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| | | |
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| | | Examples: |
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| | | >>> import soundfile |
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| | | >>> speech2text = Speech2Text("asr_config.yml", "asr.pth") |
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| | | >>> speech2text = Speech2Text("asr_config.yml", "asr.pb") |
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| | | >>> audio, rate = soundfile.read("speech.wav") |
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| | | >>> speech2text(audio) |
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| | | [(text, token, token_int, hypothesis object), ...] |
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| | |
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| | | # Input as audio signal |
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| | | if isinstance(speech, np.ndarray): |
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| | | speech = torch.tensor(speech) |
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| | | |
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| | | |
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| | | if(speech.dim()==3): |
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| | | speech = torch.squeeze(speech, 2) |
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| | | #speech = speech.unsqueeze(0).to(getattr(torch, self.dtype)) |
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| | | speech = speech.to(getattr(torch, self.dtype)) |
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| | | # lenghts: (1,) |
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| | |
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| | | ngram_weight: float = 0.9, |
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| | | nbest: int = 1, |
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| | | num_workers: int = 1, |
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| | | param_dict: dict = None, |
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| | | **kwargs, |
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| | | ): |
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| | | assert check_argument_types() |
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| | |
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| | | def _forward(data_path_and_name_and_type, |
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| | | raw_inputs: Union[np.ndarray, torch.Tensor] = None, |
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| | | output_dir_v2: Optional[str] = None, |
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| | | fs: dict = None, |
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| | | param_dict: dict = None, |
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| | | **kwargs, |
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| | | ): |
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| | | # 3. Build data-iterator |
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| | | if data_path_and_name_and_type is None and raw_inputs is not None: |
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| | |
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| | | data_path_and_name_and_type, |
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| | | dtype=dtype, |
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| | | batch_size=batch_size, |
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| | | fs=fs, |
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| | | mc=True, |
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| | | key_file=key_file, |
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| | | num_workers=num_workers, |
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| | | preprocess_fn=ASRTask.build_preprocess_fn(speech2text.asr_train_args, False), |
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| | |
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| | | return asr_result_list |
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| | | |
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| | | return _forward |
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| | | |
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| | | def set_parameters(language: str = None, |
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| | | sample_rate: Union[int, Dict[Any, int]] = None): |
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| | | if language is not None: |
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| | | global global_asr_language |
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| | | global_asr_language = language |
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| | | if sample_rate is not None: |
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| | | global global_sample_rate |
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| | | global_sample_rate = sample_rate |
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| | | |
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| | | |
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| | | def get_parser(): |
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| | | parser = config_argparse.ArgumentParser( |
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