| | |
| | | std::vector<std::vector<float>> hotwords_embedding = CompileHotwordEmbedding(asr_handle, nn_hotwords_); |
| | | |
| | | // warm up |
| | | for (size_t i = 0; i < 10; i++) |
| | | for (size_t i = 0; i < 1; i++) |
| | | { |
| | | FUNASR_RESULT result=FunOfflineInfer(asr_handle, wav_list[0].c_str(), RASR_NONE, nullptr, hotwords_embedding, audio_fs, true, decoder_handle); |
| | | if(result){ |
| | |
| | | TCLAP::ValueArg<std::int32_t> thread_num("", THREAD_NUM, "multi-thread num for rtf", false, 1, "int32_t"); |
| | | TCLAP::ValueArg<std::string> hotword("", HOTWORD, "the hotword file, one hotword perline, Format: Hotword Weight (could be: 阿里巴巴 20)", false, "", "string"); |
| | | TCLAP::SwitchArg use_gpu("", INFER_GPU, "Whether to use GPU for inference, default is false", false); |
| | | TCLAP::ValueArg<std::int32_t> batch_size("", BATCHSIZE, "batch_size for ASR model when using GPU", false, 4, "int32_t"); |
| | | |
| | | cmd.add(model_dir); |
| | | cmd.add(quantize); |
| | |
| | | cmd.add(audio_fs); |
| | | cmd.add(thread_num); |
| | | cmd.add(use_gpu); |
| | | cmd.add(batch_size); |
| | | cmd.parse(argc, argv); |
| | | |
| | | std::map<std::string, std::string> model_path; |
| | |
| | | struct timeval start, end; |
| | | gettimeofday(&start, nullptr); |
| | | bool use_gpu_ = use_gpu.getValue(); |
| | | FUNASR_HANDLE asr_handle=FunOfflineInit(model_path, 1, use_gpu_); |
| | | int batch_size_ = batch_size.getValue(); |
| | | FUNASR_HANDLE asr_handle=FunOfflineInit(model_path, 1, use_gpu_, batch_size_); |
| | | |
| | | if (!asr_handle) |
| | | { |