mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2024-11-07 16:44:13 +01:00
e30c679928
* scripts : update sync [no ci] * files : reorganize [no ci] * sync : llama.cpp * cmake : link math library * cmake : build normal ggml library * files : move headers to include * objc : fix path to ggml-metal.h * ci : fix WHISPER_CUDA -> GGML_CUDA * scripts : sync LICENSE [no ci]
467 lines
20 KiB
C++
467 lines
20 KiB
C++
#include "common.h"
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#include "common-sdl.h"
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#include "whisper.h"
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#include "json.hpp"
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#include <iostream>
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#include <cassert>
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#include <cstdio>
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#include <string>
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#include <thread>
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#include <vector>
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#include <deque>
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#include <set>
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using json = nlohmann::json;
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// command-line parameters
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struct whisper_params {
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int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
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int32_t prompt_ms = 5000;
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int32_t command_ms = 8000;
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int32_t capture_id = -1;
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int32_t max_tokens = 32;
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int32_t audio_ctx = 0;
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float vad_thold = 0.6f;
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float freq_thold = 100.0f;
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bool translate = false;
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bool print_special = false;
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bool print_energy = false;
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bool use_gpu = true;
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bool flash_attn = false;
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std::string language = "en";
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std::string model = "models/ggml-base.en.bin";
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};
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struct command {
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std::vector<whisper_token> tokens;
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std::string plaintext;
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};
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struct commandset {
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std::vector<struct command> commands;
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std::vector<whisper_token> prompt_tokens;
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// TODO: Store longest command?
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// Multi-token commands should have probabilities of subsequent logits
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// given that the prior logit is correct.
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// In this case, all commands must be iterated.
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// This however, is likely highly involved as different tokens
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// almost certainly have different spoken lengths
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// It would also have performance implications equivalent to a beam search
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};
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void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
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static bool whisper_params_parse(int argc, char ** argv, whisper_params & params) {
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for (int i = 1; i < argc; i++) {
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std::string arg = argv[i];
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if (arg == "-h" || arg == "--help") {
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whisper_print_usage(argc, argv, params);
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exit(0);
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}
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else if (arg == "-t" || arg == "--threads") { params.n_threads = std::stoi(argv[++i]); }
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else if (arg == "-pms" || arg == "--prompt-ms") { params.prompt_ms = std::stoi(argv[++i]); }
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else if (arg == "-cms" || arg == "--command-ms") { params.command_ms = std::stoi(argv[++i]); }
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else if (arg == "-c" || arg == "--capture") { params.capture_id = std::stoi(argv[++i]); }
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else if (arg == "-mt" || arg == "--max-tokens") { params.max_tokens = std::stoi(argv[++i]); }
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else if (arg == "-ac" || arg == "--audio-ctx") { params.audio_ctx = std::stoi(argv[++i]); }
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else if (arg == "-vth" || arg == "--vad-thold") { params.vad_thold = std::stof(argv[++i]); }
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else if (arg == "-fth" || arg == "--freq-thold") { params.freq_thold = std::stof(argv[++i]); }
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else if (arg == "-tr" || arg == "--translate") { params.translate = true; }
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else if (arg == "-ps" || arg == "--print-special") { params.print_special = true; }
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else if (arg == "-pe" || arg == "--print-energy") { params.print_energy = true; }
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else if (arg == "-ng" || arg == "--no-gpu") { params.use_gpu = false; }
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else if (arg == "-fa" || arg == "--flash-attn") { params.flash_attn = true; }
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else if (arg == "-l" || arg == "--language") { params.language = argv[++i]; }
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else if (arg == "-m" || arg == "--model") { params.model = argv[++i]; }
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else {
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fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
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whisper_print_usage(argc, argv, params);
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exit(0);
