mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2024-12-24 06:46:37 +00:00
5012 lines
169 KiB
C++
5012 lines
169 KiB
C++
#define WHISPER_BUILD
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#include "whisper.h"
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#include "ggml.h"
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#include <algorithm>
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#include <cassert>
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#define _USE_MATH_DEFINES
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <fstream>
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#include <map>
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#include <string>
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#include <thread>
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#include <vector>
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#include <regex>
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#include <random>
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#if defined(GGML_BIG_ENDIAN)
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#include <bit>
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template<typename T>
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static T byteswap(T value) {
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return std::byteswap(value);
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}
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template<>
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float byteswap(float value) {
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return std::bit_cast<float>(byteswap(std::bit_cast<std::uint32_t>(value)));
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}
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template<typename T>
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static void byteswap_tensor_data(ggml_tensor * tensor) {
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T * datum = reinterpret_cast<T *>(tensor->data);
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for (int i = 0; i < ggml_nelements(tensor); i++) {
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datum[i] = byteswap(datum[i]);
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}
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}
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static void byteswap_tensor(ggml_tensor * tensor) {
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switch (tensor->type) {
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case GGML_TYPE_I16: {
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byteswap_tensor_data<int16_t>(tensor);
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break;
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}
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case GGML_TYPE_F16: {
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byteswap_tensor_data<ggml_fp16_t>(tensor);
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break;
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}
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case GGML_TYPE_I32: {
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byteswap_tensor_data<int32_t>(tensor);
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break;
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}
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case GGML_TYPE_F32: {
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byteswap_tensor_data<float>(tensor);
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break;
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}
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default: { // GML_TYPE_I8
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break;
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}
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}
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}
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#define BYTESWAP_VALUE(d) d = byteswap(d)
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#define BYTESWAP_FILTERS(f) \
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do { \
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for (auto & datum : f.data) { \
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datum = byteswap(datum); \
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} \
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} while (0)
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#define BYTESWAP_TENSOR(t) \
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do { \
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byteswap_tensor(tensor); \
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} while (0)
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#else
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#define BYTESWAP_VALUE(d) do {} while (0)
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#define BYTESWAP_FILTERS(f) do {} while (0)
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#define BYTESWAP_TENSOR(t) do {} while (0)
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#endif
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#define WHISPER_ASSERT(x) \
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do { \
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if (!(x)) { \
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fprintf(stderr, "WHISPER_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
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abort(); \
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} \
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} while (0)
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// define this to enable verbose trace logging - useful for debugging purposes
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//#define WHISPER_DEBUG
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#if defined(WHISPER_DEBUG)
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#define WHISPER_PRINT_DEBUG(...) \
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do { \
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fprintf(stderr, __VA_ARGS__); \
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} while (0)
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#else
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#define WHISPER_PRINT_DEBUG(...)
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#endif
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#define WHISPER_USE_FLASH_ATTN
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//#define WHISPER_USE_FLASH_FF
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#define WHISPER_MAX_DECODERS 16
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#define WHISPER_USE_SCRATCH
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#define WHISPER_MAX_SCRATCH_BUFFERS 16
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// available whisper models
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enum e_model {
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MODEL_UNKNOWN,
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MODEL_TINY,
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MODEL_BASE,
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MODEL_SMALL,
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MODEL_MEDIUM,
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MODEL_LARGE,
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};
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static const std::map<std::string, std::pair<int, std::string>> g_lang = {
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{ "en", { 0, "english", } },
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{ "zh", { 1, "chinese", } },
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{ "de", { 2, "german", } },
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{ "es", { 3, "spanish", } },
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{ "ru", { 4, "russian", } },
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{ "ko", { 5, "korean", } },
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{ "fr", { 6, "french", } },
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{ "ja", { 7, "japanese", } },
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{ "pt", { 8, "portuguese", } },
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{ "tr", { 9, "turkish", } },
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{ "pl", { 10, "polish", } },
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{ "ca", { 11, "catalan", } },
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{ "nl", { 12, "dutch", } },
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{ "ar", { 13, "arabic", } },
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{ "sv", { 14, "swedish", } },
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{ "it", { 15, "italian", } },
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{ "id", { 16, "indonesian", } },
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{ "hi", { 17, "hindi", } },
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{ "fi", { 18, "finnish", } },
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{ "vi", { 19, "vietnamese", } },
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{ "iw", { 20, "hebrew", } },
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{ "uk", { 21, "ukrainian", } },
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{ "el", { 22, "greek", } },
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{ "ms", { 23, "malay", } },
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{ "cs", { 24, "czech", } },
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{ "ro", { 25, "romanian", } },
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{ "da", { 26, "danish", } },
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{ "hu", { 27, "hungarian", } },
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{ "ta", { 28, "tamil", } },
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{ "no", { 29, "norwegian", } },
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{ "th", { 30, "thai", } },
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{ "ur", { 31, "urdu", } },
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{ "hr", { 32, "croatian", } },
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{ "bg", { 33, "bulgarian", } },
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{ "lt", { 34, "lithuanian", } },
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{ "la", { 35, "latin", } },
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{ "mi", { 36, "maori", } },
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{ "ml", { 37, "malayalam", } },
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{ "cy", { 38, "welsh", } },
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{ "sk", { 39, "slovak", } },
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{ "te", { 40, "telugu", } },
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{ "fa", { 41, "persian", } },
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{ "lv", { 42, "latvian", } },
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{ "bn", { 43, "bengali", } },
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{ "sr", { 44, "serbian", } },
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{ "az", { 45, "azerbaijani", } },
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{ "sl", { 46, "slovenian", } },
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{ "kn", { 47, "kannada", } },
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{ "et", { 48, "estonian", } },
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{ "mk", { 49, "macedonian", } },
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{ "br", { 50, "breton", } },
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{ "eu", { 51, "basque", } },
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{ "is", { 52, "icelandic", } },
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{ "hy", { 53, "armenian", } },
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{ "ne", { 54, "nepali", } },
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{ "mn", { 55, "mongolian", } },
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{ "bs", { 56, "bosnian", } },
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{ "kk", { 57, "kazakh", } },
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{ "sq", { 58, "albanian", } },
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{ "sw", { 59, "swahili", } },
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{ "gl", { 60, "galician", } },
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{ "mr", { 61, "marathi", } },
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{ "pa", { 62, "punjabi", } },
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{ "si", { 63, "sinhala", } },
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{ "km", { 64, "khmer", } },
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{ "sn", { 65, "shona", } },
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{ "yo", { 66, "yoruba", } },
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{ "so", { 67, "somali", } },
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{ "af", { 68, "afrikaans", } },
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{ "oc", { 69, "occitan", } },
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{ "ka", { 70, "georgian", } },
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{ "be", { 71, "belarusian", } },
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{ "tg", { 72, "tajik", } },
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{ "sd", { 73, "sindhi", } },
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{ "gu", { 74, "gujarati", } },
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{ "am", { 75, "amharic", } },
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{ "yi", { 76, "yiddish", } },
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{ "lo", { 77, "lao", } },
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{ "uz", { 78, "uzbek", } },
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{ "fo", { 79, "faroese", } },
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{ "ht", { 80, "haitian creole", } },
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{ "ps", { 81, "pashto", } },
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{ "tk", { 82, "turkmen", } },
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{ "nn", { 83, "nynorsk", } },
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{ "mt", { 84, "maltese", } },
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{ "sa", { 85, "sanskrit", } },
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{ "lb", { 86, "luxembourgish", } },
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{ "my", { 87, "myanmar", } },
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{ "bo", { 88, "tibetan", } },
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{ "tl", { 89, "tagalog", } },
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{ "mg", { 90, "malagasy", } },
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{ "as", { 91, "assamese", } },
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{ "tt", { 92, "tatar", } },
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{ "haw", { 93, "hawaiian", } },
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{ "ln", { 94, "lingala", } },
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{ "ha", { 95, "hausa", } },
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{ "ba", { 96, "bashkir", } },
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{ "jw", { 97, "javanese", } },
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{ "su", { 98, "sundanese", } },
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};
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static const size_t MB = 1024*1024;
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static const std::map<e_model, size_t> MEM_REQ_SCRATCH0 = {
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{ MODEL_TINY, 12ull*MB },
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{ MODEL_BASE, 15ull*MB },
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{ MODEL_SMALL, 23ull*MB },
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{ MODEL_MEDIUM, 31ull*MB },
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{ MODEL_LARGE, 38ull*MB },
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};
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static const std::map<e_model, size_t> MEM_REQ_SCRATCH1 = {
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{ MODEL_TINY, 18ull*MB },
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{ MODEL_BASE, 24ull*MB },
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{ MODEL_SMALL, 36ull*MB },
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{ MODEL_MEDIUM, 48ull*MB },
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{ MODEL_LARGE, 60ull*MB },
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};
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static const std::map<e_model, size_t> MEM_REQ_SCRATCH2 = {
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{ MODEL_TINY, 4ull*MB },
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{ MODEL_BASE, 4ull*MB },
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{ MODEL_SMALL, 6ull*MB },
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{ MODEL_MEDIUM, 7ull*MB },
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{ MODEL_LARGE, 9ull*MB },
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};
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static const std::map<e_model, size_t> MEM_REQ_SCRATCH3 = {
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{ MODEL_TINY, 4ull*MB },
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{ MODEL_BASE, 4ull*MB },
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{ MODEL_SMALL, 6ull*MB },
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{ MODEL_MEDIUM, 7ull*MB },
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{ MODEL_LARGE, 9ull*MB },
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};
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static const std::map<e_model, size_t> MEM_REQ_MODEL = {
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{ MODEL_TINY, 74ull*MB },
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{ MODEL_BASE, 142ull*MB },
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{ MODEL_SMALL, 466ull*MB },
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{ MODEL_MEDIUM, 1464ull*MB },
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{ MODEL_LARGE, 2952ull*MB },
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};
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static const std::map<e_model, size_t> MEM_REQ_KV_SELF = {
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{ MODEL_TINY, 3ull*MB },
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{ MODEL_BASE, 6ull*MB },
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{ MODEL_SMALL, 16ull*MB },
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{ MODEL_MEDIUM, 43ull*MB },
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{ MODEL_LARGE, 71ull*MB },
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};
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static const std::map<e_model, size_t> MEM_REQ_KV_CROSS = {
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{ MODEL_TINY, 9ull*MB },
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{ MODEL_BASE, 18ull*MB },
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{ MODEL_SMALL, 53ull*MB },
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{ MODEL_MEDIUM, 141ull*MB },
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{ MODEL_LARGE, 235ull*MB },
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};
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static const std::map<e_model, size_t> MEM_REQ_ENCODE = {
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{ MODEL_TINY, 6ull*MB },
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{ MODEL_BASE, 8ull*MB },
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{ MODEL_SMALL, 13ull*MB },
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{ MODEL_MEDIUM, 22ull*MB },
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{ MODEL_LARGE, 33ull*MB },
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};
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static const std::map<e_model, size_t> MEM_REQ_DECODE = {
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{ MODEL_TINY, 3ull*MB },
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{ MODEL_BASE, 5ull*MB },
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{ MODEL_SMALL, 10ull*MB },
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{ MODEL_MEDIUM, 18ull*MB },
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{ MODEL_LARGE, 27ull*MB },
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};
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struct whisper_mel {
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int n_len;
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int n_mel;
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std::vector<float> data;
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};
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struct whisper_filters {
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int32_t n_mel;
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int32_t n_fft;
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std::vector<float> data;
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};
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struct whisper_vocab {
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using id = int32_t;
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using token = std::string;
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int n_vocab = 51864;
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std::map<token, id> token_to_id;
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std::map<id, token> id_to_token;
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id token_eot = 50256;
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id token_sot = 50257;
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id token_prev = 50360;
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id token_solm = 50361; // ??
