# -*- encoding: utf-8 -*-
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# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
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# MIT License (https://opensource.org/licenses/MIT)
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import os.path
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from pathlib import Path
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from typing import List, Union, Tuple
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import numpy as np
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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,code_mix_split_words_jieba)
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logging = get_logger()
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class CT_Transformer():
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
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https://arxiv.org/pdf/2003.01309.pdf
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"""
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def __init__(self, model_dir: Union[str, Path] = None,
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batch_size: int = 1,
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device_id: Union[str, int] = "-1",
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quantize: bool = False,
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intra_op_num_threads: int = 4,
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cache_dir: str = None,
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):
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if not Path(model_dir).exists():
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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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raise "model_dir must be model_name in modelscope or local path downloaded from modelscope, but is {}".format(
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model_dir)
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model_file = os.path.join(model_dir, 'model.onnx')
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if quantize:
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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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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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device="cpu",
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quant=quantize,
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)
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export_model.export(model_dir)
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config_file = os.path.join(model_dir, 'punc.yaml')
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config = read_yaml(config_file)
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self.converter = TokenIDConverter(config['token_list'])
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self.ort_infer = OrtInferSession(model_file, device_id, intra_op_num_threads=intra_op_num_threads)
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self.batch_size = 1
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self.punc_list = config['punc_list']
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self.period = 0
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for i in range(len(self.punc_list)):
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if self.punc_list[i] == ",":
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self.punc_list[i] = "，"
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elif self.punc_list[i] == "?":
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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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def __call__(self, text: Union[list, str], split_size=20):
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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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assert len(mini_sentences) == len(mini_sentences_id)
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cache_sent = []
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cache_sent_id = []
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new_mini_sentence = ""
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new_mini_sentence_punc = []
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cache_pop_trigger_limit = 200
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for mini_sentence_i in range(len(mini_sentences)):
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mini_sentence = mini_sentences[mini_sentence_i]
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mini_sentence_id = mini_sentences_id[mini_sentence_i]
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mini_sentence = cache_sent + mini_sentence
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mini_sentence_id = np.array(cache_sent_id + mini_sentence_id, dtype='int32')
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data = {
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"text": mini_sentence_id[None,:],
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"text_lengths": np.array([len(mini_sentence_id)], dtype='int32'),
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}
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try:
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outputs = self.infer(data['text'], data['text_lengths'])
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y = outputs[0]
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punctuations = np.argmax(y,axis=-1)[0]
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assert punctuations.size == len(mini_sentence)
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except ONNXRuntimeError:
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logging.warning("error")
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# Search for the last Period/QuestionMark as cache
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if mini_sentence_i < len(mini_sentences) - 1:
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sentenceEnd = -1
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last_comma_index = -1
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for i in range(len(punctuations) - 2, 1, -1):
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if self.punc_list[punctuations[i]] == "。" or self.punc_list[punctuations[i]] == "？":
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sentenceEnd = i
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break
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if last_comma_index < 0 and self.punc_list[punctuations[i]] == "，":
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last_comma_index = i
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if sentenceEnd < 0 and len(mini_sentence) > cache_pop_trigger_limit and last_comma_index >= 0:
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# The sentence it too long, cut off at a comma.
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sentenceEnd = last_comma_index
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punctuations[sentenceEnd] = self.period
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cache_sent = mini_sentence[sentenceEnd + 1:]
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cache_sent_id = mini_sentence_id[sentenceEnd + 1:].tolist()
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mini_sentence = mini_sentence[0:sentenceEnd + 1]
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punctuations = punctuations[0:sentenceEnd + 1]
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new_mini_sentence_punc += [int(x) for x in punctuations]
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words_with_punc = []
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for i in range(len(mini_sentence)):
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if i > 0:
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if len(mini_sentence[i][0].encode()) == 1 and len(mini_sentence[i - 1][0].encode()) == 1:
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mini_sentence[i] = " " + mini_sentence[i]
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words_with_punc.append(mini_sentence[i])
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if self.punc_list[punctuations[i]] != "_":
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words_with_punc.append(self.punc_list[punctuations[i]])
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new_mini_sentence += "".join(words_with_punc)
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# Add Period for the end of the sentence
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new_mini_sentence_out = new_mini_sentence
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new_mini_sentence_punc_out = new_mini_sentence_punc
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if mini_sentence_i == len(mini_sentences) - 1:
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if new_mini_sentence[-1] == "，" or new_mini_sentence[-1] == "、":
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new_mini_sentence_out = new_mini_sentence[:-1] + "。"
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new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.period]
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elif new_mini_sentence[-1] != "。" and new_mini_sentence[-1] != "？":
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new_mini_sentence_out = new_mini_sentence + "。"
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new_mini_sentence_punc_out = new_mini_sentence_punc[:-1] + [self.period]
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return new_mini_sentence_out, new_mini_sentence_punc_out
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def infer(self, feats: np.ndarray,
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feats_len: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
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outputs = self.ort_infer([feats, feats_len])