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}
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}
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return true;
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}
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void whisper_print_usage(int /*argc*/, char ** argv, const whisper_params & params) {
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fprintf(stderr, "\n");
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fprintf(stderr, "usage: %s [options]\n", argv[0]);
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fprintf(stderr, "\n");
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fprintf(stderr, "options:\n");
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fprintf(stderr, " -h, --help [default] show this help message and exit\n");
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fprintf(stderr, " -t N, --threads N [%-7d] number of threads to use during computation\n", params.n_threads);
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fprintf(stderr, " -pms N, --prompt-ms N [%-7d] prompt duration in milliseconds\n", params.prompt_ms);
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fprintf(stderr, " -cms N, --command-ms N [%-7d] command duration in milliseconds\n", params.command_ms);
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fprintf(stderr, " -c ID, --capture ID [%-7d] capture device ID\n", params.capture_id);
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fprintf(stderr, " -mt N, --max-tokens N [%-7d] maximum number of tokens per audio chunk\n", params.max_tokens);
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fprintf(stderr, " -ac N, --audio-ctx N [%-7d] audio context size (0 - all)\n", params.audio_ctx);
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fprintf(stderr, " -vth N, --vad-thold N [%-7.2f] voice activity detection threshold\n", params.vad_thold);
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fprintf(stderr, " -fth N, --freq-thold N [%-7.2f] high-pass frequency cutoff\n", params.freq_thold);
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fprintf(stderr, " -tr, --translate [%-7s] translate from source language to english\n", params.translate ? "true" : "false");
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fprintf(stderr, " -ps, --print-special [%-7s] print special tokens\n", params.print_special ? "true" : "false");
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fprintf(stderr, " -pe, --print-energy [%-7s] print sound energy (for debugging)\n", params.print_energy ? "true" : "false");
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fprintf(stderr, " -ng, --no-gpu [%-7s] disable GPU\n", params.use_gpu ? "false" : "true");
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fprintf(stderr, " -fa, --flash-attn [%-7s] flash attention\n", params.flash_attn ? "true" : "false");
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fprintf(stderr, " -l LANG, --language LANG [%-7s] spoken language\n", params.language.c_str());
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fprintf(stderr, " -m FNAME, --model FNAME [%-7s] model path\n", params.model.c_str());
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fprintf(stderr, "\n");
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}
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static uint64_t wait_for_vad(audio_async & audio, json jparams, const whisper_params & params, uint64_t maxlength_ms, std::vector<float> & pcmf32) {
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using namespace std::chrono;
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uint64_t time_now = time_point_cast<milliseconds>(system_clock::now()).time_since_epoch().count();
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uint64_t start_time = time_now;
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if (jparams.contains("timestamp")) {
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start_time = jparams.at("timestamp");
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}
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if(time_now - start_time < 500) {
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//wait for a backlog of audio
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std::this_thread::sleep_for(milliseconds(500 - (time_now - start_time)));
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time_now = time_point_cast<milliseconds>(system_clock::now()).time_since_epoch().count();
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} else if (time_now - start_time > 1000) {
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audio.get(time_now-start_time, pcmf32);
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size_t max_offset = pcmf32.size() - WHISPER_SAMPLE_RATE;
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for(size_t offset=0;offset < max_offset;offset+=WHISPER_SAMPLE_RATE/10) {
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std::vector<float> audio_chunk(&pcmf32[offset], &pcmf32[offset+WHISPER_SAMPLE_RATE]);
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if(::vad_simple(audio_chunk, WHISPER_SAMPLE_RATE, 1000, params.vad_thold, params.freq_thold, params.print_energy)) {
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pcmf32.resize(offset+WHISPER_SAMPLE_RATE);
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if (offset*1000/WHISPER_SAMPLE_RATE+1000 > maxlength_ms) {
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//remove samples from the beginning
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pcmf32.erase(pcmf32.begin(),pcmf32.end()-(maxlength_ms*WHISPER_SAMPLE_RATE/1000));
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fprintf(stderr, "Shortened samples");
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}
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return start_time + offset*1000/WHISPER_SAMPLE_RATE+1000;
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}
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}
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}
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size_t window_duration = std::max((uint64_t)1000, time_now-start_time);
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audio.get(window_duration, pcmf32);
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while (!::vad_simple(pcmf32, WHISPER_SAMPLE_RATE, 1000, params.vad_thold, params.freq_thold, params.print_energy)) {
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std::this_thread::sleep_for(milliseconds(100));
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time_now = time_point_cast<milliseconds>(system_clock::now()).time_since_epoch().count();
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window_duration = std::max((uint64_t)1000,time_now-start_time);
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audio.get(window_duration, pcmf32);
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}
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if (time_now - start_time > maxlength_ms) {
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audio.get(maxlength_ms, pcmf32);
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} else {
