| | |
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| | | from .utils.utils import (ONNXRuntimeError, |
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| | | OrtInferSession, get_logger, |
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| | | read_yaml) |
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| | | from .utils.utils import (TokenIDConverter, split_to_mini_sentence,code_mix_split_words) |
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| | | from .utils.utils import (TokenIDConverter, split_to_mini_sentence,code_mix_split_words,code_mix_split_words_jieba) |
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| | | logging = get_logger() |
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| | | |
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| | | |
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| | |
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| | | ): |
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| | | |
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| | | if not Path(model_dir).exists(): |
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| | | from modelscope.hub.snapshot_download import snapshot_download |
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| | | try: |
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| | | from modelscope.hub.snapshot_download import snapshot_download |
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| | | except: |
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| | | raise "You are exporting model from modelscope, please install modelscope and try it again. To install modelscope, you could:\n" \ |
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| | | "\npip3 install -U modelscope\n" \ |
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| | | "For the users in China, you could install with the command:\n" \ |
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| | | "\npip3 install -U modelscope -i https://mirror.sjtu.edu.cn/pypi/web/simple" |
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| | | try: |
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| | | model_dir = snapshot_download(model_dir, cache_dir=cache_dir) |
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| | | except: |
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| | |
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| | | model_file = os.path.join(model_dir, 'model_quant.onnx') |
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| | | if not os.path.exists(model_file): |
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| | | print(".onnx is not exist, begin to export onnx") |
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| | | from funasr.export.export_model import ModelExport |
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| | | try: |
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| | | from funasr.export.export_model import ModelExport |
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| | | except: |
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| | | raise "You are exporting onnx, please install funasr and try it again. To install funasr, you could:\n" \ |
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| | | "\npip3 install -U funasr\n" \ |
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| | | "For the users in China, you could install with the command:\n" \ |
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| | | "\npip3 install -U funasr -i https://mirror.sjtu.edu.cn/pypi/web/simple" |
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| | | export_model = ModelExport( |
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| | | cache_dir=cache_dir, |
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| | | onnx=True, |
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| | |
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| | | self.punc_list[i] = "？" |
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| | | elif self.punc_list[i] == "。": |
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| | | self.period = i |
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| | | if "seg_jieba" in config: |
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| | | self.seg_jieba = True |
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| | | self.jieba_usr_dict_path = os.path.join(model_dir, 'jieba_usr_dict') |
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| | | self.code_mix_split_words_jieba = code_mix_split_words_jieba(self.jieba_usr_dict_path) |
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| | | else: |
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| | | self.seg_jieba = False |
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| | | |
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| | | def __call__(self, text: Union[list, str], split_size=20): |
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| | | split_text = code_mix_split_words(text) |
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| | | if self.seg_jieba: |
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| | | split_text = self.code_mix_split_words_jieba(text) |
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| | | else: |
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| | | split_text = code_mix_split_words(text) |
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| | | split_text_id = self.converter.tokens2ids(split_text) |
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| | | mini_sentences = split_to_mini_sentence(split_text, split_size) |
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| | | mini_sentences_id = split_to_mini_sentence(split_text_id, split_size) |
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| | |
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| | | mini_sentence = cache_sent + mini_sentence |
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| | | mini_sentence_id = np.concatenate((cache_sent_id, mini_sentence_id), axis=0,dtype='int32') |
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| | | text_length = len(mini_sentence_id) |
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| | | vad_mask = self.vad_mask(text_length, len(cache))[None, None, :, :].astype(np.float32) |
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| | | data = { |
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| | | "input": mini_sentence_id[None,:], |
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| | | "text_lengths": np.array([text_length], dtype='int32'), |
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| | | "vad_mask": self.vad_mask(text_length, len(cache))[None, None, :, :].astype(np.float32), |
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| | | "sub_masks": np.tril(np.ones((text_length, text_length), dtype=np.float32))[None, None, :, :].astype(np.float32) |
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| | | "vad_mask": vad_mask, |
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| | | "sub_masks": vad_mask |
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| | | } |
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| | | try: |
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| | | outputs = self.infer(data['input'], data['text_lengths'], data['vad_mask'], data["sub_masks"]) |
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