init() failed\n", __func__);
	return false;
	}
	}

	// prepare tensors for the weights
	{
	auto & ctx = model.ctx;

	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, vtype, 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, vtype, 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;
	}
	}
	}

	wctx.backend = whisper_backend_init(wctx.params);
	if (!wctx.backend) {
	WHISPER_LOG_ERROR("%s: failed to initialize the backend\n", __func__);
	return false;
	}

	// allocate tensors in the backend buffers
	model.buffer = ggml_backend_alloc_ctx_tensors(model.ctx, wctx.backend);
	if (!model.buffer) {
	WHISPER_LOG_ERROR("%s: failed to allocate memory for the model\n", __func__);
	return false;
	}

	size_t size_main = ggml_backend_buffer_get_size(model.buffer);
	WHISPER_LOG_INFO("%s: %8s total size = %8.2f MB\n", __func__, ggml_backend_name(wctx.backend), size_main / 1e6);

	// load weights
	{
	size_t total_size = 0;

	model.n_loaded = 0;

	std::vector<char> read_buf;

	while (true) {
	int32_t n_dims;
	int32_t length;
	int32_t ttype;

	read_safe(loader, n_dims);
	read_safe(loader, length);
	read_safe(loader, ttype);

	if (loader->eof(loader->context)) {
	break;
	}

	int32_t nelements = 1;
	int32_t ne[4] = { 1, 1, 1, 1 };
	for (int i = 0; i < n_dims; ++i) {
	read_safe(loader, ne[i]);
	nelements *= ne[i];
	}

	std::string name;
	std::vector<char> tmp(length); // create a buffer
	loader->read(loader->context, &tmp[0], tmp.size()); // read to buffer
	name.assign(&tmp[0], tmp.size());

	if (model.tensors.find(name) == model.tensors.end()) {
	WHISPER_LOG_ERROR("%s: unknown tensor '%s' in model file\n", __func__, name.data());
	return false;
	}

	auto tensor = model.tensors[name.data()];

	if (ggml_nelements(tensor) != nelements) {
	WHISPER_LOG_ERROR("%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
	WHISPER_LOG_ERROR("%s: shape: [%d, %d, %d], expected: [%d, %d, %d]\n",
	__func__, ne[0], ne[1], ne[2], (int) tensor->ne[0], (int) tensor->ne[1], (int) tensor->ne[2]);
	return false;
	}

	if (tensor->ne[0] != ne[0] \|\| tensor->ne[1] != ne[1] \|\| tensor->ne[2] != ne[2]) {
	WHISPER_LOG_ERROR("%s: tensor '%s' has wrong shape in model file: got [%d, %d, %d], expected [%d, %d, %d]\n",
	__func__, name.data(), (int) tensor->ne[0], (int) tensor->ne[1], (int) tensor->ne[2], ne[0], ne[1], ne[2]);
	return false;
	}

	const size_t bpe = ggml_type_size(ggml_type(ttype));

	if ((nelements*bpe)/ggml_blck_size(tensor->type) != ggml_nbytes(tensor)) {
	WHISPER_LOG_ERROR("%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
	__func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
	return false;
	}

	//ggml_backend_t backend = wctx.backend;

	//printf("%s: [%5.5s] %s\n", __func__, ggml_backend_name(backend), name.c_str());

	if (ggml_backend_buffer_is_host(model.buffer)) {
	// for the CPU and Metal backend, we can read directly into the tensor
	loader->read(loader->context, tensor->data, ggml_nbytes(tensor));
	BYTESWAP_TENSOR(tensor);
	} else {
	// read into a temporary buffer first, then copy to device memory
	read_buf.resize(ggml_nbytes(tensor));

	loader->read(loader->context, read_buf.data(), read_buf.size());

	ggml_backend_tensor_set(tensor, read_buf.data(), 0, ggml_nbytes(tensor));
	}

	//printf("%48s - [%5d, %5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ne[2], ggml_type_name((ggml_type) ttype), ggml_nbytes(tensor)/1e6);
	total_size += ggml_nbytes(tensor);
	model.n_loaded++;
	}

	WHISPER_LOG_INFO("%s: model size = %7.2f MB\n", __func__, total_size/1e6);

	if (model.n_loaded == 0) {
	WHISPER_LOG_WARN("%s: WARN no tensors loaded from model file - assuming empty model for testing\n", __func__);
	} else if (model.n_loaded != (int) model.tensors.size()) {
	WHISPER_LOG_ERROR("%s: ERROR not all tensors loaded from model file - expected %zu, got %d\n", __func__, model.tensors.size(), model.n_loaded);
	return false;
	}
	}

	wctx.t_load_us = ggml_time_us() - t_start_us;

	return true;
	}

	static bool whisper_encode_external(const whisper_state & wstate) {
	GGML_UNUSED(wstate);

	#ifndef WHISPER_USE_COREML
	const bool use_coreml = false;
	#else
	const bool use_coreml = wstate.ctx_coreml != nullptr;
	#endif

	#ifndef WHISPER_USE_OPENVINO
	const bool use_openvino = false;
	#else
	const bool use_openvino = wstate.ctx_openvino != nullptr;
	#endif

	return use_coreml \|\| use_openvino;
	}

	static struct ggml_cgraph * whisper_build_graph_conv(
	whisper_context & wctx,
	whisper_state & wstate) {
	const auto & model = wctx.model;
	const auto & hparams = model.hparams;

	const int n_ctx = wstate.exp_n_audio_ctx > 0 ? wstate.exp_n_audio_ctx : hparams.n_audio_ctx;
	const int n_state = hparams.n_audio_state; GGML_UNUSED(n_state);

	const int n_mels = hparams.n_mels;

	struct ggml_init_params params = {
	/.mem_size =/ wstate.alloc_conv.meta.size(),
	/.mem_buffer =/ wstate.alloc_conv.meta.data(),
	/.no_alloc =/ true,
	};

	struct ggml_context * ctx0 = ggml_init(params);

	ggml_cgraph * gf = ggml_new_graph(ctx0);

	struct ggml_tensor * mel = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 2*n_ctx, n_mels);
	ggml_set_name(mel, "mel");
	ggml_set_input(mel);

	struct ggml_tensor * cur = nullptr;

	if (!whisper_encode_external(wstate)) {
	// convolution + gelu
	{
	cur = ggml_conv_1d_ph(ctx0, model.e_conv_1_w, mel, 1, 1);
	cur = ggml_add(ctx0, cur, model.e_conv_1_b);

	cur = ggml_gelu(ctx0, cur);

	cur = ggml_conv_1d_ph(ctx0, model.e_conv_2_w, cur, 2, 1);
	cur = ggml_add(ctx0, cur, model.e_conv_2_b);

	cur = ggml_gelu(ctx0, cur);
	}

	ggml_set_name(cur, "embd_conv");
	wstate.embd_conv = cur;
	} else {
	ggml_build_forward_expand(gf, mel);

	cur = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, n_ctx);

	ggml_set_name(cur, "embd_enc");
	wstate.embd_enc = cur;
	}

	ggml_set_output(cur);

	ggml_build_forward_expand(gf, cur);

	ggml_free(ctx0);

	return gf;
	}

	static struct ggml_cgraph * whisper_build_graph_encoder(
	whisper_context & wctx,
	whisper_state & wstate) {
	const auto & model = wctx.model;
	const auto & hparams = model.hparams;

	const int n_ctx = wstate.exp_n_audio_ctx > 0 ? wstate.exp_n_audio_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_state_head = n_state/n_head;

	auto & kv_pad = wstate.kv_pad;

	WHISPER_ASSERT(!!kv_pad.ctx);

	const int n_ctx_pad = GGML_PAD(n_ctx, 256);

	struct ggml_init_params params = {
	/.mem_size =/ wstate.alloc_encode.meta.size(),
	/.mem_buffer =/ wstate.alloc_encode.meta.data(),
	/.no_alloc =/ true,
	};

	struct ggml_context * ctx0 = ggml_init(params);

	ggml_cgraph * gf = ggml_new_graph_custom(ctx0, WHISPER_MAX_NODES, false);

	struct ggml_tensor * cur = ggml_view_tensor(ctx0, wstate.embd_conv);

	const float KQscale = 1.0f/sqrtf(float(n_state_head));

	// ===================================================================
	// NOTE: experimenting with partial evaluation of the encoder (ignore)
	//static int iter = -1;
	//const int n_iter = 1500/n_ctx;

	//iter = (iter + 1) % n_iter;

	//if (iter == 0) {
	// memset(model.memory_cross_k->data, 0, ggml_nbytes(model.memory_cross_k));
	// memset(model.memory_cross_v->data, 0, ggml_nbytes(model.memory_cross_v));
	//}

	static int iter = 0;

	const size_t e_pe_stride = model.e_pe->ne[0]*ggml_element_size(model.e_pe);
	const size_t e_pe_offset = model.e_pe->ne[0]ggml_element_size(model.e_pe)n_ctx*iter;

	struct ggml_tensor * e_pe = ggml_view_2d(ctx0, model.e_pe, model.e_pe->ne[0], n_ctx, e_pe_stride, e_pe_offset);
	cur = ggml_add(ctx0, e_pe, ggml_cont(ctx0, ggml_transpose(ctx0, cur)));

	// ===================================================================

	// original:
	//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];

	// norm
	{
	cur = ggml_norm(ctx0, inpL, hparams.eps);

	// cur = ln_0_w*cur + ln_0_b
	cur = ggml_add(ctx0,
	ggml_mul(ctx0, cur, layer.attn_ln_0_w),
	layer.attn_ln_0_b);
	}

	// self-attention
	{
	struct ggml_tensor * Qcur = ggml_mul_mat(ctx0,
	layer.attn_q_w,
	cur);

	Qcur = ggml_add(ctx0, Qcur, layer.attn_q_b);

	//Qcur = ggml_scale(ctx0, Qcur, pow(float(n_state_head), -0.25));

	// note: no bias for Key
	struct ggml_tensor * Kcur = ggml_mul_mat(ctx0,
	layer.attn_k_w,
	cur);

	//Kcur = ggml_scale(ctx0, Kcur, pow(float(n_state_head), -0.25));

	struct ggml_tensor * Vcur = ggml_mul_mat(ctx0,
	layer.attn_v_w,
	cur);

	Vcur = ggml_add(ctx0, Vcur, layer.attn_v_b);

	// ------

	struct ggml_tensor * Q =
	ggml_permute(ctx0,
	ggml_cpy(ctx0,
	Qcur,
	ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_state_head, n_head, n_ctx)),
	0, 2, 1, 3);

	if (wctx.params.flash_attn) {
	ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, ggml_view_1d(ctx0, kv_pad.k, n_ctx*n_state, 0)));
	ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, ggml_view_1d(ctx0, kv_pad.v, n_ctx*n_state, 0)));

	struct ggml_tensor * K =
	ggml_view_3d(ctx0, kv_pad.k,
	n_state_head, n_ctx_pad, n_head,
	ggml_element_size(kv_pad.k)*n_state,
	ggml_element_size(kv_pad.k)*n_state_head,
	0);

	struct ggml_tensor * V =
	ggml_view_3d(ctx0, kv_pad.v,
	n_state_head, n_ctx_pad, n_head,
	ggml_element_size(kv_pad.v)*n_state,
	ggml_element_size(kv_pad.v)*n_state_head,
	0);

	cur = ggml_flash_attn_ext(ctx0, Q, K, V, nullptr, KQscale, 0.0f, 0.0f);

	cur = ggml_reshape_2d(ctx0, cur, n_state, n_ctx);
	} else {
	struct ggml_tensor * K =
	ggml_permute(ctx0,
	ggml_cpy(ctx0,
	Kcur,
	ggml_new_tensor_3d(ctx0, wctx.itype, n_state_head, n_head, n_ctx)),
	0, 2, 1, 3);

	// K * Q
	struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);

	struct ggml_tensor * KQ_soft_max = ggml_soft_max_ext(ctx0, KQ, nullptr, KQscale, 0.0f);

	struct ggml_tensor * V =
	ggml_cpy(ctx0,
	ggml_permute(ctx0,
	ggml_reshape_3d(ctx0,
	Vcur,
	n_state_head, n_head, n_ctx),
	1, 2, 0, 3),
	ggml_new_tensor_3d(ctx0, wctx.itype, n_ctx, n_state_head, n_head)
	);

	struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);

	struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);

	cur = ggml_cpy(ctx0,
	KQV_merged,
	ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, n_ctx));
	}
	}

	// projection
	{
	cur = ggml_mul_mat(ctx0,
	layer.attn_ln_1_w,
	cur);

	cur = ggml_add(ctx0, cur, layer.attn_ln_1_b);
	}

	// add the input
	cur = ggml_add(ctx0, cur, inpL);

	struct ggml_tensor * inpFF = cur;

	// feed-forward network
	{
	// norm
	{
	cur = ggml_norm(ctx0, inpFF, hparams.eps);

	// cur = mlp_ln_w*cur + mlp_ln_b
	cur = ggml_add(ctx0,
	ggml_mul(ctx0, cur, layer.mlp_ln_w),
	layer.mlp_ln_b);
	}

	#ifdef WHISPER_USE_FLASH_FF
	cur = ggml_flash_ff(ctx0,
	ggml_cpy(ctx0, cur, ggml_new_tensor_2d(ctx0, wstate.itype, n_state, n_ctx)),
	layer.mlp_0_w, layer.mlp_0_b, layer.mlp_1_w, layer.mlp_1_b);
	#else
	// fully connected
	cur = ggml_mul_mat(ctx0,
	layer.mlp_0_w,
	cur);

	cur = ggml_add(ctx0, cur, layer.mlp_0_b);

	// GELU activation
	cur = ggml_gelu(ctx0, cur);

	// projection
	cur = ggml_mul_mat(ctx0,
	layer.mlp_1_w,
	cur);

	cur = ggml_add(ctx0, cur, layer.mlp_1_b);
	#endif
	}

	inpL = ggml_add(ctx0, cur, inpFF);
	}

	cur = inpL;

	// norm
	{
	cur = ggml_norm(ctx0, cur, hparams.eps);

	// cur = ln_f_g*cur + ln_f_b
	cur = ggml_add(ctx0,
	ggml_mul(ctx0, cur, model.e_ln_w),
	model.e_ln_b);
	}

	ggml_build_forward_expand(gf, cur);

	wstate.embd_enc = cur;

	//ggml_graph_print(gf);

	////////////////////////////////////////////////////////////////////////////

	//printf("%s: used_mem = %f MB, %f MB, %f MB %f MB %f MB\n", __func__,
	// ggml_used_mem(ctx0)/1e6,
	// wstate.get_buf_max_mem(0)/1e6,
	// wstate.get_buf_max_mem(1)/1e6,
	// wstate.get_buf_max_mem(2)/1e6,
	// wstate.get_buf_max_mem(3)/1e6);

	ggml_free(ctx0);

	return gf;
	}

	// pre-compute cross-attention memory
	static struct ggml_cgraph * whisper_build_graph_cross(
	whisper_context & wctx,
	whisper_state & wstate) {
	const auto & model = wctx.model;
	const auto & hparams = model.hparams;

	const int n_ctx = wstate.exp_n_audio_ctx > 0 ? wstate.exp_n_audio_ctx : hparams.n_audio_ctx;
	const int n_state = hparams.n_audio_state;
	const int n_head = hparams.n_audio_head;

	const int n_state_head = n_state/n_head;

	const int n_ctx_pad = GGML_PAD(n_ctx, 256);

	struct ggml_init_params params = {
	/.mem_size =/ wstate.alloc_cross.meta.size(),
	/.mem_buffer =/ wstate.alloc_cross.meta.data(),
	/.no_alloc =/ true,
	};

	struct ggml_context * ctx0 = ggml_init(params);

	ggml_cgraph * gf = ggml_new_graph(ctx0);

	struct ggml_tensor * cur = ggml_view_tensor(ctx0, wstate.embd_enc);

	const float Kscale = pow(float(n_state_head), -0.25);

	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, Kscale);

	struct ggml_tensor * Vcross = ggml_mul_mat(ctx0,
	layer.cross_attn_v_w,
	cur);

	Vcross = ggml_add(ctx0,
	Vcross,
	layer.cross_attn_v_b);

	struct ggml_tensor * k;
	struct ggml_tensor * v;

	if (wctx.params.flash_attn) {
	k = ggml_view_1d(ctx0, wstate.kv_cross.k, n_state*n_ctx,
	(ggml_element_size(wstate.kv_cross.k)n_state)(il*n_ctx_pad));

	v = ggml_view_1d(ctx0, wstate.kv_cross.v, n_state*n_ctx,
	(ggml_element_size(wstate.kv_cross.v)n_state)(il*n_ctx_pad));
	} else {
	Vcross = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcross, n_state, n_ctx));

	k = ggml_view_1d(ctx0, wstate.kv_cross.k, n_state*n_ctx,
	(ggml_element_size(wstate.kv_cross.k)n_state)(il*n_ctx));

	v = ggml_view_2d(ctx0, wstate.kv_cross.v, n_ctx, n_state,
	( n_ctx)*ggml_element_size(wstate.kv_cross.v),
	(iln_ctx)ggml_element_size(wstate.kv_cross.v)*n_state);
	}

	ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcross, k));
	ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcross, v));
	}

	//ggml_graph_print(gf);

	ggml_free(ctx0);

	return gf;
	}

	// evaluate the encoder with the given state
	//
	// 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
	//
	// - wctx: the model
	// - wstate: the state of the encoder
	// - n_threads: number of threads to use
	// - mel_offset: offset in the mel spectrogram (i.e. audio offset)
	//
	static bool whisper_encode_internal(
	whisper_context & wctx,
	whisper_state & wstate,
	const int mel_offset,
	const int n_threads,
	ggml_abort_callback abort_callback,
	void * abort_callback_data) {
	const int64_t t_start_us = ggml_time_us();

	// conv
	{
	auto & alloc = wstate.alloc_conv.alloc;

	ggml_cgraph * gf = whisper_build_graph_conv(wctx, wstate);

	if (!ggml_gallocr_alloc_graph(alloc, gf)) {
	// should never happen as we pre-allocate the memory
	return false;
	}

	struct ggml_tensor * mel = ggml_graph_get_tensor(gf, "mel");

	// set the input
	{
	const auto & mel_inp = wstate.mel;
	const int n_ctx = wstate.exp_n_audio_ctx > 0 ? wstate.exp_n_audio_ctx : wctx.model.hparams.n_audio_ctx;

	assert(mel->type == GGML_TYPE_F32);
	assert(mel_inp.n_mel == wctx.model.hparams.n_mels);

	wstate.inp_mel.resize(ggml_nelements(mel));

	float * dst = wstate.inp_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[j2n_ctx + (i - i0)] = mel_inp.data[j*mel_inp.n_len + i];
	}
	}

	ggml_backend_tensor_set(mel, wstate.inp_mel.data(), 0, ggml_nelements(mel)*sizeof(float));
	}

	if (!whisper_encode_external(wstate)) {
	if (!ggml_graph_compute_helper(wstate.backend, gf, n_threads)) {
	return false;
	}
	} else {
	#if defined(WHISPER_USE_COREML)
	whisper_coreml_encode(wstate.ctx_coreml, mel->ne[0], mel->ne[1], (float ) mel->data, (float ) wstate.embd_enc->data);
	#elif defined(WHISPER_USE_OPENVINO)
	whisper_openvino_encode(wstate.ctx_openvino, mel, wstate.embd_enc);
	#endif
	}
	}

	// encoder
	if (!whisper_encode_external(wstate)) {
	auto & alloc = wstate.alloc_encode.alloc;

	ggml_cgraph * gf = whisper_build_graph_encoder(wctx, wstate);

	if (!ggml_gallocr_alloc_graph(alloc, gf)) {
	// should never happen as we pre-allocate the memory
	return false;
	}

	if (!ggml_graph_compute_helper(wstate.backend, gf, n_threads)) {
	return false;
	}
	}

	// cross
	{
	auto & alloc = wstate.alloc_cross.alloc;

	ggml_cgraph * gf = whisper_build_graph_cross(wctx, wstate);

	if (!ggml_gallocr_alloc_graph(alloc, gf)) {
	// should never happen as we pre-allocate the memory
	return false;
	}

	if (!ggml_graph_compute_helper(wstate.backend, gf, n_threads)) {
	return false;
	}
	}

	wstate.t_encode_us += ggml_time_us() - t_start_us;
	wstate.n_encode++;

	return !(abort_callback && abort_callback(abort_callback_data));
	}

	static struct ggml_cgraph * whisper_build_graph_decoder(
	whisper_context & wctx,
	whisper_state & wstate,
	const whisper_batch & batch,
	bool save_alignment_heads_QKs,
	bool worst_case) {
	const auto & model = wctx.model;
	const auto & hparams = model.hparams;

	auto & kv_self = wstate.kv_self;

	WHISPER_ASSERT(!!kv_self.ctx);

	const int n_ctx = kv_self.size;
	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_state_head = n_state/n_head;

	const int n_tokens = batch.n_tokens;
	const int n_audio_ctx = wstate.exp_n_audio_ctx > 0 ? wstate.exp_n_audio_ctx : hparams.n_audio_ctx;

	const int n_audio_ctx_pad = GGML_PAD(n_audio_ctx, 256);

	const int32_t n_kv = worst_case ? n_ctx : kv_self.n;
	const int32_t kv_head = worst_case ? n_ctx - n_tokens : kv_self.head;

	//WHISPER_LOG_DEBUG("%s: n_past = %d, n_tokens = %d, n_audio_ctx = %d, n_ctx = %d\n", __func__, n_past, n_tokens, n_audio_ctx, n_ctx);

	struct ggml_init_params params = {
	/.mem_size =/ wstate.alloc_decode.meta.size(),
	/.mem_buffer =/ wstate.alloc_decode.meta.data(),
	/.no_alloc =/ true,
	};

	struct ggml_context * ctx0 = ggml_init(params);

	ggml_cgraph * gf = ggml_new_graph_custom(ctx0, WHISPER_MAX_NODES, false);

	struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
	ggml_set_name(embd, "embd");
	ggml_set_input(embd);

	struct ggml_tensor * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
	ggml_set_name(position, "position");
	ggml_set_input(position);

	const float KQscale = pow(float(n_state_head), -0.25);

	struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD), 1);
	ggml_set_name(KQ_mask, "KQ_mask");
	ggml_set_input(KQ_mask);

	struct ggml_tensor * KQ_mask_f16 = ggml_cast(ctx0, KQ_mask, GGML_TYPE_F16);

	// 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;

	// [EXPERIMENTAL] Token-level timestamps with DTW
	struct ggml_tensor * aheads_cross_QKs = nullptr;

	for (int il = 0; il < n_layer; ++il) {
	const auto & layer = model.layers_decoder[il];

	// norm
	{
	cur = ggml_norm(ctx0, inpL, hparams.eps);

	// cur = ln_0_w*cur + ln_0_b
	cur = ggml_add(ctx0,
	ggml_mul(ctx0,
	cur,
	layer.attn_ln_0_w),
	layer.attn_ln_0_b);
	}

	// self-attention
	{
	struct ggml_tensor * Qcur = ggml_mul_mat(ctx0,
	layer.attn_q_w,
	cur);

	Qcur = ggml_add(ctx0,
	Qcur,
	layer.attn_q_b);

	Qcur = ggml_scale(ctx0, Qcur, KQscale);

	// note: no bias for Key
	struct ggml_tensor * Kcur = ggml_mul_mat(ctx0,
	layer.attn_k_w,
	cur);

	Kcur = ggml_scale(ctx0, Kcur, KQscale);

	// store key and value to memory
	{
	struct ggml_tensor * Vcur = ggml_mul_mat(ctx0,
	layer.attn_v_w,
	cur);

	Vcur = ggml_add(ctx0,
	Vcur,
	layer.attn_v_b);

	struct ggml_tensor * k;
	struct ggml_tensor * v;

	if (wctx.params.flash_attn) {
	k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_state,
	(ggml_element_size(kv_self.k)n_state)(il*n_ctx + kv_head));

	v = ggml_view_1d(ctx0, kv_self.v, n_tokens*n_state,
	(ggml_element_size(kv_self.v)n_state)(il*n_ctx + kv_head));
	} else {
	Vcur = ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_state, n_tokens));

	k = ggml_view_1d(ctx0, kv_self.k, n_tokens*n_state,
	(ggml_element_size(kv_self.k)n_state)(il*n_ctx + kv_head));

	v = ggml_view_2d(ctx0, kv_self.v, n_tokens, n_state,
	( n_ctx)*ggml_element_size(kv_self.v),
	(iln_ctx)ggml_element_size(kv_self.v)n_state + kv_headggml_element_size(kv_self.v));
	}

	ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k));
	ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v));
	}

	// ------

	struct ggml_tensor * Q =
	ggml_permute(ctx0,
	ggml_reshape_3d(ctx0, Qcur, n_state_head, n_head, n_tokens),
	0, 2, 1, 3);

	struct ggml_tensor * K =
	ggml_view_3d(ctx0, kv_self.k,
	n_state_head, n_kv, n_head,
	ggml_element_size(kv_self.k)*n_state,
	ggml_element_size(kv_self.k)*n_state_head,
	ggml_element_size(kv_self.k)n_staten_ctx*il);

	if (wctx.params.flash_attn) {
	struct ggml_tensor * V =
	ggml_view_3d(ctx0, kv_self.v,
	n_state_head, n_kv, n_head,
	ggml_element_size(kv_self.v)*n_state,
	ggml_element_size(kv_self.v)*n_state_head,
	ggml_element_size(kv_self.v)n_staten_ctx*il);

	cur = ggml_flash_attn_ext(ctx0, Q, K, V, KQ_mask_f16, 1.0f, 0.0f, 0.0f);

	cur = ggml_reshape_2d(ctx0, cur, n_state, n_tokens);
	} else {
	// K * Q
	struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);

	struct ggml_tensor * KQ_soft_max = ggml_soft_max_ext(ctx0, KQ, KQ_mask, 1.0f, 0.0f);

	struct ggml_tensor * V =
	ggml_view_3d(ctx0, kv_self.v,
	n_kv, n_state_head, n_head,
	n_ctx*ggml_element_size(kv_self.v),
	n_ctxggml_element_size(kv_self.v)n_state_head,
	n_ctxggml_element_size(kv_self.v)n_state*il);

	struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max);

	struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);

	cur = ggml_cpy(ctx0,
	KQV_merged,
	ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, n_tokens));
	}
	}

	// projection
	{
	cur = ggml_mul_mat(ctx0,
	layer.attn_ln_1_w,
	cur);

	cur = ggml_add(ctx0,
	cur,
	layer.attn_ln_1_b);
	}

	// add the input
	struct ggml_tensor * inpCA = ggml_add(ctx0, cur, inpL);

	// norm
	{
	cur = ggml_norm(ctx0, inpCA, hparams.eps); // note: we use inpCA here

	// cur = ln_0_w*cur + ln_0_b
	cur = ggml_add(ctx0,
	ggml_mul(ctx0,
	cur,
	layer.cross_attn_ln_0_w),
	layer.cross_attn_ln_0_b);
	}

	// cross-attention
	{
	struct ggml_tensor * Qcur = ggml_mul_mat(ctx0,
	layer.cross_attn_q_w,
	cur);

	Qcur = ggml_add(ctx0,
	Qcur,
	layer.cross_attn_q_b);

	struct ggml_tensor * Q =
	ggml_permute(ctx0,
	ggml_reshape_3d(ctx0, Qcur, n_state_head, n_head, n_tokens),
	0, 2, 1, 3);

	if (wctx.params.flash_attn) {
	struct ggml_tensor * Kcross =
	ggml_view_3d(ctx0, wstate.kv_cross.k,
	n_state_head, n_audio_ctx_pad, n_head,
	ggml_element_size(wstate.kv_cross.k)*n_state,
	ggml_element_size(wstate.kv_cross.k)*n_state_head,
	ggml_element_size(wstate.kv_cross.k)n_staten_audio_ctx_pad*il);

	struct ggml_tensor * Vcross =
	ggml_view_3d(ctx0, wstate.kv_cross.v,
	n_state_head, n_audio_ctx_pad, n_head,
	ggml_element_size(wstate.kv_cross.v)*n_state,
	ggml_element_size(wstate.kv_cross.v)*n_state_head,
	ggml_element_size(wstate.kv_cross.v)n_staten_audio_ctx_pad*il);

	cur = ggml_flash_attn_ext(ctx0, Q, Kcross, Vcross, nullptr, KQscale, 0.0f, 0.0f);

	cur = ggml_reshape_2d(ctx0, cur, n_state, n_tokens);
	} else {
	struct ggml_tensor * Kcross =
	ggml_view_3d(ctx0, wstate.kv_cross.k,
	n_state_head, n_audio_ctx, n_head,
	ggml_element_size(wstate.kv_cross.k)*n_state,
	ggml_element_size(wstate.kv_cross.k)*n_state_head,
	ggml_element_size(wstate.kv_cross.k)n_staten_audio_ctx*il);

	struct ggml_tensor * Vcross =
	ggml_view_3d(ctx0, wstate.kv_cross.v,
	n_audio_ctx, n_state_head, n_head,
	n_audio_ctx*ggml_element_size(wstate.kv_cross.v),
	n_audio_ctxggml_element_size(wstate.kv_cross.v)n_state_head,
	n_audio_ctxggml_element_size(wstate.kv_cross.v)n_state*il);

	// ------

	// K * Q
	struct ggml_tensor * KQ = ggml_mul_mat(ctx0, Kcross, Q);

	struct ggml_tensor * KQ_soft_max = ggml_soft_max_ext(ctx0, KQ, nullptr, KQscale, 0.0f);

	// [EXPERIMENTAL] Token-level timestamps with DTW
	if (wctx.params.dtw_token_timestamps) {
	if (wstate.aheads_masks.m[il] != nullptr) {
	struct ggml_tensor * aheads_KQs = ggml_reshape_2d(ctx0, KQ_soft_max, KQ_soft_max->ne[0] * KQ_soft_max->ne[1], KQ_soft_max->ne[2]);
	aheads_KQs = ggml_transpose(ctx0, aheads_KQs);
	aheads_KQs = ggml_cont(ctx0, aheads_KQs);
	aheads_KQs = ggml_mul_mat(ctx0, wstate.aheads_masks.m[il], aheads_KQs);
	aheads_KQs = ggml_transpose(ctx0, aheads_KQs);
	aheads_KQs = ggml_cont(ctx0, aheads_KQs);
	aheads_KQs = ggml_reshape_3d(ctx0, aheads_KQs, KQ_soft_max->ne[0], KQ_soft_max->ne[1], wstate.aheads_masks.m[il]->ne[1]);
	if (aheads_cross_QKs == NULL) {
	aheads_cross_QKs = aheads_KQs;
	} else {
	aheads_cross_QKs = ggml_concat(ctx0, aheads_cross_QKs, aheads_KQs,2);
	}
	}
	}

	struct ggml_tensor * KQV = ggml_mul_mat(ctx0, Vcross, KQ_soft_max);

	struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);

	cur = ggml_cpy(ctx0,
	KQV_merged,
	ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, n_tokens));
	}
	}

	// projection
	{
	cur = ggml_mul_mat(ctx0,
	layer.cross_attn_ln_1_w,
	cur);

	cur = ggml_add(ctx0,
	cur,
	layer.cross_attn_ln_1_b);
	}

	// add the input
	cur = ggml_add(ctx0, cur, inpCA);

	struct ggml_tensor * inpFF = cur;

	// feed-forward network
	{
	// norm
	{
	cur = ggml_norm(ctx0, inpFF, hparams.eps);

	// cur = mlp_ln_w*cur + mlp_ln_b
	cur = ggml_add(ctx0,
	ggml_mul(ctx0,
	cur,
	layer.mlp_ln_w),
	layer.mlp_ln_b);
	}

	// fully connected
	cur = ggml_mul_mat(ctx0,
	layer.mlp_0_w,
	cur);

	cur = ggml_add(ctx0,
	cur,
	layer.mlp_0_b);

	// GELU activation
	cur = ggml_gelu(ctx0, cur);

	// projection
	cur = ggml_mul_mat(ctx0,
	layer.mlp_1_w,
	cur);

	cur = ggml_add(ctx0,
	cur,
	layer.mlp_1_b);
	}

	inpL = ggml_add(ctx0, cur, inpFF);
	}

	cur = inpL;

	// norm
	{
	cur = ggml_norm(ctx0, cur, hparams.eps);

	cur = ggml_add(ctx0,
	ggml_mul(ctx0,
	cur,
	model.d_ln_w),
	model.d_ln_b);
	}

	// compute logits only for the last token
	// comment this line to compute logits for all n_tokens
	// might be useful in the future
	//cur = ggml_view_2d(ctx0, cur, cur->ne[0], 1, cur->nb[1], (cur->ne[1] - 1)*cur->nb[1]);

	struct ggml_tensor * logits = ggml_mul_mat(ctx0, model.d_te, cur);

	// [EXPERIMENTAL] Token-level timestamps with DTW
	if (wctx.params.dtw_token_timestamps && aheads_cross_QKs != nullptr) {
	aheads_cross_QKs = ggml_transpose(ctx0, aheads_cross_QKs);
	aheads_cross_QKs = ggml_cont(ctx0, aheads_cross_QKs);
	if (save_alignment_heads_QKs) {
	ggml_build_forward_expand(gf, aheads_cross_QKs);
	wstate.aheads_cross_QKs = aheads_cross_QKs;
	}
	}

	ggml_build_forward_expand(gf, logits);

	ggml_free(ctx0);

	return gf;
	}

	// evaluate the decoder
	//
	// given text prompt + audio features -> computes the logits for the next token
	//
	// - model: the model
	// - n_threads: number of threads to use
	// - tokens: text prompt
	// - n_tokens: number of tokens in the prompt
	// - n_past: number of past tokens to prefix the prompt with
	//
	static bool whisper_decode_internal(
	whisper_context & wctx,
	whisper_state & wstate,
	const whisper_batch & batch,
	const int n_threads,
	bool save_alignment_heads_QKs,
	ggml_abort_callback abort_callback,
	void * abort_callback_data) {
	const int64_t t_start_us = ggml_time_us();

	const auto & model = wctx.model;
	const auto & hparams = model.hparams;

	const int n_vocab = hparams.n_vocab;
	const int n_tokens = batch.n_tokens;

	auto & logits_out = wstate.logits;

	struct ggml_tensor * logits;

	// find KV slot for the batch
	{
	auto & kv_self = wstate.kv_self;

	if (!whisper_kv_cache_find_slot(kv_self, batch)) {
	return false;
	}

	const uint32_t pad = whisper_kv_cache_get_padding(wctx);
	kv_self.n = std::min(kv_self.size, std::max(pad, GGML_PAD(whisper_kv_cache_cell_max(kv_self), pad)));

	//kv_self.n = std::min((int32_t) hparams.n_text_ctx, std::max(32, whisper_kv_cache_cell_max(kv_self)));
	//printf("n_tokens = %5d, kv_self.head = %5d, kv_self.n = %5d, seq_id = %5d\n", batch.n_tokens, kv_self.head, kv_self.n, batch.seq_id[0][0]);
	}

	// decoder
	{
	auto & alloc = wstate.alloc_decode.alloc;

	ggml_cgraph * gf = whisper_build_graph_decoder(wctx, wstate, batch, save_alignment_heads_QKs, false);

	if (!ggml_gallocr_alloc_graph(alloc, gf)) {
	// should never happen as we pre-allocate the memory
	return false;
	}

	// set the inputs
	{
	struct ggml_tensor * embd = ggml_graph_get_tensor(gf, "embd");
	ggml_backend_tensor_set(embd, batch.token, 0, n_tokens*ggml_element_size(embd));
	}

	{
	struct ggml_tensor * position = ggml_graph_get_tensor(gf, "position");
	for (int i = 0; i < n_tokens; ++i) {
	const int32_t val = batch.pos[i];
	ggml_backend_tensor_set(position, &val, i*sizeof(int32_t), sizeof(int32_t));
	}
	}

	{
	struct ggml_tensor * KQ_mask = ggml_graph_get_tensor(gf, "KQ_mask");

	auto & kv_self = wstate.kv_self;

	const int32_t n_kv = kv_self.n;

	wstate.inp_mask.resize(ggml_nelements(KQ_mask));

	float * data = wstate.inp_mask.data();
	memset(data, 0, ggml_nbytes(KQ_mask));

	for (int h = 0; h < 1; ++h) {
	for (int j = 0; j < n_tokens; ++j) {
	const whisper_pos pos = batch.pos[j];
	const whisper_seq_id seq_id = batch.seq_id[j][0];

	for (int i = 0; i < n_kv; ++i) {
	if (!kv_self.cells[i].has_seq_id(seq_id) \|\| kv_self.cells[i].pos > pos) {
	data[h(n_kvn_tokens) + j*n_kv + i] = -INFINITY;
	}
	}
	}

	for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) {
	for (int j = 0; j < n_kv; ++j) {
	data[h(n_kvn_tokens) + i*n_kv + j] = -INFINITY;
	}
	}
	}

	ggml_backend_tensor_set(KQ_mask, wstate.inp_mask.data(), 0, ggml_nelements(KQ_mask)*sizeof(float));
	}

	logits = ggml_graph_node(gf, -1);

	if (!ggml_graph_compute_helper(wstate.backend, gf, n_threads)) {
	return false;
	}
	}

	logits_out.resize(n_tokens*n_vocab);
	for (int i = 0; i < n_tokens; i++) {
	if (batch.logits[i] == 0) {
	continue;
	}
	ggml_backend_tensor_get(logits, logits_out.data() + (n_vocabi), sizeof(float)(n_vocabi), sizeof(float)n_vocab);
	}

	if (batch.n_tokens > 1) {
	//printf("%s: used_mem = %f MB, %f MB, %f MB %f MB %f MB\n", __func__,
	// ggml_used_mem(ctx0)/1e6,
	// wstate.get_buf_max_mem(0)/1e6,
	// wstate.get_buf_max_mem(1)/1e6,
	// wstate.get_buf_max_mem(2)/1e6,
	// wstate.get_buf_max_mem(3)/1e6);
	}

	if (batch.n_tokens == 1) {
	wstate.t_decode_us += ggml_time_us() - t_start_us;
	wstate.n_decode++;
	} else if (batch.n_tokens < 16) {
	wstate.t_batchd_us += ggml_time_us() - t_start_us;
	wstate.n_batchd += n_tokens;
	} else {
	wstate.t_prompt_us += ggml_time_us() - t_start_us;
	wstate.n_prompt += n_tokens;
	}

	return !(abort_callback && abort_callback(abort_callback_data));
	}

	// 500 -> 00:05.000
	// 6000 -> 01:00.000
	std::string to_timestamp(int64_t t, bool comma) {
	int64_t msec = t * 10;
	int64_t hr = msec / (1000 * 60 * 60);
	msec = msec - hr * (1000 * 60 * 60);
	int64_t min = msec / (1000 * 60);
	msec = msec - min * (1000 * 60);
	int64_t sec = msec / 1000;
	msec = msec - sec * 1000;

	char buf[32];
	snprintf(buf, sizeof(buf), "%02d:%02d:%02d%s%03d", (int) hr, (int) min, (int) sec, comma ? "," : ".", (int) msec);

	return std::string(buf);
	}

	#define SIN_COS_N_COUNT WHISPER_N_FFT
	static float sin_vals[SIN_COS_N_COUNT];
	static float cos_vals[SIN_COS_N_COUNT];

	// In FFT, we frequently use sine and cosine operations with the same values.
	// We can use precalculated values to speed up the process.
	static void fill_sin_cos_table() {
	static bool is_filled = false;
	if (is_filled) return;
	for (int i = 0; i < SIN_COS_N_COUNT; i++) {
	double theta = (2M_PIi)/SIN_COS_N_COUNT;
	sin_vals[i] = sinf(theta);
	cos_vals[i] = cosf(theta);
	}
	is_filled = true;
	}

	// naive Discrete Fourier Transform
	// input is real-valued
	// output is complex-valued
	static void dft(const std::vector<float> & in, std::vector<float> & out) {
	int N = in.size();

	out.resize(N*2);
	const int sin_cos_step = SIN_COS_N_COUNT / N;

	for (int k = 0; k < N; k++) {
	float re = 0;
	float im = 0;

	for (int n = 0; n < N; n++) {
	int idx = (k * n * sin_cos_step) % (SIN_COS_N_COUNT); // t = 2M_PIk*n/N
	re += in[n]*cos_vals[idx]; // cos(t)
	im -= in[n]*sin_vals[idx]; // sin(t)
	}

	out[k*2 + 0] = re;
	out[k*2 + 1] = im;
	}
	}

	// Cooley-Tukey FFT
	// poor man's implementation - use something better
	// input is real-valued
	// output is complex-valued
	static 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;

	even.reserve(N/2);
	odd.reserve(N/2);

	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);

	const int sin_cos_step = SIN_COS_N_COUNT / N;
	for (int k = 0; k < N/2; k++) {
	int idx = k * sin_cos_step; // t = 2M_PIk/N
	float re = cos_vals[idx]; // cos(t)
	float im = -sin_vals[idx]; // sin(t)

	float re_odd = odd_fft[2*k + 0];
	float im_odd = odd_fft[2*k + 1];

	out[2k + 0] = even_fft[2k + 0] + rere_odd - imim_odd;
	out[2k + 1] = even_fft[2k + 1] + reim_odd + imre_odd;

	out[2(k + N/2) + 0] = even_fft[2k + 0] - rere_odd + imim_odd;
	out[2(k + N/2) + 1] = even_fft[2k + 1] - reim_odd - imre_odd;
	}
	}

	static bool hann_window(int length, bool periodic, std::vector<float> & output) {
	if (output.size() < static_cast<size_t>(length)) {
	output.resize(length);
	}
	int offset = -1;
	if (periodic) {
	offset = 0;
	}
	for (int i = 0; i < length; i++) {
	output[i] = 0.5(1.0 - cosf((2.0M_PI*i)/(length + offset)));
	}

	return true;
	}

	static void log_mel_spectrogram_worker_thread(int ith, const std::vector<float> & hann, const std::vector<float> & samples,
	int n_samples, int frame_size, int frame_step, int n_threads,
	const whisper_filters & filters, whisper_mel & mel) {
	std::vector<float> fft_in(frame_size, 0.0);
	std::vector<float> fft_out(2 * frame_size);
	int n_fft = filters.n_fft;
	int i = ith;

	// make sure n_fft == 1 + (WHISPER_N_FFT / 2), bin_0 to bin_nyquist
	assert(n_fft == 1 + (frame_size / 2));

	// calculate FFT only when fft_in are not all zero
	for (; i < std::min(n_samples / frame_step + 1, mel.n_len); i += n_threads) {
	const int offset = i * frame_step;

	// apply Hanning window (~10% faster)
	for (int j = 0; j < std::min(frame_size, n_samples - offset); j++) {
	fft_in[j] = hann[j] * samples[offset + j];
	}
	// fill the rest with zeros
	if (n_samples - offset < frame_size) {
	std::fill(fft_in.begin() + (n_samples - offset), fft_in.end(), 0.0);
	}

	// FFT
	fft(fft_in, fft_out);

	// Calculate modulus^2 of complex numbers
	// Use pow(fft_out[2 * j + 0], 2) + pow(fft_out[2 * j + 1], 2) causes inference quality problem? Interesting.
	for (int j = 0; j < n_fft; j++) {
	fft_out[j] = (fft_out[2 * j + 0] * fft_out[2 * j + 0] + fft_out[2 * j + 1] * fft_out[2 * j + 1]);
	}

	// mel spectrogram
	for (int j = 0; j < mel.n_mel; j++) {
	double sum = 0.0;

	// unroll loop (suggested by GH user @lunixbochs)
	int k = 0;
	for (k = 0; k < n_fft - 3; k += 4) {
	sum +=
	fft_out[k + 0] * filters.data[j * n_fft + k + 0] +
	fft_out[k + 1] * filters.data[j * n_fft + k + 1] +
	fft_out[k + 2] * filters.data[j * n_fft + k + 2] +
	fft_out[k + 3] * filters.data[j * n_fft + k + 3];
	}

	// handle n_fft remainder
	for (; k < n_fft; k++) {
	sum += fft_out[k] * filters.data[j * n_fft + k];
	}

	sum = log10(std::max(sum, 1e-10));

	mel.data[j * mel.n_len + i] = sum;
	}
	}

	// Otherwise fft_out are all zero
	double sum = log10(1e-10);
	for (; i < mel.n_len; i += n_threads) {
	for (int j = 0; j < mel.n_mel; j++) {
	mel.data[j * mel.n_len + i] = sum;
	}
	}
	}

	// ref: https://github.com/openai/whisper/blob/main/whisper/audio.py#L110-L157
	static bool log_mel_spectrogram(
	whisper_state & wstate,
	const float * samples,
	const int n_samples,
	const int /sample_rate/,
	const int frame_size,
	const int frame_step,
	const int n_mel,
	const int n_threads,
	const whisper_filters & filters,
	const bool debug,
	whisper_mel & mel) {
	const int64_t t_start_us = ggml_time_us();

	// Hanning window (Use cosf to eliminate difference)
	// ref: https://pytorch.org/docs/stable/generated/torch.hann_window.html
	// ref: https://github.com/openai/whisper/blob/main/whisper/audio.py#L147
	std::vector<float> hann;
	hann_window(frame_size, true, hann);


	// Calculate the length of padding
	int64_t stage_1_pad = WHISPER_SAMPLE_RATE * 30;
	int64_t stage_2_pad = frame_size / 2;

	// Initialize a vector and copy data from C array to it.
	std::vector<float> samples_padded;
	samples_padded.resize(n_samples + stage_1_pad + stage_2_pad * 2);
	std::copy(samples, samples + n_samples, samples_padded.begin() + stage_2_pad);

	// pad 30 seconds of zeros at the end of audio (480,000 samples) + reflective pad 200 samples at the end of audio
	std::fill(samples_padded.begin() + n_samples + stage_2_pad, samples_padded.begin() + n_samples + stage_1_pad + 2 * stage_2_pad, 0);

	// reflective pad 200 samples at the beginning of audio
	std::reverse_copy(samples + 1, samples + 1 + stage_2_pad, samples_padded.begin());

	mel.n_mel = n_mel;
	// https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/SpectralOps.cpp#L936
	// Calculate number of frames + remove the last frame
	mel.n_len = (samples_padded.size() - frame_size) / frame_step;
	// Calculate semi-padded sample length to ensure compatibility
	mel.n_len_org = 1 + (n_samples + stage_2_pad - frame_size) / frame_step;
	mel.data.resize(mel.n_mel * mel.n_len);


	{
	std::vector<std::thread> workers(n_threads - 1);
	for (int iw = 0; iw < n_threads - 1; ++iw) {
	workers[iw] = std::thread(
	log_mel_spectrogram_worker_thread, iw + 1, std::cref(hann), samples_padded,
	n_samples + stage_2_pad, frame_size, frame_step, n_threads,
	std::cref(filters), std::ref(mel));
	}

	// main thread
	log_mel_spectrogram_worker_thread(0, hann, samples_padded, n_samples + stage_2_pad, frame_size, frame_step, n_threads, filters, mel);

	for (int iw = 0; iw < n_threads - 1; ++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;
	}

	wstate.t_mel_us += ggml_time_us() - t_start_us;

	// Dump log_mel_spectrogram
	if (debug) {
	std::ofstream outFile("log_mel_spectrogram.json");
	outFile << "[";
	for (uint64_t i = 0; i < mel.data.size() - 1; i++) {
	outFile << mel.data[i] << ", ";
	}
	outFile << mel.data[mel.data.size() - 1] << "]";
	outFile.close();
	}

	return true;
	}

	// split text into tokens
	//
	// ref: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/encoder.py#L53
	//
	// Regex (Python):
	// r"""'s\|'t\|'re\|'ve\|'m\|'ll\|'d\| ?\p{L}+\| ?\p{N}+\| ?[^\s\p{L}\p{N}]+\|\s+(?!\S)\|\s+"""
	//
	// Regex (C++):
	// R"('s\|'t\|'re\|'ve\|'m\|'ll\|'d\| ?[[:alpha:]]+\| ?[[:digit:]]+\| ?[^\s[:alpha:][:digit:]]+\|\s+(?!\S)\|\s+)"
	//
	static std::vector<whisper_vocab::id> tokenize(const whisper_vocab & vocab, const std::string & text) {
	std::vector<std::string> words;

	// first split the text into words
	{
	std::string str = text;
	std::string pat = R"('s\|'t\|'re\|'ve\|'m\|'ll\|'d\| ?[[:alpha:]]+\| ?[[:digit:]]+\| ?[^\s[:alpha:][:digit:]]+\|\s+(?!\S)\|\s+)";

	std::regex re(pat);
	std::smatch m;

	while (std::regex_search(str, m, re)) {
	for (auto x : m) {
	words.push_back(x);
	}
	str = m.suffix();
	}
	}

