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| import matplotlib | |
| import torch | |
| from matplotlib import pyplot as plt | |
| matplotlib.use("Agg") | |
| def convert_pad_shape(pad_shape): | |
| l = pad_shape[::-1] | |
| pad_shape = [item for sublist in l for item in sublist] | |
| return pad_shape | |
| def sequence_mask(length, max_length=None): | |
| if max_length is None: | |
| max_length = length.max() | |
| x = torch.arange(max_length, dtype=length.dtype, device=length.device) | |
| return x.unsqueeze(0) < length.unsqueeze(1) | |
| def init_weights(m, mean=0.0, std=0.01): | |
| classname = m.__class__.__name__ | |
| if classname.find("Conv") != -1: | |
| m.weight.data.normal_(mean, std) | |
| def get_padding(kernel_size, dilation=1): | |
| return int((kernel_size * dilation - dilation) / 2) | |
| def plot_mel(data, titles=None): | |
| fig, axes = plt.subplots(len(data), 1, squeeze=False) | |
| if titles is None: | |
| titles = [None for i in range(len(data))] | |
| plt.tight_layout() | |
| for i in range(len(data)): | |
| mel = data[i] | |
| if isinstance(mel, torch.Tensor): | |
| mel = mel.float().detach().cpu().numpy() | |
| axes[i][0].imshow(mel, origin="lower") | |
| axes[i][0].set_aspect(2.5, adjustable="box") | |
| axes[i][0].set_ylim(0, mel.shape[0]) | |
| axes[i][0].set_title(titles[i], fontsize="medium") | |
| axes[i][0].tick_params(labelsize="x-small", left=False, labelleft=False) | |
| axes[i][0].set_anchor("W") | |
| return fig | |
| def slice_segments(x, ids_str, segment_size=4): | |
| ret = torch.zeros_like(x[:, :, :segment_size]) | |
| for i in range(x.size(0)): | |
| idx_str = ids_str[i] | |
| idx_end = idx_str + segment_size | |
| ret[i] = x[i, :, idx_str:idx_end] | |
| return ret | |
| def rand_slice_segments(x, x_lengths=None, segment_size=4): | |
| b, d, t = x.size() | |
| if x_lengths is None: | |
| x_lengths = t | |
| ids_str_max = torch.clamp(x_lengths - segment_size + 1, min=0) | |
| ids_str = (torch.rand([b], device=x.device) * ids_str_max).to(dtype=torch.long) | |
| ret = slice_segments(x, ids_str, segment_size) | |
| return ret, ids_str | |
| def fused_add_tanh_sigmoid_multiply(in_act, n_channels): | |
| n_channels_int = n_channels[0] | |
| t_act = torch.tanh(in_act[:, :n_channels_int, :]) | |
| s_act = torch.sigmoid(in_act[:, n_channels_int:, :]) | |
| acts = t_act * s_act | |
| return acts | |
| def avg_with_mask(x, mask): | |
| assert mask.dtype == torch.float, "Mask should be float" | |
| if mask.ndim == 2: | |
| mask = mask.unsqueeze(1) | |
| if mask.shape[1] == 1: | |
| mask = mask.expand_as(x) | |
| return (x * mask).sum() / mask.sum() | |