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id token_not = 50362; // no timestamps
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id token_beg = 50363;
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// available tasks
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static const id token_translate = 50358;
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static const id token_transcribe = 50359;
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bool is_multilingual() const {
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return n_vocab == 51865;
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}
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};
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struct whisper_segment {
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int64_t t0;
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int64_t t1;
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std::string text;
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std::vector<whisper_token_data> tokens;
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};
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// medium
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// hparams: {
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// 'n_mels': 80,
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// 'n_vocab': 51864,
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// 'n_audio_ctx': 1500,
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// 'n_audio_state': 1024,
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// 'n_audio_head': 16,
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// 'n_audio_layer': 24,
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// 'n_text_ctx': 448,
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// 'n_text_state': 1024,
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// 'n_text_head': 16,
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// 'n_text_layer': 24
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// }
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//
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// default hparams (Whisper tiny)
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struct whisper_hparams {
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int32_t n_vocab = 51864;
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int32_t n_audio_ctx = 1500;
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int32_t n_audio_state = 384;
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int32_t n_audio_head = 6;
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int32_t n_audio_layer = 4;
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int32_t n_text_ctx = 448;
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int32_t n_text_state = 384;
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int32_t n_text_head = 6;
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int32_t n_text_layer = 4;
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int32_t n_mels = 80;
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int32_t f16 = 1;
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};
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// audio encoding layer
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struct whisper_layer_encoder {
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// encoder.blocks.*.attn_ln
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struct ggml_tensor * attn_ln_0_w;
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struct ggml_tensor * attn_ln_0_b;
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// encoder.blocks.*.attn.out
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struct ggml_tensor * attn_ln_1_w;
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struct ggml_tensor * attn_ln_1_b;
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// encoder.blocks.*.attn.query
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struct ggml_tensor * attn_q_w;
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struct ggml_tensor * attn_q_b;
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// encoder.blocks.*.attn.key
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struct ggml_tensor * attn_k_w;
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// encoder.blocks.*.attn.value
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struct ggml_tensor * attn_v_w;
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struct ggml_tensor * attn_v_b;
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// encoder.blocks.*.mlp_ln
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struct ggml_tensor * mlp_ln_w;
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struct ggml_tensor * mlp_ln_b;
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// encoder.blocks.*.mlp.0
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struct ggml_tensor * mlp_0_w;
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struct ggml_tensor * mlp_0_b;
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// encoder.blocks.*.mlp.2
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struct ggml_tensor * mlp_1_w;
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struct ggml_tensor * mlp_1_b;
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};
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// token decoding layer
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struct whisper_layer_decoder {
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// decoder.blocks.*.attn_ln
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struct ggml_tensor * attn_ln_0_w;
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struct ggml_tensor * attn_ln_0_b;
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// decoder.blocks.*.attn.out
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struct ggml_tensor * attn_ln_1_w;
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struct ggml_tensor * attn_ln_1_b;
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// decoder.blocks.*.attn.query
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struct ggml_tensor * attn_q_w;
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struct ggml_tensor * attn_q_b;
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// decoder.blocks.*.attn.key
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struct ggml_tensor * attn_k_w;
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// decoder.blocks.*.attn.value
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struct ggml_tensor * attn_v_w;
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struct ggml_tensor * attn_v_b;
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// decoder.blocks.*.cross_attn_ln
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struct ggml_tensor * cross_attn_ln_0_w;
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struct ggml_tensor * cross_attn_ln_0_b;
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// decoder.blocks.*.cross_attn.out
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struct ggml_tensor * cross_attn_ln_1_w;
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struct ggml_tensor * cross_attn_ln_1_b;
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// decoder.blocks.*.cross_attn.query
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struct ggml_tensor * cross_attn_q_w;
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struct ggml_tensor * cross_attn_q_b;
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// decoder.blocks.*.cross_attn.key
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struct ggml_tensor * cross_attn_k_w;
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// decoder.blocks.*.cross_attn.value
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struct ggml_tensor * cross_attn_v_w;
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struct ggml_tensor * cross_attn_v_b;
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// decoder.blocks.*.mlp_ln
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struct ggml_tensor * mlp_ln_w;
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struct ggml_tensor * mlp_ln_b;
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// decoder.blocks.*.mlp.0
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struct ggml_tensor * mlp_0_w;
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struct ggml_tensor * mlp_0_b;
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// decoder.blocks.*.mlp.2
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struct ggml_tensor * mlp_1_w;
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struct ggml_tensor * mlp_1_b;
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};
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struct whisper_kv_cache {
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struct ggml_tensor * k;
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struct ggml_tensor * v;
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struct ggml_context * ctx;
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std::vector<uint8_t> buf;
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int n; // number of tokens currently in the cache
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};
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struct whisper_model {
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e_model type = MODEL_UNKNOWN;
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whisper_hparams hparams;
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whisper_filters filters;
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// encoder.positional_embedding
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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;
|
|
|
|
// context
|
|
struct ggml_context * ctx;
|
|
|
|
// the model memory buffer is read-only and can be shared between processors
|
|
std::vector<uint8_t> * buf;
|
|
|
|
// tensors
|
|
int n_loaded;
|
|
std::map<std::string, struct ggml_tensor *> tensors;
|
|
};
|
|
|
|
struct whisper_sequence {
|
|
std::vector<whisper_token_data> tokens;
|
|
|
|
// the accumulated transcription in the current interation (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 {
|
|
// each decoders keeps its own KV-cache
|
|
whisper_kv_cache kv_self;
|
|
|
|
// the currently generated sequence of tokens
|
|
whisper_sequence sequence;
|
|
|
|
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;
|
|
|
|
std::vector<whisper_token> tokens_tmp; // used for whisper_decode calls
|
|
};
|
|
|
|
struct whisper_state {
|
|
int64_t t_sample_us = 0;
|
|
int64_t t_encode_us = 0;
|
|
int64_t t_decode_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
|
|
int32_t n_fail_p = 0; // number of logprob threshold failures
|
|
int32_t n_fail_h = 0; // number of entropy threshold failures
|
|
|
|
// cross-attention KV cache for the decoders
|
|
// shared between all decoders
|
|
whisper_kv_cache kv_cross;
|
|
whisper_mel mel;
|
|
|
|
whisper_decoder decoders[WHISPER_MAX_DECODERS] = {};
|
|
|
|
// memory buffers used by encode / decode contexts
|
|
std::vector<uint8_t> buf_compute;
|
|
std::vector<uint8_t> buf_scratch[WHISPER_MAX_SCRATCH_BUFFERS];
|
|
|
|
int buf_last = 0;
|
|
size_t buf_max_size[WHISPER_MAX_SCRATCH_BUFFERS] = { 0 };
|
|
|
|
// 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;
|
|
|
|
// work container used to avoid memory allocations
|
|
std::vector<std::pair<double, whisper_vocab::id>> logits_id;
|
|
|
|
mutable std::mt19937 rng; // used for sampling at t > 0.0
|
|
|
|
int lang_id = 0; // english by default
|
|
|
|
// [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] speed-up techniques
|
|
int32_t exp_n_audio_ctx = 0; // 0 - use default
|
|
|
|
void use_buf(struct ggml_context * ctx, int i) {
|
|
#if defined(WHISPER_USE_SCRATCH)
|
|
size_t last_size = 0;
|
|
|
|
if (i == -1) {
|
|
last_size = ggml_set_scratch(ctx, { 0, 0, nullptr, });
|
|
} else {
|
|
auto & buf = buf_scratch[i];
|
|
last_size = ggml_set_scratch(ctx, { 0, buf.size(), buf.data(), });
|
|
}
|
|
|
|
if (buf_last >= 0) {
|
|
buf_max_size[buf_last] = std::max(buf_max_size[buf_last], last_size);
|
|
}
|
|
|
|
buf_last = i;
|
|
#else
|
|
(void) i;
|
|
(void) ctx;
|
|
#endif
|
|
}
|
|
|
|
size_t get_buf_max_mem(int i) const {
|
|
#if defined(WHISPER_USE_SCRATCH)
|
|
return buf_max_size[i];
|
|
#else
|
|
(void) i;
|
|
return 0;
|
|
#endif
|
|
}
|
|
};
|
|
|
|
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 or FP16)
|
|
|
|
whisper_model model;
|
|
whisper_vocab vocab;
|
|
whisper_state * state = nullptr;
|
|
};
|
|
|
|
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(
|
|
const struct whisper_hparams & hparams,
|
|
const size_t mem_bytes,
|
|
struct whisper_kv_cache & cache,
|
|
ggml_type wtype,
|
|
int n_ctx) {
|
|
cache.buf.resize(mem_bytes);
|
|
|
|
struct ggml_init_params params;
|
|
params.mem_size = cache.buf.size();
|
|
params.mem_buffer = cache.buf.data();
|
|
|
|
cache.ctx = ggml_init(params);
|
|
|
|
if (!cache.ctx) {
|
|
fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__);
|
|
return false;
|
|
}
|
|
|
|
const int n_text_state = hparams.n_text_state;
|
|
const int n_text_layer = hparams.n_text_layer;
|
|
|
|
const int n_mem = n_text_layer*n_ctx;
|
|
const int n_elements = n_text_state*n_mem;
|
|
|
|
cache.k = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
|
|
cache.v = ggml_new_tensor_1d(cache.ctx, wtype, n_elements);
|
|
|
|
return true;
|
|
}
|
|
|
|
static bool kv_cache_reinit(struct whisper_kv_cache & cache) {
|
|
WHISPER_ASSERT(cache.ctx);
|
|
|
|
const int n_elements = ggml_nelements(cache.k);
|
|
WHISPER_ASSERT(n_elements == ggml_nelements(cache.v));
|
|
|
|
const ggml_type wtype = cache.k->type;
|
|
WHISPER_ASSERT(wtype == cache.v->type);
|
|
|
|
WHISPER_ASSERT(cache.buf.size() >= 2*n_elements*ggml_type_size(wtype));
|
|
|
|
struct ggml_init_params params;
|
|
params.mem_size = cache.buf.size();
|
|
params.mem_buffer = cache.buf.data();
|
|
|
|
cache.ctx = ggml_init(params);
|
|
|
|
if (!cache.ctx) {
|
|
fprintf(stderr, "%s: failed to allocate memory for kv cache\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);
|
|
|
|
return true;
|
|
}
|
|
|
|
static void kv_cache_free(struct whisper_kv_cache & cache) {
|
|
if (cache.ctx) {
|
|
ggml_free(cache.ctx);
|
|
cache.ctx = nullptr;
|
|
}
|
|
}
|
|
|
|
// 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) {
|
|
fprintf(stderr, "%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 != 0x67676d6c) {
|
|
fprintf(stderr, "%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.f16);
|
|
|
|
assert(hparams.n_text_state == hparams.n_audio_state);
|
|
|
|
if (hparams.n_audio_layer == 4) {
|
|
model.type = e_model::MODEL_TINY;
|
|
}
|
|
|
|
if (hparams.n_audio_layer == 6) {
|
|
model.type = e_model::MODEL_BASE;
|
|
}
|
|
|
|
if (hparams.n_audio_layer == 12) {
|
|
model.type = e_model::MODEL_SMALL;
|
|
}
|
|
|
|
if (hparams.n_audio_layer == 24) {
|
|
model.type = e_model::MODEL_MEDIUM;
|
|
}
|
|
|
|
if (hparams.n_audio_layer == 32) {
|
|
model.type = e_model::MODEL_LARGE;
|
|
}
|
|
|
|
// for the big tensors, we have the option to store the data in 16-bit floats
|
|
// in order to save memory and also to speed up the computation
|
|
wctx.wtype = model.hparams.f16 ? GGML_TYPE_F16 : GGML_TYPE_F32;
|
|
|
|
const size_t scale = model.hparams.f16 ? 1 : 2;
|
|
|
|
fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab);
|
|
fprintf(stderr, "%s: n_audio_ctx = %d\n", __func__, hparams.n_audio_ctx);
|
|
fprintf(stderr, "%s: n_audio_state = %d\n", __func__, hparams.n_audio_state);
|
|
fprintf(stderr, "%s: n_audio_head = %d\n", __func__, hparams.n_audio_head);
|
|
fprintf(stderr, "%s: n_audio_layer = %d\n", __func__, hparams.n_audio_layer);
|
|
fprintf(stderr, "%s: n_text_ctx = %d\n", __func__, hparams.n_text_ctx);
|
|
fprintf(stderr, "%s: n_text_state = %d\n", __func__, hparams.n_text_state);
|
|
fprintf(stderr, "%s: n_text_head = %d\n", __func__, hparams.n_text_head);
|
|
fprintf(stderr, "%s: n_text_layer = %d\n", __func__, hparams.n_text_layer);
|
|
fprintf(stderr, "%s: n_mels = %d\n", __func__, hparams.n_mels);
|
|
fprintf(stderr, "%s: f16 = %d\n", __func__, hparams.f16);
|
|
fprintf(stderr, "%s: type = %d\n", __func__, model.type);
|
|
|
|
// print memory requirements
|
|
{
|
|
// this is the total memory required to run the inference
|
|
const size_t mem_required =
|
|
MEM_REQ_SCRATCH0.at (model.type) +
|
|
MEM_REQ_SCRATCH1.at (model.type) +
|
|
MEM_REQ_SCRATCH2.at (model.type) +
|
|
MEM_REQ_SCRATCH3.at (model.type) +
|
|
scale*MEM_REQ_MODEL.at (model.type) +
|
|
scale*MEM_REQ_KV_CROSS.at(model.type) +
|
|
scale*std::max(MEM_REQ_ENCODE.at(model.type), MEM_REQ_DECODE.at(model.type));
|
|
|
|
// this is the memory required by one decoder
|
|
const size_t mem_required_decoder =
|
|
scale*MEM_REQ_KV_SELF.at(model.type);
|
|
|
|
fprintf(stderr, "%s: mem required = %7.2f MB (+ %7.2f MB per decoder)\n", __func__,
|
|
mem_required / 1024.0 / 1024.0, mem_required_decoder / 1024.0 / 1024.0);
|
|
}
|
|
|
|
// initialize all memory buffers
|
|
// always have at least one decoder
|
|
|
|
wctx.model.buf = new std::vector<uint8_t>();
|
|
wctx.model.buf->resize(scale*MEM_REQ_MODEL.at(model.type));
|
|
|
|
// we skip initialization of the state until it is needed
|
|
// because it might be that state will always be provided externally.
