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whisper.cpp/whisper.cpp

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#define WHISPER_BUILD
#include "whisper.h"
#include "ggml.h"
#include <algorithm>
#include <cassert>
#define _USE_MATH_DEFINES
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <map>
#include <string>
#include <thread>
#include <vector>
#include <regex>
#include <random>
#define WHISPER_ASSERT(x) \
do { \
if (!(x)) { \
fprintf(stderr, "WHISPER_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
abort(); \
} \
} while (0)
// define this to enable verbose trace logging - useful for debugging purposes
//#define WHISPER_DEBUG
#if defined(WHISPER_DEBUG)
#define WHISPER_PRINT_DEBUG(...) \
do { \
fprintf(stderr, __VA_ARGS__); \
} while (0)
#else
#define WHISPER_PRINT_DEBUG(...)
#endif
#define WHISPER_USE_FLASH_ATTN
//#define WHISPER_USE_FLASH_FF
#define WHISPER_MAX_DECODERS 16
// available whisper models
enum e_model {
MODEL_UNKNOWN,
MODEL_TINY,
MODEL_BASE,
MODEL_SMALL,
MODEL_MEDIUM,
MODEL_LARGE,
};
static const std::map<std::string, std::pair<int, std::string>> g_lang = {
{ "en", { 0, "english", } },
{ "zh", { 1, "chinese", } },
{ "de", { 2, "german", } },
{ "es", { 3, "spanish", } },
{ "ru", { 4, "russian", } },
{ "ko", { 5, "korean", } },
{ "fr", { 6, "french", } },
{ "ja", { 7, "japanese", } },
{ "pt", { 8, "portuguese", } },
{ "tr", { 9, "turkish", } },
{ "pl", { 10, "polish", } },
{ "ca", { 11, "catalan", } },
{ "nl", { 12, "dutch", } },
{ "ar", { 13, "arabic", } },
{ "sv", { 14, "swedish", } },
{ "it", { 15, "italian", } },
{ "id", { 16, "indonesian", } },
{ "hi", { 17, "hindi", } },
{ "fi", { 18, "finnish", } },
{ "vi", { 19, "vietnamese", } },
{ "iw", { 20, "hebrew", } },
{ "uk", { 21, "ukrainian", } },
{ "el", { 22, "greek", } },
{ "ms", { 23, "malay", } },
{ "cs", { 24, "czech", } },
{ "ro", { 25, "romanian", } },
{ "da", { 26, "danish", } },
{ "hu", { 27, "hungarian", } },
{ "ta", { 28, "tamil", } },
{ "no", { 29, "norwegian", } },
{ "th", { 30, "thai", } },
{ "ur", { 31, "urdu", } },
{ "hr", { 32, "croatian", } },
{ "bg", { 33, "bulgarian", } },
{ "lt", { 34, "lithuanian", } },
{ "la", { 35, "latin", } },
{ "mi", { 36, "maori", } },
{ "ml", { 37, "malayalam", } },
{ "cy", { 38, "welsh", } },
{ "sk", { 39, "slovak", } },
{ "te", { 40, "telugu", } },
{ "fa", { 41, "persian", } },
{ "lv", { 42, "latvian", } },
{ "bn", { 43, "bengali", } },
{ "sr", { 44, "serbian", } },
{ "az", { 45, "azerbaijani", } },
{ "sl", { 46, "slovenian", } },
{ "kn", { 47, "kannada", } },
{ "et", { 48, "estonian", } },
{ "mk", { 49, "macedonian", } },
{ "br", { 50, "breton", } },
{ "eu", { 51, "basque", } },
{ "is", { 52, "icelandic", } },
{ "hy", { 53, "armenian", } },
{ "ne", { 54, "nepali", } },
{ "mn", { 55, "mongolian", } },
{ "bs", { 56, "bosnian", } },
{ "kk", { 57, "kazakh", } },
{ "sq", { 58, "albanian", } },
{ "sw", { 59, "swahili", } },
{ "gl", { 60, "galician", } },
{ "mr", { 61, "marathi", } },
{ "pa", { 62, "punjabi", } },
{ "si", { 63, "sinhala", } },
{ "km", { 64, "khmer", } },
{ "sn", { 65, "shona", } },
{ "yo", { 66, "yoruba", } },
{ "so", { 67, "somali", } },
{ "af", { 68, "afrikaans", } },
{ "oc", { 69, "occitan", } },
{ "ka", { 70, "georgian", } },
{ "be", { 71, "belarusian", } },
{ "tg", { 72, "tajik", } },
{ "sd", { 73, "sindhi", } },
{ "gu", { 74, "gujarati", } },
{ "am", { 75, "amharic", } },
{ "yi", { 76, "yiddish", } },
{ "lo", { 77, "lao", } },
{ "uz", { 78, "uzbek", } },
{ "fo", { 79, "faroese", } },
{ "ht", { 80, "haitian creole", } },
{ "ps", { 81, "pashto", } },
{ "tk", { 82, "turkmen", } },
{ "nn", { 83, "nynorsk", } },
{ "mt", { 84, "maltese", } },
{ "sa", { 85, "sanskrit", } },
{ "lb", { 86, "luxembourgish", } },
{ "my", { 87, "myanmar", } },
{ "bo", { 88, "tibetan", } },
{ "tl", { 89, "tagalog", } },
{ "mg", { 90, "malagasy", } },
{ "as", { 91, "assamese", } },
{ "tt", { 92, "tatar", } },
{ "haw", { 93, "hawaiian", } },
{ "ln", { 94, "lingala", } },
{ "ha", { 95, "hausa", } },
{ "ba", { 96, "bashkir", } },
{ "jw", { 97, "javanese", } },
{ "su", { 98, "sundanese", } },
};
static const size_t MB = 1024*1024;
static const std::map<e_model, size_t> MEM_REQ_MODEL = {
{ MODEL_TINY, 74ull*MB },
{ MODEL_BASE, 142ull*MB },
{ MODEL_SMALL, 466ull*MB },
{ MODEL_MEDIUM, 1464ull*MB },
{ MODEL_LARGE, 2952ull*MB },
};
static const std::map<e_model, size_t> MEM_REQ_KV_SELF = {
{ MODEL_TINY, 3ull*MB },
{ MODEL_BASE, 6ull*MB },
{ MODEL_SMALL, 16ull*MB },
{ MODEL_MEDIUM, 43ull*MB },
{ MODEL_LARGE, 71ull*MB },
};
static const std::map<e_model, size_t> MEM_REQ_KV_CROSS = {
{ MODEL_TINY, 9ull*MB },
{ MODEL_BASE, 18ull*MB },
{ MODEL_SMALL, 53ull*MB },
{ MODEL_MEDIUM, 141ull*MB },
{ MODEL_LARGE, 235ull*MB },
};
static const std::map<e_model, size_t> MEM_REQ_ENCODE = {
{ MODEL_TINY, 80ull*MB },
{ MODEL_BASE, 128ull*MB },
{ MODEL_SMALL, 300ull*MB },
{ MODEL_MEDIUM, 680ull*MB },
{ MODEL_LARGE, 1100ull*MB },
};
static const std::map<e_model, size_t> MEM_REQ_ENCODE_LAYER = {
{ MODEL_TINY, 104ull*MB },
{ MODEL_BASE, 138ull*MB },
{ MODEL_SMALL, 208ull*MB },
{ MODEL_MEDIUM, 280ull*MB },
{ MODEL_LARGE, 354ull*MB },
};
static const std::map<e_model, size_t> MEM_REQ_DECODE = {
{ MODEL_TINY, 200ull*MB },
{ MODEL_BASE, 202ull*MB },
{ MODEL_SMALL, 204ull*MB },
{ MODEL_MEDIUM, 206ull*MB },
