#!/usr/bin/env python3
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# -*- 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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from typing import Optional
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from typing import Union
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import numpy as np
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import torch
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from funasr.build_utils.build_model_from_file import build_model_from_file
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from funasr.datasets.preprocessor import CodeMixTokenizerCommonPreprocessor
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from funasr.datasets.preprocessor import split_to_mini_sentence
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from funasr.torch_utils.device_funcs import to_device
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from funasr.torch_utils.forward_adaptor import ForwardAdaptor
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class Text2Punc:
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def __init__(
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self,
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train_config: Optional[str],
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model_file: Optional[str],
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device: str = "cpu",
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dtype: str = "float32",
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):
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# Build Model
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model, train_args = build_model_from_file(train_config, model_file, None, device, task_name="punc")
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self.device = device
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# Wrape model to make model.nll() data-parallel
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self.wrapped_model = ForwardAdaptor(model, "inference")
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self.wrapped_model.to(dtype=getattr(torch, dtype)).to(device=device).eval()
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# logging.info(f"Model:\n{model}")
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self.punc_list = train_args.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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self.preprocessor = CodeMixTokenizerCommonPreprocessor(
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train=False,
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token_type=train_args.token_type,
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token_list=train_args.token_list,
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bpemodel=train_args.bpemodel,
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text_cleaner=train_args.cleaner,
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g2p_type=train_args.g2p,
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text_name="text",
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non_linguistic_symbols=train_args.non_linguistic_symbols,
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)
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@torch.no_grad()
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def __call__(self, text: Union[list, str], split_size=20):
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data = {"text": text}
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result = self.preprocessor(data=data, uid="12938712838719")
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split_text = self.preprocessor.pop_split_text_data(result)
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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(data["text"], 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 = torch.from_numpy(np.array([], dtype='int32'))
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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.concatenate((cache_sent_id, mini_sentence_id), axis=0)
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data = {
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"text": torch.unsqueeze(torch.from_numpy(mini_sentence_id), 0),
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"text_lengths": torch.from_numpy(np.array([len(mini_sentence_id)], dtype='int32')),
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}
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data = to_device(data, self.device)
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y, _ = self.wrapped_model(**data)
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_, indices = y.view(-1, y.shape[-1]).topk(1, dim=1)
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punctuations = indices
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if indices.size()[0] != 1:
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punctuations = torch.squeeze(indices)
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assert punctuations.size()[0] == len(mini_sentence)
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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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# if len(punctuations) == 0:
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# continue
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punctuations_np = punctuations.cpu().numpy()
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new_mini_sentence_punc += [int(x) for x in punctuations_np]
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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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class Text2PuncVADRealtime:
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def __init__(
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self,
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train_config: Optional[str],
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model_file: Optional[str],
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device: str = "cpu",
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dtype: str = "float32",
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):
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# Build Model
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model, train_args = build_model_from_file(train_config, model_file, None, device, task_name="punc")
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self.device = device
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# Wrape model to make model.nll() data-parallel
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self.wrapped_model = ForwardAdaptor(model, "inference")
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self.wrapped_model.to(dtype=getattr(torch, dtype)).to(device=device).eval()
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# logging.info(f"Model:\n{model}")
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self.punc_list = train_args.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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self.preprocessor = CodeMixTokenizerCommonPreprocessor(
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train=False,
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token_type=train_args.token_type,
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token_list=train_args.token_list,
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bpemodel=train_args.bpemodel,
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text_cleaner=train_args.cleaner,
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g2p_type=train_args.g2p,
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text_name="text",
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non_linguistic_symbols=train_args.non_linguistic_symbols,
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)
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@torch.no_grad()
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def __call__(self, text: Union[list, str], cache: list, split_size=20):
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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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data = {"text": precache + " " + text}
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result = self.preprocessor(data=data, uid="12938712838719")
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split_text = self.preprocessor.pop_split_text_data(result)
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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(data["text"], 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 = torch.from_numpy(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)
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data = {
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"text": torch.unsqueeze(torch.from_numpy(mini_sentence_id), 0),
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"text_lengths": torch.from_numpy(np.array([len(mini_sentence_id)], dtype='int32')),
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"vad_indexes": torch.from_numpy(np.array([len(cache)], dtype='int32')),
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}
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data = to_device(data, self.device)
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y, _ = self.wrapped_model(**data)
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_, indices = y.view(-1, y.shape[-1]).topk(1, dim=1)
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punctuations = indices
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if indices.size()[0] != 1:
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punctuations = torch.squeeze(indices)
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assert punctuations.size()[0] == len(mini_sentence)
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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 = punctuations.cpu().numpy()
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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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return sentence_out, sentence_punc_list_out, cache_out
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