|
|
}
|
|
|
|
// 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) {
|
|
// fprintf(stderr, "%s: invalid model file '%s' (bad vocab size %d != %d)\n",
|
|
// __func__, fname.c_str(), n_vocab, model.hparams.n_vocab);
|
|
// return false;
|
|
//}
|
|
|
|
std::string word;
|
|
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)
|
|
//fprintf(stderr, "%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++;
|
|
vocab.token_prev++;
|
|
vocab.token_solm++;
|
|
vocab.token_not++;
|
|
vocab.token_beg++;
|
|
}
|
|
|
|
if (n_vocab < model.hparams.n_vocab) {
|
|
fprintf(stderr, "%s: adding %d extra tokens\n", __func__, model.hparams.n_vocab - n_vocab);
|
|
for (int i = n_vocab; i < model.hparams.n_vocab; i++) {
|
|
if (i > vocab.token_beg) {
|
|
word = "[_TT_" + std::to_string(i - vocab.token_beg) + "]";
|
|
} else if (i == vocab.token_eot) {
|
|
word = "[_EOT_]";
|
|
} else if (i == vocab.token_sot) {
|
|
word = "[_SOT_]";
|
|
} else if (i == vocab.token_prev) {
|
|
word = "[_PREV_]";
|
|
} else if (i == vocab.token_not) {
|
|
word = "[_NOT_]";
|
|
} else if (i == vocab.token_beg) {
|
|
word = "[_BEG_]";
|
|
} else {
|
|
word = "[_extra_token_" + std::to_string(i) + "]";
|
|
}
|
|
vocab.token_to_id[word] = i;
|
|
vocab.id_to_token[i] = word;
|
|
}
|
|
}
|
|
}
|
|
|
|
size_t ctx_size = 0;
|
|
|
|
const ggml_type wtype = wctx.wtype;
|
|
|
|
{
|
|
const auto & hparams = model.hparams;
|
|
|
|
const int n_vocab = hparams.n_vocab;
|
|
|
|
const int n_audio_ctx = hparams.n_audio_ctx;
|
|
const int n_audio_state = hparams.n_audio_state;
|
|
const int n_audio_layer = hparams.n_audio_layer;
|
|
|
|
const int n_text_ctx = hparams.n_text_ctx;
|
|
const int n_text_state = hparams.n_text_state;
|
|
const int n_text_layer = hparams.n_text_layer;
|
|
|
|
const int n_mels = hparams.n_mels;
|
|
|
|
// encoder
|
|
{
|
|
ctx_size += n_audio_ctx*n_audio_state*ggml_type_size(GGML_TYPE_F32); // e_pe;
|
|
|
|
ctx_size += 3*n_mels*n_audio_state*ggml_type_size(wtype); // e_conv_1_w
|
|
ctx_size += n_audio_state*ggml_type_size(GGML_TYPE_F32); // e_conv_1_b
|
|
|
|
ctx_size += 3*n_audio_state*n_audio_state*ggml_type_size(wtype); // e_conv_2_w
|
|
ctx_size += n_audio_state*ggml_type_size(GGML_TYPE_F32); // e_conv_2_b
|
|
|
|
ctx_size += n_audio_state*ggml_type_size(GGML_TYPE_F32); // e_ln_w;
|
|
ctx_size += n_audio_state*ggml_type_size(GGML_TYPE_F32); // e_ln_b;
|
|
}
|
|
|
|
// decoder
|
|
{
|
|
ctx_size += n_text_ctx*n_text_state*ggml_type_size(GGML_TYPE_F32); // d_pe;
|
|
|
|
ctx_size += n_vocab*n_text_state*ggml_type_size(wtype); // d_te;
|
|
|
|
ctx_size += n_text_state*ggml_type_size(GGML_TYPE_F32); // d_ln_w;
|
|
ctx_size += n_text_state*ggml_type_size(GGML_TYPE_F32); // d_ln_b;
|
|
}
|
|
|
|
// encoder layers
|
|
{
|
|
ctx_size += n_audio_layer*(n_audio_state*ggml_type_size(GGML_TYPE_F32)); // mlp_ln_w
|
|
ctx_size += n_audio_layer*(n_audio_state*ggml_type_size(GGML_TYPE_F32)); // mlp_ln_b
|
|
|
|
ctx_size += n_audio_layer*(4*n_audio_state*n_audio_state*ggml_type_size(wtype)); // mlp_0_w
|
|
ctx_size += n_audio_layer*( 4*n_audio_state*ggml_type_size(GGML_TYPE_F32)); // mlp_0_b
|
|
|
|
ctx_size += n_audio_layer*(4*n_audio_state*n_audio_state*ggml_type_size(wtype)); // mlp_1_w
|
|
ctx_size += n_audio_layer*( n_audio_state*ggml_type_size(GGML_TYPE_F32)); // mlp_1_b
|
|
|
|
ctx_size += n_audio_layer*(n_audio_state*ggml_type_size(GGML_TYPE_F32)); // attn_ln_0_w
|
|
ctx_size += n_audio_layer*(n_audio_state*ggml_type_size(GGML_TYPE_F32)); // attn_ln_0_b
|
|
|
|
ctx_size += n_audio_layer*(n_audio_state*n_audio_state*ggml_type_size(wtype)); // attn_q_w
|
|
ctx_size += n_audio_layer*( n_audio_state*ggml_type_size(GGML_TYPE_F32)); // attn_q_b
|
|
|
|
ctx_size += n_audio_layer*(n_audio_state*n_audio_state*ggml_type_size(wtype)); // attn_k_w
|
|
|
|
ctx_size += n_audio_layer*(n_audio_state*n_audio_state*ggml_type_size(wtype)); // attn_v_w
|
|
ctx_size += n_audio_layer*( n_audio_state*ggml_type_size(GGML_TYPE_F32)); // attn_v_b
|
|
|
|
ctx_size += n_audio_layer*(n_audio_state*n_audio_state*ggml_type_size(wtype)); // attn_ln_1_w
|
|
ctx_size += n_audio_layer*( n_audio_state*ggml_type_size(GGML_TYPE_F32)); // attn_ln_1_b
|
|
}
|
|
|
|
// decoder layers
|
|
{
|
|
ctx_size += n_text_layer*(n_text_state*ggml_type_size(GGML_TYPE_F32)); // mlp_ln_w
|
|
ctx_size += n_text_layer*(n_text_state*ggml_type_size(GGML_TYPE_F32)); // mlp_ln_b
|
|
|
|
ctx_size += n_text_layer*(4*n_text_state*n_text_state*ggml_type_size(wtype)); // mlp_0_w
|
|
ctx_size += n_text_layer*( 4*n_text_state*ggml_type_size(GGML_TYPE_F32)); // mlp_0_b
|
|
|
|
ctx_size += n_text_layer*(4*n_text_state*n_text_state*ggml_type_size(wtype)); // mlp_1_w
|
|
ctx_size += n_text_layer*( n_text_state*ggml_type_size(GGML_TYPE_F32)); // mlp_1_b
|
|
|
|
ctx_size += n_text_layer*(n_text_state*ggml_type_size(GGML_TYPE_F32)); // attn_ln_0_w
|
|
ctx_size += n_text_layer*(n_text_state*ggml_type_size(GGML_TYPE_F32)); // attn_ln_0_b
|
|
|
|
ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype)); // attn_q_w
|
|
ctx_size += n_text_layer*( n_text_state*ggml_type_size(GGML_TYPE_F32)); // attn_q_b
|
|
|
|
ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype)); // attn_k_w
|
|
|
|
ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype)); // attn_v_w
|
|
ctx_size += n_text_layer*( n_text_state*ggml_type_size(GGML_TYPE_F32)); // attn_v_b
|
|
|
|
ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype)); // attn_ln_1_w
|
|
ctx_size += n_text_layer*( n_text_state*ggml_type_size(GGML_TYPE_F32)); // attn_ln_1_b
|
|
//
|
|
ctx_size += n_text_layer*(n_text_state*ggml_type_size(GGML_TYPE_F32)); // cross_attn_ln_0_w
|
|
ctx_size += n_text_layer*(n_text_state*ggml_type_size(GGML_TYPE_F32)); // cross_attn_ln_0_b
|
|
|
|
ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype)); // cross_attn_q_w
|
|
ctx_size += n_text_layer*( n_text_state*ggml_type_size(GGML_TYPE_F32)); // cross_attn_q_b
|
|
|
|
ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype)); // cross_attn_k_w
|
|
|
|
ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype)); // cross_attn_v_w
|
|
ctx_size += n_text_layer*( n_text_state*ggml_type_size(GGML_TYPE_F32)); // cross_attn_v_b
|
|
|
|
ctx_size += n_text_layer*(n_text_state*n_text_state*ggml_type_size(wtype)); // cross_attn_ln_1_w
|
|
ctx_size += n_text_layer*( n_text_state*ggml_type_size(GGML_TYPE_F32)); // cross_attn_ln_1_b
|
|
}
|
|
|
|
ctx_size += (15 + 15*n_audio_layer + 24*n_text_layer)*256; // object overhead
|
|
|
|
fprintf(stderr, "%s: model ctx = %7.2f MB\n", __func__, ctx_size/(1024.0*1024.0));
|
|
}
|
|
|
|
// create the ggml context
|
|
{
|
|
struct ggml_init_params params;
|
|
params.mem_size = wctx.model.buf->size();
|
|
params.mem_buffer = wctx.model.buf->data();
|
|
|
|
model.ctx = ggml_init(params);
|
|
if (!model.ctx) {
|
|
fprintf(stderr, "%s: ggml_init() failed\n", __func__);
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// prepare memory 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, wtype, 3, n_mels, n_audio_state);
|
|
model.e_conv_1_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, n_audio_state);
|
|
|
|
model.e_conv_2_w = ggml_new_tensor_3d(ctx, wtype, 3, n_audio_state, n_audio_state);
|
|
model.e_conv_2_b = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, n_audio_state);
|
|
|
|
model.e_ln_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
|
|
model.e_ln_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
|
|
|
|
// map by name
|
|
model.tensors["encoder.positional_embedding"] = model.e_pe;
|
|
|
|
model.tensors["encoder.conv1.weight"] = model.e_conv_1_w;
|
|
model.tensors["encoder.conv1.bias"] = model.e_conv_1_b;
|
|
|
|
model.tensors["encoder.conv2.weight"] = model.e_conv_2_w;
|
|
model.tensors["encoder.conv2.bias"] = model.e_conv_2_b;
|
|
|
|
model.tensors["encoder.ln_post.weight"] = model.e_ln_w;
|
|
model.tensors["encoder.ln_post.bias"] = model.e_ln_b;
|
|
|
|
for (int i = 0; i < n_audio_layer; ++i) {
|
|
auto & layer = model.layers_encoder[i];
|
|
|
|
layer.mlp_ln_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
|
|
layer.mlp_ln_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
|
|
|
|
layer.mlp_0_w = ggml_new_tensor_2d(ctx, wtype, n_audio_state, 4*n_audio_state);
|
|
layer.mlp_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_audio_state);
|
|
|
|
layer.mlp_1_w = ggml_new_tensor_2d(ctx, wtype, 4*n_audio_state, n_audio_state);
|
|
layer.mlp_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
|
|
|
|
layer.attn_ln_0_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
|
|
layer.attn_ln_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
|
|
|
|
layer.attn_q_w = ggml_new_tensor_2d(ctx, wtype, n_audio_state, n_audio_state);
|
|
layer.attn_q_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
|
|
|
|
layer.attn_k_w = ggml_new_tensor_2d(ctx, wtype, n_audio_state, n_audio_state);
|
|
|
|
layer.attn_v_w = ggml_new_tensor_2d(ctx, wtype, n_audio_state, n_audio_state);
|
|
layer.attn_v_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
|
|
|
|
layer.attn_ln_1_w = ggml_new_tensor_2d(ctx, wtype, n_audio_state, n_audio_state);
|
|
layer.attn_ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_audio_state);
|
|
|
|
// map by name
|
|
model.tensors["encoder.blocks." + std::to_string(i) + ".mlp_ln.weight"] = layer.mlp_ln_w;
|
|
model.tensors["encoder.blocks." + std::to_string(i) + ".mlp_ln.bias"] = layer.mlp_ln_b;
|
|
|
|
model.tensors["encoder.blocks." + std::to_string(i) + ".mlp.0.weight"] = layer.mlp_0_w;
|
|
model.tensors["encoder.blocks." + std::to_string(i) + ".mlp.0.bias"] = layer.mlp_0_b;
|
|
|
|
model.tensors["encoder.blocks." + std::to_string(i) + ".mlp.2.weight"] = layer.mlp_1_w;
|
|
model.tensors["encoder.blocks." + std::to_string(i) + ".mlp.2.bias"] = layer.mlp_1_b;
|
|
|
|
model.tensors["encoder.blocks." + std::to_string(i) + ".attn_ln.weight"] = layer.attn_ln_0_w;
|
|
model.tensors["encoder.blocks." + std::to_string(i) + ".attn_ln.bias"] = layer.attn_ln_0_b;
|
|
|
|
model.tensors["encoder.blocks." + std::to_string(i) + ".attn.query.weight"] = layer.attn_q_w;
|
|
model.tensors["encoder.blocks." + std::to_string(i) + ".attn.query.bias"] = layer.attn_q_b;
|
|
|
|
model.tensors["encoder.blocks." + std::to_string(i) + ".attn.key.weight"] = layer.attn_k_w;
|
|
|
|
model.tensors["encoder.blocks." + std::to_string(i) + ".attn.value.weight"] = layer.attn_v_w;
|
|
model.tensors["encoder.blocks." + std::to_string(i) + ".attn.value.bias"] = layer.attn_v_b;
|
|
|
|
model.tensors["encoder.blocks." + std::to_string(i) + ".attn.out.weight"] = layer.attn_ln_1_w;
|
|
model.tensors["encoder.blocks." + std::to_string(i) + ".attn.out.bias"] = layer.attn_ln_1_b;
|
|
}
|
|
}
|
|
|
|
// decoder
|
|
{
|
|
model.d_pe = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_text_state, n_text_ctx);
|
|
|
|
model.d_te = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_vocab);
|
|
|
|
model.d_ln_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
|
|
model.d_ln_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
|
|
|
|
// map by name
|
|
model.tensors["decoder.positional_embedding"] = model.d_pe;
|
|
|
|
model.tensors["decoder.token_embedding.weight"] = model.d_te;
|
|
|
|
model.tensors["decoder.ln.weight"] = model.d_ln_w;
|
|
model.tensors["decoder.ln.bias"] = model.d_ln_b;
|
|
|
|
for (int i = 0; i < n_text_layer; ++i) {
|
|
auto & layer = model.layers_decoder[i];
|
|
|
|
layer.mlp_ln_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
|
|
layer.mlp_ln_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
|
|
|
|
layer.mlp_0_w = ggml_new_tensor_2d(ctx, wtype, n_text_state, 4*n_text_state);
|
|
layer.mlp_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 4*n_text_state);
|
|
|
|
layer.mlp_1_w = ggml_new_tensor_2d(ctx, wtype, 4*n_text_state, n_text_state);