{ MODEL_LARGE, 208ull*MB },
};
static const std::map<e_model, size_t> MEM_REQ_DECODE_LAYER = {
{ MODEL_TINY, 32ull*MB },
{ MODEL_BASE, 44ull*MB },
{ MODEL_SMALL, 64ull*MB },
{ MODEL_MEDIUM, 84ull*MB },
{ MODEL_LARGE, 110ull*MB },
};
struct whisper_mel {
int n_len;
int n_mel;
std::vector<float> data;
};
struct whisper_filters {
int32_t n_mel;
int32_t n_fft;
std::vector<float> data;
};
struct whisper_vocab {
using id = int32_t;
using token = std::string;
int n_vocab = 51864;
std::map<token, id> token_to_id;
std::map<id, token> id_to_token;
id token_eot = 50256;
id token_sot = 50257;
id token_prev = 50360;
id token_solm = 50361; // ??
id token_not = 50362; // no timestamps
id token_beg = 50363;
// available tasks
static const id token_translate = 50358;
static const id token_transcribe = 50359;
bool is_multilingual() const {
return n_vocab == 51865;
}
};
struct whisper_segment {
int64_t t0;
int64_t t1;
std::string text;
std::vector<whisper_token_data> tokens;
};
// medium
// hparams: {
// 'n_mels': 80,
// 'n_vocab': 51864,
// 'n_audio_ctx': 1500,
// 'n_audio_state': 1024,
// 'n_audio_head': 16,
// 'n_audio_layer': 24,
// 'n_text_ctx': 448,
// 'n_text_state': 1024,
// 'n_text_head': 16,
// 'n_text_layer': 24
// }
//
// default hparams (Whisper tiny)
struct whisper_hparams {
int32_t n_vocab = 51864;
int32_t n_audio_ctx = 1500;
int32_t n_audio_state = 384;
int32_t n_audio_head = 6;
int32_t n_audio_layer = 4;
int32_t n_text_ctx = 448;
int32_t n_text_state = 384;
int32_t n_text_head = 6;
int32_t n_text_layer = 4;
int32_t n_mels = 80;
int32_t f16 = 1;
};
// audio encoding layer
struct whisper_layer_encoder {
// encoder.blocks.*.attn_ln
struct ggml_tensor * attn_ln_0_w;
struct ggml_tensor * attn_ln_0_b;
// encoder.blocks.*.attn.out
struct ggml_tensor * attn_ln_1_w;
struct ggml_tensor * attn_ln_1_b;
// encoder.blocks.*.attn.query
struct ggml_tensor * attn_q_w;
struct ggml_tensor * attn_q_b;
// encoder.blocks.*.attn.key
struct ggml_tensor * attn_k_w;
// encoder.blocks.*.attn.value
struct ggml_tensor * attn_v_w;
struct ggml_tensor * attn_v_b;
// encoder.blocks.*.mlp_ln
struct ggml_tensor * mlp_ln_w;
struct ggml_tensor * mlp_ln_b;
// encoder.blocks.*.mlp.0
struct ggml_tensor * mlp_0_w;
struct ggml_tensor * mlp_0_b;
// encoder.blocks.*.mlp.2
struct ggml_tensor * mlp_1_w;
struct ggml_tensor * mlp_1_b;
};
// token decoding layer
struct whisper_layer_decoder {
// decoder.blocks.*.attn_ln
struct ggml_tensor * attn_ln_0_w;
struct ggml_tensor * attn_ln_0_b;
// decoder.blocks.*.attn.out
struct ggml_tensor * attn_ln_1_w;
struct ggml_tensor * attn_ln_1_b;
// decoder.blocks.*.attn.query
struct ggml_tensor * attn_q_w;
struct ggml_tensor * attn_q_b;
// decoder.blocks.*.attn.key
struct ggml_tensor * attn_k_w;
// decoder.blocks.*.attn.value
struct ggml_tensor * attn_v_w;
struct ggml_tensor * attn_v_b;
// decoder.blocks.*.cross_attn_ln
struct ggml_tensor * cross_attn_ln_0_w;
struct ggml_tensor * cross_attn_ln_0_b;
// decoder.blocks.*.cross_attn.out
struct ggml_tensor * cross_attn_ln_1_w;
struct ggml_tensor * cross_attn_ln_1_b;
// decoder.blocks.*.cross_attn.query
struct ggml_tensor * cross_attn_q_w;
struct ggml_tensor * cross_attn_q_b;
// decoder.blocks.*.cross_attn.key
struct ggml_tensor * cross_attn_k_w;
// decoder.blocks.*.cross_attn.value
struct ggml_tensor * cross_attn_v_w;
struct ggml_tensor * cross_attn_v_b;
// decoder.blocks.*.mlp_ln
struct ggml_tensor * mlp_ln_w;
struct ggml_tensor * mlp_ln_b;
// decoder.blocks.*.mlp.0
struct ggml_tensor * mlp_0_w;
struct ggml_tensor * mlp_0_b;
// decoder.blocks.*.mlp.2
struct ggml_tensor * mlp_1_w;
struct ggml_tensor * mlp_1_b;
};
struct whisper_kv_cache {
struct ggml_tensor * k;
struct ggml_tensor * v;
struct ggml_context * ctx;
std::vector<uint8_t> buf;
int n; // number of tokens currently in the cache
};
struct whisper_model {
e_model type = MODEL_UNKNOWN;
whisper_hparams hparams;
whisper_filters filters;
// encoder.positional_embedding
struct ggml_tensor * e_pe;
// encoder.conv1
struct ggml_tensor * e_conv_1_w;
struct ggml_tensor * e_conv_1_b;
// encoder.conv2
struct ggml_tensor * e_conv_2_w;
struct ggml_tensor * e_conv_2_b;
// encoder.ln_post
struct ggml_tensor * e_ln_w;
struct ggml_tensor * e_ln_b;
// decoder.positional_embedding
struct ggml_tensor * d_pe;
// decoder.token_embedding
struct ggml_tensor * d_te;
// decoder.ln
struct ggml_tensor * d_ln_w;
struct ggml_tensor * d_ln_b;
std::vector<whisper_layer_encoder> layers_encoder;
std::vector<whisper_layer_decoder> layers_decoder;
// 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_context {
int64_t t_load_us = 0;
int64_t t_mel_us = 0;
int64_t t_sample_us = 0;
int64_t t_encode_us = 0;
int64_t t_decode_us = 0;
int64_t t_start_us = 0;
ggml_type wtype; // weight type (FP32 or FP16)
whisper_mel mel;
whisper_model model;
whisper_vocab vocab;
// cross-attention KV cache for the decoders
// shared between all decoders
whisper_kv_cache kv_cross;
whisper_decoder decoders[WHISPER_MAX_DECODERS] = {};
// memory buffers used by encode / decode contexts
std::vector<uint8_t> buf_compute;
std::vector<uint8_t> buf_compute_layer;
// 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
// [EXPERIMENTAL] token-level timestamps data
int64_t t_beg;
int64_t t_last;
whisper_token tid_last;
std::vector<float> energy; // PCM signal energy
// [EXPERIMENTAL] speed-up techniques
