paraformer streaming bugfix

游雁

2024-03-18 cbe2ea7e07cbf364827bd89cefc42b3f643ea3be

 funasr/models/ct_transformer/model.py

@@ -3,6 +3,7 @@
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
#  MIT License  (https://opensource.org/licenses/MIT)

import copy
import torch
import numpy as np
import torch.nn.functional as F
@@ -16,8 +17,10 @@
from funasr.utils.load_utils import load_audio_text_image_video
from funasr.models.transformer.utils.nets_utils import make_pad_mask
from funasr.models.ct_transformer.utils import split_to_mini_sentence, split_words


try:
    import jieba
except:
    pass
if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
    from torch.cuda.amp import autocast
else:
@@ -69,6 +72,10 @@
        self.sos = sos
        self.eos = eos
        self.sentence_end_id = sentence_end_id
        self.jieba_usr_dict = None
        if kwargs.get("jieba_usr_dict", None) is not None:
            jieba.load_userdict(kwargs["jieba_usr_dict"])
            self.jieba_usr_dict = jieba
        
        

@@ -237,14 +244,8 @@
        # text = data_in[0]
        # text_lengths = data_lengths[0] if data_lengths is not None else None
        split_size = kwargs.get("split_size", 20)

        jieba_usr_dict = kwargs.get("jieba_usr_dict", None)
        if jieba_usr_dict and isinstance(jieba_usr_dict, str):
            import jieba
            jieba.load_userdict(jieba_usr_dict)
            jieba_usr_dict = jieba
            kwargs["jieba_usr_dict"] = "jieba_usr_dict"
        tokens = split_words(text, jieba_usr_dict=jieba_usr_dict)
        
        tokens = split_words(text, jieba_usr_dict=self.jieba_usr_dict)
        tokens_int = tokenizer.encode(tokens)

        mini_sentences = split_to_mini_sentence(tokens, split_size)
@@ -333,19 +334,41 @@
                elif new_mini_sentence[-1] == ",":
                    new_mini_sentence_out = new_mini_sentence[:-1] + "."
                    new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.sentence_end_id]
                elif new_mini_sentence[-1] != "。" and new_mini_sentence[-1] != "？" and len(new_mini_sentence[-1].encode())==0:
                elif new_mini_sentence[-1] != "。" and new_mini_sentence[-1] != "？" and len(new_mini_sentence[-1].encode())!=1:
                    new_mini_sentence_out = new_mini_sentence + "。"
                    new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.sentence_end_id]
                    if len(punctuations): punctuations[-1] = 2
                elif new_mini_sentence[-1] != "." and new_mini_sentence[-1] != "?" and len(new_mini_sentence[-1].encode())==1:
                    new_mini_sentence_out = new_mini_sentence + "."
                    new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.sentence_end_id]
            # keep a punctuations array for punc segment
                    if len(punctuations): punctuations[-1] = 2
            # keep a punctuations array for punc segment 
            if punc_array is None:
                punc_array = punctuations
            else:
                punc_array = torch.cat([punc_array, punctuations], dim=0)
        # post processing when using word level punc model
        if self.jieba_usr_dict is not None:
            len_tokens = len(tokens)
            new_punc_array = copy.copy(punc_array).tolist()
            # for i, (token, punc_id) in enumerate(zip(tokens[::-1], punc_array.tolist()[::-1])):
            for i, token in enumerate(tokens[::-1]):
                if '\u0e00' <= token[0] <= '\u9fa5': # ignore en words
                    if len(token) > 1:
                        num_append = len(token) - 1
                        ind_append = len_tokens - i - 1
                        for _ in range(num_append):
                            new_punc_array.insert(ind_append, 1)
            punc_array = torch.tensor(new_punc_array)
        
        result_i = {"key": key[0], "text": new_mini_sentence_out, "punc_array": punc_array}
        results.append(result_i)
    
        return results, meta_data

    def export(self, **kwargs):
    
        from .export_meta import export_rebuild_model
        models = export_rebuild_model(model=self, **kwargs)
        return models