|
|
layer.mlp_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
|
|
|
|
layer.attn_ln_0_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
|
|
layer.attn_ln_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
|
|
|
|
layer.attn_q_w = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_text_state);
|
|
layer.attn_q_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
|
|
|
|
layer.attn_k_w = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_text_state);
|
|
|
|
layer.attn_v_w = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_text_state);
|
|
layer.attn_v_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
|
|
|
|
layer.attn_ln_1_w = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_text_state);
|
|
layer.attn_ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
|
|
|
|
layer.cross_attn_ln_0_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
|
|
layer.cross_attn_ln_0_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
|
|
|
|
layer.cross_attn_q_w = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_text_state);
|
|
layer.cross_attn_q_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
|
|
|
|
layer.cross_attn_k_w = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_text_state);
|
|
|
|
layer.cross_attn_v_w = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_text_state);
|
|
layer.cross_attn_v_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
|
|
|
|
layer.cross_attn_ln_1_w = ggml_new_tensor_2d(ctx, wtype, n_text_state, n_text_state);
|
|
layer.cross_attn_ln_1_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_text_state);
|
|
|
|
// map by name
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".mlp_ln.weight"] = layer.mlp_ln_w;
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".mlp_ln.bias"] = layer.mlp_ln_b;
|
|
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".mlp.0.weight"] = layer.mlp_0_w;
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".mlp.0.bias"] = layer.mlp_0_b;
|
|
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".mlp.2.weight"] = layer.mlp_1_w;
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".mlp.2.bias"] = layer.mlp_1_b;
|
|
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".attn_ln.weight"] = layer.attn_ln_0_w;
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".attn_ln.bias"] = layer.attn_ln_0_b;
|
|
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".attn.query.weight"] = layer.attn_q_w;
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".attn.query.bias"] = layer.attn_q_b;
|
|
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".attn.key.weight"] = layer.attn_k_w;
|
|
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".attn.value.weight"] = layer.attn_v_w;
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".attn.value.bias"] = layer.attn_v_b;
|
|
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".attn.out.weight"] = layer.attn_ln_1_w;
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".attn.out.bias"] = layer.attn_ln_1_b;
|
|
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn_ln.weight"] = layer.cross_attn_ln_0_w;
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn_ln.bias"] = layer.cross_attn_ln_0_b;
|
|
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.query.weight"] = layer.cross_attn_q_w;
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.query.bias"] = layer.cross_attn_q_b;
|
|
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.key.weight"] = layer.cross_attn_k_w;
|
|
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.value.weight"] = layer.cross_attn_v_w;
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.value.bias"] = layer.cross_attn_v_b;
|
|
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.out.weight"] = layer.cross_attn_ln_1_w;
|
|
model.tensors["decoder.blocks." + std::to_string(i) + ".cross_attn.out.bias"] = layer.cross_attn_ln_1_b;
|
|
}
|
|
}
|
|
}
|
|
|
|
// load weights
|
|
{
|
|
size_t total_size = 0;
|
|
|
|
model.n_loaded = 0;
|
|
|
|
while (true) {
|
|
int32_t n_dims;
|
|
int32_t length;
|
|
int32_t ftype;
|
|
|
|
read_safe(loader, n_dims);
|
|
read_safe(loader, length);
|
|
read_safe(loader, ftype);
|
|
|
|
if (loader->eof(loader->context)) {
|
|
break;
|
|
}
|
|
|
|
int32_t nelements = 1;
|
|
int32_t ne[3] = { 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()) {
|
|
fprintf(stderr, "%s: unknown tensor '%s' in model file\n", __func__, name.data());
|
|
return false;
|
|
}
|
|
|
|
auto tensor = model.tensors[name.data()];
|
|
if (ggml_nelements(tensor) != nelements) {
|
|
fprintf(stderr, "%s: tensor '%s' has wrong size in model file\n", __func__, name.data());
|
|
return false;
|
|
}
|
|
|
|
if (tensor->ne[0] != ne[0] || tensor->ne[1] != ne[1] || tensor->ne[2] != ne[2]) {
|
|
fprintf(stderr, "%s: tensor '%s' has wrong shape in model file: got [%d, %d, %d], expected [%d, %d, %d]\n",
|
|
__func__, name.data(), tensor->ne[0], tensor->ne[1], tensor->ne[2], ne[0], ne[1], ne[2]);
|
|
return false;
|
|
}
|
|
|
|
const size_t bpe = (ftype == 0) ? sizeof(float) : sizeof(ggml_fp16_t);
|
|
|
|
if (nelements*bpe != ggml_nbytes(tensor)) {
|
|
fprintf(stderr, "%s: tensor '%s' has wrong size in model file: got %zu, expected %zu\n",
|
|
__func__, name.data(), ggml_nbytes(tensor), nelements*bpe);
|
|
return false;
|
|
}
|
|
|
|
loader->read(loader->context, tensor->data, ggml_nbytes(tensor));
|
|
BYTESWAP_TENSOR(tensor);
|
|
|
|
//printf("%48s - [%5d, %5d, %5d], type = %6s, %6.2f MB\n", name.data(), ne[0], ne[1], ne[2], ftype == 0 ? "float" : "f16", ggml_nbytes(tensor)/1024.0/1024.0);
|
|
total_size += ggml_nbytes(tensor);
|
|
model.n_loaded++;
|
|
}
|
|
|
|
fprintf(stderr, "%s: model size = %7.2f MB\n", __func__, total_size/1024.0/1024.0);
|
|
|
|
if (model.n_loaded == 0) {
|
|
fprintf(stderr, "%s: WARN no tensors loaded from model file - assuming empty model for testing\n", __func__);
|
|
} else if (model.n_loaded != (int) model.tensors.size()) {
|
|
fprintf(stderr, "%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;
|
|
}
|
|
|
|
// 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){
|
|
|
|
const int64_t t_start_us = ggml_time_us();
|
|
|
|
const auto & model = wctx.model;
|
|
const auto & mel_inp = wstate.mel;
|
|
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_mels = hparams.n_mels;
|
|
assert(mel_inp.n_mel == n_mels);
|
|
|
|
struct ggml_init_params params;
|
|
params.mem_size = wstate.buf_compute.size();
|
|
params.mem_buffer = wstate.buf_compute.data();
|
|
|
|
struct ggml_context * ctx0 = ggml_init(params);
|
|
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
struct ggml_tensor * mel = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 2*n_ctx, n_mels);
|
|
assert(mel->type == GGML_TYPE_F32);
|
|
{
|
|
float * dst = (float *) mel->data;
|
|
memset(dst, 0, ggml_nbytes(mel));
|
|
|
|
const int i0 = std::min(mel_offset, mel_inp.n_len);
|
|
const int i1 = std::min(mel_offset + 2*n_ctx, mel_inp.n_len);
|
|
|
|
for (int j = 0; j < mel_inp.n_mel; ++j) {
|
|
for (int i = i0; i < i1; ++i) {
|
|
dst[j*2*n_ctx + (i - i0)] = mel_inp.data[j*mel_inp.n_len + i];
|
|
}
|
|
}
|
|
}
|
|
|
|
struct ggml_tensor * cur;
|
|
|
|
// convolution + gelu
|
|
{
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
cur = ggml_conv_1d_1s(ctx0, model.e_conv_1_w, mel);
|
|
cur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0,
|
|
model.e_conv_1_b,
|
|
cur),
|
|
cur);
|
|
|
|
cur = ggml_gelu(ctx0, cur);
|
|
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
cur = ggml_conv_1d_2s(ctx0, model.e_conv_2_w, cur);
|
|
cur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0,
|
|
model.e_conv_2_b,
|
|
cur),
|
|
cur);
|
|
|
|
cur = ggml_gelu(ctx0, cur);
|
|
}
|
|
|
|
wstate.use_buf(ctx0, 3);
|
|
|
|
// ===================================================================
|
|
// 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_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
|
|
{
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
cur = ggml_norm(ctx0, inpL);
|
|
|
|
// cur = ln_0_w*cur + ln_0_b
|
|
cur = ggml_add(ctx0,
|
|
ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, layer.attn_ln_0_w, cur),
|
|
cur),
|
|
ggml_repeat(ctx0, layer.attn_ln_0_b, cur));
|
|
}
|
|
|
|
// self-attention
|
|
{
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0,
|
|
layer.attn_q_w,
|
|
cur);
|
|
|
|
Qcur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0,
|
|
layer.attn_q_b,
|
|
Qcur),
|
|
Qcur);
|
|
|
|
//Qcur = ggml_scale(ctx0, Qcur, ggml_new_f32(ctx0, pow(float(n_state)/n_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, ggml_new_f32(ctx0, pow(float(n_state)/n_head, -0.25)));
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0,
|
|
layer.attn_v_w,
|
|
cur);
|
|
|
|
Vcur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0,
|
|
layer.attn_v_b,
|
|
Vcur),
|
|
Vcur);
|
|
|
|
// ------
|
|
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
#ifdef WHISPER_USE_FLASH_ATTN
|
|
struct ggml_tensor * Q =
|
|
ggml_permute(ctx0,
|
|
ggml_cpy(ctx0,
|
|
Qcur,
|
|
ggml_new_tensor_3d(ctx0, wctx.wtype, n_state/n_head, n_head, n_ctx)),
|
|
0, 2, 1, 3);
|
|
|
|
struct ggml_tensor * K =
|
|
ggml_permute(ctx0,
|
|
ggml_cpy(ctx0,
|
|
Kcur,
|
|
ggml_new_tensor_3d(ctx0, wctx.wtype, n_state/n_head, n_head, n_ctx)),
|
|
0, 2, 1, 3);
|
|
|
|
struct ggml_tensor * V =
|
|
ggml_cpy(ctx0,
|
|
ggml_permute(ctx0,
|
|
ggml_reshape_3d(ctx0,
|
|
Vcur,
|
|
n_state/n_head, n_head, n_ctx),
|
|
1, 2, 0, 3),
|
|
ggml_new_tensor_3d(ctx0, wctx.wtype, n_ctx, n_state/n_head, n_head)
|
|
);
|
|
|
|
struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, false);
|
|
#else
|
|
struct ggml_tensor * Q =
|
|
ggml_permute(ctx0,
|
|
ggml_cpy(ctx0,
|
|
Qcur,
|
|
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_state/n_head, n_head, n_ctx)),
|
|
0, 2, 1, 3);
|
|
|
|
struct ggml_tensor * K =
|
|
ggml_permute(ctx0,
|
|
ggml_cpy(ctx0,
|
|
Kcur,
|
|
ggml_new_tensor_3d(ctx0, wctx.wtype, n_state/n_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_scaled =
|
|
ggml_scale(ctx0,
|
|
KQ,
|
|
ggml_new_f32(ctx0, 1.0f/sqrt(float(n_state)/n_head))
|
|
);
|
|
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_scaled);
|
|
|
|
//struct ggml_tensor * V_trans =
|
|
// ggml_permute(ctx0,
|
|
// ggml_cpy(ctx0,
|
|
// Vcur,
|
|
// ggml_new_tensor_3d(ctx0, wctx.wtype, n_state/n_head, n_head, n_ctx)),
|
|
// 1, 2, 0, 3);
|
|
|
|
//struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
|
|
|
|
struct ggml_tensor * V =
|
|
ggml_cpy(ctx0,
|
|
ggml_permute(ctx0,
|
|
ggml_reshape_3d(ctx0,
|
|
Vcur,
|
|
n_state/n_head, n_head, n_ctx),
|
|
0, 2, 1, 3),
|
|
ggml_new_tensor_3d(ctx0, wctx.wtype, n_state/n_head, n_ctx, n_head)
|
|
);
|
|
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, ggml_transpose(ctx0, V), KQ_soft_max);
|
|
#endif
|
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
cur = ggml_cpy(ctx0,
|
|
KQV_merged,
|
|
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, n_ctx));
|
|
}
|
|
|
|
// projection
|
|
{
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
cur = ggml_mul_mat(ctx0,
|
|
layer.attn_ln_1_w,
|
|
cur);
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
cur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0, layer.attn_ln_1_b, cur),
|
|
cur);
|
|
}
|
|
|
|
wstate.use_buf(ctx0, 2);
|
|
|
|
// add the input
|
|
cur = ggml_add(ctx0, cur, inpL);
|
|
|
|
struct ggml_tensor * inpFF = cur;
|
|
|
|
// feed-forward network
|
|
{
|
|
// norm
|
|
{
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
cur = ggml_norm(ctx0, inpFF);
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
// cur = mlp_ln_w*cur + mlp_ln_b
|
|
cur = ggml_add(ctx0,
|
|
ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, layer.mlp_ln_w, cur),
|
|
cur),
|
|
ggml_repeat(ctx0, layer.mlp_ln_b, cur));