int32_t exp_n_audio_ctx; // 0 - use default
};
template<typename T>
static void read_safe(whisper_model_loader * loader, T & dest) {
loader->read(loader->context, &dest, sizeof(T));
}
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 =
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)) +
scale*std::max(MEM_REQ_ENCODE_LAYER.at(model.type), MEM_REQ_DECODE_LAYER.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));
if (!kv_cache_init(model.hparams, scale*MEM_REQ_KV_SELF.at(model.type), wctx.decoders[0].kv_self, wctx.wtype, model.hparams.n_text_ctx)) {
fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__);
return false;
}
{
const size_t memory_size = ggml_nbytes(wctx.decoders[0].kv_self.k) + ggml_nbytes(wctx.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(model.hparams, scale*MEM_REQ_KV_CROSS.at(model.type), wctx.kv_cross, wctx.wtype, model.hparams.n_audio_ctx)) {
fprintf(stderr, "%s: kv_cache_init() failed for cross-attention cache\n", __func__);
return false;
}
{
const size_t memory_size = ggml_nbytes(wctx.kv_cross.k) + ggml_nbytes(wctx.kv_cross.v);
fprintf(stderr, "%s: kv cross size = %7.2f MB\n", __func__, memory_size/1024.0/1024.0);
}
wctx.buf_compute.resize (scale*std::max(MEM_REQ_ENCODE.at(model.type), MEM_REQ_DECODE.at(model.type)));
wctx.buf_compute_layer.resize(scale*std::max(MEM_REQ_ENCODE_LAYER.at(model.type), MEM_REQ_DECODE_LAYER.at(model.type)));
}
// 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));
}
// 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;
}
}
wctx.logits.reserve(vocab.n_vocab*model.hparams.n_text_ctx);
wctx.logits_id.reserve(n_vocab);
// TAGS: WHISPER_DECODER_INIT
wctx.decoders[0].sequence.tokens.reserve(model.hparams.n_text_ctx);
wctx.decoders[0].probs.reserve (vocab.n_vocab);
wctx.decoders[0].logits.reserve (vocab.n_vocab);
wctx.decoders[0].logprobs.reserve(vocab.n_vocab);
}
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));
//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.rng = std::mt19937(0);
wctx.t_load_us = ggml_time_us() - t_start_us;
return true;
}
// evaluate the encoder
//
// given audio recording (more specifically, its log mel spectrogram), runs forward pass of the encoder
// part of the transformer model and returns the encoded features
//
// - model: the model
// - n_threads: number of threads to use
// - mel_offset: offset in the mel spectrogram (i.e. audio offset)
//
static bool whisper_encode(
whisper_context & wctx,
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 = wctx.mel;
const auto & hparams = model.hparams;
const int n_ctx = wctx.exp_n_audio_ctx > 0 ? wctx.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 = wctx.buf_compute.size();
params.mem_buffer = wctx.buf_compute.data();
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_tensor * mel = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 2*n_ctx, n_mels);
assert(mel->type == GGML_TYPE_F32);
{
float * dst = (float *) mel->data;
memset(dst, 0, ggml_nbytes(mel));
const int i0 = std::min(mel_offset, mel_inp.n_len);
const int i1 = std::min(mel_offset + 2*n_ctx, mel_inp.n_len);
for (int j = 0; j < mel_inp.n_mel; ++j) {
for (int i = i0; i < i1; ++i) {
dst[j*2*n_ctx + (i - i0)] = mel_inp.data[j*mel_inp.n_len + i];
}
}
}
struct ggml_tensor * cur;
// convolution + gelu
{
cur = ggml_conv_1d_1s(ctx0, model.e_conv_1_w, mel);
cur = ggml_add(ctx0,
ggml_repeat(ctx0,
model.e_conv_1_b,
cur),
cur);
cur = ggml_gelu(ctx0, cur);
cur = ggml_conv_1d_2s(ctx0, model.e_conv_2_w, cur);
cur = ggml_add(ctx0,
ggml_repeat(ctx0,
model.e_conv_2_b,
cur),
cur);
cur = ggml_gelu(ctx0, cur);
}
// ===================================================================
// 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];
// create separate context for each layer to reduce memory usage
struct ggml_init_params paramsL;
paramsL.mem_size = wctx.buf_compute_layer.size();
paramsL.mem_buffer = wctx.buf_compute_layer.data();
struct ggml_context * ctxL = ggml_init(paramsL);
// norm
{
cur = ggml_norm(ctxL, inpL);
// cur = ln_0_w*cur + ln_0_b
cur = ggml_add(ctxL,
ggml_mul(ctxL,
ggml_repeat(ctxL, layer.attn_ln_0_w, cur),
cur),
ggml_repeat(ctxL, layer.attn_ln_0_b, cur));
}
// self-attention
{
struct ggml_tensor * Qcur = ggml_mul_mat(ctxL,
layer.attn_q_w,
cur);
Qcur = ggml_add(ctxL,
ggml_repeat(ctxL,
layer.attn_q_b,
Qcur),
Qcur);
//Qcur = ggml_scale(ctxL, Qcur, ggml_new_f32(ctxL, pow(float(n_state)/n_head, -0.25)));
// note: no bias for Key
struct ggml_tensor * Kcur = ggml_mul_mat(ctxL,
layer.attn_k_w,
cur);
//Kcur = ggml_scale(ctxL, Kcur, ggml_new_f32(ctxL, pow(float(n_state)/n_head, -0.25)));
struct ggml_tensor * Vcur = ggml_mul_mat(ctxL,
layer.attn_v_w,
cur);
Vcur = ggml_add(ctxL,
ggml_repeat(ctxL,
layer.attn_v_b,
Vcur),
Vcur);
// ------
#ifdef WHISPER_USE_FLASH_ATTN
struct ggml_tensor * Q =
ggml_permute(ctxL,
ggml_cpy(ctxL,
Qcur,
ggml_new_tensor_3d(ctxL, wctx.wtype, n_state/n_head, n_head, n_ctx)),
0, 2, 1, 3);
struct ggml_tensor * K =
ggml_permute(ctxL,
ggml_cpy(ctxL,
Kcur,
ggml_new_tensor_3d(ctxL, wctx.wtype, n_state/n_head, n_head, n_ctx)),
0, 2, 1, 3);
struct ggml_tensor * V =
ggml_cpy(ctxL,
ggml_permute(ctxL,
ggml_reshape_3d(ctxL,
Vcur,
n_state/n_head, n_head, n_ctx),
1, 2, 0, 3),
ggml_new_tensor_3d(ctxL, wctx.wtype, n_ctx, n_state/n_head, n_head)
);
struct ggml_tensor * KQV = ggml_flash_attn(ctxL, Q, K, V, false);
