--- /dev/null
+#include "whisper.h"
+
+// third-party utilities
+// use your favorite implementations
+#define DR_WAV_IMPLEMENTATION
+#include "dr_wav.h"
+
+#include <cmath>
+#include <fstream>
+#include <cstdio>
+#include <string>
+#include <thread>
+#include <vector>
+
+// Terminal color map. 10 colors grouped in ranges [0.0, 0.1, ..., 0.9]
+// Lowest is red, middle is yellow, highest is green.
+const std::vector<std::string> k_colors = {
+ "\033[38;5;196m", "\033[38;5;202m", "\033[38;5;208m", "\033[38;5;214m", "\033[38;5;220m",
+ "\033[38;5;226m", "\033[38;5;190m", "\033[38;5;154m", "\033[38;5;118m", "\033[38;5;82m",
+};
+
+// 500 -> 00:05.000
+// 6000 -> 01:00.000
+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);
+}
+
+// command-line parameters
+struct whisper_params {
+ int32_t seed = -1; // RNG seed, not used currently
+ int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
+ int32_t offset_t_ms = 0;
+ int32_t offset_n = 0;
+
+ bool verbose = false;
+ bool translate = false;
+ bool output_txt = false;
+ bool output_vtt = false;
+ bool output_srt = false;
+ bool print_special_tokens = false;
+ bool print_colors = false;
+ bool no_timestamps = false;
+
+ std::string language = "en";
+ std::string model = "models/ggml-base.en.bin";
+
+ std::vector<std::string> fname_inp = {};
+};
+
+void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
+
+bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
+ for (int i = 1; i < argc; i++) {
+ std::string arg = argv[i];
+
+ if (arg[0] != '-') {
+ params.fname_inp.push_back(arg);
+ continue;
+ }
+
+ if (arg == "-s" || arg == "--seed") {
+ params.seed = std::stoi(argv[++i]);
+ } else if (arg == "-t" || arg == "--threads") {
+ params.n_threads = std::stoi(argv[++i]);
+ } else if (arg == "-ot" || arg == "--offset-t") {
+ params.offset_t_ms = std::stoi(argv[++i]);
+ } else if (arg == "-on" || arg == "--offset-n") {
+ params.offset_n = std::stoi(argv[++i]);
+ } else if (arg == "-v" || arg == "--verbose") {
+ params.verbose = true;
+ } else if (arg == "--translate") {
+ params.translate = true;
+ } else if (arg == "-l" || arg == "--language") {
+ params.language = argv[++i];
+ if (whisper_lang_id(params.language.c_str()) == -1) {
+ fprintf(stderr, "error: unknown language '%s'\n", params.language.c_str());
+ whisper_print_usage(argc, argv, params);
+ exit(0);
+ }
+ } else if (arg == "-otxt" || arg == "--output-txt") {
+ params.output_txt = true;
+ } else if (arg == "-ovtt" || arg == "--output-vtt") {
+ params.output_vtt = true;
+ } else if (arg == "-osrt" || arg == "--output-srt") {
+ params.output_srt = true;
+ } else if (arg == "-ps" || arg == "--print_special") {
+ params.print_special_tokens = true;
+ } else if (arg == "-pc" || arg == "--print_colors") {
+ params.print_colors = true;
+ } else if (arg == "-nt" || arg == "--no_timestamps") {
+ params.no_timestamps = true;
+ } else if (arg == "-m" || arg == "--model") {
+ params.model = argv[++i];
+ } else if (arg == "-f" || arg == "--file") {
+ params.fname_inp.push_back(argv[++i]);
+ } else if (arg == "-h" || arg == "--help") {
+ whisper_print_usage(argc, argv, params);
+ exit(0);
+ } else {
+ fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
+ whisper_print_usage(argc, argv, params);
+ exit(0);
+ }
+ }
+
+ return true;
+}
+
+void whisper_print_usage(int argc, char ** argv, const whisper_params & params) {
+ fprintf(stderr, "\n");
+ fprintf(stderr, "usage: %s [options] file0.wav file1.wav ...\n", argv[0]);
+ fprintf(stderr, "\n");
+ fprintf(stderr, "options:\n");
+ fprintf(stderr, " -h, --help show this help message and exit\n");
+ fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n");
+ fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
+ fprintf(stderr, " -ot N, --offset-t N time offset in milliseconds (default: %d)\n", params.offset_t_ms);
+ fprintf(stderr, " -on N, --offset-n N segment index offset (default: %d)\n", params.offset_n);
+ fprintf(stderr, " -v, --verbose verbose output\n");
+ fprintf(stderr, " --translate translate from source language to english\n");
+ fprintf(stderr, " -otxt, --output-txt output result in a text file\n");
+ fprintf(stderr, " -ovtt, --output-vtt output result in a vtt file\n");
+ fprintf(stderr, " -osrt, --output-srt output result in a srt file\n");
+ fprintf(stderr, " -ps, --print_special print special tokens\n");
+ fprintf(stderr, " -pc, --print_colors print colors\n");
+ fprintf(stderr, " -nt, --no_timestamps do not print timestamps\n");
+ fprintf(stderr, " -l LANG, --language LANG spoken language (default: %s)\n", params.language.c_str());
+ fprintf(stderr, " -m FNAME, --model FNAME model path (default: %s)\n", params.model.c_str());
+ fprintf(stderr, " -f FNAME, --file FNAME input WAV file path\n");
+ fprintf(stderr, "\n");
+}
+
+void whisper_print_segment_callback(struct whisper_context * ctx, void * user_data) {
+ const whisper_params & params = *(whisper_params *) user_data;
+
+ const int n_segments = whisper_full_n_segments(ctx);
+
+ // print the last segment
+ const int i = n_segments - 1;
+ if (i == 0) {
+ printf("\n");
+ }
+
+ if (params.no_timestamps) {
+ if (params.print_colors) {
+ for (int j = 0; j < whisper_full_n_tokens(ctx, i); ++j) {
+ if (params.print_special_tokens == false) {
+ const whisper_token id = whisper_full_get_token_id(ctx, i, j);
+ if (id >= whisper_token_eot(ctx)) {
+ continue;
+ }
+ }
+
+ const char * text = whisper_full_get_token_text(ctx, i, j);
+ const float p = whisper_full_get_token_p (ctx, i, j);
+
+ const int col = std::max(0, std::min((int) k_colors.size(), (int) (std::pow(p, 3)*float(k_colors.size()))));
+
+ printf("%s%s%s", k_colors[col].c_str(), text, "\033[0m");
+ }
+ } else {
+ const char * text = whisper_full_get_segment_text(ctx, i);
+ printf("%s", text);
+ }
+ fflush(stdout);
+ } else {
+ const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
+ const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
+
+ if (params.print_colors) {
+ printf("[%s --> %s] ", to_timestamp(t0).c_str(), to_timestamp(t1).c_str());
+ for (int j = 0; j < whisper_full_n_tokens(ctx, i); ++j) {
+ if (params.print_special_tokens == false) {
+ const whisper_token id = whisper_full_get_token_id(ctx, i, j);
+ if (id >= whisper_token_eot(ctx)) {
+ continue;
+ }
+ }
+
+ const char * text = whisper_full_get_token_text(ctx, i, j);
+ const float p = whisper_full_get_token_p (ctx, i, j);
+
+ const int col = std::max(0, std::min((int) k_colors.size(), (int) (std::pow(p, 3)*float(k_colors.size()))));
+
+ printf("%s%s%s", k_colors[col].c_str(), text, "\033[0m");
+ }
+ printf("\n");
+ } else {
+ const char * text = whisper_full_get_segment_text(ctx, i);
+
+ printf("[%s --> %s] %s\n", to_timestamp(t0).c_str(), to_timestamp(t1).c_str(), text);
+ }
+ }
+}
+
+bool output_txt(struct whisper_context * ctx, const char * fname) {
+ std::ofstream fout(fname);
+ if (!fout.is_open()) {
+ fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
+ return false;
+ }
+
+ fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
+
+ const int n_segments = whisper_full_n_segments(ctx);
+ for (int i = 0; i < n_segments; ++i) {
+ const char * text = whisper_full_get_segment_text(ctx, i);
+ fout << text;
+ }
+
+ return true;
+}
+
+bool output_vtt(struct whisper_context * ctx, const char * fname) {
+ std::ofstream fout(fname);
+ if (!fout.is_open()) {
+ fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
+ return 9;
+ }
+
+ fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
+
+ fout << "WEBVTT\n\n";
+
+ const int n_segments = whisper_full_n_segments(ctx);
+ for (int i = 0; i < n_segments; ++i) {
+ const char * text = whisper_full_get_segment_text(ctx, i);
+ const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
+ const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
+
+ fout << to_timestamp(t0) << " --> " << to_timestamp(t1) << "\n";
+ fout << text << "\n\n";
+ }
+
+ return true;
+}
+
+bool output_srt(struct whisper_context * ctx, const char * fname, const whisper_params & params) {
+ std::ofstream fout(fname);
+ if (!fout.is_open()) {
+ fprintf(stderr, "%s: failed to open '%s' for writing\n", __func__, fname);
+ return false;
+ }
+
+ fprintf(stderr, "%s: saving output to '%s'\n", __func__, fname);
+
+ const int n_segments = whisper_full_n_segments(ctx);
+ for (int i = 0; i < n_segments; ++i) {
+ const char * text = whisper_full_get_segment_text(ctx, i);
+ const int64_t t0 = whisper_full_get_segment_t0(ctx, i);
+ const int64_t t1 = whisper_full_get_segment_t1(ctx, i);
+
+ fout << i + 1 + params.offset_n << "\n";
+ fout << to_timestamp(t0, true) << " --> " << to_timestamp(t1, true) << "\n";
+ fout << text << "\n\n";
+ }
+
+ return true;
+}
+
+int main(int argc, char ** argv) {
+ whisper_params params;
+
+ if (whisper_params_parse(argc, argv, params) == false) {
+ return 1;
+ }
+
+ if (params.seed < 0) {
+ params.seed = time(NULL);
+ }
+
+ if (params.fname_inp.empty()) {
+ fprintf(stderr, "error: no input files specified\n");
+ whisper_print_usage(argc, argv, params);
+ return 2;
+ }
+
+ // whisper init
+
+ struct whisper_context * ctx = whisper_init(params.model.c_str());
+
+ if (ctx == nullptr) {
+ fprintf(stderr, "error: failed to initialize whisper context\n");
+ return 3;
+ }
+
+ for (int f = 0; f < (int) params.fname_inp.size(); ++f) {
+ const auto fname_inp = params.fname_inp[f];
+
+ // WAV input
+ std::vector<float> pcmf32;
+ {
+ drwav wav;
+ if (!drwav_init_file(&wav, fname_inp.c_str(), NULL)) {
+ fprintf(stderr, "%s: failed to open WAV file '%s' - check your input\n", argv[0], fname_inp.c_str());
+ whisper_print_usage(argc, argv, {});
+ return 4;
+ }
+
+ if (wav.channels != 1 && wav.channels != 2) {
+ fprintf(stderr, "%s: WAV file '%s' must be mono or stereo\n", argv[0], fname_inp.c_str());
+ return 5;
+ }
+
+ if (wav.sampleRate != WHISPER_SAMPLE_RATE) {
+ fprintf(stderr, "%s: WAV file '%s' must be 16 kHz\n", argv[0], fname_inp.c_str());
+ return 6;
+ }
+
+ if (wav.bitsPerSample != 16) {
+ fprintf(stderr, "%s: WAV file '%s' must be 16-bit\n", argv[0], fname_inp.c_str());
+ return 7;
+ }
+
+ int n = wav.totalPCMFrameCount;
+
+ std::vector<int16_t> pcm16;
+ pcm16.resize(n*wav.channels);
+ drwav_read_pcm_frames_s16(&wav, n, pcm16.data());
+ drwav_uninit(&wav);
+
+ // convert to mono, float
+ pcmf32.resize(n);
+ if (wav.channels == 1) {
+ for (int i = 0; i < n; i++) {
+ pcmf32[i] = float(pcm16[i])/32768.0f;
+ }
+ } else {
+ for (int i = 0; i < n; i++) {
+ pcmf32[i] = float(pcm16[2*i] + pcm16[2*i + 1])/65536.0f;
+ }
+ }
+ }
+
+ // print system information
+ {
+ fprintf(stderr, "\n");
+ fprintf(stderr, "system_info: n_threads = %d / %d | %s\n", params.n_threads, std::thread::hardware_concurrency(), whisper_print_system_info());
+ }
+
+ // print some info about the processing
+ {
+ fprintf(stderr, "\n");
+ if (!whisper_is_multilingual(ctx)) {
+ if (params.language != "en" || params.translate) {
+ params.language = "en";
+ params.translate = false;
+ fprintf(stderr, "%s: WARNING: model is not multilingual, ignoring language and translation options\n", __func__);
