Conformer

class torchaudio.models.Conformer(input_dim: int, num_heads: int, ffn_dim: int, num_layers: int, depthwise_conv_kernel_size: int, dropout: float = 0.0, use_group_norm: bool = False, convolution_first: bool = False)

Conformer architecture introduced in Conformer: Convolution-augmented Transformer for Speech Recognition [Gulati et al., 2020].

Parameters:

• input_dim (int) – input dimension.

• num_heads (int) – number of attention heads in each Conformer layer.

• ffn_dim (int) – hidden layer dimension of feedforward networks.

• num_layers (int) – number of Conformer layers to instantiate.

• depthwise_conv_kernel_size (int) – kernel size of each Conformer layer’s depthwise convolution layer.

• dropout (float, optional) – dropout probability. (Default: 0.0)

• use_group_norm (bool, optional) – use GroupNorm rather than BatchNorm1d in the convolution module. (Default: False)

• convolution_first (bool, optional) – apply the convolution module ahead of the attention module. (Default: False)

Examples

>>> conformer = Conformer(
>>>     input_dim=80,
>>>     num_heads=4,
>>>     ffn_dim=128,
>>>     num_layers=4,
>>>     depthwise_conv_kernel_size=31,
>>> )
>>> lengths = torch.randint(1, 400, (10,))  # (batch,)
>>> input = torch.rand(10, int(lengths.max()), input_dim)  # (batch, num_frames, input_dim)
>>> output = conformer(input, lengths)

forward

Conformer.forward(input: Tensor, lengths: Tensor) → Tuple[Tensor, Tensor]

Parameters:

• input (torch.Tensor) – with shape (B, T, input_dim).

• lengths (torch.Tensor) – with shape (B,) and i-th element representing number of valid frames for i-th batch element in input.

Returns:

(torch.Tensor, torch.Tensor)

torch.Tensor

output frames, with shape (B, T, input_dim)

torch.Tensor

output lengths, with shape (B,) and i-th element representing number of valid frames for i-th batch element in output frames.