#else
struct ggml_tensor * Q =
ggml_permute(ctxL,
ggml_cpy(ctxL,
Qcur,
ggml_new_tensor_3d(ctxL, GGML_TYPE_F32, n_state/n_head, n_head, n_ctx)),
0, 2, 1, 3);
struct ggml_tensor * K =
ggml_permute(ctxL,
ggml_cpy(ctxL,
Kcur,
ggml_new_tensor_3d(ctxL, wctx.wtype, n_state/n_head, n_head, n_ctx)),
0, 2, 1, 3);
// K * Q
struct ggml_tensor * KQ = ggml_mul_mat(ctxL, K, Q);
struct ggml_tensor * KQ_scaled =
ggml_scale(ctxL,
KQ,
ggml_new_f32(ctxL, 1.0f/sqrt(float(n_state)/n_head))
);
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctxL, KQ_scaled);
//struct ggml_tensor * V_trans =
// ggml_permute(ctxL,
// ggml_cpy(ctxL,
// Vcur,
// ggml_new_tensor_3d(ctxL, wctx.wtype, n_state/n_head, n_head, n_ctx)),
// 1, 2, 0, 3);
//struct ggml_tensor * KQV = ggml_mul_mat(ctxL, V_trans, KQ_soft_max);
struct ggml_tensor * V =
ggml_cpy(ctxL,
ggml_permute(ctxL,
ggml_reshape_3d(ctxL,
Vcur,
n_state/n_head, n_head, n_ctx),
0, 2, 1, 3),
ggml_new_tensor_3d(ctxL, wctx.wtype, n_state/n_head, n_ctx, n_head)
);
struct ggml_tensor * KQV = ggml_mul_mat(ctxL, ggml_transpose(ctxL, V), KQ_soft_max);
#endif
struct ggml_tensor * KQV_merged = ggml_permute(ctxL, KQV, 0, 2, 1, 3);
cur = ggml_cpy(ctxL,
KQV_merged,
ggml_new_tensor_2d(ctxL, GGML_TYPE_F32, n_state, n_ctx));
}
// projection
{
cur = ggml_mul_mat(ctxL,
layer.attn_ln_1_w,
cur);
cur = ggml_add(ctxL,
ggml_repeat(ctxL, layer.attn_ln_1_b, cur),
cur);
}
// add the input
cur = ggml_add(ctxL, cur, inpL);
struct ggml_tensor * inpFF = cur;
// feed-forward network
{
// norm
{
cur = ggml_norm(ctxL, inpFF);
// cur = mlp_ln_w*cur + mlp_ln_b
cur = ggml_add(ctxL,
ggml_mul(ctxL,
ggml_repeat(ctxL, layer.mlp_ln_w, cur),
cur),
ggml_repeat(ctxL, layer.mlp_ln_b, cur));
}
#ifdef WHISPER_USE_FLASH_FF
cur = ggml_flash_ff(ctxL,
ggml_cpy(ctxL, cur, ggml_new_tensor_2d(ctxL, wctx.wtype, n_state, N)),
layer.mlp_0_w, layer.mlp_0_b, layer.mlp_1_w, layer.mlp_1_b);
#else
// fully connected
cur = ggml_mul_mat(ctxL,
layer.mlp_0_w,
cur);
cur = ggml_add(ctxL,
ggml_repeat(ctxL, layer.mlp_0_b, cur),
cur);
// GELU activation
cur = ggml_gelu(ctxL, cur);
// projection
cur = ggml_mul_mat(ctxL,
layer.mlp_1_w,
cur);
cur = ggml_add(ctxL,
ggml_repeat(ctxL, layer.mlp_1_b, cur),
cur);
#endif
}
// output from this layer
struct ggml_tensor * inpO = ggml_add(ctxL, cur, inpFF);
{
struct ggml_cgraph gf = {};
gf.n_threads = n_threads;
ggml_build_forward_expand(&gf, inpO);
ggml_graph_compute (ctxL, &gf);
//ggml_graph_print(&gf);
}
// TODO: this is a hack to have per-layer computation graphs - need to come up with something better
// input for next layer (inpO -> inpL)
memcpy(inpL->data, inpO->data, ggml_nbytes(inpL));
inpL->op = GGML_OP_NONE;
inpL->src0 = nullptr;
inpL->src1 = nullptr;
//printf("%s: - used_mem(%d) = %f MB\n", __func__, il, ggml_used_mem(ctxL)/1024.0/1024.0);
ggml_free(ctxL);
}
cur = inpL;
// norm
{
cur = ggml_norm(ctx0, cur);
// cur = ln_f_g*cur + ln_f_b
cur = ggml_add(ctx0,
ggml_mul(ctx0,
ggml_repeat(ctx0, model.e_ln_w, cur),
cur),
ggml_repeat(ctx0, model.e_ln_b, cur));
}
// run the computation
{
struct ggml_cgraph gf = {};
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];
struct ggml_tensor * Kcross = ggml_mul_mat(ctx0,
layer.cross_attn_k_w,
cur);
Kcross = ggml_scale(ctx0, Kcross, ggml_new_f32(ctx0, pow(float(n_state)/n_head, -0.25)));
struct ggml_tensor * Vcross = ggml_mul_mat(ctx0,
layer.cross_attn_v_w,
cur);
Vcross = ggml_add(ctx0,
ggml_repeat(ctx0,
layer.cross_attn_v_b,
Vcross),
Vcross);
//struct ggml_tensor * k = ggml_view_1d(ctx0, wctx.kv_cross.k, n_state*n_ctx, (ggml_element_size(wctx.kv_cross.k)*n_state)*(il*hparams.n_audio_ctx + iter*n_ctx));
//struct ggml_tensor * v = ggml_view_1d(ctx0, wctx.kv_cross.v, n_state*n_ctx, (ggml_element_size(wctx.kv_cross.v)*n_state)*(il*hparams.n_audio_ctx + iter*n_ctx));
struct ggml_tensor * k = ggml_view_1d(ctx0, wctx.kv_cross.k, n_state*n_ctx, (ggml_element_size(wctx.kv_cross.k)*n_state)*(il*n_ctx));
struct ggml_tensor * v = ggml_view_1d(ctx0, wctx.kv_cross.v, n_state*n_ctx, (ggml_element_size(wctx.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\n", __func__, ggml_used_mem(ctx0)/1024.0/1024.0);
ggml_free(ctx0);
wctx.t_encode_us += ggml_time_us() - t_start_us;
return true;
}
// evaluate the decoder
//
// given text prompt + audio features -> predicts the probabilities for the next token
//
// - model: the model
// - n_threads: number of threads to use
// - 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(
whisper_context & wctx,
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 = wctx.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 = wctx.exp_n_audio_ctx > 0 ? wctx.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 = wctx.buf_compute.size();
params.mem_buffer = wctx.buf_compute.data();
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N);
memcpy(embd->data, 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;
}
// token encoding + position encoding
struct ggml_tensor * cur =
ggml_add(ctx0,
ggml_get_rows(ctx0, model.d_te, embd),
ggml_get_rows(ctx0, model.d_pe, position));
struct ggml_tensor * inpL = cur;
for (int il = 0; il < n_layer; ++il) {
const auto & layer = model.layers_decoder[il];
struct ggml_init_params paramsL;
paramsL.mem_size = wctx.buf_compute_layer.size();
paramsL.mem_buffer = wctx.buf_compute_layer.data();
