Compute convolution module. Args: x (torch.Tensor): Input tensor (#batch, time, channels). mask_pad (torch.Tensor): us

Forward function. Args: xs: input tensor (B, L, D) Returns: output tensor, (B, L, D)

Return Swish activation function.

Compute scaled dot product attention. Args: query (torch.Tensor): Query tensor (#batch, time1, size). key (torch.Tens

Compute 'Scaled Dot Product Attention' with rel. positional encoding. Args: query (torch.Tensor): Query tensor (#batch, time1, siz

Compute encoded features. Args: x (torch.Tensor): (#batch, time, size) mask (torch.Tensor): Mask tensor for the input

Add positional encoding. Args: x (torch.Tensor): Input tensor (batch, time, `*`). Returns: torch.Tensor: Enc

Input x. Args: x (torch.Tensor): Input tensor (#batch, time, idim). x_mask (torch.Tensor): Input mask (#batch, 1, tim

Embed positions in tensor. Args: xs: padded input tensor (B, T, D) xs_lens: input length (B) decoding_chu

Make mask tensor containing indices of padded part. See description of make_non_pad_mask. Args: lengths (torch.Tensor): Batch of len

对文本进行标准化处理，去除标点符号，转为小写（如果适用）

Args: match (re.Match) Returns: str

Args: match (re.Match) Returns: str

Args: match (re.Match) Returns: str

Args: match (re.Match) Returns: str

Args: match (re.Match) Returns: str

Args: match (re.Match) Returns: str

Args: match (re.Match) Returns: str

Args: match (re.Match) Returns: str

Args: match (re.Match) Returns: str

Args: match (re.Match) Returns: str

Forward process in flow-matching Args: sample (`torch.FloatTensor`): The input sample. times

Forward process in flow-matching Args: sample (`torch.FloatTensor`): The input sample. times

Forward process in flow-matching Args: sample (`torch.FloatTensor`): The input sample. times

Sets the begin index for the scheduler. This function should be run from pipeline before the inference. Args: begin_inde

Sets the begin index for the scheduler. This function should be run from pipeline before the inference. Args: begin_inde

Sets the begin index for the scheduler. This function should be run from pipeline before the inference. Args: begin_inde

Sets the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`)

Sets the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`)

Sets the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`)

Preprocess the input text

Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned m

The index counter for current timestep. It will increase 1 after each scheduler step.

The index counter for current timestep. It will increase 1 after each scheduler step.

The index counter for current timestep. It will increase 1 after each scheduler step.

Create mask for subsequent steps (size, size). This mask is used only in decoder which works in an auto-regressive mode. This means the curre