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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,2 +1,3 @@ | ||
| build/ | ||
| fc_base/ | ||
| wheels* |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,115 @@ | ||
| import os | ||
| from typing import List | ||
|
|
||
| import jieba | ||
| import torch | ||
| from wenet.cli.hub import Hub | ||
| from wenet.paraformer.search import _isAllAlpha | ||
| from wenet.text.char_tokenizer import CharTokenizer | ||
|
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||
|
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| class PuncModel: | ||
|
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| def __init__(self, model_dir: str) -> None: | ||
| self.model_dir = model_dir | ||
| model_path = os.path.join(model_dir, 'final.zip') | ||
| units_path = os.path.join(model_dir, 'units.txt') | ||
|
|
||
| self.model = torch.jit.load(model_path) | ||
| self.tokenizer = CharTokenizer(units_path) | ||
| self.device = torch.device("cpu") | ||
| self.use_jieba = False | ||
|
|
||
| self.punc_table = ['<unk>', '', '，', '。', '？', '、'] | ||
|
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| def split_words(self, text: str): | ||
| if not self.use_jieba: | ||
| self.use_jieba = True | ||
| import logging | ||
|
|
||
| # Disable jieba's logger | ||
| logging.getLogger('jieba').disabled = True | ||
| jieba.load_userdict(os.path.join(self.model_dir, 'jieba_usr_dict')) | ||
|
|
||
| result_list = [] | ||
| tokens = text.split() | ||
| current_language = None | ||
| buffer = [] | ||
|
|
||
| for token in tokens: | ||
| is_english = token.isascii() | ||
| if is_english: | ||
| language = "English" | ||
| else: | ||
| language = "Chinese" | ||
|
|
||
| if current_language and language != current_language: | ||
| if current_language == "Chinese": | ||
| result_list.extend(jieba.cut(''.join(buffer), HMM=False)) | ||
| else: | ||
| result_list.extend(buffer) | ||
| buffer = [] | ||
|
|
||
| buffer.append(token) | ||
| current_language = language | ||
|
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| if buffer: | ||
| if current_language == "Chinese": | ||
| result_list.extend(jieba.cut(''.join(buffer), HMM=False)) | ||
| else: | ||
| result_list.extend(buffer) | ||
|
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| return result_list | ||
|
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| def add_punc_batch(self, texts: List[str]): | ||
| batch_text_words = [] | ||
| batch_text_ids = [] | ||
| batch_text_lens = [] | ||
|
|
||
| for text in texts: | ||
| words = self.split_words(text) | ||
| ids = self.tokenizer.tokens2ids(words) | ||
| batch_text_words.append(words) | ||
| batch_text_ids.append(ids) | ||
| batch_text_lens.append(len(ids)) | ||
|
|
||
| texts_tensor = torch.tensor(batch_text_ids, | ||
| device=self.device, | ||
| dtype=torch.int64) | ||
| texts_lens_tensor = torch.tensor(batch_text_lens, | ||
| device=self.device, | ||
| dtype=torch.int64) | ||
|
|
||
| log_probs, _ = self.model(texts_tensor, texts_lens_tensor) | ||
| result = [] | ||
| outs = log_probs.argmax(-1).cpu().numpy() | ||
| for i, out in enumerate(outs): | ||
| punc_id = out[:batch_text_lens[i]] | ||
| sentence = '' | ||
| for j, word in enumerate(batch_text_words[i]): | ||
| if _isAllAlpha(word): | ||
| word = '▁' + word | ||
| word += self.punc_table[punc_id[j]] | ||
| sentence += word | ||
| result.append(sentence.replace('▁', ' ')) | ||
| return result | ||
|
|
||
| def __call__(self, text: str): | ||
| if text != '': | ||
| r = self.add_punc_batch([text])[0] | ||
| return r | ||
| return '' | ||
|
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||
|
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| def load_model(model_dir: str = None, | ||
| gpu: int = -1, | ||
| device: str = "cpu") -> PuncModel: | ||
| if model_dir is None: | ||
| model_dir = Hub.get_model_by_lang('punc') | ||
| if gpu != -1: | ||
| # remain the original usage of gpu | ||
| device = "cuda" | ||
| punc = PuncModel(model_dir) | ||
| punc.device = torch.device(device) | ||
| punc.model.to(device) | ||
| return punc |
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