struct ggml_context * ctxL = ggml_init(paramsL);
struct ggml_cgraph gf = {};
gf.n_threads = n_threads;
// norm
{
cur = ggml_norm(ctxL, inpL);
// cur = ln_0_w*cur + ln_0_b
cur = ggml_add(ctxL,
ggml_mul(ctxL,
ggml_repeat(ctxL, layer.attn_ln_0_w, cur),
cur),
ggml_repeat(ctxL, layer.attn_ln_0_b, cur));
}
// self-attention
{
struct ggml_tensor * Qcur = ggml_mul_mat(ctxL,
layer.attn_q_w,
cur);
Qcur = ggml_add(ctxL,
ggml_repeat(ctxL,
layer.attn_q_b,
Qcur),
Qcur);
Qcur = ggml_scale(ctxL, Qcur, ggml_new_f32(ctxL, pow(float(n_state)/n_head, -0.25)));
// note: no bias for Key
struct ggml_tensor * Kcur = ggml_mul_mat(ctxL,
layer.attn_k_w,
cur);
Kcur = ggml_scale(ctxL, Kcur, ggml_new_f32(ctxL, pow(float(n_state)/n_head, -0.25)));
struct ggml_tensor * Vcur = ggml_mul_mat(ctxL,
layer.attn_v_w,
cur);
Vcur = ggml_add(ctxL,
ggml_repeat(ctxL,
layer.attn_v_b,
Vcur),
Vcur);
// store key and value to memory
{
struct ggml_tensor * k = ggml_view_1d(ctxL, 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(ctxL, 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(ctxL, Kcur, k));
ggml_build_forward_expand(&gf, ggml_cpy(ctxL, Vcur, v));
}
// ------
struct ggml_tensor * Q =
ggml_permute(ctxL,
ggml_cpy(ctxL,
Qcur,
ggml_new_tensor_3d(ctxL, GGML_TYPE_F32, n_state/n_head, n_head, N)),
0, 2, 1, 3);
struct ggml_tensor * K =
ggml_permute(ctxL,
ggml_reshape_3d(ctxL,
ggml_view_1d(ctxL, 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);
// K * Q
struct ggml_tensor * KQ = ggml_mul_mat(ctxL, K, Q);
//struct ggml_tensor * KQ_scaled =
// ggml_scale(ctxL,
// KQ,
// ggml_new_f32(ctxL, 1.0f/sqrt(float(n_state)/n_head))
// );
struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctxL, KQ, n_past);
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctxL, KQ_masked);
struct ggml_tensor * V_trans =
ggml_permute(ctxL,
ggml_reshape_3d(ctxL,
ggml_view_1d(ctxL, 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);
struct ggml_tensor * KQV = ggml_mul_mat(ctxL, V_trans, KQ_soft_max);
struct ggml_tensor * KQV_merged = ggml_permute(ctxL, KQV, 0, 2, 1, 3);
cur = ggml_cpy(ctxL,
KQV_merged,
ggml_new_tensor_2d(ctxL, GGML_TYPE_F32, n_state, N));
}
{
cur = ggml_mul_mat(ctxL,
layer.attn_ln_1_w,
cur);
cur = ggml_add(ctxL,
ggml_repeat(ctxL, layer.attn_ln_1_b, cur),
cur);
}
// add the input
struct ggml_tensor * inpCA = ggml_add(ctxL, cur, inpL);
// norm
{
cur = ggml_norm(ctxL, inpCA); // note: we use inpCA here
// cur = ln_0_w*cur + ln_0_b
cur = ggml_add(ctxL,
ggml_mul(ctxL,
ggml_repeat(ctxL, layer.cross_attn_ln_0_w, cur),
cur),
ggml_repeat(ctxL, layer.cross_attn_ln_0_b, cur));
}
// cross-attention
{
struct ggml_tensor * Qcur = ggml_mul_mat(ctxL,
layer.cross_attn_q_w,
cur);
Qcur = ggml_add(ctxL,
ggml_repeat(ctxL,
layer.cross_attn_q_b,
Qcur),
Qcur);
Qcur = ggml_scale(ctxL, Qcur, ggml_new_f32(ctxL, pow(float(n_state)/n_head, -0.25)));
// Kcross is already scaled
struct ggml_tensor * Kcross =
ggml_reshape_3d(ctxL,
ggml_view_1d(ctxL, wctx.kv_cross.k, M*n_state, il*M*ggml_element_size(wctx.kv_cross.k)*n_state),
n_state/n_head, n_head, M);
struct ggml_tensor * Vcross =
ggml_reshape_3d(ctxL,
ggml_view_1d(ctxL, wctx.kv_cross.v, M*n_state, il*M*ggml_element_size(wctx.kv_cross.v)*n_state),
n_state/n_head, n_head, M);
// ------
struct ggml_tensor * Q =
ggml_permute(ctxL,
ggml_cpy(ctxL,
Qcur,
ggml_new_tensor_3d(ctxL, GGML_TYPE_F32, n_state/n_head, n_head, N)),
0, 2, 1, 3);
struct ggml_tensor * K = ggml_permute(ctxL, Kcross, 0, 2, 1, 3);
// K * Q
struct ggml_tensor * KQ = ggml_mul_mat(ctxL, K, Q);
//struct ggml_tensor * KQ_scaled =
// ggml_scale(ctxL,
// KQ,
// ggml_new_f32(ctxL, 1.0f/sqrt(float(n_state)/n_head))
// );
// no masking for cross-attention
//struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctxL, KQ_scaled, n_past);
struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctxL, KQ);
struct ggml_tensor * V_trans = ggml_permute(ctxL, Vcross, 1, 2, 0, 3);
struct ggml_tensor * KQV = ggml_mul_mat(ctxL, V_trans, KQ_soft_max);
struct ggml_tensor * KQV_merged = ggml_permute(ctxL, KQV, 0, 2, 1, 3);
// cur = KQV_merged.contiguous().view(n_state, N)
cur = ggml_cpy(ctxL,
KQV_merged,
ggml_new_tensor_2d(ctxL, GGML_TYPE_F32, n_state, N));
}
// projection
{
cur = ggml_mul_mat(ctxL,
layer.cross_attn_ln_1_w,
cur);
cur = ggml_add(ctxL,
ggml_repeat(ctxL, layer.cross_attn_ln_1_b, cur),
cur);
}
// add the input
cur = ggml_add(ctxL, cur, inpCA);
struct ggml_tensor * inpFF = cur;
// feed-forward network
{
// norm
{
cur = ggml_norm(ctxL, inpFF);
// cur = mlp_ln_w*cur + mlp_ln_b
cur = ggml_add(ctxL,
ggml_mul(ctxL,
ggml_repeat(ctxL, layer.mlp_ln_w, cur),
cur),
ggml_repeat(ctxL, layer.mlp_ln_b, cur));
}
// fully connected
cur = ggml_mul_mat(ctxL,
layer.mlp_0_w,
cur);
cur = ggml_add(ctxL,
ggml_repeat(ctxL, layer.mlp_0_b, cur),
cur);
// GELU activation
cur = ggml_gelu(ctxL, cur);
// projection
cur = ggml_mul_mat(ctxL,
layer.mlp_1_w,
cur);
cur = ggml_add(ctxL,
ggml_repeat(ctxL, layer.mlp_1_b, cur),
cur);
}
// output from this layer
struct ggml_tensor * inpO = ggml_add(ctxL, cur, inpFF);
{
ggml_build_forward_expand(&gf, inpO);
ggml_graph_compute (ctxL, &gf);
//ggml_graph_print(&gf);
}
// TODO: this is a hack to have per-layer computation graphs - need to come up with something better
// input for next layer (inpO -> inpL)
memcpy(inpL->data, inpO->data, ggml_nbytes(inpL));
inpL->op = GGML_OP_NONE;
