import torch
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import random
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from funasr.register import tables
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from funasr.utils.load_utils import extract_fbank, load_audio_text_image_video
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@tables.register("dataset_classes", "AudioDataset")
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class AudioDataset(torch.utils.data.Dataset):
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"""
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AudioDataset
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"""
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def __init__(self,
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path,
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index_ds: str = None,
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frontend=None,
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tokenizer=None,
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int_pad_value: int = -1,
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float_pad_value: float = 0.0,
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**kwargs):
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super().__init__()
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index_ds_class = tables.index_ds_classes.get(index_ds)
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self.index_ds = index_ds_class(path, **kwargs)
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preprocessor_speech = kwargs.get("preprocessor_speech", None)
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if preprocessor_speech:
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preprocessor_speech_class = tables.preprocessor_classes.get(preprocessor_speech)
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preprocessor_speech = preprocessor_speech_class(**kwargs.get("preprocessor_speech_conf"))
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self.preprocessor_speech = preprocessor_speech
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preprocessor_text = kwargs.get("preprocessor_text", None)
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if preprocessor_text:
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preprocessor_text_class = tables.preprocessor_classes.get(preprocessor_text)
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preprocessor_text = preprocessor_text_class(**kwargs.get("preprocessor_text_conf"))
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self.preprocessor_text = preprocessor_text
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self.frontend = frontend
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self.fs = 16000 if frontend is None else frontend.fs
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self.data_type = "sound"
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self.tokenizer = tokenizer
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self.int_pad_value = int_pad_value
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self.float_pad_value = float_pad_value
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def get_source_len(self, index):
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item = self.index_ds[index]
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return self.index_ds.get_source_len(item)
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def get_target_len(self, index):
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item = self.index_ds[index]
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return self.index_ds.get_target_len(item)
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def __len__(self):
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return len(self.index_ds)
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def __getitem__(self, index):
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item = self.index_ds[index]
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# import pdb;
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# pdb.set_trace()
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source = item["source"]
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data_src = load_audio_text_image_video(source, fs=self.fs)
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if self.preprocessor_speech:
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data_src = self.preprocessor_speech(data_src, fs=self.fs)
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speech, speech_lengths = extract_fbank(data_src, data_type=self.data_type, frontend=self.frontend, is_final=True) # speech: [b, T, d]
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target = item["target"]
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if self.preprocessor_text:
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target = self.preprocessor_text(target)
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if self.tokenizer:
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ids = self.tokenizer.encode(target)
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text = torch.tensor(ids, dtype=torch.int64)
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else:
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ids = target
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text = ids
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ids_lengths = len(ids)
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text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
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return {"speech": speech[0, :, :],
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"speech_lengths": speech_lengths,
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"text": text,
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"text_lengths": text_lengths,
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}
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def collator(self, samples: list=None):
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outputs = {}
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for sample in samples:
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for key in sample.keys():
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if key not in outputs:
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outputs[key] = []
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outputs[key].append(sample[key])
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for key, data_list in outputs.items():
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if isinstance(data_list[0], torch.Tensor):
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if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
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pad_value = self.int_pad_value
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else:
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pad_value = self.float_pad_value
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outputs[key] = torch.nn.utils.rnn.pad_sequence(data_list, batch_first=True, padding_value=pad_value)
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return outputs
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@tables.register("dataset_classes", "AudioDatasetHotword")
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class AudioDatasetHotword(AudioDataset):
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# for finetuning contextual_paraformer and seaco_paraformer
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def __init__(
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self,
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*args,
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seaco_id: bool = 0,
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**kwargs,
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):
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super().__init__(*args, **kwargs)
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self.seaco_id = seaco_id
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def __getitem__(self, index):
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item = self.index_ds[index]
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# import pdb;
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# pdb.set_trace()
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source = item["source"]
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data_src = load_audio_text_image_video(source, fs=self.fs)
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if self.preprocessor_speech:
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data_src = self.preprocessor_speech(data_src, fs=self.fs)
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speech, speech_lengths = extract_fbank(data_src, data_type=self.data_type, frontend=self.frontend, is_final=True) # speech: [b, T, d]