|
|
}
|
|
|
|
#ifdef WHISPER_USE_FLASH_FF
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
cur = ggml_flash_ff(ctx0,
|
|
ggml_cpy(ctx0, cur, ggml_new_tensor_2d(ctx0, wstate.wtype, n_state, n_ctx)),
|
|
layer.mlp_0_w, layer.mlp_0_b, layer.mlp_1_w, layer.mlp_1_b);
|
|
#else
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
// fully connected
|
|
cur = ggml_mul_mat(ctx0,
|
|
layer.mlp_0_w,
|
|
cur);
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
cur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0, layer.mlp_0_b, cur),
|
|
cur);
|
|
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
// GELU activation
|
|
cur = ggml_gelu(ctx0, cur);
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
// projection
|
|
cur = ggml_mul_mat(ctx0,
|
|
layer.mlp_1_w,
|
|
cur);
|
|
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
cur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0, layer.mlp_1_b, cur),
|
|
cur);
|
|
#endif
|
|
}
|
|
|
|
wstate.use_buf(ctx0, 3);
|
|
|
|
inpL = ggml_add(ctx0, cur, inpFF);
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
// norm
|
|
{
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
cur = ggml_norm(ctx0, cur);
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
// cur = ln_f_g*cur + ln_f_b
|
|
cur = ggml_add(ctx0,
|
|
ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model.e_ln_w, cur),
|
|
cur),
|
|
ggml_repeat(ctx0, model.e_ln_b, cur));
|
|
}
|
|
|
|
wstate.use_buf(ctx0, -1);
|
|
|
|
// run the computation
|
|
{
|
|
struct ggml_cgraph gf = {};
|
|
gf.n_threads = n_threads;
|
|
|
|
ggml_build_forward_expand(&gf, cur);
|
|
ggml_graph_compute(ctx0, &gf);
|
|
|
|
//ggml_graph_print(&gf);
|
|
}
|
|
|
|
// cur
|
|
//{
|
|
// printf("ne0 = %d\n", cur->ne[0]);
|
|
// printf("ne1 = %d\n", cur->ne[1]);
|
|
// for (int i = 0; i < 10; ++i) {
|
|
// printf("%8.4f ", ((float *)(cur->data))[i]);
|
|
// }
|
|
// printf("... ");
|
|
// for (int i = cur->ne[0] - 10; i < cur->ne[0]; ++i) {
|
|
// printf("%8.4f ", ((float *)(cur->data))[i]);
|
|
// }
|
|
// printf("\n");
|
|
//}
|
|
|
|
// pre-compute cross-attention memory
|
|
{
|
|
struct ggml_cgraph gf = {};
|
|
gf.n_threads = n_threads;
|
|
|
|
// TODO: hack to disconnect the encoded features from the previous graph
|
|
cur->op = GGML_OP_NONE;
|
|
cur->src0 = nullptr;
|
|
cur->src1 = nullptr;
|
|
|
|
for (int il = 0; il < model.hparams.n_text_layer; ++il) {
|
|
auto& layer = model.layers_decoder[il];
|
|
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
struct ggml_tensor* Kcross = ggml_mul_mat(ctx0,
|
|
layer.cross_attn_k_w,
|
|
cur);
|
|
|
|
Kcross = ggml_scale(ctx0, Kcross, ggml_new_f32(ctx0, pow(float(n_state) / n_head, -0.25)));
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
struct ggml_tensor* Vcross = ggml_mul_mat(ctx0,
|
|
layer.cross_attn_v_w,
|
|
cur);
|
|
|
|
Vcross = ggml_add(ctx0,
|
|
ggml_repeat(ctx0,
|
|
layer.cross_attn_v_b,
|
|
Vcross),
|
|
Vcross);
|
|
|
|
wstate.use_buf(ctx0, -1);
|
|
|
|
//struct ggml_tensor * k = ggml_view_1d(ctx0, wstate.kv_cross.k, n_state*n_ctx, (ggml_element_size(wstate.kv_cross.k)*n_state)*(il*hparams.n_audio_ctx + iter*n_ctx));
|
|
//struct ggml_tensor * v = ggml_view_1d(ctx0, wstate.kv_cross.v, n_state*n_ctx, (ggml_element_size(wstate.kv_cross.v)*n_state)*(il*hparams.n_audio_ctx + iter*n_ctx));
|
|
struct ggml_tensor* 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));
|
|
struct ggml_tensor* 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));
|
|
|
|
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcross, k));
|
|
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcross, v));
|
|
}
|
|
|
|
ggml_graph_compute(ctx0, &gf);
|
|
//ggml_graph_print(&gf);
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////
|
|
|
|
//printf("%s: used_mem = %f MB, %f MB, %f MB %f MB %f MB\n", __func__,
|
|
// ggml_used_mem(ctx0)/1024.0/1024.0,
|
|
// wctx.get_buf_max_mem(0)/1024.0/1024.0,
|
|
// wctx.get_buf_max_mem(1)/1024.0/1024.0,
|
|
// wctx.get_buf_max_mem(2)/1024.0/1024.0,
|
|
// wctx.get_buf_max_mem(3)/1024.0/1024.0);
|
|
|
|
ggml_free(ctx0);
|
|
|
|
wstate.t_encode_us += ggml_time_us() - t_start_us;
|
|
wstate.n_encode++;
|
|
|
|
return true;
|
|
}
|
|
|
|
// 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,
|
|
whisper_decoder & decoder,
|
|
const whisper_token * tokens,
|
|
const int n_tokens,
|
|
const int n_past,
|
|
const int n_threads) {
|
|
const int64_t t_start_us = ggml_time_us();
|
|
|
|
const auto & model = wctx.model;
|
|
const auto & hparams = model.hparams;
|
|
|
|
auto & kv_self = decoder.kv_self;
|
|
|
|
WHISPER_ASSERT(!!kv_self.ctx);
|
|
|
|
auto & logits_out = wstate.logits;
|
|
|
|
const int n_vocab = hparams.n_vocab;
|
|
|
|
const int n_ctx = hparams.n_text_ctx;
|
|
const int n_state = hparams.n_text_state;
|
|
const int n_head = hparams.n_text_head;
|
|
const int n_layer = hparams.n_text_layer;
|
|
|
|
const int N = n_tokens;
|
|
const int M = wstate.exp_n_audio_ctx > 0 ? wstate.exp_n_audio_ctx : hparams.n_audio_ctx;
|
|
|
|
//WHISPER_PRINT_DEBUG("%s: n_past = %d, N = %d, M = %d, n_ctx = %d\n", __func__, n_past, N, M, n_ctx);
|
|
|
|
struct ggml_init_params params;
|
|
params.mem_size = wstate.buf_compute.size();
|
|
params.mem_buffer = wstate.buf_compute.data();
|
|
|
|
struct ggml_context * ctx0 = ggml_init(params);
|
|
|
|
struct ggml_cgraph gf = {};
|
|
gf.n_threads = n_threads;
|
|
|
|
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
|
memcpy(embd->data, tokens, N*ggml_element_size(embd));
|
|
|
|
struct ggml_tensor * position = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
|
|
for (int i = 0; i < N; ++i) {
|
|
((int32_t *) position->data)[i] = n_past + i;
|
|
}
|
|
|
|
wstate.use_buf(ctx0, 3);
|
|
|
|
// token encoding + position encoding
|
|
struct ggml_tensor * cur =
|
|
ggml_add(ctx0,
|
|
ggml_get_rows(ctx0, model.d_te, embd),
|
|
ggml_get_rows(ctx0, model.d_pe, position));
|
|
|
|
struct ggml_tensor * inpL = cur;
|
|
|
|
for (int il = 0; il < n_layer; ++il) {
|
|
const auto & layer = model.layers_decoder[il];
|
|
|
|
// norm
|
|
{
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
cur = ggml_norm(ctx0, inpL);
|
|
|
|
// cur = ln_0_w*cur + ln_0_b
|
|
cur = ggml_add(ctx0,
|
|
ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, layer.attn_ln_0_w, cur),
|
|
cur),
|
|
ggml_repeat(ctx0, layer.attn_ln_0_b, cur));
|
|
}
|
|
|
|
// self-attention
|
|
{
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0,
|
|
layer.attn_q_w,
|
|
cur);
|
|
|
|
Qcur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0,
|
|
layer.attn_q_b,
|
|
Qcur),
|
|
Qcur);
|
|
|
|
Qcur = ggml_scale(ctx0, Qcur, ggml_new_f32(ctx0, pow(float(n_state)/n_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, ggml_new_f32(ctx0, pow(float(n_state)/n_head, -0.25)));
|
|
|
|
struct ggml_tensor * Vcur = ggml_mul_mat(ctx0,
|
|
layer.attn_v_w,
|
|
cur);
|
|
|
|
Vcur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0,
|
|
layer.attn_v_b,
|
|
Vcur),
|
|
Vcur);
|
|
|
|
// store key and value to memory
|
|
{
|
|
struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_state, (ggml_element_size(kv_self.k)*n_state)*(il*n_ctx + n_past));
|
|
struct ggml_tensor * v = ggml_view_1d(ctx0, kv_self.v, N*n_state, (ggml_element_size(kv_self.v)*n_state)*(il*n_ctx + n_past));
|
|
|
|
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcur, k));
|
|
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcur, v));
|
|
}
|
|
|
|
// ------
|
|
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
struct ggml_tensor * Q =
|
|
ggml_permute(ctx0,
|
|
ggml_cpy(ctx0,
|
|
Qcur,
|
|
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_state/n_head, n_head, N)),
|
|
0, 2, 1, 3);
|
|
|
|
struct ggml_tensor * K =
|
|
ggml_permute(ctx0,
|
|
ggml_reshape_3d(ctx0,
|
|
ggml_view_1d(ctx0, kv_self.k, (n_past + N)*n_state, il*n_ctx*ggml_element_size(kv_self.k)*n_state),
|
|
n_state/n_head, n_head, n_past + N),
|
|
0, 2, 1, 3);
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
// K * Q
|
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
//struct ggml_tensor * KQ_scaled =
|
|
// ggml_scale(ctx0,
|
|
// KQ,
|
|
// ggml_new_f32(ctx0, 1.0f/sqrt(float(n_state)/n_head))
|
|
// );
|
|
|
|
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ, n_past);
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked);
|
|
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
struct ggml_tensor * V_trans =
|
|
ggml_permute(ctx0,
|
|
ggml_reshape_3d(ctx0,
|
|
ggml_view_1d(ctx0, kv_self.v, (n_past + N)*n_state, il*n_ctx*ggml_element_size(kv_self.v)*n_state),
|
|
n_state/n_head, n_head, n_past + N),
|
|
1, 2, 0, 3);
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, 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));
|
|
}
|
|
|
|
// projection
|
|
{
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
cur = ggml_mul_mat(ctx0,
|
|
layer.attn_ln_1_w,
|
|
cur);
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
cur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0, layer.attn_ln_1_b, cur),
|
|
cur);
|
|
}
|
|
|
|
wstate.use_buf(ctx0, 2);
|
|
|
|
// add the input
|
|
struct ggml_tensor * inpCA = ggml_add(ctx0, cur, inpL);
|
|
|
|
// norm
|
|
{
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
cur = ggml_norm(ctx0, inpCA); // note: we use inpCA here
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
// cur = ln_0_w*cur + ln_0_b
|
|
cur = ggml_add(ctx0,
|
|
ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, layer.cross_attn_ln_0_w, cur),
|
|
cur),
|
|
ggml_repeat(ctx0, layer.cross_attn_ln_0_b, cur));
|
|
}
|
|
|
|
// cross-attention
|
|
{
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
struct ggml_tensor * Qcur = ggml_mul_mat(ctx0,
|
|
layer.cross_attn_q_w,
|
|
cur);
|
|
|
|
Qcur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0,
|
|
layer.cross_attn_q_b,
|
|
Qcur),
|
|
Qcur);
|
|
|
|
Qcur = ggml_scale(ctx0, Qcur, ggml_new_f32(ctx0, pow(float(n_state)/n_head, -0.25)));
|
|
|
|
// Kcross is already scaled
|
|
struct ggml_tensor * Kcross =
|
|
ggml_reshape_3d(ctx0,
|
|
ggml_view_1d(ctx0, wstate.kv_cross.k, M*n_state, il*M*ggml_element_size(wstate.kv_cross.k)*n_state),
|
|
n_state/n_head, n_head, M);
|
|
|
|
struct ggml_tensor * Vcross =
|
|
ggml_reshape_3d(ctx0,
|
|
ggml_view_1d(ctx0, wstate.kv_cross.v, M*n_state, il*M*ggml_element_size(wstate.kv_cross.v)*n_state),
|
|
n_state/n_head, n_head, M);
|
|
|
|
struct ggml_tensor * V_trans = ggml_permute(ctx0, Vcross, 1, 2, 0, 3);
|
|
|
|
// ------
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
struct ggml_tensor * Q =
|
|
ggml_permute(ctx0,
|
|
ggml_cpy(ctx0,
|
|
Qcur,
|
|
ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_state/n_head, n_head, N)),
|
|
0, 2, 1, 3);
|
|
|
|
struct ggml_tensor * K = ggml_permute(ctx0, Kcross, 0, 2, 1, 3);
|
|
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
// K * Q
|
|
struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q);
|
|
|
|
//struct ggml_tensor * KQ_scaled =
|
|
// ggml_scale(ctx0,
|
|
// KQ,
|
|
// ggml_new_f32(ctx0, 1.0f/sqrt(float(n_state)/n_head))
|
|
// );
|
|
|
|
// no masking for cross-attention
|
|
//struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past);
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ);
|
|
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V_trans, KQ_soft_max);
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3);
|
|
|
|
// cur = KQV_merged.contiguous().view(n_state, N)
|
|
cur = ggml_cpy(ctx0,
|
|
KQV_merged,
|
|
ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_state, N));
|
|
}
|
|
|
|
// projection
|
|
{
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
cur = ggml_mul_mat(ctx0,
|
|
layer.cross_attn_ln_1_w,
|
|
cur);
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
