speechbrain.lobes.models.transformer.TransformerST module

Transformer for ST in the SpeechBrain style.

Authors * YAO FEI, CHENG 2021

Summary

Classes:

TransformerST

This is an implementation of transformer model for ST.

Reference

class speechbrain.lobes.models.transformer.TransformerST.TransformerST(tgt_vocab, input_size, d_model=512, nhead=8, num_encoder_layers=6, num_decoder_layers=6, d_ffn=2048, dropout=0.1, activation=<class 'torch.nn.modules.activation.ReLU'>, positional_encoding='fixed_abs_sine', normalize_before=False, kernel_size: int | None = 31, bias: bool | None = True, encoder_module: str | None = 'transformer', conformer_activation: ~torch.nn.modules.module.Module | None = <class 'speechbrain.nnet.activations.Swish'>, attention_type: str | None = 'regularMHA', max_length: int | None = 2500, causal: bool | None = True, ctc_weight: float = 0.0, asr_weight: float = 0.0, mt_weight: float = 0.0, asr_tgt_vocab: int = 0, mt_src_vocab: int = 0)

Bases: TransformerASR

This is an implementation of transformer model for ST.

The architecture is based on the paper “Attention Is All You Need”: https://arxiv.org/pdf/1706.03762.pdf

Parameters:

• tgt_vocab (int) – Size of vocabulary.

• input_size (int) – Input feature size.

• d_model (int, optional) – Embedding dimension size. (default=512).

• nhead (int, optional) – The number of heads in the multi-head attention models (default=8).

• num_encoder_layers (int, optional) – The number of sub-encoder-layers in the encoder (default=6).

• num_decoder_layers (int, optional) – The number of sub-decoder-layers in the decoder (default=6).

• d_ffn (int, optional) – The dimension of the feedforward network model (default=2048).

• dropout (int, optional) – The dropout value (default=0.1).

• activation (torch.nn.Module, optional) – The activation function of FFN layers. Recommended: relu or gelu (default=relu).

• 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.

• ctc_weight (float) – The weight of ctc for asr task

• asr_weight (float) – The weight of asr task for calculating loss

• mt_weight (float) – The weight of mt task for calculating loss

• asr_tgt_vocab (int) – The size of the asr target language

• mt_src_vocab (int) – The size of the mt source language

Example

>>> src = torch.rand([8, 120, 512])
>>> tgt = torch.randint(0, 720, [8, 120])
>>> net = TransformerST(
...     720, 512, 512, 8, 1, 1, 1024, activation=torch.nn.GELU,
...     ctc_weight=1, asr_weight=0.3,
... )
>>> enc_out, dec_out = net.forward(src, tgt)
>>> enc_out.shape
torch.Size([8, 120, 512])
>>> dec_out.shape
torch.Size([8, 120, 512])

forward_asr(encoder_out, src, tgt, wav_len, pad_idx=0)

This method implements a decoding step for asr task

Parameters:

• encoder_out (torch.Tensor) – The representation of the encoder (required).

• src (torch.Tensor) – Input sequence (required).

• tgt (torch.Tensor) – The sequence to the decoder (transcription) (required).

• wav_len (torch.Tensor) – Length of input tensors (required).

• pad_idx (int) – The index for <pad> token (default=0).

Returns:

asr_decoder_out – One step of asr decoder.

Return type:

torch.Tensor

forward_mt(src, tgt, pad_idx=0)

This method implements a forward step for mt task

Parameters:

• src (torch.Tensor) – The sequence to the encoder (transcription) (required).

• tgt (torch.Tensor) – The sequence to the decoder (translation) (required).

• pad_idx (int) – The index for <pad> token (default=0).

Returns:

• encoder_out (torch.Tensor) – Output of encoder

• decoder_out (torch.Tensor) – Output of decoder

forward_mt_decoder_only(src, tgt, pad_idx=0)

This method implements a forward step for mt task using a wav2vec encoder (same than above, but without the encoder stack)

Parameters:

• (transcription) (src) – output features from the w2v2 encoder

• (translation) (tgt) – The sequence to the decoder (required).

• pad_idx (int) – The index for <pad> token (default=0).

decode_asr(tgt, encoder_out)

This method implements a decoding step for the transformer model.

Parameters:

• tgt (torch.Tensor) – The sequence to the decoder.

• encoder_out (torch.Tensor) – Hidden output of the encoder.

Returns:

• prediction (torch.Tensor) – The predicted outputs.

• multihead_attns (torch.Tensor) – The last step of attention.

make_masks_for_mt(src, tgt, pad_idx=0)

This method generates the masks for training the transformer model.

Parameters:

• src (torch.Tensor) – The sequence to the encoder (required).

• tgt (torch.Tensor) – The sequence to the decoder (required).

• pad_idx (int) – The index for <pad> token (default=0).

Returns:

• src_key_padding_mask (torch.Tensor) – Timesteps to mask due to padding

• tgt_key_padding_mask (torch.Tensor) – Timesteps to mask due to padding

• src_mask (torch.Tensor) – Timesteps to mask for causality

• tgt_mask (torch.Tensor) – Timesteps to mask for causality