#!/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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import torch
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import numpy as np
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from contextlib import contextmanager
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from distutils.version import LooseVersion
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from funasr.register import tables
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from funasr.train_utils.device_funcs import to_device
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from funasr.models.ct_transformer.model import CTTransformer
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from funasr.utils.load_utils import load_audio_text_image_video
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from funasr.models.ct_transformer.utils import split_to_mini_sentence, split_words
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if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"):
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from torch.cuda.amp import autocast
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else:
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# Nothing to do if torch<1.6.0
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@contextmanager
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def autocast(enabled=True):
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yield
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@tables.register("model_classes", "CTTransformerStreaming")
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class CTTransformerStreaming(CTTransformer):
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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__(
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self,
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*args,
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**kwargs,
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):
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super().__init__(*args, **kwargs)
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def punc_forward(self, text: torch.Tensor, text_lengths: torch.Tensor, vad_indexes: torch.Tensor, **kwargs):
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"""Compute loss value from buffer sequences.
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Args:
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input (torch.Tensor): Input ids. (batch, len)
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hidden (torch.Tensor): Target ids. (batch, len)
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"""
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x = self.embed(text)
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# mask = self._target_mask(input)
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h, _, _ = self.encoder(x, text_lengths, vad_indexes=vad_indexes)
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y = self.decoder(h)
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return y, None
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def with_vad(self):
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return True
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def inference(self,
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data_in,
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data_lengths=None,
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key: list = None,
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tokenizer=None,
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frontend=None,
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cache: dict = {},
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**kwargs,
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):
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assert len(data_in) == 1
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if len(cache) == 0:
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cache["pre_text"] = []
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text = load_audio_text_image_video(data_in, data_type=kwargs.get("kwargs", "text"))[0]
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text = "".join(cache["pre_text"]) + " " + text
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split_size = kwargs.get("split_size", 20)
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tokens = split_words(text)
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tokens_int = tokenizer.encode(tokens)
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mini_sentences = split_to_mini_sentence(tokens, split_size)
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mini_sentences_id = split_to_mini_sentence(tokens_int, 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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skip_num = 0
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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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results = []
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meta_data = {}
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punc_array = None
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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["pre_text"])], dtype='int32')),
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}
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data = to_device(data, kwargs["device"])
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# y, _ = self.wrapped_model(**data)
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y, _ = self.punc_forward(**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.sentence_end_id
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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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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["pre_text"]):
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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["pre_text"]):
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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["pre_text"] = 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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# keep a punctuations array for punc segment
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if punc_array is None:
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punc_array = punctuations
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else:
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punc_array = torch.cat([punc_array, punctuations], dim=0)
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result_i = {"key": key[0], "text": sentence_out, "punc_array": punc_array}
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results.append(result_i)
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return results, meta_data
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def export(
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self,
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**kwargs,
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):
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is_onnx = kwargs.get("type", "onnx") == "onnx"
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encoder_class = tables.encoder_classes.get(kwargs["encoder"] + "Export")
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self.encoder = encoder_class(self.encoder, onnx=is_onnx)
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self.forward = self._export_forward
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return self
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def _export_forward(self, inputs: torch.Tensor,
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text_lengths: torch.Tensor,
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vad_indexes: torch.Tensor,
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sub_masks: torch.Tensor,
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):
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"""Compute loss value from buffer sequences.
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Args:
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input (torch.Tensor): Input ids. (batch, len)
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hidden (torch.Tensor): Target ids. (batch, len)
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"""
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x = self.embed(inputs)
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# mask = self._target_mask(input)
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h, _ = self.encoder(x, text_lengths, vad_indexes, sub_masks)
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y = self.decoder(h)
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return y
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def export_dummy_inputs(self):
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length = 120
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text_indexes = torch.randint(0, self.embed.num_embeddings, (1, length)).type(torch.int32)
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text_lengths = torch.tensor([length], dtype=torch.int32)
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vad_mask = torch.ones(length, length, dtype=torch.float32)[None, None, :, :]
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sub_masks = torch.ones(length, length, dtype=torch.float32)
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sub_masks = torch.tril(sub_masks).type(torch.float32)
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return (text_indexes, text_lengths, vad_mask, sub_masks[None, None, :, :])
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def export_input_names(self):
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return ['inputs', 'text_lengths', 'vad_masks', 'sub_masks']
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def export_output_names(self):
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return ['logits']
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def export_dynamic_axes(self):
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return {
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'inputs': {
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1: 'feats_length'
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},
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'vad_masks': {
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2: 'feats_length1',
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3: 'feats_length2'
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},
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'sub_masks': {
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2: 'feats_length1',
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3: 'feats_length2'
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},
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'logits': {
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1: 'logits_length'
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},
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}
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def export_name(self):
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return "model.onnx"
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