import torch
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from torch.nn import functional as F
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import numpy as np
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def sequence_mask(lengths, maxlen=None, dtype=torch.float32, device=None):
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if maxlen is None:
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maxlen = lengths.max()
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row_vector = torch.arange(0, maxlen, 1).to(lengths.device)
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matrix = torch.unsqueeze(lengths, dim=-1)
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mask = row_vector < matrix
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mask = mask.detach()
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return mask.type(dtype).to(device) if device is not None else mask.type(dtype)
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def apply_cmvn(inputs, mvn):
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device = inputs.device
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dtype = inputs.dtype
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frame, dim = inputs.shape
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meams = np.tile(mvn[0:1, :dim], (frame, 1))
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vars = np.tile(mvn[1:2, :dim], (frame, 1))
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inputs -= torch.from_numpy(meams).type(dtype).to(device)
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inputs *= torch.from_numpy(vars).type(dtype).to(device)
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return inputs.type(torch.float32)
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def drop_and_add(inputs: torch.Tensor,
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outputs: torch.Tensor,
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training: bool,
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dropout_rate: float = 0.1,
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stoch_layer_coeff: float = 1.0):
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outputs = F.dropout(outputs, p=dropout_rate, training=training, inplace=True)
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outputs *= stoch_layer_coeff
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input_dim = inputs.size(-1)
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output_dim = outputs.size(-1)
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if input_dim == output_dim:
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outputs += inputs
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return outputs
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