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examples/whisper/cpp/src/whisper.cpp Executable file
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/*
* Copyright (C) 2024–2025 Amlogic, Inc. All rights reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "whisper.h"
#include <atomic>
#include <algorithm>
#define _USE_MATH_DEFINES
#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstdarg>
#include <cstring>
#include <fstream>
#include <set>
#include <thread>
#include <vector>
#include <regex>
#include <random>
#include <functional>
#include <codecvt>
struct whisper_mel {
int n_len;
int n_len_org;
int n_mel;
std::vector<float> data;
};
struct whisper_filters {
int32_t n_mel = 80;
int32_t n_fft = 201;
std::vector<float> data;
};
struct whisper_state {
int64_t t_sample_us = 0;
int64_t t_encode_us = 0;
int64_t t_decode_us = 0;
int64_t t_batchd_us = 0;
int64_t t_prompt_us = 0;
int64_t t_mel_us = 0;
int32_t n_sample = 0; // number of tokens sampled
int32_t n_encode = 0; // number of encoder calls
int32_t n_decode = 0; // number of decoder calls with n_tokens == 1 (text-generation)
int32_t n_batchd = 0; // number of decoder calls with n_tokens < 16 (batch decoding)
int32_t n_prompt = 0; // number of decoder calls with n_tokens > 1 (prompt encoding)
int32_t n_fail_p = 0; // number of logprob threshold failures
int32_t n_fail_h = 0; // number of entropy threshold failures
whisper_mel mel;
std::vector<float> logits;
std::vector<whisper_token> prompt_past;
int lang_id = 0; // english by default
std::string path_model; // populated by whisper_init_from_file_with_params()
// [EXPERIMENTAL] token-level timestamps data
int64_t t_beg = 0;
int64_t t_last = 0;
whisper_token tid_last;
std::vector<float> energy; // PCM signal energy
// [EXPERIMENTAL] speed-up techniques
int32_t exp_n_audio_ctx = 0; // 0 - use default
};
struct whisper_model {
whisper_filters filters;
};
struct whisper_context {
int64_t t_load_us = 0;
int64_t t_start_us = 0;
whisper_model model;
whisper_vocab vocab;
whisper_state * state = nullptr;
};
#define SIN_COS_N_COUNT WHISPER_N_FFT
static float sin_vals[SIN_COS_N_COUNT];
static float cos_vals[SIN_COS_N_COUNT];
// In FFT, we frequently use sine and cosine operations with the same values.
// We can use precalculated values to speed up the process.
static void fill_sin_cos_table() {
static bool is_filled = false;
if (is_filled) return;
for (int i = 0; i < SIN_COS_N_COUNT; i++) {
double theta = (2*M_PI*i)/SIN_COS_N_COUNT;
sin_vals[i] = sinf(theta);
cos_vals[i] = cosf(theta);
}
is_filled = true;
}
// 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);
const int sin_cos_step = SIN_COS_N_COUNT / N;
for (int k = 0; k < N; k++) {
float re = 0;
float im = 0;
for (int n = 0; n < N; n++) {
int idx = (k * n * sin_cos_step) % (SIN_COS_N_COUNT); // t = 2*M_PI*k*n/N
re += in[n]*cos_vals[idx]; // cos(t)
im -= in[n]*sin_vals[idx]; // sin(t)
}
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);
const int sin_cos_step = SIN_COS_N_COUNT / N;
for (int k = 0; k < N/2; k++) {
int idx = k * sin_cos_step; // t = 2*M_PI*k/N
float re = cos_vals[idx]; // cos(t)
float im = -sin_vals[idx]; // sin(t)
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;
}
}
static bool hann_window(int length, bool periodic, std::vector<float> & output) {
if (output.size() < static_cast<size_t>(length)) {
output.resize(length);
}
int offset = -1;
if (periodic) {
offset = 0;
}
for (int i = 0; i < length; i++) {
output[i] = 0.5*(1.0 - cosf((2.0*M_PI*i)/(length + offset)));
}
return true;
}
static void log_mel_spectrogram_worker_thread(int ith, const std::vector<float> & hann, const std::vector<float> & samples,
int n_samples, int frame_size, int frame_step, int n_threads,
const whisper_filters & filters, whisper_mel & mel) {
std::vector<float> fft_in(frame_size, 0.0);
std::vector<float> fft_out(2 * frame_size);
int n_fft = filters.n_fft;
int i = ith;
assert(n_fft == 1 + (frame_size / 2));
// calculate FFT only when fft_in are not all zero
for (; i < std::min(n_samples / frame_step + 1, mel.n_len); i += n_threads) {
const int offset = i * frame_step;
// apply Hanning window (~10% faster)
for (int j = 0; j < std::min(frame_size, n_samples - offset); j++) {
fft_in[j] = hann[j] * samples[offset + j];
}
// fill the rest with zeros
if (n_samples - offset < frame_size) {
std::fill(fft_in.begin() + (n_samples - offset), fft_in.end(), 0.0);
}
// FFT
fft(fft_in, fft_out);
// Calculate modulus^2 of complex numbers
// Use pow(fft_out[2 * j + 0], 2) + pow(fft_out[2 * j + 1], 2) causes inference quality problem? Interesting.
