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| | | from typing import List, Union, Tuple |
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| | | import copy |
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| | | import torch |
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| | | import librosa |
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| | | import numpy as np |
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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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| | | from .utils.utils import pad_list |
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| | | |
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| | | logging = get_logger() |
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| | | # index from bias_embed |
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| | | bias_embed = bias_embed.transpose(1, 0, 2) |
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| | | _ind = np.arange(0, len(hotwords)).tolist() |
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| | | bias_embed = bias_embed[_ind, hotwords_length.cpu().numpy().tolist()] |
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| | | bias_embed = bias_embed[_ind, hotwords_length.tolist()] |
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| | | waveform_list = self.load_data(wav_content, self.frontend.opts.frame_opts.samp_freq) |
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| | | waveform_nums = len(waveform_list) |
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| | | asr_res = [] |
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| | | hotwords = hotwords.split(" ") |
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| | | hotwords_length = [len(i) - 1 for i in hotwords] |
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| | | hotwords_length.append(0) |
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| | | hotwords_length = torch.Tensor(hotwords_length).to(torch.int32) |
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| | | hotwords_length = np.array(hotwords_length) |
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| | | # hotwords.append('<s>') |
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| | | def word_map(word): |
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| | | hotwords = [] |
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| | | logging.warning("oov character {} found in hotword {}, replaced by <unk>".format(c, word)) |
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| | | else: |
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| | | hotwords.append(self.vocab[c]) |
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| | | return torch.tensor(hotwords) |
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| | | return np.array(hotwords) |
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| | | hotword_int = [word_map(i) for i in hotwords] |
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| | | # import pdb; pdb.set_trace() |
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| | | hotword_int.append(torch.tensor([1])) |
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| | | hotword_int.append(np.array([1])) |
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| | | hotwords = pad_list(hotword_int, pad_value=0, max_len=10) |
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| | | # import pdb; pdb.set_trace() |
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| | | return hotwords, hotwords_length |
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| | | |
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| | | def bb_infer(self, feats: np.ndarray, |
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| | | return outputs |
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| | | def eb_infer(self, hotwords, hotwords_length): |
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| | | outputs = self.ort_infer_eb([hotwords.to(torch.int32).numpy(), hotwords_length.to(torch.int32).numpy()]) |
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| | | outputs = self.ort_infer_eb([hotwords.astype(np.int32), hotwords_length.astype(np.int32)]) |
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| | | return outputs |
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| | | def decode(self, am_scores: np.ndarray, token_nums: int) -> List[str]: |
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