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import math |
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import torch |
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from torch.nn import functional as F |
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import torch.jit |
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def script_method(fn, _rcb=None): |
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return fn |
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def script(obj, optimize=True, _frames_up=0, _rcb=None): |
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return obj |
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torch.jit.script_method = script_method |
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torch.jit.script = script |
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def init_weights(m, mean=0.0, std=0.01): |
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classname = m.__class__.__name__ |
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if classname.find("Conv") != -1: |
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m.weight.data.normal_(mean, std) |
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def get_padding(kernel_size, dilation=1): |
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return int((kernel_size*dilation - dilation)/2) |
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def intersperse(lst, item): |
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result = [item] * (len(lst) * 2 + 1) |
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result[1::2] = lst |
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return result |
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def slice_segments(x, ids_str, segment_size=4): |
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ret = torch.zeros_like(x[:, :, :segment_size]) |
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for i in range(x.size(0)): |
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idx_str = ids_str[i] |
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idx_end = idx_str + segment_size |
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ret[i] = x[i, :, idx_str:idx_end] |
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return ret |
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def rand_slice_segments(x, x_lengths=None, segment_size=4): |
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b, d, t = x.size() |
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if x_lengths is None: |
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x_lengths = t |
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ids_str_max = x_lengths - segment_size + 1 |
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ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long) |
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ret = slice_segments(x, ids_str, segment_size) |
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return ret, ids_str |
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def subsequent_mask(length): |
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mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0) |
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return mask |
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@torch.jit.script |
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def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels): |
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n_channels_int = n_channels[0] |
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in_act = input_a + input_b |
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t_act = torch.tanh(in_act[:, :n_channels_int, :]) |
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s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) |
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acts = t_act * s_act |
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return acts |
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def convert_pad_shape(pad_shape): |
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l = pad_shape[::-1] |
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pad_shape = [item for sublist in l for item in sublist] |
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return pad_shape |
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def sequence_mask(length, max_length=None): |
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if max_length is None: |
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max_length = length.max() |
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x = torch.arange(max_length, dtype=length.dtype, device=length.device) |
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return x.unsqueeze(0) < length.unsqueeze(1) |
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def generate_path(duration, mask): |
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""" |
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duration: [b, 1, t_x] |
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mask: [b, 1, t_y, t_x] |
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""" |
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device = duration.device |
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b, _, t_y, t_x = mask.shape |
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cum_duration = torch.cumsum(duration, -1) |
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cum_duration_flat = cum_duration.view(b * t_x) |
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path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype) |
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path = path.view(b, t_x, t_y) |
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path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1] |
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path = path.unsqueeze(1).transpose(2,3) * mask |
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return path |
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