for (int j = 0; j < n_fft; j++) {
fft_out[j] = (fft_out[2 * j + 0] * fft_out[2 * j + 0] + fft_out[2 * j + 1] * fft_out[2 * j + 1]);
}
// mel spectrogram
for (int j = 0; j < mel.n_mel; j++) {
double sum = 0.0;
// unroll loop (suggested by GH user @lunixbochs)
int k = 0;
for (k = 0; k < n_fft - 3; k += 4) {
sum +=
fft_out[k + 0] * filters.data[j * n_fft + k + 0] +
fft_out[k + 1] * filters.data[j * n_fft + k + 1] +
fft_out[k + 2] * filters.data[j * n_fft + k + 2] +
fft_out[k + 3] * filters.data[j * n_fft + k + 3];
}
// handle n_fft remainder
for (; k < n_fft; k++) {
sum += fft_out[k] * filters.data[j * n_fft + k];
}
sum = log10(std::max(sum, 1e-10));
mel.data[j * mel.n_len + i] = sum;
}
}
// Otherwise fft_out are all zero
double sum = log10(1e-10);
for (; i < mel.n_len; i += n_threads) {
for (int j = 0; j < mel.n_mel; j++) {
mel.data[j * mel.n_len + i] = sum;
}
}
}
static bool log_mel_spectrogram(
whisper_state & wstate,
const float * samples,
const int n_samples,
const int /*sample_rate*/,
const int frame_size,
const int frame_step,
const int n_mel,
const int n_threads,
whisper_filters & filters,
const bool debug,
whisper_mel & mel) {
// Hanning window (Use cosf to eliminate difference)
// ref: https://pytorch.org/docs/stable/generated/torch.hann_window.html
// ref: https://github.com/openai/whisper/blob/main/whisper/audio.py#L147
fill_sin_cos_table();
// auto & filters = filters;
filters.data.resize(filters.n_mel*filters.n_fft);
auto fin = std::ifstream("./data_bin/data.bin", std::ios::binary);
if (!fin)
{
fprintf(stderr, "%s : fail to open '%s'\n", __func__, "./data_bin/data.bin");
}
fin.read((char *)filters.data.data(), filters.data.size()*sizeof(float));
fin.eof();
fin.close();
std::vector<float> hann;
hann_window(frame_size, true, hann);
// Calculate the length of padding
int64_t stage_1_pad = WHISPER_SAMPLE_RATE * 30;
int64_t stage_2_pad = frame_size / 2;
// Initialize a vector and copy data from C array to it.
std::vector<float> samples_padded;
samples_padded.resize(n_samples + stage_1_pad + stage_2_pad * 2);
std::copy(samples, samples + n_samples, samples_padded.begin() + stage_2_pad);
// pad 30 seconds of zeros at the end of audio (480,000 samples) + reflective pad 200 samples at the end of audio
std::fill(samples_padded.begin() + n_samples + stage_2_pad, samples_padded.begin() + n_samples + stage_1_pad + 2 * stage_2_pad, 0);
// reflective pad 200 samples at the beginning of audio
std::reverse_copy(samples + 1, samples + 1 + stage_2_pad, samples_padded.begin());
mel.n_mel = n_mel;
// https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/SpectralOps.cpp#L936
// Calculate number of frames + remove the last frame
mel.n_len = (samples_padded.size() - frame_size) / frame_step;
// Calculate semi-padded sample length to ensure compatibility
mel.n_len_org = 1 + (n_samples + stage_2_pad - frame_size) / frame_step;
mel.data.resize(mel.n_mel * mel.n_len);
{
std::vector<std::thread> workers(n_threads - 1);
for (int iw = 0; iw < n_threads - 1; ++iw) {
workers[iw] = std::thread(
log_mel_spectrogram_worker_thread, iw + 1, std::cref(hann), samples_padded,
n_samples + stage_2_pad, frame_size, frame_step, n_threads,
std::cref(filters), std::ref(mel));
}
// main thread
log_mel_spectrogram_worker_thread(0, hann, samples_padded, n_samples + stage_2_pad, frame_size, frame_step, n_threads, filters, mel);
for (int iw = 0; iw < n_threads - 1; ++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];
}
}
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;
}
return true;
}
int whisper_pcm_to_mel_with_state(struct whisper_context * ctx, struct whisper_state * state, const float * samples, int n_samples, int n_threads) {
if (!log_mel_spectrogram(*state, samples, n_samples, WHISPER_SAMPLE_RATE, WHISPER_N_FFT, WHISPER_HOP_LENGTH, 80, n_threads, ctx->model.filters, true, state->mel)) {
printf("%s: failed to compute mel spectrogram\n", __func__);
return -1;
}
return 0;
}
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;
bool found = false;
while (j > i) {
auto sub = word.substr(i, j-i);
auto it = vocab.token_to_id.find(sub);
if (it != vocab.token_to_id.end()) {
tokens.push_back(it->second);
i = j;
found = true;
break;
}
--j;
}
if (!found) {
printf("unknown token\n");
++i;
}
}
}
return tokens;
}