	// find the longest tokens that form the words:
	std::vector<whisper_vocab::id> tokens;
	for (const auto & word : words) {
	if (word.empty()) continue;

	int i = 0;
	int n = word.size();
	while (i < n) {
	int j = n;
	bool found = false;
	while (j > i) {
	auto sub = word.substr(i, j-i);
	auto it = vocab.token_to_id.find(sub);
	if (it != vocab.token_to_id.end()) {
	tokens.push_back(it->second);
	i = j;
	found = true;
	break;
	}
	--j;
	}
	if (!found) {
	WHISPER_LOG_ERROR("unknown token\n");
	++i;
	}
	}
	}

	return tokens;
	}

	//
	// interface implementation
	//

	#ifdef WHISPER_USE_COREML
	// replace .bin with -encoder.mlmodelc
	static std::string whisper_get_coreml_path_encoder(std::string path_bin) {
	auto pos = path_bin.rfind('.');
	if (pos != std::string::npos) {
	path_bin = path_bin.substr(0, pos);
	}

	// match "-qx_x"
	pos = path_bin.rfind('-');
	if (pos != std::string::npos) {
	auto sub = path_bin.substr(pos);
	if (sub.size() == 5 && sub[1] == 'q' && sub[3] == '_') {
	path_bin = path_bin.substr(0, pos);
	}
	}

	path_bin += "-encoder.mlmodelc";

	return path_bin;
	}
	#endif

	#ifdef WHISPER_USE_OPENVINO
	// replace .bin with-encoder-openvino.xml
	static std::string whisper_openvino_get_path_encoder(std::string path_bin) {
	auto pos = path_bin.rfind('.');
	if (pos != std::string::npos) {
	path_bin = path_bin.substr(0, pos);
	}

	path_bin += "-encoder-openvino.xml";

	return path_bin;
	}

	static std::string whisper_openvino_get_path_cache(std::string path_bin) {
	auto pos = path_bin.rfind('.');
	if (pos != std::string::npos) {
	path_bin = path_bin.substr(0, pos);
	}

	path_bin += "-encoder-openvino-cache";

	return path_bin;
	}
	#endif

	struct whisper_state * whisper_init_state(whisper_context * ctx) {
	fill_sin_cos_table();

	whisper_state * state = new whisper_state;

	state->backend = whisper_backend_init(ctx->params);
	if (!state->backend) {
	WHISPER_LOG_ERROR("%s: whisper_backend_init() failed\n", __func__);
	whisper_free_state(state);
	return nullptr;
	}

	// at this point, we don't know yet how many decoders will be used, so we overallocate 3x ctx
	// in theory, there can be a case where this is not enough, but in practice it should always be enough
	const int factor = 3;

	if (!kv_cache_init(state->kv_self, ctx->backend, ctx->itype,
	ctx->model.hparams.n_text_state,
	ctx->model.hparams.n_text_layer,
	GGML_PAD(ctx->model.hparams.n_text_ctx, 256)*factor)) {
	WHISPER_LOG_ERROR("%s: kv_cache_init() failed for self-attention cache\n", __func__);
	whisper_free_state(state);
	return nullptr;
	}

	{
	const size_t memory_size = ggml_nbytes(state->kv_self.k) + ggml_nbytes(state->kv_self.v);
	WHISPER_LOG_INFO("%s: kv self size = %7.2f MB\n", __func__, memory_size / 1e6);
	}

	if (!kv_cache_init(state->kv_cross, ctx->backend, ctx->itype,
	ctx->model.hparams.n_text_state,
	ctx->model.hparams.n_text_layer,
	GGML_PAD(ctx->model.hparams.n_audio_ctx, 256))) {
	WHISPER_LOG_ERROR("%s: kv_cache_init() failed for cross-attention cache\n", __func__);
	whisper_free_state(state);
	return nullptr;
	}

	{
	const size_t memory_size = ggml_nbytes(state->kv_cross.k) + ggml_nbytes(state->kv_cross.v);
	WHISPER_LOG_INFO("%s: kv cross size = %7.2f MB\n", __func__, memory_size / 1e6);
	}

	if (!kv_cache_init(state->kv_pad, ctx->backend, ctx->itype,
	ctx->model.hparams.n_audio_state,
	1,
	GGML_PAD(ctx->model.hparams.n_audio_ctx, 256))) {
	WHISPER_LOG_ERROR("%s: kv_cache_init() failed for self-attention cache\n", __func__);
	whisper_free_state(state);
	return nullptr;
	}

	{
	const size_t memory_size = ggml_nbytes(state->kv_pad.k) + ggml_nbytes(state->kv_pad.v);
	WHISPER_LOG_INFO("%s: kv pad size = %7.2f MB\n", __func__, memory_size / 1e6);
	}

	// [EXPERIMENTAL] Token-level timestamps with DTW
	if (ctx->params.dtw_token_timestamps) {
	if (!aheads_masks_init(ctx->params, ctx->model.hparams, state->aheads_masks, ctx->backend)) {
	WHISPER_LOG_ERROR("%s: aheads_masks_init() failed for alignment heads masks\n", __func__);
	whisper_free_state(state);
	return nullptr;
	}
	const size_t memory_size = aheads_masks_nbytes(state->aheads_masks);
	WHISPER_LOG_INFO("%s: alignment heads masks size = %ld B\n", __func__, memory_size);
	}

	#ifdef WHISPER_USE_COREML
	const auto path_coreml = whisper_get_coreml_path_encoder(ctx->path_model);

	WHISPER_LOG_INFO("%s: loading Core ML model from '%s'\n", __func__, path_coreml.c_str());
	WHISPER_LOG_INFO("%s: first run on a device may take a while ...\n", __func__);

	state->ctx_coreml = whisper_coreml_init(path_coreml.c_str());
	if (!state->ctx_coreml) {
	WHISPER_LOG_ERROR("%s: failed to load Core ML model from '%s'\n", __func__, path_coreml.c_str());
	#ifndef WHISPER_COREML_ALLOW_FALLBACK
	whisper_free_state(state);
	return nullptr;
	#endif
	} else {
	WHISPER_LOG_INFO("%s: Core ML model loaded\n", __func__);
	}
	#endif

	state->logits.reserve(ctx->vocab.n_vocab * ctx->model.hparams.n_text_ctx);

	state->batch = whisper_batch_init(ctx->model.hparams.n_text_ctx, WHISPER_MAX_DECODERS);

	// TAGS: WHISPER_DECODER_INIT
	state->decoders[0].sequence.tokens.reserve(ctx->model.hparams.n_text_ctx);

	state->decoders[0].probs.reserve (ctx->vocab.n_vocab);
	state->decoders[0].logits.reserve (ctx->vocab.n_vocab);
	state->decoders[0].logprobs.reserve (ctx->vocab.n_vocab);
	state->decoders[0].logits_id.reserve(ctx->model.hparams.n_vocab);

	state->decoders[0].rng = std::mt19937(0);

	// conv allocator
	{
	bool ok = whisper_allocr_graph_init(state->alloc_conv, ctx->backend,
	[&]() {
	return whisper_build_graph_conv(ctx, state);
	});

	if (!ok) {
	WHISPER_LOG_ERROR("%s: failed to init conv allocator\n", __func__);
	whisper_free_state(state);
	return nullptr;
	}

	WHISPER_LOG_INFO("%s: compute buffer (conv) = %7.2f MB\n", __func__, whisper_allocr_size(state->alloc_conv) / 1e6);
	}

	// encoder allocator
	if (!whisper_encode_external(*state)) {
	bool ok = whisper_allocr_graph_init(state->alloc_encode, ctx->backend,
	[&]() {
	return whisper_build_graph_encoder(ctx, state);
	});

	if (!ok) {
	WHISPER_LOG_ERROR("%s: failed to init encoder allocator\n", __func__);
	whisper_free_state(state);
	return nullptr;
	}

	WHISPER_LOG_INFO("%s: compute buffer (encode) = %7.2f MB\n", __func__, whisper_allocr_size(state->alloc_encode) / 1e6);
	}

	// cross allocator
	{
	bool ok = whisper_allocr_graph_init(state->alloc_cross, ctx->backend,
	[&]() {
	return whisper_build_graph_cross(ctx, state);
	});

	if (!ok) {
	WHISPER_LOG_ERROR("%s: failed to init cross allocator\n", __func__);
	whisper_free_state(state);
	return nullptr;
	}

	WHISPER_LOG_INFO("%s: compute buffer (cross) = %7.2f MB\n", __func__, whisper_allocr_size(state->alloc_cross) / 1e6);
	}

	// decoder allocator
	{
	bool ok = whisper_allocr_graph_init(state->alloc_decode, ctx->backend,
	[&]() {
	const auto & hparams = ctx->model.hparams;

	// TODO: make sure this is the worst-case scenario
	const int n_tokens = hparams.n_text_ctx;
	const int n_past = 0;

	whisper_batch_prep_legacy(state->batch, nullptr, n_tokens, n_past, 0);

	return whisper_build_graph_decoder(ctx, state, state->batch, ctx->params.dtw_token_timestamps, true);
	});

	if (!ok) {
	WHISPER_LOG_ERROR("%s: failed to init decoder allocator\n", __func__);
	whisper_free_state(state);
	return nullptr;
	}

	WHISPER_LOG_INFO("%s: compute buffer (decode) = %7.2f MB\n", __func__, whisper_allocr_size(state->alloc_decode) / 1e6);
	}

	return state;
	}

	int whisper_ctx_init_openvino_encoder(
	struct whisper_context * ctx,
	const char * model_path,
	const char * device,
	const char * cache_dir) {
	#ifndef WHISPER_USE_OPENVINO
	(void)(ctx);
	(void)(model_path);
	(void)(device);
	(void)(cache_dir);

	return 1;
	#else
	if (!model_path && ctx->path_model.empty()) {
	WHISPER_LOG_ERROR("%s: model_path is nullptr, and ctx has no model_path set.\n", __func__);
	return 1;
	}

	std::string path_encoder;
	if (!model_path) {
	//if model_path is not set, attempt to find it in the same directory as ggml-<model>.bin model
	path_encoder = whisper_openvino_get_path_encoder(ctx->path_model);
	} else {
	path_encoder = model_path;
	}

	std::string path_cache;
	if (!cache_dir) {
	//if cache_dir is not set, set it as a dir residing next to ggml-<model>.bin
	path_cache = whisper_openvino_get_path_cache(ctx->path_model);
	} else {
	path_cache = cache_dir;
	}

	WHISPER_LOG_INFO("%s: loading OpenVINO model from '%s'\n", __func__, path_encoder.c_str());
	WHISPER_LOG_INFO("%s: first run on a device may take a while ...\n", __func__);

	ctx->state->ctx_openvino = whisper_openvino_init(path_encoder.c_str(), device, path_cache.c_str());
	if (!ctx->state->ctx_openvino) {
	WHISPER_LOG_ERROR("%s: failed to init OpenVINO encoder from '%s'\n", __func__, path_encoder.c_str());
	return 1;
	} else {
	WHISPER_LOG_INFO("%s: OpenVINO model loaded\n", __func__);
	}

	return 0;
	#endif
	}

	struct whisper_context_params whisper_context_default_params() {
	struct whisper_context_params result = {
	/.use_gpu =/ true,
	/.flash_attn =/ false,
	/.gpu_device =/ 0,

	/.dtw_token_timestamps =/ false,
	/.dtw_aheads_preset =/ WHISPER_AHEADS_NONE,
	/.dtw_n_top =/ -1,
	/.dtw_aheads =/ {
	/.n_heads =/ 0,
	/.heads =/ NULL,
	},
	/.dtw_mem_size =/ 10241024128,
	};
	return result;
	}

	struct whisper_context * whisper_init_from_file_with_params_no_state(const char * path_model, struct whisper_context_params params) {
	WHISPER_LOG_INFO("%s: loading model from '%s'\n", __func__, path_model);
	#ifdef _MSC_VER
	// Convert UTF-8 path to wide string (UTF-16) for Windows, resolving character encoding issues.
	std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
	std::wstring path_model_wide = converter.from_bytes(path_model);
	auto fin = std::ifstream(path_model_wide, std::ios::binary);
	#else
	auto fin = std::ifstream(path_model, std::ios::binary);
	#endif
	if (!fin) {
	WHISPER_LOG_ERROR("%s: failed to open '%s'\n", __func__, path_model);
	return nullptr;
	}

	whisper_model_loader loader = {};

	loader.context = &fin;

	loader.read = [](void * ctx, void * output, size_t read_size) {
	std::ifstream * fin = (std::ifstream*)ctx;
	fin->read((char *)output, read_size);
	return read_size;
	};

	loader.eof = [](void * ctx) {
	std::ifstream * fin = (std::ifstream*)ctx;
	return fin->eof();
	};

	loader.close = [](void * ctx) {
	std::ifstream * fin = (std::ifstream*)ctx;
	fin->close();
	};

	auto ctx = whisper_init_with_params_no_state(&loader, params);

	if (ctx) {
	ctx->path_model = path_model;
	}

	return ctx;
	}

	struct whisper_context * whisper_init_from_buffer_with_params_no_state(void * buffer, size_t buffer_size, struct whisper_context_params params) {
	struct buf_context {
	uint8_t* buffer;
	size_t size;
	size_t current_offset;
	};

	buf_context ctx = { reinterpret_cast<uint8_t*>(buffer), buffer_size, 0 };

	WHISPER_LOG_INFO("%s: loading model from buffer\n", __func__);

	whisper_model_loader loader = {};

	loader.context = &ctx;

	loader.read = [](void * ctx, void * output, size_t read_size) {
	buf_context * buf = reinterpret_cast<buf_context *>(ctx);

	size_t size_to_copy = buf->current_offset + read_size < buf->size ? read_size : buf->size - buf->current_offset;

	memcpy(output, buf->buffer + buf->current_offset, size_to_copy);
	buf->current_offset += size_to_copy;

	return size_to_copy;
	};

	loader.eof = [](void * ctx) {
	buf_context * buf = reinterpret_cast<buf_context *>(ctx);

	return buf->current_offset >= buf->size;
	};

	loader.close = [](void * /ctx/) { };

	return whisper_init_with_params_no_state(&loader, params);
	}

	struct whisper_context * whisper_init_with_params_no_state(struct whisper_model_loader * loader, struct whisper_context_params params) {
	ggml_time_init();

	if (params.flash_attn && params.dtw_token_timestamps) {
	WHISPER_LOG_WARN("%s: dtw_token_timestamps is not supported with flash_attn - disabling\n", __func__);
	params.dtw_token_timestamps = false;
	}

	WHISPER_LOG_INFO("%s: use gpu = %d\n", __func__, params.use_gpu);
	WHISPER_LOG_INFO("%s: flash attn = %d\n", __func__, params.flash_attn);
	WHISPER_LOG_INFO("%s: gpu_device = %d\n", __func__, params.gpu_device);
	WHISPER_LOG_INFO("%s: dtw = %d\n", __func__, params.dtw_token_timestamps);

	whisper_context * ctx = new whisper_context;
	ctx->params = params;

	if (!whisper_model_load(loader, *ctx)) {
	loader->close(loader->context);
	WHISPER_LOG_ERROR("%s: failed to load model\n", __func__);
	delete ctx;
	return nullptr;
	}

	loader->close(loader->context);

	return ctx;
	}

	struct whisper_context * whisper_init_from_file_with_params(const char * path_model, struct whisper_context_params params) {
	whisper_context * ctx = whisper_init_from_file_with_params_no_state(path_model, params);
	if (!ctx) {
	return nullptr;
	}

	ctx->state = whisper_init_state(ctx);
	if (!ctx->state) {
	whisper_free(ctx);
	return nullptr;
	}

	return ctx;
	}

	struct whisper_context * whisper_init_from_buffer_with_params(void * buffer, size_t buffer_size, struct whisper_context_params params) {
	whisper_context * ctx = whisper_init_from_buffer_with_params_no_state(buffer, buffer_size, params);
	if (!ctx) {
	return nullptr;
	}

	ctx->state = whisper_init_state(ctx);
	if (!ctx->state) {
	whisper_free(ctx);
	return nullptr;
	}

	return ctx;
	}

	struct whisper_context * whisper_init_with_params(struct whisper_model_loader * loader, struct whisper_context_params params) {
	whisper_context * ctx = whisper_init_with_params_no_state(loader, params);
	if (!ctx) {
	return nullptr;
	}

	ctx->state = whisper_init_state(ctx);
	if (!ctx->state) {
	whisper_free(ctx);
	return nullptr;
	}

	return ctx;
	}

	struct whisper_context * whisper_init_from_file(const char * path_model) {
	return whisper_init_from_file_with_params(path_model, whisper_context_default_params());
	}

	struct whisper_context * whisper_init_from_buffer(void * buffer, size_t buffer_size) {
	return whisper_init_from_buffer_with_params(buffer, buffer_size, whisper_context_default_params());
	}

	struct whisper_context * whisper_init(struct whisper_model_loader * loader) {
	return whisper_init_with_params(loader, whisper_context_default_params());
	}

	struct whisper_context * whisper_init_from_file_no_state(const char * path_model) {
	return whisper_init_from_file_with_params_no_state(path_model, whisper_context_default_params());
	}

	struct whisper_context * whisper_init_from_buffer_no_state(void * buffer, size_t buffer_size) {
	return whisper_init_from_buffer_with_params_no_state(buffer, buffer_size, whisper_context_default_params());
	}

	struct whisper_context * whisper_init_no_state(struct whisper_model_loader * loader) {
	return whisper_init_with_params_no_state(loader, whisper_context_default_params());
	}

	void whisper_free_state(struct whisper_state * state) {
	if (state) {
	kv_cache_free(state->kv_self);
	kv_cache_free(state->kv_cross);
	kv_cache_free(state->kv_pad);

	#ifdef WHISPER_USE_COREML
	if (state->ctx_coreml != nullptr) {
	whisper_coreml_free(state->ctx_coreml);
	state->ctx_coreml = nullptr;
	}
	#endif

	#ifdef WHISPER_USE_OPENVINO
	if (state->ctx_openvino != nullptr) {
	whisper_openvino_free(state->ctx_openvino);
	state->ctx_openvino = nullptr;
	}
	#endif

	whisper_batch_free(state->batch);

	ggml_gallocr_free(state->alloc_conv.alloc);
	ggml_gallocr_free(state->alloc_encode.alloc);
	ggml_gallocr_free(state->alloc_cross.alloc);
	ggml_gallocr_free(state->alloc_decode.alloc);

	ggml_backend_free(state->backend);

	// [EXPERIMENTAL] Token-level timestamps with DTW
	aheads_masks_free(state->aheads_masks);

	delete state;
	}
	}

	void whisper_free(struct whisper_context * ctx) {
	if (ctx) {
	ggml_free(ctx->model.ctx);

	ggml_backend_buffer_free(ctx->model.buffer);

	whisper_free_state(ctx->state);

	ggml_backend_free(ctx->backend);

	delete ctx;
	}
	}

	void whisper_free_context_params(struct whisper_context_params * params) {
	if (params) {
	delete params;
	}
	}

	void whisper_free_params(struct whisper_full_params * params) {
	if (params) {
	delete params;
	}
	}

	int whisper_pcm_to_mel_with_state(struct whisper_context * ctx, struct whisper_state * state, const float * samples, int n_samples, int n_threads) {
	if (!log_mel_spectrogram(*state, samples, n_samples, WHISPER_SAMPLE_RATE, WHISPER_N_FFT, WHISPER_HOP_LENGTH, ctx->model.filters.n_mel, n_threads, ctx->model.filters, false, state->mel)) {
	WHISPER_LOG_ERROR("%s: failed to compute mel spectrogram\n", __func__);
	return -1;
	}

	return 0;
	}

	int whisper_pcm_to_mel(struct whisper_context * ctx, const float * samples, int n_samples, int n_threads) {
	return whisper_pcm_to_mel_with_state(ctx, ctx->state, samples, n_samples, n_threads);
	}

	// same as whisper_pcm_to_mel, but applies a Phase Vocoder to speed up the audio x2 (PV without phase lock is not good)
	int whisper_pcm_to_mel_phase_vocoder_with_state(struct whisper_context * ctx, struct whisper_state * state, const float * samples, int n_samples, int n_threads) {
	if (!log_mel_spectrogram(state, samples, n_samples, WHISPER_SAMPLE_RATE, 2 WHISPER_N_FFT, 2 * WHISPER_HOP_LENGTH, ctx->model.filters.n_mel, n_threads, ctx->model.filters, false, state->mel)) {
	WHISPER_LOG_ERROR("%s: failed to compute mel spectrogram\n", __func__);
	return -1;
	}

	return 0;
	}

	// same as whisper_pcm_to_mel, but applies a Phase Vocoder to speed up the audio x2 (PV without phase lock is not good)
	int whisper_pcm_to_mel_phase_vocoder(struct whisper_context * ctx, const float * samples, int n_samples, int n_threads) {
	return whisper_pcm_to_mel_phase_vocoder_with_state(ctx, ctx->state, samples, n_samples, n_threads);
	}

	// same as whisper_pcm_to_mel, but applies WSOLA to speed up the audio x2
	// TODO

	// same as whisper_pcm_to_mel, but applies HPTSM to speed up the audio x2
	// TODO

	// same as whisper_pcm_to_mel, but applies PV (with phase lock) to speed up the audio x2
	// TODO

	int whisper_set_mel_with_state(
	struct whisper_context * ctx,
	struct whisper_state * state,
	const float * data,
	int n_len,
	int n_mel) {
	if (n_mel != ctx->model.filters.n_mel) {
	WHISPER_LOG_ERROR("%s: invalid number of mel bands: %d (expected %d)\n", __func__, n_mel, ctx->model.filters.n_mel);
	return -1;
	}

	state->mel.n_len = n_len;
	state->mel.n_len_org = n_len;
	state->mel.n_mel = n_mel;

	state->mel.data.resize(n_len*n_mel);
	memcpy(state->mel.data.data(), data, n_lenn_melsizeof(float));

	return 0;
	}

	int whisper_set_mel(
	struct whisper_context * ctx,
	const float * data,
	int n_len,
	int n_mel) {
	return whisper_set_mel_with_state(ctx, ctx->state, data, n_len, n_mel);
	}

	int whisper_encode_with_state(struct whisper_context * ctx, struct whisper_state * state, int offset, int n_threads) {
	if (!whisper_encode_internal(ctx, state, offset, n_threads, nullptr, nullptr)) {
	WHISPER_LOG_ERROR("%s: failed to eval\n", __func__);
	return -1;
	}

	return 0;
	}

	int whisper_encode(struct whisper_context * ctx, int offset, int n_threads) {
	if (!whisper_encode_internal(ctx, ctx->state, offset, n_threads, nullptr, nullptr)) {
	WHISPER_LOG_ERROR("%s: failed to eval\n", __func__);
	return -1;
	}

	return 0;
	}

	int whisper_decode_with_state(struct whisper_context * ctx, struct whisper_state * state, const whisper_token * tokens, int n_tokens, int n_past, int n_threads) {
	whisper_batch_prep_legacy(state->batch, tokens, n_tokens, n_past, 0);

	whisper_kv_cache_seq_rm(state->kv_self, 0, n_past, -1);

	if (!whisper_decode_internal(ctx, state, state->batch, n_threads, false, nullptr, nullptr)) {
	WHISPER_LOG_ERROR("%s: failed to eval\n", __func__);
	return 1;
	}

	return 0;
	}

	int whisper_decode(struct whisper_context * ctx, const whisper_token * tokens, int n_tokens, int n_past, int n_threads) {
	if (ctx->state == nullptr) {
	WHISPER_LOG_ERROR("%s: ERROR state was not loaded.\n", __func__);
	return -1;
	}

	return whisper_decode_with_state(ctx, ctx->state, tokens, n_tokens, n_past, n_threads);
	}

	int whisper_tokenize(struct whisper_context * ctx, const char * text, whisper_token * tokens, int n_max_tokens) {
	const auto res = tokenize(ctx->vocab, text);

	if (n_max_tokens < (int) res.size()) {
	WHISPER_LOG_ERROR("%s: too many resulting tokens: %d (max %d)\n", __func__, (int) res.size(), n_max_tokens);
	return -(int) res.size();
	}

	for (int i = 0; i < (int) res.size(); i++) {
	tokens[i] = res[i];
	}

	return res.size();
	}

	int whisper_token_count(struct whisper_context * ctx, const char * text) {
	return -whisper_tokenize(ctx, text, NULL, 0);
	}

	int whisper_lang_max_id() {
	auto max_id = 0;
	for (const auto & kv : g_lang) {
	max_id = std::max(max_id, kv.second.first);
	}

	return max_id;
	}

	int whisper_lang_id(const char * lang) {
	if (!g_lang.count(lang)) {
	for (const auto & kv : g_lang) {
	if (kv.second.second == lang) {
	return kv.second.first;
	}
	}

