| | |
|---|
| | | return xs_pad, ilens, None |
|---|
| | | |
|---|
| | | |
|---|
| | | @tables.register("encoder_classes", "SANMTPEncoder") |
|---|
| | | class SANMTPEncoder(nn.Module): |
|---|
| | | """ |
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| | | Author: Speech Lab of DAMO Academy, Alibaba Group |
|---|
| | | SCAMA: Streaming chunk-aware multihead attention for online end-to-end speech recognition |
|---|
| | | https://arxiv.org/abs/2006.01713 |
|---|
| | | """ |
|---|
| | | def __init__( |
|---|
| | | self, |
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| | | input_size: int, |
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| | | output_size: int = 256, |
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| | | attention_heads: int = 4, |
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| | | linear_units: int = 2048, |
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| | | num_blocks: int = 6, |
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| | | tp_blocks: int = 0, |
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| | | dropout_rate: float = 0.1, |
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| | | positional_dropout_rate: float = 0.1, |
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| | | attention_dropout_rate: float = 0.0, |
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| | | stochastic_depth_rate: float = 0.0, |
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| | | input_layer: Optional[str] = "conv2d", |
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| | | pos_enc_class=SinusoidalPositionEncoder, |
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| | | normalize_before: bool = True, |
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| | | concat_after: bool = False, |
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| | | positionwise_layer_type: str = "linear", |
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| | | positionwise_conv_kernel_size: int = 1, |
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| | | padding_idx: int = -1, |
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| | | kernel_size: int = 11, |
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| | | sanm_shfit: int = 0, |
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| | | selfattention_layer_type: str = "sanm", |
|---|
| | | ): |
|---|
| | | super().__init__() |
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| | | self._output_size = output_size |
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| | | if input_layer == "linear": |
|---|
| | | self.embed = torch.nn.Sequential( |
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| | | torch.nn.Linear(input_size, output_size), |
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| | | torch.nn.LayerNorm(output_size), |
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| | | torch.nn.Dropout(dropout_rate), |
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| | | torch.nn.ReLU(), |
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| | | eval(pos_enc_class)(output_size, positional_dropout_rate), |
|---|
| | | ) |
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| | | elif input_layer == "linear_no_pos": |
|---|
| | | self.embed = torch.nn.Sequential( |
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| | | torch.nn.Linear(input_size, output_size), |
|---|
| | | torch.nn.LayerNorm(output_size), |
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| | | torch.nn.Dropout(dropout_rate), |
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| | | eval(pos_enc_class)(output_size, positional_dropout_rate, use_pos=False), |
|---|
| | | ) |
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| | | elif input_layer == "conv2d": |
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| | | self.embed = Conv2dSubsampling(input_size, output_size, dropout_rate) |
|---|
