Source code for espnet2.asr_transducer.decoder.stateless_decoder

"""Stateless decoder definition for Transducer models."""

from typing import Any, List, Optional, Tuple

import torch
from typeguard import typechecked

from espnet2.asr_transducer.beam_search_transducer import Hypothesis
from espnet2.asr_transducer.decoder.abs_decoder import AbsDecoder


[docs]class StatelessDecoder(AbsDecoder): """Stateless Transducer decoder module. Args: vocab_size: Output size. embed_size: Embedding size. embed_dropout_rate: Dropout rate for embedding layer. embed_pad: Embed/Blank symbol ID. """ @typechecked def __init__( self, vocab_size: int, embed_size: int = 256, embed_dropout_rate: float = 0.0, embed_pad: int = 0, ) -> None: """Construct a StatelessDecoder object.""" super().__init__() self.embed = torch.nn.Embedding(vocab_size, embed_size, padding_idx=embed_pad) self.embed_dropout_rate = torch.nn.Dropout(p=embed_dropout_rate) self.output_size = embed_size self.vocab_size = vocab_size self.device = next(self.parameters()).device self.score_cache = {}
[docs] def forward( self, labels: torch.Tensor, states: Optional[Any] = None, ) -> torch.Tensor: """Encode source label sequences. Args: labels: Label ID sequences. (B, L) states: Decoder hidden states. None Returns: embed: Decoder output sequences. (B, U, D_emb) """ embed = self.embed_dropout_rate(self.embed(labels)) return embed
[docs] def score( self, label_sequence: List[int], states: Optional[Any] = None, ) -> Tuple[torch.Tensor, None]: """One-step forward hypothesis. Args: label_sequence: Current label sequence. states: Decoder hidden states. None Returns: : Decoder output sequence. (1, D_emb) state: Decoder hidden states. None """ str_labels = "_".join(map(str, label_sequence)) if str_labels in self.score_cache: embed = self.score_cache[str_labels] else: label = torch.full( (1, 1), label_sequence[-1], dtype=torch.long, device=self.device, ) embed = self.embed(label) self.score_cache[str_labels] = embed return embed[0], None
[docs] def batch_score(self, hyps: List[Hypothesis]) -> Tuple[torch.Tensor, None]: """One-step forward hypotheses. Args: hyps: Hypotheses. Returns: out: Decoder output sequences. (B, D_dec) states: Decoder hidden states. None """ labels = torch.tensor( [[h.yseq[-1]] for h in hyps], dtype=torch.long, device=self.device ) embed = self.embed(labels) return embed.squeeze(1), None
[docs] def set_device(self, device: torch.device) -> None: """Set GPU device to use. Args: device: Device ID. """ self.device = device
[docs] def init_state(self, batch_size: int) -> None: """Initialize decoder states. Args: batch_size: Batch size. Returns: : Initial decoder hidden states. None """ return None
[docs] def select_state(self, states: Optional[torch.Tensor], idx: int) -> None: """Get specified ID state from decoder hidden states. Args: states: Decoder hidden states. None idx: State ID to extract. Returns: : Decoder hidden state for given ID. None """ return None
[docs] def create_batch_states( self, new_states: List[Optional[torch.Tensor]], ) -> None: """Create decoder hidden states. Args: new_states: Decoder hidden states. [N x None] Returns: states: Decoder hidden states. None """ return None