speechbrain.lobes.models.transformer.TransformerST module
Transformer for ST in the SpeechBrain sytle.
Authors * YAO FEI, CHENG 2021
Summary
Classes:
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: ~typing.Optional[int] = 31, bias: ~typing.Optional[bool] = True, encoder_module: ~typing.Optional[str] = 'transformer', conformer_activation: ~typing.Optional[~torch.nn.modules.module.Module] = <class 'speechbrain.nnet.activations.Swish'>, attention_type: ~typing.Optional[str] = 'regularMHA', max_length: ~typing.Optional[int] = 2500, causal: ~typing.Optional[bool] = 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)[source]
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).
dim_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)[source]
This method implements a decoding step for asr task
- Parameters
encoder_out (tensor) – The representation of the encoder (required).
(transcription) (tgt) – The sequence to the decoder (required).
pad_idx (int) – The index for <pad> token (default=0).
- forward_mt(src, tgt, pad_idx=0)[source]
This method implements a forward step for mt task
- Parameters
(transcription) (src) – The sequence to the encoder (required).
(translation) (tgt) – The sequence to the decoder (required).
pad_idx (int) – The index for <pad> token (default=0).
- decode_asr(tgt, encoder_out)[source]
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.