Update modelscope_models.md

chenmengzheAAA

2023-09-14 30c40c643c19f6e2ac8679fa76d09d0f9ceccc65

 funasr/runtime/python/onnxruntime/funasr_onnx/utils/utils.py

@@ -6,16 +6,67 @@
from pathlib import Path
from typing import Any, Dict, Iterable, List, NamedTuple, Set, Tuple, Union

import re
import torch
import numpy as np
import yaml
from onnxruntime import (GraphOptimizationLevel, InferenceSession,
                         SessionOptions, get_available_providers, get_device)

try:
    from onnxruntime import (GraphOptimizationLevel, InferenceSession,
                             SessionOptions, get_available_providers, get_device)
except:
    print("please pip3 install onnxruntime")
import jieba
import warnings

root_dir = Path(__file__).resolve().parent

logger_initialized = {}


def pad_list(xs, pad_value, max_len=None):
    n_batch = len(xs)
    if max_len is None:
        max_len = max(x.size(0) for x in xs)
    pad = xs[0].new(n_batch, max_len, *xs[0].size()[1:]).fill_(pad_value)

    for i in range(n_batch):
        pad[i, : xs[i].size(0)] = xs[i]

    return pad


def make_pad_mask(lengths, xs=None, length_dim=-1, maxlen=None):
    if length_dim == 0:
        raise ValueError("length_dim cannot be 0: {}".format(length_dim))

    if not isinstance(lengths, list):
        lengths = lengths.tolist()
    bs = int(len(lengths))
    if maxlen is None:
        if xs is None:
            maxlen = int(max(lengths))
        else:
            maxlen = xs.size(length_dim)
    else:
        assert xs is None
        assert maxlen >= int(max(lengths))

    seq_range = torch.arange(0, maxlen, dtype=torch.int64)
    seq_range_expand = seq_range.unsqueeze(0).expand(bs, maxlen)
    seq_length_expand = seq_range_expand.new(lengths).unsqueeze(-1)
    mask = seq_range_expand >= seq_length_expand

    if xs is not None:
        assert xs.size(0) == bs, (xs.size(0), bs)

        if length_dim < 0:
            length_dim = xs.dim() + length_dim
        # ind = (:, None, ..., None, :, , None, ..., None)
        ind = tuple(
            slice(None) if i in (0, length_dim) else None for i in range(xs.dim())
        )
        mask = mask[ind].expand_as(xs).to(xs.device)
    return mask


class TokenIDConverter():
@@ -230,6 +281,64 @@
            words.append(current_word)
    return words

def isEnglish(text:str):
    if re.search('^[a-zA-Z\']+$', text):
        return True
    else:
        return False

def join_chinese_and_english(input_list):
    line = ''
    for token in input_list:
        if isEnglish(token):
            line = line + ' ' + token
        else:
            line = line + token

    line = line.strip()
    return line

def code_mix_split_words_jieba(seg_dict_file: str):
    jieba.load_userdict(seg_dict_file)

    def _fn(text: str):
        input_list = text.split()
        token_list_all = []
        langauge_list = []
        token_list_tmp = []
        language_flag = None
        for token in input_list:
            if isEnglish(token) and language_flag == 'Chinese':
                token_list_all.append(token_list_tmp)
                langauge_list.append('Chinese')
                token_list_tmp = []
            elif not isEnglish(token) and language_flag == 'English':
                token_list_all.append(token_list_tmp)
                langauge_list.append('English')
                token_list_tmp = []
    
            token_list_tmp.append(token)
    
            if isEnglish(token):
                language_flag = 'English'
            else:
                language_flag = 'Chinese'
    
        if token_list_tmp:
            token_list_all.append(token_list_tmp)
            langauge_list.append(language_flag)
    
        result_list = []
        for token_list_tmp, language_flag in zip(token_list_all, langauge_list):
            if language_flag == 'English':
                result_list.extend(token_list_tmp)
            else:
                seg_list = jieba.cut(join_chinese_and_english(token_list_tmp), HMM=False)
                result_list.extend(seg_list)
    
        return result_list
    return _fn

def read_yaml(yaml_path: Union[str, Path]) -> Dict:
    if not Path(yaml_path).exists():
        raise FileExistsError(f'The {yaml_path} does not exist.')