inpL->src0 = nullptr;
inpL->src1 = nullptr;
if (N > 1) {
//printf("%s: - used_mem(%d) = %f MB\n", __func__, il, ggml_used_mem(ctxL)/1024.0/1024.0);
}
ggml_free(ctxL);
}
cur = inpL;
// norm
{
cur = ggml_norm(ctx0, cur);
cur = ggml_add(ctx0,
ggml_mul(ctx0,
ggml_repeat(ctx0, model.d_ln_w, cur),
cur),
ggml_repeat(ctx0, model.d_ln_b, cur));
}
struct ggml_tensor * logits = ggml_mul_mat(ctx0, model.d_te, cur);
// run the computation
{
struct ggml_cgraph gf = {};
gf.n_threads = n_threads;
ggml_build_forward_expand(&gf, logits);
ggml_graph_compute (ctx0, &gf);
}
logits_out.resize(N*n_vocab);
memcpy(logits_out.data(), ggml_get_data(logits), sizeof(float)*N*n_vocab);
if (N > 1) {
//const float mem_per_token = ggml_used_mem(ctx0)/1024.0/1024.0/N;
//printf("%s: used_mem = %f MB / %f per token\n", __func__, ggml_used_mem(ctx0)/1024.0/1024.0, mem_per_token);
//printf("%s: max mem = %f MB\n", __func__, mem_per_token*model.hparams.n_text_ctx);
}
ggml_free(ctx0);
wctx.t_decode_us += ggml_time_us() - t_start_us;
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_context & wctx,
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;
}
wctx.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_context * whisper_init_from_file(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(&loader);
}
struct whisper_context * whisper_init_from_buffer(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(&loader);
}
struct whisper_context * whisper_init(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;
}
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;
}
if (ctx->kv_cross.ctx) {
ggml_free(ctx->kv_cross.ctx);
}
for (int i = 0; i < WHISPER_MAX_DECODERS; ++i) {
if (ctx->decoders[i].kv_self.ctx) {
ggml_free(ctx->decoders[i].kv_self.ctx);
}
}
delete ctx;
}
}
int whisper_pcm_to_mel(struct whisper_context * ctx, const float * samples, int n_samples, int n_threads) {
if (!log_mel_spectrogram(*ctx, samples, n_samples, WHISPER_SAMPLE_RATE, WHISPER_N_FFT, WHISPER_HOP_LENGTH, WHISPER_N_MEL, n_threads, ctx->model.filters, false, ctx->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) {
if (!log_mel_spectrogram(*ctx, samples, n_samples, WHISPER_SAMPLE_RATE, 2*WHISPER_N_FFT, 2*WHISPER_HOP_LENGTH, WHISPER_N_MEL, n_threads, ctx->model.filters, true, ctx->mel)) {
fprintf(stderr, "%s: failed to compute mel spectrogram\n", __func__);
return -1;
}
return 0;
}
int whisper_set_mel(
struct whisper_context * ctx,
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;
}
ctx->mel.n_len = n_len;
ctx->mel.n_mel = n_mel;
ctx->mel.data.resize(n_len*n_mel);
memcpy(ctx->mel.data.data(), data, n_len*n_mel*sizeof(float));
return 0;
}
int whisper_encode(struct whisper_context * ctx, int offset, int n_threads) {
if (!whisper_encode(*ctx, offset, 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 context
const int selected_decoder_id = 0;
if (!whisper_decode(*ctx, ctx->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(
struct whisper_context * ctx,
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 >= ctx->mel.n_len) {
fprintf(stderr, "%s: offset %dms is past the end of the audio (%dms)\n", __func__, offset_ms, ctx->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(ctx, prompt.data(), prompt.size(), 0, n_threads) != 0) {
fprintf(stderr, "%s: failed to decode\n", __func__);
return -7;
}
auto & logits_id = ctx->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(ctx->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_n_len(struct whisper_context * ctx) {
return ctx->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->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);
fprintf(stderr, "%s: mel time = %8.2f ms\n", __func__, ctx->t_mel_us/1000.0f);
fprintf(stderr, "%s: sample time = %8.2f ms\n", __func__, ctx->t_sample_us/1000.0f);
fprintf(stderr, "%s: encode time = %8.2f ms / %.2f ms per layer\n", __func__, ctx->t_encode_us/1000.0f, ctx->t_encode_us/1000.0f/ctx->model.hparams.n_audio_layer);
fprintf(stderr, "%s: decode time = %8.2f ms / %.2f ms per layer\n", __func__, ctx->t_decode_us/1000.0f, ctx->t_decode_us/1000.0f/ctx->model.hparams.n_text_layer);
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) {
ctx->t_sample_us = 0;
ctx->t_encode_us = 0;
ctx->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 =*/ WHISPER_SAMPLING_GREEDY,
/*.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 =*/ false,
/*.single_segment =*/ false,
/*.print_special =*/ false,
/*.print_progress =*/ true,
/*.print_realtime =*/ false,
/*.print_timestamps =*/ true,
/*.token_timestamps =*/ false,
/*.thold_pt =*/ 0.01f,
/*.thold_ptsum =*/ 0.01f,
/*.max_len =*/ 0,
/*.max_tokens =*/ 0,
/*.speed_up =*/ false,
/*.audio_ctx =*/ 0,
/*.prompt_tokens =*/ nullptr,
/*.prompt_n_tokens =*/ 0,
/*.language =*/ "en",
/*.suppress_blank =*/ true,
/*.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,
};
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,
int i_segment,
float thold_pt,
float thold_ptsum);
// wrap the last segment to max_len characters
// returns the number of new segments
static int whisper_wrap_segment(struct whisper_context & ctx, int max_len) {
auto segment = ctx.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) {
// split here
ctx.result_all.back().text = std::move(text);
ctx.result_all.back().t1 = token.t0;
ctx.result_all.back().tokens.resize(i);