	WHISPER_LOG_ERROR("%s: unknown language '%s'\n", __func__, lang);
	return -1;
	}
	return g_lang.at(lang).first;
	}

	const char * whisper_lang_str(int id) {
	for (const auto & kv : g_lang) {
	if (kv.second.first == id) {
	return kv.first.c_str();
	}
	}

	WHISPER_LOG_ERROR("%s: unknown language id %d\n", __func__, id);
	return nullptr;
	}

	const char * whisper_lang_str_full(int id) {
	for (const auto & kv : g_lang) {
	if (kv.second.first == id) {
	return kv.second.second.c_str();
	}
	}

	WHISPER_LOG_ERROR("%s: unknown language id %d\n", __func__, id);
	return nullptr;
	}

	int whisper_lang_auto_detect_with_state(
	struct whisper_context * ctx,
	struct whisper_state * state,
	int offset_ms,
	int n_threads,
	float * lang_probs) {
	const int seek = offset_ms/10;

	if (seek < 0) {
	WHISPER_LOG_ERROR("%s: offset %dms is before the start of the audio\n", __func__, offset_ms);
	return -1;
	}

	if (seek >= state->mel.n_len_org) {
	WHISPER_LOG_ERROR("%s: offset %dms is past the end of the audio (%dms)\n", __func__, offset_ms, state->mel.n_len_org*10);
	return -2;
	}

	// run the encoder
	if (whisper_encode_with_state(ctx, state, seek, n_threads) != 0) {
	WHISPER_LOG_ERROR("%s: failed to encode\n", __func__);
	return -6;
	}

	const std::vector<whisper_token> prompt = { whisper_token_sot(ctx) };

	if (whisper_decode_with_state(ctx, state, prompt.data(), prompt.size(), 0, n_threads) != 0) {
	WHISPER_LOG_ERROR("%s: failed to decode\n", __func__);
	return -7;
	}

	auto & logits_id = state->decoders[0].logits_id;
	logits_id.clear();

	for (const auto & kv : g_lang) {
	const auto token_lang = whisper_token_lang(ctx, kv.second.first);
	logits_id.emplace_back(state->logits[token_lang], kv.second.first);
	}

	// sort descending
	{
	using pair_type = std::remove_reference<decltype(logits_id)>::type::value_type;
	std::sort(logits_id.begin(), logits_id.end(), [](const pair_type & a, const pair_type & b) {
	return a.first > b.first;
	});
	}

	// softmax
	{
	const auto max = logits_id[0].first;

	double sum = 0.0f;
	for (auto & kv : logits_id) {
	kv.first = exp(kv.first - max);
	sum += kv.first;
	}

	for (auto & kv : logits_id) {
	kv.first /= sum;
	}
	}

	{
	for (const auto & prob : logits_id) {
	if (lang_probs) {
	lang_probs[prob.second] = prob.first;
	}

	//printf("%s: lang %2d (%3s): %f\n", __func__, prob.second, whisper_lang_str(prob.second), prob.first);
	}
	}

	return logits_id[0].second;
	}

	int whisper_lang_auto_detect(
	struct whisper_context * ctx,
	int offset_ms,
	int n_threads,
	float * lang_probs) {
	return whisper_lang_auto_detect_with_state(ctx, ctx->state, offset_ms, n_threads, lang_probs);
	}

	int whisper_model_n_vocab(struct whisper_context * ctx) {
	return ctx->model.hparams.n_vocab;
	}

	int whisper_model_n_audio_ctx(struct whisper_context * ctx) {
	return ctx->model.hparams.n_audio_ctx;
	}

	int whisper_model_n_audio_state(struct whisper_context * ctx) {
	return ctx->model.hparams.n_audio_state;
	}

	int whisper_model_n_audio_head(struct whisper_context * ctx) {
	return ctx->model.hparams.n_audio_head;
	}

	int whisper_model_n_audio_layer(struct whisper_context * ctx) {
	return ctx->model.hparams.n_audio_layer;
	}

	int whisper_model_n_text_ctx(struct whisper_context * ctx) {
	return ctx->model.hparams.n_text_ctx;
	}

	int whisper_model_n_text_state(struct whisper_context * ctx) {
	return ctx->model.hparams.n_text_state;
	}

	int whisper_model_n_text_head(struct whisper_context * ctx) {
	return ctx->model.hparams.n_text_head;
	}

	int whisper_model_n_text_layer(struct whisper_context * ctx) {
	return ctx->model.hparams.n_text_layer;
	}

	int whisper_model_n_mels(struct whisper_context * ctx) {
	return ctx->model.hparams.n_mels;
	}

	int whisper_model_ftype(struct whisper_context * ctx) {
	return ctx->model.hparams.ftype;
	}

	int whisper_model_type(struct whisper_context * ctx) {
	return ctx->model.type;
	}

	const char whisper_model_type_readable(struct whisper_context ctx) {
	switch (ctx->model.type) {
	case e_model::MODEL_TINY:
	return "tiny";
	case e_model::MODEL_BASE:
	return "base";
	case e_model::MODEL_SMALL:
	return "small";
	case e_model::MODEL_MEDIUM:
	return "medium";
	case e_model::MODEL_LARGE:
	return "large";
	default:
	return "unknown";
	}
	}

	int whisper_n_len_from_state(struct whisper_state * state) {
	return state->mel.n_len_org;
	}

	int whisper_n_len(struct whisper_context * ctx) {
	return ctx->state->mel.n_len_org;
	}

	int whisper_n_vocab(struct whisper_context * ctx) {
	return ctx->vocab.n_vocab;
	}

	int whisper_n_text_ctx(struct whisper_context * ctx) {
	return ctx->model.hparams.n_text_ctx;
	}

	int whisper_n_audio_ctx(struct whisper_context * ctx) {
	return ctx->model.hparams.n_audio_ctx;
	}

	int whisper_is_multilingual(struct whisper_context * ctx) {
	return ctx->vocab.is_multilingual() ? 1 : 0;
	}

	float * whisper_get_logits(struct whisper_context * ctx) {
	return ctx->state->logits.data();
	}

	float * whisper_get_logits_from_state(struct whisper_state * state) {
	return state->logits.data();
	}

	const char * whisper_token_to_str(struct whisper_context * ctx, whisper_token token) {
	return ctx->vocab.id_to_token.at(token).c_str();
	}

	whisper_token whisper_token_eot(struct whisper_context * ctx) {
	return ctx->vocab.token_eot;
	}

	whisper_token whisper_token_sot(struct whisper_context * ctx) {
	return ctx->vocab.token_sot;
	}

	whisper_token whisper_token_solm(struct whisper_context * ctx) {
	return ctx->vocab.token_solm;
	}

	whisper_token whisper_token_prev(struct whisper_context * ctx) {
	return ctx->vocab.token_prev;
	}

	whisper_token whisper_token_nosp(struct whisper_context * ctx) {
	return ctx->vocab.token_nosp;
	}

	whisper_token whisper_token_not(struct whisper_context * ctx) {
	return ctx->vocab.token_not;
	}

	whisper_token whisper_token_beg(struct whisper_context * ctx) {
	return ctx->vocab.token_beg;
	}

	whisper_token whisper_token_lang(struct whisper_context * ctx, int lang_id) {
	return whisper_token_sot(ctx) + 1 + lang_id;
	}

	whisper_token whisper_token_translate(struct whisper_context * ctx) {
	return ctx->vocab.token_translate;
	}

	whisper_token whisper_token_transcribe(struct whisper_context * ctx) {
	return ctx->vocab.token_transcribe;
	}

	void whisper_print_timings(struct whisper_context * ctx) {
	const int64_t t_end_us = ggml_time_us();

	WHISPER_LOG_INFO("\n");
	WHISPER_LOG_INFO("%s: load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0f);
	if (ctx->state != nullptr) {

	const int32_t n_sample = std::max(1, ctx->state->n_sample);
	const int32_t n_encode = std::max(1, ctx->state->n_encode);
	const int32_t n_decode = std::max(1, ctx->state->n_decode);
	const int32_t n_batchd = std::max(1, ctx->state->n_batchd);
	const int32_t n_prompt = std::max(1, ctx->state->n_prompt);

	WHISPER_LOG_INFO("%s: fallbacks = %3d p / %3d h\n", __func__, ctx->state->n_fail_p, ctx->state->n_fail_h);
	WHISPER_LOG_INFO("%s: mel time = %8.2f ms\n", __func__, ctx->state->t_mel_us / 1000.0f);
	WHISPER_LOG_INFO("%s: sample time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->state->t_sample_us, n_sample, 1e-3f * ctx->state->t_sample_us / n_sample);
	WHISPER_LOG_INFO("%s: encode time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->state->t_encode_us, n_encode, 1e-3f * ctx->state->t_encode_us / n_encode);
	WHISPER_LOG_INFO("%s: decode time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->state->t_decode_us, n_decode, 1e-3f * ctx->state->t_decode_us / n_decode);
	WHISPER_LOG_INFO("%s: batchd time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->state->t_batchd_us, n_batchd, 1e-3f * ctx->state->t_batchd_us / n_batchd);
	WHISPER_LOG_INFO("%s: prompt time = %8.2f ms / %5d runs (%8.2f ms per run)\n", __func__, 1e-3f * ctx->state->t_prompt_us, n_prompt, 1e-3f * ctx->state->t_prompt_us / n_prompt);
	}
	WHISPER_LOG_INFO("%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0f);
	}

	void whisper_reset_timings(struct whisper_context * ctx) {
	ctx->t_start_us = ggml_time_us();
	if (ctx->state != nullptr) {
	ctx->state->t_mel_us = 0;
	ctx->state->t_sample_us = 0;
	ctx->state->t_encode_us = 0;
	ctx->state->t_decode_us = 0;
	ctx->state->t_batchd_us = 0;
	ctx->state->t_prompt_us = 0;
	ctx->state->n_sample = 0;
	ctx->state->n_encode = 0;
	ctx->state->n_decode = 0;
	ctx->state->n_batchd = 0;
	ctx->state->n_prompt = 0;
	}
	}

	static int whisper_has_coreml(void) {
	#ifdef WHISPER_USE_COREML
	return 1;
	#else
	return 0;
	#endif
	}

	static int whisper_has_openvino(void) {
	#ifdef WHISPER_USE_OPENVINO
	return 1;
	#else
	return 0;
	#endif
	}

	const char * whisper_print_system_info(void) {
	static std::string s;

	s = "";
	s += "AVX = " + std::to_string(ggml_cpu_has_avx()) + " \| ";
	s += "AVX2 = " + std::to_string(ggml_cpu_has_avx2()) + " \| ";
	s += "AVX512 = " + std::to_string(ggml_cpu_has_avx512()) + " \| ";
	s += "FMA = " + std::to_string(ggml_cpu_has_fma()) + " \| ";
	s += "NEON = " + std::to_string(ggml_cpu_has_neon()) + " \| ";
	s += "ARM_FMA = " + std::to_string(ggml_cpu_has_arm_fma()) + " \| ";
	s += "F16C = " + std::to_string(ggml_cpu_has_f16c()) + " \| ";
	s += "FP16_VA = " + std::to_string(ggml_cpu_has_fp16_va()) + " \| ";
	s += "WASM_SIMD = " + std::to_string(ggml_cpu_has_wasm_simd()) + " \| ";
	s += "SSE3 = " + std::to_string(ggml_cpu_has_sse3()) + " \| ";
	s += "SSSE3 = " + std::to_string(ggml_cpu_has_ssse3()) + " \| ";
	s += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " \| ";
	s += "COREML = " + std::to_string(whisper_has_coreml()) + " \| ";
	s += "OPENVINO = " + std::to_string(whisper_has_openvino()) ;

	return s.c_str();
	}

	//////////////////////////////////
	// Grammar - ported from llama.cpp
	//////////////////////////////////

	// Decodes a UTF-8 string which may end in an incomplete sequence. Adds a terminating 0 for use as
	// pointer. If an invalid sequence is encountered, returns `whisper_partial_utf8.n_remain == -1`.
	std::pair<std::vector<uint32_t>, whisper_partial_utf8> decode_utf8(
	const char * src,
	whisper_partial_utf8 partial_start) {
	static const int lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 2, 2, 3, 4 };
	const char * pos = src;
	std::vector<uint32_t> code_points;
	uint32_t value = partial_start.value;
	int n_remain = partial_start.n_remain;

	// continue previous decode, if applicable
	while (*pos != 0 && n_remain > 0) {
	uint8_t next_byte = static_cast<uint8_t>(*pos);
	if ((next_byte >> 6) != 2) {
	// invalid sequence, abort
	code_points.push_back(0);
	return std::make_pair(std::move(code_points), whisper_partial_utf8{ 0, -1 });
	}
	value = (value << 6) + (next_byte & 0x3F);
	++pos;
	--n_remain;
	}

	if (partial_start.n_remain > 0 && n_remain == 0) {
	code_points.push_back(value);
	}

	// decode any subsequent utf-8 sequences, which may end in an incomplete one
	while (*pos != 0) {
	uint8_t first_byte = static_cast<uint8_t>(*pos);
	uint8_t highbits = first_byte >> 4;
	n_remain = lookup[highbits] - 1;

	if (n_remain < 0) {
	// invalid sequence, abort
	code_points.clear();
	code_points.push_back(0);
	return std::make_pair(std::move(code_points), whisper_partial_utf8{ 0, n_remain });
	}

	uint8_t mask = (1 << (7 - n_remain)) - 1;
	value = first_byte & mask;
	++pos;
	while (*pos != 0 && n_remain > 0) {
	value = (value << 6) + (static_cast<uint8_t>(*pos) & 0x3F);
	++pos;
	--n_remain;
	}
	if (n_remain == 0) {
	code_points.push_back(value);
	}
	}
	code_points.push_back(0);

	return std::make_pair(std::move(code_points), whisper_partial_utf8{ value, n_remain });
	}

	// returns true iff pos points to the end of one of the definitions of a rule
	static bool whisper_grammar_is_end_of_sequence(const whisper_grammar_element * pos) {
	switch (pos->type) {
	case WHISPER_GRETYPE_END: return true; // NOLINT
	case WHISPER_GRETYPE_ALT: return true; // NOLINT
	default: return false;
	}
	}

	// returns true iff chr satisfies the char range at pos (regular or inverse range)
	// asserts that pos is pointing to a char range element
	static std::pair<bool, const whisper_grammar_element *> whisper_grammar_match_char(
	const whisper_grammar_element * pos,
	const uint32_t chr) {

	bool found = false;
	bool is_positive_char = pos->type == WHISPER_GRETYPE_CHAR;

	WHISPER_ASSERT(is_positive_char \|\| pos->type == WHISPER_GRETYPE_CHAR_NOT); // NOLINT

	do {
	if (pos[1].type == WHISPER_GRETYPE_CHAR_RNG_UPPER) {
	// inclusive range, e.g. [a-z]
	found = found \|\| (pos->value <= chr && chr <= pos[1].value);
	pos += 2;
	} else {
	// exact char match, e.g. [a] or "a"
	found = found \|\| pos->value == chr;
	pos += 1;
	}
	} while (pos->type == WHISPER_GRETYPE_CHAR_ALT);

	return std::make_pair(found == is_positive_char, pos);
	}

	// returns true iff some continuation of the given partial UTF-8 sequence could satisfy the char
	// range at pos (regular or inverse range)
	// asserts that pos is pointing to a char range element
	static bool whisper_grammar_match_partial_char(
	const whisper_grammar_element * pos,
	const whisper_partial_utf8 partial_utf8) {

	bool is_positive_char = pos->type == WHISPER_GRETYPE_CHAR;
	WHISPER_ASSERT(is_positive_char \|\| pos->type == WHISPER_GRETYPE_CHAR_NOT);

	uint32_t partial_value = partial_utf8.value;
	int n_remain = partial_utf8.n_remain;

	// invalid sequence or 7-bit char split across 2 bytes (overlong)
	if (n_remain < 0 \|\| (n_remain == 1 && partial_value < 2)) {
	return false;
	}

	// range of possible code points this partial UTF-8 sequence could complete to
	uint32_t low = partial_value << (n_remain * 6);
	uint32_t high = low \| ((1 << (n_remain * 6)) - 1);

	if (low == 0) {
	if (n_remain == 2) {
	low = 1 << 11;
	} else if (n_remain == 3) {
	low = 1 << 16;
	}
	}

	do {
	if (pos[1].type == WHISPER_GRETYPE_CHAR_RNG_UPPER) {
	// inclusive range, e.g. [a-z]
	if (pos->value <= high && low <= pos[1].value) {
	return is_positive_char;
	}
	pos += 2;
	} else {
	// exact char match, e.g. [a] or "a"
	if (low <= pos->value && pos->value <= high) {
	return is_positive_char;
	}
	pos += 1;
	}
	} while (pos->type == WHISPER_GRETYPE_CHAR_ALT);

	return !is_positive_char;
	}


	// transforms a grammar pushdown stack into N possible stacks, all ending
	// at a character range (terminal element)
	static void whisper_grammar_advance_stack(
	const std::vector<std::vector<whisper_grammar_element>> & rules,
	const std::vector<const whisper_grammar_element *> & stack,
	std::vector<std::vector<const whisper_grammar_element *>> & new_stacks) {

	if (stack.empty()) {
	new_stacks.push_back(stack);
	return;
	}

	const whisper_grammar_element * pos = stack.back();

	switch (pos->type) {
	case WHISPER_GRETYPE_RULE_REF: {
	const size_t rule_id = static_cast<size_t>(pos->value);
	const whisper_grammar_element * subpos = rules[rule_id].data();
	do {
	// init new stack without the top (pos)
	std::vector<const whisper_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
	if (!whisper_grammar_is_end_of_sequence(pos + 1)) {
	// if this rule ref is followed by another element, add that to stack
	new_stack.push_back(pos + 1);
	}
	if (!whisper_grammar_is_end_of_sequence(subpos)) {
	// if alternate is nonempty, add to stack
	new_stack.push_back(subpos);
	}
	whisper_grammar_advance_stack(rules, new_stack, new_stacks);
	while (!whisper_grammar_is_end_of_sequence(subpos)) {
	// scan to end of alternate def
	subpos++;
	}
	if (subpos->type == WHISPER_GRETYPE_ALT) {
	// there's another alternate def of this rule to process
	subpos++;
	} else {
	break;
	}
	} while (true);
	break;
	}
	case WHISPER_GRETYPE_CHAR:
	case WHISPER_GRETYPE_CHAR_NOT:
	new_stacks.push_back(stack);
	break;
	default:
	// end of alternate (WHISPER_GRETYPE_END, WHISPER_GRETYPE_ALT) or middle of char range
	// (WHISPER_GRETYPE_CHAR_ALT, WHISPER_GRETYPE_CHAR_RNG_UPPER); stack should never be left on
	// those
	WHISPER_ASSERT(false);
	}
	}

	// takes a set of possible pushdown stacks on a grammar, which are required to
	// be positioned at a character range (see `whisper_grammar_advance_stack`), and
	// produces the N possible stacks if the given char is accepted at those
	// positions
	static std::vector<std::vector<const whisper_grammar_element *>> whisper_grammar_accept(
	const std::vector<std::vector<whisper_grammar_element>> & rules,
	const std::vector<std::vector<const whisper_grammar_element *>> & stacks,
	const uint32_t chr) {

	std::vector<std::vector<const whisper_grammar_element *>> new_stacks;

	for (const auto & stack : stacks) {
	if (stack.empty()) {
	continue;
	}

	auto match = whisper_grammar_match_char(stack.back(), chr);
	if (match.first) {
	const whisper_grammar_element * pos = match.second;

	// update top of stack to next element, if any
	std::vector<const whisper_grammar_element *> new_stack(stack.begin(), stack.end() - 1);
	if (!whisper_grammar_is_end_of_sequence(pos)) {
	new_stack.push_back(pos);
	}
	whisper_grammar_advance_stack(rules, new_stack, new_stacks);
	}
	}

	return new_stacks;
	}

	static std::vector<whisper_grammar_candidate> whisper_grammar_reject_candidates(
	const std::vector<std::vector<whisper_grammar_element>> & rules,
	const std::vector<std::vector<const whisper_grammar_element *>> & stacks,
	const std::vector<whisper_grammar_candidate> & candidates);

	static std::vector<whisper_grammar_candidate> whisper_grammar_reject_candidates_for_stack(
	const std::vector<std::vector<whisper_grammar_element>> & rules,
	const std::vector<const whisper_grammar_element *> & stack,
	const std::vector<whisper_grammar_candidate> & candidates) {

	std::vector<whisper_grammar_candidate> rejects;

	if (stack.empty()) {
	for (auto tok : candidates) {
	if (*tok.code_points != 0 \|\| tok.partial_utf8.n_remain != 0) {
	rejects.push_back(tok);
	}
	}
	return rejects;
	}

	const whisper_grammar_element * stack_pos = stack.back();

	std::vector<whisper_grammar_candidate> next_candidates;
	for (auto tok : candidates) {
	if (*tok.code_points == 0) {
	// reached end of full codepoints in token, reject iff it ended in a partial sequence
	// that cannot satisfy this position in grammar
	if (tok.partial_utf8.n_remain != 0 && !whisper_grammar_match_partial_char(stack_pos, tok.partial_utf8)) {
	rejects.push_back(tok);
	}
	} else if (whisper_grammar_match_char(stack_pos, *tok.code_points).first) {
	next_candidates.push_back({ tok.id, tok.code_points + 1, tok.partial_utf8 });
	} else {
	rejects.push_back(tok);
	}
	}

	const auto * stack_pos_after = whisper_grammar_match_char(stack_pos, 0).second;

	// update top of stack to next element, if any
	std::vector<const whisper_grammar_element *> stack_after(stack.begin(), stack.end() - 1);
	if (!whisper_grammar_is_end_of_sequence(stack_pos_after)) {
	stack_after.push_back(stack_pos_after);
	}
	std::vector<std::vector<const whisper_grammar_element *>> next_stacks;
	whisper_grammar_advance_stack(rules, stack_after, next_stacks);

	auto next_rejects = whisper_grammar_reject_candidates(rules, next_stacks, next_candidates);
	for (auto tok : next_rejects) {
	rejects.push_back({ tok.id, tok.code_points - 1, tok.partial_utf8 });
	}

	return rejects;
	}

	static std::vector<whisper_grammar_candidate> whisper_grammar_reject_candidates(
	const std::vector<std::vector<whisper_grammar_element>> & rules,
	const std::vector<std::vector<const whisper_grammar_element *>> & stacks,
	const std::vector<whisper_grammar_candidate> & candidates) {
	if (candidates.empty() \|\| stacks.empty()) {
	return std::vector<whisper_grammar_candidate>();
	}

	auto rejects = whisper_grammar_reject_candidates_for_stack(rules, stacks.front(), candidates);

	for (size_t i = 1, size = stacks.size(); i < size; ++i) {
	rejects = whisper_grammar_reject_candidates_for_stack(rules, stacks[i], rejects);
	}
	return rejects;
	}

	static struct whisper_grammar whisper_grammar_init(
	const whisper_grammar_element ** rules,
	size_t n_rules,
	size_t i_start_rule) {
	const whisper_grammar_element * pos;

	// copy rule definitions into vectors
	std::vector<std::vector<whisper_grammar_element>> vec_rules(n_rules);
	for (size_t i = 0; i < n_rules; i++) {
	for (pos = rules[i]; pos->type != WHISPER_GRETYPE_END; pos++) {
	vec_rules[i].push_back(*pos);
	}
	vec_rules[i].push_back({WHISPER_GRETYPE_END, 0});
	}

