import numpy as np
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import torch
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from torch.nn.utils.rnn import pad_sequence
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def padding(data, float_pad_value=0.0, int_pad_value=-1):
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assert isinstance(data, list)
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assert "key" in data[0]
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assert "speech" in data[0] or "text" in data[0]
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keys = [x["key"] for x in data]
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batch = {}
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data_names = data[0].keys()
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for data_name in data_names:
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if data_name == "key" or data_name == "sampling_rate":
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continue
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else:
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if data_name != 'hotword_indxs':
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if data[0][data_name].dtype.kind == "i":
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pad_value = int_pad_value
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tensor_type = torch.int64
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else:
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pad_value = float_pad_value
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tensor_type = torch.float32
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tensor_list = [torch.tensor(np.copy(d[data_name]), dtype=tensor_type) for d in data]
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tensor_lengths = torch.tensor([len(d[data_name]) for d in data], dtype=torch.int32)
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tensor_pad = pad_sequence(tensor_list,
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batch_first=True,
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padding_value=pad_value)
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batch[data_name] = tensor_pad
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batch[data_name + "_lengths"] = tensor_lengths
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# SAC LABEL INCLUDE
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if "hotword_indxs" in batch:
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# if hotword indxs in batch
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# use it to slice hotwords out
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hotword_list = []
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hotword_lengths = []
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text = batch['text']
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text_lengths = batch['text_lengths']
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hotword_indxs = batch['hotword_indxs']
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dha_pad = torch.ones_like(text) * -1
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_, t1 = text.shape
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t1 += 1 # TODO: as parameter which is same as predictor_bias
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nth_hw = 0
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for b, (hotword_indx, one_text, length) in enumerate(zip(hotword_indxs, text, text_lengths)):
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dha_pad[b][:length] = 8405
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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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dha_pad[b][start: end+1] = one_text[start: end+1]
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nth_hw += 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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dha_pad[b][start: end+1] = one_text[start: end+1]
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nth_hw += 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 = pad_sequence(hotword_list,
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batch_first=True,
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padding_value=0)
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batch["hotword_pad"] = hotword_pad
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batch["hotword_lengths"] = torch.tensor(hotword_lengths, dtype=torch.int32)
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batch['dha_pad'] = dha_pad
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del batch['hotword_indxs']
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del batch['hotword_indxs_lengths']
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return keys, batch
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