Method run

src/framework/audio/rnnoise.cpp:1225–1258

    }

    RnnoiseSequenceRunOutput run(
        const float * features,
        int64_t frames,
        RnnoiseSequenceState & state) const {
        if (features == nullptr || frames <= 0 || frames > capacity_frames_) {
            throw std::runtime_error("RNNoise sequence graph input shape mismatch");
        }
        std::vector<float> padded_features(static_cast<size_t>(capacity_frames_ * kRnnoiseFeatures), 0.0f);
        std::copy(features, features + frames * kRnnoiseFeatures, padded_features.begin());
        ggml_backend_tensor_set(features_, padded_features.data(), 0, padded_features.size() * sizeof(float));
        ggml_backend_tensor_set(conv1_memory_in_, state.conv1_memory.data(), 0, state.conv1_memory.size() * sizeof(float));
        ggml_backend_tensor_set(conv2_memory_in_, state.conv2_memory.data(), 0, state.conv2_memory.size() * sizeof(float));
        ggml_backend_tensor_set(gru1_hidden_in_, state.gru1_hidden.data(), 0, state.gru1_hidden.size() * sizeof(float));
        ggml_backend_tensor_set(gru2_hidden_in_, state.gru2_hidden.data(), 0, state.gru2_hidden.size() * sizeof(float));
        ggml_backend_tensor_set(gru3_hidden_in_, state.gru3_hidden.data(), 0, state.gru3_hidden.size() * sizeof(float));
        const auto status = core::compute_backend_graph(weights_.backend.get(), graph_, plan_, "RNNoise sequence");
        ggml_backend_synchronize(weights_.backend.get());
        if (status != GGML_STATUS_SUCCESS) {
            throw std::runtime_error("RNNoise sequence GGML graph compute failed");
        }
        RnnoiseSequenceRunOutput output;
        output.gains.resize(static_cast<size_t>(capacity_frames_ * weights_.config.gain_bands));
        output.vad.resize(static_cast<size_t>(capacity_frames_));
        ggml_backend_tensor_get(gains_, output.gains.data(), 0, output.gains.size() * sizeof(float));
        ggml_backend_tensor_get(vad_, output.vad.data(), 0, output.vad.size() * sizeof(float));
        output.gains.resize(static_cast<size_t>(frames * weights_.config.gain_bands));
        output.vad.resize(static_cast<size_t>(frames));
        read_output(conv1_memory_out_, state.conv1_memory);
        read_output(conv2_memory_out_, state.conv2_memory);
        read_output(gru1_hidden_out_, state.gru1_hidden);
        read_output(gru2_hidden_out_, state.gru2_hidden);
        read_output(gru3_hidden_out_, state.gru3_hidden);
        return output;
    }

private:
    static void read_output(ggml_tensor * tensor, std::vector<float> & values) {