cur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0, layer.cross_attn_ln_1_b, cur),
|
|
cur);
|
|
}
|
|
|
|
wstate.use_buf(ctx0, 2);
|
|
|
|
// add the input
|
|
cur = ggml_add(ctx0, cur, inpCA);
|
|
|
|
struct ggml_tensor * inpFF = cur;
|
|
|
|
// feed-forward network
|
|
{
|
|
// norm
|
|
{
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
cur = ggml_norm(ctx0, inpFF);
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
// cur = mlp_ln_w*cur + mlp_ln_b
|
|
cur = ggml_add(ctx0,
|
|
ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, layer.mlp_ln_w, cur),
|
|
cur),
|
|
ggml_repeat(ctx0, layer.mlp_ln_b, cur));
|
|
}
|
|
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
// fully connected
|
|
cur = ggml_mul_mat(ctx0,
|
|
layer.mlp_0_w,
|
|
cur);
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
cur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0, layer.mlp_0_b, cur),
|
|
cur);
|
|
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
// GELU activation
|
|
cur = ggml_gelu(ctx0, cur);
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
// projection
|
|
cur = ggml_mul_mat(ctx0,
|
|
layer.mlp_1_w,
|
|
cur);
|
|
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
cur = ggml_add(ctx0,
|
|
ggml_repeat(ctx0, layer.mlp_1_b, cur),
|
|
cur);
|
|
}
|
|
|
|
wstate.use_buf(ctx0, 3);
|
|
|
|
inpL = ggml_add(ctx0, cur, inpFF);
|
|
}
|
|
|
|
cur = inpL;
|
|
|
|
// norm
|
|
{
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
cur = ggml_norm(ctx0, cur);
|
|
|
|
wstate.use_buf(ctx0, 1);
|
|
|
|
cur = ggml_add(ctx0,
|
|
ggml_mul(ctx0,
|
|
ggml_repeat(ctx0, model.d_ln_w, cur),
|
|
cur),
|
|
ggml_repeat(ctx0, model.d_ln_b, cur));
|
|
}
|
|
|
|
wstate.use_buf(ctx0, 0);
|
|
|
|
// 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);
|
|
|
|
wstate.use_buf(ctx0, -1);
|
|
|
|
// run the computation
|
|
{
|
|
ggml_build_forward_expand(&gf, logits);
|
|
ggml_graph_compute (ctx0, &gf);
|
|
}
|
|
|
|
// print the time for computing the last ggml_mul_mat that computes logits
|
|
// also print the total decoder time
|
|
// these need to be called after ggml_graph_compute()
|
|
printf("logits t = %7.3f ms (%2d runs, N = %3d, ggml_mul_mat: [%d x %d] * [%d x %d])\n", 1e-3*double(logits->perf_time_us)/logits->perf_runs, logits->perf_runs, N, logits->ne[0], logits->ne[1], cur->ne[1], cur->ne[0]);
|
|
printf("total t = %7.3f ms (%2d runs)\n", 1e-3*double(gf.perf_time_us)/gf.perf_runs, gf.perf_runs);
|
|
|
|
// extract logits for all N tokens
|
|
//logits_out.resize(N*n_vocab);
|
|
//memcpy(logits_out.data(), ggml_get_data(logits), sizeof(float)*N*n_vocab);
|
|
|
|
// extract logits only for the last token
|
|
logits_out.resize(n_vocab);
|
|
memcpy(logits_out.data(), ggml_get_data(logits), sizeof(float)*n_vocab);
|
|
|
|
if (N > 1) {
|
|
//printf("%s: used_mem = %f MB, %f MB, %f MB %f MB %f MB\n", __func__,
|
|
// ggml_used_mem(ctx0)/1024.0/1024.0,
|
|
// wctx.get_buf_max_mem(0)/1024.0/1024.0,
|
|
// wctx.get_buf_max_mem(1)/1024.0/1024.0,
|
|
// wctx.get_buf_max_mem(2)/1024.0/1024.0,
|
|
// wctx.get_buf_max_mem(3)/1024.0/1024.0);
|
|
}
|
|
|
|
ggml_free(ctx0);
|
|
|
|
wstate.t_decode_us += ggml_time_us() - t_start_us;
|
|
wstate.n_decode++;
|
|
|
|
return true;
|
|
}
|
|
|
|
// 500 -> 00:05.000
|
|
// 6000 -> 01:00.000
|
|
static std::string to_timestamp(int64_t t, bool comma = false) {
|
|
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);
|
|
}
|
|
|
|
// 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);
|
|
|
|
for (int k = 0; k < N; k++) {
|
|
float re = 0;
|
|
float im = 0;
|
|
|
|
for (int n = 0; n < N; n++) {
|
|
float angle = 2*M_PI*k*n/N;
|
|
re += in[n]*cos(angle);
|
|
im -= in[n]*sin(angle);
|
|
}
|
|
|
|
out[k*2 + 0] = re;
|
|
out[k*2 + 1] = im;
|
|
}
|
|
}
|
|
|
|
// Cooley-Tukey FFT
|
|
// poor man's 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);
|
|
|
|
for (int k = 0; k < N/2; k++) {
|
|
float theta = 2*M_PI*k/N;
|
|
|
|
float re = cos(theta);
|
|
float im = -sin(theta);
|
|
|
|
float re_odd = odd_fft[2*k + 0];
|
|
float im_odd = odd_fft[2*k + 1];
|
|
|
|
out[2*k + 0] = even_fft[2*k + 0] + re*re_odd - im*im_odd;
|
|
out[2*k + 1] = even_fft[2*k + 1] + re*im_odd + im*re_odd;
|
|
|
|
out[2*(k + N/2) + 0] = even_fft[2*k + 0] - re*re_odd + im*im_odd;
|
|
out[2*(k + N/2) + 1] = even_fft[2*k + 1] - re*im_odd - im*re_odd;
|
|
}
|
|
}
|
|
|
|
// ref: https://github.com/openai/whisper/blob/main/whisper/audio.py#L92-L124
|
|
static bool log_mel_spectrogram(
|
|
whisper_state & wstate,
|
|
const float * samples,
|
|
const int n_samples,
|
|
const int /*sample_rate*/,
|
|
const int fft_size,
|
|
const int fft_step,
|
|
const int n_mel,
|
|
const int n_threads,
|
|
const whisper_filters & filters,
|
|
const bool speed_up,
|
|
whisper_mel & mel) {
|
|
const int64_t t_start_us = ggml_time_us();
|
|
|
|
// Hanning window
|
|
std::vector<float> hann;
|
|
hann.resize(fft_size);
|
|
for (int i = 0; i < fft_size; i++) {
|
|
hann[i] = 0.5*(1.0 - cos((2.0*M_PI*i)/(fft_size)));
|
|
}
|
|
|
|
mel.n_mel = n_mel;
|
|
mel.n_len = (n_samples)/fft_step;
|
|
mel.data.resize(mel.n_mel*mel.n_len);
|
|
|
|
const int n_fft = 1 + (speed_up ? fft_size/4 : fft_size/2);
|
|
|
|
//printf("%s: n_samples = %d, n_len = %d\n", __func__, n_samples, mel.n_len);
|
|
//printf("%s: recording length: %f s\n", __func__, (float) n_samples/sample_rate);
|
|
|
|
std::vector<std::thread> workers(n_threads);
|
|
for (int iw = 0; iw < n_threads; ++iw) {
|
|
workers[iw] = std::thread([&](int ith) {
|
|
std::vector<float> fft_in;
|
|
fft_in.resize(fft_size);
|
|
for (int i = 0; i < fft_size; i++) {
|
|
fft_in[i] = 0.0;
|
|
}
|
|
|
|
std::vector<float> fft_out;
|
|
fft_out.resize(2*fft_size);
|
|
|
|
for (int i = ith; i < mel.n_len; i += n_threads) {
|
|
const int offset = i*fft_step;
|
|
|
|
// apply Hanning window
|
|
for (int j = 0; j < fft_size; j++) {
|
|
if (offset + j < n_samples) {
|
|
fft_in[j] = hann[j]*samples[offset + j];
|
|
} else {
|
|
fft_in[j] = 0.0;
|
|
}
|
|
}
|
|
|
|
// FFT -> mag^2
|
|
fft(fft_in, fft_out);
|
|
|
|
for (int j = 0; j < fft_size; j++) {
|
|
fft_out[j] = (fft_out[2*j + 0]*fft_out[2*j + 0] + fft_out[2*j + 1]*fft_out[2*j + 1]);
|
|
}
|
|
for (int j = 1; j < fft_size/2; j++) {
|
|
//if (i == 0) {
|
|
// printf("%d: %f %f\n", j, fft_out[j], fft_out[fft_size - j]);
|
|
//}
|
|
fft_out[j] += fft_out[fft_size - j];
|
|
}
|
|
if (i == 0) {
|
|
//for (int j = 0; j < fft_size; j++) {
|
|
// printf("%d: %e\n", j, fft_out[j]);
|
|
//}
|
|
}
|
|
|
|
if (speed_up) {
|
|
// scale down in the frequency domain results in a speed up in the time domain
|
|
for (int j = 0; j < n_fft; j++) {
|
|
fft_out[j] = 0.5*(fft_out[2*j] + fft_out[2*j + 1]);
|
|
}
|
|
}
|
|
|
|
// mel spectrogram
|
|
for (int j = 0; j < mel.n_mel; j++) {
|
|
double sum = 0.0;
|
|
|
|
for (int k = 0; k < n_fft; k++) {
|
|
sum += fft_out[k]*filters.data[j*n_fft + k];
|
|
}
|
|
if (sum < 1e-10) {
|
|
sum = 1e-10;
|
|
}
|
|
|
|
sum = log10(sum);
|
|
|
|
mel.data[j*mel.n_len + i] = sum;
|
|
}
|
|
}
|
|
}, iw);
|
|
}
|
|
|
|
for (int iw = 0; iw < n_threads; ++iw) {
|
|
workers[iw].join();
|
|
}
|
|
|
|
// clamping and normalization
|
|
double mmax = -1e20;
|
|
for (int i = 0; i < mel.n_mel*mel.n_len; i++) {
|
|
if (mel.data[i] > mmax) {
|
|
mmax = mel.data[i];
|
|
}
|
|
}
|
|
//printf("%s: max = %f\n", __func__, mmax);
|
|
|
|
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;
|
|
|
|
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;
|
|
while (j > i) {
|
|
auto it = vocab.token_to_id.find(word.substr(i, j-i));
|
|
if (it != vocab.token_to_id.end()) {
|
|
tokens.push_back(it->second);
|
|
i = j;
|
|
break;
|
|
}
|
|
--j;
|
|
}
|
|
if (i == n) {
|
|
break;
|
|
}
|
|
if (j == i) {
|
|
auto sub = word.substr(i, 1);
|
|
if (vocab.token_to_id.find(sub) != vocab.token_to_id.end()) {
|
|
tokens.push_back(vocab.token_to_id.at(sub));
|
|
} else {
|
|
fprintf(stderr, "%s: unknown token '%s'\n", __func__, sub.data());
|
|
}
|
|
++i;
|
|
}
|
|
}
|
|
}
|
|
|
|
return tokens;
|
|
}
|
|
|
|
//
|
|
// interface implementation
|
|
//
|
|
|
|
struct whisper_state * whisper_init_state(whisper_context * ctx) {
|
|
whisper_state * state = new whisper_state;
|
|
|
|
const size_t scale = ctx->model.hparams.f16 ? 1 : 2;
|
|
|
|
|
|
if (!kv_cache_init(ctx->model.hparams, scale * MEM_REQ_KV_SELF.at(ctx->model.type), state->decoders[0].kv_self, ctx->wtype, ctx->model.hparams.n_text_ctx)) {
|
|
fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
|
|
return nullptr;
|
|
}
|
|
|
|
{
|
|
const size_t memory_size = ggml_nbytes(state->decoders[0].kv_self.k) + ggml_nbytes(state->decoders[0].kv_self.v);
|
|
fprintf(stderr, "%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
|
|
}
|
|
|
|
if (!kv_cache_init(ctx->model.hparams, scale * MEM_REQ_KV_CROSS.at(ctx->model.type), state->kv_cross, ctx->wtype, ctx->model.hparams.n_audio_ctx)) {
|
|
fprintf(stderr, "%s: kv_cache_init() failed for cross-attention cache\n", __func__);
|
|
return nullptr;
|
|
}
|
|
|
|
{
|
|
const size_t memory_size = ggml_nbytes(state->kv_cross.k) + ggml_nbytes(state->kv_cross.v);
|
|
fprintf(stderr, "%s: kv cross size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0);
|
|
}
|
|
|
|
|
|
state->logits.reserve(ctx->vocab.n_vocab * ctx->model.hparams.n_text_ctx);
|
|
|
|
state->logits_id.reserve(ctx->model.hparams.n_vocab);
|
|
|
|
// 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->buf_compute.resize(scale * std::max(MEM_REQ_ENCODE.at(ctx->model.type), MEM_REQ_DECODE.at(ctx->model.type)));
|
|
|
|
state->buf_scratch[0].resize(MEM_REQ_SCRATCH0.at(ctx->model.type));
|
|
state->buf_scratch[1].resize(MEM_REQ_SCRATCH1.at(ctx->model.type));
|
|
state->buf_scratch[2].resize(MEM_REQ_SCRATCH2.at(ctx->model.type));
|
|
state->buf_scratch[3].resize(MEM_REQ_SCRATCH3.at(ctx->model.type));
|
|
|
|
state->rng = std::mt19937(0);
|
|
|
|
return state;
|
|
}
|
|
|
|
struct whisper_context * whisper_init_from_file_no_state(const char * path_model) {
|
|
whisper_model_loader loader = {};
|
|
|
|
fprintf(stderr, "%s: loading model from '%s'\n", __func__, path_model);
|
|
|
|
auto fin = std::ifstream(path_model, std::ios::binary);
|
|
if (!fin) {
|
|
fprintf(stderr, "%s: failed to open '%s'\n", __func__, path_model);
|
|
return nullptr;
|
|
}
|
|
|
|
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();
|
|
};
|
|
|
|
return whisper_init_no_state(&loader);
|
|
}
|
|
|
|
struct whisper_context * whisper_init_from_buffer_no_state(void * buffer, size_t buffer_size) {
|
|
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_model_loader loader = {};
|
|
|
|
fprintf(stderr, "%s: loading model from buffer\n", __func__);
|
|
|
|
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_no_state(&loader);
|
|
}
|
|
|
|
struct whisper_context * whisper_init_no_state(struct whisper_model_loader * loader) {
|
|
ggml_time_init();
|
|
|
|
whisper_context * ctx = new whisper_context;
|
|
|
|
if (!whisper_model_load(loader, *ctx)) {
|
|
loader->close(loader->context);
|
|
fprintf(stderr, "%s: failed to load model\n", __func__);
|
|
delete ctx;
|
|
return nullptr;
|
|
}
|
|
|
|
loader->close(loader->context);
|
|
|
|
return ctx;
|
|
}
|
|
|
|
struct whisper_context * whisper_init_from_file(const char * path_model) {
|
|
whisper_context * ctx = whisper_init_from_file_no_state(path_model);
|
|
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(void * buffer, size_t buffer_size) {
|
|
whisper_context * ctx = whisper_init_from_buffer_no_state(buffer, buffer_size);
|
|
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(struct whisper_model_loader * loader) {
|
|
whisper_context * ctx = whisper_init_no_state(loader);
|
|
if (!ctx) {
|
|
return nullptr;
|
|
}
|
|
|
|
ctx->state = whisper_init_state(ctx);
|
|
if (!ctx->state) {
|
|
whisper_free(ctx);
|
|
return nullptr;
|
|
}
|
|
|
|