+ }
+ }
+ fprintf(stderr, "%s: processing '%s' (%d samples, %.1f sec), %d threads, lang = %s, task = %s, timestamps = %d ...\n",
+ __func__, fname_inp.c_str(), int(pcmf32.size()), float(pcmf32.size())/WHISPER_SAMPLE_RATE, params.n_threads,
+ params.language.c_str(),
+ params.translate ? "translate" : "transcribe",
+ params.no_timestamps ? 0 : 1);
+
+ fprintf(stderr, "\n");
+ }
+
+
+ // run the inference
+ {
+ whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
+
+ wparams.print_realtime = false;
+ wparams.print_progress = false;
+ wparams.print_timestamps = !params.no_timestamps;
+ wparams.print_special_tokens = params.print_special_tokens;
+ wparams.translate = params.translate;
+ wparams.language = params.language.c_str();
+ wparams.n_threads = params.n_threads;
+ wparams.offset_ms = params.offset_t_ms;
+
+ // this callback is called on each new segment
+ if (!wparams.print_realtime) {
+ wparams.new_segment_callback = whisper_print_segment_callback;
+ wparams.new_segment_callback_user_data = ¶ms;
+ }
+
+ if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
+ fprintf(stderr, "%s: failed to process audio\n", argv[0]);
+ return 8;
+ }
+
+ printf("\n");
+
+ // output to text file
+ if (params.output_txt) {
+ const auto fname_txt = fname_inp + ".txt";
+ output_txt(ctx, fname_txt.c_str());
+ }
+
+ // output to VTT file
+ if (params.output_vtt) {
+ const auto fname_vtt = fname_inp + ".vtt";
+ output_vtt(ctx, fname_vtt.c_str());
+ }
+
+ // output to SRT file
+ if (params.output_srt) {
+ const auto fname_srt = fname_inp + ".srt";
+ output_srt(ctx, fname_srt.c_str(), params);
+ }
+ }
+ }
+
+ whisper_print_timings(ctx);
+ whisper_free(ctx);
+
+ return 0;
+}
std::vector<float> probs;
std::vector<float> logits;
- std::vector<whisper_token_data> tokens_cur;
std::vector<whisper_segment> result_all;
std::vector<whisper_token> prompt_past;
//
// see the convert-pt-to-ggml.py script for details
//
-bool whisper_model_load(const std::string & fname, whisper_context & wctx) {
+bool whisper_model_load(const std::string & fname, const int n_processors, whisper_context & wctx) {
fprintf(stderr, "%s: loading model from '%s'\n", __func__, fname.c_str());
auto & model = wctx.model;
ctx_size += n_text_layer*( n_text_state*ggml_type_size(GGML_TYPE_F32)); // cross_attn_ln_1_b
}
- ctx_size += n_text_layer*n_text_ctx*n_text_state*ggml_type_size(GGML_TYPE_F16); // memory_k
- ctx_size += n_text_layer*n_text_ctx*n_text_state*ggml_type_size(GGML_TYPE_F16); // memory_v
+ ctx_size += n_processors*n_text_layer*n_text_ctx*n_text_state*ggml_type_size(GGML_TYPE_F16); // memory_k
+ ctx_size += n_processors*n_text_layer*n_text_ctx*n_text_state*ggml_type_size(GGML_TYPE_F16); // memory_v
- ctx_size += n_text_layer*n_audio_ctx*n_text_state*ggml_type_size(GGML_TYPE_F16); // memory_cross_k
- ctx_size += n_text_layer*n_audio_ctx*n_text_state*ggml_type_size(GGML_TYPE_F16); // memory_cross_v
+ ctx_size += n_processors*n_text_layer*n_audio_ctx*n_text_state*ggml_type_size(GGML_TYPE_F16); // memory_cross_k
+ ctx_size += n_processors*n_text_layer*n_audio_ctx*n_text_state*ggml_type_size(GGML_TYPE_F16); // memory_cross_v
ctx_size += (15 + 15*n_audio_layer + 24*n_text_layer)*256; // object overhead
// key/value memory for the self-attention layer
{
const int n_mem = n_text_layer*n_text_ctx;
- const int n_elements = n_text_state*n_mem;
+ const int n_elements = n_text_state*n_mem*n_processors;
model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
const int n_audio_ctx = hparams.n_audio_ctx;
const int n_mem = n_text_layer*n_audio_ctx;
- const int n_elements = n_text_state*n_mem;