ctx.result_all.push_back({});
ctx.result_all.back().t0 = token.t0;
ctx.result_all.back().t1 = segment.t1;
// add tokens [i, end] to the new segment
ctx.result_all.back().tokens.insert(
ctx.result_all.back().tokens.end(),
segment.tokens.begin() + i,
segment.tokens.end());
acc = 0;
text = "";
segment = ctx.result_all.back();
i = -1;
res++;
} else {
acc += cur;
text += txt;
}
}
ctx.result_all.back().text = std::move(text);
return res;
}
// process the logits for the selected decoder
// - applies logit filters
// - computes logprobs and probs
static void whisper_process_logits(
const struct whisper_context & ctx,
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(), ctx.logits.data() + (ctx.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;
// 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;
}
}
// 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(
const whisper_context & ctx,
const whisper_decoder & decoder,
bool best) {
whisper_token_data result = {
0, 0, 0.0f, 0.0f, 0.0f, 0.0f, -1, -1, 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(ctx.rng);
result.p = probs[result.id];
result.plog = logprobs[result.id];
}
if (result.id >= vocab.token_beg) {
result.tid = result.id;
result.pt = result.p;
}
return result;
}
static std::vector<whisper_token_data> whisper_sample_token_topk(
whisper_context & ctx,
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 = ctx.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;
float pt;
float ptsum;
{
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;
}
}
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(
struct whisper_context * ctx,
struct whisper_full_params params,
const float * samples,
int n_samples) {
// clear old results
auto & result_all = ctx->result_all;
result_all.clear();
// compute log mel spectrogram
if (params.speed_up) {
if (whisper_pcm_to_mel_phase_vocoder(ctx, 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(ctx, 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(ctx, 0, params.n_threads, probs.data());
if (lang_id < 0) {
fprintf(stderr, "%s: failed to auto-detect language\n", __func__);
return -3;
}
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) {
ctx->t_beg = 0;
ctx->t_last = 0;
ctx->tid_last = 0;
ctx->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(ctx) : 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 = ctx->decoders[j];
if (decoder.kv_self.ctx == nullptr) {
decoder.kv_self = ctx->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(ctx->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 = ctx->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;
}
ctx->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);
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, 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(*ctx, 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 = ctx->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) {
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(*ctx, ctx->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, params, ctx->decoders[0], t_cur);
ctx->decoders[0].kv_self.n += prompt.size();
for (int j = 1; j < n_decoders_cur; ++j) {
auto & decoder = ctx->decoders[j];
memcpy(decoder.kv_self.k->data, ctx->decoders[0].kv_self.k->data, ggml_nbytes(decoder.kv_self.k));
memcpy(decoder.kv_self.v->data, ctx->decoders[0].kv_self.v->data, ggml_nbytes(decoder.kv_self.v));
decoder.kv_self.n += prompt.size();
memcpy(decoder.probs.data(), ctx->decoders[0].probs.data(), decoder.probs.size()*sizeof(decoder.probs[0]));
memcpy(decoder.logits.data(), ctx->decoders[0].logits.data(), decoder.logits.size()*sizeof(decoder.logits[0]));
memcpy(decoder.logprobs.data(), ctx->decoders[0].logprobs.data(), decoder.logprobs.size()*sizeof(decoder.logprobs[0]));
}
ctx->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 = ctx->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 = ctx->decoders[j];
if (decoder.completed || decoder.failed) {
continue;
}
switch (params.strategy) {
case whisper_sampling_strategy::WHISPER_SAMPLING_GREEDY:
{
if (t_cur < 1e-6f) {
decoder.sequence.tokens.push_back(whisper_sample_token(*ctx, decoder, true));
} else {
decoder.sequence.tokens.push_back(whisper_sample_token(*ctx, decoder, false));
}
decoder.sequence.sum_logprobs_all += decoder.sequence.tokens.back().plog;
} break;
case whisper_sampling_strategy::WHISPER_SAMPLING_BEAM_SEARCH:
{
const auto tokens_new = whisper_sample_token_topk(*ctx, decoder, params.beam_search.beam_size);
for (const auto & token : tokens_new) {
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;
});
int cur_c = 0;
for (int j = 0; j < n_decoders_cur; ++j) {
auto & decoder = ctx->decoders[j];
if (decoder.completed || decoder.failed) {
continue;
}
auto & cur = beam_candidates[cur_c++];
while (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 = ctx->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 = ctx->decoders[j];
if (decoder.completed || decoder.failed) {
continue;
}
completed_all = false;
}
if (completed_all) {
break;
}
}
ctx->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 = ctx->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(*ctx, 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, params, decoder, t_cur);
++decoder.kv_self.n;