	// loop over alternates of start rule to build initial stacks
	std::vector<std::vector<const whisper_grammar_element *>> stacks;
	pos = rules[i_start_rule];
	do {
	std::vector<const whisper_grammar_element *> stack;
	if (!whisper_grammar_is_end_of_sequence(pos)) {
	// if alternate is nonempty, add to stack
	stack.push_back(pos);
	}
	whisper_grammar_advance_stack(vec_rules, stack, stacks);
	while (!whisper_grammar_is_end_of_sequence(pos)) {
	// scan to end of alternate def
	pos++;
	}
	if (pos->type == WHISPER_GRETYPE_ALT) {
	// there's another alternate def of this rule to process
	pos++;
	} else {
	break;
	}
	} while (true);

	return { std::move(vec_rules), std::move(stacks), {} };
	}

	static void whisper_suppress_invalid_grammar(
	whisper_context & ctx,
	const whisper_full_params & params,
	std::vector<float> & logits,
	const whisper_grammar & grammar) {

	if (grammar.rules.empty() \|\| grammar.stacks.empty()) {
	return;
	}

	//bool allow_eot = false;
	//for (const auto & stack : grammar.stacks) {
	// if (stack.empty()) {
	// allow_eot = true;
	// break;
	// }
	//}

	const whisper_token eot = whisper_token_eot(&ctx);

	std::vector<std::pair<std::vector<uint32_t>, whisper_partial_utf8>> candidates_decoded;
	std::vector<whisper_grammar_candidate> candidates_grammar;

	for (whisper_token id = 0; id < eot; ++id) {
	const std::string & text = ctx.vocab.id_to_token[id];
	if (!text.empty()) {
	candidates_decoded.push_back(decode_utf8(text.c_str(), grammar.partial_utf8));
	candidates_grammar.push_back({ id, candidates_decoded.back().first.data(), candidates_decoded.back().second });
	}
	}

	const auto rejects = whisper_grammar_reject_candidates(grammar.rules, grammar.stacks, candidates_grammar);

	for (const auto & reject : rejects) {
	logits[reject.id] -= params.grammar_penalty;
	}

	// when the grammar allows a continuation, we penalize the end-of-text token
	//if (!allow_eot) {
	// logits[eot] -= params.grammar_penalty;
	//}
	//fprintf(stderr, "Allowed: (%zu tokens)\n", size - rejects.size());
	}

	static void whisper_grammar_accept_token(whisper_context & ctx, whisper_grammar & grammar, whisper_token token) {
	if (grammar.rules.empty() \|\| grammar.stacks.empty()) {
	return;
	}

	//fprintf(stderr, "Accept: '%s'\n", ctx.vocab.id_to_token[token].c_str());

	const std::string & text = ctx.vocab.id_to_token[token];

	if (text.rfind("[_", 0) == 0) {
	// fprintf(stderr, " (skipped)\n");
	return;
	}
	// fprintf(stderr, "\n");

	// Note terminating 0 in decoded string
	const auto decoded = decode_utf8(text.c_str(), grammar.partial_utf8);
	const auto & code_points = decoded.first;
	for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) {
	grammar.stacks = whisper_grammar_accept(grammar.rules, grammar.stacks, *it);
	}
	grammar.partial_utf8 = decoded.second;
	}

	//////////////
	// END grammar
	//////////////

	////////////////////////////////////////////////////////////////////////////

	struct whisper_context_params * whisper_context_default_params_by_ref() {
	struct whisper_context_params params = whisper_context_default_params();

	struct whisper_context_params* result = new whisper_context_params();
	*result = params;
	return result;
	}

	struct whisper_full_params * whisper_full_default_params_by_ref(enum whisper_sampling_strategy strategy) {
	struct whisper_full_params params = whisper_full_default_params(strategy);

	struct whisper_full_params* result = new whisper_full_params();
	*result = params;
	return result;
	}

	struct whisper_full_params whisper_full_default_params(enum whisper_sampling_strategy strategy) {
	struct whisper_full_params result = {
	/.strategy =/ strategy,

	/.n_threads =/ std::min(4, (int32_t) std::thread::hardware_concurrency()),
	/.n_max_text_ctx =/ 16384,
	/.offset_ms =/ 0,
	/.duration_ms =/ 0,

	/.translate =/ false,
	/.no_context =/ true,
	/.no_timestamps =/ false,
	/.single_segment =/ false,
	/.print_special =/ false,
	/.print_progress =/ true,
	/.print_realtime =/ false,
	/.print_timestamps =/ true,

	/.token_timestamps =/ false,
	/.thold_pt =/ 0.01f,
	/.thold_ptsum =/ 0.01f,
	/.max_len =/ 0,
	/.split_on_word =/ false,
	/.max_tokens =/ 0,

	/.speed_up =/ false,
	/.debug_mode =/ false,
	/.audio_ctx =/ 0,

	/.tdrz_enable =/ false,

	/* suppress_regex =*/ nullptr,

	/.initial_prompt =/ nullptr,
	/.prompt_tokens =/ nullptr,
	/.prompt_n_tokens =/ 0,

	/.language =/ "en",
	/.detect_language =/ false,

	/.suppress_blank =/ true,
	/.suppress_non_speech_tokens =/ false,

	/.temperature =/ 0.0f,
	/.max_initial_ts =/ 1.0f,
	/.length_penalty =/ -1.0f,

	/.temperature_inc =/ 0.2f,
	/.entropy_thold =/ 2.4f,
	/.logprob_thold =/ -1.0f,
	/.no_speech_thold =/ 0.6f,

	/.greedy =/ {
	/.best_of =/ -1,
	},

	/.beam_search =/ {
	/.beam_size =/ -1,

	/.patience =/ -1.0f,
	},

	/.new_segment_callback =/ nullptr,
	/.new_segment_callback_user_data =/ nullptr,

	/.progress_callback =/ nullptr,
	/.progress_callback_user_data =/ nullptr,

	/.encoder_begin_callback =/ nullptr,
	/.encoder_begin_callback_user_data =/ nullptr,

	/.abort_callback =/ nullptr,
	/.abort_callback_user_data =/ nullptr,

	/.logits_filter_callback =/ nullptr,
	/.logits_filter_callback_user_data =/ nullptr,

	/.grammar_rules =/ nullptr,
	/.n_grammar_rules =/ 0,
	/.i_start_rule =/ 0,
	/.grammar_penalty =/ 100.0f,
	};

	switch (strategy) {
	case WHISPER_SAMPLING_GREEDY:
	{
	result.greedy = {
	/.best_of =/ 5,
	};
	} break;
	case WHISPER_SAMPLING_BEAM_SEARCH:
	{
	result.beam_search = {
	/.beam_size =/ 5,

	/.patience =/ -1.0f,
	};
	} break;
	}

	return result;
	}

	// forward declarations
	static std::vector<float> get_signal_energy(const float * signal, int n_samples, int n_samples_per_half_window);
	static void whisper_exp_compute_token_level_timestamps(
	struct whisper_context & ctx,
	struct whisper_state & state,
	int i_segment,
	float thold_pt,
	float thold_ptsum);

	static inline bool should_split_on_word(const char * txt, bool split_on_word) {
	if (!split_on_word) return true;

	return txt[0] == ' ';
	}

	static void whisper_exp_compute_token_level_timestamps_dtw(
	struct whisper_context * ctx,
	struct whisper_state * state,
	struct whisper_full_params params,
	int i_segment,
	size_t n_segments,
	int seek,
	int n_frames,
	int medfilt_width,
	int n_threads);

	// wrap the last segment to max_len characters
	// returns the number of new segments
	static int whisper_wrap_segment(struct whisper_context & ctx, struct whisper_state & state, int max_len, bool split_on_word) {
	auto segment = state.result_all.back();

	int res = 1;
	int acc = 0;

	std::string text;

	for (int i = 0; i < (int) segment.tokens.size(); i++) {
	const auto & token = segment.tokens[i];
	if (token.id >= whisper_token_eot(&ctx)) {
	continue;
	}

	const auto txt = whisper_token_to_str(&ctx, token.id);
	const int cur = strlen(txt);

	if (acc + cur > max_len && i > 0 && should_split_on_word(txt, split_on_word)) {
	state.result_all.back().text = std::move(text);
	state.result_all.back().t1 = token.t0;
	state.result_all.back().tokens.resize(i);
	state.result_all.back().speaker_turn_next = false;

	state.result_all.push_back({});
	state.result_all.back().t0 = token.t0;
	state.result_all.back().t1 = segment.t1;

	// add tokens [i, end] to the new segment
	state.result_all.back().tokens.insert(
	state.result_all.back().tokens.end(),
	segment.tokens.begin() + i,
	segment.tokens.end());

	state.result_all.back().speaker_turn_next = segment.speaker_turn_next;

	acc = 0;
	text = "";

	segment = state.result_all.back();
	i = -1;

	res++;
	} else {
	acc += cur;
	text += txt;
	}
	}

	state.result_all.back().text = std::move(text);

	return res;
	}

	static const std::vector<std::string> non_speech_tokens = {
	"\"", "#", "(", ")", "*", "+", "/", ":", ";", "<", "=", ">", "@", "[", "\\", "]", "^",
	"_", "`", "{", "\|", "}", "~", "「", "」", "『", "』", "<<", ">>", "<<<", ">>>", "--",
	"---", "-(", "-[", "('", "(\"", "((", "))", "(((", ")))", "[[", "]]", "{{", "}}", "♪♪",
	"♪♪♪","♩", "♪", "♫", "♬", "♭", "♮", "♯"
	};

	// process the logits for the selected decoder
	// - applies logit filters
	// - computes logprobs and probs
	// TODO: optimize
	static void whisper_process_logits(
	struct whisper_context & ctx,
	struct whisper_state & state,
	struct whisper_decoder & decoder,
	const struct whisper_full_params params,
	float temperature) {
	const auto & vocab = ctx.vocab;
	const auto & tokens_cur = decoder.sequence.tokens;

	const bool is_initial = tokens_cur.size() == 0;
	const int n_logits = vocab.id_to_token.size();

	WHISPER_ASSERT(n_logits == ctx.vocab.n_vocab);

	// extract the logits for the last token
	// we will be mutating, and therefore we don't want to use the ctx.logits buffer directly
	auto & probs = decoder.probs;
	auto & logits = decoder.logits;
	auto & logprobs = decoder.logprobs;
	{
	logits.resize(n_logits);
	memcpy(logits.data(), state.logits.data() + decoder.i_batchn_logits, n_logitssizeof(float));

	if (temperature > 0.0f) {
	for (int i = 0; i < n_logits; i++) {
	logits[i] /= temperature;
	}
	}

	// will be populated a bit later
	probs.resize(n_logits);
	logprobs.resize(n_logits);
	}

	// apply logit filters here
	// ref: https://github.com/openai/whisper/blob/0b1ba3d46ebf7fe6f953acfd8cad62a4f851b49f/whisper/decoding.py#L480-L493
	{
	// suppress blank
	// https://github.com/openai/whisper/blob/0b1ba3d46ebf7fe6f953acfd8cad62a4f851b49f/whisper/decoding.py#L388-L390
	if (params.suppress_blank) {
	if (is_initial) {
	logits[vocab.token_eot] = -INFINITY;
	logits[vocab.token_to_id.at(" ")] = -INFINITY;
	}
	}

	// suppress <\|notimestamps\|> token
	// ref: https://github.com/openai/whisper/blob/0b1ba3d46ebf7fe6f953acfd8cad62a4f851b49f/whisper/decoding.py#L410-L412
	logits[vocab.token_not] = -INFINITY;
	if (params.no_timestamps) {
	for (int i = vocab.token_beg; i < n_logits; ++i) {
	logits[i] = -INFINITY;
	}
	}

	// suppress sot and nosp tokens
	logits[vocab.token_sot] = -INFINITY;
	logits[vocab.token_nosp] = -INFINITY; // TODO: ignore this token for now

	// [TDRZ] when tinydiarize is disabled, suppress solm token
	if (params.tdrz_enable == false) {
	logits[vocab.token_solm] = -INFINITY;
	}

	// suppress task tokens
	logits[vocab.token_translate] = -INFINITY;
	logits[vocab.token_transcribe] = -INFINITY;
	logits[vocab.token_prev] = -INFINITY;

	// suppress lang tokens
	for (size_t i = 0; i < g_lang.size(); ++i) {
	logits[whisper_token_lang(&ctx, i)] = -INFINITY;
	}

	// suppress prev token
	logits[vocab.token_prev] = -INFINITY;

	if (params.logits_filter_callback) {
	params.logits_filter_callback(&ctx, &state, tokens_cur.data(), tokens_cur.size(), logits.data(), params.logits_filter_callback_user_data);
	}

	// suppress any tokens matching a regular expression
	// ref: https://github.com/openai/whisper/discussions/1041
	if (params.suppress_regex != nullptr) {
	std::regex re(params.suppress_regex);
	for (std::pair<whisper_vocab::token, whisper_vocab::id> token_id : vocab.token_to_id) {
	if (std::regex_match(token_id.first, re)) {
	logits[token_id.second] = -INFINITY;
	}
	}
	}

	// suppress non-speech tokens
	// ref: https://github.com/openai/whisper/blob/7858aa9c08d98f75575035ecd6481f462d66ca27/whisper/tokenizer.py#L224-L253
	if (params.suppress_non_speech_tokens) {
	for (const std::string & token : non_speech_tokens) {
	const std::string suppress_tokens[] = {token, " " + token};
	for (const std::string & suppress_token : suppress_tokens) {
	if (vocab.token_to_id.find(suppress_token) != vocab.token_to_id.end()) {
	logits[vocab.token_to_id.at(suppress_token)] = -INFINITY;
	}
	}
	}

	// allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word
	if (vocab.token_to_id.find(" -") != vocab.token_to_id.end()) {
	logits[vocab.token_to_id.at(" -")] = -INFINITY;
	}
	if (vocab.token_to_id.find(" '") != vocab.token_to_id.end()) {
	logits[vocab.token_to_id.at(" '")] = -INFINITY;
	}
	}

	// timestamps have to appear in pairs, except directly before EOT; mask logits accordingly
	// https://github.com/openai/whisper/blob/0b1ba3d46ebf7fe6f953acfd8cad62a4f851b49f/whisper/decoding.py#L414-L424
	{
	const bool last_was_timestamp = tokens_cur.size() > 0 && tokens_cur.back().id >= vocab.token_beg;
	const bool penultimate_was_timestamp = tokens_cur.size() < 2 \|\| tokens_cur[tokens_cur.size() - 2].id >= vocab.token_beg;

	//WHISPER_LOG_INFO("last_was_timestamp=%d penultimate_was_timestamp=%d\n", last_was_timestamp, penultimate_was_timestamp);

	if (last_was_timestamp) {
	if (penultimate_was_timestamp) {
	for (int i = vocab.token_beg; i < n_logits; ++i) {
	logits[i] = -INFINITY;
	}
	} else {
	for (int i = 0; i < vocab.token_eot; ++i) {
	logits[i] = -INFINITY;
	}
	}
	}
	}

	// the initial timestamp cannot be larger than max_initial_ts
	// ref: https://github.com/openai/whisper/blob/0b1ba3d46ebf7fe6f953acfd8cad62a4f851b49f/whisper/decoding.py#L426-L429
	if (is_initial && params.max_initial_ts > 0.0f) {
	const float precision = float(WHISPER_CHUNK_SIZE)/ctx.model.hparams.n_audio_ctx;
	const int tid0 = std::round(params.max_initial_ts/precision);

	for (int i = vocab.token_beg + tid0 + 1; i < n_logits; ++i) {
	logits[i] = -INFINITY;
	}
	}

	// condition timestamp tokens to be increasing
	// ref: https://github.com/openai/whisper/pull/831#issuecomment-1385910556
	if (decoder.has_ts) {
	const int tid0 = decoder.seek_delta/2;

	for (int i = vocab.token_beg; i < vocab.token_beg + tid0; ++i) {
	logits[i] = -INFINITY;
	}
	}

	// populate the logprobs array (log_softmax)
	{
	const float logit_max = *std::max_element(logits.begin(), logits.end());
	float logsumexp = 0.0f;
	for (int i = 0; i < n_logits; ++i) {
	if (logits[i] > -INFINITY) {
	logsumexp += expf(logits[i] - logit_max);
	}
	}
	logsumexp = logf(logsumexp) + logit_max;

	for (int i = 0; i < n_logits; ++i) {
	if (logits[i] > -INFINITY) {
	logprobs[i] = logits[i] - logsumexp;
	} else {
	logprobs[i] = -INFINITY;
	}
	}
	}

	// if sum of probability over timestamps is above any other token, sample timestamp
	// ref: https://github.com/openai/whisper/blob/0b1ba3d46ebf7fe6f953acfd8cad62a4f851b49f/whisper/decoding.py#L431-L437
	{
	// logsumexp over timestamps
	float timestamp_logprob = -INFINITY;
	{
	float logsumexp = 0.0f;
	const float logprob_max = *std::max_element(logprobs.begin() + vocab.token_beg, logprobs.end());
	for (int i = vocab.token_beg; i < n_logits; ++i) {
	if (logprobs[i] > -INFINITY) {
	logsumexp += expf(logprobs[i] - logprob_max);
	}
	}
	if (logsumexp > 0.0f) {
	timestamp_logprob = logf(logsumexp) + logprob_max;
	}
	}

	const float max_text_token_logprob = *std::max_element(logprobs.begin(), logprobs.begin() + vocab.token_beg);

	//WHISPER_LOG_INFO("timestamp_logprob=%f max_text_token_logprob=%f\n", timestamp_logprob, max_text_token_logprob);

	if (timestamp_logprob > max_text_token_logprob) {
	for (int i = 0; i < vocab.token_beg; ++i) {
	logits[i] = -INFINITY;
	logprobs[i] = -INFINITY;
	}
	} else {
	if (params.n_grammar_rules > 0) {
	whisper_suppress_invalid_grammar(ctx, params, logits, decoder.grammar);

	// populate the logprobs array (log_softmax)
	{
	const float logit_max = *std::max_element(logits.begin(), logits.end());
	float logsumexp = 0.0f;
	for (int i = 0; i < n_logits; ++i) {
	if (logits[i] > -INFINITY) {
	logsumexp += expf(logits[i] - logit_max);
	}
	}
	logsumexp = logf(logsumexp) + logit_max;

	for (int i = 0; i < n_logits; ++i) {
	if (logits[i] > -INFINITY) {
	logprobs[i] = logits[i] - logsumexp;
	} else {
	logprobs[i] = -INFINITY;
	}
	}
	}
	}
	}
	}
	}

	// compute probs
	{
	for (int i = 0; i < n_logits; ++i) {
	if (logits[i] == -INFINITY) {
	probs[i] = 0.0f;
	} else {
	probs[i] = expf(logprobs[i]);
	}
	}
	}

	#if 0
	// print first 100 logits - token string : logit
	//for (int i = 0; i < 10; i++) {
	// const auto token = vocab.id_to_token.at(i);
	// const auto prob = probs[i];
	// const auto logit = logits[i];
	// const auto logprob = logprobs[i];
	// printf("%16s : prob=%9.5f logit=%9.5f logprob=%9.5f\n", token.c_str(), prob, logit, logprob);
	//}

	// print sorted
	{
	std::vector<std::pair<float, int>> pairs;

	for (int i = 0; i < n_logits; ++i) {
	pairs.push_back(std::make_pair(probs[i], i));
	}

	std::sort(pairs.begin(), pairs.end(), [](const std::pair<float, int>& a, const std::pair<float, int>& b) {
	return a.first > b.first;
	});

	for (int i = 0; i < 10; i++) {
	const auto token = vocab.id_to_token.at(pairs[i].second);
	const auto prob = pairs[i].first;
	const auto logit = logits[pairs[i].second];
	const auto logprob = logprobs[pairs[i].second];
	printf("%16s : id=%6d prob=%9.5f logit=%9.5f logprob=%9.5f '%s'\n", token.c_str(), pairs[i].second, prob, logit, logprob, token.c_str());
	}

	printf("----------------\n");
	}

	// "And", "and", " And", " and"
	//printf("logits[\"and\"] = %f\n", logits[vocab.token_to_id.at("and")]);
	//printf("logits[\"And\"] = %f\n", logits[vocab.token_to_id.at("And")]);
	//printf("logits[\" and\"] = %f\n", logits[vocab.token_to_id.at(" and")]);
	//printf("logits[\" And\"] = %f\n", logits[vocab.token_to_id.at(" And")]);
	//printf("logits[\" so\"] = %f\n", logits[vocab.token_to_id.at(" so")]);

	//printf("logprobs[\"and\"] = %f\n", logprobs[vocab.token_to_id.at("and")]);
	//printf("logprobs[\"And\"] = %f\n", logprobs[vocab.token_to_id.at("And")]);
	//printf("logprobs[\" and\"] = %f\n", logprobs[vocab.token_to_id.at(" and")]);
	//printf("logprobs[\" And\"] = %f\n", logprobs[vocab.token_to_id.at(" And")]);
	//printf("logprobs[\" so\"] = %f\n", logprobs[vocab.token_to_id.at(" so")]);

	//printf("probs[\"and\"] = %f\n", probs[vocab.token_to_id.at("and")]);
	//printf("probs[\"And\"] = %f\n", probs[vocab.token_to_id.at("And")]);
	//printf("probs[\" and\"] = %f\n", probs[vocab.token_to_id.at(" and")]);
	//printf("probs[\" And\"] = %f\n", probs[vocab.token_to_id.at(" And")]);
	//printf("probs[\" so\"] = %f\n", probs[vocab.token_to_id.at(" so")]);
	#endif
	}

	static bool whisper_sequence_tokens_equal(const whisper_sequence & a, const whisper_sequence & b) {
	if (a.tokens.size() != b.tokens.size()) {
	return false;
	}
	// sequences are more likely to diverge at the end
	for (int i = a.tokens.size() - 1; i >= 0; i--) {
	if (a.tokens[i].id != b.tokens[i].id) {
	return false;
	}
	}
	return true;
	}

	static whisper_token_data whisper_sample_token(
	whisper_context & ctx,
	const whisper_decoder & decoder,
	bool best) {
	whisper_token_data result = {
	0, 0, 0.0f, 0.0f, 0.0f, 0.0f, -1, -1, -1, 0.0f,
	};

	const auto & vocab = ctx.vocab;

	const auto & probs = decoder.probs;
	const auto & logprobs = decoder.logprobs;

	const int n_logits = vocab.n_vocab;

	{
	double sum_ts = 0.0;
	double max_ts = 0.0;

	for (int i = vocab.token_beg; i < n_logits; i++) {
	if (probs[i] == -INFINITY) {
	continue;
	}

	sum_ts += probs[i];
	if (max_ts < probs[i]) {
	max_ts = probs[i];
	result.tid = i;
	}
	}

	result.pt = max_ts/(sum_ts + 1e-10);
	result.ptsum = sum_ts;
	}

	if (best) {
	for (int i = 0; i < n_logits; ++i) {
	if (result.p < probs[i]) {
	result.id = i;
	result.p = probs[i];
	result.plog = logprobs[i];
	}
	}
	} else {
	std::discrete_distribution<> dist(probs.begin(), probs.end());

	result.id = dist(decoder.rng);
	result.p = probs[result.id];
	result.plog = logprobs[result.id];
	}

	if (result.id >= vocab.token_beg) {
	result.tid = result.id;
	result.pt = result.p;
	}

	return result;
	}

	static std::vector<whisper_token_data> whisper_sample_token_topk(
	whisper_context & ctx,
	whisper_decoder & decoder,
	int k) {
	const auto & vocab = ctx.vocab;

	const auto & probs = decoder.probs;
	const auto & logits = decoder.logits;
	const auto & logprobs = decoder.logprobs;

	const int n_logits = vocab.n_vocab;

	auto & logits_id = decoder.logits_id;

	logits_id.resize(n_logits);
	for (int i = 0; i < n_logits; ++i) {
	logits_id[i].first = logits[i];
	logits_id[i].second = i;
	}

	{
	using pair_type = std::remove_reference<decltype(logits_id)>::type::value_type;
	std::partial_sort(
	logits_id.begin(),
	logits_id.begin() + k, logits_id.end(),
	[](const pair_type & a, const pair_type & b) {
	return a.first > b.first;
	});
	}

	std::vector<whisper_token_data> result;
	result.reserve(k);

	whisper_token tid = vocab.token_beg;

	float pt = 0.0;
	float ptsum = 0.0;

	{
	double sum_ts = 0.0;
	double max_ts = 0.0;

	for (int i = vocab.token_beg; i < n_logits; i++) {
	if (probs[i] == -INFINITY) {
	continue;
	}

	sum_ts += probs[i];
	if (max_ts < probs[i]) {
	max_ts = probs[i];
	tid = i;
	}
	}

	pt = max_ts/(sum_ts + 1e-10);
	ptsum = sum_ts;
	}

	std::discrete_distribution<> dist(probs.begin(), probs.end());

	for (int i = 0; i < k; ++i) {
	const auto id = dist(decoder.rng);
	//printf("XXX %d %d %f %f %f %f\n", id, tid, probs[id], logprobs[id], pt, ptsum);

	result.push_back({ id, tid, probs[id], logprobs[id], pt, ptsum, -1, -1, -1, 0.0f, });

	if (result[i].id >= vocab.token_beg) {
	result[i].tid = result[i].id;
	result[i].pt = result[i].p;
	}
	}

	return result;
	}

	// ref: https://github.com/openai/whisper/blob/0b1ba3d46ebf7fe6f953acfd8cad62a4f851b49f/whisper/decoding.py#L178-L192
	static void whisper_sequence_score(
	const struct whisper_full_params & params,
	whisper_sequence & sequence) {
	if (sequence.result_len == 0) {
	return;
	}

	double result = 0.0f;

	for (int i = 0; i < sequence.result_len; ++i) {
	result += sequence.tokens[i].plog;
	}

	sequence.sum_logprobs = result;
	sequence.avg_logprobs = result/sequence.result_len;

	double penalty = sequence.result_len;

	if (params.length_penalty > 0.0f) {
	penalty = pow((5.0 + penalty)/6.0, params.length_penalty);
	}

	sequence.score = result/penalty;