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audio.get(time_now - start_time, pcmf32);
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}
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return time_now;
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}
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static json unguided_transcription(struct whisper_context * ctx, audio_async &audio, json jparams, const whisper_params ¶ms) {
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std::vector<whisper_token> prompt_tokens;
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std::vector<float> pcmf32;
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uint64_t unprocessed_audio_timestamp = wait_for_vad(audio, jparams, params, 10000U, pcmf32);
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whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
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if (jparams.contains("prompt")) {
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// unlikely to see much use. Under normal circumstances, no_context would be set to false
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std::string prompt = jparams.at("prompt");
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prompt_tokens.resize(1024);
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int n = whisper_tokenize(ctx, prompt.c_str(), prompt_tokens.data(), 1024);
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prompt_tokens.resize(n);
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wparams.prompt_tokens = prompt_tokens.data();
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wparams.prompt_n_tokens = prompt_tokens.size();
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}
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wparams.print_progress = false;
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wparams.print_special = params.print_special;
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wparams.print_realtime = false;
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wparams.print_timestamps = false;
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wparams.translate = params.translate;
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wparams.no_context = jparams.value("no_context", true);
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wparams.single_segment = true;
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wparams.max_tokens = params.max_tokens;
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wparams.language = params.language.c_str();
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wparams.n_threads = params.n_threads;
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wparams.audio_ctx = params.audio_ctx;
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wparams.suppress_non_speech_tokens = true;
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// run the transformer and a single decoding pass
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if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
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fprintf(stderr, "%s: ERROR: whisper_full() failed\n", __func__);
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throw json{
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{"code", -32803},
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{"message", "ERROR: whisper_full() failed"}
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};
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}
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std::string result = whisper_full_get_segment_text(ctx,0);
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return json {
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{"transcription", result},
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{"timestamp", unprocessed_audio_timestamp}
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};
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}
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// command-list mode
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// guide the transcription to match the most likely command from a provided list
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static json guided_transcription(struct whisper_context * ctx, audio_async &audio, const whisper_params ¶ms, json jparams, std::vector<struct commandset> commandset_list) {
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struct commandset cs = commandset_list[jparams.value("commandset_index", commandset_list.size()-1)];
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std::vector<float> pcmf32;
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uint64_t unprocessed_audio_timestamp = wait_for_vad(audio, jparams, params, 2000U, pcmf32);
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fprintf(stderr, "%s: Speech detected! Processing ...\n", __func__);
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whisper_full_params wparams = whisper_full_default_params(WHISPER_SAMPLING_GREEDY);
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wparams.print_progress = false;
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wparams.print_special = params.print_special;
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wparams.print_realtime = false;
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wparams.print_timestamps = false;
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wparams.translate = params.translate;
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wparams.no_context = true;
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wparams.single_segment = true;
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wparams.max_tokens = 1;
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wparams.language = params.language.c_str();
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wparams.n_threads = params.n_threads;
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wparams.audio_ctx = params.audio_ctx;
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// TODO: Do some time testing. Does an overly long prompt slow down processing?
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// Set up command sets/precompute prompts
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wparams.prompt_tokens = cs.prompt_tokens.data();
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wparams.prompt_n_tokens = cs.prompt_tokens.size();
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// TODO: properly expose as option
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wparams.suppress_non_speech_tokens = true;
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// run the transformer and a single decoding pass
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if (whisper_full(ctx, wparams, pcmf32.data(), pcmf32.size()) != 0) {
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fprintf(stderr, "%s: ERROR: whisper_full() failed\n", __func__);
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throw json{
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{"code", -32803},
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{"message", "ERROR: whisper_full() failed"}//TODO: format string (sprintf?)
