| | |
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| | | from .utils.utils import (CharTokenizer, Hypothesis, ONNXRuntimeError, |
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| | | OrtInferSession, TokenIDConverter, get_logger, |
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| | | read_yaml) |
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| | | from .utils.postprocess_utils import sentence_postprocess |
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| | | from .utils.postprocess_utils import (sentence_postprocess, |
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| | | sentence_postprocess_sentencepiece) |
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| | | from .utils.frontend import WavFrontend |
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| | | from .utils.timestamp_utils import time_stamp_lfr6_onnx |
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| | | from .utils.utils import pad_list, make_pad_mask |
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| | |
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| | | self.pred_bias = config['model_conf']['predictor_bias'] |
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| | | else: |
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| | | self.pred_bias = 0 |
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| | | if "lang" in config: |
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| | | self.language = config['lang'] |
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| | | else: |
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| | | self.language = None |
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| | | |
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| | | def __call__(self, wav_content: Union[str, np.ndarray, List[str]], **kwargs) -> List: |
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| | | waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq) |
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| | |
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| | | preds = self.decode(am_scores, valid_token_lens) |
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| | | if us_peaks is None: |
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| | | for pred in preds: |
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| | | pred = sentence_postprocess(pred) |
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| | | if self.language == "en-bpe": |
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| | | pred = sentence_postprocess_sentencepiece(pred) |
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| | | else: |
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| | | pred = sentence_postprocess(pred) |
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| | | asr_res.append({'preds': pred}) |
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| | | else: |
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| | | for pred, us_peaks_ in zip(preds, us_peaks): |
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