	// compute the entropy of the sequence of the last 32 tokens
	{
	const int n = 32;

	int cnt = 0;
	double entropy = 0.0f;

	std::map<whisper_token, int> token_counts;
	for (int i = std::max(0, sequence.result_len - n); i < sequence.result_len; ++i) {
	token_counts[sequence.tokens[i].id]++;
	cnt++;
	}

	for (const auto & kv : token_counts) {
	const auto p = kv.second/(double)cnt;
	entropy -= p*log(p);

	//WHISPER_LOG_DEBUG("entropy: %d %f %f, count %d\n", kv.first, p, log(p), kv.second);
	}

	sequence.entropy = entropy;
	}
	}

	int whisper_full_with_state(
	struct whisper_context * ctx,
	struct whisper_state * state,
	struct whisper_full_params params,
	const float * samples,
	int n_samples) {
	// clear old results
	auto & result_all = state->result_all;

	result_all.clear();

	if (n_samples > 0) {
	// compute log mel spectrogram
	if (params.speed_up) {
	// TODO: Replace PV with more advanced algorithm
	WHISPER_LOG_ERROR("%s: failed to compute log mel spectrogram\n", __func__);
	return -1;
	} else {
	if (whisper_pcm_to_mel_with_state(ctx, state, samples, n_samples, params.n_threads) != 0) {
	WHISPER_LOG_ERROR("%s: failed to compute log mel spectrogram\n", __func__);
	return -2;
	}
	}
	}

	// auto-detect language if not specified
	if (params.language == nullptr \|\| strlen(params.language) == 0 \|\| strcmp(params.language, "auto") == 0 \|\| params.detect_language) {
	std::vector<float> probs(whisper_lang_max_id() + 1, 0.0f);

	const auto lang_id = whisper_lang_auto_detect_with_state(ctx, state, 0, params.n_threads, probs.data());
	if (lang_id < 0) {
	if(params.debug_mode)
	{
	printf("\n%s: failed to auto-detect language\n", __func__);
	}
	return -3;
	}
	state->lang_id = lang_id;
	params.language = whisper_lang_str(lang_id);

	if(params.debug_mode)
	{
	printf("\n%s: auto-detected language: %s (p = %f)\n", __func__, params.language, probs[whisper_lang_id(params.language)]);
	}
	if (params.detect_language) {
	return 0;
	}
	}

	if (params.token_timestamps) {
	state->t_beg = 0;
	state->t_last = 0;
	state->tid_last = 0;
	if (n_samples > 0) {
	state->energy = get_signal_energy(samples, n_samples, 32);
	}
	}

	const int seek_start = params.offset_ms/10;
	const int seek_end = params.duration_ms == 0 ? whisper_n_len_from_state(state) : seek_start + params.duration_ms/10;

	// if length of spectrogram is less than 1.0s (100 frames), then return
	// basically don't process anything that is less than 1.0s
	// see issue #39: https://github.com/ggerganov/whisper.cpp/issues/39
	if (seek_end < seek_start + (params.speed_up ? 50 : 100)) {
	WHISPER_LOG_WARN("%s: input is too short - %d ms < 1000 ms. consider padding the input audio with silence\n", __func__, (seek_end - seek_start)*10);
	return 0;
	}

	// a set of temperatures to use
	// [ t0, t0 + delta, t0 + 2*delta, ..., < 1.0f + 1e-6f ]
	std::vector<float> temperatures;
	if (params.temperature_inc > 0.0f) {
	for (float t = params.temperature; t < 1.0f + 1e-6f; t += params.temperature_inc) {
	temperatures.push_back(t);
	}
	} else {
	temperatures.push_back(params.temperature);
	}

	// initialize the decoders
	int n_decoders = 1;

	switch (params.strategy) {
	case WHISPER_SAMPLING_GREEDY:
	{
	n_decoders = params.greedy.best_of;
	} break;
	case WHISPER_SAMPLING_BEAM_SEARCH:
	{
	n_decoders = std::max(params.greedy.best_of, params.beam_search.beam_size);
	} break;
	};

	n_decoders = std::max(1, n_decoders);

	if (n_decoders > WHISPER_MAX_DECODERS) {
	WHISPER_LOG_ERROR("%s: too many decoders requested (%d), max = %d\n", __func__, n_decoders, WHISPER_MAX_DECODERS);
	return -4;
	}

	// TAGS: WHISPER_DECODER_INIT
	for (int j = 1; j < n_decoders; j++) {
	auto & decoder = state->decoders[j];

	decoder.sequence.tokens.reserve(state->decoders[0].sequence.tokens.capacity());

	decoder.probs.resize (ctx->vocab.n_vocab);
	decoder.logits.resize (ctx->vocab.n_vocab);
	decoder.logprobs.resize(ctx->vocab.n_vocab);
	decoder.logits_id.reserve(ctx->model.hparams.n_vocab);

	decoder.rng = std::mt19937(0);
	}

	// the accumulated text context so far
	auto & prompt_past = state->prompt_past;
	if (params.no_context) {
	prompt_past.clear();
	}

	// prepare prompt
	{
	std::vector<whisper_token> prompt_tokens;

	// initial prompt
	if (!params.prompt_tokens && params.initial_prompt) {
	prompt_tokens.resize(1024);
	int n_needed = whisper_tokenize(ctx, params.initial_prompt, prompt_tokens.data(), prompt_tokens.size());
	if (n_needed < 0) {
	prompt_tokens.resize(-n_needed);
	n_needed = whisper_tokenize(ctx, params.initial_prompt, prompt_tokens.data(), prompt_tokens.size());
	}
	prompt_tokens.resize(n_needed);
	params.prompt_tokens = prompt_tokens.data();
	params.prompt_n_tokens = prompt_tokens.size();
	}

	// prepend the prompt tokens to the prompt_past
	if (params.prompt_tokens && params.prompt_n_tokens > 0) {
	// parse tokens from the pointer
	for (int i = 0; i < params.prompt_n_tokens; i++) {
	prompt_past.push_back(params.prompt_tokens[i]);
	}
	std::rotate(prompt_past.begin(), prompt_past.end() - params.prompt_n_tokens, prompt_past.end());
	}
	}

	// overwrite audio_ctx, max allowed is hparams.n_audio_ctx
	if (params.audio_ctx > whisper_n_audio_ctx(ctx)) {
	WHISPER_LOG_ERROR("%s: audio_ctx is larger than the maximum allowed (%d > %d)\n", __func__, params.audio_ctx, whisper_n_audio_ctx(ctx));
	return -5;
	}
	state->exp_n_audio_ctx = params.audio_ctx;

	// these tokens determine the task that will be performed
	std::vector<whisper_token> prompt_init = { whisper_token_sot(ctx), };

	if (whisper_is_multilingual(ctx)) {
	int lang_id = whisper_lang_id(params.language);
	if(lang_id<0)
	{
	lang_id = 0; //default to english
	}
	state->lang_id = lang_id;
	prompt_init.push_back(whisper_token_lang(ctx, lang_id));
	if (params.translate) {
	prompt_init.push_back(whisper_token_translate(ctx));
	} else {
	prompt_init.push_back(whisper_token_transcribe(ctx));
	}
	}

	// first release distilled models require the "no_timestamps" token
	{
	const bool is_distil = ctx->model.hparams.n_text_layer == 2 && ctx->model.hparams.n_vocab != 51866;
	if (is_distil && !params.no_timestamps) {
	WHISPER_LOG_WARN("%s: using first release distilled models - forcing no_timestamps\n", __func__);
	params.no_timestamps = true;
	}
	}

	if (params.no_timestamps) {
	prompt_init.push_back(whisper_token_not(ctx));
	}

	int seek = seek_start;

	std::vector<whisper_token> prompt;
	prompt.reserve(whisper_n_text_ctx(ctx));

	struct beam_candidate {
	int decoder_idx;
	int seek_delta;

	bool has_ts;

	whisper_sequence sequence;
	whisper_grammar grammar;
	};

	std::vector<std::vector<beam_candidate>> bc_per_dec(n_decoders);
	std::vector<beam_candidate> beam_candidates;

	// main loop
	while (true) {
	if (params.progress_callback) {
	const int progress_cur = (100*(seek - seek_start))/(seek_end - seek_start);

	params.progress_callback(
	ctx, state, progress_cur, params.progress_callback_user_data);
	}

	// if only 1 second left, then stop
	if (seek + 100 >= seek_end) {
	break;
	}

	if (params.encoder_begin_callback) {
	if (params.encoder_begin_callback(ctx, state, params.encoder_begin_callback_user_data) == false) {
	WHISPER_LOG_ERROR("%s: encoder_begin_callback returned false - aborting\n", __func__);
	break;
	}
	}

	// encode audio features starting at offset seek
	if (!whisper_encode_internal(ctx, state, seek, params.n_threads, params.abort_callback, params.abort_callback_user_data)) {
	WHISPER_LOG_ERROR("%s: failed to encode\n", __func__);
	return -6;
	}

	// if there is a very short audio segment left to process, we remove any past prompt since it tends
	// to confuse the decoder and often make it repeat or hallucinate stuff
	if (seek > seek_start && seek + 500 >= seek_end) {
	prompt_past.clear();
	}

	int best_decoder_id = 0;

	for (int it = 0; it < (int) temperatures.size(); ++it) {
	const float t_cur = temperatures[it];

	int n_decoders_cur = 1;

	switch (params.strategy) {
	case whisper_sampling_strategy::WHISPER_SAMPLING_GREEDY:
	{
	if (t_cur > 0.0f) {
	n_decoders_cur = params.greedy.best_of;
	}
	} break;
	case whisper_sampling_strategy::WHISPER_SAMPLING_BEAM_SEARCH:
	{
	if (t_cur > 0.0f) {
	n_decoders_cur = params.greedy.best_of;
	} else {
	n_decoders_cur = params.beam_search.beam_size;
	}
	} break;
	};

	n_decoders_cur = std::max(1, n_decoders_cur);

	WHISPER_LOG_DEBUG("\n%s: strategy = %d, decoding with %d decoders, temperature = %.2f\n", __func__, params.strategy, n_decoders_cur, t_cur);

	// TAGS: WHISPER_DECODER_INIT
	for (int j = 0; j < n_decoders_cur; ++j) {
	auto & decoder = state->decoders[j];

	decoder.sequence.tokens.clear();
	decoder.sequence.result_len = 0;
	decoder.sequence.sum_logprobs_all = 0.0;
	decoder.sequence.sum_logprobs = -INFINITY;
	decoder.sequence.avg_logprobs = -INFINITY;
	decoder.sequence.entropy = 0.0;
	decoder.sequence.score = -INFINITY;

	decoder.seek_delta = 100*WHISPER_CHUNK_SIZE;

	decoder.failed = false;
	decoder.completed = false;
	decoder.has_ts = false;

	if (params.grammar_rules != nullptr) {
	decoder.grammar = whisper_grammar_init(params.grammar_rules, params.n_grammar_rules, params.i_start_rule);
	} else {
	decoder.grammar = {};
	}
	}

	// init prompt and kv cache for the current iteration
	// TODO: do not recompute the prompt if it is the same as previous time
	{
	prompt.clear();

	// if we have already generated some text, use it as a prompt to condition the next generation
	if (!prompt_past.empty() && t_cur < 0.5f && params.n_max_text_ctx > 0) {
	int n_take = std::min(std::min(params.n_max_text_ctx, whisper_n_text_ctx(ctx)/2), int(prompt_past.size()));

	prompt = { whisper_token_prev(ctx) };
	prompt.insert(prompt.begin() + 1, prompt_past.end() - n_take, prompt_past.end());
	}

	// init new transcription with sot, language (opt) and task tokens
	prompt.insert(prompt.end(), prompt_init.begin(), prompt_init.end());

	// print the prompt
	WHISPER_LOG_DEBUG("\n\n");
	for (int i = 0; i < (int) prompt.size(); i++) {
	WHISPER_LOG_DEBUG("%s: prompt[%d] = %s\n", __func__, i, ctx->vocab.id_to_token.at(prompt[i]).c_str());
	}
	WHISPER_LOG_DEBUG("\n\n");

	whisper_kv_cache_clear(state->kv_self);

	whisper_batch_prep_legacy(state->batch, prompt.data(), prompt.size(), 0, 0);

	if (!whisper_decode_internal(ctx, state, state->batch, params.n_threads, false, params.abort_callback, params.abort_callback_user_data)) {
	WHISPER_LOG_ERROR("%s: failed to decode\n", __func__);
	return -7;
	}

	{
	const int64_t t_start_sample_us = ggml_time_us();

	state->decoders[0].i_batch = prompt.size() - 1;

	whisper_process_logits(ctx, state, state->decoders[0], params, t_cur);

	for (int j = 1; j < n_decoders_cur; ++j) {
	auto & decoder = state->decoders[j];

	whisper_kv_cache_seq_cp(state->kv_self, 0, j, -1, -1);

	memcpy(decoder.probs.data(), state->decoders[0].probs.data(), decoder.probs.size()*sizeof(decoder.probs[0]));
	memcpy(decoder.logits.data(), state->decoders[0].logits.data(), decoder.logits.size()*sizeof(decoder.logits[0]));
	memcpy(decoder.logprobs.data(), state->decoders[0].logprobs.data(), decoder.logprobs.size()*sizeof(decoder.logprobs[0]));
	}

	state->t_sample_us += ggml_time_us() - t_start_sample_us;
	}
	}

	for (int i = 0, n_max = whisper_n_text_ctx(ctx)/2 - 4; i < n_max; ++i) {
	const int64_t t_start_sample_us = ggml_time_us();

	if (params.strategy == whisper_sampling_strategy::WHISPER_SAMPLING_BEAM_SEARCH) {
	for (auto & bc : bc_per_dec) {
	bc.clear();
	}
	}

	// sampling
	// TODO: avoid memory allocations, optimize, avoid threads?
	{
	std::atomic<int> j_cur(0);

	auto process = [&]() {
	while (true) {
	const int j = j_cur.fetch_add(1);

	if (j >= n_decoders_cur) {
	break;
	}

	auto & decoder = state->decoders[j];

	if (decoder.completed \|\| decoder.failed) {
	continue;
	}

	switch (params.strategy) {
	case whisper_sampling_strategy::WHISPER_SAMPLING_GREEDY:
	{
	if (t_cur < 1e-6f) {
	decoder.sequence.tokens.push_back(whisper_sample_token(*ctx, decoder, true));
	} else {
	decoder.sequence.tokens.push_back(whisper_sample_token(*ctx, decoder, false));
	}

	decoder.sequence.sum_logprobs_all += decoder.sequence.tokens.back().plog;
	} break;
	case whisper_sampling_strategy::WHISPER_SAMPLING_BEAM_SEARCH:
	{
	const auto tokens_new = whisper_sample_token_topk(*ctx, decoder, params.beam_search.beam_size);

	for (const auto & token : tokens_new) {
	bc_per_dec[j].push_back({ j, decoder.seek_delta, decoder.has_ts, decoder.sequence, decoder.grammar, });
	bc_per_dec[j].back().sequence.tokens.push_back(token);
	bc_per_dec[j].back().sequence.sum_logprobs_all += token.plog;
	}
	} break;
	};
	}
	};

	const int n_threads = std::min(params.n_threads, n_decoders_cur);

	if (n_threads == 1) {
	process();
	} else {
	std::vector<std::thread> threads(n_threads - 1);

	for (int t = 0; t < n_threads - 1; ++t) {
	threads[t] = std::thread(process);
	}

	process();

	for (int t = 0; t < n_threads - 1; ++t) {
	threads[t].join();
	}
	}
	}

	beam_candidates.clear();
	for (const auto & bc : bc_per_dec) {
	beam_candidates.insert(beam_candidates.end(), bc.begin(), bc.end());

	if (!bc.empty()) {
	state->n_sample += 1;
	}
	}

	// for beam-search, choose the top candidates and update the KV caches
	if (params.strategy == whisper_sampling_strategy::WHISPER_SAMPLING_BEAM_SEARCH) {
	std::sort(
	beam_candidates.begin(),
	beam_candidates.end(),
	[](const beam_candidate & a, const beam_candidate & b) {
	if (a.sequence.sum_logprobs_all != b.sequence.sum_logprobs_all) {
	return a.sequence.sum_logprobs_all > b.sequence.sum_logprobs_all;
	}
	return a.decoder_idx < b.decoder_idx;
	});

	uint32_t cur_c = 0;

	for (int j = 0; j < n_decoders_cur; ++j) {
	auto & decoder = state->decoders[j];

	if (decoder.completed \|\| decoder.failed) {
	continue;
	}

	if (cur_c >= beam_candidates.size()) {
	cur_c = 0;
	}

	auto & cur = beam_candidates[cur_c++];

	while (beam_candidates.size() > cur_c && whisper_sequence_tokens_equal(beam_candidates[cur_c].sequence, cur.sequence) && i > 0) {
	++cur_c;
	}

	decoder.seek_delta = cur.seek_delta;
	decoder.has_ts = cur.has_ts;
	decoder.sequence = cur.sequence;
	decoder.grammar = cur.grammar;

	whisper_kv_cache_seq_cp(state->kv_self, cur.decoder_idx, WHISPER_MAX_DECODERS + j, -1, -1);

	WHISPER_LOG_DEBUG("%s: beam search: decoder %d: from decoder %d: token = %10s, plog = %8.5f, sum_logprobs = %8.5f\n",
	__func__, j, cur.decoder_idx, ctx->vocab.id_to_token.at(decoder.sequence.tokens.back().id).c_str(), decoder.sequence.tokens.back().plog, decoder.sequence.sum_logprobs_all);
	}

	for (int j = 0; j < n_decoders_cur; ++j) {
	auto & decoder = state->decoders[j];

	if (decoder.completed \|\| decoder.failed) {
	continue;
	}

	whisper_kv_cache_seq_rm(state->kv_self, j, -1, -1);
	whisper_kv_cache_seq_cp(state->kv_self, WHISPER_MAX_DECODERS + j, j, -1, -1);
	whisper_kv_cache_seq_rm(state->kv_self, WHISPER_MAX_DECODERS + j, -1, -1);
	}
	}

	// update the decoder state
	// - check if the sequence is completed
	// - check if the sequence is failed
	// - update sliding window based on timestamp tokens
	for (int j = 0; j < n_decoders_cur; ++j) {
	auto & decoder = state->decoders[j];

	if (decoder.completed \|\| decoder.failed) {
	continue;
	}

	auto & has_ts = decoder.has_ts;
	auto & failed = decoder.failed;
	auto & completed = decoder.completed;
	auto & seek_delta = decoder.seek_delta;
	auto & result_len = decoder.sequence.result_len;

	{
	const auto & token = decoder.sequence.tokens.back();

	// timestamp token - update sliding window
	if (token.id > whisper_token_beg(ctx)) {
	const int seek_delta_new = 2*(token.id - whisper_token_beg(ctx));

	// do not allow to go back in time
	if (has_ts && seek_delta > seek_delta_new && result_len < i) {
	WHISPER_LOG_DEBUG("%s: decoder %d: failed due to seek_delta (%d > %d)\n", __func__, j, seek_delta, seek_delta_new);
	failed = true; // TODO: maybe this is not a failure ?
	continue;
	}

	seek_delta = seek_delta_new;
	result_len = i + 1;
	has_ts = true;
	}

	whisper_grammar_accept_token(*ctx, decoder.grammar, token.id);

	#ifdef WHISPER_DEBUG
	{
	const auto tt = token.pt > 0.10 ? ctx->vocab.id_to_token.at(token.tid) : "[?]";
	WHISPER_LOG_DEBUG("%s: id = %3d, decoder = %d, token = %6d, p = %6.3f, ts = %10s, %6.3f, result_len = %4d '%s'\n",
	__func__, i, j, token.id, token.p, tt.c_str(), token.pt, result_len, ctx->vocab.id_to_token.at(token.id).c_str());
	}
	#endif

	// end of segment
	if (token.id == whisper_token_eot(ctx) \|\| // end of text token
	(params.max_tokens > 0 && i >= params.max_tokens) \|\| // max tokens per segment reached
	(has_ts && seek + seek_delta + 100 >= seek_end) // end of audio reached
	) {
	if (result_len == 0 && !params.no_timestamps) {
	if (seek + seek_delta + 100 >= seek_end) {
	result_len = i + 1;
	} else {
	WHISPER_LOG_DEBUG("%s: decoder %d failed (result_len = 0)\n", __func__, j);
	failed = true;
	continue;
	}
	}

	if (params.single_segment \|\| params.no_timestamps) {
	result_len = i + 1;
	seek_delta = 100*WHISPER_CHUNK_SIZE;
	}

	WHISPER_LOG_DEBUG("%s: decoder %d completed\n", __func__, j);
	completed = true;
	continue;
	}

	// TESTS: if no tensors are loaded, it means we are running tests
	if (ctx->model.n_loaded == 0) {
	seek_delta = 100*WHISPER_CHUNK_SIZE;
	completed = true;
	continue;
	}
	}

	// sometimes, the decoding can get stuck in a repetition loop
	// this is an attempt to mitigate such cases - we flag the decoding as failed and use a fallback strategy
	if (i == n_max - 1 && (result_len == 0 \|\| seek_delta < 100*WHISPER_CHUNK_SIZE/2)) {
	WHISPER_LOG_DEBUG("%s: decoder %d: failed due to repetition loop\n", __func__, j);
	failed = true;
	continue;
	}
	}

	// check if all decoders have finished (i.e. completed or failed)
	{
	bool completed_all = true;

	for (int j = 0; j < n_decoders_cur; ++j) {
	auto & decoder = state->decoders[j];

	if (decoder.completed \|\| decoder.failed) {
	continue;
	}

	completed_all = false;
	}

	if (completed_all) {
	break;
	}
	}

	state->t_sample_us += ggml_time_us() - t_start_sample_us;

	// obtain logits for the next token
	{
	auto & batch = state->batch;

	batch.n_tokens = 0;

	const int n_past = prompt.size() + i;

	for (int j = 0; j < n_decoders_cur; ++j) {
	auto & decoder = state->decoders[j];

	if (decoder.failed \|\| decoder.completed) {
	continue;
	}

	//WHISPER_LOG_DEBUG("%s: decoder %d: token %d, seek_delta %d\n", __func__, j, decoder.sequence.tokens.back().id, decoder.seek_delta);

	decoder.i_batch = batch.n_tokens;

	batch.token [batch.n_tokens] = decoder.sequence.tokens.back().id;
	batch.pos [batch.n_tokens] = n_past;
	batch.n_seq_id[batch.n_tokens] = 1;
	batch.seq_id [batch.n_tokens][0] = j;
	batch.logits [batch.n_tokens] = 1;
	batch.n_tokens++;
	}

	assert(batch.n_tokens > 0);

	if (!whisper_decode_internal(ctx, state, state->batch, params.n_threads, false, params.abort_callback, params.abort_callback_user_data)) {
	WHISPER_LOG_ERROR("%s: failed to decode\n", __func__);
	return -8;
	}

	const int64_t t_start_sample_us = ggml_time_us();

	// TODO: avoid memory allocations, optimize, avoid threads?
	{
	std::atomic<int> j_cur(0);

	auto process = [&]() {
	while (true) {
	const int j = j_cur.fetch_add(1);

	if (j >= n_decoders_cur) {
	break;
	}

	auto & decoder = state->decoders[j];

	if (decoder.failed \|\| decoder.completed) {
	continue;
	}

	whisper_process_logits(ctx, state, decoder, params, t_cur);
	}
	};

	const int n_threads = std::min(params.n_threads, n_decoders_cur);

	if (n_threads == 1) {
	process();
	} else {
	std::vector<std::thread> threads(n_threads - 1);

	for (int t = 0; t < n_threads - 1; ++t) {
	threads[t] = std::thread(process);
	}

	process();

	for (int t = 0; t < n_threads - 1; ++t) {
	threads[t].join();
	}
	}
	}

	state->t_sample_us += ggml_time_us() - t_start_sample_us;
	}
	}

	// rank the resulting sequences and select the best one
	{
	double best_score = -INFINITY;

	for (int j = 0; j < n_decoders_cur; ++j) {
	auto & decoder = state->decoders[j];

	if (decoder.failed) {
	continue;
	}

	decoder.sequence.tokens.resize(decoder.sequence.result_len);
	whisper_sequence_score(params, decoder.sequence);