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};
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}
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// estimate command probability
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// NOTE: not optimal
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{
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const auto * logits = whisper_get_logits(ctx);
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std::vector<float> probs(whisper_n_vocab(ctx), 0.0f);
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// compute probs from logits via softmax
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{
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float max = -1e9;
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for (int i = 0; i < (int) probs.size(); ++i) {
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max = std::max(max, logits[i]);
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}
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float sum = 0.0f;
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for (int i = 0; i < (int) probs.size(); ++i) {
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probs[i] = expf(logits[i] - max);
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sum += probs[i];
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}
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for (int i = 0; i < (int) probs.size(); ++i) {
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probs[i] /= sum;
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}
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}
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std::vector<std::pair<float, int>> probs_id;
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// In my testing, the most verbose token is always the desired.
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// TODO: Trim commandset struct once efficacy has been verified
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for (int i = 0; i < (int) cs.commands.size(); ++i) {
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probs_id.emplace_back(probs[cs.commands[i].tokens[0]], i);
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}
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// sort descending
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{
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using pair_type = decltype(probs_id)::value_type;
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std::sort(probs_id.begin(), probs_id.end(), [](const pair_type & a, const pair_type & b) {
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return a.first > b.first;
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});
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}
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int id = probs_id[0].second;
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return json{
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{"command_index", id},
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{"command_text", cs.commands[id].plaintext},
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{"timestamp", unprocessed_audio_timestamp},
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};
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}
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}
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static json register_commandset(struct whisper_context * ctx, json jparams, std::vector<struct commandset> &commandset_list) {
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// TODO: check for token collision
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struct commandset cs;
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std::string k_prompt = " select one from the available words: ";
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std::set<whisper_token> token_set;
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whisper_token tokens[32];
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for (std::string s : jparams) {
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std::vector<whisper_token> token_vec;
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// The existing command implementation uses a nested for loop to tokenize single characters
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// I fail to see the purpose of this when ' a' has a wholly different pronunciation than the start of ' apple'
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const int n = whisper_tokenize(ctx, (" " + s).c_str(), tokens, 32);
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if (n < 0) {
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fprintf(stderr, "%s: error: failed to tokenize command '%s'\n", __func__, s.c_str());
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return 3;
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}
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token_vec.push_back(tokens[0]);
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if (!token_set.insert(tokens[0]).second) {
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fprintf(stderr, "%s: warning: %s is a duplicate of an existing token\n", __func__, s.c_str());
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throw json{
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{"code",-31000},
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{"message", "Duplicate token in token set: " + s}
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};
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}
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if (n > 1) {// empty string if n=0? Should never occur
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fprintf(stderr, "%s: error: command is more than a single token: %s\n", __func__, s.c_str());
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}
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struct command command = {token_vec, s};
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cs.commands.push_back(command);
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k_prompt += s;
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}
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k_prompt = k_prompt.substr(0,k_prompt.length()-2) + ". Selected word:";
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cs.prompt_tokens.resize(1024);
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int n = whisper_tokenize(ctx, k_prompt.c_str(), cs.prompt_tokens.data(), 1024);
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cs.prompt_tokens.resize(n);
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// prepare response
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int index = commandset_list.size();
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commandset_list.push_back(cs);
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return json{{"index",index}};
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}
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static json seek(struct whisper_context * /*ctx*/, audio_async & /*audio*/, json /*params*/) {
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// whisper_state has the pertinent offsets, but there also seem to be a large
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// number of scratch buffers that would prevent rewinding context in a manner similar to llama
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// I'll give this a another pass once everything else is implemented,