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return outputs
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class CT_Transformer_VadRealtime(CT_Transformer):
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"""
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Author: Speech Lab of DAMO Academy, Alibaba Group
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CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
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https://arxiv.org/pdf/2003.01309.pdf
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"""
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def __init__(self, model_dir: Union[str, Path] = None,
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batch_size: int = 1,
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device_id: Union[str, int] = "-1",
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quantize: bool = False,
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intra_op_num_threads: int = 4,
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cache_dir: str = None
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):
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super(CT_Transformer_VadRealtime, self).__init__(model_dir, batch_size, device_id, quantize, intra_op_num_threads, cache_dir=cache_dir)
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def __call__(self, text: str, param_dict: map, split_size=20):
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cache_key = "cache"
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assert cache_key in param_dict
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cache = param_dict[cache_key]
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if cache is not None and len(cache) > 0:
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precache = "".join(cache)
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else:
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precache = ""
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cache = []
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full_text = precache + " " + text
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split_text = code_mix_split_words(full_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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new_mini_sentence_punc = []
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assert len(mini_sentences) == len(mini_sentences_id)
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cache_sent = []
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cache_sent_id = np.array([], dtype='int32')
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sentence_punc_list = []
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sentence_words_list = []
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cache_pop_trigger_limit = 200
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skip_num = 0
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for mini_sentence_i in range(len(mini_sentences)):
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mini_sentence = mini_sentences[mini_sentence_i]
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mini_sentence_id = mini_sentences_id[mini_sentence_i]
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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": 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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y = outputs[0]
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punctuations = np.argmax(y,axis=-1)[0]
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assert punctuations.size == len(mini_sentence)
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except ONNXRuntimeError:
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logging.warning("error")
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# Search for the last Period/QuestionMark as cache
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if mini_sentence_i < len(mini_sentences) - 1:
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sentenceEnd = -1
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last_comma_index = -1
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for i in range(len(punctuations) - 2, 1, -1):
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if self.punc_list[punctuations[i]] == "。" or self.punc_list[punctuations[i]] == "？":
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sentenceEnd = i
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break
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if last_comma_index < 0 and self.punc_list[punctuations[i]] == "，":
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last_comma_index = i
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if sentenceEnd < 0 and len(mini_sentence) > cache_pop_trigger_limit and last_comma_index >= 0:
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# The sentence it too long, cut off at a comma.
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sentenceEnd = last_comma_index
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punctuations[sentenceEnd] = self.period
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cache_sent = mini_sentence[sentenceEnd + 1:]
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cache_sent_id = mini_sentence_id[sentenceEnd + 1:]
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mini_sentence = mini_sentence[0:sentenceEnd + 1]
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punctuations = punctuations[0:sentenceEnd + 1]
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punctuations_np = [int(x) for x in punctuations]
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new_mini_sentence_punc += punctuations_np
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sentence_punc_list += [self.punc_list[int(x)] for x in punctuations_np]
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sentence_words_list += mini_sentence
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assert len(sentence_punc_list) == len(sentence_words_list)
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words_with_punc = []
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sentence_punc_list_out = []
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for i in range(0, len(sentence_words_list)):
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if i > 0:
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if len(sentence_words_list[i][0].encode()) == 1 and len(sentence_words_list[i - 1][-1].encode()) == 1:
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sentence_words_list[i] = " " + sentence_words_list[i]
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if skip_num < len(cache):
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skip_num += 1
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else:
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words_with_punc.append(sentence_words_list[i])
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if skip_num >= len(cache):
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sentence_punc_list_out.append(sentence_punc_list[i])
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if sentence_punc_list[i] != "_":
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words_with_punc.append(sentence_punc_list[i])
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sentence_out = "".join(words_with_punc)
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sentenceEnd = -1
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for i in range(len(sentence_punc_list) - 2, 1, -1):
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if sentence_punc_list[i] == "。" or sentence_punc_list[i] == "？":
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sentenceEnd = i
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break
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cache_out = sentence_words_list[sentenceEnd + 1:]
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if sentence_out[-1] in self.punc_list:
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sentence_out = sentence_out[:-1]
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sentence_punc_list_out[-1] = "_"
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param_dict[cache_key] = cache_out
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return sentence_out, sentence_punc_list_out, cache_out
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def vad_mask(self, size, vad_pos, dtype=bool):
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"""Create mask for decoder self-attention.
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:param int size: size of mask
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:param int vad_pos: index of vad index
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:param torch.dtype dtype: result dtype
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:rtype: torch.Tensor (B, Lmax, Lmax)
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"""
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ret = np.ones((size, size), dtype=dtype)
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if vad_pos <= 0 or vad_pos >= size:
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return ret
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sub_corner = np.zeros(
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(vad_pos - 1, size - vad_pos), dtype=dtype)
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ret[0:vad_pos - 1, vad_pos:] = sub_corner
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return ret
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def infer(self, feats: np.ndarray,
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feats_len: np.ndarray,
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vad_mask: np.ndarray,
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sub_masks: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
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outputs = self.ort_infer([feats, feats_len, vad_mask, sub_masks])
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return outputs
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