espnet2.asr.decoder.transformer_decoder.TransformerMDDecoder

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class espnet2.asr.decoder.transformer_decoder.TransformerMDDecoder(vocab_size: int, encoder_output_size: int, attention_heads: int = 4, linear_units: int = 2048, num_blocks: int = 6, dropout_rate: float = 0.1, positional_dropout_rate: float = 0.1, self_attention_dropout_rate: float = 0.0, src_attention_dropout_rate: float = 0.0, input_layer: str = 'embed', use_output_layer: bool = True, pos_enc_class=<class 'espnet.nets.pytorch_backend.transformer.embedding.PositionalEncoding'>, normalize_before: bool = True, concat_after: bool = False, use_speech_attn: bool = True)

Bases: BaseTransformerDecoder

Initializes internal Module state, shared by both nn.Module and ScriptModule.

batch_score(ys: Tensor, states: List[Any], xs: Tensor, speech: Tensor | None = None) → Tuple[Tensor, List[Any]]

Score new token batch.

• Parameters:
  • ys (torch.Tensor) – torch.int64 prefix tokens (n_batch, ylen).
  • states (List *[*Any ]) – Scorer states for prefix tokens.
  • xs (torch.Tensor) – The encoder feature that generates ys (n_batch, xlen, n_feat).
• Returns: Tuple of : batchfied scores for next token with shape of (n_batch, n_vocab) and next state list for ys.
• Return type: tuple[torch.Tensor, List[Any]]

forward(hs_pad: Tensor, hlens: Tensor, ys_in_pad: Tensor, ys_in_lens: Tensor, speech: Tensor | None = None, speech_lens: Tensor | None = None, return_hs: bool = False) → Tuple[Tensor, Tensor]

Forward decoder.

• Parameters:

  • hs_pad – encoded memory, float32 (batch, maxlen_in, feat)
  • hlens – (batch)
  • ys_in_pad – input token ids, int64 (batch, maxlen_out) if input_layer == “embed” input tensor (batch, maxlen_out, #mels) in the other cases
  • ys_in_lens – (batch)
  • return_hs – dec hidden state corresponding to ys, used for searchable hidden ints

• Returns: tuple containing:

  x: decoded token score before softmax (batch, maxlen_out, token) : if use_output_layer is True,

  olens: (batch, )

• Return type: (tuple)

forward_one_step(tgt: Tensor, tgt_mask: Tensor, memory: Tensor, memory_mask: Tensor | None = None, *, speech: Tensor | None = None, speech_mask: Tensor | None = None, cache: List[Tensor] | None = None, return_hs: bool = False) → Tuple[Tensor, List[Tensor]]

Forward one step.

• Parameters:
  • tgt – input token ids, int64 (batch, maxlen_out)
  • tgt_mask – input token mask, (batch, maxlen_out) dtype=torch.uint8 in PyTorch 1.2- dtype=torch.bool in PyTorch 1.2+ (include 1.2)
  • memory – encoded memory, float32 (batch, maxlen_in, feat)
  • memory_mask – encoded memory mask (batch, 1, maxlen_in)
  • speech – encoded speech, float32 (batch, maxlen_in, feat)
  • speech_mask – encoded memory mask (batch, 1, maxlen_in)
  • cache – cached output list of (batch, max_time_out-1, size)
  • return_hs – dec hidden state corresponding to ys, used for searchable hidden ints
• Returns: NN output value and cache per self.decoders. y.shape` is (batch, maxlen_out, token)
• Return type: y, cache

score(ys, state, x, speech=None)

Score.