return ctx;
|
|
}
|
|
|
|
void whisper_free_state(struct whisper_state * state)
|
|
{
|
|
if (state) {
|
|
kv_cache_free(state->kv_cross);
|
|
|
|
for (int i = 0; i < WHISPER_MAX_DECODERS; ++i) {
|
|
kv_cache_free(state->decoders[i].kv_self);
|
|
}
|
|
|
|
delete state;
|
|
}
|
|
}
|
|
|
|
void whisper_free(struct whisper_context * ctx) {
|
|
if (ctx) {
|
|
if (ctx->model.ctx) {
|
|
ggml_free(ctx->model.ctx);
|
|
}
|
|
if (ctx->model.buf) {
|
|
delete ctx->model.buf;
|
|
}
|
|
|
|
whisper_free_state(ctx->state);
|
|
|
|
delete ctx;
|
|
}
|
|
}
|
|
|
|
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, WHISPER_N_MEL, n_threads, ctx->model.filters, false, state->mel)) {
|
|
fprintf(stderr, "%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
|
|
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, WHISPER_N_MEL, n_threads, ctx->model.filters, true, state->mel)) {
|
|
fprintf(stderr, "%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
|
|
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);
|
|
}
|
|
|
|
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 != WHISPER_N_MEL) {
|
|
fprintf(stderr, "%s: invalid number of mel bands: %d (expected %d)\n", __func__, n_mel, WHISPER_N_MEL);
|
|
return -1;
|
|
}
|
|
|
|
state->mel.n_len = n_len;
|
|
state->mel.n_mel = n_mel;
|
|
|
|
state->mel.data.resize(n_len*n_mel);
|
|
memcpy(state->mel.data.data(), data, n_len*n_mel*sizeof(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)) {
|
|
fprintf(stderr, "%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)) {
|
|
fprintf(stderr, "%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) {
|
|
const int selected_decoder_id = 0;
|
|
|
|
if (!whisper_decode_internal(*ctx, *state, state->decoders[selected_decoder_id], tokens, n_tokens, n_past, n_threads)) {
|
|
fprintf(stderr, "%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) {
|
|
// TODO: add selected_decoder_id to state
|
|
const int selected_decoder_id = 0;
|
|
|
|
if (ctx->state == nullptr) {
|
|
fprintf(stderr, "%s: ERROR state was not loaded.\n", __func__);
|
|
return false;
|
|
}
|
|
|
|
|
|
if (!whisper_decode_internal(*ctx, *ctx->state, ctx->state->decoders[selected_decoder_id], tokens, n_tokens, n_past, n_threads)) {
|
|
fprintf(stderr, "%s: failed to eval\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
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()) {
|
|
fprintf(stderr, "%s: too many resulting tokens: %d (max %d)\n", __func__, (int) res.size(), n_max_tokens);
|
|
return -1;
|
|
}
|
|
|
|
for (int i = 0; i < (int) res.size(); i++) {
|
|
tokens[i] = res[i];
|
|
}
|
|
|
|
return res.size();
|
|
}
|
|
|
|
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;
|
|
}
|
|
}
|
|
|
|
fprintf(stderr, "%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();
|
|
}
|
|
}
|
|
|
|
fprintf(stderr, "%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) {
|
|
fprintf(stderr, "%s: offset %dms is before the start of the audio\n", __func__, offset_ms);
|
|
return -1;
|
|
}
|
|
|
|
if (seek >= state->mel.n_len) {
|
|
fprintf(stderr, "%s: offset %dms is past the end of the audio (%dms)\n", __func__, offset_ms, state->mel.n_len*10);
|
|
return -2;
|
|
}
|
|
|
|
// run the encoder
|
|
if (whisper_encode(ctx, seek, n_threads) != 0) {
|
|
fprintf(stderr, "%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) {
|
|
fprintf(stderr, "%s: failed to decode\n", __func__);
|
|
return -7;
|
|
}
|
|
|
|
auto & logits_id = state->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_n_len_from_state(struct whisper_state * state) {
|
|
return state->mel.n_len;
|
|
}
|
|
|
|
int whisper_n_len(struct whisper_context * ctx) {
|
|
return ctx->state->mel.n_len;
|
|
}
|
|
|
|
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_prev(struct whisper_context * ctx) {
|
|
return ctx->vocab.token_prev;
|
|
}
|
|
|
|
whisper_token whisper_token_solm(struct whisper_context * ctx) {
|
|
return ctx->vocab.token_solm;
|
|
}
|
|
|
|
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(void) {
|
|
return whisper_vocab::token_translate;
|
|
}
|
|
|
|
whisper_token whisper_token_transcribe(void) {
|
|
return whisper_vocab::token_transcribe;
|
|
}
|
|
|
|
void whisper_print_timings(struct whisper_context * ctx) {
|
|
const int64_t t_end_us = ggml_time_us();
|
|
|
|
fprintf(stderr, "\n");
|
|
fprintf(stderr, "%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);
|
|
|
|
fprintf(stderr, "%s: fallbacks = %3d p / %3d h\n", __func__, ctx->state->n_fail_p, ctx->state->n_fail_h);
|
|
fprintf(stderr, "%s: mel time = %8.2f ms\n", __func__, ctx->state->t_mel_us / 1000.0f);
|
|
fprintf(stderr, "%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);
|
|
fprintf(stderr, "%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);
|
|
fprintf(stderr, "%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);
|
|
}
|
|
fprintf(stderr, "%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) {
|
|
if (ctx->state != nullptr) {
|
|
ctx->state->t_sample_us = 0;
|
|
ctx->state->t_encode_us = 0;
|
|
ctx->state->t_decode_us = 0;
|
|
}
|
|
}
|
|
|
|
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 += "BLAS = " + std::to_string(ggml_cpu_has_blas()) + " | ";
|
|
s += "SSE3 = " + std::to_string(ggml_cpu_has_sse3()) + " | ";
|
|
s += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " | ";
|
|
|
|
return s.c_str();
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////
|
|
|
|
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,
|
|
/*.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,
|
|
/*.audio_ctx =*/ 0,
|
|
|
|
/*.prompt_tokens =*/ nullptr,
|
|
/*.prompt_n_tokens =*/ 0,
|
|
|
|
/*.language =*/ "en",
|
|
|
|
/*.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,
|
|
|
|
/*.encoder_begin_callback =*/ nullptr,
|
|
/*.encoder_begin_callback_user_data =*/ nullptr,
|
|
|
|
/*.logits_filter_callback =*/ nullptr,
|
|
/*.logits_filter_callback_user_data =*/ nullptr,
|
|
};
|
|
|
|
switch (strategy) {
|
|
case WHISPER_SAMPLING_GREEDY:
|
|
{
|
|
result.greedy = {
|
|
/*.best_of =*/ 1,
|
|
};
|
|
} 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);
|
|
|
|
// trim from start (in place)
|
|
static inline void ltrim(std::string &s) {
|
|
s.erase(s.begin(), std::find_if(s.begin(), s.end(), [](unsigned char ch) {
|
|
return !std::isspace(ch);
|
|
}));
|
|
}
|
|
|
|
// trim from end (in place)
|
|
static inline void rtrim(std::string &s) {
|
|
s.erase(std::find_if(s.rbegin(), s.rend(), [](unsigned char ch) {
|
|
return !std::isspace(ch);
|
|
}).base(), s.end());
|
|
}
|
|
|
|
// trim from both ends (in place)
|
|
static inline void trim(std::string &s) {
|
|
rtrim(s);
|
|
ltrim(s);
|
|
}
|
|
|
|
static inline bool should_split_on_word(const char * txt, bool split_on_word) {
|
|
if (!split_on_word) return true;
|
|
|
|
return txt[0] == ' ';
|
|
}
|
|
|
|
// 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)) {
|
|
// split here
|
|
if (split_on_word) {
|
|
trim(text);
|
|
}
|
|
|
|
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.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());
|
|
|
|
acc = 0;
|
|
text = "";
|
|
|
|
segment = state.result_all.back();
|
|
i = -1;
|
|
|
|
res++;
|
|
} else {
|
|
acc += cur;
|
|
text += txt;
|
|
}
|
|
}
|
|
|
|
if (split_on_word) {
|
|
trim(text);
|
|
}
|
|
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
|
|
static void whisper_process_logits(
|
|
struct whisper_context & ctx,
|
|
struct whisper_state & state,
|
|
const struct whisper_full_params params,
|
|
struct whisper_decoder & decoder,
|
|
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() + (state.logits.size() - n_logits), n_logits*sizeof(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;
|
|
|
|
// suppress sot and solm tokens
|
|
logits[vocab.token_sot] = -INFINITY;
|
|
logits[vocab.token_solm] = -INFINITY;
|
|
|
|
// suppress task tokens
|
|
logits[vocab.token_translate] = -INFINITY;
|
|
logits[vocab.token_transcribe] = -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 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;
|
|
|
|
//fprintf(stderr, "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);
|
|
|
|
//fprintf(stderr, "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;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// 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 < 100; 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("%s : prob=%9.5f logit=%9.5f logprob=%9.5f\n", token.c_str(), prob, logit, logprob);
|
|
}
|
|
|
|
// "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 whisper_token_data whisper_sample_token(
|
|
whisper_context & ctx,
|
|
whisper_state & state,
|
|
const whisper_decoder & decoder,
|
|
bool best) {
|
|
whisper_token_data result = {
|
|
0, 0, 0.0f, 0.0f, 0.0f, 0.0f, -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(state.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;
|
|
}
|
|
|
|
state.n_sample++;
|
|
|
|
return result;
|
|
}
|
|
|
|
static std::vector<whisper_token_data> whisper_sample_token_topk(
|
|
whisper_context & ctx,
|
|
whisper_state & state,
|
|
const 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 = state.logits_id;
|
|
|
|
logits_id.clear();
|
|
for (int i = 0; i < n_logits; ++i) {
|
|
logits_id.push_back({ logits[i], i });
|
|
}
|
|
|
|
std::partial_sort(
|
|
logits_id.begin(),
|
|
logits_id.begin() + k, logits_id.end(),
|
|
[](const std::pair<double, whisper_token> & a, const std::pair<double, whisper_token> & 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;
|
|
}
|
|
|
|
for (int i = 0; i < k; ++i) {
|
|
const auto id = logits_id[i].second;
|
|
|
|
result.push_back({ id, tid, probs[id], logprobs[id], pt, ptsum, -1, -1, 0.0f, });
|
|
|
|
if (result[i].id >= vocab.token_beg) {
|
|
result[i].tid = result[i].id;
|
|
result[i].pt = result[i].p;
|
|
}
|
|
}
|
|
|
|
state.n_sample++;
|
|
|
|
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_PRINT_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();
|
|
|
|
// compute log mel spectrogram
|
|
if (params.speed_up) {
|
|
if (whisper_pcm_to_mel_phase_vocoder_with_state(ctx, state, samples, n_samples, params.n_threads) != 0) {
|
|
fprintf(stderr, "%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) {
|
|
fprintf(stderr, "%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) {
|
|
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) {
|
|
fprintf(stderr, "%s: failed to auto-detect language\n", __func__);
|
|
return -3;
|
|
}
|
|
state->lang_id = lang_id;
|
|
params.language = whisper_lang_str(lang_id);
|
|
|
|
fprintf(stderr, "%s: auto-detected language: %s (p = %f)\n", __func__, params.language, probs[whisper_lang_id(params.language)]);
|
|
}
|
|
|
|
if (params.token_timestamps) {
|
|
state->t_beg = 0;
|
|
state->t_last = 0;
|
|
state->tid_last = 0;
|
|
state->energy = get_signal_energy(samples, n_samples, 32);
|
|
}
|
|
|
|
const int seek_start = params.offset_ms/10;
|
|
const int seek_end = seek_start + (params.duration_ms == 0 ? whisper_n_len_from_state(state) : params.duration_ms/10);
|
|
|
|
// if length of spectrogram is less than 1s (100 samples), then return
|
|
// basically don't process anything that is less than 1s
|
|
// see issue #39: https://github.com/ggerganov/whisper.cpp/issues/39
|
|
if (seek_end < seek_start + (params.speed_up ? 50 : 100)) {
|
|
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);
|
|
|
|
// TAGS: WHISPER_DECODER_INIT
|
|
for (int j = 1; j < n_decoders; j++) {
|
|
auto & decoder = state->decoders[j];
|
|
|
|
if (decoder.kv_self.ctx == nullptr) {
|
|
decoder.kv_self = state->decoders[0].kv_self;
|
|
if (!kv_cache_reinit(decoder.kv_self)) {
|
|
fprintf(stderr, "%s: kv_cache_reinit() failed for self-attention, decoder %d\n", __func__, j);
|
|
return -4;
|
|
}
|
|
|
|
