Function main

tensorflow/examples/speech_commands/label_wav.cc:101–177

}  // namespace

int main(int argc, char* argv[]) {
  string wav = "";
  string graph = "";
  string labels = "";
  string input_name = "wav_data";
  string output_name = "labels_softmax";
  int32 how_many_labels = 3;
  std::vector<Flag> flag_list = {
      Flag("wav", &wav, "audio file to be identified"),
      Flag("graph", &graph, "model to be executed"),
      Flag("labels", &labels, "path to file containing labels"),
      Flag("input_name", &input_name, "name of input node in model"),
      Flag("output_name", &output_name, "name of output node in model"),
      Flag("how_many_labels", &how_many_labels, "number of results to show"),
  };
  string usage = tensorflow::Flags::Usage(argv[0], flag_list);
  const bool parse_result = tensorflow::Flags::Parse(&argc, argv, flag_list);
  if (!parse_result) {
    LOG(ERROR) << usage;
    return -1;
  }

  // We need to call this to set up global state for TensorFlow.
  tensorflow::port::InitMain(argv[0], &argc, &argv);
  if (argc > 1) {
    LOG(ERROR) << "Unknown argument " << argv[1] << "\n" << usage;
    return -1;
  }

  // First we load and initialize the model.
  std::unique_ptr<tensorflow::Session> session;
  Status load_graph_status = LoadGraph(graph, &session);
  if (!load_graph_status.ok()) {
    LOG(ERROR) << load_graph_status;
    return -1;
  }

  std::vector<string> labels_list;
  Status read_labels_status = ReadLabelsFile(labels, &labels_list);
  if (!read_labels_status.ok()) {
    LOG(ERROR) << read_labels_status;
    return -1;
  }

  string wav_string;
  Status read_wav_status = tensorflow::ReadFileToString(
      tensorflow::Env::Default(), wav, &wav_string);
  if (!read_wav_status.ok()) {
    LOG(ERROR) << read_wav_status;
    return -1;
  }
  Tensor wav_tensor(tensorflow::DT_STRING, tensorflow::TensorShape({}));
  wav_tensor.scalar<tstring>()() = wav_string;

  // Actually run the audio through the model.
  std::vector<Tensor> outputs;
  Status run_status =
      session->Run({{input_name, wav_tensor}}, {output_name}, {}, &outputs);