from typing import List from typing import Optional from typing import Sequence from typing import Tuple from typing import Union import logging import torch import torch.nn as nn from funasr.models.scama.chunk_utilis import overlap_chunk import numpy as np from funasr.models.transformer.utils.nets_utils import make_pad_mask from funasr.models.sond.attention import MultiHeadSelfAttention from funasr.models.transformer.embedding import SinusoidalPositionEncoder from funasr.models.transformer.layer_norm import LayerNorm from funasr.models.transformer.utils.multi_layer_conv import Conv1dLinear from funasr.models.transformer.utils.multi_layer_conv import MultiLayeredConv1d from funasr.models.transformer.positionwise_feed_forward import ( PositionwiseFeedForward, # noqa: H301 ) from funasr.models.transformer.utils.repeat import repeat from funasr.models.transformer.utils.subsampling import Conv2dSubsampling from funasr.models.transformer.utils.subsampling import Conv2dSubsampling2 from funasr.models.transformer.utils.subsampling import Conv2dSubsampling6 from funasr.models.transformer.utils.subsampling import Conv2dSubsampling8 from funasr.models.transformer.utils.subsampling import TooShortUttError from funasr.models.transformer.utils.subsampling import check_short_utt from funasr.models.ctc import CTC from funasr.models.encoder.abs_encoder import AbsEncoder class EncoderLayer(nn.Module): def __init__( self, in_size, size, self_attn, feed_forward, dropout_rate, normalize_before=True, concat_after=False, stochastic_depth_rate=0.0, ): """Construct an EncoderLayer object.""" super(EncoderLayer, self).__init__() self.self_attn = self_attn self.feed_forward = feed_forward self.norm1 = LayerNorm(in_size) self.norm2 = LayerNorm(size) self.dropout = nn.Dropout(dropout_rate) self.in_size = in_size self.size = size self.normalize_before = normalize_before self.concat_after = concat_after if self.concat_after: self.concat_linear = nn.Linear(size + size, size) self.stochastic_depth_rate = stochastic_depth_rate self.dropout_rate = dropout_rate def forward(self, x, mask, cache=None, mask_att_chunk_encoder=None): """Compute encoded features. Args: x_input (torch.Tensor): Input tensor (#batch, time, size). mask (torch.Tensor): Mask tensor for the input (#batch, time). cache (torch.Tensor): Cache tensor of the input (#batch, time - 1, size). Returns: torch.Tensor: Output tensor (#batch, time, size). torch.Tensor: Mask tensor (#batch, time). """ skip_layer = False # with stochastic depth, residual connection `x + f(x)` becomes # `x <- x + 1 / (1 - p) * f(x)` at training time. stoch_layer_coeff = 1.0 if self.training and self.stochastic_depth_rate > 0: skip_layer = torch.rand(1).item() < self.stochastic_depth_rate stoch_layer_coeff = 1.0 / (1 - self.stochastic_depth_rate) if skip_layer: if cache is not None: x = torch.cat([cache, x], dim=1) return x, mask residual = x if self.normalize_before: x = self.norm1(x) if self.concat_after: x_concat = torch.cat( (x, self.self_attn(x, mask, mask_att_chunk_encoder=mask_att_chunk_encoder)), dim=-1 ) if self.in_size == self.size: x = residual + stoch_layer_coeff * self.concat_linear(x_concat) else: x = stoch_layer_coeff * self.concat_linear(x_concat) else: if self.in_size == self.size: x = residual + stoch_layer_coeff * self.dropout( self.self_attn(x, mask, mask_att_chunk_encoder=mask_att_chunk_encoder) ) else: x = stoch_layer_coeff * self.dropout( self.self_attn(x, mask, mask_att_chunk_encoder=mask_att_chunk_encoder) ) if not self.normalize_before: x = self.norm1(x) residual = x if self.normalize_before: x = self.norm2(x) x = residual + stoch_layer_coeff * self.dropout(self.feed_forward(x)) if not self.normalize_before: x = self.norm2(x) return x, mask, cache, mask_att_chunk_encoder class SelfAttentionEncoder(AbsEncoder): """ Author: Speech Lab of DAMO Academy, Alibaba Group Self attention encoder in OpenNMT framework """ def __init__( self, input_size: int, output_size: int = 256, attention_heads: int = 4, linear_units: int = 2048, num_blocks: int = 6, dropout_rate: float = 0.1, positional_dropout_rate: float = 0.1, attention_dropout_rate: float = 0.0, input_layer: Optional[str] = "conv2d", pos_enc_class=SinusoidalPositionEncoder, normalize_before: bool = True, concat_after: bool = False, positionwise_layer_type: str = "linear", positionwise_conv_kernel_size: int = 1, padding_idx: int = -1, interctc_layer_idx: List[int] = [], interctc_use_conditioning: bool = False, tf2torch_tensor_name_prefix_torch: str = "encoder", tf2torch_tensor_name_prefix_tf: str = "seq2seq/encoder", out_units=None, ): super().__init__() self._output_size = output_size if input_layer == "linear": self.embed = torch.nn.Sequential( torch.nn.Linear(input_size, output_size), torch.nn.LayerNorm(output_size), torch.nn.Dropout(dropout_rate), torch.nn.ReLU(), pos_enc_class(output_size, positional_dropout_rate), ) elif input_layer == "conv2d": 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( torch.nn.Embedding(input_size, output_size, padding_idx=padding_idx), SinusoidalPositionEncoder(), ) elif input_layer is None: if input_size == output_size: self.embed = None else: self.embed = torch.nn.Linear(input_size, output_size) elif input_layer == "pe": self.embed = SinusoidalPositionEncoder() elif input_layer == "null": self.embed = None else: raise ValueError("unknown input_layer: " + input_layer) 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.") self.encoders = repeat( num_blocks, lambda lnum: ( EncoderLayer( output_size, output_size, MultiHeadSelfAttention( attention_heads, output_size, output_size, attention_dropout_rate, ), positionwise_layer(*positionwise_layer_args), dropout_rate, normalize_before, concat_after, ) if lnum > 0 else EncoderLayer( input_size, output_size, MultiHeadSelfAttention( attention_heads, input_size if input_layer == "pe" or input_layer == "null" else output_size, output_size, attention_dropout_rate, ), positionwise_layer(*positionwise_layer_args), dropout_rate, normalize_before, concat_after, ) ), ) if self.normalize_before: self.after_norm = LayerNorm(output_size) self.interctc_layer_idx = interctc_layer_idx if len(interctc_layer_idx) > 0: assert 0 < min(interctc_layer_idx) and max(interctc_layer_idx) < num_blocks self.interctc_use_conditioning = interctc_use_conditioning self.conditioning_layer = None self.dropout = nn.Dropout(dropout_rate) self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf self.out_units = out_units if out_units is not None: self.output_linear = nn.Linear(output_size, out_units) def output_size(self) -> int: return self._output_size def forward( self, xs_pad: torch.Tensor, ilens: torch.Tensor, prev_states: torch.Tensor = None, ctc: CTC = None, ) -> 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 = 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) xs_pad = self.dropout(xs_pad) # encoder_outs = self.encoders0(xs_pad, masks) # xs_pad, masks = encoder_outs[0], encoder_outs[1] intermediate_outs = [] if len(self.interctc_layer_idx) == 0: encoder_outs = self.encoders(xs_pad, masks) xs_pad, masks = encoder_outs[0], encoder_outs[1] else: for layer_idx, encoder_layer in enumerate(self.encoders): encoder_outs = encoder_layer(xs_pad, masks) xs_pad, masks = encoder_outs[0], encoder_outs[1] if layer_idx + 1 in self.interctc_layer_idx: encoder_out = xs_pad # intermediate outputs are also normalized if self.normalize_before: encoder_out = self.after_norm(encoder_out) intermediate_outs.append((layer_idx + 1, encoder_out)) if self.interctc_use_conditioning: ctc_out = ctc.softmax(encoder_out) xs_pad = xs_pad + self.conditioning_layer(ctc_out) if self.normalize_before: xs_pad = self.after_norm(xs_pad) if self.out_units is not None: xs_pad = self.output_linear(xs_pad) olens = masks.squeeze(1).sum(1) if len(intermediate_outs) > 0: return (xs_pad, intermediate_outs), olens, None return xs_pad, olens, None