Source code for speechbrain.lobes.models.g2p.model

"""The Attentional RNN model for Grapheme-to-Phoneme

 * Mirco Ravinelli 2021
 * Artem Ploujnikov 2021

from speechbrain.lobes.models.transformer.Transformer import (

import torch
from torch import nn
from speechbrain.nnet.linear import Linear
from speechbrain.nnet import normalization

[docs]class AttentionSeq2Seq(nn.Module): """ The Attentional RNN encoder-decoder model Arguments --------- enc: torch.nn.Module the encoder module encoder_emb: torch.nn.Module the encoder_embedding_module emb: torch.nn.Module the embedding module dec: torch.nn.Module the decoder module lin: torch.nn.Module the linear module out: torch.nn.Module the output layer (typically log_softmax) use_word_emb: bool whether or not to use word embedding bos_token: int the index of teh Beginning-of-Sentence token word_emb_enc: nn.Module a module to encode word embeddings Returns ------- result: tuple a (p_seq, char_lens) tuple """ def __init__( self, enc, encoder_emb, emb, dec, lin, out, bos_token=0, use_word_emb=False, word_emb_enc=None, ): super().__init__() self.enc = enc self.encoder_emb = encoder_emb self.emb = emb self.dec = dec self.lin = lin self.out = out self.bos_token = bos_token self.use_word_emb = use_word_emb self.word_emb_enc = word_emb_enc if use_word_emb else None
[docs] def forward( self, grapheme_encoded, phn_encoded=None, word_emb=None, **kwargs ): """Computes the forward pass Arguments --------- grapheme_encoded: torch.Tensor graphemes encoded as a Torch tensor phn_encoded: torch.Tensor the encoded phonemes word_emb: torch.Tensor word embeddings (optional) Returns ------- p_seq: torch.Tensor a (batch x position x token) tensor of token probabilities in each position char_lens: torch.Tensor a tensor of character sequence lengths encoder_out: the raw output of the encoder """ chars, char_lens = grapheme_encoded if phn_encoded is None: phn_bos = get_dummy_phonemes(chars.size(0), chars.device) else: phn_bos, _ = phn_encoded emb_char = self.encoder_emb(chars) if self.use_word_emb: emb_char = _apply_word_emb(self.word_emb_enc, emb_char, word_emb) encoder_out, _ = self.enc(emb_char) e_in = self.emb(phn_bos) h, w = self.dec(e_in, encoder_out, char_lens) logits = self.lin(h) p_seq = self.out(logits) return p_seq, char_lens, encoder_out, w
def _apply_word_emb(self, emb_char, word_emb): """Concatenate character embeddings with word embeddeings, possibly encoding the word embeddings if an encoder is provided Arguments --------- emb_char: torch.Tensor the character embedding tensor word_emb: torch.Tensor the word embedding tensor Returns ------- result: torch.Tensor the concatenation of the tensor""" word_emb_enc = ( self.word_emb_enc(word_emb) if self.word_emb_enc is not None else word_emb ) return[emb_char, word_emb_enc], dim=-1)
[docs]class WordEmbeddingEncoder(nn.Module): """A small encoder module that reduces the dimensionality and normalizes word embeddings Arguments --------- word_emb_dim: int the dimension of the original word embeddings word_emb_enc_dim: int the dimension of the encoded word embeddings norm: torch.nn.Module the normalization to be used ( e.g. speechbrain.nnet.normalization.LayerNorm) norm_type: str the type of normalization to be used """ def __init__( self, word_emb_dim, word_emb_enc_dim, norm=None, norm_type=None ): super().__init__() self.word_emb_dim = word_emb_dim self.word_emb_enc_dim = word_emb_enc_dim if norm_type: self.norm = self._get_norm(norm_type, word_emb_dim) else: self.norm = norm self.lin = Linear(n_neurons=word_emb_enc_dim, input_size=word_emb_dim) self.activation = nn.Tanh() def _get_norm(self, norm, dim): """Determines the type of normalizer Arguments --------- norm: str the normalization type: "batch", "layer" or "instance dim: int the dimensionality of the inputs """ norm_cls = self.NORMS.get(norm) if not norm_cls: raise ValueError(f"Invalid norm: {norm}") return norm_cls(input_size=dim)
[docs] def forward(self, emb): """Computes the forward pass of the embedding Arguments --------- emb: torch.Tensor the original word embeddings Returns ------- emb_enc: torch.Tensor encoded word embeddings """ if self.norm is not None: x = self.norm(emb) x = self.lin(x) x = self.activation(x) return x
NORMS = { "batch": normalization.BatchNorm1d, "layer": normalization.LayerNorm, "instance": normalization.InstanceNorm1d, }
