Emformer

class torchaudio.models.Emformer(input_dim: int, num_heads: int, ffn_dim: int, num_layers: int, segment_length: int, dropout: float = 0.0, activation: str = 'relu', left_context_length: int = 0, right_context_length: int = 0, max_memory_size: int = 0, weight_init_scale_strategy: Optional[str] = 'depthwise', tanh_on_mem: bool = False, negative_inf: float = -100000000.0)

Emformer architecture introduced in Emformer: Efficient Memory Transformer Based Acoustic Model for Low Latency Streaming Speech Recognition [Shi et al., 2021].

See also

• emformer_rnnt_model(), emformer_rnnt_base(): factory functions.

• torchaudio.pipelines.RNNTBundle: ASR pipelines with pretrained model.

Parameters:

• input_dim (int) – input dimension.

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

• ffn_dim (int) – hidden layer dimension of each Emformer layer’s feedforward network.

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

• segment_length (int) – length of each input segment.

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

• activation (str, optional) – activation function to use in each Emformer layer’s feedforward network. Must be one of (“relu”, “gelu”, “silu”). (Default: “relu”)

• left_context_length (int, optional) – length of left context. (Default: 0)

• right_context_length (int, optional) – length of right context. (Default: 0)

• max_memory_size (int, optional) – maximum number of memory elements to use. (Default: 0)

• weight_init_scale_strategy (str or None, optional) – per-layer weight initialization scaling strategy. Must be one of (“depthwise”, “constant”, None). (Default: “depthwise”)

• tanh_on_mem (bool, optional) – if True, applies tanh to memory elements. (Default: False)

• negative_inf (float, optional) – value to use for negative infinity in attention weights. (Default: -1e8)

Examples

>>> emformer = Emformer(512, 8, 2048, 20, 4, right_context_length=1)
>>> input = torch.rand(128, 400, 512)  # batch, num_frames, feature_dim
>>> lengths = torch.randint(1, 200, (128,))  # batch
>>> output, lengths = emformer(input, lengths)
>>> input = torch.rand(128, 5, 512)
>>> lengths = torch.ones(128) * 5
>>> output, lengths, states = emformer.infer(input, lengths, None)

forward

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

Forward pass for training and non-streaming inference.

B: batch size; T: max number of input frames in batch; D: feature dimension of each frame.

Parameters:

• input (torch.Tensor) – utterance frames right-padded with right context frames, with shape (B, T + right_context_length, D).

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

Returns:

Tensor

output frames, with shape (B, T, D).

Tensor

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

Return type:

(Tensor, Tensor)

infer

Emformer.infer(input: Tensor, lengths: Tensor, states: Optional[List[List[Tensor]]] = None) → Tuple[Tensor, Tensor, List[List[Tensor]]]

Forward pass for streaming inference.

B: batch size; D: feature dimension of each frame.

Parameters:

• input (torch.Tensor) – utterance frames right-padded with right context frames, with shape (B, segment_length + right_context_length, D).

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

• states (List[List[torch.Tensor]] or None, optional) – list of lists of tensors representing internal state generated in preceding invocation of infer. (Default: None)

Returns:

Tensor

output frames, with shape (B, segment_length, D).

Tensor

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

List[List[Tensor]]

output states; list of lists of tensors representing internal state generated in current invocation of infer.

Return type:

(Tensor, Tensor, List[List[Tensor]])