espnet2.asr_transducer.decoder.rnn_decoder.RNNDecoder

source

class espnet2.asr_transducer.decoder.rnn_decoder.RNNDecoder(vocab_size: int, embed_size: int = 256, hidden_size: int = 256, rnn_type: str = 'lstm', num_layers: int = 1, dropout_rate: float = 0.0, embed_dropout_rate: float = 0.0, embed_pad: int = 0)

Bases: AbsDecoder

RNN decoder module.

• Parameters:
  • vocab_size – Vocabulary size.
  • embed_size – Embedding size.
  • hidden_size – Hidden size..
  • rnn_type – Decoder layers type.
  • num_layers – Number of decoder layers.
  • dropout_rate – Dropout rate for decoder layers.
  • embed_dropout_rate – Dropout rate for embedding layer.
  • embed_pad – Embedding padding symbol ID.

Construct a RNNDecoder object.

batch_score(hyps: List[Hypothesis]) → Tuple[Tensor, Tuple[Tensor, Tensor | None]]

One-step forward hypotheses.

• Parameters:hyps – Hypotheses.
• Returns: Decoder output sequences. (B, D_dec) states: Decoder hidden states. ((N, B, D_dec), (N, B, D_dec) or None)
• Return type: out

create_batch_states(new_states: List[Tuple[Tensor, Tensor | None]]) → Tuple[Tensor, Tensor | None]

Create decoder hidden states.

• Parameters:new_states – Decoder hidden states. [B x ((N, 1, D_dec), (N, 1, D_dec) or None)]
• Returns: Decoder hidden states. ((N, B, D_dec), (N, B, D_dec) or None)
• Return type: states

forward(labels: Tensor) → Tensor

Encode source label sequences.

• Parameters:labels – Label ID sequences. (B, L)
• Returns: Decoder output sequences. (B, U, D_dec)
• Return type: out

init_state(batch_size: int) → Tuple[Tensor, tensor | None]

Initialize decoder states.

• Parameters:batch_size – Batch size.
• Returns: Initial decoder hidden states. ((N, B, D_dec), (N, B, D_dec) or None)

rnn_forward(x: Tensor, state: Tuple[Tensor, Tensor | None]) → Tuple[Tensor, Tuple[Tensor, Tensor | None]]

Encode source label sequences.

• Parameters:
  • x – RNN input sequences. (B, D_emb)
  • state – Decoder hidden states. ((N, B, D_dec), (N, B, D_dec) or None)
• Returns: RNN output sequences. (B, D_dec) (h_next, c_next): Decoder hidden states.

  (N, B, D_dec), (N, B, D_dec) or None)

• Return type: x

score(label_sequence: List[int], states: Tuple[Tensor, Tensor | None]) → Tuple[Tensor, Tuple[Tensor, Tensor | None]]

One-step forward hypothesis.

• Parameters:
  • label_sequence – Current label sequence.
  • states – Decoder hidden states. ((N, 1, D_dec), (N, 1, D_dec) or None)
• Returns: Decoder output sequence. (1, D_dec) states: Decoder hidden states.

  ((N, 1, D_dec), (N, 1, D_dec) or None)

• Return type: out

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

Get specified ID state from decoder hidden states.

• Parameters:
  • states – Decoder hidden states. ((N, B, D_dec), (N, B, D_dec) or None)
  • idx – State ID to extract.
• Returns: Decoder hidden state for given ID. ((N, 1, D_dec), (N, 1, D_dec) or None)

set_device(device: device) → None

Set GPU device to use.

• Parameters:device – Device ID.