espnet2.asr.decoder.rnn_decoder.RNNDecoder

source

class espnet2.asr.decoder.rnn_decoder.RNNDecoder(vocab_size: int, encoder_output_size: int, rnn_type: str = 'lstm', num_layers: int = 1, hidden_size: int = 320, sampling_probability: float = 0.0, dropout: float = 0.0, context_residual: bool = False, replace_sos: bool = False, num_encs: int = 1, att_conf: dict = {'aconv_chans': 10, 'aconv_filts': 100, 'adim': 320, 'aheads': 4, 'atype': 'location', 'awin': 5, 'han_conv_chans': -1, 'han_conv_filts': 100, 'han_dim': 320, 'han_heads': 4, 'han_mode': False, 'han_type': None, 'han_win': 5, 'num_att': 1, 'num_encs': 1})

Bases: AbsDecoder

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

forward(hs_pad, hlens, ys_in_pad, ys_in_lens, strm_idx=0)

Defines the computation performed at every call.

Should be overridden by all subclasses.

NOTE

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

init_state(x)

Get an initial state for decoding (optional).

• Parameters:x (torch.Tensor) – The encoded feature tensor

Returns: initial state

rnn_forward(ey, z_list, c_list, z_prev, c_prev)

score(yseq, state, x)

Score new token (required).

• Parameters:
  • y (torch.Tensor) – 1D torch.int64 prefix tokens.
  • state – Scorer state for prefix tokens
  • x (torch.Tensor) – The encoder feature that generates ys.
• Returns: Tuple of : scores for next token that has a shape of (n_vocab) and next state for ys
• Return type: tuple[torch.Tensor, Any]

zero_state(hs_pad)