[docs]class TransformerG2P(TransformerInterface): """ A Transformer-based Grapheme-to-Phoneme model Arguments ---------- emb: torch.nn.Module the embedding module encoder_emb: torch.nn.Module the encoder embedding module char_lin: torch.nn.Module a linear module connecting the inputs to the transformer phn_lin: torch.nn.Module a linear module connecting the outputs to the transformer out: torch.nn.Module the decoder module (usually Softmax) lin: torch.nn.Module the linear module for outputs d_model: int The number of expected features in the encoder/decoder inputs (default=512). nhead: int The number of heads in the multi-head attention models (default=8). num_encoder_layers: int, optional The number of encoder layers in1ì the encoder. num_decoder_layers: int, optional The number of decoder layers in the decoder. dim_ffn: int, optional The dimension of the feedforward network model hidden layer. dropout: int, optional The dropout value. activation: torch.nn.Module, optional The activation function for Feed-Forward Netowrk layer, e.g., relu or gelu or swish. custom_src_module: torch.nn.Module, optional Module that processes the src features to expected feature dim. custom_tgt_module: torch.nn.Module, optional Module that processes the src features to expected feature dim. positional_encoding: str, optional Type of positional encoding used. e.g. 'fixed_abs_sine' for fixed absolute positional encodings. normalize_before: bool, optional Whether normalization should be applied before or after MHA or FFN in Transformer layers. Defaults to True as this was shown to lead to better performance and training stability. kernel_size: int, optional Kernel size in convolutional layers when Conformer is used. bias: bool, optional Whether to use bias in Conformer convolutional layers. encoder_module: str, optional Choose between Conformer and Transformer for the encoder. The decoder is fixed to be a Transformer. conformer_activation: torch.nn.Module, optional Activation module used after Conformer convolutional layers. E.g. Swish, ReLU etc. it has to be a torch Module. attention_type: str, optional Type of attention layer used in all Transformer or Conformer layers. e.g. regularMHA or RelPosMHA. max_length: int, optional Max length for the target and source sequence in input. Used for positional encodings. causal: bool, optional Whether the encoder should be causal or not (the decoder is always causal). If causal the Conformer convolutional layer is causal. pad_idx: int the padding index (for masks) encoder_kdim: int, optional Dimension of the key for the encoder. encoder_vdim: int, optional Dimension of the value for the encoder. decoder_kdim: int, optional Dimension of the key for the decoder. decoder_vdim: int, optional Dimension of the value for the decoder. """ def __init__( self, emb, encoder_emb, char_lin, phn_lin, lin, out, d_model=512, nhead=8, num_encoder_layers=6, num_decoder_layers=6, d_ffn=2048, dropout=0.1, activation=nn.ReLU, custom_src_module=None, custom_tgt_module=None, positional_encoding="fixed_abs_sine", normalize_before=True, kernel_size=15, bias=True, encoder_module="transformer", attention_type="regularMHA", max_length=2500, causal=False, pad_idx=0, encoder_kdim=None, encoder_vdim=None, decoder_kdim=None, decoder_vdim=None, use_word_emb=False, word_emb_enc=None, ): super().__init__( d_model=d_model, nhead=nhead, num_encoder_layers=num_encoder_layers, num_decoder_layers=num_decoder_layers, d_ffn=d_ffn, dropout=dropout, activation=activation, custom_src_module=custom_src_module, custom_tgt_module=custom_tgt_module, positional_encoding=positional_encoding, normalize_before=normalize_before, kernel_size=kernel_size, bias=bias, encoder_module=encoder_module, attention_type=attention_type, max_length=max_length, causal=causal, encoder_kdim=encoder_kdim, encoder_vdim=encoder_vdim, decoder_kdim=decoder_kdim, decoder_vdim=decoder_vdim, ) self.emb = emb self.encoder_emb = encoder_emb self.char_lin = char_lin self.phn_lin = phn_lin self.lin = lin self.out = out self.pad_idx = pad_idx self.use_word_emb = use_word_emb self.word_emb_enc = word_emb_enc self._reset_params()
[docs] def forward( self, grapheme_encoded, phn_encoded=None, word_emb=None, **kwargs ): """Computes the forward pass Arguments --------- grapheme_encoded: torch.Tensor graphemes encoded as a Torch tensor phn_encoded: torch.Tensor the encoded phonemes word_emb: torch.Tensor word embeddings (if applicable) Returns ------- p_seq: torch.Tensor the log-probabilities of individual tokens i a sequence char_lens: torch.Tensor the character length syntax encoder_out: torch.Tensor the encoder state attention: torch.Tensor the attention state """ chars, char_lens = grapheme_encoded if phn_encoded is None: phn = get_dummy_phonemes(chars.size(0), chars.device) else: phn, _ = phn_encoded emb_char = self.encoder_emb(chars) if self.use_word_emb: emb_char = _apply_word_emb(self.word_emb_enc, emb_char, word_emb) src = self.char_lin(emb_char) tgt = self.emb(phn) tgt = self.phn_lin(tgt) ( src_key_padding_mask, tgt_key_padding_mask, src_mask, tgt_mask, ) = self.make_masks(src, tgt, char_lens, pad_idx=self.pad_idx) pos_embs_encoder = None if self.attention_type == "RelPosMHAXL": pos_embs_encoder = self.positional_encoding(src) elif self.positional_encoding_type == "fixed_abs_sine": src = src + self.positional_encoding(src) # add the encodings here pos_embs_encoder = None encoder_out, _ = self.encoder( src=src, src_mask=src_mask, src_key_padding_mask=src_key_padding_mask, pos_embs=pos_embs_encoder, ) if self.attention_type == "RelPosMHAXL": # use standard sinusoidal pos encoding in decoder tgt = tgt + self.positional_encoding_decoder(tgt) src = src + self.positional_encoding_decoder(src) pos_embs_encoder = None pos_embs_target = None elif self.positional_encoding_type == "fixed_abs_sine": tgt = tgt + self.positional_encoding(tgt) pos_embs_target = None pos_embs_encoder = None decoder_out, _, attention = self.decoder( tgt=tgt, memory=encoder_out, memory_mask=src_mask, tgt_mask=tgt_mask, tgt_key_padding_mask=tgt_key_padding_mask, memory_key_padding_mask=src_key_padding_mask, pos_embs_tgt=pos_embs_target, pos_embs_src=pos_embs_encoder, ) logits = self.lin(decoder_out) p_seq = self.out(logits) return p_seq, char_lens, encoder_out, attention
def _reset_params(self): """Resets the parameters of the model""" for p in self.parameters(): if p.dim() > 1: torch.nn.init.xavier_normal_(p)
[docs] def make_masks(self, src, tgt, src_len=None, pad_idx=0): """This method generates the masks for training the transformer model. Arguments --------- src : tensor The sequence to the encoder (required). tgt : tensor The sequence to the decoder (required). pad_idx : int The index for <pad> token (default=0). Returns ------- src_key_padding_mask: torch.Tensor the source key padding mask tgt_key_padding_mask: torch.Tensor the target key padding masks src_mask: torch.Tensor the source mask tgt_mask: torch.Tensor the target mask """ if src_len is not None: abs_len = torch.round(src_len * src.shape[1]) src_key_padding_mask = ( torch.arange(src.shape[1])[None, :].to(abs_len) > abs_len[:, None] ) tgt_key_padding_mask = get_key_padding_mask(tgt, pad_idx=pad_idx) src_mask = None tgt_mask = get_lookahead_mask(tgt) return src_key_padding_mask, tgt_key_padding_mask, src_mask, tgt_mask
[docs] def decode(self, tgt, encoder_out): """This method implements a decoding step for the transformer model. Arguments --------- tgt : torch.Tensor The sequence to the decoder. encoder_out : torch.Tensor Hidden output of the encoder. Returns ------- prediction: torch.Tensor the predicted sequence attention: torch.Tensor the attention matrix corresponding to the last attention head (useful for plotting attention) """ tgt_mask = get_lookahead_mask(tgt) tgt = self.emb(tgt) tgt = self.phn_lin(tgt) if self.attention_type == "RelPosMHAXL": # we use fixed positional encodings in the decoder tgt = tgt + self.positional_encoding_decoder(tgt) encoder_out = encoder_out + self.positional_encoding_decoder( encoder_out ) elif self.positional_encoding_type == "fixed_abs_sine": tgt = tgt + self.positional_encoding(tgt) # add the encodings here prediction, self_attns, multihead_attns = self.decoder( tgt, encoder_out, tgt_mask=tgt_mask, pos_embs_tgt=None, pos_embs_src=None, ) attention = multihead_attns[-1] return prediction, attention
[docs]def input_dim(use_word_emb, embedding_dim, word_emb_enc_dim): """Computes the input dimension (intended for hparam files) Arguments --------- use_word_emb: bool whether to use word embeddings embedding_dim: int the embedding dimension word_emb_enc_dim: int the dimension of encoded word embeddings Returns ------- input_dim: int the input dimension """ return embedding_dim + use_word_emb * word_emb_enc_dim
def _apply_word_emb(word_emb_enc, emb_char, word_emb): """ Concatenates character and word embeddings together, possibly applying a custom encoding/transformation Arguments --------- word_emb_enc: callable an encoder to apply (typically, speechbrain.lobes.models.g2p.model.WordEmbeddingEncoder) emb_char: torch.Tensor character embeddings word_emb: char word embeddings Returns ------- result: torch.Tensor the resulting (concatenated) tensor """ word_emb_enc = ( word_emb_enc( if word_emb_enc is not None else ) return[emb_char, word_emb_enc], dim=-1)
[docs]def get_dummy_phonemes(batch_size, device): """ Creates a dummy phoneme sequence Arguments --------- batch_size: int the batch size device: str the target device Returns ------- result: torch.Tensor """ return torch.tensor([0], device=device).expand(batch_size, 1)