WHISPER_PRINT_DEBUG("%s: initialized self-attention kv cache, decoder %d\n", __func__, 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);
|
|
}
|
|
}
|
|
|
|
// the accumulated text context so far
|
|
auto & prompt_past = state->prompt_past;
|
|
if (params.no_context) {
|
|
prompt_past.clear();
|
|
}
|
|
|
|
// 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)) {
|
|
fprintf(stderr, "%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)) {
|
|
const int lang_id = whisper_lang_id(params.language);
|
|
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());
|
|
} else {
|
|
prompt_init.push_back(whisper_token_transcribe());
|
|
}
|
|
}
|
|
|
|
int progress_prev = 0;
|
|
int progress_step = 5;
|
|
|
|
int seek = seek_start;
|
|
|
|
std::vector<whisper_token> prompt;
|
|
prompt.reserve(whisper_n_text_ctx(ctx));
|
|
|
|
// beam-search helpers
|
|
struct kv_buf {
|
|
std::vector<uint8_t> k;
|
|
std::vector<uint8_t> v;
|
|
};
|
|
|
|
std::vector<kv_buf> kv_bufs;
|
|
|
|
struct beam_candidate {
|
|
int decoder_idx;
|
|
int seek_delta;
|
|
|
|
bool has_ts;
|
|
|
|
whisper_sequence sequence;
|
|
};
|
|
|
|
std::vector<beam_candidate> beam_candidates;
|
|
|
|
// main loop
|
|
while (true) {
|
|
const int progress_cur = (100*(seek - seek_start))/(seek_end - seek_start);
|
|
while (progress_cur >= progress_prev + progress_step) {
|
|
progress_prev += progress_step;
|
|
if (params.print_progress) {
|
|
fprintf(stderr, "%s: progress = %3d%%\n", __func__, progress_prev);
|
|
}
|
|
}
|
|
|
|
// of 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) {
|
|
fprintf(stderr, "%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)) {
|
|
fprintf(stderr, "%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_PRINT_DEBUG("\n%s: decoding with %d decoders, temperature = %.2f\n", __func__, n_decoders_cur, t_cur);
|
|
|
|
// TAGS: WHISPER_DECODER_INIT
|
|
for (int j = 0; j < n_decoders_cur; ++j) {
|
|
auto & decoder = state->decoders[j];
|
|
|
|
decoder.kv_self.n = 0;
|
|
|
|
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;
|
|
}
|
|
|
|
// init prompt and kv cache for the current iteration
|
|
// run whisper_decoder() only for decoder 0 and copy the results for the other decoders
|
|
{
|
|
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_PRINT_DEBUG("\n\n");
|
|
for (int i = 0; i < (int) prompt.size(); i++) {
|
|
WHISPER_PRINT_DEBUG("%s: prompt[%d] = %s\n", __func__, i, ctx->vocab.id_to_token.at(prompt[i]).c_str());
|
|
}
|
|
WHISPER_PRINT_DEBUG("\n\n");
|
|
|
|
if (!whisper_decode_internal(*ctx, *state, state->decoders[0], prompt.data(), prompt.size(), 0, params.n_threads)) {
|
|
fprintf(stderr, "%s: failed to decode\n", __func__);
|
|
return -7;
|
|
}
|
|
|
|
{
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
whisper_process_logits(*ctx, *state, params, state->decoders[0], t_cur);
|
|
|
|
state->decoders[0].kv_self.n += prompt.size();
|
|
|
|
for (int j = 1; j < n_decoders_cur; ++j) {
|
|
auto & decoder = state->decoders[j];
|
|
|
|
memcpy(decoder.kv_self.k->data, state->decoders[0].kv_self.k->data, ggml_nbytes(decoder.kv_self.k));
|
|
memcpy(decoder.kv_self.v->data, state->decoders[0].kv_self.v->data, ggml_nbytes(decoder.kv_self.v));
|
|
|
|
decoder.kv_self.n += prompt.size();
|
|
|
|
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();
|
|
|
|
// store the KV caches of all decoders when doing beam-search
|
|
if (params.strategy == whisper_sampling_strategy::WHISPER_SAMPLING_BEAM_SEARCH) {
|
|
kv_bufs.resize(n_decoders_cur);
|
|
for (int j = 0; j < n_decoders_cur; ++j) {
|
|
auto & decoder = state->decoders[j];
|
|
|
|
if (decoder.completed || decoder.failed) {
|
|
continue;
|
|
}
|
|
|
|
kv_bufs[j].k.resize(ggml_nbytes(decoder.kv_self.k));
|
|
kv_bufs[j].v.resize(ggml_nbytes(decoder.kv_self.v));
|
|
|
|
memcpy(kv_bufs[j].k.data(), decoder.kv_self.k->data, kv_bufs[j].k.size());
|
|
memcpy(kv_bufs[j].v.data(), decoder.kv_self.v->data, kv_bufs[j].v.size());
|
|
}
|
|
|
|
beam_candidates.clear();
|
|
}
|
|
|
|
// generate new sequence candidates for each decoder
|
|
for (int j = 0; j < n_decoders_cur; ++j) {
|
|
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, *state, decoder, true));
|
|
} else {
|
|
decoder.sequence.tokens.push_back(whisper_sample_token(*ctx, *state, 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, *state, decoder, params.beam_search.beam_size);
|
|
|
|
for (const auto & token : tokens_new) {
|
|
beam_candidates.push_back({ j, decoder.seek_delta, decoder.has_ts, decoder.sequence });
|
|
beam_candidates.back().sequence.tokens.push_back(token);
|
|
beam_candidates.back().sequence.sum_logprobs_all += token.plog;
|
|
|
|
//WHISPER_PRINT_DEBUG("%s: beam candidate: %s (%f, %f)\n", __func__, ctx->vocab.id_to_token.at(token.id).c_str(), token.plog, beam_candidates.back().sequence.sum_logprobs_all);
|
|
}
|
|
} break;
|
|
};
|
|
}
|
|
|
|
// 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) {
|
|
return a.sequence.sum_logprobs_all > b.sequence.sum_logprobs_all;
|
|
});
|
|
|
|
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;
|
|
}
|
|
|
|
auto & cur = beam_candidates[cur_c++];
|
|
|
|
while (beam_candidates.size() > cur_c && beam_candidates[cur_c].sequence.sum_logprobs_all == cur.sequence.sum_logprobs_all && i > 0) {
|
|
++cur_c;
|
|
}
|
|
|
|
decoder.sequence = cur.sequence;
|
|
decoder.seek_delta = cur.seek_delta;
|
|
decoder.has_ts = cur.has_ts;
|
|
|
|
memcpy(decoder.kv_self.k->data, kv_bufs[cur.decoder_idx].k.data(), kv_bufs[cur.decoder_idx].k.size());
|
|
memcpy(decoder.kv_self.v->data, kv_bufs[cur.decoder_idx].v.data(), kv_bufs[cur.decoder_idx].v.size());
|
|
|
|
WHISPER_PRINT_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);
|
|
}
|
|
}
|
|
|
|
// 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) {
|
|
failed = true; // TODO: maybe this is not a failure ?
|
|
continue;
|
|
}
|
|
|
|
seek_delta = seek_delta_new;
|
|
result_len = i + 1;
|
|
has_ts = true;
|
|
}
|
|
|
|
#ifdef WHISPER_DEBUG
|
|
{
|
|
const auto tt = token.pt > 0.10 ? ctx->vocab.id_to_token.at(token.tid) : "[?]";
|
|
WHISPER_PRINT_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) {
|
|
if (seek + seek_delta + 100 >= seek_end) {
|
|
result_len = i + 1;
|
|
} else {
|
|
failed = true;
|
|
continue;
|
|
}
|
|
}
|
|
|
|
if (params.single_segment) {
|
|
result_len = i + 1;
|
|
seek_delta = 100*WHISPER_CHUNK_SIZE;
|
|
}
|
|
|
|
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)) {
|
|
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
|
|
for (int j = 0; j < n_decoders_cur; ++j) {
|
|
auto & decoder = state->decoders[j];
|
|
|
|
if (decoder.failed || decoder.completed) {
|
|
continue;
|
|
}
|
|
|
|
decoder.tokens_tmp.resize(1);
|
|
decoder.tokens_tmp[0] = decoder.sequence.tokens.back().id;
|
|
|
|
//WHISPER_PRINT_DEBUG("%s: decoder %d: token %d, kv_self.n %d, seek_delta %d\n", __func__, j, decoder.tokens_tmp[0], decoder.kv_self.n, decoder.seek_delta);
|
|
|
|
if (!whisper_decode_internal(*ctx, *state, decoder, decoder.tokens_tmp.data(), decoder.tokens_tmp.size(), decoder.kv_self.n, params.n_threads)) {
|
|
fprintf(stderr, "%s: failed to decode\n", __func__);
|
|
return -8;
|
|
}
|
|
|
|
{
|
|
const int64_t t_start_sample_us = ggml_time_us();
|
|
|
|
whisper_process_logits(*ctx, *state, params, decoder, t_cur);
|
|
|
|
++decoder.kv_self.n;
|
|
|
|
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_PRINT_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_PRINT_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_PRINT_DEBUG("%s: best decoder = %d\n", __func__, best_decoder_id);
|
|
}
|
|
|
|
// was the decoding successful for the current temperature?
|
|
{
|
|
bool success = true;
|
|
|
|
const auto & decoder = state->decoders[best_decoder_id];
|
|
|
|
if (decoder.failed || 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_PRINT_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_PRINT_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;
|
|
|
|
//WHISPER_PRINT_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);
|
|
}
|
|
|
|
// store the text from this iteration
|
|
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;
|
|
|
|
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 == false && tokens_cur[i].id >= whisper_token_eot(ctx)) {
|
|
} else {
|
|
text += whisper_token_to_str(ctx, tokens_cur[i].id);
|
|
}
|
|
|
|
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, {} });
|
|
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;
|
|
}
|
|
}
|
|
|
|
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, {} });
|
|
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);
|
|
}
|
|
}
|
|
}
|
|
|
|
// update audio window
|
|
seek += seek_delta;
|
|
|
|
WHISPER_PRINT_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;
|
|
|
|
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;
|
|
|
|
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
|
|
fprintf(stderr, "\n");
|
|
fprintf(stderr, "%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) {
|
|
fprintf(stderr, "%s: split %d - %s\n", __func__, (i + 1), to_timestamp(100*((i + 1)*n_samples_per_processor)/WHISPER_SAMPLE_RATE + offset_t).c_str());
|
|
}
|
|
fprintf(stderr, "%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;
|
|
}
|
|
|
|
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 = 50;
|
|
size_t arr = n_threads > 0 ? 1024 : n_threads; // trick to avoid compiler optimizations
|
|
|
|
// 1 GB array
|
|
const size_t size = arr*1024llu*1024llu;
|
|
|
|
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[0] = rand();
|
|
}
|
|
|
|
snprintf(strbuf, sizeof(strbuf), "memcpy: %.2f GB/s\n", (double) (n*size)/(tsum*1024llu*1024llu*1024llu));
|
|
s += strbuf;
|
|
|
|
// needed to prevent the compile from optimizing the memcpy away
|
|
{
|
|
double sum = 0.0;
|
|
|
|
for (size_t i = 0; i < size; i++) sum += dst[i];
|
|
|
|
snprintf(strbuf, sizeof(strbuf), "sum: %s %f\n", sum == -536870910.00 ? "ok" : "error", sum);
|
|
s += strbuf;
|
|
}
|
|
|
|
free(src);
|
|
free(dst);
|
|
|
|
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: N*N*sizeof(float)
|
|
// b: N*N*sizeof(float)
|
|
// c: N*N*sizeof(float)
|
|
// when F16 is used, there is an extra work buffer of size N*N*sizeof(float)
|
|
std::vector<char> buf(4llu*N_max*N_max*sizeof(float) + 4*256);
|
|
|
|
for (size_t i = 0; i < buf.size(); i++) buf[i] = i;
|
|
|
|
for (int j = 0; j < (int) sizes.size(); j++) {
|
|
int n_fp16 = 0;
|
|
int n_fp32 = 0;
|
|
|
|
// GFLOPS/s
|
|
double s_fp16 = 0.0;
|
|
double s_fp32 = 0.0;
|
|
|
|
const size_t N = sizes[j];
|
|
|
|
for (int k = 0; k < 2; ++k) {
|
|
const ggml_type wtype = k == 0 ? GGML_TYPE_F16 : GGML_TYPE_F32;
|
|
|
|
double & s = k == 0 ? s_fp16 : s_fp32;
|
|
int & n = k == 0 ? n_fp16 : n_fp32;
|
|
|
|
struct ggml_init_params gparams = {
|
|
/*.mem_size =*/ buf.size(),
|
|
/*.mem_buffer =*/ buf.data(),
|
|
};
|
|
|
|
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_build_forward(c);
|
|
|
|
gf.n_threads = n_threads;
|
|
|
|
double tsum = 0.0;
|
|
|
|
// heat-up
|
|
ggml_graph_compute(ctx0, &gf);
|
|
|
|
for (int i = 0; i < n_max; ++i) {
|
|
const int64_t t0 = ggml_time_us();
|
|
|
|
ggml_graph_compute(ctx0, &gf);
|
|
|
|
const int64_t t1 = ggml_time_us();
|
|
|
|
tsum += (t1 - t0)*1e-6;
|
|
n++;
|
|
|
|
if (tsum > 1.0 && n >= 3) {
|
|
break;
|
|
}
|
|
}
|
|
|
|
ggml_free(ctx0);
|
|
|
|
s = ((2.0*N*N*N*n)/tsum)*1e-9;
|
|
}
|
|
|
|
snprintf(strbuf, sizeof(strbuf), "ggml_mul_mat: %5zu x %5zu: F16 %8.1f GFLOPS (%3d runs) / F32 %8.1f 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
|
|
//
|
|
|
|
static 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)));
|
|
}
|
|
|
|
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) {
|
|
fprintf(stderr, "%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;
|
|
// }
|
|
//}
|
|
}
|