snapshot of https://github.com/ggml-org/whisper.cpp.git @ da54572229bcf64ba367d96c7ef15770376c4280 (2026-09-15)

examples/bench/CMakeLists.txt

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+set(TARGET whisper-bench)
+add_executable(${TARGET} bench.cpp)
+
+include(DefaultTargetOptions)
+
+target_link_libraries(${TARGET} PRIVATE whisper ${CMAKE_THREAD_LIBS_INIT})
+
+install(TARGETS ${TARGET} RUNTIME)

examples/bench/README.md

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+# whisper.cpp/examples/bench
+
+A very basic tool for benchmarking the inference performance on your device. The tool simply runs the Encoder part of
+the transformer on some random audio data and records the execution time. This way we can have an objective comparison
+of the performance of the model for various setups.
+
+Benchmark results are tracked in the following Github issue: https://github.com/ggml-org/whisper.cpp/issues/89
+
+```bash
+# run the bench too on the small.en model using 4 threads
+$ ./build/bin/whisper-bench -m ./models/ggml-small.en.bin -t 4
+
+whisper_model_load: loading model from './models/ggml-small.en.bin'
+whisper_model_load: n_vocab       = 51864
+whisper_model_load: n_audio_ctx   = 1500
+whisper_model_load: n_audio_state = 768
+whisper_model_load: n_audio_head  = 12
+whisper_model_load: n_audio_layer = 12
+whisper_model_load: n_text_ctx    = 448
+whisper_model_load: n_text_state  = 768
+whisper_model_load: n_text_head   = 12
+whisper_model_load: n_text_layer  = 12
+whisper_model_load: n_mels        = 80
+whisper_model_load: f16           = 1
+whisper_model_load: type          = 3
+whisper_model_load: mem_required  = 1048.00 MB
+whisper_model_load: adding 1607 extra tokens
+whisper_model_load: ggml ctx size = 533.05 MB
+whisper_model_load: memory size =    68.48 MB 
+whisper_model_load: model size  =   464.44 MB
+
+whisper_print_timings:     load time =   240.82 ms
+whisper_print_timings:      mel time =     0.00 ms
+whisper_print_timings:   sample time =     0.00 ms
+whisper_print_timings:   encode time =  1062.21 ms / 88.52 ms per layer
+whisper_print_timings:   decode time =     0.00 ms / 0.00 ms per layer
+whisper_print_timings:    total time =  1303.04 ms
+
+system_info: n_threads = 4 | AVX2 = 0 | AVX512 = 0 | NEON = 1 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | 
+
+If you wish, you can submit these results here:
+
+  https://github.com/ggml-org/whisper.cpp/issues/89
+
+Please include the following information:
+
+  - CPU model
+  - Operating system
+  - Compiler
+
+```