+ const int n_elements = n_text_state*n_mem*n_processors;
model.memory_cross_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
model.memory_cross_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, n_elements);
ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v) +
ggml_nbytes(model.memory_cross_k) + ggml_nbytes(model.memory_cross_v);
- fprintf(stderr, "%s: memory size = %8.2f MB \n", __func__, memory_size/1024.0/1024.0);
+ fprintf(stderr, "%s: memory size = %8.2f MB (%d processors)\n", __func__, memory_size/1024.0/1024.0, n_processors);
}
// load weights
bool whisper_encode(
whisper_context & wctx,
const int n_threads,
- const int mel_offset) {
+ const int mel_offset,
+ const int processor_id) {
const auto & model = wctx.model;
const auto & mel_inp = wctx.mel;
const auto & hparams = model.hparams;
Vcross),
Vcross);
- struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_cross_k, n_state*n_ctx, (ggml_element_size(model.memory_cross_k)*n_state)*(il*n_ctx));
- struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_cross_v, n_state*n_ctx, (ggml_element_size(model.memory_cross_v)*n_state)*(il*n_ctx));
+ const size_t offset_k = processor_id*(ggml_element_size(model.memory_cross_k)*n_state)*(model.hparams.n_text_layer*n_ctx);
+ const size_t offset_v = processor_id*(ggml_element_size(model.memory_cross_v)*n_state)*(model.hparams.n_text_layer*n_ctx);
+
+ struct ggml_tensor * k = ggml_view_1d(ctx0, model.memory_cross_k, n_state*n_ctx, offset_k + (ggml_element_size(model.memory_cross_k)*n_state)*(il*n_ctx));
+ struct ggml_tensor * v = ggml_view_1d(ctx0, model.memory_cross_v, n_state*n_ctx, offset_v + (ggml_element_size(model.memory_cross_v)*n_state)*(il*n_ctx));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Kcross, k));
ggml_build_forward_expand(&gf, ggml_cpy(ctx0, Vcross, v));
const int n_threads,
const whisper_token * tokens,
const int n_tokens,
- const int n_past) {
+ const int n_past,
+ const int processor_id) {
const auto & model = wctx.model;
const auto & hparams = model.hparams;
Vcur),
Vcur);
+ const size_t offset_k = processor_id*(ggml_element_size(model.memory_k)*n_state)*(n_layer*n_ctx);
+ const size_t offset_v = processor_id*(ggml_element_size(model.memory_v)*n_state)*(n_layer*n_ctx);
+
// store key and value to memory
{
- struct ggml_tensor * k = ggml_view_1d(ctxL, model.memory_k, N*n_state, (ggml_element_size(model.memory_k)*n_state)*(il*n_ctx + n_past));
- struct ggml_tensor * v = ggml_view_1d(ctxL, model.memory_v, N*n_state, (ggml_element_size(model.memory_v)*n_state)*(il*n_ctx + n_past));
+ struct ggml_tensor * k = ggml_view_1d(ctxL, model.memory_k, N*n_state, offset_k + (ggml_element_size(model.memory_k)*n_state)*(il*n_ctx + n_past));
+ struct ggml_tensor * v = ggml_view_1d(ctxL, model.memory_v, N*n_state, offset_v + (ggml_element_size(model.memory_v)*n_state)*(il*n_ctx + n_past));
ggml_build_forward_expand(&gf, ggml_cpy(ctxL, Kcur, k));
ggml_build_forward_expand(&gf, ggml_cpy(ctxL, Vcur, v));
struct ggml_tensor * K =
ggml_permute(ctxL,
ggml_reshape_3d(ctxL,
- ggml_view_1d(ctxL, model.memory_k, (n_past + N)*n_state, il*n_ctx*ggml_element_size(model.memory_k)*n_state),
+ ggml_view_1d(ctxL, model.memory_k, (n_past + N)*n_state, offset_k + il*n_ctx*ggml_element_size(model.memory_k)*n_state),
n_state/n_head, n_head, n_past + N),
0, 2, 1, 3);
struct ggml_tensor * V_trans =
ggml_permute(ctxL,
ggml_reshape_3d(ctxL,
- ggml_view_1d(ctxL, model.memory_v, (n_past + N)*n_state, il*n_ctx*ggml_element_size(model.memory_v)*n_state),
+ ggml_view_1d(ctxL, model.memory_v, (n_past + N)*n_state, offset_v + il*n_ctx*ggml_element_size(model.memory_v)*n_state),
n_state/n_head, n_head, n_past + N),
