mirror of
https://github.com/ggerganov/whisper.cpp.git
synced 2024-12-19 12:47:52 +00:00
126 lines
3.4 KiB
C++
126 lines
3.4 KiB
C++
// Various helper functions and utilities
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#pragma once
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#include <string>
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#include <map>
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#include <vector>
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#include <random>
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#include <thread>
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#define COMMON_SAMPLE_RATE 16000
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//
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// CLI argument parsing
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//
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struct gpt_params {
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int32_t seed = -1; // RNG seed
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int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
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int32_t n_predict = 200; // new tokens to predict
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// sampling parameters
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int32_t top_k = 40;
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float top_p = 0.9f;
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float temp = 0.9f;
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int32_t n_batch = 8; // batch size for prompt processing
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std::string model = "models/gpt-2-117M/ggml-model.bin"; // model path
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std::string prompt;
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};
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bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
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void gpt_print_usage(int argc, char ** argv, const gpt_params & params);
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std::string gpt_random_prompt(std::mt19937 & rng);
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//
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// Vocab utils
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//
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std::string trim(const std::string & s);
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std::string replace(
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const std::string & s,
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const std::string & from,
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const std::string & to);
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struct gpt_vocab {
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using id = int32_t;
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using token = std::string;
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std::map<token, id> token_to_id;
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std::map<id, token> id_to_token;
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std::vector<std::string> special_tokens;
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void add_special_token(const std::string & token);
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};
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// poor-man's JSON parsing
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std::map<std::string, int32_t> json_parse(const std::string & fname);
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// split text into tokens
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//
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// ref: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/encoder.py#L53
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//
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// Regex (Python):
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// r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
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//
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// Regex (C++):
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// R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)"
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//
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std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text);
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// load the tokens from encoder.json
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bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab);
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// sample next token given probabilities for each embedding
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//
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// - consider only the top K tokens
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// - from them, consider only the top tokens with cumulative probability > P
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//
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// TODO: not sure if this implementation is correct
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// TODO: temperature is not implemented
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//
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gpt_vocab::id gpt_sample_top_k_top_p(
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const gpt_vocab & vocab,
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const float * logits,
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int top_k,
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double top_p,
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double temp,
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std::mt19937 & rng);
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//
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// Audio utils
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//
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// Read WAV audio file and store the PCM data into pcmf32
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// The sample rate of the audio must be equal to COMMON_SAMPLE_RATE
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// If stereo flag is set and the audio has 2 channels, the pcmf32s will contain 2 channel PCM
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bool read_wav(
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const std::string & fname,
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std::vector<float> & pcmf32,
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std::vector<std::vector<float>> & pcmf32s,
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bool stereo);
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// Apply a high-pass frequency filter to PCM audio
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// Suppresses frequencies below cutoff Hz
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void high_pass_filter(
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std::vector<float> & data,
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float cutoff,
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float sample_rate);
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// Basic voice activity detection (VAD) using audio energy adaptive threshold
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bool vad_simple(
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std::vector<float> & pcmf32,
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int sample_rate,
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int last_ms,
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float vad_thold,
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float freq_thold,
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bool verbose);
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// compute similarity between two strings using Levenshtein distance
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float similarity(const std::string & s0, const std::string & s1);
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