add decoding model parameters

仁迷

2023-02-15 b22c0d228493fe6a662bc8f188b3d46090c09b9c

add decoding model parameters

 funasr/bin/asr_inference_uniasr.py

@@ -397,7 +397,7 @@
        device = "cuda"
    else:
        device = "cpu"

    
    # 1. Set random-seed
    set_all_random_seed(seed)

@@ -439,6 +439,18 @@
            if isinstance(raw_inputs, torch.Tensor):
                raw_inputs = raw_inputs.numpy()
            data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
        if param_dict is not None and "decoding_model" in param_dict:
            if param_dict["decoding_model"] == "fast":
                speech2text.decoding_ind = 0
                speech2text.decoding_mode = "model1"
            elif param_dict["decoding_model"] == "normal":
                speech2text.decoding_ind = 0
                speech2text.decoding_mode = "model2"
            elif param_dict["decoding_model"] == "offline":
                speech2text.decoding_ind = 1
                speech2text.decoding_mode = "model2"
            else:
                raise NotImplementedError("unsupported decoding model {}".format(param_dict["decoding_model"]))
        loader = ASRTask.build_streaming_iterator(
            data_path_and_name_and_type,
            dtype=dtype,

 funasr/bin/asr_inference_uniasr_vad.py

@@ -439,6 +439,18 @@
            if isinstance(raw_inputs, torch.Tensor):
                raw_inputs = raw_inputs.numpy()
            data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
        if param_dict is not None and "decoding_model" in param_dict:
            if param_dict["decoding_model"] == "fast":
                speech2text.decoding_ind = 0
                speech2text.decoding_mode = "model1"
            elif param_dict["decoding_model"] == "normal":
                speech2text.decoding_ind = 0
                speech2text.decoding_mode = "model2"
            elif param_dict["decoding_model"] == "offline":
                speech2text.decoding_ind = 1
                speech2text.decoding_mode = "model2"
            else:
                raise NotImplementedError("unsupported decoding model {}".format(param_dict["decoding_model"]))
        loader = ASRTask.build_streaming_iterator(
            data_path_and_name_and_type,
            dtype=dtype,