Decorator to make any fx with this use the lazy property :param fn: :return:

Entry point into a DP thread :param gpu_idx: :param model: :param cluster_obj: :return:

Converts a space-separated string of tokens to a list of ids.

Find files recursively. Args: root_dir (str): Root root_dir to find. query (str): Query to find. include_root_dir (bool):

:param spec: [B, 1, M, T] :param diffusion_step: [B, 1] :param cond: [B, M, T] :return:

:param spec: [B, 1, 80, T] :param diffusion_step: [B, 1] :param cond: [B, M, T] :return:

Calculate forward propagation. Args: xs (Tensor): Batch of input sequences (B, Tmax, idim). x_masks (ByteTensor, optio

Example (no batch dim version): 1. dur = [2,2,3] 2. token_idx = [[1],[2],[3]], dur_cumsum = [2,4,7], dur_cumsum_prev

:param xs: [B, T, H] :return: [B, T, H]

:param x: [B, T, C] :param padding_mask: [B, T] :return: [B, T, C] or [L, B, T, C]

:param txt_tokens: [B, T] :return: { 'encoder_out': [T x B x C] }

:param x: [B, T, 80] :return: [L, B, T, H], [B, T, H]

:param x: [B, C, T] :return: [B, C, T]

:param x: [B, T, H] :return: [B, T, H]

Calculate forward propagation. Args: x (Tensor): Predicted signal (B, T). y (Tensor): Groundtruth signal (B, T).

Calculate forward propagation. Args: x (Tensor): Predicted signal (B, T). y (Tensor): Groundtruth signal (B, T).

Calculate forward propagation. Args: x (Tensor): Input tensor (B, residual_channels, T). c (Tensor): Local conditioni

Calculate forward propagation. Args: c (Tensor): Input tensor (B, channels, T). Returns: Tensor: Output tens

Calculate forward propagation. Args: x (Tensor): Input tensor (B, C, F, T). Returns: Tensor: Interpolated te

Calculate forward propagation. Args: c : Input tensor (B, C, T). Returns: Tensor: Upsampled tensor (B, C, T'

Calculate forward propagation. Args: c : Input tensor (B, C, T'). Returns: Tensor: Upsampled tensor (B, C, T

Calculate forward propagation. Args: x (Tensor): Input tensor (B, in_channels, T). Returns: Tensor: Output t

Calculate forward propagation. Args: x (Tensor): Input tensor (B, in_channels, T_in). Returns: Tensor: Outpu

Calculate forward propagation. Args: x (Tensor): Input noise signal (B, C_in, T). c (Tensor): Local conditioning auxi

Calculate forward propagation. Args: x (Tensor): Input noise signal (B, 1, T). Returns: Tensor: Output tenso

Calculate forward propagation. Args: x (Tensor): Input noise signal (B, 1, T). Returns: Tensor: Output tenso

Calculate forward propagation. Args: c (Tensor): Input tensor (B, channels, T). Returns: Tensor: Output tens

Calculate forward propagation. Args: x (Tensor): Input noise signal (B, 1, T). Returns: List: List of output

Calculate forward propagation. Args: x (Tensor): Input noise signal (B, 1, T). Returns: List: List of list o

sine_tensor, uv = forward(f0) input F0: tensor(batchsize=1, length, dim=1) f0 for unvoiced steps should be 0 output

Pulse train generator pulse_train, uv = forward(f0) input F0: tensor(batchsize=1, length, dim=1) f0 for unvoiced st

output = forward(signal, system_ir) signal: (batchsize, length1, dim) system_ir: (length2, dim) output: (batchsize, l

Producde cyclic-noise

cyc, noise, uv = SourceModuleCycNoise_v1(F0, beta) F0_upsampled (batchsize, length, 1) beta (1) cyc (batchsize, lengt

Sine_source, noise_source = SourceModuleHnNSF(F0_sampled) F0_sampled (batchsize, length, 1) Sine_source (batchsize, length, 1

Calculate forward propagation. Args: x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).

Calculate forward propagation. Args: x_mag (Tensor): Magnitude spectrogram of predicted signal (B, #frames, #freq_bins).

Calculate forward propagation. Args: x (Tensor): Predicted signal (B, T). y (Tensor): Groundtruth signal (B, T).

Calculate forward propagation. Args: x (Tensor): Predicted signal (B, T). y (Tensor): Groundtruth signal (B, T).

Input is expected to be of size [bsz x seqlen].

Input shape: Time x Batch x Channel Args: key_padding_mask (ByteTensor, optional): mask to exclude keys that are

:param x: [T, B, C] :return: [T, B, C]