examples/bench/bench.cpp

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+#include "whisper.h"
+
+#include <cstdio>
+#include <cstring>
+#include <string>
+#include <thread>
+
+// command-line parameters
+struct whisper_params {
+    int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
+    int32_t what = 0; // what to benchmark: 0 - whisper encoder, 1 - memcpy, 2 - ggml_mul_mat
+
+    std::string model = "models/ggml-base.en.bin";
+
+    bool use_gpu    = true;
+    bool flash_attn = true;
+};
+
+void whisper_print_usage(int argc, char ** argv, const whisper_params & params);
+
+static 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 == "-h" || arg == "--help") {
+            whisper_print_usage(argc, argv, params);
+            exit(0);
+        }
+        else if (arg == "-t"     || arg == "--threads")       { params.n_threads  = std::stoi(argv[++i]); }
+        else if (arg == "-m"     || arg == "--model")         { params.model      = argv[++i]; }
+        else if (arg == "-w"     || arg == "--what")          { params.what       = atoi(argv[++i]); }
+        else if (arg == "-ng"    || arg == "--no-gpu")        { params.use_gpu    = false; }
+        else if (arg == "-fa"    || arg == "--flash-attn")    { params.flash_attn = true; }
+        else if (arg == "-nfa"   || arg == "--no-flash-attn") { params.flash_attn = false; }
+        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]\n", argv[0]);
+    fprintf(stderr, "\n");
+    fprintf(stderr, "options:\n");
+    fprintf(stderr, "  -h,       --help          [default] show this help message and exit\n");
+    fprintf(stderr, "  -t N,     --threads N     [%-7d] number of threads to use during computation\n", params.n_threads);
+    fprintf(stderr, "  -m FNAME, --model FNAME   [%-7s] model path\n",                                  params.model.c_str());
+    fprintf(stderr, "  -w N,     --what N        [%-7d] what to benchmark:\n",                          params.what);
+    fprintf(stderr, "                             %-7s  0 - whisper\n",                                 "");
+    fprintf(stderr, "                             %-7s  1 - memcpy\n",                                  "");
+    fprintf(stderr, "                             %-7s  2 - ggml_mul_mat\n",                            "");
+    fprintf(stderr, "  -ng,      --no-gpu        [%-7s] disable GPU\n",                                 params.use_gpu ? "false" : "true");
+    fprintf(stderr, "  -fa,      --flash-attn    [%-7s] enable flash attention\n",                      params.flash_attn ? "true" : "false");
+    fprintf(stderr, "  -nfa,     --no-flash-attn [%-7s] disable flash attention\n",                     params.flash_attn ? "false" : "true");
+    fprintf(stderr, "\n");
+}
+
+static int whisper_bench_full(const whisper_params & params) {
+    // whisper init
+
+    struct whisper_context_params cparams = whisper_context_default_params();
+
+    cparams.use_gpu    = params.use_gpu;
+    cparams.flash_attn = params.flash_attn;
+
+    {
+        fprintf(stderr, "\n");
+        fprintf(stderr, "system_info: n_threads = %d / %d | %s\n", params.n_threads, std::thread::hardware_concurrency(), whisper_print_system_info());
+    }
+
+    struct whisper_context * ctx = whisper_init_from_file_with_params(params.model.c_str(), cparams);
+    if (ctx == nullptr) {
+        fprintf(stderr, "error: failed to initialize whisper context\n");
+        return 2;
+    }
+
+    const int n_mels = whisper_model_n_mels(ctx);
+
+    if (int ret = whisper_set_mel(ctx, nullptr, 0, n_mels)) {
+        fprintf(stderr, "error: failed to set mel: %d\n", ret);
+        return 3;
+    }
+
+    whisper_token tokens[512];
+    memset(tokens, 0, sizeof(tokens));
+
+    // TODO: need 2 loops because of the current graph capture logic in the CUDA backend
+    //       https://github.com/ggml-org/llama.cpp/pull/19754
+    for (int h = 0; h < 2; ++h) {
+        // heat encoder
+        if (int ret = whisper_encode(ctx, 0, params.n_threads) != 0) {
+            fprintf(stderr, "error: failed to encode: %d\n", ret);
+            return 4;
+        }
+
+        // prompt heat
+        if (int ret = whisper_decode(ctx, tokens, 256, 0, params.n_threads) != 0) {
+            fprintf(stderr, "error: failed to decode: %d\n", ret);
+            return 4;
+        }
+
+        // text-generation heat
+        for (int i = 0; i < 256; i++) {
+            if (int ret = whisper_decode(ctx, tokens, 1, i, params.n_threads) != 0) {
+                fprintf(stderr, "error: failed to decode: %d\n", ret);
+                return 4;
+            }
+        }
+
+        // batched heat
+        if (int ret = whisper_decode(ctx, tokens, 5, 0, params.n_threads) != 0) {
+            fprintf(stderr, "error: failed to decode: %d\n", ret);
+            return 4;
+        }
+    }
+
+    whisper_reset_timings(ctx);
+
+    // actual run
+    if (int ret = whisper_encode(ctx, 0, params.n_threads) != 0) {
+        fprintf(stderr, "error: failed to encode: %d\n", ret);
+        return 4;
+    }
+
+    // text-generation
+    for (int i = 0; i < 256; i++) {
+        if (int ret = whisper_decode(ctx, tokens, 1, i, params.n_threads) != 0) {
+            fprintf(stderr, "error: failed to decode: %d\n", ret);
+            return 4;
+        }
+    }
+
+    // batched decoding
+    for (int i = 0; i < 64; i++) {
+        if (int ret = whisper_decode(ctx, tokens, 5, 0, params.n_threads) != 0) {
+            fprintf(stderr, "error: failed to decode: %d\n", ret);
+            return 4;
+        }
+    }
+
+    // prompt processing
+    for (int i = 0; i < 16; i++) {
+        if (int ret = whisper_decode(ctx, tokens, 256, 0, params.n_threads) != 0) {
+            fprintf(stderr, "error: failed to decode: %d\n", ret);
+            return 4;
+        }
+    }
+
+    whisper_print_timings(ctx);
+    whisper_free(ctx);
+
+    fprintf(stderr, "\n");
+    fprintf(stderr, "If you wish, you can submit these results here:\n");
+    fprintf(stderr, "\n");
+    fprintf(stderr, "  https://github.com/ggml-org/whisper.cpp/issues/89\n");
+    fprintf(stderr, "\n");
+    fprintf(stderr, "Please include the following information:\n");
+    fprintf(stderr, "\n");
+    fprintf(stderr, "  - CPU model\n");
+    fprintf(stderr, "  - Operating system\n");
+    fprintf(stderr, "  - Compiler\n");
+    fprintf(stderr, "\n");
+
+    return 0;
+}
+
+int main(int argc, char ** argv) {
+    ggml_backend_load_all();
+
+    whisper_params params;
+
+    if (whisper_params_parse(argc, argv, params) == false) {
+        return 1;
+    }
+
+    int ret = -1;
+
+    switch (params.what) {
+        case 0: ret = whisper_bench_full(params);                break;
+        case 1: ret = whisper_bench_memcpy(params.n_threads);       break;
+        case 2: ret = whisper_bench_ggml_mul_mat(params.n_threads); break;
+        default: fprintf(stderr, "error: unknown benchmark: %d\n", params.what); break;
+    }
+
+    return ret;
+}