Fabio
c36510c21e
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62 lines
3.7 KiB
C++
62 lines
3.7 KiB
C++
#include "app/cli/args.h"
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#include "engine/community_models/realtime_reuse/frontend.h"
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#include "engine/framework/assets/tensor_source.h"
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#include "engine/framework/debug/profiler.h"
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#include "engine/framework/io/safetensors.h"
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#include <cmath>
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#include <iostream>
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int main(int argc, char ** argv) {
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try {
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const auto model = minitts::cli::find_arg(argc, argv, "--model").value();
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const auto reference = minitts::cli::find_arg(argc, argv, "--reference").value();
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const auto backend = minitts::cli::find_arg(argc, argv, "--backend").value_or("cuda");
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const auto log = minitts::cli::find_arg(argc, argv, "--log-file").value();
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if (!minitts::cli::has_arg(argc, argv, "--log")) throw std::runtime_error("--log is required");
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minitts::cli::require_known_args(argc, argv);
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engine::debug::configure_logging({true, log});
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auto tensor_source = engine::assets::open_tensor_source(reference);
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auto index = engine::io::load_safetensors_index(reference);
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const auto shape = index.tensors.at("features").shape;
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const auto samples = index.tensors.at("waveform").shape[0];
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auto waveform = tensor_source->require_f32("waveform", {samples});
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auto expected = tensor_source->require_f32("expected", {samples});
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auto features = tensor_source->require_f32("features", shape);
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const int sr = std::stoi(index.metadata.at("sample_rate"));
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auto spectrum = engine::models::realtime_reuse::analyze_spectrum(waveform, sr, 8);
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double max_abs = 0;
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size_t jumps = 0;
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for (size_t i = 0; i < features.size(); ++i) {
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max_abs = std::max(max_abs, std::abs(double(features[i]) - spectrum.features[i]));
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jumps += std::abs(features[i] - spectrum.features[i]) > 1;
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}
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engine::debug::trace_log_scalar("realtime_reuse.reference.feature_max_abs", max_abs);
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engine::debug::trace_log_scalar("realtime_reuse.reference.feature_jumps", jumps);
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engine::debug::trace_log_f32("realtime_reuse.reference.features", {shape[0], shape[1], shape[2]}, features);
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engine::debug::trace_log_f32("realtime_reuse.frontend.features", {shape[0], shape[1], shape[2]}, spectrum.features);
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engine::core::ExecutionContext execution({minitts::cli::parse_backend(backend), 0, 8});
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engine::models::realtime_reuse::Config config;
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config.exit_layer = std::stoi(index.metadata.at("exit_layer"));
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config.lookahead_frames = std::stoi(index.metadata.at("lookahead_frames"));
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engine::models::realtime_reuse::Runtime runtime(engine::models::realtime_reuse::load_assets(model), execution,
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engine::assets::TensorStorageType::Native, config);
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const auto started = std::chrono::steady_clock::now();
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auto heads = runtime.infer(features, shape[1], shape[2], false);
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auto output = engine::models::realtime_reuse::synthesize_spectrum(spectrum, heads, 8);
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double aa = 0, bb = 0, ab = 0;
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for (size_t i = 0; i < output.size(); ++i) {
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aa += double(output[i]) * output[i];
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bb += double(expected[i]) * expected[i];
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ab += double(output[i]) * expected[i];
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}
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const double cosine = ab / std::sqrt(aa * bb);
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engine::debug::trace_log_scalar("realtime_reuse.reference.waveform_cosine", cosine);
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engine::debug::timing_log_scalar("session.wall_ms", engine::debug::elapsed_ms(started));
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if (!(cosine > .99999)) throw std::runtime_error("Aligned-input waveform parity failed");
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std::cout << "Real-time RE-USE aligned-input waveform parity passed\n";
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} catch (const std::exception & error) {
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std::cerr << error.what() << '\n';
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return 1;
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}
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}
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