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target = item["target"]
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if self.preprocessor_text:
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target = self.preprocessor_text(target)
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if self.tokenizer:
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ids = self.tokenizer.encode(target)
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text = torch.tensor(ids, dtype=torch.int64)
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else:
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ids = target
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text = ids
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ids_lengths = len(ids)
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text_lengths = torch.tensor([ids_lengths], dtype=torch.int32)
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def generate_index(length,
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hotword_min_length=2,
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hotword_max_length=8,
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sample_rate=0.75,
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double_rate=0.1,
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pre_prob=0.0,
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pre_index=None,
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pre_hwlist=None):
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if length < hotword_min_length:
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return [-1]
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if random.random() < sample_rate:
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if pre_prob > 0 and random.random() < pre_prob and pre_index is not None:
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return pre_index
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if length == hotword_min_length:
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return [0, length-1]
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elif random.random() < double_rate and length > hotword_max_length + hotword_min_length + 2:
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# sample two hotwords in a sentence
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_max_hw_length = min(hotword_max_length, length // 2)
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# first hotword
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start1 = random.randint(0, length // 3)
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end1 = random.randint(start1 + hotword_min_length - 1, start1 + _max_hw_length - 1)
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# second hotword
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start2 = random.randint(end1 + 1, length - hotword_min_length)
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end2 = random.randint(min(length-1, start2+hotword_min_length-1), min(length-1, start2+hotword_max_length-1))
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return [start1, end1, start2, end2]
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else: # single hotword
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start = random.randint(0, length - hotword_min_length)
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end = random.randint(min(length-1, start+hotword_min_length-1), min(length-1, start+hotword_max_length-1))
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return [start, end]
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else:
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return [-1]
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hotword_indx = generate_index(text_lengths[0])
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return {"speech": speech[0, :, :],
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"speech_lengths": speech_lengths,
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"text": text,
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"text_lengths": text_lengths,
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"hotword_indx": hotword_indx,
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"seaco_id": self.seaco_id,
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}
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def collator(self, samples: list=None):
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outputs = {}
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hotword_indxs = []
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seaco_id = samples[0]['seaco_id']
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for sample in samples:
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for key in sample.keys():
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if key == 'seaco_id':
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continue
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elif key == 'hotword_indx':
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hotword_indxs.append(sample[key])
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else:
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if key not in outputs:
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outputs[key] = []
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outputs[key].append(sample[key])
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for key, data_list in outputs.items():
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if isinstance(data_list[0], torch.Tensor):
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if data_list[0].dtype == torch.int64 or data_list[0].dtype == torch.int32:
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pad_value = self.int_pad_value
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else:
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pad_value = self.float_pad_value
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outputs[key] = torch.nn.utils.rnn.pad_sequence(data_list, batch_first=True, padding_value=pad_value)
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hotword_list, hotword_lengths = [], []
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text = outputs['text']
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seaco_label_pad = torch.ones_like(text) * -1 if seaco_id else None
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for b, (hotword_indx, one_text, length) in enumerate(zip(hotword_indxs, text, outputs['text_lengths'])):
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length = length[0]
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if seaco_label_pad is not None: seaco_label_pad[b][:length] = seaco_id
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if hotword_indx[0] != -1:
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start, end = int(hotword_indx[0]), int(hotword_indx[1])
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hotword = one_text[start: end+1]
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hotword_list.append(hotword)
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hotword_lengths.append(end-start+1)
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if seaco_label_pad is not None: seaco_label_pad[b][start: end+1] = one_text[start: end+1]
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if len(hotword_indx) == 4 and hotword_indx[2] != -1:
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# the second hotword if exist
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start, end = int(hotword_indx[2]), int(hotword_indx[3])
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hotword_list.append(one_text[start: end+1])
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hotword_lengths.append(end-start+1)
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if seaco_label_pad is not None: seaco_label_pad[b][start: end+1] = one_text[start: end+1]
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hotword_list.append(torch.tensor([1]))
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hotword_lengths.append(1)
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hotword_pad = torch.nn.utils.rnn.pad_sequence(hotword_list,
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batch_first=True,
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padding_value=0)
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outputs["hotword_pad"] = hotword_pad
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outputs["hotword_lengths"] = torch.tensor(hotword_lengths, dtype=torch.int32)
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if seaco_label_pad is not None: outputs['seaco_label_pad'] = seaco_label_pad
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return outputs
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