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// but for now, it's unsupported
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throw json {
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{"code", -32601},
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{"message", "Seeking is not yet supported."}
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};
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}
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static json parse_job(const json &body, struct whisper_context * ctx, audio_async &audio, const whisper_params ¶ms, std::vector<struct commandset> &commandset_list) {
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// See: https://www.jsonrpc.org/specification
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json id = body.at("id");
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try {
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std::string version = body.at("jsonrpc");
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if (version != "2.0") {
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// unsupported version
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throw json{
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{"code", -3260},
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{"message", "invalid jsonrpc version"}
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};
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}
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std::string method = body.at("method");
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json jparams = json{{"dummy", "dummy"}};
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if (body.contains("params"))
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jparams = body.at("params");
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json res;
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// TODO: be consistent about argument order
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fprintf(stderr, "Dispatching a job\n");
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if (method == "unguided") { res = unguided_transcription(ctx, audio, jparams, params); }
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else if (method == "guided") { res = guided_transcription(ctx, audio, params, jparams, commandset_list); }
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else if (method == "seek") { res = seek(ctx, audio, jparams); }
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else if (method == "registerCommandset") { res = register_commandset(ctx, jparams, commandset_list); }
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else if (method == "echo") { res = jparams; }
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return json{
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{"jsonrpc", "2.0"},
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{"result", res},
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{"id", id}
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};
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} catch(json ex) {
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return json {
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{"jsonrpc", "2.0"},
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{"error", ex},
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{"id", id}
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};
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}
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}
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static void process_loop(struct whisper_context * ctx, audio_async &audio, const whisper_params ¶ms) {
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std::deque<json> jobqueue;
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std::vector<struct commandset> commandset_list;
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while (true) {
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// For eventual cancellation support, shouldn't block if job exists
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if (std::cin.rdbuf()->in_avail() > 22 || jobqueue.size() == 0) {
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int content_length;
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if (scanf("Content-Length: %d", &content_length) != 1) {
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fprintf(stderr, "Could not read input: %d", std::cin.peek());
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return;
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}
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// scanf leaves the new lines intact
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std::cin.ignore(2);
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if (std::cin.peek() != 13) {
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// Content-Type. jsonrpc necessitates utf8.
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std::cin.ignore(200,10);
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}
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std::cin.ignore(2);
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// A message is being sent and blocking is acceptable
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std::string content(content_length,'\0');
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std::cin.read(&content[0], content_length);
|
|
json job = json::parse(content);
|
|
// TODO: Some messages(cancellation) should skip queue here
|
|
if (job.is_array()) {
|
|
// response must also be batched. Will implement later
|
|
// for (subjob : job.begin())
|
|
// TODO: At the very least respond with an unsupported error.
|
|
} else {
|
|
jobqueue.push_back(job);
|
|
}
|
|
}
|
|
assert(jobqueue.size() > 0);
|
|
json job = jobqueue.front();
|
|
json resp = parse_job(job, ctx, audio, params, commandset_list);
|
|
if (resp != "unfinished") {
|
|
jobqueue.pop_front();
|
|
// send response
|
|
std::string data = resp.dump(-1, ' ', false, json::error_handler_t::replace);
|
|
fprintf(stdout, "Content-Length: %d\r\n\r\n%s\n", (int)data.length()+1, data.c_str());
|
|
std::cout.flush();
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
whisper_params params;
|
|
if (whisper_params_parse(argc, argv, params) == false) {
|
|
return 1;
|
|
}
|
|
|
|
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);
|
|
}
|
|
|
|
// whisper init
|
|
struct whisper_context_params cparams = whisper_context_default_params();
|
|
|
|
cparams.use_gpu = params.use_gpu;
|
|
cparams.flash_attn = params.flash_attn;
|
|
|
|
struct whisper_context * ctx = whisper_init_from_file_with_params(params.model.c_str(), cparams);
|
|
// init audio
|
|
|
|
audio_async audio(30*1000);
|
|
if (!audio.init(params.capture_id, WHISPER_SAMPLE_RATE)) {
|
|
fprintf(stderr, "%s: audio.init() failed!\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
audio.resume();
|
|
// TODO: Investigate why this is required. An extra second of startup latency is not great
|
|
// wait for 1 second to avoid any buffered noise
|
|
std::this_thread::sleep_for(std::chrono::milliseconds(1000));
|
|
audio.clear();
|
|
// TODO: consider some sort of indicator to designate loading has finished?
|
|
// Potentially better for the client to just start with a non-blocking message (register commands)
|
|
process_loop(ctx, audio, params);
|
|
|
|
audio.pause();
|
|
whisper_print_timings(ctx);
|
|
whisper_free(ctx);
|
|
|
|
return 0;
|
|
}
|