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167f5d0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 | #!/usr/bin/env python3
# -*- encoding: utf-8 -*-
# Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved.
# MIT License (https://opensource.org/licenses/MIT)
import torch
import numpy as np
from contextlib import contextmanager
from distutils.version import LooseVersion
from funasr.register import tables
from funasr.train_utils.device_funcs import to_device
from funasr.models.ct_transformer.model import CTTransformer
from funasr.utils.load_utils import load_audio_text_image_video
from funasr.models.ct_transformer.utils import split_to_mini_sentence, split_words
if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
from torch.cuda.amp import autocast
else:
# Nothing to do if torch<1.6.0
@contextmanager
def autocast(enabled=True):
yield
@tables.register("model_classes", "CTTransformerStreaming")
class CTTransformerStreaming(CTTransformer):
"""
Author: Speech Lab of DAMO Academy, Alibaba Group
CT-Transformer: Controllable time-delay transformer for real-time punctuation prediction and disfluency detection
https://arxiv.org/pdf/2003.01309.pdf
"""
def __init__(
self,
*args,
**kwargs,
):
super().__init__(*args, **kwargs)
def punc_forward(
self, text: torch.Tensor, text_lengths: torch.Tensor, vad_indexes: torch.Tensor, **kwargs
):
"""Compute loss value from buffer sequences.
Args:
input (torch.Tensor): Input ids. (batch, len)
hidden (torch.Tensor): Target ids. (batch, len)
"""
x = self.embed(text)
# mask = self._target_mask(input)
h, _, _ = self.encoder(x, text_lengths, vad_indexes=vad_indexes)
y = self.decoder(h)
return y, None
def with_vad(self):
return True
def inference(
self,
data_in,
data_lengths=None,
key: list = None,
tokenizer=None,
frontend=None,
cache: dict = {},
**kwargs,
):
assert len(data_in) == 1
if len(cache) == 0:
cache["pre_text"] = []
text = load_audio_text_image_video(data_in, data_type=kwargs.get("kwargs", "text"))[0]
text = "".join(cache["pre_text"]) + " " + text
split_size = kwargs.get("split_size", 20)
tokens = split_words(text)
tokens_int = tokenizer.encode(tokens)
mini_sentences = split_to_mini_sentence(tokens, split_size)
mini_sentences_id = split_to_mini_sentence(tokens_int, split_size)
assert len(mini_sentences) == len(mini_sentences_id)
cache_sent = []
cache_sent_id = torch.from_numpy(np.array([], dtype="int32"))
skip_num = 0
sentence_punc_list = []
sentence_words_list = []
cache_pop_trigger_limit = 200
results = []
meta_data = {}
punc_array = None
for mini_sentence_i in range(len(mini_sentences)):
mini_sentence = mini_sentences[mini_sentence_i]
mini_sentence_id = mini_sentences_id[mini_sentence_i]
mini_sentence = cache_sent + mini_sentence
mini_sentence_id = np.concatenate((cache_sent_id, mini_sentence_id), axis=0)
data = {
"text": torch.unsqueeze(torch.from_numpy(mini_sentence_id), 0),
"text_lengths": torch.from_numpy(np.array([len(mini_sentence_id)], dtype="int32")),
"vad_indexes": torch.from_numpy(np.array([len(cache["pre_text"])], dtype="int32")),
}
data = to_device(data, kwargs["device"])
# y, _ = self.wrapped_model(**data)
y, _ = self.punc_forward(**data)
_, indices = y.view(-1, y.shape[-1]).topk(1, dim=1)
punctuations = indices
if indices.size()[0] != 1:
punctuations = torch.squeeze(indices)
assert punctuations.size()[0] == len(mini_sentence)
# Search for the last Period/QuestionMark as cache
if mini_sentence_i < len(mini_sentences) - 1:
sentenceEnd = -1
last_comma_index = -1
for i in range(len(punctuations) - 2, 1, -1):
if (
self.punc_list[punctuations[i]] == "。"
or self.punc_list[punctuations[i]] == "？"
):
sentenceEnd = i
break
if last_comma_index < 0 and self.punc_list[punctuations[i]] == "，":
last_comma_index = i
if (
sentenceEnd < 0
and len(mini_sentence) > cache_pop_trigger_limit
and last_comma_index >= 0
):
# The sentence it too long, cut off at a comma.
sentenceEnd = last_comma_index
punctuations[sentenceEnd] = self.sentence_end_id
cache_sent = mini_sentence[sentenceEnd + 1 :]
cache_sent_id = mini_sentence_id[sentenceEnd + 1 :]
mini_sentence = mini_sentence[0 : sentenceEnd + 1]
punctuations = punctuations[0 : sentenceEnd + 1]
# if len(punctuations) == 0:
# continue
punctuations_np = punctuations.cpu().numpy()
sentence_punc_list += [self.punc_list[int(x)] for x in punctuations_np]
sentence_words_list += mini_sentence
assert len(sentence_punc_list) == len(sentence_words_list)
words_with_punc = []
sentence_punc_list_out = []
for i in range(0, len(sentence_words_list)):
if i > 0:
if (
len(sentence_words_list[i][0].encode()) == 1
and len(sentence_words_list[i - 1][-1].encode()) == 1
):
sentence_words_list[i] = " " + sentence_words_list[i]
if skip_num < len(cache["pre_text"]):
skip_num += 1
else:
words_with_punc.append(sentence_words_list[i])
if skip_num >= len(cache["pre_text"]):
sentence_punc_list_out.append(sentence_punc_list[i])
if sentence_punc_list[i] != "_":
words_with_punc.append(sentence_punc_list[i])
sentence_out = "".join(words_with_punc)
sentenceEnd = -1
for i in range(len(sentence_punc_list) - 2, 1, -1):
if sentence_punc_list[i] == "。" or sentence_punc_list[i] == "？":
sentenceEnd = i
break
cache["pre_text"] = sentence_words_list[sentenceEnd + 1 :]
if sentence_out[-1] in self.punc_list:
sentence_out = sentence_out[:-1]
sentence_punc_list_out[-1] = "_"
# 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)
result_i = {"key": key[0], "text": 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
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