	WHISPER_LOG_DEBUG("%s: decoder %2d: score = %8.5f, result_len = %3d, avg_logprobs = %8.5f, entropy = %8.5f\n",
	__func__, j, decoder.sequence.score, decoder.sequence.result_len, decoder.sequence.avg_logprobs, decoder.sequence.entropy);

	if (decoder.sequence.result_len > 32 && decoder.sequence.entropy < params.entropy_thold) {
	WHISPER_LOG_DEBUG("%s: decoder %2d: failed due to entropy %8.5f < %8.5f\n",
	__func__, j, decoder.sequence.entropy, params.entropy_thold);

	decoder.failed = true;
	state->n_fail_h++;

	continue;
	}

	if (best_score < decoder.sequence.score) {
	best_score = decoder.sequence.score;
	best_decoder_id = j;
	}
	}

	WHISPER_LOG_DEBUG("%s: best decoder = %d\n", __func__, best_decoder_id);
	}

	bool success = true;

	// was the decoding successful for the current temperature?
	// do fallback only if:
	// - we are not at the last temperature
	if (it != (int) temperatures.size() - 1) {
	const auto & decoder = state->decoders[best_decoder_id];

	if (decoder.failed \|\| decoder.sequence.avg_logprobs < params.logprob_thold) {
	WHISPER_LOG_DEBUG("%s: failed due to avg_logprobs %8.5f < %8.5f\n", __func__, decoder.sequence.avg_logprobs, params.logprob_thold);
	success = false;
	state->n_fail_p++;
	}
	}

	if (success) {
	//for (auto & token : ctx->decoders[best_decoder_id].sequence.tokens) {
	// WHISPER_LOG_DEBUG("%s: token = %d, p = %6.3f, pt = %6.3f, ts = %s, str = %s\n", __func__, token.id, token.p, token.pt, ctx->vocab.id_to_token.at(token.tid).c_str(), ctx->vocab.id_to_token.at(token.id).c_str());
	//}

	break;
	}

	WHISPER_LOG_DEBUG("\n%s: failed to decode with temperature = %.2f\n", __func__, t_cur);
	}

	// output results through a user-provided callback
	{
	const auto & best_decoder = state->decoders[best_decoder_id];

	const auto seek_delta = best_decoder.seek_delta;
	const auto result_len = best_decoder.sequence.result_len;

	const auto & tokens_cur = best_decoder.sequence.tokens;

	// [EXPERIMENTAL] Token-level timestamps with DTW
	const auto n_segments_before = state->result_all.size();

	//WHISPER_LOG_DEBUG("prompt_init.size() = %d, prompt.size() = %d, result_len = %d, seek_delta = %d\n", prompt_init.size(), prompt.size(), result_len, seek_delta);

	// update prompt_past
	prompt_past.clear();
	if (prompt.front() == whisper_token_prev(ctx)) {
	prompt_past.insert(prompt_past.end(), prompt.begin() + 1, prompt.end() - prompt_init.size());
	}

	for (int i = 0; i < result_len; ++i) {
	prompt_past.push_back(tokens_cur[i].id);
	}

	if (!tokens_cur.empty() && ctx->model.n_loaded > 0) {
	int i0 = 0;
	auto t0 = seek + 2*(tokens_cur.front().tid - whisper_token_beg(ctx));

	std::string text;
	bool speaker_turn_next = false;

	for (int i = 0; i < (int) tokens_cur.size(); i++) {
	//printf("%s: %18s %6.3f %18s %6.3f\n", __func__,
	// ctx->vocab.id_to_token[tokens_cur[i].id].c_str(), tokens_cur[i].p,
	// ctx->vocab.id_to_token[tokens_cur[i].tid].c_str(), tokens_cur[i].pt);

	if (params.print_special \|\| tokens_cur[i].id < whisper_token_eot(ctx)) {
	text += whisper_token_to_str(ctx, tokens_cur[i].id);
	}

	// [TDRZ] record if speaker turn was predicted after current segment
	if (params.tdrz_enable && tokens_cur[i].id == whisper_token_solm(ctx)) {
	speaker_turn_next = true;
	}

	if (tokens_cur[i].id > whisper_token_beg(ctx) && !params.single_segment) {
	const auto t1 = seek + 2*(tokens_cur[i].tid - whisper_token_beg(ctx));

	if (!text.empty()) {
	const auto tt0 = params.speed_up ? 2*t0 : t0;
	const auto tt1 = params.speed_up ? 2*t1 : t1;

	if (params.print_realtime) {
	if (params.print_timestamps) {
	printf("[%s --> %s] %s\n", to_timestamp(tt0).c_str(), to_timestamp(tt1).c_str(), text.c_str());
	} else {
	printf("%s", text.c_str());
	fflush(stdout);
	}
	}

	//printf("tt0 = %d, tt1 = %d, text = %s, token = %s, token_id = %d, tid = %d\n", tt0, tt1, text.c_str(), ctx->vocab.id_to_token[tokens_cur[i].id].c_str(), tokens_cur[i].id, tokens_cur[i].tid);

	result_all.push_back({ tt0, tt1, text, {}, speaker_turn_next });
	for (int j = i0; j <= i; j++) {
	result_all.back().tokens.push_back(tokens_cur[j]);
	}

	int n_new = 1;

	if (params.token_timestamps) {
	whisper_exp_compute_token_level_timestamps(
	ctx, state, result_all.size() - 1, params.thold_pt, params.thold_ptsum);

	if (params.max_len > 0) {
	n_new = whisper_wrap_segment(ctx, state, params.max_len, params.split_on_word);
	}
	}
	if (params.new_segment_callback) {
	params.new_segment_callback(ctx, state, n_new, params.new_segment_callback_user_data);
	}
	}
	text = "";
	while (i < (int) tokens_cur.size() && tokens_cur[i].id > whisper_token_beg(ctx)) {
	i++;
	}
	i--;
	t0 = t1;
	i0 = i + 1;
	speaker_turn_next = false;
	}
	}

	if (!text.empty()) {
	const auto t1 = seek + seek_delta;

	const auto tt0 = params.speed_up ? 2*t0 : t0;
	const auto tt1 = params.speed_up ? 2*t1 : t1;

	if (params.print_realtime) {
	if (params.print_timestamps) {
	printf("[%s --> %s] %s\n", to_timestamp(tt0).c_str(), to_timestamp(tt1).c_str(), text.c_str());
	} else {
	printf("%s", text.c_str());
	fflush(stdout);
	}
	}

	result_all.push_back({ tt0, tt1, text, {} , speaker_turn_next });
	for (int j = i0; j < (int) tokens_cur.size(); j++) {
	result_all.back().tokens.push_back(tokens_cur[j]);
	}

	int n_new = 1;

	if (params.token_timestamps) {
	whisper_exp_compute_token_level_timestamps(
	ctx, state, result_all.size() - 1, params.thold_pt, params.thold_ptsum);

	if (params.max_len > 0) {
	n_new = whisper_wrap_segment(ctx, state, params.max_len, params.split_on_word);
	}
	}
	if (params.new_segment_callback) {
	params.new_segment_callback(ctx, state, n_new, params.new_segment_callback_user_data);
	}
	}
	}

	// FIXME: will timestamp offsets be correct?
	// [EXPERIMENTAL] Token-level timestamps with DTW
	{
	const auto n_segments = state->result_all.size() - n_segments_before;
	if (ctx->params.dtw_token_timestamps && n_segments) {
	const int n_frames = std::min(std::min(WHISPER_CHUNK_SIZE * 100, seek_delta), seek_end - seek);
	whisper_exp_compute_token_level_timestamps_dtw(
	ctx, state, params, result_all.size() - n_segments, n_segments, seek, n_frames, 7, params.n_threads);
	}
	}

	// update audio window
	seek += seek_delta;

	WHISPER_LOG_DEBUG("seek = %d, seek_delta = %d\n", seek, seek_delta);
	}
	}

	return 0;
	}

	int whisper_full(
	struct whisper_context * ctx,
	struct whisper_full_params params,
	const float * samples,
	int n_samples) {
	return whisper_full_with_state(ctx, ctx->state, params, samples, n_samples);
	}

	int whisper_full_parallel(
	struct whisper_context * ctx,
	struct whisper_full_params params,
	const float * samples,
	int n_samples,
	int n_processors) {
	if (n_processors == 1) {
	return whisper_full(ctx, params, samples, n_samples);
	}
	int ret = 0;

	// prepare separate states for each thread
	std::vector<whisper_state*> states;

	const int offset_samples = (WHISPER_SAMPLE_RATE*params.offset_ms)/1000;
	const int n_samples_per_processor = (n_samples - offset_samples)/n_processors;

	// the calling thread will process the first chunk
	// while the other threads will process the remaining chunks

	std::vector<std::thread> workers(n_processors - 1);
	for (int i = 0; i < n_processors - 1; ++i) {
	// create a new state for each thread
	states.push_back(whisper_init_state(ctx));

	const int start_samples = offset_samples + (i + 1)*n_samples_per_processor;
	const int n_samples_cur = (i == n_processors - 2) ? n_samples - start_samples : n_samples_per_processor;

	auto params_cur = params;

	params_cur.offset_ms = 0;
	params_cur.print_progress = false;
	params_cur.print_realtime = false;

	params_cur.new_segment_callback = nullptr;
	params_cur.new_segment_callback_user_data = nullptr;

	params_cur.progress_callback = nullptr;
	params_cur.progress_callback_user_data = nullptr;

	workers[i] = std::thread(whisper_full_with_state, ctx, states[i], std::move(params_cur), samples + start_samples, n_samples_cur);
	}

	{
	auto params_cur = params;

	// We need to disable the print real-time for this one as well, otherwise it will show only for the first chunk.
	params_cur.print_realtime = false;

	// Run the first transformation using default state but only for the first chunk.
	ret = whisper_full_with_state(ctx, ctx->state, std::move(params_cur), samples, offset_samples + n_samples_per_processor);
	}

	for (int i = 0; i < n_processors - 1; ++i) {
	workers[i].join();
	}

	const int64_t offset_t = (int64_t) params.offset_ms/10.0;

	// combine results into result_state->result_all from all other states
	for (int i = 0; i < n_processors - 1; ++i) {
	auto& results_i = states[i]->result_all;

	for (auto& result : results_i) {
	// correct the segment timestamp taking into account the offset
	result.t0 += 100 * ((i + 1) * n_samples_per_processor) / WHISPER_SAMPLE_RATE + offset_t;
	result.t1 += 100 * ((i + 1) * n_samples_per_processor) / WHISPER_SAMPLE_RATE + offset_t;

	// make sure that segments are not overlapping
	if (!ctx->state->result_all.empty()) {
	result.t0 = std::max(result.t0, ctx->state->result_all.back().t1);
	}

	ctx->state->result_all.push_back(std::move(result));

	// call the new_segment_callback for each segment
	if (params.new_segment_callback) {
	params.new_segment_callback(ctx, ctx->state, 1, params.new_segment_callback_user_data);
	}
	}

	ctx->state->t_mel_us += states[i]->t_mel_us;

	ctx->state->t_sample_us += states[i]->t_sample_us;
	ctx->state->t_encode_us += states[i]->t_encode_us;
	ctx->state->t_decode_us += states[i]->t_decode_us;
	ctx->state->t_batchd_us += states[i]->t_batchd_us;
	ctx->state->t_prompt_us += states[i]->t_prompt_us;

	ctx->state->n_sample += states[i]->n_sample;
	ctx->state->n_encode += states[i]->n_encode;
	ctx->state->n_decode += states[i]->n_decode;
	ctx->state->n_batchd += states[i]->n_batchd;
	ctx->state->n_prompt += states[i]->n_prompt;

	whisper_free_state(states[i]);
	}

	// average the timings
	ctx->state->t_mel_us /= n_processors;
	ctx->state->t_sample_us /= n_processors;
	ctx->state->t_encode_us /= n_processors;
	ctx->state->t_decode_us /= n_processors;

	// print information about the audio boundaries
	WHISPER_LOG_WARN("\n");
	WHISPER_LOG_WARN("%s: the audio has been split into %d chunks at the following times:\n", __func__, n_processors);
	for (int i = 0; i < n_processors - 1; ++i) {
	WHISPER_LOG_WARN("%s: split %d - %s\n", __func__, (i + 1), to_timestamp(100((i + 1)n_samples_per_processor)/WHISPER_SAMPLE_RATE + offset_t).c_str());
	}
	WHISPER_LOG_WARN("%s: the transcription quality may be degraded near these boundaries\n", __func__);

	return ret;
	}

	int whisper_full_n_segments_from_state(struct whisper_state * state) {
	return state->result_all.size();
	}

	int whisper_full_n_segments(struct whisper_context * ctx) {
	return ctx->state->result_all.size();
	}

	int whisper_full_lang_id_from_state(struct whisper_state * state) {
	return state->lang_id;
	}

	int whisper_full_lang_id(struct whisper_context * ctx) {
	return ctx->state->lang_id;
	}

	int64_t whisper_full_get_segment_t0_from_state(struct whisper_state * state, int i_segment) {
	return state->result_all[i_segment].t0;
	}

	int64_t whisper_full_get_segment_t0(struct whisper_context * ctx, int i_segment) {
	return ctx->state->result_all[i_segment].t0;
	}

	int64_t whisper_full_get_segment_t1_from_state(struct whisper_state * state, int i_segment) {
	return state->result_all[i_segment].t1;
	}

	int64_t whisper_full_get_segment_t1(struct whisper_context * ctx, int i_segment) {
	return ctx->state->result_all[i_segment].t1;
	}

	bool whisper_full_get_segment_speaker_turn_next_from_state(struct whisper_state * state, int i_segment) {
	return state->result_all[i_segment].speaker_turn_next;
	}

	bool whisper_full_get_segment_speaker_turn_next(struct whisper_context * ctx, int i_segment) {
	return ctx->state->result_all[i_segment].speaker_turn_next;
	}

	const char * whisper_full_get_segment_text_from_state(struct whisper_state * state, int i_segment) {
	return state->result_all[i_segment].text.c_str();
	}

	const char * whisper_full_get_segment_text(struct whisper_context * ctx, int i_segment) {
	return ctx->state->result_all[i_segment].text.c_str();
	}

	int whisper_full_n_tokens_from_state(struct whisper_state * state, int i_segment) {
	return state->result_all[i_segment].tokens.size();
	}

	int whisper_full_n_tokens(struct whisper_context * ctx, int i_segment) {
	return ctx->state->result_all[i_segment].tokens.size();
	}

	const char * whisper_full_get_token_text_from_state(struct whisper_context * ctx, struct whisper_state * state, int i_segment, int i_token) {
	return ctx->vocab.id_to_token[state->result_all[i_segment].tokens[i_token].id].c_str();
	}

	const char* whisper_full_get_token_text(struct whisper_context * ctx, int i_segment, int i_token) {
	return ctx->vocab.id_to_token[ctx->state->result_all[i_segment].tokens[i_token].id].c_str();
	}

	whisper_token whisper_full_get_token_id_from_state(struct whisper_state * state, int i_segment, int i_token) {
	return state->result_all[i_segment].tokens[i_token].id;
	}

	whisper_token whisper_full_get_token_id(struct whisper_context * ctx, int i_segment, int i_token) {
	return ctx->state->result_all[i_segment].tokens[i_token].id;
	}

	struct whisper_token_data whisper_full_get_token_data_from_state(struct whisper_state * state, int i_segment, int i_token) {
	return state->result_all[i_segment].tokens[i_token];
	}

	struct whisper_token_data whisper_full_get_token_data(struct whisper_context * ctx, int i_segment, int i_token) {
	return ctx->state->result_all[i_segment].tokens[i_token];
	}

	float whisper_full_get_token_p_from_state(struct whisper_state * state, int i_segment, int i_token) {
	return state->result_all[i_segment].tokens[i_token].p;
	}

	float whisper_full_get_token_p(struct whisper_context * ctx, int i_segment, int i_token) {
	return ctx->state->result_all[i_segment].tokens[i_token].p;
	}

	// =================================================================================================

	//
	// Temporary interface needed for exposing ggml interface
	// Will be removed in the future when ggml becomes a separate library
	//

	WHISPER_API int whisper_bench_memcpy(int n_threads) {
	fputs(whisper_bench_memcpy_str(n_threads), stderr);
	return 0;
	}

	WHISPER_API const char * whisper_bench_memcpy_str(int n_threads) {
	static std::string s;
	s = "";
	char strbuf[256];

	ggml_time_init();

	size_t n = 20;
	size_t arr = n_threads > 0 ? 1024llu : n_threads; // trick to avoid compiler optimizations

	// 1GB array
	const size_t size = arr*1e6;

	double sum = 0.0;

	// heat-up
	{
	char * src = (char *) malloc(size);
	char * dst = (char *) malloc(size);

	for (size_t i = 0; i < size; i++) src[i] = i;

	memcpy(dst, src, size); // heat-up

	double tsum = 0.0;

	for (size_t i = 0; i < n; i++) {
	const int64_t t0 = ggml_time_us();

	memcpy(dst, src, size);

	const int64_t t1 = ggml_time_us();

	tsum += (t1 - t0)*1e-6;

	src[rand() % size] = rand() % 256;
	}

	snprintf(strbuf, sizeof(strbuf), "memcpy: %7.2f GB/s (heat-up)\n", (double) (nsize)/(tsum1e9));
	s += strbuf;

	// needed to prevent the compiler from optimizing the memcpy away
	{
	for (size_t i = 0; i < size; i++) sum += dst[i];
	}

	free(src);
	free(dst);
	}

	// single-thread
	{
	char * src = (char *) malloc(size);
	char * dst = (char *) malloc(size);

	for (size_t i = 0; i < size; i++) src[i] = i;

	memcpy(dst, src, size); // heat-up

	double tsum = 0.0;

	for (size_t i = 0; i < n; i++) {
	const int64_t t0 = ggml_time_us();

	memcpy(dst, src, size);

	const int64_t t1 = ggml_time_us();

	tsum += (t1 - t0)*1e-6;

	src[rand() % size] = rand() % 256;
	}

	snprintf(strbuf, sizeof(strbuf), "memcpy: %7.2f GB/s ( 1 thread)\n", (double) (nsize)/(tsum1e9));
	s += strbuf;

	// needed to prevent the compiler from optimizing the memcpy away
	{
	for (size_t i = 0; i < size; i++) sum += dst[i];
	}

	free(src);
	free(dst);
	}

	// multi-thread

	for (int32_t k = 1; k <= n_threads; k++) {
	char * src = (char *) malloc(size);
	char * dst = (char *) malloc(size);

	for (size_t i = 0; i < size; i++) src[i] = i;

	memcpy(dst, src, size); // heat-up

	double tsum = 0.0;

	auto helper = [&](int th) {
	const int64_t i0 = (th + 0)*size/k;
	const int64_t i1 = (th + 1)*size/k;

	for (size_t i = 0; i < n; i++) {
	memcpy(dst + i0, src + i0, i1 - i0);

	src[i0 + rand() % (i1 - i0)] = rand() % 256;
	};
	};

	const int64_t t0 = ggml_time_us();

	std::vector<std::thread> threads(k - 1);
	for (int32_t th = 0; th < k - 1; ++th) {
	threads[th] = std::thread(helper, th);
	}

	helper(k - 1);

	for (int32_t th = 0; th < k - 1; ++th) {
	threads[th].join();
	}

	const int64_t t1 = ggml_time_us();

	tsum += (t1 - t0)*1e-6;

	snprintf(strbuf, sizeof(strbuf), "memcpy: %7.2f GB/s (%2d thread)\n", (double) (nsize)/(tsum1e9), k);
	s += strbuf;

	// needed to prevent the compiler from optimizing the memcpy away
	{
	for (size_t i = 0; i < size; i++) sum += dst[i];
	}

	free(src);
	free(dst);
	}

	snprintf(strbuf, sizeof(strbuf), "sum: %f\n", sum);
	s += strbuf;

	return s.c_str();
	}

	WHISPER_API int whisper_bench_ggml_mul_mat(int n_threads) {
	fputs(whisper_bench_ggml_mul_mat_str(n_threads), stderr);
	return 0;
	}

	WHISPER_API const char * whisper_bench_ggml_mul_mat_str(int n_threads) {
	static std::string s;
	s = "";
	char strbuf[256];

	ggml_time_init();

	const int n_max = 128;

	const std::vector<size_t> sizes = {
	64, 128, 256, 512, 1024, 2048, 4096,
	};

	const size_t N_max = sizes.back();

	// a: NNsizeof(float)
	// b: NNsizeof(float)
	// c: NNsizeof(float)
	// when F16 is used, there is an extra work buffer of size NNsizeof(float)
	std::vector<uint8_t> buf(3lluN_maxN_maxsizeof(float) + 3ggml_tensor_overhead() + ggml_graph_overhead());
	std::vector<uint8_t> work;

	// put a bunch of random data in the buffer
	for (size_t i = 0; i < buf.size(); i++) buf[i] = i;

	for (int j = 0; j < (int) sizes.size(); j++) {
	int n_q4_0 = 0;
	int n_q4_1 = 0;
	int n_q5_0 = 0;
	int n_q5_1 = 0;
	int n_q8_0 = 0;
	int n_fp16 = 0;
	int n_fp32 = 0;

	// GFLOPS/s
	double s_q4_0 = 0.0;
	double s_q4_1 = 0.0;
	double s_q5_0 = 0.0;
	double s_q5_1 = 0.0;
	double s_q8_0 = 0.0;
	double s_fp16 = 0.0;
	double s_fp32 = 0.0;

	const size_t N = sizes[j];

	for (int k = 0; k < 7; ++k) {
	const ggml_type wtype =
	k == 0 ? GGML_TYPE_Q4_0 :
	k == 1 ? GGML_TYPE_Q4_1 :
	k == 2 ? GGML_TYPE_Q5_0 :
	k == 3 ? GGML_TYPE_Q5_1 :
	k == 4 ? GGML_TYPE_Q8_0 :
	k == 5 ? GGML_TYPE_F16 : GGML_TYPE_F32;

	double & s = k == 0 ? s_q4_0 : k == 1 ? s_q4_1 : k == 2 ? s_q5_0 : k == 3 ? s_q5_1 : k == 4 ? s_q8_0 : k == 5 ? s_fp16 : /k == 6/ s_fp32;
	int & n = k == 0 ? n_q4_0 : k == 1 ? n_q4_1 : k == 2 ? n_q5_0 : k == 3 ? n_q5_1 : k == 4 ? n_q8_0 : k == 5 ? n_fp16 : /k == 6/ n_fp32;

	struct ggml_init_params gparams = {
	/.mem_size =/ buf.size(),
	/.mem_buffer =/ buf.data(),
	/.no_alloc =/ false,
	};

	struct ggml_context * ctx0 = ggml_init(gparams);

	struct ggml_tensor * a = ggml_new_tensor_2d(ctx0, wtype, N, N);
	struct ggml_tensor * b = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, N, N);

	struct ggml_tensor * c = ggml_mul_mat(ctx0, a, b);

	struct ggml_cgraph * gf = ggml_new_graph(ctx0);

	ggml_build_forward_expand(gf, c);

	double tsum = 0.0;

	// heat-up
	ggml_graph_compute_helper(gf, work, n_threads, nullptr, nullptr);

	for (int i = 0; i < n_max; ++i) {
	const int64_t t0 = ggml_time_us();

	ggml_graph_compute_helper(gf, work, n_threads, nullptr, nullptr);

	const int64_t t1 = ggml_time_us();

	tsum += (t1 - t0)*1e-6;
	n++;

	if (tsum > 1.0 && n >= 3) {
	break;
	}
	}

	ggml_free(ctx0);

	s = ((2.0NNNn)/tsum)*1e-9;
	}

	// Q4_0 \| Q4_1
	snprintf(strbuf, sizeof(strbuf), "%4zu x %4zu: Q4_0 %7.1f GFLOPS (%3d runs) \| Q4_1 %7.1f GFLOPS (%3d runs)\n",
	N, N, s_q4_0, n_q4_0, s_q4_1, n_q4_1);
	s += strbuf;