1, 2, 0, 3);
Qcur = ggml_scale(ctxL, Qcur, ggml_new_f32(ctxL, pow(float(n_state)/n_head, -0.25)));
+ const size_t offset_k = processor_id*(ggml_element_size(model.memory_cross_k)*n_state)*(n_layer*M);
+ const size_t offset_v = processor_id*(ggml_element_size(model.memory_cross_v)*n_state)*(n_layer*M);
+
// Kcross is already scaled
struct ggml_tensor * Kcross =
ggml_reshape_3d(ctxL,
- ggml_view_1d(ctxL, model.memory_cross_k, M*n_state, il*M*ggml_element_size(model.memory_cross_k)*n_state),
+ ggml_view_1d(ctxL, model.memory_cross_k, M*n_state, offset_k + il*M*ggml_element_size(model.memory_cross_k)*n_state),
n_state/n_head, n_head, M);
struct ggml_tensor * Vcross =
ggml_reshape_3d(ctxL,
- ggml_view_1d(ctxL, model.memory_cross_v, M*n_state, il*M*ggml_element_size(model.memory_cross_v)*n_state),
+ ggml_view_1d(ctxL, model.memory_cross_v, M*n_state, offset_v + il*M*ggml_element_size(model.memory_cross_v)*n_state),
n_state/n_head, n_head, M);
// ------
ctx->t_start_us = t_start_us;
- if (!whisper_model_load(path_model, *ctx)) {
+ if (!whisper_model_load(path_model, 1, *ctx)) {
+ fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, path_model);
+ return NULL;
+ }
+
+ ctx->t_load_us = ggml_time_us() - t_start_us;
+
+ return ctx;
+}
+
+struct whisper_context * whisper_init_parallel(const char * path_model, int n_processors) {
+ ggml_time_init();
+
+ whisper_context * ctx = new whisper_context;
+
+ const int64_t t_start_us = ggml_time_us();
+
+ ctx->t_start_us = t_start_us;
+
+ if (!whisper_model_load(path_model, n_processors, *ctx)) {
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, path_model);
return NULL;
}
int whisper_encode(struct whisper_context * ctx, int offset, int n_threads) {
const int64_t t_start_us = ggml_time_us();
- if (!whisper_encode(*ctx, n_threads, offset)) {
+ if (!whisper_encode(*ctx, n_threads, offset, 0)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
return -1;
}
int whisper_decode(struct whisper_context * ctx, const whisper_token * tokens, int n_tokens, int n_past, int n_threads) {
const int64_t t_start_us = ggml_time_us();
- if (!whisper_decode(*ctx, n_threads, tokens, n_tokens, n_past)) {
+ if (!whisper_decode(*ctx, n_threads, tokens, n_tokens, n_past, 0)) {
fprintf(stderr, "%s: failed to eval\n", __func__);
return 1;
}
/*.n_threads =*/ std::min(4, (int32_t) std::thread::hardware_concurrency()),
/*.offset_ms =*/ 0,
+ /*.n_processors =*/ 1,
/*.translate =*/ false,
/*.no_context =*/ false,
/*.n_threads =*/ std::min(4, (int32_t) std::thread::hardware_concurrency()),
/*.offset_ms =*/ 0,
+ /*.n_processors =*/ 1,
/*.translate =*/ false,
/*.no_context =*/ false,
int n_samples) {
// clear old results
auto & result_all = ctx->result_all;
- auto & tokens_cur = ctx->tokens_cur;
result_all.clear();
return -1;
}
+ const int seek_start = params.offset_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 (whisper_n_len(ctx) < 100) {
+ if (whisper_n_len(ctx) < 100 + seek_start) {
return 0;
}
int progress_prev = 0;
int progress_step = 5;
+ std::vector<whisper_token_data> tokens_cur;
+ tokens_cur.reserve(whisper_n_text_ctx(ctx));
+
+ std::vector<whisper_token> prompt;
+ prompt.reserve(whisper_n_text_ctx(ctx));
+
// main loop
- int seek = params.offset_ms/10;
+ int seek = seek_start;
while (true) {
int progress_cur = (100*seek)/whisper_n_len(ctx);
while (progress_cur >= progress_prev + progress_step) {
return 7;
}
- std::vector<whisper_token> prompt;
-
int n_past = 0;
+ prompt.clear();
// if we have already generated some text, use it as a prompt to condition the next generation
if (prompt_past.size() > 0) {
overview / pkg / ggml / sources / whisper.cpp / commitdiff