| | | elif input_layer == "conv2d2": |
|---|
| | | self.embed = Conv2dSubsampling2(input_size, output_size, dropout_rate) |
|---|
| | | elif input_layer == "conv2d6": |
|---|
| | | self.embed = Conv2dSubsampling6(input_size, output_size, dropout_rate) |
|---|
| | | elif input_layer == "conv2d8": |
|---|
| | | self.embed = Conv2dSubsampling8(input_size, output_size, dropout_rate) |
|---|
| | | elif input_layer == "embed": |
|---|
| | | self.embed = torch.nn.Sequential( |
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| | | torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx), |
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| | | eval(pos_enc_class)(output_size, positional_dropout_rate), |
|---|
| | | ) |
|---|
| | | elif input_layer is None: |
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| | | if input_size == output_size: |
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| | | self.embed = None |
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| | | else: |
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| | | self.embed = torch.nn.Linear(input_size, output_size) |
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| | | elif input_layer == "pe": |
|---|
| | | self.embed = SinusoidalPositionEncoder() |
|---|
| | | elif input_layer == "pe_online": |
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| | | self.embed = StreamSinusoidalPositionEncoder() |
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| | | else: |
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| | | raise ValueError("unknown input_layer: " + input_layer) |
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| | | self.normalize_before = normalize_before |
|---|
| | | if positionwise_layer_type == "linear": |
|---|
| | | positionwise_layer = PositionwiseFeedForward |
|---|
| | | positionwise_layer_args = ( |
|---|
| | | output_size, |
|---|
| | | linear_units, |
|---|
| | | dropout_rate, |
|---|
| | | ) |
|---|
| | | elif positionwise_layer_type == "conv1d": |
|---|
| | | positionwise_layer = MultiLayeredConv1d |
|---|
| | | positionwise_layer_args = ( |
|---|
| | | output_size, |
|---|
| | | linear_units, |
|---|
| | | positionwise_conv_kernel_size, |
|---|
| | | dropout_rate, |
|---|
| | | ) |
|---|
| | | elif positionwise_layer_type == "conv1d-linear": |
|---|
| | | positionwise_layer = Conv1dLinear |
|---|
| | | positionwise_layer_args = ( |
|---|
| | | output_size, |
|---|
| | | linear_units, |
|---|
| | | positionwise_conv_kernel_size, |
|---|
| | | dropout_rate, |
|---|
| | | ) |
|---|
| | | else: |
|---|
| | | raise NotImplementedError("Support only linear or conv1d.") |
|---|
| | | if selfattention_layer_type == "selfattn": |
|---|
| | | encoder_selfattn_layer = MultiHeadedAttention |
|---|
| | | encoder_selfattn_layer_args = ( |
|---|
| | | attention_heads, |
|---|
| | | output_size, |
|---|
| | | attention_dropout_rate, |
|---|
| | | ) |
|---|
| | | elif selfattention_layer_type == "sanm": |
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| | | encoder_selfattn_layer = MultiHeadedAttentionSANM |
|---|
| | | encoder_selfattn_layer_args0 = ( |
|---|
| | | attention_heads, |
|---|
| | | input_size, |
|---|
| | | output_size, |
|---|
| | | attention_dropout_rate, |
|---|
| | | kernel_size, |
|---|
| | | sanm_shfit, |
|---|
| | | ) |
|---|
| | | encoder_selfattn_layer_args = ( |
|---|
| | | attention_heads, |
|---|
| | | output_size, |
|---|
| | | output_size, |
|---|
| | | attention_dropout_rate, |
|---|
| | | kernel_size, |
|---|
| | | sanm_shfit, |
|---|
| | | ) |
|---|
| | | self.encoders0 = repeat( |
|---|
| | | 1, |
|---|
| | | lambda lnum: EncoderLayerSANM( |
|---|
| | | input_size, |
|---|
| | | output_size, |
|---|
| | | encoder_selfattn_layer(*encoder_selfattn_layer_args0), |
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| | | positionwise_layer(*positionwise_layer_args), |
|---|
| | | dropout_rate, |
|---|