	// Q5_0 \| Q5_1 \| Q8_0
	snprintf(strbuf, sizeof(strbuf), "%4zu x %4zu: Q5_0 %7.1f GFLOPS (%3d runs) \| Q5_1 %7.1f GFLOPS (%3d runs) \| Q8_0 %7.1f GFLOPS (%3d runs)\n",
	N, N, s_q5_0, n_q5_0, s_q5_1, n_q5_1, s_q8_0, n_q8_0);
	s += strbuf;

	// F16 \| F32
	snprintf(strbuf, sizeof(strbuf), "%4zu x %4zu: F16 %7.1f GFLOPS (%3d runs) \| F32 %7.1f GFLOPS (%3d runs)\n",
	N, N, s_fp16, n_fp16, s_fp32, n_fp32);
	s += strbuf;
	}

	return s.c_str();
	}

	// =================================================================================================

	// =================================================================================================

	//
	// Experimental stuff below
	//
	// Not sure if these should be part of the library at all, because the quality of the results is not
	// guaranteed. Might get removed at some point unless a robust algorithm implementation is found
	//

	// =================================================================================================

	//
	// token-level timestamps
	//

	int timestamp_to_sample(int64_t t, int n_samples) {
	return std::max(0, std::min((int) n_samples - 1, (int) ((t*WHISPER_SAMPLE_RATE)/100)));
	}
	int timestamp_to_sample(int64_t t, int n_samples, int whisper_sample_rate) {
	return std::max(0, std::min((int) n_samples - 1, (int) ((t*whisper_sample_rate)/100)));
	}

	static int64_t sample_to_timestamp(int i_sample) {
	return (100ll*i_sample)/WHISPER_SAMPLE_RATE;
	}

	// a cost-function / heuristic that is high for text that takes longer to pronounce
	// obviously, can be improved
	static float voice_length(const std::string & text) {
	float res = 0.0f;

	for (char c : text) {
	if (c == ' ') {
	res += 0.01f;
	} else if (c == ',') {
	res += 2.00f;
	} else if (c == '.') {
	res += 3.00f;
	} else if (c == '!') {
	res += 3.00f;
	} else if (c == '?') {
	res += 3.00f;
	} else if (c >= '0' && c <= '9') {
	res += 3.00f;
	} else {
	res += 1.00f;
	}
	}

	return res;
	}

	// average the fabs of the signal
	static std::vector<float> get_signal_energy(const float * signal, int n_samples, int n_samples_per_half_window) {
	const int hw = n_samples_per_half_window;

	std::vector<float> result(n_samples);

	for (int i = 0; i < n_samples; i++) {
	float sum = 0;
	for (int j = -hw; j <= hw; j++) {
	if (i + j >= 0 && i + j < n_samples) {
	sum += fabs(signal[i + j]);
	}
	}
	result[i] = sum/(2*hw + 1);
	}

	return result;
	}

	static void whisper_exp_compute_token_level_timestamps(
	struct whisper_context & ctx,
	struct whisper_state & state,
	int i_segment,
	float thold_pt,
	float thold_ptsum) {
	auto & segment = state.result_all[i_segment];
	auto & tokens = segment.tokens;

	const int n_samples = state.energy.size();

	if (n_samples == 0) {
	WHISPER_LOG_ERROR("%s: no signal data available\n", __func__);
	return;
	}

	const int64_t t0 = segment.t0;
	const int64_t t1 = segment.t1;

	const int n = tokens.size();

	if (n == 0) {
	return;
	}

	if (n == 1) {
	tokens[0].t0 = t0;
	tokens[0].t1 = t1;

	return;
	}

	auto & t_beg = state.t_beg;
	auto & t_last = state.t_last;
	auto & tid_last = state.tid_last;

	for (int j = 0; j < n; ++j) {
	auto & token = tokens[j];

	if (j == 0) {
	if (token.id == whisper_token_beg(&ctx)) {
	tokens[j ].t0 = t0;
	tokens[j ].t1 = t0;
	tokens[j + 1].t0 = t0;

	t_beg = t0;
	t_last = t0;
	tid_last = whisper_token_beg(&ctx);
	} else {
	tokens[j ].t0 = t_last;
	}
	}

	const int64_t tt = t_beg + 2*(token.tid - whisper_token_beg(&ctx));

	tokens[j].id = token.id;
	tokens[j].tid = token.tid;
	tokens[j].p = token.p;
	tokens[j].pt = token.pt;
	tokens[j].ptsum = token.ptsum;

	tokens[j].vlen = voice_length(whisper_token_to_str(&ctx, token.id));

	if (token.pt > thold_pt && token.ptsum > thold_ptsum && token.tid > tid_last && tt <= t1) {
	if (j > 0) {
	tokens[j - 1].t1 = tt;
	}
	tokens[j].t0 = tt;
	tid_last = token.tid;
	}
	}

	tokens[n - 2].t1 = t1;
	tokens[n - 1].t0 = t1;
	tokens[n - 1].t1 = t1;

	t_last = t1;

	// find intervals of tokens with unknown timestamps
	// fill the timestamps by proportionally splitting the interval based on the token voice lengths
	{
	int p0 = 0;
	int p1 = 0;

	while (true) {
	while (p1 < n && tokens[p1].t1 < 0) {
	p1++;
	}

	if (p1 >= n) {
	p1--;
	}

	//printf("p0=%d p1=%d t0=%lld t1=%lld\n", p0, p1, tokens[p0].t0, tokens[p1].t1);

	if (p1 > p0) {
	double psum = 0.0;
	for (int j = p0; j <= p1; j++) {
	psum += tokens[j].vlen;
	}

	//printf("analyzing %d - %d, psum = %f\n", p0, p1, psum);

	const double dt = tokens[p1].t1 - tokens[p0].t0;

	// split the time proportionally to the voice length
	for (int j = p0 + 1; j <= p1; j++) {
	const double ct = tokens[j - 1].t0 + dt*tokens[j - 1].vlen/psum;

	tokens[j - 1].t1 = ct;
	tokens[j ].t0 = ct;
	}
	}

	p1++;
	p0 = p1;
	if (p1 >= n) {
	break;
	}
	}
	}

	// fix up (just in case)
	for (int j = 0; j < n - 1; j++) {
	if (tokens[j].t1 < 0) {
	tokens[j + 1].t0 = tokens[j].t1;
	}

	if (j > 0) {
	if (tokens[j - 1].t1 > tokens[j].t0) {
	tokens[j].t0 = tokens[j - 1].t1;
	tokens[j].t1 = std::max(tokens[j].t0, tokens[j].t1);
	}
	}
	}

	// VAD
	// expand or contract tokens based on voice activity
	{
	const int hw = WHISPER_SAMPLE_RATE/8;

	for (int j = 0; j < n; j++) {
	if (tokens[j].id >= whisper_token_eot(&ctx)) {
	continue;
	}

	int s0 = timestamp_to_sample(tokens[j].t0, n_samples);
	int s1 = timestamp_to_sample(tokens[j].t1, n_samples);

	const int ss0 = std::max(s0 - hw, 0);
	const int ss1 = std::min(s1 + hw, n_samples);

	const int ns = ss1 - ss0;

	float sum = 0.0f;

	for (int k = ss0; k < ss1; k++) {
	sum += state.energy[k];
	}

	const float thold = 0.5*sum/ns;

	{
	int k = s0;
	if (state.energy[k] > thold && j > 0) {
	while (k > 0 && state.energy[k] > thold) {
	k--;
	}
	tokens[j].t0 = sample_to_timestamp(k);
	if (tokens[j].t0 < tokens[j - 1].t1) {
	tokens[j].t0 = tokens[j - 1].t1;
	} else {
	s0 = k;
	}
	} else {
	while (state.energy[k] < thold && k < s1) {
	k++;
	}
	s0 = k;
	tokens[j].t0 = sample_to_timestamp(k);
	}
	}

	{
	int k = s1;
	if (state.energy[k] > thold) {
	while (k < n_samples - 1 && state.energy[k] > thold) {
	k++;
	}
	tokens[j].t1 = sample_to_timestamp(k);
	if (j < ns - 1 && tokens[j].t1 > tokens[j + 1].t0) {
	tokens[j].t1 = tokens[j + 1].t0;
	} else {
	s1 = k;
	}
	} else {
	while (state.energy[k] < thold && k > s0) {
	k--;
	}
	s1 = k;
	tokens[j].t1 = sample_to_timestamp(k);
	}
	}
	}
	}

	// fixed token expand (optional)
	//{
	// const int t_expand = 0;

	// for (int j = 0; j < n; j++) {
	// if (j > 0) {
	// tokens[j].t0 = std::max(0, (int) (tokens[j].t0 - t_expand));
	// }
	// if (j < n - 1) {
	// tokens[j].t1 = tokens[j].t1 + t_expand;
	// }
	// }
	//}

	// debug info
	//for (int j = 0; j < n; ++j) {
	// const auto & token = tokens[j];
	// const auto tt = token.pt > thold_pt && token.ptsum > 0.01 ? whisper_token_to_str(&ctx, token.tid) : "[?]";
	// printf("%s: %10s %6.3f %6.3f %6.3f %6.3f %5d %5d '%s'\n", __func__,
	// tt, token.p, token.pt, token.ptsum, token.vlen, (int) token.t0, (int) token.t1, whisper_token_to_str(&ctx, token.id));

	// if (tokens[j].id >= whisper_token_eot(&ctx)) {
	// continue;
	// }
	//}
	}

	//
	// token level timestamps - dtw version
	//

	// n_text_layer -> total text layers on model
	// n_head -> total heads per text layer on model
	static std::vector<uint32_t> get_alignment_heads_by_layer(const whisper_context_params & cparams, int il, int n_text_layer, int n_head) {
	std::vector<uint32_t> ret;
	if (cparams.dtw_aheads_preset == WHISPER_AHEADS_NONE) {
	return ret;
	} else if (cparams.dtw_aheads_preset == WHISPER_AHEADS_N_TOP_MOST) {
	if (il >= n_text_layer - cparams.dtw_n_top) {
	for (int32_t i = 0; i < n_head; ++i) {
	ret.push_back(i);
	}
	}
	} else {
	const auto aheads = cparams.dtw_aheads_preset == WHISPER_AHEADS_CUSTOM ? cparams.dtw_aheads : g_aheads.at(cparams.dtw_aheads_preset);
	for (size_t i = 0; i < aheads.n_heads; ++i) {
	if (aheads.heads[i].n_text_layer == il) {
	ret.push_back(aheads.heads[i].n_head);
	}
	}
	}
	return ret;
	}

	// dtw + backtrace to return found path
	// based on
	// https://github.com/openai/whisper/blob/main/whisper/timing.py#L83
	static ggml_tensor * dtw_and_backtrace(ggml_context * ctx, ggml_tensor * x) {
	WHISPER_ASSERT(ggml_n_dims(x) == 2);

	int64_t N = x->ne[0];
	int64_t M = x->ne[1];
	struct ggml_tensor * cost = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, N + 1, M + 1);
	struct ggml_tensor * trace = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, N + 1, M + 1);

	cost = ggml_set_f32(cost, INFINITY);
	trace = ggml_set_f32(trace, -1);
	ggml_set_f32_nd(cost, 0, 0, 0, 0, 0.0);

	// dtw
	// supposedly can be optmized by computing diagonals in parallel ?
	// Not sure it is worth it since x will be GENERATED_TOKENS*1500 size at most.
	for (int64_t j = 1; j < M + 1; ++j) {
	for (int64_t i = 1; i < N + 1; ++i) {
	float c0 = ggml_get_f32_nd(cost, i - 1, j - 1, 0, 0);
	float c1 = ggml_get_f32_nd(cost, i - 1, j, 0, 0);
	float c2 = ggml_get_f32_nd(cost, i, j - 1, 0, 0);

	float c;
	int32_t t;
	if (c0 < c1 && c0 < c2) {
	c = c0;
	t = 0;
	} else if (c1 < c0 && c1 < c2) {
	c = c1;
	t = 1;
	} else {
	c = c2;
	t = 2;
	}

	c = ggml_get_f32_nd(x, i - 1, j - 1, 0, 0) + c;
	ggml_set_f32_nd(cost, i, j, 0, 0, c);
	ggml_set_i32_nd(trace, i, j, 0, 0, t);
	}
	}

	// Backtrace
	const int64_t BT_MAX_ROWS = N + M - 1;
	struct ggml_tensor * bt = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, BT_MAX_ROWS, 2);
	// trace[0, :] = 2;
	for (int64_t i = 0; i < M + 1; ++i)
	ggml_set_i32_nd(trace, 0, i, 0, 0, 2);
	//trace[:, 0] = 1;
	for (int64_t i = 0; i < N + 1; ++i)
	ggml_set_i32_nd(trace, i, 0, 0, 0, 1);
	int bt_row_idx = BT_MAX_ROWS - 1;
	int64_t i = N;
	int64_t j = M;
	while (i > 0 \|\| j > 0) {
	ggml_set_i32_nd(bt, bt_row_idx, 0, 0, 0, i - 1);
	ggml_set_i32_nd(bt, bt_row_idx, 1, 0, 0, j - 1);
	--bt_row_idx;

	int32_t t = ggml_get_i32_nd(trace, i, j, 0, 0);
	if (t == 0) {
	--i;
	--j;
	} else if (t == 1) {
	--i;
	} else if (t == 2) {
	--j;
	} else {
	WHISPER_ASSERT(0);
	}
	}

	// FIXME: manual clip/transpose might not be the most efficient way? (e.g. use ggml funcs)
	// Clip + transpose
	// This might not be entirely necessary for our case, but leaving it for now so output matrix
	// is identical to dtw on openAI timing.py
	const int64_t result_n_cols = BT_MAX_ROWS-bt_row_idx-1;
	ggml_tensor * r = ggml_new_tensor_2d(ctx, GGML_TYPE_I32, 2, result_n_cols);
	for (int64_t i = 0; i < 2; ++i) {
	for (int64_t j = 0; j < result_n_cols; ++j) {
	int32_t v = ggml_get_i32_nd(bt, j+bt_row_idx+1, i, 0, 0);
	ggml_set_i32_nd(r, i, j, 0, 0, v);
	}
	}

	return r;
	}

	struct median_filter_user_data {
	int filter_width;
	};

	static void median_filter(struct ggml_tensor * dst , const struct ggml_tensor * a, int ith, int nth, void * userdata) {
	int filter_width = ((median_filter_user_data *) userdata)->filter_width;
	WHISPER_ASSERT(nth == 1);
	WHISPER_ASSERT(ith == 0);
	WHISPER_ASSERT(filter_width < a->ne[2]);
	WHISPER_ASSERT(filter_width % 2);
	WHISPER_ASSERT(ggml_n_dims(a) == 3);
	WHISPER_ASSERT(a->type == GGML_TYPE_F32);

	std::vector<float> filter;
	filter.reserve(filter_width);
	for (int64_t i = 0; i < a->ne[0]; ++i) {
	for (int64_t j = 0; j < a->ne[1]; ++j) {
	for (int64_t k = 0; k < a->ne[2]; ++k) {
	for (int64_t off = -filter_width/2; off <= filter_width/2; ++off) {
	// "reflect" padding
	int64_t idx = k + off;
	if (idx < 0) {
	idx = -idx;
	} else if (idx >= a->ne[2]) {
	idx = 2*(a->ne[2] - 1) - idx;
	}

	filter.push_back(ggml_get_f32_nd(a, i, j, idx, 0));
	}
	std::sort(filter.begin(), filter.end());
	const float v = filter[filter.size()/2];
	ggml_set_f32_nd(dst, i, j, k, 0, v);
	filter.clear();
	}
	}
	}
	}

	static void whisper_exp_compute_token_level_timestamps_dtw(
	struct whisper_context * ctx,
	struct whisper_state * state,
	struct whisper_full_params params,
	int i_segment,
	size_t n_segments,
	int seek,
	int n_frames,
	int medfilt_width,
	int n_threads)
	{
	const int n_audio_ctx = state->exp_n_audio_ctx > 0 ? state->exp_n_audio_ctx : ctx->model.hparams.n_audio_ctx;
	WHISPER_ASSERT(medfilt_width % 2);
	WHISPER_ASSERT(n_frames <= n_audio_ctx * 2);
	WHISPER_ASSERT(ctx->params.dtw_aheads_preset != WHISPER_AHEADS_NONE);

	// FIXME: Allocating mem everytime we call this func
	// Our ggml buffer should be pre-allocated somewhere during init and reused
	// when we call this function
	struct ggml_init_params gparams = {
	/.mem_size =/ ctx->params.dtw_mem_size,
	/.mem_buffer =/ NULL,
	/.no_alloc =/ false,
	};
	struct ggml_context * gctx = ggml_init(gparams);

	// Build token sequence that will be passed to decoder
	// sot + [lang] + text result + eot
	std::vector<whisper_token> tokens = { whisper_token_sot(ctx), };
	if (whisper_is_multilingual(ctx)) {
	const int lang_id = whisper_lang_id(params.language);
	state->lang_id = lang_id;
	tokens.push_back(whisper_token_lang(ctx, lang_id));
	}
	const size_t sot_sequence_length = tokens.size();
	tokens.push_back(whisper_token_not(ctx));
	for (size_t i = i_segment; i < i_segment + n_segments; ++i) {
	auto & segment = state->result_all[i];
	for (auto &t: segment.tokens) {
	// Only text tokens
	if (t.id < whisper_token_eot(ctx)) {
	tokens.push_back(t.id);
	}
	}
	}
	tokens.push_back(whisper_token_eot(ctx));

	// Get result tokens, pass then along to decoder to get cross attention QKs
	// used in timestamping
	// Decoder already returns only alignment head QKs, already concatenated in
	// one tensor.
	whisper_kv_cache_clear(state->kv_self);
	whisper_batch_prep_legacy(state->batch, tokens.data(), tokens.size(), 0, 0);
	whisper_kv_cache_seq_rm(state->kv_self, 0, 0, -1);
	if (!whisper_decode_internal(ctx, state, state->batch, n_threads, true, nullptr, nullptr)) {
	WHISPER_LOG_INFO("DECODER FAILED\n");
	WHISPER_ASSERT(0);
	}
	WHISPER_ASSERT(state->aheads_cross_QKs != nullptr);

	const auto n_audio_tokens = n_frames/2;
	WHISPER_ASSERT(state->aheads_cross_QKs != NULL);
	WHISPER_ASSERT(n_audio_tokens <= state->aheads_cross_QKs->ne[1]);
	const auto n_tokens = state->aheads_cross_QKs->ne[0];
	const auto n_heads = state->aheads_cross_QKs->ne[2];

	// Copy data from decoder buffer to a local CPU tensor, discarding unused audio
	// tokens (i.e. discarding rows at the end of tensor)
	// IN: Tensor with N_TOKENSaudio_ctxN_ALIGNMENT_HEADS dims
	// OUT: Tensor with N_TOKENSN_AUDIO_TOKENSN_ALIGNMENT_HEADS dims
	WHISPER_ASSERT(state->aheads_cross_QKs->type == GGML_TYPE_F32);
	WHISPER_ASSERT(ggml_is_contiguous(state->aheads_cross_QKs));
	ggml_tensor * w = ggml_new_tensor_3d(gctx, GGML_TYPE_F32, n_tokens, n_audio_tokens, n_heads);
	auto & data = state->aheads_cross_QKs_data;
	data.resize(n_tokens * n_audio_ctx * n_heads);
	ggml_backend_tensor_get(state->aheads_cross_QKs, data.data(), 0, sizeof(float) * n_tokens * n_audio_ctx * n_heads);
	for (int k = 0; k < n_heads; ++k) {
	for (int j = 0; j < n_audio_tokens; ++j) {
	memcpy(
	(char ) w->data + j w->nb[1] + k * w->nb[2],
	data.data() + j * n_tokens + k * n_tokens * n_audio_ctx,
	n_tokens * sizeof(float)
	);
	}
	}

	// Normalize - in original OpenAI code, this is done over dim=-2. In this case,
	// we already permuted N_TOKENS dimension to columns on last loop, becase ggml_norm
	// operates over columns. Afterwards, permute to a shape that facilitates mean
	// operation (after median filter)
	// IN: Tensor with N_TOKENSN_AUDIO_TOKENSN_ALIGNMENT_HEADS dims
	// OUT: Tensor with N_ALIGNMENT_HEADSN_TOKENSN_AUDIO_TOKENS dims
	w = ggml_norm(gctx, w, 1e-9);
	w = ggml_permute(gctx, ggml_permute(gctx, w, 2, 1, 0 ,3), 0, 2, 1, 3);

	// Pass median filter - this is done over AUDIO_TOKENS dimension.
	// IN: Tensor with N_ALIGNMENT_HEADSN_TOKENSN_AUDIO_TOKENS dims
	// OUT: Same dims
	median_filter_user_data mf_user_data = {medfilt_width};
	w = ggml_map_custom1(gctx, w, median_filter, 1, &mf_user_data);

	// Take mean over columns, scale by -1, reshape to 2D tensor, remove SOT sequence and EOT
	// IN: Tensor with N_ALIGNMENT_HEADSN_TOKENSN_AUDIO_TOKENS dims
	// OUT: Tensor with N_TOKENS*N_AUDIO_TOKENS dims
	w = ggml_mean(gctx, w);
	w = ggml_scale(gctx, w, -1.0);
	w = ggml_reshape_2d(gctx, w, w->ne[1], w->ne[2]);

	// Remove SOT sequence and EOT
	// Out dimension is (N_TOKENS-sot_sequence_length-1)*N_AUDIO_TOKENS
	w = ggml_view_2d(gctx, w, w->ne[0] - sot_sequence_length - 1, w->ne[1], w->nb[1], sot_sequence_length * w->nb[0]);

	// Compute
	struct ggml_cgraph * gf = ggml_new_graph(gctx);
	ggml_build_forward_expand(gf, w);
	ggml_graph_compute_with_ctx(gctx, gf, n_threads);

	ggml_tensor * alignment = dtw_and_backtrace(gctx, w);

	// Place timestamps on segments
	int32_t last_v = 0;
	auto seg_i = state->result_all.begin() + i_segment;
	auto tok_i = seg_i->tokens.begin();
	for (int i = 0; i < alignment->ne[1]; ++i) {
	int32_t v = ggml_get_i32_nd(alignment, 0, i, 0, 0);
	if (v != last_v) {
	int32_t time_index = ggml_get_i32_nd(alignment, 1, i, 0, 0);
	int64_t timestamp = (time_index * 2) + seek; // Each index on DTW result = 20mS audio
	last_v = v;

	// Skip non-text tokens
	while (!(tok_i->id < whisper_token_eot(ctx))) {
	++tok_i;
	if (tok_i == seg_i->tokens.end()) {
	++seg_i;
	tok_i = seg_i->tokens.begin();
	}
	}

	tok_i->t_dtw = timestamp;
	++tok_i;
	if (tok_i == seg_i->tokens.end()) {
	++seg_i;
	tok_i = seg_i->tokens.begin();
	}
	}
	}

	// Print DTW timestamps
	/*for (size_t i = i_segment; i < i_segment + n_segments; ++i) {
	auto & segment = state->result_all[i];
	for (auto &t: segment.tokens) {
	const char * tok = whisper_token_to_str(ctx, t.id);
	fprintf(stderr, "\|%s\|(%.2f) ", tok, (float)t.t_dtw/100);
	}
	fprintf(stderr, "\n");
	}*/

	ggml_free(gctx);
	}

	void whisper_log_set(ggml_log_callback log_callback, void * user_data) {
	g_state.log_callback = log_callback ? log_callback : whisper_log_callback_default;
	g_state.log_callback_user_data = user_data;
	}

	GGML_ATTRIBUTE_FORMAT(2, 3)
	static void whisper_log_internal(ggml_log_level level, const char * format, ...) {
	va_list args;
	va_start(args, format);
	char buffer[1024];
	int len = vsnprintf(buffer, 1024, format, args);
	if (len < 1024) {
	g_state.log_callback(level, buffer, g_state.log_callback_user_data);
	} else {
	char* buffer2 = new char[len+1];
	vsnprintf(buffer2, len+1, format, args);
	buffer2[len] = 0;
	g_state.log_callback(level, buffer2, g_state.log_callback_user_data);
	delete[] buffer2;
	}
	va_end(args);
	}

	static void whisper_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
	(void) level;
	(void) user_data;
	fputs(text, stderr);
	fflush(stderr);
	}



	bool is_file_exist(const char *fileName)
	{
	std::ifstream infile(fileName);
	return infile.good();
	}