espnet2.legacy.nets.pytorch_backend.transducer.custom_decoder.CustomDecoder

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class espnet2.legacy.nets.pytorch_backend.transducer.custom_decoder.CustomDecoder(odim: int, dec_arch: List, input_layer: str = 'embed', repeat_block: int = 0, joint_activation_type: str = 'tanh', positional_encoding_type: str = 'abs_pos', positionwise_layer_type: str = 'linear', positionwise_activation_type: str = 'relu', input_layer_dropout_rate: float = 0.0, blank_id: int = 0)

Bases: TransducerDecoderInterface, Module

Custom decoder module for Transducer model.

• Parameters:
  • odim – Output dimension.
  • dec_arch – Decoder block architecture (type and parameters).
  • input_layer – Input layer type.
  • repeat_block – Number of times dec_arch is repeated.
  • joint_activation_type – Type of activation for joint network.
  • positional_encoding_type – Positional encoding type.
  • positionwise_layer_type – Positionwise layer type.
  • positionwise_activation_type – Positionwise activation type.
  • input_layer_dropout_rate – Dropout rate for input layer.
  • blank_id – Blank symbol ID.

Construct a CustomDecoder object.

batch_score(hyps: List[Hypothesis] | List[ExtendedHypothesis], dec_states: List[Tensor | None], cache: Dict[str, Any], use_lm: bool) → Tuple[Tensor, List[Tensor | None], Tensor]

One-step forward hypotheses.

• Parameters:
  • hyps – Hypotheses.
  • dec_states – Decoder hidden states. [N x (B, U, D_dec)]
  • cache – Pairs of (h_dec, dec_states) for each label sequences. (keys)
  • use_lm – Whether to compute label ID sequences for LM.
• Returns: Decoder output sequences. (B, D_dec) dec_states: Decoder hidden states. [N x (B, U, D_dec)] lm_labels: Label ID sequences for LM. (B,)
• Return type: dec_out

create_batch_states(states: List[Tensor | None], new_states: List[Tensor | None], check_list: List[List[int]]) → List[Tensor | None]

Create decoder hidden states sequences.

• Parameters:
  • states – Decoder hidden states. [N x (B, U, D_dec)]
  • new_states – Decoder hidden states. [B x [N x (1, U, D_dec)]]
  • check_list – Label ID sequences.
• Returns: New decoder hidden states. [N x (B, U, D_dec)]
• Return type: states

forward(dec_input: Tensor, dec_mask: Tensor) → Tuple[Tensor, Tensor]

Encode label ID sequences.

• Parameters:
  • dec_input – Label ID sequences. (B, U)
  • dec_mask – Label mask sequences. (B, U)
• Returns: Decoder output sequences. (B, U, D_dec) dec_output_mask: Mask of decoder output sequences. (B, U)
• Return type: dec_output

init_state(batch_size: int | None = None) → List[Tensor | None]

Initialize decoder states.

• Parameters:batch_size – Batch size.
• Returns: Initial decoder hidden states. [N x None]
• Return type: state

score(hyp: Hypothesis, cache: Dict[str, Any]) → Tuple[Tensor, List[Tensor | None], Tensor]

One-step forward hypothesis.

• Parameters:
  • hyp – Hypothesis.
  • cache – Pairs of (dec_out, dec_state) for each label sequence. (key)
• Returns: Decoder output sequence. (1, D_dec) dec_state: Decoder hidden states. [N x (1, U, D_dec)] lm_label: Label ID for LM. (1,)
• Return type: dec_out

select_state(states: List[Tensor | None], idx: int) → List[Tensor | None]

Get specified ID state from decoder hidden states.

• Parameters:
  • states – Decoder hidden states. [N x (B, U, D_dec)]
  • idx – State ID to extract.
• Returns: Decoder hidden state for given ID. [N x (1, U, D_dec)]
• Return type: state_idx

set_device(device: device)

Set GPU device to use.

• Parameters:device – Device ID.