ctx->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 = ctx->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 > 8 && 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;
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 = ctx->decoders[best_decoder_id];
if (decoder.failed || decoder.sequence.avg_logprobs < params.logprob_thold) {
success = false;
}
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 = ctx->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, result_all.size() - 1, params.thold_pt, params.thold_ptsum);
if (params.max_len > 0) {
n_new = whisper_wrap_segment(*ctx, params.max_len);
}
}
if (params.new_segment_callback) {
params.new_segment_callback(ctx, 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, result_all.size() - 1, params.thold_pt, params.thold_ptsum);
if (params.max_len > 0) {
n_new = whisper_wrap_segment(*ctx, params.max_len);
}
}
if (params.new_segment_callback) {
params.new_segment_callback(ctx, 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_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 contexts for each thread
std::vector<struct whisper_context> ctxs(n_processors - 1);
for (int i = 0; i < n_processors - 1; ++i) {
auto & ctx_p = ctxs[i];
ctx_p = *ctx;
ctx_p.logits.reserve(ctx_p.vocab.n_vocab*ctx_p.model.hparams.n_text_ctx);
ctx_p.logits_id.reserve(ctx_p.vocab.n_vocab);
if (!kv_cache_reinit(ctx_p.kv_cross)) {
fprintf(stderr, "%s: kv_cache_reinit() failed for cross-attention, processor %d\n", __func__, i);
return false;
}
// TAGS: WHISPER_DECODER_INIT
for (int j = 0; j < WHISPER_MAX_DECODERS; ++j) {
if (ctx_p.decoders[j].kv_self.ctx && !kv_cache_reinit(ctx_p.decoders[j].kv_self)) {
fprintf(stderr, "%s: kv_cache_reinit() failed for self-attention, decoder %d, processor %d\n", __func__, j, i);
return false;
}
ctx_p.decoders[j].sequence.tokens.reserve(ctx_p.model.hparams.n_text_ctx);
ctx_p.decoders[j].probs.reserve (ctx_p.vocab.n_vocab);
ctx_p.decoders[j].logits.reserve (ctx_p.vocab.n_vocab);
ctx_p.decoders[j].logprobs.reserve(ctx_p.vocab.n_vocab);
}
}
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) {
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, &ctxs[i], std::move(params_cur), samples + start_samples, n_samples_cur);
}
{
auto params_cur = params;
ret = whisper_full(ctx, 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 ctx->result_all
for (int i = 0; i < n_processors - 1; ++i) {
auto & results_i = ctxs[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->result_all.empty()) {
result.t0 = std::max(result.t0, ctx->result_all.back().t1);
}
ctx->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, 1, params.new_segment_callback_user_data);
}
}
ctx->t_mel_us += ctxs[i].t_mel_us;
ctx->t_sample_us += ctxs[i].t_sample_us;
ctx->t_encode_us += ctxs[i].t_encode_us;
ctx->t_decode_us += ctxs[i].t_decode_us;
kv_cache_free(ctx->kv_cross);
for (int j = 0; j < WHISPER_MAX_DECODERS; ++j) {
kv_cache_free(ctx->decoders[j].kv_self);
}
}
// average the timings
ctx->t_mel_us /= n_processors;
ctx->t_sample_us /= n_processors;
ctx->t_encode_us /= n_processors;
ctx->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(struct whisper_context * ctx) {
return ctx->result_all.size();
}
int64_t whisper_full_get_segment_t0(struct whisper_context * ctx, int i_segment) {
return ctx->result_all[i_segment].t0;
}
int64_t whisper_full_get_segment_t1(struct whisper_context * ctx, int i_segment) {
return ctx->result_all[i_segment].t1;
}
const char * whisper_full_get_segment_text(struct whisper_context * ctx, int i_segment) {
return ctx->result_all[i_segment].text.c_str();
}
int whisper_full_n_tokens(struct whisper_context * ctx, int i_segment) {
return ctx->result_all[i_segment].tokens.size();
}
const char * whisper_full_get_token_text(struct whisper_context * ctx, int i_segment, int i_token) {
return ctx->vocab.id_to_token[ctx->result_all[i_segment].tokens[i_token].id].c_str();
}
whisper_token whisper_full_get_token_id(struct whisper_context * ctx, int i_segment, int i_token) {
return ctx->result_all[i_segment].tokens[i_token].id;
}
struct whisper_token_data whisper_full_get_token_data(struct whisper_context * ctx, int i_segment, int i_token) {
return ctx->result_all[i_segment].tokens[i_token];
}
float whisper_full_get_token_p(struct whisper_context * ctx, int i_segment, int i_token) {
return ctx->result_all[i_segment].tokens[i_token].p;
}
// =================================================================================================
//
// 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,
int i_segment,
float thold_pt,
float thold_ptsum) {
auto & segment = ctx.result_all[i_segment];
auto & tokens = segment.tokens;
const int n_samples = ctx.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 = ctx.t_beg;
auto & t_last = ctx.t_last;
auto & tid_last = ctx.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 += ctx.energy[k];
}
const float thold = 0.5*sum/ns;
{
int k = s0;
if (ctx.energy[k] > thold && j > 0) {
while (k > 0 && ctx.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 (ctx.energy[k] < thold && k < s1) {
k++;
}
s0 = k;
tokens[j].t0 = sample_to_timestamp(k);
}
}
{
int k = s1;
if (ctx.energy[k] > thold) {
while (k < n_samples - 1 && ctx.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 (ctx.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;
// }
//}
}
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