| | | normalize_before, |
|---|
| | | concat_after, |
|---|
| | | ), |
|---|
| | | ) |
|---|
| | | self.encoders = repeat( |
|---|
| | | num_blocks - 1, |
|---|
| | | lambda lnum: EncoderLayerSANM( |
|---|
| | | output_size, |
|---|
| | | output_size, |
|---|
| | | encoder_selfattn_layer(*encoder_selfattn_layer_args), |
|---|
| | | positionwise_layer(*positionwise_layer_args), |
|---|
| | | dropout_rate, |
|---|
| | | normalize_before, |
|---|
| | | concat_after, |
|---|
| | | stochastic_depth_rate, |
|---|
| | | ), |
|---|
| | | ) |
|---|
| | | self.tp_encoders = repeat( |
|---|
| | | tp_blocks, |
|---|
| | | lambda lnum: EncoderLayerSANM( |
|---|
| | | output_size, |
|---|
| | | output_size, |
|---|
| | | encoder_selfattn_layer(*encoder_selfattn_layer_args), |
|---|
| | | positionwise_layer(*positionwise_layer_args), |
|---|
| | | dropout_rate, |
|---|
| | | normalize_before, |
|---|
| | | concat_after, |
|---|
| | | stochastic_depth_rate, |
|---|
| | | ), |
|---|
| | | ) |
|---|
| | | if self.normalize_before: |
|---|
| | | self.after_norm = LayerNorm(output_size) |
|---|
| | | self.tp_blocks = tp_blocks |
|---|
| | | if self.tp_blocks > 0: |
|---|
| | | self.tp_norm = LayerNorm(output_size) |
|---|
| | | def output_size(self) -> int: |
|---|
| | | return self._output_size |
|---|
| | | def forward( |
|---|
| | | self, |
|---|
| | | xs_pad: torch.Tensor, |
|---|
| | | ilens: torch.Tensor, |
|---|
| | | ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]: |
|---|
| | | """Embed positions in tensor. |
|---|
| | | Args: |
|---|
| | | xs_pad: input tensor (B, L, D) |
|---|
| | | ilens: input length (B) |
|---|
| | | prev_states: Not to be used now. |
|---|
| | | Returns: |
|---|
| | | position embedded tensor and mask |
|---|
| | | """ |
|---|
| | | masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device) |
|---|
| | | xs_pad *= self.output_size() ** 0.5 |
|---|
| | | if self.embed is None: |
|---|
| | | xs_pad = xs_pad |
|---|
| | | elif ( |
|---|
| | | isinstance(self.embed, Conv2dSubsampling) |
|---|
| | | or isinstance(self.embed, Conv2dSubsampling2) |
|---|
| | | or isinstance(self.embed, Conv2dSubsampling6) |
|---|
| | | or isinstance(self.embed, Conv2dSubsampling8) |
|---|
| | | ): |
|---|
| | | short_status, limit_size = check_short_utt(self.embed, xs_pad.size(1)) |
|---|
| | | if short_status: |
|---|
| | | raise TooShortUttError( |
|---|
| | | f"has {xs_pad.size(1)} frames and is too short for subsampling " |
|---|
| | | + f"(it needs more than {limit_size} frames), return empty results", |
|---|
| | | xs_pad.size(1), |
|---|
| | | limit_size, |
|---|
| | | ) |
|---|
| | | xs_pad, masks = self.embed(xs_pad, masks) |
|---|
| | | else: |
|---|
| | | xs_pad = self.embed(xs_pad) |
|---|
| | | # forward encoder1 |
|---|
| | | mask_shfit_chunk, mask_att_chunk_encoder = None, None |
|---|
| | | encoder_outs = self.encoders0(xs_pad, masks, None, mask_shfit_chunk, mask_att_chunk_encoder) |
|---|
| | | xs_pad, masks = encoder_outs[0], encoder_outs[1] |
|---|
| | | encoder_outs = self.encoders(xs_pad, masks, None, mask_shfit_chunk, mask_att_chunk_encoder) |
|---|
| | | xs_pad, masks = encoder_outs[0], encoder_outs[1] |
|---|
| | | if self.normalize_before: |
|---|
| | | xs_pad = self.after_norm(xs_pad) |
|---|
| | | # forward encoder2 |
|---|
| | | olens = masks.squeeze(1).sum(1) |
|---|
| | | mask_shfit_chunk2, mask_att_chunk_encoder2 = None, None |
|---|
| | | for layer_idx, encoder_layer in enumerate(self.tp_encoders): |
|---|
| | | encoder_outs = encoder_layer(xs_pad, masks, None, mask_shfit_chunk2, mask_att_chunk_encoder2) |
|---|
| | | xs_pad, masks = encoder_outs[0], encoder_outs[1] |
|---|
| | | if self.tp_blocks > 0: |
|---|
| | | xs_pad = self.tp_norm(xs_pad) |
|---|
| | | return xs_pad, olens |
|---|
| | | |
|---|
| | | |
|---|
| | | class EncoderLayerSANMExport(nn.Module): |
|---|
| | | def __init__( |
|---|
| | | self, |
|---|
