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https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
fix uniasr decoding bug
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parent
7bb2dfba0c
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@ -398,6 +398,19 @@ def inference_modelscope(
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else:
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device = "cpu"
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if param_dict is not None and "decoding_model" in param_dict:
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if param_dict["decoding_model"] == "fast":
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decoding_ind = 0
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decoding_mode = "model1"
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elif param_dict["decoding_model"] == "normal":
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decoding_ind = 0
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decoding_mode = "model2"
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elif param_dict["decoding_model"] == "offline":
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decoding_ind = 1
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decoding_mode = "model2"
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else:
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raise NotImplementedError("unsupported decoding model {}".format(param_dict["decoding_model"]))
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# 1. Set random-seed
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set_all_random_seed(seed)
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@ -440,18 +453,6 @@ def inference_modelscope(
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if isinstance(raw_inputs, torch.Tensor):
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raw_inputs = raw_inputs.numpy()
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data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
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if param_dict is not None and "decoding_model" in param_dict:
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if param_dict["decoding_model"] == "fast":
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speech2text.decoding_ind = 0
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speech2text.decoding_mode = "model1"
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elif param_dict["decoding_model"] == "normal":
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speech2text.decoding_ind = 0
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speech2text.decoding_mode = "model2"
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elif param_dict["decoding_model"] == "offline":
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speech2text.decoding_ind = 1
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speech2text.decoding_mode = "model2"
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else:
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raise NotImplementedError("unsupported decoding model {}".format(param_dict["decoding_model"]))
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loader = ASRTask.build_streaming_iterator(
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data_path_and_name_and_type,
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dtype=dtype,
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@ -398,6 +398,19 @@ def inference_modelscope(
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else:
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device = "cpu"
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if param_dict is not None and "decoding_model" in param_dict:
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if param_dict["decoding_model"] == "fast":
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decoding_ind = 0
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decoding_mode = "model1"
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elif param_dict["decoding_model"] == "normal":
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decoding_ind = 0
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decoding_mode = "model2"
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elif param_dict["decoding_model"] == "offline":
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decoding_ind = 1
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decoding_mode = "model2"
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else:
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raise NotImplementedError("unsupported decoding model {}".format(param_dict["decoding_model"]))
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# 1. Set random-seed
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set_all_random_seed(seed)
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@ -440,18 +453,6 @@ def inference_modelscope(
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if isinstance(raw_inputs, torch.Tensor):
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raw_inputs = raw_inputs.numpy()
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data_path_and_name_and_type = [raw_inputs, "speech", "waveform"]
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if param_dict is not None and "decoding_model" in param_dict:
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if param_dict["decoding_model"] == "fast":
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speech2text.decoding_ind = 0
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speech2text.decoding_mode = "model1"
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elif param_dict["decoding_model"] == "normal":
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speech2text.decoding_ind = 0
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speech2text.decoding_mode = "model2"
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elif param_dict["decoding_model"] == "offline":
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speech2text.decoding_ind = 1
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speech2text.decoding_mode = "model2"
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else:
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raise NotImplementedError("unsupported decoding model {}".format(param_dict["decoding_model"]))
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loader = ASRTask.build_streaming_iterator(
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data_path_and_name_and_type,
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dtype=dtype,
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